init codes

demo/inference_on_a_image.py

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+import argparse
+import os
+import sys
+
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+
+import numpy as np
+import torch
+from PIL import Image, ImageDraw, ImageFont
+
+import groundingdino.datasets.transforms as T
+from groundingdino.models import build_model
+from groundingdino.util import box_ops
+from groundingdino.util.slconfig import SLConfig
+from groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap
+
+
+def plot_boxes_to_image(image_pil, tgt):
+    H, W = tgt["size"]
+    boxes = tgt["boxes"]
+    labels = tgt["labels"]
+    assert len(boxes) == len(labels), "boxes and labels must have same length"
+
+    draw = ImageDraw.Draw(image_pil)
+    mask = Image.new("L", image_pil.size, 0)
+    mask_draw = ImageDraw.Draw(mask)
+
+    # draw boxes and masks
+    for box, label in zip(boxes, labels):
+        # from 0..1 to 0..W, 0..H
+        box = box * torch.Tensor([W, H, W, H])
+        # from xywh to xyxy
+        box[:2] -= box[2:] / 2
+        box[2:] += box[:2]
+        # random color
+        color = tuple(np.random.randint(0, 255, size=3).tolist())
+        # draw
+        x0, y0, x1, y1 = box
+        x0, y0, x1, y1 = int(x0), int(y0), int(x1), int(y1)
+
+        draw.rectangle([x0, y0, x1, y1], outline=color, width=6)
+        # draw.text((x0, y0), str(label), fill=color)
+
+        bbox = draw.textbbox((x0, y0), str(label))
+        draw.rectangle(bbox, fill=color)
+        draw.text((x0, y0), str(label), fill="white")
+
+        mask_draw.rectangle([x0, y0, x1, y1], fill=255, width=6)
+
+    return image_pil, mask
+
+
+def load_image(image_path):
+    # load image
+    image_pil = Image.open(image_path).convert("RGB")  # load image
+
+    transform = T.Compose(
+        [
+            T.RandomResize([800], max_size=1333),
+            T.ToTensor(),
+            T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+        ]
+    )
+    image, _ = transform(image_pil, None)  # 3, h, w
+    return image_pil, image
+
+
+def load_model(model_config_path, model_checkpoint_path):
+    args = SLConfig.fromfile(model_config_path)
+    args.device = "cuda"
+    model = build_model(args)
+    checkpoint = torch.load(model_checkpoint_path, map_location="cpu")
+    load_res = model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False)
+    print(load_res)
+    _ = model.eval()
+    return model
+
+
+def get_grounding_output(model, image, caption, box_threshold, text_threshold, with_logits=True):
+    caption = caption.lower()
+    caption = caption.strip()
+    if not caption.endswith("."):
+        caption = caption + "."
+    model = model.cuda()
+    image = image.cuda()
+    with torch.no_grad():
+        outputs = model(image[None], captions=[caption])
+    logits = outputs["pred_logits"].cpu().sigmoid()[0]  # (nq, 256)
+    boxes = outputs["pred_boxes"].cpu()[0]  # (nq, 4)
+    logits.shape[0]
+    
+
+    # filter output
+    logits_filt = logits.clone()
+    boxes_filt = boxes.clone()
+    filt_mask = logits_filt.max(dim=1)[0] > box_threshold
+    logits_filt = logits_filt[filt_mask]  # num_filt, 256
+    boxes_filt = boxes_filt[filt_mask]  # num_filt, 4
+    logits_filt.shape[0]
+
+    # get phrase
+    tokenlizer = model.tokenizer
+    tokenized = tokenlizer(caption)
+    # build pred
+    pred_phrases = []
+    for logit, box in zip(logits_filt, boxes_filt):
+        pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, caption)
+        if with_logits:
+            pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})")
+        else:
+            pred_phrases.append(pred_phrase)
+
+    return boxes_filt, pred_phrases
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser("Grounding DINO example", add_help=True)
+    parser.add_argument("--config_file", "-c", type=str, required=True, help="path to config file")
+    parser.add_argument(
+        "--checkpoint_path", "-p", type=str, required=True, help="path to checkpoint file"
+    )
+    parser.add_argument("--image_path", "-i", type=str, required=True, help="path to image file")
+    parser.add_argument("--text_prompt", "-t", type=str, required=True, help="text prompt")
+    parser.add_argument(
+        "--output_dir", "-o", type=str, default="outputs", required=True, help="output directory"
+    )
+
+    parser.add_argument(
+        "--box_threshold", type=float, default=0.3, help="box threshold"
+    )
+    parser.add_argument(
+        "--text_threshold", type=float, default=0.25, help="text threshold"
+    )
+    args = parser.parse_args()
+
+    # cfg
+    config_file = args.config_file  # change the path of the model config file
+    checkpoint_path = args.checkpoint_path  # change the path of the model
+    image_path = args.image_path
+    text_prompt = args.text_prompt
+    output_dir = args.output_dir
+    box_threshold = args.box_threshold
+    text_threshold = args.box_threshold
+
+    # make dir
+    os.makedirs(output_dir, exist_ok=True)
+    # load image
+    image_pil, image = load_image(image_path)
+    # load model
+    model = load_model(config_file, checkpoint_path)
+
+    # visualize raw image
+    image_pil.save(os.path.join(output_dir, "raw_image.jpg"))
+
+    # run model
+    boxes_filt, pred_phrases = get_grounding_output(
+        model, image, text_prompt, box_threshold, text_threshold
+    )
+
+    # visualize pred
+    size = image_pil.size
+    pred_dict = {
+        "boxes": boxes_filt,
+        "size": [size[1], size[0]],  # H,W
+        "labels": pred_phrases,
+    }
+    # import ipdb; ipdb.set_trace()
+    image_with_box = plot_boxes_to_image(image_pil, pred_dict)[0]
+    image_with_box.save(os.path.join(output_dir, "pred.jpg"))

groundingdino/config/GroundingDINO_SwinT_OGC.py

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+batch_size = 1
+modelname = "groundingdino"
+backbone = "swin_T_224_1k"
+position_embedding = "sine"
+pe_temperatureH = 20
+pe_temperatureW = 20
+return_interm_indices = [1, 2, 3]
+backbone_freeze_keywords = None
+enc_layers = 6
+dec_layers = 6
+pre_norm = False
+dim_feedforward = 2048
+hidden_dim = 256
+dropout = 0.0
+nheads = 8
+num_queries = 900
+query_dim = 4
+num_patterns = 0
+num_feature_levels = 4
+enc_n_points = 4
+dec_n_points = 4
+two_stage_type = "standard"
+two_stage_bbox_embed_share = False
+two_stage_class_embed_share = False
+transformer_activation = "relu"
+dec_pred_bbox_embed_share = True
+dn_box_noise_scale = 1.0
+dn_label_noise_ratio = 0.5
+dn_label_coef = 1.0
+dn_bbox_coef = 1.0
+embed_init_tgt = True
+dn_labelbook_size = 2000
+max_text_len = 256
+text_encoder_type = "bert-base-uncased"
+use_text_enhancer = True
+use_fusion_layer = True
+use_checkpoint = True
+use_transformer_ckpt = True
+use_text_cross_attention = True
+text_dropout = 0.0
+fusion_dropout = 0.0
+fusion_droppath = 0.1
+sub_sentence_present = True

groundingdino/datasets/transforms.py

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+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+"""
+Transforms and data augmentation for both image + bbox.
+"""
+import os
+import random
+
+import PIL
+import torch
+import torchvision.transforms as T
+import torchvision.transforms.functional as F
+
+from groundingdino.util.box_ops import box_xyxy_to_cxcywh
+from groundingdino.util.misc import interpolate
+
+
+def crop(image, target, region):
+    cropped_image = F.crop(image, *region)
+
+    target = target.copy()
+    i, j, h, w = region
+
+    # should we do something wrt the original size?
+    target["size"] = torch.tensor([h, w])
+
+    fields = ["labels", "area", "iscrowd", "positive_map"]
+
+    if "boxes" in target:
+        boxes = target["boxes"]
+        max_size = torch.as_tensor([w, h], dtype=torch.float32)
+        cropped_boxes = boxes - torch.as_tensor([j, i, j, i])
+        cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)
+        cropped_boxes = cropped_boxes.clamp(min=0)
+        area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1)
+        target["boxes"] = cropped_boxes.reshape(-1, 4)
+        target["area"] = area
+        fields.append("boxes")
+
+    if "masks" in target:
+        # FIXME should we update the area here if there are no boxes?
+        target["masks"] = target["masks"][:, i : i + h, j : j + w]
+        fields.append("masks")
+
+    # remove elements for which the boxes or masks that have zero area
+    if "boxes" in target or "masks" in target:
+        # favor boxes selection when defining which elements to keep
+        # this is compatible with previous implementation
+        if "boxes" in target:
+            cropped_boxes = target["boxes"].reshape(-1, 2, 2)
+            keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1)
+        else:
+            keep = target["masks"].flatten(1).any(1)
+
+        for field in fields:
+            if field in target:
+                target[field] = target[field][keep]
+
+    if os.environ.get("IPDB_SHILONG_DEBUG", None) == "INFO":
+        # for debug and visualization only.
+        if "strings_positive" in target:
+            target["strings_positive"] = [
+                _i for _i, _j in zip(target["strings_positive"], keep) if _j
+            ]
+
+    return cropped_image, target
+
+
+def hflip(image, target):
+    flipped_image = F.hflip(image)
+
+    w, h = image.size
+
+    target = target.copy()
+    if "boxes" in target:
+        boxes = target["boxes"]
+        boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor(
+            [w, 0, w, 0]
+        )
+        target["boxes"] = boxes
+
+    if "masks" in target:
+        target["masks"] = target["masks"].flip(-1)
+
+    return flipped_image, target
+
+
+def resize(image, target, size, max_size=None):
+    # size can be min_size (scalar) or (w, h) tuple
+
+    def get_size_with_aspect_ratio(image_size, size, max_size=None):
+        w, h = image_size
+        if max_size is not None:
+            min_original_size = float(min((w, h)))
+            max_original_size = float(max((w, h)))
+            if max_original_size / min_original_size * size > max_size:
+                size = int(round(max_size * min_original_size / max_original_size))
+
+        if (w <= h and w == size) or (h <= w and h == size):
+            return (h, w)
+
+        if w < h:
+            ow = size
+            oh = int(size * h / w)
+        else:
+            oh = size
+            ow = int(size * w / h)
+
+        return (oh, ow)
+
+    def get_size(image_size, size, max_size=None):
+        if isinstance(size, (list, tuple)):
+            return size[::-1]
+        else:
+            return get_size_with_aspect_ratio(image_size, size, max_size)
+
+    size = get_size(image.size, size, max_size)
+    rescaled_image = F.resize(image, size)
+
+    if target is None:
+        return rescaled_image, None
+
+    ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size))
+    ratio_width, ratio_height = ratios
+
+    target = target.copy()
+    if "boxes" in target:
+        boxes = target["boxes"]
+        scaled_boxes = boxes * torch.as_tensor(
+            [ratio_width, ratio_height, ratio_width, ratio_height]
+        )
+        target["boxes"] = scaled_boxes
+
+    if "area" in target:
+        area = target["area"]
+        scaled_area = area * (ratio_width * ratio_height)
+        target["area"] = scaled_area
+
+    h, w = size
+    target["size"] = torch.tensor([h, w])
+
+    if "masks" in target:
+        target["masks"] = (
+            interpolate(target["masks"][:, None].float(), size, mode="nearest")[:, 0] > 0.5
+        )
+
+    return rescaled_image, target
+
+
+def pad(image, target, padding):
+    # assumes that we only pad on the bottom right corners
+    padded_image = F.pad(image, (0, 0, padding[0], padding[1]))
+    if target is None:
+        return padded_image, None
+    target = target.copy()
+    # should we do something wrt the original size?
+    target["size"] = torch.tensor(padded_image.size[::-1])
+    if "masks" in target:
+        target["masks"] = torch.nn.functional.pad(target["masks"], (0, padding[0], 0, padding[1]))
+    return padded_image, target
+
+
+class ResizeDebug(object):
+    def __init__(self, size):
+        self.size = size
+
+    def __call__(self, img, target):
+        return resize(img, target, self.size)
+
+
+class RandomCrop(object):
+    def __init__(self, size):
+        self.size = size
+
+    def __call__(self, img, target):
+        region = T.RandomCrop.get_params(img, self.size)
+        return crop(img, target, region)
+
+
+class RandomSizeCrop(object):
+    def __init__(self, min_size: int, max_size: int, respect_boxes: bool = False):
+        # respect_boxes:    True to keep all boxes
+        #                   False to tolerence box filter
+        self.min_size = min_size
+        self.max_size = max_size
+        self.respect_boxes = respect_boxes
+
+    def __call__(self, img: PIL.Image.Image, target: dict):
+        init_boxes = len(target["boxes"])
+        max_patience = 10
+        for i in range(max_patience):
+            w = random.randint(self.min_size, min(img.width, self.max_size))
+            h = random.randint(self.min_size, min(img.height, self.max_size))
+            region = T.RandomCrop.get_params(img, [h, w])
+            result_img, result_target = crop(img, target, region)
+            if (
+                not self.respect_boxes
+                or len(result_target["boxes"]) == init_boxes
+                or i == max_patience - 1
+            ):
+                return result_img, result_target
+        return result_img, result_target
+
+
+class CenterCrop(object):
+    def __init__(self, size):
+        self.size = size
+
+    def __call__(self, img, target):
+        image_width, image_height = img.size
+        crop_height, crop_width = self.size
+        crop_top = int(round((image_height - crop_height) / 2.0))
+        crop_left = int(round((image_width - crop_width) / 2.0))
+        return crop(img, target, (crop_top, crop_left, crop_height, crop_width))
+
+
+class RandomHorizontalFlip(object):
+    def __init__(self, p=0.5):
+        self.p = p
+
+    def __call__(self, img, target):
+        if random.random() < self.p:
+            return hflip(img, target)
+        return img, target
+
+
+class RandomResize(object):
+    def __init__(self, sizes, max_size=None):
+        assert isinstance(sizes, (list, tuple))
+        self.sizes = sizes
+        self.max_size = max_size
+
+    def __call__(self, img, target=None):
+        size = random.choice(self.sizes)
+        return resize(img, target, size, self.max_size)
+
+
+class RandomPad(object):
+    def __init__(self, max_pad):
+        self.max_pad = max_pad
+
+    def __call__(self, img, target):
+        pad_x = random.randint(0, self.max_pad)
+        pad_y = random.randint(0, self.max_pad)
+        return pad(img, target, (pad_x, pad_y))
+
+
+class RandomSelect(object):
+    """
+    Randomly selects between transforms1 and transforms2,
+    with probability p for transforms1 and (1 - p) for transforms2
+    """
+
+    def __init__(self, transforms1, transforms2, p=0.5):
+        self.transforms1 = transforms1
+        self.transforms2 = transforms2
+        self.p = p
+
+    def __call__(self, img, target):
+        if random.random() < self.p:
+            return self.transforms1(img, target)
+        return self.transforms2(img, target)
+
+
+class ToTensor(object):
+    def __call__(self, img, target):
+        return F.to_tensor(img), target
+
+
+class RandomErasing(object):
+    def __init__(self, *args, **kwargs):
+        self.eraser = T.RandomErasing(*args, **kwargs)
+
+    def __call__(self, img, target):
+        return self.eraser(img), target
+
+
+class Normalize(object):
+    def __init__(self, mean, std):
+        self.mean = mean
+        self.std = std
+
+    def __call__(self, image, target=None):
+        image = F.normalize(image, mean=self.mean, std=self.std)
+        if target is None:
+            return image, None
+        target = target.copy()
+        h, w = image.shape[-2:]
+        if "boxes" in target:
+            boxes = target["boxes"]
+            boxes = box_xyxy_to_cxcywh(boxes)
+            boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32)
+            target["boxes"] = boxes
+        return image, target
+
+
+class Compose(object):
+    def __init__(self, transforms):
+        self.transforms = transforms
+
+    def __call__(self, image, target):
+        for t in self.transforms:
+            image, target = t(image, target)
+        return image, target
+
+    def __repr__(self):
+        format_string = self.__class__.__name__ + "("
+        for t in self.transforms:
+            format_string += "\n"
+            format_string += "    {0}".format(t)
+        format_string += "\n)"
+        return format_string

groundingdino/models/GroundingDINO/__init__.py

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+# ------------------------------------------------------------------------
+# Conditional DETR
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+
+from .groundingdino import build_groundingdino

groundingdino/models/GroundingDINO/backbone/__init__.py

@@ -0,0 +1 @@
+from .backbone import build_backbone

groundingdino/models/GroundingDINO/backbone/backbone.py

@@ -0,0 +1,216 @@
+# ------------------------------------------------------------------------
+# Conditional DETR
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+
+"""
+Backbone modules.
+"""
+
+from typing import Dict, List
+
+import torch
+import torch.nn.functional as F
+import torchvision
+from torch import nn
+from torchvision.models._utils import IntermediateLayerGetter
+
+from groundingdino.util.misc import NestedTensor, clean_state_dict, is_main_process
+
+from .position_encoding import build_position_encoding
+from .swin_transformer import build_swin_transformer
+
+
+class FrozenBatchNorm2d(torch.nn.Module):
+    """
+    BatchNorm2d where the batch statistics and the affine parameters are fixed.
+
+    Copy-paste from torchvision.misc.ops with added eps before rqsrt,
+    without which any other models than torchvision.models.resnet[18,34,50,101]
+    produce nans.
+    """
+
+    def __init__(self, n):
+        super(FrozenBatchNorm2d, self).__init__()
+        self.register_buffer("weight", torch.ones(n))
+        self.register_buffer("bias", torch.zeros(n))
+        self.register_buffer("running_mean", torch.zeros(n))
+        self.register_buffer("running_var", torch.ones(n))
+
+    def _load_from_state_dict(
+        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+    ):
+        num_batches_tracked_key = prefix + "num_batches_tracked"
+        if num_batches_tracked_key in state_dict:
+            del state_dict[num_batches_tracked_key]
+
+        super(FrozenBatchNorm2d, self)._load_from_state_dict(
+            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+        )
+
+    def forward(self, x):
+        # move reshapes to the beginning
+        # to make it fuser-friendly
+        w = self.weight.reshape(1, -1, 1, 1)
+        b = self.bias.reshape(1, -1, 1, 1)
+        rv = self.running_var.reshape(1, -1, 1, 1)
+        rm = self.running_mean.reshape(1, -1, 1, 1)
+        eps = 1e-5
+        scale = w * (rv + eps).rsqrt()
+        bias = b - rm * scale
+        return x * scale + bias
+
+
+class BackboneBase(nn.Module):
+    def __init__(
+        self,
+        backbone: nn.Module,
+        train_backbone: bool,
+        num_channels: int,
+        return_interm_indices: list,
+    ):
+        super().__init__()
+        for name, parameter in backbone.named_parameters():
+            if (
+                not train_backbone
+                or "layer2" not in name
+                and "layer3" not in name
+                and "layer4" not in name
+            ):
+                parameter.requires_grad_(False)
+
+        return_layers = {}
+        for idx, layer_index in enumerate(return_interm_indices):
+            return_layers.update(
+                {"layer{}".format(5 - len(return_interm_indices) + idx): "{}".format(layer_index)}
+            )
+
+        # if len:
+        #     if use_stage1_feature:
+        #         return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"}
+        #     else:
+        #         return_layers = {"layer2": "0", "layer3": "1", "layer4": "2"}
+        # else:
+        #     return_layers = {'layer4': "0"}
+        self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)
+        self.num_channels = num_channels
+
+    def forward(self, tensor_list: NestedTensor):
+        xs = self.body(tensor_list.tensors)
+        out: Dict[str, NestedTensor] = {}
+        for name, x in xs.items():
+            m = tensor_list.mask
+            assert m is not None
+            mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
+            out[name] = NestedTensor(x, mask)
+        # import ipdb; ipdb.set_trace()
+        return out
+
+
+class Backbone(BackboneBase):
+    """ResNet backbone with frozen BatchNorm."""
+
+    def __init__(
+        self,
+        name: str,
+        train_backbone: bool,
+        dilation: bool,
+        return_interm_indices: list,
+        batch_norm=FrozenBatchNorm2d,
+    ):
+        if name in ["resnet18", "resnet34", "resnet50", "resnet101"]:
+            backbone = getattr(torchvision.models, name)(
+                replace_stride_with_dilation=[False, False, dilation],
+                pretrained=is_main_process(),
+                norm_layer=batch_norm,
+            )
+        else:
+            raise NotImplementedError("Why you can get here with name {}".format(name))
+        # num_channels = 512 if name in ('resnet18', 'resnet34') else 2048
+        assert name not in ("resnet18", "resnet34"), "Only resnet50 and resnet101 are available."
+        assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]
+        num_channels_all = [256, 512, 1024, 2048]
+        num_channels = num_channels_all[4 - len(return_interm_indices) :]
+        super().__init__(backbone, train_backbone, num_channels, return_interm_indices)
+
+
+class Joiner(nn.Sequential):
+    def __init__(self, backbone, position_embedding):
+        super().__init__(backbone, position_embedding)
+
+    def forward(self, tensor_list: NestedTensor):
+        xs = self[0](tensor_list)
+        out: List[NestedTensor] = []
+        pos = []
+        for name, x in xs.items():
+            out.append(x)
+            # position encoding
+            pos.append(self[1](x).to(x.tensors.dtype))
+
+        return out, pos
+
+
+def build_backbone(args):
+    """
+    Useful args:
+        - backbone: backbone name
+        - lr_backbone:
+        - dilation
+        - return_interm_indices: available: [0,1,2,3], [1,2,3], [3]
+        - backbone_freeze_keywords:
+        - use_checkpoint: for swin only for now
+
+    """
+    position_embedding = build_position_encoding(args)
+    train_backbone = True
+    if not train_backbone:
+        raise ValueError("Please set lr_backbone > 0")
+    return_interm_indices = args.return_interm_indices
+    assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]
+    args.backbone_freeze_keywords
+    use_checkpoint = getattr(args, "use_checkpoint", False)
+
+    if args.backbone in ["resnet50", "resnet101"]:
+        backbone = Backbone(
+            args.backbone,
+            train_backbone,
+            args.dilation,
+            return_interm_indices,
+            batch_norm=FrozenBatchNorm2d,
+        )
+        bb_num_channels = backbone.num_channels
+    elif args.backbone in [
+        "swin_T_224_1k",
+        "swin_B_224_22k",
+        "swin_B_384_22k",
+        "swin_L_224_22k",
+        "swin_L_384_22k",
+    ]:
+        pretrain_img_size = int(args.backbone.split("_")[-2])
+        backbone = build_swin_transformer(
+            args.backbone,
+            pretrain_img_size=pretrain_img_size,
+            out_indices=tuple(return_interm_indices),
+            dilation=False,
+            use_checkpoint=use_checkpoint,
+        )
+
+        bb_num_channels = backbone.num_features[4 - len(return_interm_indices) :]
+    else:
+        raise NotImplementedError("Unknown backbone {}".format(args.backbone))
+
+    assert len(bb_num_channels) == len(
+        return_interm_indices
+    ), f"len(bb_num_channels) {len(bb_num_channels)} != len(return_interm_indices) {len(return_interm_indices)}"
+
+    model = Joiner(backbone, position_embedding)
+    model.num_channels = bb_num_channels
+    assert isinstance(
+        bb_num_channels, List
+    ), "bb_num_channels is expected to be a List but {}".format(type(bb_num_channels))
+    # import ipdb; ipdb.set_trace()
+    return model

groundingdino/models/GroundingDINO/backbone/position_encoding.py

@@ -0,0 +1,181 @@
+# ------------------------------------------------------------------------
+# DINO
+# Copyright (c) 2022 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Conditional DETR
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+
+"""
+Various positional encodings for the transformer.
+"""
+import math
+
+import torch
+from torch import nn
+
+from groundingdino.util.misc import NestedTensor
+
+
+class PositionEmbeddingSine(nn.Module):
+    """
+    This is a more standard version of the position embedding, very similar to the one
+    used by the Attention is all you need paper, generalized to work on images.
+    """
+
+    def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
+        super().__init__()
+        self.num_pos_feats = num_pos_feats
+        self.temperature = temperature
+        self.normalize = normalize
+        if scale is not None and normalize is False:
+            raise ValueError("normalize should be True if scale is passed")
+        if scale is None:
+            scale = 2 * math.pi
+        self.scale = scale
+
+    def forward(self, tensor_list: NestedTensor):
+        x = tensor_list.tensors
+        mask = tensor_list.mask
+        assert mask is not None
+        not_mask = ~mask
+        y_embed = not_mask.cumsum(1, dtype=torch.float32)
+        x_embed = not_mask.cumsum(2, dtype=torch.float32)
+        if self.normalize:
+            eps = 1e-6
+            # if os.environ.get("SHILONG_AMP", None) == '1':
+            #     eps = 1e-4
+            # else:
+            #     eps = 1e-6
+            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
+
+        pos_x = x_embed[:, :, :, None] / dim_t
+        pos_y = y_embed[:, :, :, None] / dim_t
+        pos_x = torch.stack(
+            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
+        ).flatten(3)
+        pos_y = torch.stack(
+            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
+        ).flatten(3)
+        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+        return pos
+
+
+class PositionEmbeddingSineHW(nn.Module):
+    """
+    This is a more standard version of the position embedding, very similar to the one
+    used by the Attention is all you need paper, generalized to work on images.
+    """
+
+    def __init__(
+        self, num_pos_feats=64, temperatureH=10000, temperatureW=10000, normalize=False, scale=None
+    ):
+        super().__init__()
+        self.num_pos_feats = num_pos_feats
+        self.temperatureH = temperatureH
+        self.temperatureW = temperatureW
+        self.normalize = normalize
+        if scale is not None and normalize is False:
+            raise ValueError("normalize should be True if scale is passed")
+        if scale is None:
+            scale = 2 * math.pi
+        self.scale = scale
+
+    def forward(self, tensor_list: NestedTensor):
+        x = tensor_list.tensors
+        mask = tensor_list.mask
+        assert mask is not None
+        not_mask = ~mask
+        y_embed = not_mask.cumsum(1, dtype=torch.float32)
+        x_embed = not_mask.cumsum(2, dtype=torch.float32)
+
+        # import ipdb; ipdb.set_trace()
+
+        if self.normalize:
+            eps = 1e-6
+            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+        dim_tx = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+        dim_tx = self.temperatureW ** (2 * (dim_tx // 2) / self.num_pos_feats)
+        pos_x = x_embed[:, :, :, None] / dim_tx
+
+        dim_ty = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+        dim_ty = self.temperatureH ** (2 * (dim_ty // 2) / self.num_pos_feats)
+        pos_y = y_embed[:, :, :, None] / dim_ty
+
+        pos_x = torch.stack(
+            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
+        ).flatten(3)
+        pos_y = torch.stack(
+            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
+        ).flatten(3)
+        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+
+        # import ipdb; ipdb.set_trace()
+
+        return pos
+
+
+class PositionEmbeddingLearned(nn.Module):
+    """
+    Absolute pos embedding, learned.
+    """
+
+    def __init__(self, num_pos_feats=256):
+        super().__init__()
+        self.row_embed = nn.Embedding(50, num_pos_feats)
+        self.col_embed = nn.Embedding(50, num_pos_feats)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.uniform_(self.row_embed.weight)
+        nn.init.uniform_(self.col_embed.weight)
+
+    def forward(self, tensor_list: NestedTensor):
+        x = tensor_list.tensors
+        h, w = x.shape[-2:]
+        i = torch.arange(w, device=x.device)
+        j = torch.arange(h, device=x.device)
+        x_emb = self.col_embed(i)
+        y_emb = self.row_embed(j)
+        pos = (
+            torch.cat(
+                [
+                    x_emb.unsqueeze(0).repeat(h, 1, 1),
+                    y_emb.unsqueeze(1).repeat(1, w, 1),
+                ],
+                dim=-1,
+            )
+            .permute(2, 0, 1)
+            .unsqueeze(0)
+            .repeat(x.shape[0], 1, 1, 1)
+        )
+        return pos
+
+
+def build_position_encoding(args):
+    N_steps = args.hidden_dim // 2
+    if args.position_embedding in ("v2", "sine"):
+        # TODO find a better way of exposing other arguments
+        position_embedding = PositionEmbeddingSineHW(
+            N_steps,
+            temperatureH=args.pe_temperatureH,
+            temperatureW=args.pe_temperatureW,
+            normalize=True,
+        )
+    elif args.position_embedding in ("v3", "learned"):
+        position_embedding = PositionEmbeddingLearned(N_steps)
+    else:
+        raise ValueError(f"not supported {args.position_embedding}")
+
+    return position_embedding

groundingdino/models/GroundingDINO/backbone/swin_transformer.py

@@ -0,0 +1,797 @@
+# ------------------------------------------------------------------------
+# DINO
+# Copyright (c) 2022 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# --------------------------------------------------------
+# modified from https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/master/mmdet/models/backbones/swin_transformer.py
+# --------------------------------------------------------
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+from groundingdino.util.misc import NestedTensor
+
+
+class Mlp(nn.Module):
+    """Multilayer perceptron."""
+
+    def __init__(
+        self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0
+    ):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, H, W, C)
+        window_size (int): window size
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, window_size, H, W):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (int): Window size
+        H (int): Height of image
+        W (int): Width of image
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+
+class WindowAttention(nn.Module):
+    """Window based multi-head self attention (W-MSA) module with relative position bias.
+    It supports both of shifted and non-shifted window.
+    Args:
+        dim (int): Number of input channels.
+        window_size (tuple[int]): The height and width of the window.
+        num_heads (int): Number of attention heads.
+        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+    """
+
+    def __init__(
+        self,
+        dim,
+        window_size,
+        num_heads,
+        qkv_bias=True,
+        qk_scale=None,
+        attn_drop=0.0,
+        proj_drop=0.0,
+    ):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim**-0.5
+
+        # define a parameter table of relative position bias
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)
+        )  # 2*Wh-1 * 2*Ww-1, nH
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(self.window_size[0])
+        coords_w = torch.arange(self.window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        self.register_buffer("relative_position_index", relative_position_index)
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+        trunc_normal_(self.relative_position_bias_table, std=0.02)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=None):
+        """Forward function.
+        Args:
+            x: input features with shape of (num_windows*B, N, C)
+            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+        """
+        B_, N, C = x.shape
+        qkv = (
+            self.qkv(x)
+            .reshape(B_, N, 3, self.num_heads, C // self.num_heads)
+            .permute(2, 0, 3, 1, 4)
+        )
+        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
+
+        q = q * self.scale
+        attn = q @ k.transpose(-2, -1)
+
+        relative_position_bias = self.relative_position_bias_table[
+            self.relative_position_index.view(-1)
+        ].view(
+            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1
+        )  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(
+            2, 0, 1
+        ).contiguous()  # nH, Wh*Ww, Wh*Ww
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class SwinTransformerBlock(nn.Module):
+    """Swin Transformer Block.
+    Args:
+        dim (int): Number of input channels.
+        num_heads (int): Number of attention heads.
+        window_size (int): Window size.
+        shift_size (int): Shift size for SW-MSA.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float, optional): Stochastic depth rate. Default: 0.0
+        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(
+        self,
+        dim,
+        num_heads,
+        window_size=7,
+        shift_size=0,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop=0.0,
+        attn_drop=0.0,
+        drop_path=0.0,
+        act_layer=nn.GELU,
+        norm_layer=nn.LayerNorm,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention(
+            dim,
+            window_size=to_2tuple(self.window_size),
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            attn_drop=attn_drop,
+            proj_drop=drop,
+        )
+
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(
+            in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop
+        )
+
+        self.H = None
+        self.W = None
+
+    def forward(self, x, mask_matrix):
+        """Forward function.
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+            mask_matrix: Attention mask for cyclic shift.
+        """
+        B, L, C = x.shape
+        H, W = self.H, self.W
+        assert L == H * W, "input feature has wrong size"
+
+        shortcut = x
+        x = self.norm1(x)
+        x = x.view(B, H, W, C)
+
+        # pad feature maps to multiples of window size
+        pad_l = pad_t = 0
+        pad_r = (self.window_size - W % self.window_size) % self.window_size
+        pad_b = (self.window_size - H % self.window_size) % self.window_size
+        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
+        _, Hp, Wp, _ = x.shape
+
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+            attn_mask = mask_matrix
+        else:
+            shifted_x = x
+            attn_mask = None
+
+        # partition windows
+        x_windows = window_partition(
+            shifted_x, self.window_size
+        )  # nW*B, window_size, window_size, C
+        x_windows = x_windows.view(
+            -1, self.window_size * self.window_size, C
+        )  # nW*B, window_size*window_size, C
+
+        # W-MSA/SW-MSA
+        attn_windows = self.attn(x_windows, mask=attn_mask)  # nW*B, window_size*window_size, C
+
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+        shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # B H' W' C
+
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+        else:
+            x = shifted_x
+
+        if pad_r > 0 or pad_b > 0:
+            x = x[:, :H, :W, :].contiguous()
+
+        x = x.view(B, H * W, C)
+
+        # FFN
+        x = shortcut + self.drop_path(x)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+        return x
+
+
+class PatchMerging(nn.Module):
+    """Patch Merging Layer
+    Args:
+        dim (int): Number of input channels.
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, dim, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(4 * dim)
+
+    def forward(self, x, H, W):
+        """Forward function.
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+        """
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+
+        x = x.view(B, H, W, C)
+
+        # padding
+        pad_input = (H % 2 == 1) or (W % 2 == 1)
+        if pad_input:
+            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
+
+        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+        x = self.norm(x)
+        x = self.reduction(x)
+
+        return x
+
+
+class BasicLayer(nn.Module):
+    """A basic Swin Transformer layer for one stage.
+    Args:
+        dim (int): Number of feature channels
+        depth (int): Depths of this stage.
+        num_heads (int): Number of attention head.
+        window_size (int): Local window size. Default: 7.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+    """
+
+    def __init__(
+        self,
+        dim,
+        depth,
+        num_heads,
+        window_size=7,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop=0.0,
+        attn_drop=0.0,
+        drop_path=0.0,
+        norm_layer=nn.LayerNorm,
+        downsample=None,
+        use_checkpoint=False,
+    ):
+        super().__init__()
+        self.window_size = window_size
+        self.shift_size = window_size // 2
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList(
+            [
+                SwinTransformerBlock(
+                    dim=dim,
+                    num_heads=num_heads,
+                    window_size=window_size,
+                    shift_size=0 if (i % 2 == 0) else window_size // 2,
+                    mlp_ratio=mlp_ratio,
+                    qkv_bias=qkv_bias,
+                    qk_scale=qk_scale,
+                    drop=drop,
+                    attn_drop=attn_drop,
+                    drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                    norm_layer=norm_layer,
+                )
+                for i in range(depth)
+            ]
+        )
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(dim=dim, norm_layer=norm_layer)
+        else:
+            self.downsample = None
+
+    def forward(self, x, H, W):
+        """Forward function.
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+        """
+
+        # calculate attention mask for SW-MSA
+        Hp = int(np.ceil(H / self.window_size)) * self.window_size
+        Wp = int(np.ceil(W / self.window_size)) * self.window_size
+        img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1
+        h_slices = (
+            slice(0, -self.window_size),
+            slice(-self.window_size, -self.shift_size),
+            slice(-self.shift_size, None),
+        )
+        w_slices = (
+            slice(0, -self.window_size),
+            slice(-self.window_size, -self.shift_size),
+            slice(-self.shift_size, None),
+        )
+        cnt = 0
+        for h in h_slices:
+            for w in w_slices:
+                img_mask[:, h, w, :] = cnt
+                cnt += 1
+
+        mask_windows = window_partition(
+            img_mask, self.window_size
+        )  # nW, window_size, window_size, 1
+        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
+            attn_mask == 0, float(0.0)
+        )
+
+        for blk in self.blocks:
+            blk.H, blk.W = H, W
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x, attn_mask)
+            else:
+                x = blk(x, attn_mask)
+        if self.downsample is not None:
+            x_down = self.downsample(x, H, W)
+            Wh, Ww = (H + 1) // 2, (W + 1) // 2
+            return x, H, W, x_down, Wh, Ww
+        else:
+            return x, H, W, x, H, W
+
+
+class PatchEmbed(nn.Module):
+    """Image to Patch Embedding
+    Args:
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        patch_size = to_2tuple(patch_size)
+        self.patch_size = patch_size
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+        if norm_layer is not None:
+            self.norm = norm_layer(embed_dim)
+        else:
+            self.norm = None
+
+    def forward(self, x):
+        """Forward function."""
+        # padding
+        _, _, H, W = x.size()
+        if W % self.patch_size[1] != 0:
+            x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
+        if H % self.patch_size[0] != 0:
+            x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
+
+        x = self.proj(x)  # B C Wh Ww
+        if self.norm is not None:
+            Wh, Ww = x.size(2), x.size(3)
+            x = x.flatten(2).transpose(1, 2)
+            x = self.norm(x)
+            x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
+
+        return x
+
+
+class SwinTransformer(nn.Module):
+    """Swin Transformer backbone.
+        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
+          https://arxiv.org/pdf/2103.14030
+    Args:
+        pretrain_img_size (int): Input image size for training the pretrained model,
+            used in absolute postion embedding. Default 224.
+        patch_size (int | tuple(int)): Patch size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        depths (tuple[int]): Depths of each Swin Transformer stage.
+        num_heads (tuple[int]): Number of attention head of each stage.
+        window_size (int): Window size. Default: 7.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
+        drop_rate (float): Dropout rate.
+        attn_drop_rate (float): Attention dropout rate. Default: 0.
+        drop_path_rate (float): Stochastic depth rate. Default: 0.2.
+        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
+        patch_norm (bool): If True, add normalization after patch embedding. Default: True.
+        out_indices (Sequence[int]): Output from which stages.
+        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+            -1 means not freezing any parameters.
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+        dilation (bool): if True, the output size if 16x downsample, ow 32x downsample.
+    """
+
+    def __init__(
+        self,
+        pretrain_img_size=224,
+        patch_size=4,
+        in_chans=3,
+        embed_dim=96,
+        depths=[2, 2, 6, 2],
+        num_heads=[3, 6, 12, 24],
+        window_size=7,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop_rate=0.0,
+        attn_drop_rate=0.0,
+        drop_path_rate=0.2,
+        norm_layer=nn.LayerNorm,
+        ape=False,
+        patch_norm=True,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=-1,
+        dilation=False,
+        use_checkpoint=False,
+    ):
+        super().__init__()
+
+        self.pretrain_img_size = pretrain_img_size
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.ape = ape
+        self.patch_norm = patch_norm
+        self.out_indices = out_indices
+        self.frozen_stages = frozen_stages
+        self.dilation = dilation
+
+        # if use_checkpoint:
+        #     print("use_checkpoint!!!!!!!!!!!!!!!!!!!!!!!!")
+
+        # split image into non-overlapping patches
+        self.patch_embed = PatchEmbed(
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None,
+        )
+
+        # absolute position embedding
+        if self.ape:
+            pretrain_img_size = to_2tuple(pretrain_img_size)
+            patch_size = to_2tuple(patch_size)
+            patches_resolution = [
+                pretrain_img_size[0] // patch_size[0],
+                pretrain_img_size[1] // patch_size[1],
+            ]
+
+            self.absolute_pos_embed = nn.Parameter(
+                torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1])
+            )
+            trunc_normal_(self.absolute_pos_embed, std=0.02)
+
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        # stochastic depth
+        dpr = [
+            x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
+        ]  # stochastic depth decay rule
+
+        # build layers
+        self.layers = nn.ModuleList()
+        # prepare downsample list
+        downsamplelist = [PatchMerging for i in range(self.num_layers)]
+        downsamplelist[-1] = None
+        num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)]
+        if self.dilation:
+            downsamplelist[-2] = None
+            num_features[-1] = int(embed_dim * 2 ** (self.num_layers - 1)) // 2
+        for i_layer in range(self.num_layers):
+            layer = BasicLayer(
+                # dim=int(embed_dim * 2 ** i_layer),
+                dim=num_features[i_layer],
+                depth=depths[i_layer],
+                num_heads=num_heads[i_layer],
+                window_size=window_size,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                qk_scale=qk_scale,
+                drop=drop_rate,
+                attn_drop=attn_drop_rate,
+                drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
+                norm_layer=norm_layer,
+                # downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
+                downsample=downsamplelist[i_layer],
+                use_checkpoint=use_checkpoint,
+            )
+            self.layers.append(layer)
+
+        # num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
+        self.num_features = num_features
+
+        # add a norm layer for each output
+        for i_layer in out_indices:
+            layer = norm_layer(num_features[i_layer])
+            layer_name = f"norm{i_layer}"
+            self.add_module(layer_name, layer)
+
+        self._freeze_stages()
+
+    def _freeze_stages(self):
+        if self.frozen_stages >= 0:
+            self.patch_embed.eval()
+            for param in self.patch_embed.parameters():
+                param.requires_grad = False
+
+        if self.frozen_stages >= 1 and self.ape:
+            self.absolute_pos_embed.requires_grad = False
+
+        if self.frozen_stages >= 2:
+            self.pos_drop.eval()
+            for i in range(0, self.frozen_stages - 1):
+                m = self.layers[i]
+                m.eval()
+                for param in m.parameters():
+                    param.requires_grad = False
+
+    # def init_weights(self, pretrained=None):
+    #     """Initialize the weights in backbone.
+    #     Args:
+    #         pretrained (str, optional): Path to pre-trained weights.
+    #             Defaults to None.
+    #     """
+
+    #     def _init_weights(m):
+    #         if isinstance(m, nn.Linear):
+    #             trunc_normal_(m.weight, std=.02)
+    #             if isinstance(m, nn.Linear) and m.bias is not None:
+    #                 nn.init.constant_(m.bias, 0)
+    #         elif isinstance(m, nn.LayerNorm):
+    #             nn.init.constant_(m.bias, 0)
+    #             nn.init.constant_(m.weight, 1.0)
+
+    #     if isinstance(pretrained, str):
+    #         self.apply(_init_weights)
+    #         logger = get_root_logger()
+    #         load_checkpoint(self, pretrained, strict=False, logger=logger)
+    #     elif pretrained is None:
+    #         self.apply(_init_weights)
+    #     else:
+    #         raise TypeError('pretrained must be a str or None')
+
+    def forward_raw(self, x):
+        """Forward function."""
+        x = self.patch_embed(x)
+
+        Wh, Ww = x.size(2), x.size(3)
+        if self.ape:
+            # interpolate the position embedding to the corresponding size
+            absolute_pos_embed = F.interpolate(
+                self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic"
+            )
+            x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C
+        else:
+            x = x.flatten(2).transpose(1, 2)
+        x = self.pos_drop(x)
+
+        outs = []
+        for i in range(self.num_layers):
+            layer = self.layers[i]
+            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
+            # import ipdb; ipdb.set_trace()
+
+            if i in self.out_indices:
+                norm_layer = getattr(self, f"norm{i}")
+                x_out = norm_layer(x_out)
+
+                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
+                outs.append(out)
+        # in:
+        #   torch.Size([2, 3, 1024, 1024])
+        # outs:
+        #   [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \
+        #       torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])]
+        return tuple(outs)
+
+    def forward(self, tensor_list: NestedTensor):
+        x = tensor_list.tensors
+
+        """Forward function."""
+        x = self.patch_embed(x)
+
+        Wh, Ww = x.size(2), x.size(3)
+        if self.ape:
+            # interpolate the position embedding to the corresponding size
+            absolute_pos_embed = F.interpolate(
+                self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic"
+            )
+            x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C
+        else:
+            x = x.flatten(2).transpose(1, 2)
+        x = self.pos_drop(x)
+
+        outs = []
+        for i in range(self.num_layers):
+            layer = self.layers[i]
+            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
+
+            if i in self.out_indices:
+                norm_layer = getattr(self, f"norm{i}")
+                x_out = norm_layer(x_out)
+
+                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
+                outs.append(out)
+        # in:
+        #   torch.Size([2, 3, 1024, 1024])
+        # out:
+        #   [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \
+        #       torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])]
+
+        # collect for nesttensors
+        outs_dict = {}
+        for idx, out_i in enumerate(outs):
+            m = tensor_list.mask
+            assert m is not None
+            mask = F.interpolate(m[None].float(), size=out_i.shape[-2:]).to(torch.bool)[0]
+            outs_dict[idx] = NestedTensor(out_i, mask)
+
+        return outs_dict
+
+    def train(self, mode=True):
+        """Convert the model into training mode while keep layers freezed."""
+        super(SwinTransformer, self).train(mode)
+        self._freeze_stages()
+
+
+def build_swin_transformer(modelname, pretrain_img_size, **kw):
+    assert modelname in [
+        "swin_T_224_1k",
+        "swin_B_224_22k",
+        "swin_B_384_22k",
+        "swin_L_224_22k",
+        "swin_L_384_22k",
+    ]
+
+    model_para_dict = {
+        "swin_T_224_1k": dict(
+            embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7
+        ),
+        "swin_B_224_22k": dict(
+            embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=7
+        ),
+        "swin_B_384_22k": dict(
+            embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=12
+        ),
+        "swin_L_224_22k": dict(
+            embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=7
+        ),
+        "swin_L_384_22k": dict(
+            embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12
+        ),
+    }
+    kw_cgf = model_para_dict[modelname]
+    kw_cgf.update(kw)
+    model = SwinTransformer(pretrain_img_size=pretrain_img_size, **kw_cgf)
+    return model
+
+
+if __name__ == "__main__":
+    model = build_swin_transformer("swin_L_384_22k", 384, dilation=True)
+    x = torch.rand(2, 3, 1024, 1024)
+    y = model.forward_raw(x)
+    import ipdb
+
+    ipdb.set_trace()
+    x = torch.rand(2, 3, 384, 384)
+    y = model.forward_raw(x)

groundingdino/models/GroundingDINO/bertwarper.py

@@ -0,0 +1,266 @@
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from torch import Tensor, nn
+from torchvision.ops.boxes import nms
+from transformers import BertConfig, BertModel, BertPreTrainedModel
+from transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions
+
+
+class BertModelWarper(nn.Module):
+    def __init__(self, bert_model):
+        super().__init__()
+        # self.bert = bert_modelc
+
+        self.config = bert_model.config
+        self.embeddings = bert_model.embeddings
+        self.encoder = bert_model.encoder
+        self.pooler = bert_model.pooler
+
+        self.get_extended_attention_mask = bert_model.get_extended_attention_mask
+        self.invert_attention_mask = bert_model.invert_attention_mask
+        self.get_head_mask = bert_model.get_head_mask
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
+            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
+            the model is configured as a decoder.
+        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
+            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+
+            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
+            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
+            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
+        use_cache (:obj:`bool`, `optional`):
+            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
+            decoding (see :obj:`past_key_values`).
+        """
+        output_attentions = (
+            output_attentions if output_attentions is not None else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if self.config.is_decoder:
+            use_cache = use_cache if use_cache is not None else self.config.use_cache
+        else:
+            use_cache = False
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            input_shape = input_ids.size()
+            batch_size, seq_length = input_shape
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+            batch_size, seq_length = input_shape
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+        # past_key_values_length
+        past_key_values_length = (
+            past_key_values[0][0].shape[2] if past_key_values is not None else 0
+        )
+
+        if attention_mask is None:
+            attention_mask = torch.ones(
+                ((batch_size, seq_length + past_key_values_length)), device=device
+            )
+        if token_type_ids is None:
+            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
+
+        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+        # ourselves in which case we just need to make it broadcastable to all heads.
+        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
+            attention_mask, input_shape, device
+        )
+
+        # If a 2D or 3D attention mask is provided for the cross-attention
+        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
+        if self.config.is_decoder and encoder_hidden_states is not None:
+            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
+            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
+            if encoder_attention_mask is None:
+                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
+            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
+        else:
+            encoder_extended_attention_mask = None
+        # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
+        #     import ipdb; ipdb.set_trace()
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+        embedding_output = self.embeddings(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            token_type_ids=token_type_ids,
+            inputs_embeds=inputs_embeds,
+            past_key_values_length=past_key_values_length,
+        )
+
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_mask=extended_attention_mask,
+            head_mask=head_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_extended_attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = encoder_outputs[0]
+        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+        if not return_dict:
+            return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPoolingAndCrossAttentions(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            past_key_values=encoder_outputs.past_key_values,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+            cross_attentions=encoder_outputs.cross_attentions,
+        )
+
+
+class TextEncoderShell(nn.Module):
+    def __init__(self, text_encoder):
+        super().__init__()
+        self.text_encoder = text_encoder
+        self.config = self.text_encoder.config
+
+    def forward(self, **kw):
+        # feed into text encoder
+        return self.text_encoder(**kw)
+
+
+def generate_masks_with_special_tokens(tokenized, special_tokens_list, tokenizer):
+    """Generate attention mask between each pair of special tokens
+    Args:
+        input_ids (torch.Tensor): input ids. Shape: [bs, num_token]
+        special_tokens_mask (list): special tokens mask.
+    Returns:
+        torch.Tensor: attention mask between each special tokens.
+    """
+    input_ids = tokenized["input_ids"]
+    bs, num_token = input_ids.shape
+    # special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens
+    special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool()
+    for special_token in special_tokens_list:
+        special_tokens_mask |= input_ids == special_token
+
+    # idxs: each row is a list of indices of special tokens
+    idxs = torch.nonzero(special_tokens_mask)
+
+    # generate attention mask and positional ids
+    attention_mask = (
+        torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1)
+    )
+    position_ids = torch.zeros((bs, num_token), device=input_ids.device)
+    previous_col = 0
+    for i in range(idxs.shape[0]):
+        row, col = idxs[i]
+        if (col == 0) or (col == num_token - 1):
+            attention_mask[row, col, col] = True
+            position_ids[row, col] = 0
+        else:
+            attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True
+            position_ids[row, previous_col + 1 : col + 1] = torch.arange(
+                0, col - previous_col, device=input_ids.device
+            )
+
+        previous_col = col
+
+    # # padding mask
+    # padding_mask = tokenized['attention_mask']
+    # attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool()
+
+    return attention_mask, position_ids.to(torch.long)
+
+
+def generate_masks_with_special_tokens_and_transfer_map(tokenized, special_tokens_list, tokenizer):
+    """Generate attention mask between each pair of special tokens
+    Args:
+        input_ids (torch.Tensor): input ids. Shape: [bs, num_token]
+        special_tokens_mask (list): special tokens mask.
+    Returns:
+        torch.Tensor: attention mask between each special tokens.
+    """
+    input_ids = tokenized["input_ids"]
+    bs, num_token = input_ids.shape
+    # special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens
+    special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool()
+    for special_token in special_tokens_list:
+        special_tokens_mask |= input_ids == special_token
+
+    # idxs: each row is a list of indices of special tokens
+    idxs = torch.nonzero(special_tokens_mask)
+
+    # generate attention mask and positional ids
+    attention_mask = (
+        torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1)
+    )
+    position_ids = torch.zeros((bs, num_token), device=input_ids.device)
+    cate_to_token_mask_list = [[] for _ in range(bs)]
+    previous_col = 0
+    for i in range(idxs.shape[0]):
+        row, col = idxs[i]
+        if (col == 0) or (col == num_token - 1):
+            attention_mask[row, col, col] = True
+            position_ids[row, col] = 0
+        else:
+            attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True
+            position_ids[row, previous_col + 1 : col + 1] = torch.arange(
+                0, col - previous_col, device=input_ids.device
+            )
+            c2t_maski = torch.zeros((num_token), device=input_ids.device).bool()
+            c2t_maski[previous_col + 1 : col] = True
+            cate_to_token_mask_list[row].append(c2t_maski)
+        previous_col = col
+
+    cate_to_token_mask_list = [
+        torch.stack(cate_to_token_mask_listi, dim=0)
+        for cate_to_token_mask_listi in cate_to_token_mask_list
+    ]
+
+    # # padding mask
+    # padding_mask = tokenized['attention_mask']
+    # attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool()
+
+    return attention_mask, position_ids.to(torch.long), cate_to_token_mask_list

groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn.h

@@ -0,0 +1,64 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+#pragma once
+
+#include "ms_deform_attn_cpu.h"
+
+#ifdef WITH_CUDA
+#include "ms_deform_attn_cuda.h"
+#endif
+
+namespace groundingdino {
+
+at::Tensor
+ms_deform_attn_forward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const int im2col_step)
+{
+    if (value.type().is_cuda())
+    {
+#ifdef WITH_CUDA
+        return ms_deform_attn_cuda_forward(
+            value, spatial_shapes, level_start_index, sampling_loc, attn_weight, im2col_step);
+#else
+        AT_ERROR("Not compiled with GPU support");
+#endif
+    }
+    AT_ERROR("Not implemented on the CPU");
+}
+
+std::vector<at::Tensor>
+ms_deform_attn_backward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const at::Tensor &grad_output,
+    const int im2col_step)
+{
+    if (value.type().is_cuda())
+    {
+#ifdef WITH_CUDA
+        return ms_deform_attn_cuda_backward(
+            value, spatial_shapes, level_start_index, sampling_loc, attn_weight, grad_output, im2col_step);
+#else
+        AT_ERROR("Not compiled with GPU support");
+#endif
+    }
+    AT_ERROR("Not implemented on the CPU");
+}
+
+} // namespace groundingdino

groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.cpp

@@ -0,0 +1,43 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+#include <vector>
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+
+namespace groundingdino {
+
+at::Tensor
+ms_deform_attn_cpu_forward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const int im2col_step)
+{
+    AT_ERROR("Not implement on cpu");
+}
+
+std::vector<at::Tensor>
+ms_deform_attn_cpu_backward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const at::Tensor &grad_output,
+    const int im2col_step)
+{
+    AT_ERROR("Not implement on cpu");
+}
+
+} // namespace groundingdino

groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.h

@@ -0,0 +1,35 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+#pragma once
+#include <torch/extension.h>
+
+namespace groundingdino {
+
+at::Tensor
+ms_deform_attn_cpu_forward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const int im2col_step);
+
+std::vector<at::Tensor>
+ms_deform_attn_cpu_backward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const at::Tensor &grad_output,
+    const int im2col_step);
+
+} // namespace groundingdino

groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.cu

@@ -0,0 +1,156 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+#include <vector>
+#include "ms_deform_im2col_cuda.cuh"
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <cuda.h>
+#include <cuda_runtime.h>
+
+namespace groundingdino {
+
+at::Tensor ms_deform_attn_cuda_forward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const int im2col_step)
+{
+    AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous");
+    AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous");
+    AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous");
+    AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous");
+    AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous");
+
+    AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor");
+    AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor");
+    AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor");
+    AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor");
+    AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor");
+
+    const int batch = value.size(0);
+    const int spatial_size = value.size(1);
+    const int num_heads = value.size(2);
+    const int channels = value.size(3);
+
+    const int num_levels = spatial_shapes.size(0);
+
+    const int num_query = sampling_loc.size(1);
+    const int num_point = sampling_loc.size(4);
+
+    const int im2col_step_ = std::min(batch, im2col_step);
+
+    AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_);
+    
+    auto output = at::zeros({batch, num_query, num_heads, channels}, value.options());
+
+    const int batch_n = im2col_step_;
+    auto output_n = output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels});
+    auto per_value_size = spatial_size * num_heads * channels;
+    auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;
+    auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;
+    for (int n = 0; n < batch/im2col_step_; ++n)
+    {
+        auto columns = output_n.select(0, n);
+        AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_forward_cuda", ([&] {
+            ms_deformable_im2col_cuda(at::cuda::getCurrentCUDAStream(),
+                value.data<scalar_t>() + n * im2col_step_ * per_value_size,
+                spatial_shapes.data<int64_t>(),
+                level_start_index.data<int64_t>(),
+                sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
+                attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size,
+                batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,
+                columns.data<scalar_t>());
+
+        }));
+    }
+
+    output = output.view({batch, num_query, num_heads*channels});
+
+    return output;
+}
+
+
+std::vector<at::Tensor> ms_deform_attn_cuda_backward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const at::Tensor &grad_output,
+    const int im2col_step)
+{
+
+    AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous");
+    AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous");
+    AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous");
+    AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous");
+    AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous");
+    AT_ASSERTM(grad_output.is_contiguous(), "grad_output tensor has to be contiguous");
+
+    AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor");
+    AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor");
+    AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor");
+    AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor");
+    AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor");
+    AT_ASSERTM(grad_output.type().is_cuda(), "grad_output must be a CUDA tensor");
+
+    const int batch = value.size(0);
+    const int spatial_size = value.size(1);
+    const int num_heads = value.size(2);
+    const int channels = value.size(3);
+
+    const int num_levels = spatial_shapes.size(0);
+
+    const int num_query = sampling_loc.size(1);
+    const int num_point = sampling_loc.size(4);
+
+    const int im2col_step_ = std::min(batch, im2col_step);
+
+    AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_);
+
+    auto grad_value = at::zeros_like(value);
+    auto grad_sampling_loc = at::zeros_like(sampling_loc);
+    auto grad_attn_weight = at::zeros_like(attn_weight);
+
+    const int batch_n = im2col_step_;
+    auto per_value_size = spatial_size * num_heads * channels;
+    auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;
+    auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;
+    auto grad_output_n = grad_output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels});
+    
+    for (int n = 0; n < batch/im2col_step_; ++n)
+    {
+        auto grad_output_g = grad_output_n.select(0, n);
+        AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_backward_cuda", ([&] {
+            ms_deformable_col2im_cuda(at::cuda::getCurrentCUDAStream(),
+                                    grad_output_g.data<scalar_t>(),
+                                    value.data<scalar_t>() + n * im2col_step_ * per_value_size,
+                                    spatial_shapes.data<int64_t>(),
+                                    level_start_index.data<int64_t>(),
+                                    sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
+                                    attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size,
+                                    batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,
+                                    grad_value.data<scalar_t>() +  n * im2col_step_ * per_value_size,
+                                    grad_sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
+                                    grad_attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size);
+
+        }));
+    }
+
+    return {
+        grad_value, grad_sampling_loc, grad_attn_weight
+    };
+}
+
+} // namespace groundingdino

groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.h

@@ -0,0 +1,33 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+#pragma once
+#include <torch/extension.h>
+
+namespace groundingdino {
+
+at::Tensor ms_deform_attn_cuda_forward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const int im2col_step);
+
+std::vector<at::Tensor> ms_deform_attn_cuda_backward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const at::Tensor &grad_output,
+    const int im2col_step);
+
+} // namespace groundingdino

groundingdino/models/GroundingDINO/csrc/cuda_version.cu

@@ -0,0 +1,7 @@
+#include <cuda_runtime_api.h>
+
+namespace groundingdino {
+int get_cudart_version() {
+  return CUDART_VERSION;
+}
+} // namespace groundingdino

groundingdino/models/GroundingDINO/csrc/vision.cpp

@@ -0,0 +1,58 @@
+// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+
+#include "MsDeformAttn/ms_deform_attn.h"
+
+namespace groundingdino {
+
+#ifdef WITH_CUDA
+extern int get_cudart_version();
+#endif
+
+std::string get_cuda_version() {
+#ifdef WITH_CUDA
+  std::ostringstream oss;
+
+  // copied from
+  // https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/cuda/detail/CUDAHooks.cpp#L231
+  auto printCudaStyleVersion = [&](int v) {
+    oss << (v / 1000) << "." << (v / 10 % 100);
+    if (v % 10 != 0) {
+      oss << "." << (v % 10);
+    }
+  };
+  printCudaStyleVersion(get_cudart_version());
+  return oss.str();
+#else
+  return std::string("not available");
+#endif
+}
+
+// similar to
+// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/Version.cpp
+std::string get_compiler_version() {
+  std::ostringstream ss;
+#if defined(__GNUC__)
+#ifndef __clang__
+  { ss << "GCC " << __GNUC__ << "." << __GNUC_MINOR__; }
+#endif
+#endif
+
+#if defined(__clang_major__)
+  {
+    ss << "clang " << __clang_major__ << "." << __clang_minor__ << "."
+       << __clang_patchlevel__;
+  }
+#endif
+
+#if defined(_MSC_VER)
+  { ss << "MSVC " << _MSC_FULL_VER; }
+#endif
+  return ss.str();
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def("ms_deform_attn_forward", &ms_deform_attn_forward, "ms_deform_attn_forward");
+  m.def("ms_deform_attn_backward", &ms_deform_attn_backward, "ms_deform_attn_backward");
+}
+
+} // namespace groundingdino

groundingdino/models/GroundingDINO/fuse_modules.py

@@ -0,0 +1,290 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from timm.models.layers import DropPath
+
+
+class FeatureResizer(nn.Module):
+    """
+    This class takes as input a set of embeddings of dimension C1 and outputs a set of
+    embedding of dimension C2, after a linear transformation, dropout and normalization (LN).
+    """
+
+    def __init__(self, input_feat_size, output_feat_size, dropout, do_ln=True):
+        super().__init__()
+        self.do_ln = do_ln
+        # Object feature encoding
+        self.fc = nn.Linear(input_feat_size, output_feat_size, bias=True)
+        self.layer_norm = nn.LayerNorm(output_feat_size, eps=1e-12)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, encoder_features):
+        x = self.fc(encoder_features)
+        if self.do_ln:
+            x = self.layer_norm(x)
+        output = self.dropout(x)
+        return output
+
+
+def l1norm(X, dim, eps=1e-8):
+    """L1-normalize columns of X"""
+    norm = torch.abs(X).sum(dim=dim, keepdim=True) + eps
+    X = torch.div(X, norm)
+    return X
+
+
+def l2norm(X, dim, eps=1e-8):
+    """L2-normalize columns of X"""
+    norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
+    X = torch.div(X, norm)
+    return X
+
+
+def func_attention(query, context, smooth=1, raw_feature_norm="softmax", eps=1e-8):
+    """
+    query: (n_context, queryL, d)
+    context: (n_context, sourceL, d)
+    """
+    batch_size_q, queryL = query.size(0), query.size(1)
+    batch_size, sourceL = context.size(0), context.size(1)
+
+    # Get attention
+    # --> (batch, d, queryL)
+    queryT = torch.transpose(query, 1, 2)
+
+    # (batch, sourceL, d)(batch, d, queryL)
+    # --> (batch, sourceL, queryL)
+    attn = torch.bmm(context, queryT)
+    if raw_feature_norm == "softmax":
+        # --> (batch*sourceL, queryL)
+        attn = attn.view(batch_size * sourceL, queryL)
+        attn = nn.Softmax()(attn)
+        # --> (batch, sourceL, queryL)
+        attn = attn.view(batch_size, sourceL, queryL)
+    elif raw_feature_norm == "l2norm":
+        attn = l2norm(attn, 2)
+    elif raw_feature_norm == "clipped_l2norm":
+        attn = nn.LeakyReLU(0.1)(attn)
+        attn = l2norm(attn, 2)
+    else:
+        raise ValueError("unknown first norm type:", raw_feature_norm)
+    # --> (batch, queryL, sourceL)
+    attn = torch.transpose(attn, 1, 2).contiguous()
+    # --> (batch*queryL, sourceL)
+    attn = attn.view(batch_size * queryL, sourceL)
+    attn = nn.Softmax()(attn * smooth)
+    # --> (batch, queryL, sourceL)
+    attn = attn.view(batch_size, queryL, sourceL)
+    # --> (batch, sourceL, queryL)
+    attnT = torch.transpose(attn, 1, 2).contiguous()
+
+    # --> (batch, d, sourceL)
+    contextT = torch.transpose(context, 1, 2)
+    # (batch x d x sourceL)(batch x sourceL x queryL)
+    # --> (batch, d, queryL)
+    weightedContext = torch.bmm(contextT, attnT)
+    # --> (batch, queryL, d)
+    weightedContext = torch.transpose(weightedContext, 1, 2)
+
+    return weightedContext, attnT
+
+
+class BiMultiHeadAttention(nn.Module):
+    def __init__(self, v_dim, l_dim, embed_dim, num_heads, dropout=0.1, cfg=None):
+        super(BiMultiHeadAttention, self).__init__()
+
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.head_dim = embed_dim // num_heads
+        self.v_dim = v_dim
+        self.l_dim = l_dim
+
+        assert (
+            self.head_dim * self.num_heads == self.embed_dim
+        ), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})."
+        self.scale = self.head_dim ** (-0.5)
+        self.dropout = dropout
+
+        self.v_proj = nn.Linear(self.v_dim, self.embed_dim)
+        self.l_proj = nn.Linear(self.l_dim, self.embed_dim)
+        self.values_v_proj = nn.Linear(self.v_dim, self.embed_dim)
+        self.values_l_proj = nn.Linear(self.l_dim, self.embed_dim)
+
+        self.out_v_proj = nn.Linear(self.embed_dim, self.v_dim)
+        self.out_l_proj = nn.Linear(self.embed_dim, self.l_dim)
+
+        self.stable_softmax_2d = True
+        self.clamp_min_for_underflow = True
+        self.clamp_max_for_overflow = True
+
+        self._reset_parameters()
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def _reset_parameters(self):
+        nn.init.xavier_uniform_(self.v_proj.weight)
+        self.v_proj.bias.data.fill_(0)
+        nn.init.xavier_uniform_(self.l_proj.weight)
+        self.l_proj.bias.data.fill_(0)
+        nn.init.xavier_uniform_(self.values_v_proj.weight)
+        self.values_v_proj.bias.data.fill_(0)
+        nn.init.xavier_uniform_(self.values_l_proj.weight)
+        self.values_l_proj.bias.data.fill_(0)
+        nn.init.xavier_uniform_(self.out_v_proj.weight)
+        self.out_v_proj.bias.data.fill_(0)
+        nn.init.xavier_uniform_(self.out_l_proj.weight)
+        self.out_l_proj.bias.data.fill_(0)
+
+    def forward(self, v, l, attention_mask_v=None, attention_mask_l=None):
+        """_summary_
+
+        Args:
+            v (_type_): bs, n_img, dim
+            l (_type_): bs, n_text, dim
+            attention_mask_v (_type_, optional): _description_. bs, n_img
+            attention_mask_l (_type_, optional): _description_. bs, n_text
+
+        Returns:
+            _type_: _description_
+        """
+        # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
+        #     import ipdb; ipdb.set_trace()
+        bsz, tgt_len, _ = v.size()
+
+        query_states = self.v_proj(v) * self.scale
+        key_states = self._shape(self.l_proj(l), -1, bsz)
+        value_v_states = self._shape(self.values_v_proj(v), -1, bsz)
+        value_l_states = self._shape(self.values_l_proj(l), -1, bsz)
+
+        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
+        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
+        key_states = key_states.view(*proj_shape)
+        value_v_states = value_v_states.view(*proj_shape)
+        value_l_states = value_l_states.view(*proj_shape)
+
+        src_len = key_states.size(1)
+        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))  # bs*nhead, nimg, ntxt
+
+        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+            )
+
+        if self.stable_softmax_2d:
+            attn_weights = attn_weights - attn_weights.max()
+
+        if self.clamp_min_for_underflow:
+            attn_weights = torch.clamp(
+                attn_weights, min=-50000
+            )  # Do not increase -50000, data type half has quite limited range
+        if self.clamp_max_for_overflow:
+            attn_weights = torch.clamp(
+                attn_weights, max=50000
+            )  # Do not increase 50000, data type half has quite limited range
+
+        attn_weights_T = attn_weights.transpose(1, 2)
+        attn_weights_l = attn_weights_T - torch.max(attn_weights_T, dim=-1, keepdim=True)[0]
+        if self.clamp_min_for_underflow:
+            attn_weights_l = torch.clamp(
+                attn_weights_l, min=-50000
+            )  # Do not increase -50000, data type half has quite limited range
+        if self.clamp_max_for_overflow:
+            attn_weights_l = torch.clamp(
+                attn_weights_l, max=50000
+            )  # Do not increase 50000, data type half has quite limited range
+
+        # mask vison for language
+        if attention_mask_v is not None:
+            attention_mask_v = (
+                attention_mask_v[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)
+            )
+            attn_weights_l.masked_fill_(attention_mask_v, float("-inf"))
+
+        attn_weights_l = attn_weights_l.softmax(dim=-1)
+
+        # mask language for vision
+        if attention_mask_l is not None:
+            attention_mask_l = (
+                attention_mask_l[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)
+            )
+            attn_weights.masked_fill_(attention_mask_l, float("-inf"))
+        attn_weights_v = attn_weights.softmax(dim=-1)
+
+        attn_probs_v = F.dropout(attn_weights_v, p=self.dropout, training=self.training)
+        attn_probs_l = F.dropout(attn_weights_l, p=self.dropout, training=self.training)
+
+        attn_output_v = torch.bmm(attn_probs_v, value_l_states)
+        attn_output_l = torch.bmm(attn_probs_l, value_v_states)
+
+        if attn_output_v.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output_v` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output_v.size()}"
+            )
+
+        if attn_output_l.size() != (bsz * self.num_heads, src_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output_l` should be of size {(bsz, self.num_heads, src_len, self.head_dim)}, but is {attn_output_l.size()}"
+            )
+
+        attn_output_v = attn_output_v.view(bsz, self.num_heads, tgt_len, self.head_dim)
+        attn_output_v = attn_output_v.transpose(1, 2)
+        attn_output_v = attn_output_v.reshape(bsz, tgt_len, self.embed_dim)
+
+        attn_output_l = attn_output_l.view(bsz, self.num_heads, src_len, self.head_dim)
+        attn_output_l = attn_output_l.transpose(1, 2)
+        attn_output_l = attn_output_l.reshape(bsz, src_len, self.embed_dim)
+
+        attn_output_v = self.out_v_proj(attn_output_v)
+        attn_output_l = self.out_l_proj(attn_output_l)
+
+        return attn_output_v, attn_output_l
+
+
+# Bi-Direction MHA (text->image, image->text)
+class BiAttentionBlock(nn.Module):
+    def __init__(
+        self,
+        v_dim,
+        l_dim,
+        embed_dim,
+        num_heads,
+        dropout=0.1,
+        drop_path=0.0,
+        init_values=1e-4,
+        cfg=None,
+    ):
+        """
+        Inputs:
+            embed_dim - Dimensionality of input and attention feature vectors
+            hidden_dim - Dimensionality of hidden layer in feed-forward network
+                         (usually 2-4x larger than embed_dim)
+            num_heads - Number of heads to use in the Multi-Head Attention block
+            dropout - Amount of dropout to apply in the feed-forward network
+        """
+        super(BiAttentionBlock, self).__init__()
+
+        # pre layer norm
+        self.layer_norm_v = nn.LayerNorm(v_dim)
+        self.layer_norm_l = nn.LayerNorm(l_dim)
+        self.attn = BiMultiHeadAttention(
+            v_dim=v_dim, l_dim=l_dim, embed_dim=embed_dim, num_heads=num_heads, dropout=dropout
+        )
+
+        # add layer scale for training stability
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+        self.gamma_v = nn.Parameter(init_values * torch.ones((v_dim)), requires_grad=True)
+        self.gamma_l = nn.Parameter(init_values * torch.ones((l_dim)), requires_grad=True)
+
+    def forward(self, v, l, attention_mask_v=None, attention_mask_l=None):
+        v = self.layer_norm_v(v)
+        l = self.layer_norm_l(l)
+        delta_v, delta_l = self.attn(
+            v, l, attention_mask_v=attention_mask_v, attention_mask_l=attention_mask_l
+        )
+        # v, l = v + delta_v, l + delta_l
+        v = v + self.drop_path(self.gamma_v * delta_v)
+        l = l + self.drop_path(self.gamma_l * delta_l)
+        return v, l
+
+    # def forward(self, v:List[torch.Tensor], l, attention_mask_v=None, attention_mask_l=None)

groundingdino/models/GroundingDINO/groundingdino.py

@@ -0,0 +1,394 @@
+# ------------------------------------------------------------------------
+# DINO
+# Copyright (c) 2022 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Conditional DETR model and criterion classes.
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Modified from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# ------------------------------------------------------------------------
+import copy
+from typing import List
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torchvision.ops.boxes import nms
+from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast
+
+from groundingdino.util import box_ops, get_tokenlizer
+from groundingdino.util.misc import (
+    NestedTensor,
+    accuracy,
+    get_world_size,
+    interpolate,
+    inverse_sigmoid,
+    is_dist_avail_and_initialized,
+    nested_tensor_from_tensor_list,
+)
+from groundingdino.util.utils import get_phrases_from_posmap
+from groundingdino.util.visualizer import COCOVisualizer
+from groundingdino.util.vl_utils import create_positive_map_from_span
+
+from ..registry import MODULE_BUILD_FUNCS
+from .backbone import build_backbone
+from .bertwarper import (
+    BertModelWarper,
+    generate_masks_with_special_tokens,
+    generate_masks_with_special_tokens_and_transfer_map,
+)
+from .transformer import build_transformer
+from .utils import MLP, ContrastiveEmbed, sigmoid_focal_loss
+
+
+class GroundingDINO(nn.Module):
+    """This is the Cross-Attention Detector module that performs object detection"""
+
+    def __init__(
+        self,
+        backbone,
+        transformer,
+        num_queries,
+        aux_loss=False,
+        iter_update=False,
+        query_dim=2,
+        num_feature_levels=1,
+        nheads=8,
+        # two stage
+        two_stage_type="no",  # ['no', 'standard']
+        dec_pred_bbox_embed_share=True,
+        two_stage_class_embed_share=True,
+        two_stage_bbox_embed_share=True,
+        num_patterns=0,
+        dn_number=100,
+        dn_box_noise_scale=0.4,
+        dn_label_noise_ratio=0.5,
+        dn_labelbook_size=100,
+        text_encoder_type="bert-base-uncased",
+        sub_sentence_present=True,
+        max_text_len=256,
+    ):
+        """Initializes the model.
+        Parameters:
+            backbone: torch module of the backbone to be used. See backbone.py
+            transformer: torch module of the transformer architecture. See transformer.py
+            num_queries: number of object queries, ie detection slot. This is the maximal number of objects
+                         Conditional DETR can detect in a single image. For COCO, we recommend 100 queries.
+            aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used.
+        """
+        super().__init__()
+        self.num_queries = num_queries
+        self.transformer = transformer
+        self.hidden_dim = hidden_dim = transformer.d_model
+        self.num_feature_levels = num_feature_levels
+        self.nheads = nheads
+        self.max_text_len = 256
+        self.sub_sentence_present = sub_sentence_present
+
+        # setting query dim
+        self.query_dim = query_dim
+        assert query_dim == 4
+
+        # for dn training
+        self.num_patterns = num_patterns
+        self.dn_number = dn_number
+        self.dn_box_noise_scale = dn_box_noise_scale
+        self.dn_label_noise_ratio = dn_label_noise_ratio
+        self.dn_labelbook_size = dn_labelbook_size
+
+        # bert
+        self.tokenizer = get_tokenlizer.get_tokenlizer(text_encoder_type)
+        self.bert = get_tokenlizer.get_pretrained_language_model(text_encoder_type)
+        self.bert.pooler.dense.weight.requires_grad_(False)
+        self.bert.pooler.dense.bias.requires_grad_(False)
+        self.bert = BertModelWarper(bert_model=self.bert)
+
+        self.feat_map = nn.Linear(self.bert.config.hidden_size, self.hidden_dim, bias=True)
+        nn.init.constant_(self.feat_map.bias.data, 0)
+        nn.init.xavier_uniform_(self.feat_map.weight.data)
+        # freeze
+
+        # special tokens
+        self.specical_tokens = self.tokenizer.convert_tokens_to_ids(["[CLS]", "[SEP]", ".", "?"])
+
+        # prepare input projection layers
+        if num_feature_levels > 1:
+            num_backbone_outs = len(backbone.num_channels)
+            input_proj_list = []
+            for _ in range(num_backbone_outs):
+                in_channels = backbone.num_channels[_]
+                input_proj_list.append(
+                    nn.Sequential(
+                        nn.Conv2d(in_channels, hidden_dim, kernel_size=1),
+                        nn.GroupNorm(32, hidden_dim),
+                    )
+                )
+            for _ in range(num_feature_levels - num_backbone_outs):
+                input_proj_list.append(
+                    nn.Sequential(
+                        nn.Conv2d(in_channels, hidden_dim, kernel_size=3, stride=2, padding=1),
+                        nn.GroupNorm(32, hidden_dim),
+                    )
+                )
+                in_channels = hidden_dim
+            self.input_proj = nn.ModuleList(input_proj_list)
+        else:
+            assert two_stage_type == "no", "two_stage_type should be no if num_feature_levels=1 !!!"
+            self.input_proj = nn.ModuleList(
+                [
+                    nn.Sequential(
+                        nn.Conv2d(backbone.num_channels[-1], hidden_dim, kernel_size=1),
+                        nn.GroupNorm(32, hidden_dim),
+                    )
+                ]
+            )
+
+        self.backbone = backbone
+        self.aux_loss = aux_loss
+        self.box_pred_damping = box_pred_damping = None
+
+        self.iter_update = iter_update
+        assert iter_update, "Why not iter_update?"
+
+        # prepare pred layers
+        self.dec_pred_bbox_embed_share = dec_pred_bbox_embed_share
+        # prepare class & box embed
+        _class_embed = ContrastiveEmbed()
+
+        _bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3)
+        nn.init.constant_(_bbox_embed.layers[-1].weight.data, 0)
+        nn.init.constant_(_bbox_embed.layers[-1].bias.data, 0)
+
+        if dec_pred_bbox_embed_share:
+            box_embed_layerlist = [_bbox_embed for i in range(transformer.num_decoder_layers)]
+        else:
+            box_embed_layerlist = [
+                copy.deepcopy(_bbox_embed) for i in range(transformer.num_decoder_layers)
+            ]
+        class_embed_layerlist = [_class_embed for i in range(transformer.num_decoder_layers)]
+        self.bbox_embed = nn.ModuleList(box_embed_layerlist)
+        self.class_embed = nn.ModuleList(class_embed_layerlist)
+        self.transformer.decoder.bbox_embed = self.bbox_embed
+        self.transformer.decoder.class_embed = self.class_embed
+
+        # two stage
+        self.two_stage_type = two_stage_type
+        assert two_stage_type in ["no", "standard"], "unknown param {} of two_stage_type".format(
+            two_stage_type
+        )
+        if two_stage_type != "no":
+            if two_stage_bbox_embed_share:
+                assert dec_pred_bbox_embed_share
+                self.transformer.enc_out_bbox_embed = _bbox_embed
+            else:
+                self.transformer.enc_out_bbox_embed = copy.deepcopy(_bbox_embed)
+
+            if two_stage_class_embed_share:
+                assert dec_pred_bbox_embed_share
+                self.transformer.enc_out_class_embed = _class_embed
+            else:
+                self.transformer.enc_out_class_embed = copy.deepcopy(_class_embed)
+
+            self.refpoint_embed = None
+
+        self._reset_parameters()
+
+    def _reset_parameters(self):
+        # init input_proj
+        for proj in self.input_proj:
+            nn.init.xavier_uniform_(proj[0].weight, gain=1)
+            nn.init.constant_(proj[0].bias, 0)
+
+    def init_ref_points(self, use_num_queries):
+        self.refpoint_embed = nn.Embedding(use_num_queries, self.query_dim)
+
+    def forward(self, samples: NestedTensor, targets: List = None, **kw):
+        """The forward expects a NestedTensor, which consists of:
+           - samples.tensor: batched images, of shape [batch_size x 3 x H x W]
+           - samples.mask: a binary mask of shape [batch_size x H x W], containing 1 on padded pixels
+
+        It returns a dict with the following elements:
+           - "pred_logits": the classification logits (including no-object) for all queries.
+                            Shape= [batch_size x num_queries x num_classes]
+           - "pred_boxes": The normalized boxes coordinates for all queries, represented as
+                           (center_x, center_y, width, height). These values are normalized in [0, 1],
+                           relative to the size of each individual image (disregarding possible padding).
+                           See PostProcess for information on how to retrieve the unnormalized bounding box.
+           - "aux_outputs": Optional, only returned when auxilary losses are activated. It is a list of
+                            dictionnaries containing the two above keys for each decoder layer.
+        """
+        if targets is None:
+            captions = kw["captions"]
+        else:
+            captions = [t["caption"] for t in targets]
+        len(captions)
+
+        # encoder texts
+        tokenized = self.tokenizer(captions, padding="longest", return_tensors="pt").to(
+            samples.device
+        )
+        (
+            text_self_attention_masks,
+            position_ids,
+            cate_to_token_mask_list,
+        ) = generate_masks_with_special_tokens_and_transfer_map(
+            tokenized, self.specical_tokens, self.tokenizer
+        )
+
+        if text_self_attention_masks.shape[1] > self.max_text_len:
+            text_self_attention_masks = text_self_attention_masks[
+                :, : self.max_text_len, : self.max_text_len
+            ]
+            position_ids = position_ids[:, : self.max_text_len]
+            tokenized["input_ids"] = tokenized["input_ids"][:, : self.max_text_len]
+            tokenized["attention_mask"] = tokenized["attention_mask"][:, : self.max_text_len]
+            tokenized["token_type_ids"] = tokenized["token_type_ids"][:, : self.max_text_len]
+
+        # extract text embeddings
+        if self.sub_sentence_present:
+            tokenized_for_encoder = {k: v for k, v in tokenized.items() if k != "attention_mask"}
+            tokenized_for_encoder["attention_mask"] = text_self_attention_masks
+            tokenized_for_encoder["position_ids"] = position_ids
+        else:
+            # import ipdb; ipdb.set_trace()
+            tokenized_for_encoder = tokenized
+
+        bert_output = self.bert(**tokenized_for_encoder)  # bs, 195, 768
+
+        encoded_text = self.feat_map(bert_output["last_hidden_state"])  # bs, 195, d_model
+        text_token_mask = tokenized.attention_mask.bool()  # bs, 195
+        # text_token_mask: True for nomask, False for mask
+        # text_self_attention_masks: True for nomask, False for mask
+
+        if encoded_text.shape[1] > self.max_text_len:
+            encoded_text = encoded_text[:, : self.max_text_len, :]
+            text_token_mask = text_token_mask[:, : self.max_text_len]
+            position_ids = position_ids[:, : self.max_text_len]
+            text_self_attention_masks = text_self_attention_masks[
+                :, : self.max_text_len, : self.max_text_len
+            ]
+
+        text_dict = {
+            "encoded_text": encoded_text,  # bs, 195, d_model
+            "text_token_mask": text_token_mask,  # bs, 195
+            "position_ids": position_ids,  # bs, 195
+            "text_self_attention_masks": text_self_attention_masks,  # bs, 195,195
+        }
+
+        # import ipdb; ipdb.set_trace()
+
+        if isinstance(samples, (list, torch.Tensor)):
+            samples = nested_tensor_from_tensor_list(samples)
+        features, poss = self.backbone(samples)
+
+        srcs = []
+        masks = []
+        for l, feat in enumerate(features):
+            src, mask = feat.decompose()
+            srcs.append(self.input_proj[l](src))
+            masks.append(mask)
+            assert mask is not None
+        if self.num_feature_levels > len(srcs):
+            _len_srcs = len(srcs)
+            for l in range(_len_srcs, self.num_feature_levels):
+                if l == _len_srcs:
+                    src = self.input_proj[l](features[-1].tensors)
+                else:
+                    src = self.input_proj[l](srcs[-1])
+                m = samples.mask
+                mask = F.interpolate(m[None].float(), size=src.shape[-2:]).to(torch.bool)[0]
+                pos_l = self.backbone[1](NestedTensor(src, mask)).to(src.dtype)
+                srcs.append(src)
+                masks.append(mask)
+                poss.append(pos_l)
+
+        input_query_bbox = input_query_label = attn_mask = dn_meta = None
+        hs, reference, hs_enc, ref_enc, init_box_proposal = self.transformer(
+            srcs, masks, input_query_bbox, poss, input_query_label, attn_mask, text_dict
+        )
+
+        # deformable-detr-like anchor update
+        outputs_coord_list = []
+        for dec_lid, (layer_ref_sig, layer_bbox_embed, layer_hs) in enumerate(
+            zip(reference[:-1], self.bbox_embed, hs)
+        ):
+            layer_delta_unsig = layer_bbox_embed(layer_hs)
+            layer_outputs_unsig = layer_delta_unsig + inverse_sigmoid(layer_ref_sig)
+            layer_outputs_unsig = layer_outputs_unsig.sigmoid()
+            outputs_coord_list.append(layer_outputs_unsig)
+        outputs_coord_list = torch.stack(outputs_coord_list)
+
+        # output
+        outputs_class = torch.stack(
+            [
+                layer_cls_embed(layer_hs, text_dict)
+                for layer_cls_embed, layer_hs in zip(self.class_embed, hs)
+            ]
+        )
+        out = {"pred_logits": outputs_class[-1], "pred_boxes": outputs_coord_list[-1]}
+
+        # # for intermediate outputs
+        # if self.aux_loss:
+        #     out['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord_list)
+
+        # # for encoder output
+        # if hs_enc is not None:
+        #     # prepare intermediate outputs
+        #     interm_coord = ref_enc[-1]
+        #     interm_class = self.transformer.enc_out_class_embed(hs_enc[-1], text_dict)
+        #     out['interm_outputs'] = {'pred_logits': interm_class, 'pred_boxes': interm_coord}
+        #     out['interm_outputs_for_matching_pre'] = {'pred_logits': interm_class, 'pred_boxes': init_box_proposal}
+
+        return out
+
+    @torch.jit.unused
+    def _set_aux_loss(self, outputs_class, outputs_coord):
+        # this is a workaround to make torchscript happy, as torchscript
+        # doesn't support dictionary with non-homogeneous values, such
+        # as a dict having both a Tensor and a list.
+        return [
+            {"pred_logits": a, "pred_boxes": b}
+            for a, b in zip(outputs_class[:-1], outputs_coord[:-1])
+        ]
+
+
+@MODULE_BUILD_FUNCS.registe_with_name(module_name="groundingdino")
+def build_groundingdino(args):
+
+    backbone = build_backbone(args)
+    transformer = build_transformer(args)
+
+    dn_labelbook_size = args.dn_labelbook_size
+    dec_pred_bbox_embed_share = args.dec_pred_bbox_embed_share
+    sub_sentence_present = args.sub_sentence_present
+
+    model = GroundingDINO(
+        backbone,
+        transformer,
+        num_queries=args.num_queries,
+        aux_loss=True,
+        iter_update=True,
+        query_dim=4,
+        num_feature_levels=args.num_feature_levels,
+        nheads=args.nheads,
+        dec_pred_bbox_embed_share=dec_pred_bbox_embed_share,
+        two_stage_type=args.two_stage_type,
+        two_stage_bbox_embed_share=args.two_stage_bbox_embed_share,
+        two_stage_class_embed_share=args.two_stage_class_embed_share,
+        num_patterns=args.num_patterns,
+        dn_number=0,
+        dn_box_noise_scale=args.dn_box_noise_scale,
+        dn_label_noise_ratio=args.dn_label_noise_ratio,
+        dn_labelbook_size=dn_labelbook_size,
+        text_encoder_type=args.text_encoder_type,
+        sub_sentence_present=sub_sentence_present,
+        max_text_len=args.max_text_len,
+    )
+
+    return model

groundingdino/models/GroundingDINO/ms_deform_attn.py

@@ -0,0 +1,419 @@
+# coding=utf-8
+# Copyright 2022 The IDEA Authors. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ------------------------------------------------------------------------------------------------
+# Deformable DETR
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------------------------------
+# Modified from:
+# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/functions/ms_deform_attn_func.py
+# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/modules/ms_deform_attn.py
+# https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/multi_scale_deform_attn.py
+# ------------------------------------------------------------------------------------------------
+
+import math
+import warnings
+from typing import Optional
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Function
+from torch.autograd.function import once_differentiable
+from torch.nn.init import constant_, xavier_uniform_
+from groundingdino import _C
+
+
+# helpers
+def _is_power_of_2(n):
+    if (not isinstance(n, int)) or (n < 0):
+        raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n)))
+    return (n & (n - 1) == 0) and n != 0
+
+
+class MultiScaleDeformableAttnFunction(Function):
+    @staticmethod
+    def forward(
+        ctx,
+        value,
+        value_spatial_shapes,
+        value_level_start_index,
+        sampling_locations,
+        attention_weights,
+        im2col_step,
+    ):
+        ctx.im2col_step = im2col_step
+        output = _C.ms_deform_attn_forward(
+            value,
+            value_spatial_shapes,
+            value_level_start_index,
+            sampling_locations,
+            attention_weights,
+            ctx.im2col_step,
+        )
+        ctx.save_for_backward(
+            value,
+            value_spatial_shapes,
+            value_level_start_index,
+            sampling_locations,
+            attention_weights,
+        )
+        return output
+
+    @staticmethod
+    @once_differentiable
+    def backward(ctx, grad_output):
+        (
+            value,
+            value_spatial_shapes,
+            value_level_start_index,
+            sampling_locations,
+            attention_weights,
+        ) = ctx.saved_tensors
+        grad_value, grad_sampling_loc, grad_attn_weight = _C.ms_deform_attn_backward(
+            value,
+            value_spatial_shapes,
+            value_level_start_index,
+            sampling_locations,
+            attention_weights,
+            grad_output,
+            ctx.im2col_step,
+        )
+
+        return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None
+
+
+def multi_scale_deformable_attn_pytorch(
+    value: torch.Tensor,
+    value_spatial_shapes: torch.Tensor,
+    sampling_locations: torch.Tensor,
+    attention_weights: torch.Tensor,
+) -> torch.Tensor:
+
+    bs, _, num_heads, embed_dims = value.shape
+    _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape
+    value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)
+    sampling_grids = 2 * sampling_locations - 1
+    sampling_value_list = []
+    for level, (H_, W_) in enumerate(value_spatial_shapes):
+        # bs, H_*W_, num_heads, embed_dims ->
+        # bs, H_*W_, num_heads*embed_dims ->
+        # bs, num_heads*embed_dims, H_*W_ ->
+        # bs*num_heads, embed_dims, H_, W_
+        value_l_ = (
+            value_list[level].flatten(2).transpose(1, 2).reshape(bs * num_heads, embed_dims, H_, W_)
+        )
+        # bs, num_queries, num_heads, num_points, 2 ->
+        # bs, num_heads, num_queries, num_points, 2 ->
+        # bs*num_heads, num_queries, num_points, 2
+        sampling_grid_l_ = sampling_grids[:, :, :, level].transpose(1, 2).flatten(0, 1)
+        # bs*num_heads, embed_dims, num_queries, num_points
+        sampling_value_l_ = F.grid_sample(
+            value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False
+        )
+        sampling_value_list.append(sampling_value_l_)
+    # (bs, num_queries, num_heads, num_levels, num_points) ->
+    # (bs, num_heads, num_queries, num_levels, num_points) ->
+    # (bs, num_heads, 1, num_queries, num_levels*num_points)
+    attention_weights = attention_weights.transpose(1, 2).reshape(
+        bs * num_heads, 1, num_queries, num_levels * num_points
+    )
+    output = (
+        (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights)
+        .sum(-1)
+        .view(bs, num_heads * embed_dims, num_queries)
+    )
+    return output.transpose(1, 2).contiguous()
+
+
+class MultiScaleDeformableAttention(nn.Module):
+    """Multi-Scale Deformable Attention Module used in Deformable-DETR
+
+    `Deformable DETR: Deformable Transformers for End-to-End Object Detection.
+    <https://arxiv.org/pdf/2010.04159.pdf>`_.
+
+    Args:
+        embed_dim (int): The embedding dimension of Attention. Default: 256.
+        num_heads (int): The number of attention heads. Default: 8.
+        num_levels (int): The number of feature map used in Attention. Default: 4.
+        num_points (int): The number of sampling points for each query
+            in each head. Default: 4.
+        img2col_steps (int): The step used in image_to_column. Defualt: 64.
+            dropout (float): Dropout layer used in output. Default: 0.1.
+        batch_first (bool): if ``True``, then the input and output tensor will be
+            provided as `(bs, n, embed_dim)`. Default: False. `(n, bs, embed_dim)`
+    """
+
+    def __init__(
+        self,
+        embed_dim: int = 256,
+        num_heads: int = 8,
+        num_levels: int = 4,
+        num_points: int = 4,
+        img2col_step: int = 64,
+        batch_first: bool = False,
+    ):
+        super().__init__()
+        if embed_dim % num_heads != 0:
+            raise ValueError(
+                "embed_dim must be divisible by num_heads, but got {} and {}".format(
+                    embed_dim, num_heads
+                )
+            )
+        head_dim = embed_dim // num_heads
+
+        self.batch_first = batch_first
+
+        if not _is_power_of_2(head_dim):
+            warnings.warn(
+                """
+                You'd better set d_model in MSDeformAttn to make sure that
+                each dim of the attention head a power of 2, which is more efficient.
+                """
+            )
+
+        self.im2col_step = img2col_step
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.num_levels = num_levels
+        self.num_points = num_points
+        self.sampling_offsets = nn.Linear(embed_dim, num_heads * num_levels * num_points * 2)
+        self.attention_weights = nn.Linear(embed_dim, num_heads * num_levels * num_points)
+        self.value_proj = nn.Linear(embed_dim, embed_dim)
+        self.output_proj = nn.Linear(embed_dim, embed_dim)
+
+        self.init_weights()
+
+    def _reset_parameters(self):
+        return self.init_weights()
+
+    def init_weights(self):
+        """
+        Default initialization for Parameters of Module.
+        """
+        constant_(self.sampling_offsets.weight.data, 0.0)
+        thetas = torch.arange(self.num_heads, dtype=torch.float32) * (
+            2.0 * math.pi / self.num_heads
+        )
+        grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
+        grid_init = (
+            (grid_init / grid_init.abs().max(-1, keepdim=True)[0])
+            .view(self.num_heads, 1, 1, 2)
+            .repeat(1, self.num_levels, self.num_points, 1)
+        )
+        for i in range(self.num_points):
+            grid_init[:, :, i, :] *= i + 1
+        with torch.no_grad():
+            self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
+        constant_(self.attention_weights.weight.data, 0.0)
+        constant_(self.attention_weights.bias.data, 0.0)
+        xavier_uniform_(self.value_proj.weight.data)
+        constant_(self.value_proj.bias.data, 0.0)
+        xavier_uniform_(self.output_proj.weight.data)
+        constant_(self.output_proj.bias.data, 0.0)
+
+    def freeze_sampling_offsets(self):
+        print("Freeze sampling offsets")
+        self.sampling_offsets.weight.requires_grad = False
+        self.sampling_offsets.bias.requires_grad = False
+
+    def freeze_attention_weights(self):
+        print("Freeze attention weights")
+        self.attention_weights.weight.requires_grad = False
+        self.attention_weights.bias.requires_grad = False
+
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: Optional[torch.Tensor] = None,
+        value: Optional[torch.Tensor] = None,
+        query_pos: Optional[torch.Tensor] = None,
+        key_padding_mask: Optional[torch.Tensor] = None,
+        reference_points: Optional[torch.Tensor] = None,
+        spatial_shapes: Optional[torch.Tensor] = None,
+        level_start_index: Optional[torch.Tensor] = None,
+        **kwargs
+    ) -> torch.Tensor:
+
+        """Forward Function of MultiScaleDeformableAttention
+
+        Args:
+            query (torch.Tensor): Query embeddings with shape
+                `(num_query, bs, embed_dim)`
+            key (torch.Tensor): Key embeddings with shape
+                `(num_key, bs, embed_dim)`
+            value (torch.Tensor): Value embeddings with shape
+                `(num_key, bs, embed_dim)`
+            query_pos (torch.Tensor): The position embedding for `query`. Default: None.
+            key_padding_mask (torch.Tensor): ByteTensor for `query`, with shape `(bs, num_key)`,
+                indicating which elements within `key` to be ignored in attention.
+            reference_points (torch.Tensor): The normalized reference points
+                with shape `(bs, num_query, num_levels, 2)`,
+                all elements is range in [0, 1], top-left (0, 0),
+                bottom-right (1, 1), including padding are.
+                or `(N, Length_{query}, num_levels, 4)`, add additional
+                two dimensions `(h, w)` to form reference boxes.
+            spatial_shapes (torch.Tensor): Spatial shape of features in different levels.
+                With shape `(num_levels, 2)`, last dimension represents `(h, w)`.
+            level_start_index (torch.Tensor): The start index of each level. A tensor with
+                shape `(num_levels, )` which can be represented as
+                `[0, h_0 * w_0, h_0 * w_0 + h_1 * w_1, ...]`.
+
+        Returns:
+            torch.Tensor: forward results with shape `(num_query, bs, embed_dim)`
+        """
+
+        if value is None:
+            value = query
+
+        if query_pos is not None:
+            query = query + query_pos
+
+        if not self.batch_first:
+            # change to (bs, num_query ,embed_dims)
+            query = query.permute(1, 0, 2)
+            value = value.permute(1, 0, 2)
+
+        bs, num_query, _ = query.shape
+        bs, num_value, _ = value.shape
+
+        assert (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == num_value
+
+
+        value = self.value_proj(value)
+        if key_padding_mask is not None:
+            value = value.masked_fill(key_padding_mask[..., None], float(0))
+        value = value.view(bs, num_value, self.num_heads, -1)
+        sampling_offsets = self.sampling_offsets(query).view(
+            bs, num_query, self.num_heads, self.num_levels, self.num_points, 2
+        )
+        attention_weights = self.attention_weights(query).view(
+            bs, num_query, self.num_heads, self.num_levels * self.num_points
+        )
+        attention_weights = attention_weights.softmax(-1)
+        attention_weights = attention_weights.view(
+            bs,
+            num_query,
+            self.num_heads,
+            self.num_levels,
+            self.num_points,
+        )
+
+        # bs, num_query, num_heads, num_levels, num_points, 2
+        if reference_points.shape[-1] == 2:
+            offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1)
+            sampling_locations = (
+                reference_points[:, :, None, :, None, :]
+                + sampling_offsets / offset_normalizer[None, None, None, :, None, :]
+            )
+        elif reference_points.shape[-1] == 4:
+            sampling_locations = (
+                reference_points[:, :, None, :, None, :2]
+                + sampling_offsets
+                / self.num_points
+                * reference_points[:, :, None, :, None, 2:]
+                * 0.5
+            )
+        else:
+            raise ValueError(
+                "Last dim of reference_points must be 2 or 4, but get {} instead.".format(
+                    reference_points.shape[-1]
+                )
+            )
+        if torch.cuda.is_available() and value.is_cuda:
+            halffloat = False
+            if value.dtype == torch.float16:
+                halffloat = True
+                value = value.float()
+                sampling_locations = sampling_locations.float()
+                attention_weights = attention_weights.float()
+
+
+            output = MultiScaleDeformableAttnFunction.apply(
+                value,
+                spatial_shapes,
+                level_start_index,
+                sampling_locations,
+                attention_weights,
+                self.im2col_step,
+            )
+
+            if halffloat:
+                output = output.half()
+        else:
+            output = multi_scale_deformable_attn_pytorch(
+                value, spatial_shapes, sampling_locations, attention_weights
+            )
+
+        output = self.output_proj(output)
+
+        if not self.batch_first:
+            output = output.permute(1, 0, 2)
+
+        return output
+
+
+def create_dummy_class(klass, dependency, message=""):
+    """
+    When a dependency of a class is not available, create a dummy class which throws ImportError
+    when used.
+
+    Args:
+        klass (str): name of the class.
+        dependency (str): name of the dependency.
+        message: extra message to print
+    Returns:
+        class: a class object
+    """
+    err = "Cannot import '{}', therefore '{}' is not available.".format(dependency, klass)
+    if message:
+        err = err + " " + message
+
+    class _DummyMetaClass(type):
+        # throw error on class attribute access
+        def __getattr__(_, __):  # noqa: B902
+            raise ImportError(err)
+
+    class _Dummy(object, metaclass=_DummyMetaClass):
+        # throw error on constructor
+        def __init__(self, *args, **kwargs):
+            raise ImportError(err)
+
+    return _Dummy
+
+
+def create_dummy_func(func, dependency, message=""):
+    """
+    When a dependency of a function is not available, create a dummy function which throws
+    ImportError when used.
+
+    Args:
+        func (str): name of the function.
+        dependency (str or list[str]): name(s) of the dependency.
+        message: extra message to print
+    Returns:
+        function: a function object
+    """
+    err = "Cannot import '{}', therefore '{}' is not available.".format(dependency, func)
+    if message:
+        err = err + " " + message
+
+    if isinstance(dependency, (list, tuple)):
+        dependency = ",".join(dependency)
+
+    def _dummy(*args, **kwargs):
+        raise ImportError(err)
+
+    return _dummy
+

groundingdino/models/GroundingDINO/transformer.py

@@ -0,0 +1,942 @@
+# ------------------------------------------------------------------------
+# DINO
+# Copyright (c) 2022 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Conditional DETR Transformer class.
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Modified from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+
+from typing import Optional
+
+import torch
+import torch.utils.checkpoint as checkpoint
+from torch import Tensor, nn
+
+from groundingdino.util.misc import inverse_sigmoid
+
+from .fuse_modules import BiAttentionBlock
+from .ms_deform_attn import MultiScaleDeformableAttention as MSDeformAttn
+from .transformer_vanilla import TransformerEncoderLayer
+from .utils import (
+    MLP,
+    _get_activation_fn,
+    _get_clones,
+    gen_encoder_output_proposals,
+    gen_sineembed_for_position,
+    get_sine_pos_embed,
+)
+
+
+class Transformer(nn.Module):
+    def __init__(
+        self,
+        d_model=256,
+        nhead=8,
+        num_queries=300,
+        num_encoder_layers=6,
+        num_unicoder_layers=0,
+        num_decoder_layers=6,
+        dim_feedforward=2048,
+        dropout=0.0,
+        activation="relu",
+        normalize_before=False,
+        return_intermediate_dec=False,
+        query_dim=4,
+        num_patterns=0,
+        # for deformable encoder
+        num_feature_levels=1,
+        enc_n_points=4,
+        dec_n_points=4,
+        # init query
+        learnable_tgt_init=False,
+        # two stage
+        two_stage_type="no",  # ['no', 'standard', 'early', 'combine', 'enceachlayer', 'enclayer1']
+        embed_init_tgt=False,
+        # for text
+        use_text_enhancer=False,
+        use_fusion_layer=False,
+        use_checkpoint=False,
+        use_transformer_ckpt=False,
+        use_text_cross_attention=False,
+        text_dropout=0.1,
+        fusion_dropout=0.1,
+        fusion_droppath=0.0,
+    ):
+        super().__init__()
+        self.num_feature_levels = num_feature_levels
+        self.num_encoder_layers = num_encoder_layers
+        self.num_unicoder_layers = num_unicoder_layers
+        self.num_decoder_layers = num_decoder_layers
+        self.num_queries = num_queries
+        assert query_dim == 4
+
+        # choose encoder layer type
+        encoder_layer = DeformableTransformerEncoderLayer(
+            d_model, dim_feedforward, dropout, activation, num_feature_levels, nhead, enc_n_points
+        )
+
+        if use_text_enhancer:
+            text_enhance_layer = TransformerEncoderLayer(
+                d_model=d_model,
+                nhead=nhead // 2,
+                dim_feedforward=dim_feedforward // 2,
+                dropout=text_dropout,
+            )
+        else:
+            text_enhance_layer = None
+
+        if use_fusion_layer:
+            feature_fusion_layer = BiAttentionBlock(
+                v_dim=d_model,
+                l_dim=d_model,
+                embed_dim=dim_feedforward // 2,
+                num_heads=nhead // 2,
+                dropout=fusion_dropout,
+                drop_path=fusion_droppath,
+            )
+        else:
+            feature_fusion_layer = None
+
+        encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
+        assert encoder_norm is None
+        self.encoder = TransformerEncoder(
+            encoder_layer,
+            num_encoder_layers,
+            d_model=d_model,
+            num_queries=num_queries,
+            text_enhance_layer=text_enhance_layer,
+            feature_fusion_layer=feature_fusion_layer,
+            use_checkpoint=use_checkpoint,
+            use_transformer_ckpt=use_transformer_ckpt,
+        )
+
+        # choose decoder layer type
+        decoder_layer = DeformableTransformerDecoderLayer(
+            d_model,
+            dim_feedforward,
+            dropout,
+            activation,
+            num_feature_levels,
+            nhead,
+            dec_n_points,
+            use_text_cross_attention=use_text_cross_attention,
+        )
+
+        decoder_norm = nn.LayerNorm(d_model)
+        self.decoder = TransformerDecoder(
+            decoder_layer,
+            num_decoder_layers,
+            decoder_norm,
+            return_intermediate=return_intermediate_dec,
+            d_model=d_model,
+            query_dim=query_dim,
+            num_feature_levels=num_feature_levels,
+        )
+
+        self.d_model = d_model
+        self.nhead = nhead
+        self.dec_layers = num_decoder_layers
+        self.num_queries = num_queries  # useful for single stage model only
+        self.num_patterns = num_patterns
+        if not isinstance(num_patterns, int):
+            Warning("num_patterns should be int but {}".format(type(num_patterns)))
+            self.num_patterns = 0
+
+        if num_feature_levels > 1:
+            if self.num_encoder_layers > 0:
+                self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))
+            else:
+                self.level_embed = None
+
+        self.learnable_tgt_init = learnable_tgt_init
+        assert learnable_tgt_init, "why not learnable_tgt_init"
+        self.embed_init_tgt = embed_init_tgt
+        if (two_stage_type != "no" and embed_init_tgt) or (two_stage_type == "no"):
+            self.tgt_embed = nn.Embedding(self.num_queries, d_model)
+            nn.init.normal_(self.tgt_embed.weight.data)
+        else:
+            self.tgt_embed = None
+
+        # for two stage
+        self.two_stage_type = two_stage_type
+        assert two_stage_type in ["no", "standard"], "unknown param {} of two_stage_type".format(
+            two_stage_type
+        )
+        if two_stage_type == "standard":
+            # anchor selection at the output of encoder
+            self.enc_output = nn.Linear(d_model, d_model)
+            self.enc_output_norm = nn.LayerNorm(d_model)
+            self.two_stage_wh_embedding = None
+
+        if two_stage_type == "no":
+            self.init_ref_points(num_queries)  # init self.refpoint_embed
+
+        self.enc_out_class_embed = None
+        self.enc_out_bbox_embed = None
+
+        self._reset_parameters()
+
+    def _reset_parameters(self):
+        for p in self.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_uniform_(p)
+        for m in self.modules():
+            if isinstance(m, MSDeformAttn):
+                m._reset_parameters()
+        if self.num_feature_levels > 1 and self.level_embed is not None:
+            nn.init.normal_(self.level_embed)
+
+    def get_valid_ratio(self, mask):
+        _, H, W = mask.shape
+        valid_H = torch.sum(~mask[:, :, 0], 1)
+        valid_W = torch.sum(~mask[:, 0, :], 1)
+        valid_ratio_h = valid_H.float() / H
+        valid_ratio_w = valid_W.float() / W
+        valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
+        return valid_ratio
+
+    def init_ref_points(self, use_num_queries):
+        self.refpoint_embed = nn.Embedding(use_num_queries, 4)
+
+    def forward(self, srcs, masks, refpoint_embed, pos_embeds, tgt, attn_mask=None, text_dict=None):
+        """
+        Input:
+            - srcs: List of multi features [bs, ci, hi, wi]
+            - masks: List of multi masks [bs, hi, wi]
+            - refpoint_embed: [bs, num_dn, 4]. None in infer
+            - pos_embeds: List of multi pos embeds [bs, ci, hi, wi]
+            - tgt: [bs, num_dn, d_model]. None in infer
+
+        """
+        # prepare input for encoder
+        src_flatten = []
+        mask_flatten = []
+        lvl_pos_embed_flatten = []
+        spatial_shapes = []
+        for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):
+            bs, c, h, w = src.shape
+            spatial_shape = (h, w)
+            spatial_shapes.append(spatial_shape)
+
+            src = src.flatten(2).transpose(1, 2)  # bs, hw, c
+            mask = mask.flatten(1)  # bs, hw
+            pos_embed = pos_embed.flatten(2).transpose(1, 2)  # bs, hw, c
+            if self.num_feature_levels > 1 and self.level_embed is not None:
+                lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)
+            else:
+                lvl_pos_embed = pos_embed
+            lvl_pos_embed_flatten.append(lvl_pos_embed)
+            src_flatten.append(src)
+            mask_flatten.append(mask)
+        src_flatten = torch.cat(src_flatten, 1)  # bs, \sum{hxw}, c
+        mask_flatten = torch.cat(mask_flatten, 1)  # bs, \sum{hxw}
+        lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)  # bs, \sum{hxw}, c
+        spatial_shapes = torch.as_tensor(
+            spatial_shapes, dtype=torch.long, device=src_flatten.device
+        )
+        level_start_index = torch.cat(
+            (spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])
+        )
+        valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)
+
+        # two stage
+        enc_topk_proposals = enc_refpoint_embed = None
+
+        #########################################################
+        # Begin Encoder
+        #########################################################
+        memory, memory_text = self.encoder(
+            src_flatten,
+            pos=lvl_pos_embed_flatten,
+            level_start_index=level_start_index,
+            spatial_shapes=spatial_shapes,
+            valid_ratios=valid_ratios,
+            key_padding_mask=mask_flatten,
+            memory_text=text_dict["encoded_text"],
+            text_attention_mask=~text_dict["text_token_mask"],
+            # we ~ the mask . False means use the token; True means pad the token
+            position_ids=text_dict["position_ids"],
+            text_self_attention_masks=text_dict["text_self_attention_masks"],
+        )
+        #########################################################
+        # End Encoder
+        # - memory: bs, \sum{hw}, c
+        # - mask_flatten: bs, \sum{hw}
+        # - lvl_pos_embed_flatten: bs, \sum{hw}, c
+        # - enc_intermediate_output: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c)
+        # - enc_intermediate_refpoints: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c)
+        #########################################################
+        text_dict["encoded_text"] = memory_text
+        # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1':
+        #     if memory.isnan().any() | memory.isinf().any():
+        #         import ipdb; ipdb.set_trace()
+
+        if self.two_stage_type == "standard":
+            output_memory, output_proposals = gen_encoder_output_proposals(
+                memory, mask_flatten, spatial_shapes
+            )
+            output_memory = self.enc_output_norm(self.enc_output(output_memory))
+
+            if text_dict is not None:
+                enc_outputs_class_unselected = self.enc_out_class_embed(output_memory, text_dict)
+            else:
+                enc_outputs_class_unselected = self.enc_out_class_embed(output_memory)
+
+            topk_logits = enc_outputs_class_unselected.max(-1)[0]
+            enc_outputs_coord_unselected = (
+                self.enc_out_bbox_embed(output_memory) + output_proposals
+            )  # (bs, \sum{hw}, 4) unsigmoid
+            topk = self.num_queries
+
+            topk_proposals = torch.topk(topk_logits, topk, dim=1)[1]  # bs, nq
+
+            # gather boxes
+            refpoint_embed_undetach = torch.gather(
+                enc_outputs_coord_unselected, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)
+            )  # unsigmoid
+            refpoint_embed_ = refpoint_embed_undetach.detach()
+            init_box_proposal = torch.gather(
+                output_proposals, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)
+            ).sigmoid()  # sigmoid
+
+            # gather tgt
+            tgt_undetach = torch.gather(
+                output_memory, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, self.d_model)
+            )
+            if self.embed_init_tgt:
+                tgt_ = (
+                    self.tgt_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)
+                )  # nq, bs, d_model
+            else:
+                tgt_ = tgt_undetach.detach()
+
+            if refpoint_embed is not None:
+                refpoint_embed = torch.cat([refpoint_embed, refpoint_embed_], dim=1)
+                tgt = torch.cat([tgt, tgt_], dim=1)
+            else:
+                refpoint_embed, tgt = refpoint_embed_, tgt_
+
+        elif self.two_stage_type == "no":
+            tgt_ = (
+                self.tgt_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)
+            )  # nq, bs, d_model
+            refpoint_embed_ = (
+                self.refpoint_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)
+            )  # nq, bs, 4
+
+            if refpoint_embed is not None:
+                refpoint_embed = torch.cat([refpoint_embed, refpoint_embed_], dim=1)
+                tgt = torch.cat([tgt, tgt_], dim=1)
+            else:
+                refpoint_embed, tgt = refpoint_embed_, tgt_
+
+            if self.num_patterns > 0:
+                tgt_embed = tgt.repeat(1, self.num_patterns, 1)
+                refpoint_embed = refpoint_embed.repeat(1, self.num_patterns, 1)
+                tgt_pat = self.patterns.weight[None, :, :].repeat_interleave(
+                    self.num_queries, 1
+                )  # 1, n_q*n_pat, d_model
+                tgt = tgt_embed + tgt_pat
+
+            init_box_proposal = refpoint_embed_.sigmoid()
+
+        else:
+            raise NotImplementedError("unknown two_stage_type {}".format(self.two_stage_type))
+        #########################################################
+        # End preparing tgt
+        # - tgt: bs, NQ, d_model
+        # - refpoint_embed(unsigmoid): bs, NQ, d_model
+        #########################################################
+
+        #########################################################
+        # Begin Decoder
+        #########################################################
+        hs, references = self.decoder(
+            tgt=tgt.transpose(0, 1),
+            memory=memory.transpose(0, 1),
+            memory_key_padding_mask=mask_flatten,
+            pos=lvl_pos_embed_flatten.transpose(0, 1),
+            refpoints_unsigmoid=refpoint_embed.transpose(0, 1),
+            level_start_index=level_start_index,
+            spatial_shapes=spatial_shapes,
+            valid_ratios=valid_ratios,
+            tgt_mask=attn_mask,
+            memory_text=text_dict["encoded_text"],
+            text_attention_mask=~text_dict["text_token_mask"],
+            # we ~ the mask . False means use the token; True means pad the token
+        )
+        #########################################################
+        # End Decoder
+        # hs: n_dec, bs, nq, d_model
+        # references: n_dec+1, bs, nq, query_dim
+        #########################################################
+
+        #########################################################
+        # Begin postprocess
+        #########################################################
+        if self.two_stage_type == "standard":
+            hs_enc = tgt_undetach.unsqueeze(0)
+            ref_enc = refpoint_embed_undetach.sigmoid().unsqueeze(0)
+        else:
+            hs_enc = ref_enc = None
+        #########################################################
+        # End postprocess
+        # hs_enc: (n_enc+1, bs, nq, d_model) or (1, bs, nq, d_model) or (n_enc, bs, nq, d_model) or None
+        # ref_enc: (n_enc+1, bs, nq, query_dim) or (1, bs, nq, query_dim) or (n_enc, bs, nq, d_model) or None
+        #########################################################
+
+        return hs, references, hs_enc, ref_enc, init_box_proposal
+        # hs: (n_dec, bs, nq, d_model)
+        # references: sigmoid coordinates. (n_dec+1, bs, bq, 4)
+        # hs_enc: (n_enc+1, bs, nq, d_model) or (1, bs, nq, d_model) or None
+        # ref_enc: sigmoid coordinates. \
+        #           (n_enc+1, bs, nq, query_dim) or (1, bs, nq, query_dim) or None
+
+
+class TransformerEncoder(nn.Module):
+    def __init__(
+        self,
+        encoder_layer,
+        num_layers,
+        d_model=256,
+        num_queries=300,
+        enc_layer_share=False,
+        text_enhance_layer=None,
+        feature_fusion_layer=None,
+        use_checkpoint=False,
+        use_transformer_ckpt=False,
+    ):
+        """_summary_
+
+        Args:
+            encoder_layer (_type_): _description_
+            num_layers (_type_): _description_
+            norm (_type_, optional): _description_. Defaults to None.
+            d_model (int, optional): _description_. Defaults to 256.
+            num_queries (int, optional): _description_. Defaults to 300.
+            enc_layer_share (bool, optional): _description_. Defaults to False.
+
+        """
+        super().__init__()
+        # prepare layers
+        self.layers = []
+        self.text_layers = []
+        self.fusion_layers = []
+        if num_layers > 0:
+            self.layers = _get_clones(encoder_layer, num_layers, layer_share=enc_layer_share)
+
+            if text_enhance_layer is not None:
+                self.text_layers = _get_clones(
+                    text_enhance_layer, num_layers, layer_share=enc_layer_share
+                )
+            if feature_fusion_layer is not None:
+                self.fusion_layers = _get_clones(
+                    feature_fusion_layer, num_layers, layer_share=enc_layer_share
+                )
+        else:
+            self.layers = []
+            del encoder_layer
+
+            if text_enhance_layer is not None:
+                self.text_layers = []
+                del text_enhance_layer
+            if feature_fusion_layer is not None:
+                self.fusion_layers = []
+                del feature_fusion_layer
+
+        self.query_scale = None
+        self.num_queries = num_queries
+        self.num_layers = num_layers
+        self.d_model = d_model
+
+        self.use_checkpoint = use_checkpoint
+        self.use_transformer_ckpt = use_transformer_ckpt
+
+    @staticmethod
+    def get_reference_points(spatial_shapes, valid_ratios, device):
+        reference_points_list = []
+        for lvl, (H_, W_) in enumerate(spatial_shapes):
+
+            ref_y, ref_x = torch.meshgrid(
+                torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),
+                torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device),
+            )
+            ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)
+            ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)
+            ref = torch.stack((ref_x, ref_y), -1)
+            reference_points_list.append(ref)
+        reference_points = torch.cat(reference_points_list, 1)
+        reference_points = reference_points[:, :, None] * valid_ratios[:, None]
+        return reference_points
+
+    def forward(
+        self,
+        # for images
+        src: Tensor,
+        pos: Tensor,
+        spatial_shapes: Tensor,
+        level_start_index: Tensor,
+        valid_ratios: Tensor,
+        key_padding_mask: Tensor,
+        # for texts
+        memory_text: Tensor = None,
+        text_attention_mask: Tensor = None,
+        pos_text: Tensor = None,
+        text_self_attention_masks: Tensor = None,
+        position_ids: Tensor = None,
+    ):
+        """
+        Input:
+            - src: [bs, sum(hi*wi), 256]
+            - pos: pos embed for src. [bs, sum(hi*wi), 256]
+            - spatial_shapes: h,w of each level [num_level, 2]
+            - level_start_index: [num_level] start point of level in sum(hi*wi).
+            - valid_ratios: [bs, num_level, 2]
+            - key_padding_mask: [bs, sum(hi*wi)]
+
+            - memory_text: bs, n_text, 256
+            - text_attention_mask: bs, n_text
+                False for no padding; True for padding
+            - pos_text: bs, n_text, 256
+
+            - position_ids: bs, n_text
+        Intermedia:
+            - reference_points: [bs, sum(hi*wi), num_level, 2]
+        Outpus:
+            - output: [bs, sum(hi*wi), 256]
+        """
+
+        output = src
+
+        # preparation and reshape
+        if self.num_layers > 0:
+            reference_points = self.get_reference_points(
+                spatial_shapes, valid_ratios, device=src.device
+            )
+
+        if self.text_layers:
+            # generate pos_text
+            bs, n_text, text_dim = memory_text.shape
+            if pos_text is None and position_ids is None:
+                pos_text = (
+                    torch.arange(n_text, device=memory_text.device)
+                    .float()
+                    .unsqueeze(0)
+                    .unsqueeze(-1)
+                    .repeat(bs, 1, 1)
+                )
+                pos_text = get_sine_pos_embed(pos_text, num_pos_feats=256, exchange_xy=False)
+            if position_ids is not None:
+                pos_text = get_sine_pos_embed(
+                    position_ids[..., None], num_pos_feats=256, exchange_xy=False
+                )
+
+        # main process
+        for layer_id, layer in enumerate(self.layers):
+            # if output.isnan().any() or memory_text.isnan().any():
+            #     if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
+            #         import ipdb; ipdb.set_trace()
+            if self.fusion_layers:
+                if self.use_checkpoint:
+                    output, memory_text = checkpoint.checkpoint(
+                        self.fusion_layers[layer_id],
+                        output,
+                        memory_text,
+                        key_padding_mask,
+                        text_attention_mask,
+                    )
+                else:
+                    output, memory_text = self.fusion_layers[layer_id](
+                        v=output,
+                        l=memory_text,
+                        attention_mask_v=key_padding_mask,
+                        attention_mask_l=text_attention_mask,
+                    )
+
+            if self.text_layers:
+                memory_text = self.text_layers[layer_id](
+                    src=memory_text.transpose(0, 1),
+                    src_mask=~text_self_attention_masks,  # note we use ~ for mask here
+                    src_key_padding_mask=text_attention_mask,
+                    pos=(pos_text.transpose(0, 1) if pos_text is not None else None),
+                ).transpose(0, 1)
+
+            # main process
+            if self.use_transformer_ckpt:
+                output = checkpoint.checkpoint(
+                    layer,
+                    output,
+                    pos,
+                    reference_points,
+                    spatial_shapes,
+                    level_start_index,
+                    key_padding_mask,
+                )
+            else:
+                output = layer(
+                    src=output,
+                    pos=pos,
+                    reference_points=reference_points,
+                    spatial_shapes=spatial_shapes,
+                    level_start_index=level_start_index,
+                    key_padding_mask=key_padding_mask,
+                )
+
+        return output, memory_text
+
+
+class TransformerDecoder(nn.Module):
+    def __init__(
+        self,
+        decoder_layer,
+        num_layers,
+        norm=None,
+        return_intermediate=False,
+        d_model=256,
+        query_dim=4,
+        num_feature_levels=1,
+    ):
+        super().__init__()
+        if num_layers > 0:
+            self.layers = _get_clones(decoder_layer, num_layers)
+        else:
+            self.layers = []
+        self.num_layers = num_layers
+        self.norm = norm
+        self.return_intermediate = return_intermediate
+        assert return_intermediate, "support return_intermediate only"
+        self.query_dim = query_dim
+        assert query_dim in [2, 4], "query_dim should be 2/4 but {}".format(query_dim)
+        self.num_feature_levels = num_feature_levels
+
+        self.ref_point_head = MLP(query_dim // 2 * d_model, d_model, d_model, 2)
+        self.query_pos_sine_scale = None
+
+        self.query_scale = None
+        self.bbox_embed = None
+        self.class_embed = None
+
+        self.d_model = d_model
+
+        self.ref_anchor_head = None
+
+    def forward(
+        self,
+        tgt,
+        memory,
+        tgt_mask: Optional[Tensor] = None,
+        memory_mask: Optional[Tensor] = None,
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        memory_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+        refpoints_unsigmoid: Optional[Tensor] = None,  # num_queries, bs, 2
+        # for memory
+        level_start_index: Optional[Tensor] = None,  # num_levels
+        spatial_shapes: Optional[Tensor] = None,  # bs, num_levels, 2
+        valid_ratios: Optional[Tensor] = None,
+        # for text
+        memory_text: Optional[Tensor] = None,
+        text_attention_mask: Optional[Tensor] = None,
+    ):
+        """
+        Input:
+            - tgt: nq, bs, d_model
+            - memory: hw, bs, d_model
+            - pos: hw, bs, d_model
+            - refpoints_unsigmoid: nq, bs, 2/4
+            - valid_ratios/spatial_shapes: bs, nlevel, 2
+        """
+        output = tgt
+
+        intermediate = []
+        reference_points = refpoints_unsigmoid.sigmoid()
+        ref_points = [reference_points]
+
+        for layer_id, layer in enumerate(self.layers):
+
+            if reference_points.shape[-1] == 4:
+                reference_points_input = (
+                    reference_points[:, :, None]
+                    * torch.cat([valid_ratios, valid_ratios], -1)[None, :]
+                )  # nq, bs, nlevel, 4
+            else:
+                assert reference_points.shape[-1] == 2
+                reference_points_input = reference_points[:, :, None] * valid_ratios[None, :]
+            query_sine_embed = gen_sineembed_for_position(
+                reference_points_input[:, :, 0, :]
+            )  # nq, bs, 256*2
+
+            # conditional query
+            raw_query_pos = self.ref_point_head(query_sine_embed)  # nq, bs, 256
+            pos_scale = self.query_scale(output) if self.query_scale is not None else 1
+            query_pos = pos_scale * raw_query_pos
+            # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1':
+            #     if query_pos.isnan().any() | query_pos.isinf().any():
+            #         import ipdb; ipdb.set_trace()
+
+            # main process
+            output = layer(
+                tgt=output,
+                tgt_query_pos=query_pos,
+                tgt_query_sine_embed=query_sine_embed,
+                tgt_key_padding_mask=tgt_key_padding_mask,
+                tgt_reference_points=reference_points_input,
+                memory_text=memory_text,
+                text_attention_mask=text_attention_mask,
+                memory=memory,
+                memory_key_padding_mask=memory_key_padding_mask,
+                memory_level_start_index=level_start_index,
+                memory_spatial_shapes=spatial_shapes,
+                memory_pos=pos,
+                self_attn_mask=tgt_mask,
+                cross_attn_mask=memory_mask,
+            )
+            if output.isnan().any() | output.isinf().any():
+                print(f"output layer_id {layer_id} is nan")
+                try:
+                    num_nan = output.isnan().sum().item()
+                    num_inf = output.isinf().sum().item()
+                    print(f"num_nan {num_nan}, num_inf {num_inf}")
+                except Exception as e:
+                    print(e)
+                    # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1':
+                    #     import ipdb; ipdb.set_trace()
+
+            # iter update
+            if self.bbox_embed is not None:
+                # box_holder = self.bbox_embed(output)
+                # box_holder[..., :self.query_dim] += inverse_sigmoid(reference_points)
+                # new_reference_points = box_holder[..., :self.query_dim].sigmoid()
+
+                reference_before_sigmoid = inverse_sigmoid(reference_points)
+                delta_unsig = self.bbox_embed[layer_id](output)
+                outputs_unsig = delta_unsig + reference_before_sigmoid
+                new_reference_points = outputs_unsig.sigmoid()
+
+                reference_points = new_reference_points.detach()
+                # if layer_id != self.num_layers - 1:
+                ref_points.append(new_reference_points)
+
+            intermediate.append(self.norm(output))
+
+        return [
+            [itm_out.transpose(0, 1) for itm_out in intermediate],
+            [itm_refpoint.transpose(0, 1) for itm_refpoint in ref_points],
+        ]
+
+
+class DeformableTransformerEncoderLayer(nn.Module):
+    def __init__(
+        self,
+        d_model=256,
+        d_ffn=1024,
+        dropout=0.1,
+        activation="relu",
+        n_levels=4,
+        n_heads=8,
+        n_points=4,
+    ):
+        super().__init__()
+
+        # self attention
+        self.self_attn = MSDeformAttn(embed_dim=d_model, num_levels=n_levels, num_heads=n_heads, num_points=n_points, batch_first=True)
+        self.dropout1 = nn.Dropout(dropout)
+        self.norm1 = nn.LayerNorm(d_model)
+
+        # ffn
+        self.linear1 = nn.Linear(d_model, d_ffn)
+        self.activation = _get_activation_fn(activation, d_model=d_ffn)
+        self.dropout2 = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(d_ffn, d_model)
+        self.dropout3 = nn.Dropout(dropout)
+        self.norm2 = nn.LayerNorm(d_model)
+
+    @staticmethod
+    def with_pos_embed(tensor, pos):
+        return tensor if pos is None else tensor + pos
+
+    def forward_ffn(self, src):
+        src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))
+        src = src + self.dropout3(src2)
+        src = self.norm2(src)
+        return src
+
+    def forward(
+        self, src, pos, reference_points, spatial_shapes, level_start_index, key_padding_mask=None
+    ):
+        # self attention
+        # import ipdb; ipdb.set_trace()
+        src2 = self.self_attn(
+            query=self.with_pos_embed(src, pos),
+            reference_points=reference_points,
+            value=src,
+            spatial_shapes=spatial_shapes,
+            level_start_index=level_start_index,
+            key_padding_mask=key_padding_mask,
+        )
+        src = src + self.dropout1(src2)
+        src = self.norm1(src)
+
+        # ffn
+        src = self.forward_ffn(src)
+
+        return src
+
+
+class DeformableTransformerDecoderLayer(nn.Module):
+    def __init__(
+        self,
+        d_model=256,
+        d_ffn=1024,
+        dropout=0.1,
+        activation="relu",
+        n_levels=4,
+        n_heads=8,
+        n_points=4,
+        use_text_feat_guide=False,
+        use_text_cross_attention=False,
+    ):
+        super().__init__()
+
+        # cross attention
+        self.cross_attn = MSDeformAttn(embed_dim=d_model, num_levels=n_levels, num_heads=n_heads, num_points=n_points, batch_first=True)
+        self.dropout1 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
+        self.norm1 = nn.LayerNorm(d_model)
+
+        # cross attention text
+        if use_text_cross_attention:
+            self.ca_text = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
+            self.catext_dropout = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
+            self.catext_norm = nn.LayerNorm(d_model)
+
+        # self attention
+        self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
+        self.dropout2 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
+        self.norm2 = nn.LayerNorm(d_model)
+
+        # ffn
+        self.linear1 = nn.Linear(d_model, d_ffn)
+        self.activation = _get_activation_fn(activation, d_model=d_ffn, batch_dim=1)
+        self.dropout3 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
+        self.linear2 = nn.Linear(d_ffn, d_model)
+        self.dropout4 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
+        self.norm3 = nn.LayerNorm(d_model)
+
+        self.key_aware_proj = None
+        self.use_text_feat_guide = use_text_feat_guide
+        assert not use_text_feat_guide
+        self.use_text_cross_attention = use_text_cross_attention
+
+    def rm_self_attn_modules(self):
+        self.self_attn = None
+        self.dropout2 = None
+        self.norm2 = None
+
+    @staticmethod
+    def with_pos_embed(tensor, pos):
+        return tensor if pos is None else tensor + pos
+
+    def forward_ffn(self, tgt):
+        with torch.cuda.amp.autocast(enabled=False):
+            tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt))))
+        tgt = tgt + self.dropout4(tgt2)
+        tgt = self.norm3(tgt)
+        return tgt
+
+    def forward(
+        self,
+        # for tgt
+        tgt: Optional[Tensor],  # nq, bs, d_model
+        tgt_query_pos: Optional[Tensor] = None,  # pos for query. MLP(Sine(pos))
+        tgt_query_sine_embed: Optional[Tensor] = None,  # pos for query. Sine(pos)
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        tgt_reference_points: Optional[Tensor] = None,  # nq, bs, 4
+        memory_text: Optional[Tensor] = None,  # bs, num_token, d_model
+        text_attention_mask: Optional[Tensor] = None,  # bs, num_token
+        # for memory
+        memory: Optional[Tensor] = None,  # hw, bs, d_model
+        memory_key_padding_mask: Optional[Tensor] = None,
+        memory_level_start_index: Optional[Tensor] = None,  # num_levels
+        memory_spatial_shapes: Optional[Tensor] = None,  # bs, num_levels, 2
+        memory_pos: Optional[Tensor] = None,  # pos for memory
+        # sa
+        self_attn_mask: Optional[Tensor] = None,  # mask used for self-attention
+        cross_attn_mask: Optional[Tensor] = None,  # mask used for cross-attention
+    ):
+        """
+        Input:
+            - tgt/tgt_query_pos: nq, bs, d_model
+            -
+        """
+        assert cross_attn_mask is None
+
+        # self attention
+        if self.self_attn is not None:
+            # import ipdb; ipdb.set_trace()
+            q = k = self.with_pos_embed(tgt, tgt_query_pos)
+            tgt2 = self.self_attn(q, k, tgt, attn_mask=self_attn_mask)[0]
+            tgt = tgt + self.dropout2(tgt2)
+            tgt = self.norm2(tgt)
+
+        if self.use_text_cross_attention:
+            tgt2 = self.ca_text(
+                self.with_pos_embed(tgt, tgt_query_pos),
+                memory_text.transpose(0, 1),
+                memory_text.transpose(0, 1),
+                key_padding_mask=text_attention_mask,
+            )[0]
+            tgt = tgt + self.catext_dropout(tgt2)
+            tgt = self.catext_norm(tgt)
+
+        tgt2 = self.cross_attn(
+            query=self.with_pos_embed(tgt, tgt_query_pos).transpose(0, 1),
+            reference_points=tgt_reference_points.transpose(0, 1).contiguous(),
+            value=memory.transpose(0, 1),
+            spatial_shapes=memory_spatial_shapes,
+            level_start_index=memory_level_start_index,
+            key_padding_mask=memory_key_padding_mask,
+        ).transpose(0, 1)
+        tgt = tgt + self.dropout1(tgt2)
+        tgt = self.norm1(tgt)
+
+        # ffn
+        tgt = self.forward_ffn(tgt)
+
+        return tgt
+
+
+def build_transformer(args):
+    return Transformer(
+        d_model=args.hidden_dim,
+        dropout=args.dropout,
+        nhead=args.nheads,
+        num_queries=args.num_queries,
+        dim_feedforward=args.dim_feedforward,
+        num_encoder_layers=args.enc_layers,
+        num_decoder_layers=args.dec_layers,
+        normalize_before=args.pre_norm,
+        return_intermediate_dec=True,
+        query_dim=args.query_dim,
+        activation=args.transformer_activation,
+        num_patterns=args.num_patterns,
+        num_feature_levels=args.num_feature_levels,
+        enc_n_points=args.enc_n_points,
+        dec_n_points=args.dec_n_points,
+        learnable_tgt_init=True,
+        # two stage
+        two_stage_type=args.two_stage_type,  # ['no', 'standard', 'early']
+        embed_init_tgt=args.embed_init_tgt,
+        use_text_enhancer=args.use_text_enhancer,
+        use_fusion_layer=args.use_fusion_layer,
+        use_checkpoint=args.use_checkpoint,
+        use_transformer_ckpt=args.use_transformer_ckpt,
+        use_text_cross_attention=args.use_text_cross_attention,
+        text_dropout=args.text_dropout,
+        fusion_dropout=args.fusion_dropout,
+        fusion_droppath=args.fusion_droppath,
+    )

groundingdino/models/GroundingDINO/transformer_vanilla.py

@@ -0,0 +1,117 @@
+# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+"""
+DETR Transformer class.
+
+Copy-paste from torch.nn.Transformer with modifications:
+    * positional encodings are passed in MHattention
+    * extra LN at the end of encoder is removed
+    * decoder returns a stack of activations from all decoding layers
+"""
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from torch import Tensor, nn
+
+from .utils import (
+    MLP,
+    _get_activation_fn,
+    _get_clones,
+    gen_encoder_output_proposals,
+    gen_sineembed_for_position,
+    sigmoid_focal_loss,
+)
+
+
+class TextTransformer(nn.Module):
+    def __init__(self, num_layers, d_model=256, nheads=8, dim_feedforward=2048, dropout=0.1):
+        super().__init__()
+        self.num_layers = num_layers
+        self.d_model = d_model
+        self.nheads = nheads
+        self.dim_feedforward = dim_feedforward
+        self.norm = None
+
+        single_encoder_layer = TransformerEncoderLayer(
+            d_model=d_model, nhead=nheads, dim_feedforward=dim_feedforward, dropout=dropout
+        )
+        self.layers = _get_clones(single_encoder_layer, num_layers)
+
+    def forward(self, memory_text: torch.Tensor, text_attention_mask: torch.Tensor):
+        """
+
+        Args:
+            text_attention_mask: bs, num_token
+            memory_text: bs, num_token, d_model
+
+        Raises:
+            RuntimeError: _description_
+
+        Returns:
+            output: bs, num_token, d_model
+        """
+
+        output = memory_text.transpose(0, 1)
+
+        for layer in self.layers:
+            output = layer(output, src_key_padding_mask=text_attention_mask)
+
+        if self.norm is not None:
+            output = self.norm(output)
+
+        return output.transpose(0, 1)
+
+
+class TransformerEncoderLayer(nn.Module):
+    def __init__(
+        self,
+        d_model,
+        nhead,
+        dim_feedforward=2048,
+        dropout=0.1,
+        activation="relu",
+        normalize_before=False,
+    ):
+        super().__init__()
+        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+        # Implementation of Feedforward model
+        self.linear1 = nn.Linear(d_model, dim_feedforward)
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+
+        self.activation = _get_activation_fn(activation)
+        self.normalize_before = normalize_before
+        self.nhead = nhead
+
+    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+        return tensor if pos is None else tensor + pos
+
+    def forward(
+        self,
+        src,
+        src_mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+    ):
+        # repeat attn mask
+        if src_mask.dim() == 3 and src_mask.shape[0] == src.shape[1]:
+            # bs, num_q, num_k
+            src_mask = src_mask.repeat(self.nhead, 1, 1)
+
+        q = k = self.with_pos_embed(src, pos)
+
+        src2 = self.self_attn(q, k, value=src, attn_mask=src_mask)[0]
+
+        # src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]
+        src = src + self.dropout1(src2)
+        src = self.norm1(src)
+        src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
+        src = src + self.dropout2(src2)
+        src = self.norm2(src)
+        return src

groundingdino/models/GroundingDINO/utils.py

@@ -0,0 +1,267 @@
+# ------------------------------------------------------------------------
+# DINO
+# Copyright (c) 2022 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+
+import copy
+import math
+
+import torch
+import torch.nn.functional as F
+from torch import Tensor, nn
+
+
+def _get_clones(module, N, layer_share=False):
+    # import ipdb; ipdb.set_trace()
+    if layer_share:
+        return nn.ModuleList([module for i in range(N)])
+    else:
+        return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
+
+
+def get_sine_pos_embed(
+    pos_tensor: torch.Tensor,
+    num_pos_feats: int = 128,
+    temperature: int = 10000,
+    exchange_xy: bool = True,
+):
+    """generate sine position embedding from a position tensor
+    Args:
+        pos_tensor (torch.Tensor): shape: [..., n].
+        num_pos_feats (int): projected shape for each float in the tensor.
+        temperature (int): temperature in the sine/cosine function.
+        exchange_xy (bool, optional): exchange pos x and pos y. \
+            For example, input tensor is [x,y], the results will be [pos(y), pos(x)]. Defaults to True.
+    Returns:
+        pos_embed (torch.Tensor): shape: [..., n*num_pos_feats].
+    """
+    scale = 2 * math.pi
+    dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=pos_tensor.device)
+    dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats)
+
+    def sine_func(x: torch.Tensor):
+        sin_x = x * scale / dim_t
+        sin_x = torch.stack((sin_x[..., 0::2].sin(), sin_x[..., 1::2].cos()), dim=3).flatten(2)
+        return sin_x
+
+    pos_res = [sine_func(x) for x in pos_tensor.split([1] * pos_tensor.shape[-1], dim=-1)]
+    if exchange_xy:
+        pos_res[0], pos_res[1] = pos_res[1], pos_res[0]
+    pos_res = torch.cat(pos_res, dim=-1)
+    return pos_res
+
+
+def gen_encoder_output_proposals(
+    memory: Tensor, memory_padding_mask: Tensor, spatial_shapes: Tensor, learnedwh=None
+):
+    """
+    Input:
+        - memory: bs, \sum{hw}, d_model
+        - memory_padding_mask: bs, \sum{hw}
+        - spatial_shapes: nlevel, 2
+        - learnedwh: 2
+    Output:
+        - output_memory: bs, \sum{hw}, d_model
+        - output_proposals: bs, \sum{hw}, 4
+    """
+    N_, S_, C_ = memory.shape
+    proposals = []
+    _cur = 0
+    for lvl, (H_, W_) in enumerate(spatial_shapes):
+        mask_flatten_ = memory_padding_mask[:, _cur : (_cur + H_ * W_)].view(N_, H_, W_, 1)
+        valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1)
+        valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1)
+
+        # import ipdb; ipdb.set_trace()
+
+        grid_y, grid_x = torch.meshgrid(
+            torch.linspace(0, H_ - 1, H_, dtype=torch.float32, device=memory.device),
+            torch.linspace(0, W_ - 1, W_, dtype=torch.float32, device=memory.device),
+        )
+        grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1)  # H_, W_, 2
+
+        scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N_, 1, 1, 2)
+        grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale
+
+        if learnedwh is not None:
+            # import ipdb; ipdb.set_trace()
+            wh = torch.ones_like(grid) * learnedwh.sigmoid() * (2.0**lvl)
+        else:
+            wh = torch.ones_like(grid) * 0.05 * (2.0**lvl)
+
+        # scale = torch.cat([W_[None].unsqueeze(-1), H_[None].unsqueeze(-1)], 1).view(1, 1, 1, 2).repeat(N_, 1, 1, 1)
+        # grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale
+        # wh = torch.ones_like(grid) / scale
+        proposal = torch.cat((grid, wh), -1).view(N_, -1, 4)
+        proposals.append(proposal)
+        _cur += H_ * W_
+    # import ipdb; ipdb.set_trace()
+    output_proposals = torch.cat(proposals, 1)
+    output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(
+        -1, keepdim=True
+    )
+    output_proposals = torch.log(output_proposals / (1 - output_proposals))  # unsigmoid
+    output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float("inf"))
+    output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf"))
+
+    output_memory = memory
+    output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0))
+    output_memory = output_memory.masked_fill(~output_proposals_valid, float(0))
+
+    # output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float('inf'))
+    # output_memory = output_memory.masked_fill(~output_proposals_valid, float('inf'))
+
+    return output_memory, output_proposals
+
+
+class RandomBoxPerturber:
+    def __init__(
+        self, x_noise_scale=0.2, y_noise_scale=0.2, w_noise_scale=0.2, h_noise_scale=0.2
+    ) -> None:
+        self.noise_scale = torch.Tensor(
+            [x_noise_scale, y_noise_scale, w_noise_scale, h_noise_scale]
+        )
+
+    def __call__(self, refanchors: Tensor) -> Tensor:
+        nq, bs, query_dim = refanchors.shape
+        device = refanchors.device
+
+        noise_raw = torch.rand_like(refanchors)
+        noise_scale = self.noise_scale.to(device)[:query_dim]
+
+        new_refanchors = refanchors * (1 + (noise_raw - 0.5) * noise_scale)
+        return new_refanchors.clamp_(0, 1)
+
+
+def sigmoid_focal_loss(
+    inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2, no_reduction=False
+):
+    """
+    Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
+    Args:
+        inputs: A float tensor of arbitrary shape.
+                The predictions for each example.
+        targets: A float tensor with the same shape as inputs. Stores the binary
+                 classification label for each element in inputs
+                (0 for the negative class and 1 for the positive class).
+        alpha: (optional) Weighting factor in range (0,1) to balance
+                positive vs negative examples. Default = -1 (no weighting).
+        gamma: Exponent of the modulating factor (1 - p_t) to
+               balance easy vs hard examples.
+    Returns:
+        Loss tensor
+    """
+    prob = inputs.sigmoid()
+    ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
+    p_t = prob * targets + (1 - prob) * (1 - targets)
+    loss = ce_loss * ((1 - p_t) ** gamma)
+
+    if alpha >= 0:
+        alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
+        loss = alpha_t * loss
+
+    if no_reduction:
+        return loss
+
+    return loss.mean(1).sum() / num_boxes
+
+
+class MLP(nn.Module):
+    """Very simple multi-layer perceptron (also called FFN)"""
+
+    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
+        super().__init__()
+        self.num_layers = num_layers
+        h = [hidden_dim] * (num_layers - 1)
+        self.layers = nn.ModuleList(
+            nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
+        )
+
+    def forward(self, x):
+        for i, layer in enumerate(self.layers):
+            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
+        return x
+
+
+def _get_activation_fn(activation, d_model=256, batch_dim=0):
+    """Return an activation function given a string"""
+    if activation == "relu":
+        return F.relu
+    if activation == "gelu":
+        return F.gelu
+    if activation == "glu":
+        return F.glu
+    if activation == "prelu":
+        return nn.PReLU()
+    if activation == "selu":
+        return F.selu
+
+    raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
+
+
+def gen_sineembed_for_position(pos_tensor):
+    # n_query, bs, _ = pos_tensor.size()
+    # sineembed_tensor = torch.zeros(n_query, bs, 256)
+    scale = 2 * math.pi
+    dim_t = torch.arange(128, dtype=torch.float32, device=pos_tensor.device)
+    dim_t = 10000 ** (2 * (dim_t // 2) / 128)
+    x_embed = pos_tensor[:, :, 0] * scale
+    y_embed = pos_tensor[:, :, 1] * scale
+    pos_x = x_embed[:, :, None] / dim_t
+    pos_y = y_embed[:, :, None] / dim_t
+    pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2)
+    pos_y = torch.stack((pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3).flatten(2)
+    if pos_tensor.size(-1) == 2:
+        pos = torch.cat((pos_y, pos_x), dim=2)
+    elif pos_tensor.size(-1) == 4:
+        w_embed = pos_tensor[:, :, 2] * scale
+        pos_w = w_embed[:, :, None] / dim_t
+        pos_w = torch.stack((pos_w[:, :, 0::2].sin(), pos_w[:, :, 1::2].cos()), dim=3).flatten(2)
+
+        h_embed = pos_tensor[:, :, 3] * scale
+        pos_h = h_embed[:, :, None] / dim_t
+        pos_h = torch.stack((pos_h[:, :, 0::2].sin(), pos_h[:, :, 1::2].cos()), dim=3).flatten(2)
+
+        pos = torch.cat((pos_y, pos_x, pos_w, pos_h), dim=2)
+    else:
+        raise ValueError("Unknown pos_tensor shape(-1):{}".format(pos_tensor.size(-1)))
+    return pos
+
+
+class ContrastiveEmbed(nn.Module):
+    def __init__(self, max_text_len=256):
+        """
+        Args:
+            max_text_len: max length of text.
+        """
+        super().__init__()
+        self.max_text_len = max_text_len
+
+    def forward(self, x, text_dict):
+        """_summary_
+
+        Args:
+            x (_type_): _description_
+            text_dict (_type_): _description_
+            {
+                'encoded_text': encoded_text, # bs, 195, d_model
+                'text_token_mask': text_token_mask, # bs, 195
+                        # True for used tokens. False for padding tokens
+            }
+        Returns:
+            _type_: _description_
+        """
+        assert isinstance(text_dict, dict)
+
+        y = text_dict["encoded_text"]
+        text_token_mask = text_dict["text_token_mask"]
+
+        res = x @ y.transpose(-1, -2)
+        res.masked_fill_(~text_token_mask[:, None, :], float("-inf"))
+
+        # padding to max_text_len
+        new_res = torch.full((*res.shape[:-1], self.max_text_len), float("-inf"), device=res.device)
+        new_res[..., : res.shape[-1]] = res
+
+        return new_res

groundingdino/models/__init__.py

@@ -0,0 +1,17 @@
+# ------------------------------------------------------------------------
+# DINO
+# Copyright (c) 2022 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+from .GroundingDINO import build_groundingdino
+
+
+def build_model(args):
+    # we use register to maintain models from catdet6 on.
+    from .registry import MODULE_BUILD_FUNCS
+
+    assert args.modelname in MODULE_BUILD_FUNCS._module_dict
+    build_func = MODULE_BUILD_FUNCS.get(args.modelname)
+    model = build_func(args)
+    return model

groundingdino/models/registry.py

@@ -0,0 +1,60 @@
+# -*- coding: utf-8 -*-
+# @Author: Yihao Chen
+# @Date:   2021-08-16 16:03:17
+# @Last Modified by:   Shilong Liu
+# @Last Modified time: 2022-01-23 15:26
+# modified from mmcv
+
+import inspect
+from functools import partial
+
+
+class Registry(object):
+    def __init__(self, name):
+        self._name = name
+        self._module_dict = dict()
+
+    def __repr__(self):
+        format_str = self.__class__.__name__ + "(name={}, items={})".format(
+            self._name, list(self._module_dict.keys())
+        )
+        return format_str
+
+    def __len__(self):
+        return len(self._module_dict)
+
+    @property
+    def name(self):
+        return self._name
+
+    @property
+    def module_dict(self):
+        return self._module_dict
+
+    def get(self, key):
+        return self._module_dict.get(key, None)
+
+    def registe_with_name(self, module_name=None, force=False):
+        return partial(self.register, module_name=module_name, force=force)
+
+    def register(self, module_build_function, module_name=None, force=False):
+        """Register a module build function.
+        Args:
+            module (:obj:`nn.Module`): Module to be registered.
+        """
+        if not inspect.isfunction(module_build_function):
+            raise TypeError(
+                "module_build_function must be a function, but got {}".format(
+                    type(module_build_function)
+                )
+            )
+        if module_name is None:
+            module_name = module_build_function.__name__
+        if not force and module_name in self._module_dict:
+            raise KeyError("{} is already registered in {}".format(module_name, self.name))
+        self._module_dict[module_name] = module_build_function
+
+        return module_build_function
+
+
+MODULE_BUILD_FUNCS = Registry("model build functions")

groundingdino/util/__init__.py

@@ -0,0 +1 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved

groundingdino/util/box_ops.py

@@ -0,0 +1,140 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+"""
+Utilities for bounding box manipulation and GIoU.
+"""
+import torch
+from torchvision.ops.boxes import box_area
+
+
+def box_cxcywh_to_xyxy(x):
+    x_c, y_c, w, h = x.unbind(-1)
+    b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)]
+    return torch.stack(b, dim=-1)
+
+
+def box_xyxy_to_cxcywh(x):
+    x0, y0, x1, y1 = x.unbind(-1)
+    b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)]
+    return torch.stack(b, dim=-1)
+
+
+# modified from torchvision to also return the union
+def box_iou(boxes1, boxes2):
+    area1 = box_area(boxes1)
+    area2 = box_area(boxes2)
+
+    # import ipdb; ipdb.set_trace()
+    lt = torch.max(boxes1[:, None, :2], boxes2[:, :2])  # [N,M,2]
+    rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:])  # [N,M,2]
+
+    wh = (rb - lt).clamp(min=0)  # [N,M,2]
+    inter = wh[:, :, 0] * wh[:, :, 1]  # [N,M]
+
+    union = area1[:, None] + area2 - inter
+
+    iou = inter / (union + 1e-6)
+    return iou, union
+
+
+def generalized_box_iou(boxes1, boxes2):
+    """
+    Generalized IoU from https://giou.stanford.edu/
+
+    The boxes should be in [x0, y0, x1, y1] format
+
+    Returns a [N, M] pairwise matrix, where N = len(boxes1)
+    and M = len(boxes2)
+    """
+    # degenerate boxes gives inf / nan results
+    # so do an early check
+    assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
+    assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
+    # except:
+    #     import ipdb; ipdb.set_trace()
+    iou, union = box_iou(boxes1, boxes2)
+
+    lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])
+    rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])
+
+    wh = (rb - lt).clamp(min=0)  # [N,M,2]
+    area = wh[:, :, 0] * wh[:, :, 1]
+
+    return iou - (area - union) / (area + 1e-6)
+
+
+# modified from torchvision to also return the union
+def box_iou_pairwise(boxes1, boxes2):
+    area1 = box_area(boxes1)
+    area2 = box_area(boxes2)
+
+    lt = torch.max(boxes1[:, :2], boxes2[:, :2])  # [N,2]
+    rb = torch.min(boxes1[:, 2:], boxes2[:, 2:])  # [N,2]
+
+    wh = (rb - lt).clamp(min=0)  # [N,2]
+    inter = wh[:, 0] * wh[:, 1]  # [N]
+
+    union = area1 + area2 - inter
+
+    iou = inter / union
+    return iou, union
+
+
+def generalized_box_iou_pairwise(boxes1, boxes2):
+    """
+    Generalized IoU from https://giou.stanford.edu/
+
+    Input:
+        - boxes1, boxes2: N,4
+    Output:
+        - giou: N, 4
+    """
+    # degenerate boxes gives inf / nan results
+    # so do an early check
+    assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
+    assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
+    assert boxes1.shape == boxes2.shape
+    iou, union = box_iou_pairwise(boxes1, boxes2)  # N, 4
+
+    lt = torch.min(boxes1[:, :2], boxes2[:, :2])
+    rb = torch.max(boxes1[:, 2:], boxes2[:, 2:])
+
+    wh = (rb - lt).clamp(min=0)  # [N,2]
+    area = wh[:, 0] * wh[:, 1]
+
+    return iou - (area - union) / area
+
+
+def masks_to_boxes(masks):
+    """Compute the bounding boxes around the provided masks
+
+    The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.
+
+    Returns a [N, 4] tensors, with the boxes in xyxy format
+    """
+    if masks.numel() == 0:
+        return torch.zeros((0, 4), device=masks.device)
+
+    h, w = masks.shape[-2:]
+
+    y = torch.arange(0, h, dtype=torch.float)
+    x = torch.arange(0, w, dtype=torch.float)
+    y, x = torch.meshgrid(y, x)
+
+    x_mask = masks * x.unsqueeze(0)
+    x_max = x_mask.flatten(1).max(-1)[0]
+    x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
+
+    y_mask = masks * y.unsqueeze(0)
+    y_max = y_mask.flatten(1).max(-1)[0]
+    y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
+
+    return torch.stack([x_min, y_min, x_max, y_max], 1)
+
+
+if __name__ == "__main__":
+    x = torch.rand(5, 4)
+    y = torch.rand(3, 4)
+    iou, union = box_iou(x, y)
+    import ipdb
+
+    ipdb.set_trace()

groundingdino/util/get_tokenlizer.py

@@ -0,0 +1,26 @@
+from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast
+
+
+def get_tokenlizer(text_encoder_type):
+    if not isinstance(text_encoder_type, str):
+        # print("text_encoder_type is not a str")
+        if hasattr(text_encoder_type, "text_encoder_type"):
+            text_encoder_type = text_encoder_type.text_encoder_type
+        elif text_encoder_type.get("text_encoder_type", False):
+            text_encoder_type = text_encoder_type.get("text_encoder_type")
+        else:
+            raise ValueError(
+                "Unknown type of text_encoder_type: {}".format(type(text_encoder_type))
+            )
+    print("final text_encoder_type: {}".format(text_encoder_type))
+
+    tokenizer = AutoTokenizer.from_pretrained(text_encoder_type)
+    return tokenizer
+
+
+def get_pretrained_language_model(text_encoder_type):
+    if text_encoder_type == "bert-base-uncased":
+        return BertModel.from_pretrained(text_encoder_type)
+    if text_encoder_type == "roberta-base":
+        return RobertaModel.from_pretrained(text_encoder_type)
+    raise ValueError("Unknown text_encoder_type {}".format(text_encoder_type))

groundingdino/util/logger.py

@@ -0,0 +1,93 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+import functools
+import logging
+import os
+import sys
+
+from termcolor import colored
+
+
+class _ColorfulFormatter(logging.Formatter):
+    def __init__(self, *args, **kwargs):
+        self._root_name = kwargs.pop("root_name") + "."
+        self._abbrev_name = kwargs.pop("abbrev_name", "")
+        if len(self._abbrev_name):
+            self._abbrev_name = self._abbrev_name + "."
+        super(_ColorfulFormatter, self).__init__(*args, **kwargs)
+
+    def formatMessage(self, record):
+        record.name = record.name.replace(self._root_name, self._abbrev_name)
+        log = super(_ColorfulFormatter, self).formatMessage(record)
+        if record.levelno == logging.WARNING:
+            prefix = colored("WARNING", "red", attrs=["blink"])
+        elif record.levelno == logging.ERROR or record.levelno == logging.CRITICAL:
+            prefix = colored("ERROR", "red", attrs=["blink", "underline"])
+        else:
+            return log
+        return prefix + " " + log
+
+
+# so that calling setup_logger multiple times won't add many handlers
+@functools.lru_cache()
+def setup_logger(output=None, distributed_rank=0, *, color=True, name="imagenet", abbrev_name=None):
+    """
+    Initialize the detectron2 logger and set its verbosity level to "INFO".
+
+    Args:
+        output (str): a file name or a directory to save log. If None, will not save log file.
+            If ends with ".txt" or ".log", assumed to be a file name.
+            Otherwise, logs will be saved to `output/log.txt`.
+        name (str): the root module name of this logger
+
+    Returns:
+        logging.Logger: a logger
+    """
+    logger = logging.getLogger(name)
+    logger.setLevel(logging.DEBUG)
+    logger.propagate = False
+
+    if abbrev_name is None:
+        abbrev_name = name
+
+    plain_formatter = logging.Formatter(
+        "[%(asctime)s.%(msecs)03d]: %(message)s", datefmt="%m/%d %H:%M:%S"
+    )
+    # stdout logging: master only
+    if distributed_rank == 0:
+        ch = logging.StreamHandler(stream=sys.stdout)
+        ch.setLevel(logging.DEBUG)
+        if color:
+            formatter = _ColorfulFormatter(
+                colored("[%(asctime)s.%(msecs)03d]: ", "green") + "%(message)s",
+                datefmt="%m/%d %H:%M:%S",
+                root_name=name,
+                abbrev_name=str(abbrev_name),
+            )
+        else:
+            formatter = plain_formatter
+        ch.setFormatter(formatter)
+        logger.addHandler(ch)
+
+    # file logging: all workers
+    if output is not None:
+        if output.endswith(".txt") or output.endswith(".log"):
+            filename = output
+        else:
+            filename = os.path.join(output, "log.txt")
+        if distributed_rank > 0:
+            filename = filename + f".rank{distributed_rank}"
+        os.makedirs(os.path.dirname(filename), exist_ok=True)
+
+        fh = logging.StreamHandler(_cached_log_stream(filename))
+        fh.setLevel(logging.DEBUG)
+        fh.setFormatter(plain_formatter)
+        logger.addHandler(fh)
+
+    return logger
+
+
+# cache the opened file object, so that different calls to `setup_logger`
+# with the same file name can safely write to the same file.
+@functools.lru_cache(maxsize=None)
+def _cached_log_stream(filename):
+    return open(filename, "a")

groundingdino/util/misc.py

@@ -0,0 +1,717 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+"""
+Misc functions, including distributed helpers.
+
+Mostly copy-paste from torchvision references.
+"""
+import colorsys
+import datetime
+import functools
+import io
+import json
+import os
+import pickle
+import subprocess
+import time
+from collections import OrderedDict, defaultdict, deque
+from typing import List, Optional
+
+import numpy as np
+import torch
+import torch.distributed as dist
+
+# needed due to empty tensor bug in pytorch and torchvision 0.5
+import torchvision
+from torch import Tensor
+
+__torchvision_need_compat_flag = float(torchvision.__version__.split(".")[1]) < 7
+if __torchvision_need_compat_flag:
+    from torchvision.ops import _new_empty_tensor
+    from torchvision.ops.misc import _output_size
+
+
+class SmoothedValue(object):
+    """Track a series of values and provide access to smoothed values over a
+    window or the global series average.
+    """
+
+    def __init__(self, window_size=20, fmt=None):
+        if fmt is None:
+            fmt = "{median:.4f} ({global_avg:.4f})"
+        self.deque = deque(maxlen=window_size)
+        self.total = 0.0
+        self.count = 0
+        self.fmt = fmt
+
+    def update(self, value, n=1):
+        self.deque.append(value)
+        self.count += n
+        self.total += value * n
+
+    def synchronize_between_processes(self):
+        """
+        Warning: does not synchronize the deque!
+        """
+        if not is_dist_avail_and_initialized():
+            return
+        t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda")
+        dist.barrier()
+        dist.all_reduce(t)
+        t = t.tolist()
+        self.count = int(t[0])
+        self.total = t[1]
+
+    @property
+    def median(self):
+        d = torch.tensor(list(self.deque))
+        if d.shape[0] == 0:
+            return 0
+        return d.median().item()
+
+    @property
+    def avg(self):
+        d = torch.tensor(list(self.deque), dtype=torch.float32)
+        return d.mean().item()
+
+    @property
+    def global_avg(self):
+        if os.environ.get("SHILONG_AMP", None) == "1":
+            eps = 1e-4
+        else:
+            eps = 1e-6
+        return self.total / (self.count + eps)
+
+    @property
+    def max(self):
+        return max(self.deque)
+
+    @property
+    def value(self):
+        return self.deque[-1]
+
+    def __str__(self):
+        return self.fmt.format(
+            median=self.median,
+            avg=self.avg,
+            global_avg=self.global_avg,
+            max=self.max,
+            value=self.value,
+        )
+
+
+@functools.lru_cache()
+def _get_global_gloo_group():
+    """
+    Return a process group based on gloo backend, containing all the ranks
+    The result is cached.
+    """
+
+    if dist.get_backend() == "nccl":
+        return dist.new_group(backend="gloo")
+
+    return dist.group.WORLD
+
+
+def all_gather_cpu(data):
+    """
+    Run all_gather on arbitrary picklable data (not necessarily tensors)
+    Args:
+        data: any picklable object
+    Returns:
+        list[data]: list of data gathered from each rank
+    """
+
+    world_size = get_world_size()
+    if world_size == 1:
+        return [data]
+
+    cpu_group = _get_global_gloo_group()
+
+    buffer = io.BytesIO()
+    torch.save(data, buffer)
+    data_view = buffer.getbuffer()
+    device = "cuda" if cpu_group is None else "cpu"
+    tensor = torch.ByteTensor(data_view).to(device)
+
+    # obtain Tensor size of each rank
+    local_size = torch.tensor([tensor.numel()], device=device, dtype=torch.long)
+    size_list = [torch.tensor([0], device=device, dtype=torch.long) for _ in range(world_size)]
+    if cpu_group is None:
+        dist.all_gather(size_list, local_size)
+    else:
+        print("gathering on cpu")
+        dist.all_gather(size_list, local_size, group=cpu_group)
+    size_list = [int(size.item()) for size in size_list]
+    max_size = max(size_list)
+    assert isinstance(local_size.item(), int)
+    local_size = int(local_size.item())
+
+    # receiving Tensor from all ranks
+    # we pad the tensor because torch all_gather does not support
+    # gathering tensors of different shapes
+    tensor_list = []
+    for _ in size_list:
+        tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=device))
+    if local_size != max_size:
+        padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device=device)
+        tensor = torch.cat((tensor, padding), dim=0)
+    if cpu_group is None:
+        dist.all_gather(tensor_list, tensor)
+    else:
+        dist.all_gather(tensor_list, tensor, group=cpu_group)
+
+    data_list = []
+    for size, tensor in zip(size_list, tensor_list):
+        tensor = torch.split(tensor, [size, max_size - size], dim=0)[0]
+        buffer = io.BytesIO(tensor.cpu().numpy())
+        obj = torch.load(buffer)
+        data_list.append(obj)
+
+    return data_list
+
+
+def all_gather(data):
+    """
+    Run all_gather on arbitrary picklable data (not necessarily tensors)
+    Args:
+        data: any picklable object
+    Returns:
+        list[data]: list of data gathered from each rank
+    """
+
+    if os.getenv("CPU_REDUCE") == "1":
+        return all_gather_cpu(data)
+
+    world_size = get_world_size()
+    if world_size == 1:
+        return [data]
+
+    # serialized to a Tensor
+    buffer = pickle.dumps(data)
+    storage = torch.ByteStorage.from_buffer(buffer)
+    tensor = torch.ByteTensor(storage).to("cuda")
+
+    # obtain Tensor size of each rank
+    local_size = torch.tensor([tensor.numel()], device="cuda")
+    size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)]
+    dist.all_gather(size_list, local_size)
+    size_list = [int(size.item()) for size in size_list]
+    max_size = max(size_list)
+
+    # receiving Tensor from all ranks
+    # we pad the tensor because torch all_gather does not support
+    # gathering tensors of different shapes
+    tensor_list = []
+    for _ in size_list:
+        tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda"))
+    if local_size != max_size:
+        padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda")
+        tensor = torch.cat((tensor, padding), dim=0)
+    dist.all_gather(tensor_list, tensor)
+
+    data_list = []
+    for size, tensor in zip(size_list, tensor_list):
+        buffer = tensor.cpu().numpy().tobytes()[:size]
+        data_list.append(pickle.loads(buffer))
+
+    return data_list
+
+
+def reduce_dict(input_dict, average=True):
+    """
+    Args:
+        input_dict (dict): all the values will be reduced
+        average (bool): whether to do average or sum
+    Reduce the values in the dictionary from all processes so that all processes
+    have the averaged results. Returns a dict with the same fields as
+    input_dict, after reduction.
+    """
+    world_size = get_world_size()
+    if world_size < 2:
+        return input_dict
+    with torch.no_grad():
+        names = []
+        values = []
+        # sort the keys so that they are consistent across processes
+        for k in sorted(input_dict.keys()):
+            names.append(k)
+            values.append(input_dict[k])
+        values = torch.stack(values, dim=0)
+        dist.all_reduce(values)
+        if average:
+            values /= world_size
+        reduced_dict = {k: v for k, v in zip(names, values)}
+    return reduced_dict
+
+
+class MetricLogger(object):
+    def __init__(self, delimiter="\t"):
+        self.meters = defaultdict(SmoothedValue)
+        self.delimiter = delimiter
+
+    def update(self, **kwargs):
+        for k, v in kwargs.items():
+            if isinstance(v, torch.Tensor):
+                v = v.item()
+            assert isinstance(v, (float, int))
+            self.meters[k].update(v)
+
+    def __getattr__(self, attr):
+        if attr in self.meters:
+            return self.meters[attr]
+        if attr in self.__dict__:
+            return self.__dict__[attr]
+        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr))
+
+    def __str__(self):
+        loss_str = []
+        for name, meter in self.meters.items():
+            # print(name, str(meter))
+            # import ipdb;ipdb.set_trace()
+            if meter.count > 0:
+                loss_str.append("{}: {}".format(name, str(meter)))
+        return self.delimiter.join(loss_str)
+
+    def synchronize_between_processes(self):
+        for meter in self.meters.values():
+            meter.synchronize_between_processes()
+
+    def add_meter(self, name, meter):
+        self.meters[name] = meter
+
+    def log_every(self, iterable, print_freq, header=None, logger=None):
+        if logger is None:
+            print_func = print
+        else:
+            print_func = logger.info
+
+        i = 0
+        if not header:
+            header = ""
+        start_time = time.time()
+        end = time.time()
+        iter_time = SmoothedValue(fmt="{avg:.4f}")
+        data_time = SmoothedValue(fmt="{avg:.4f}")
+        space_fmt = ":" + str(len(str(len(iterable)))) + "d"
+        if torch.cuda.is_available():
+            log_msg = self.delimiter.join(
+                [
+                    header,
+                    "[{0" + space_fmt + "}/{1}]",
+                    "eta: {eta}",
+                    "{meters}",
+                    "time: {time}",
+                    "data: {data}",
+                    "max mem: {memory:.0f}",
+                ]
+            )
+        else:
+            log_msg = self.delimiter.join(
+                [
+                    header,
+                    "[{0" + space_fmt + "}/{1}]",
+                    "eta: {eta}",
+                    "{meters}",
+                    "time: {time}",
+                    "data: {data}",
+                ]
+            )
+        MB = 1024.0 * 1024.0
+        for obj in iterable:
+            data_time.update(time.time() - end)
+            yield obj
+            # import ipdb; ipdb.set_trace()
+            iter_time.update(time.time() - end)
+            if i % print_freq == 0 or i == len(iterable) - 1:
+                eta_seconds = iter_time.global_avg * (len(iterable) - i)
+                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
+                if torch.cuda.is_available():
+                    print_func(
+                        log_msg.format(
+                            i,
+                            len(iterable),
+                            eta=eta_string,
+                            meters=str(self),
+                            time=str(iter_time),
+                            data=str(data_time),
+                            memory=torch.cuda.max_memory_allocated() / MB,
+                        )
+                    )
+                else:
+                    print_func(
+                        log_msg.format(
+                            i,
+                            len(iterable),
+                            eta=eta_string,
+                            meters=str(self),
+                            time=str(iter_time),
+                            data=str(data_time),
+                        )
+                    )
+            i += 1
+            end = time.time()
+        total_time = time.time() - start_time
+        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+        print_func(
+            "{} Total time: {} ({:.4f} s / it)".format(
+                header, total_time_str, total_time / len(iterable)
+            )
+        )
+
+
+def get_sha():
+    cwd = os.path.dirname(os.path.abspath(__file__))
+
+    def _run(command):
+        return subprocess.check_output(command, cwd=cwd).decode("ascii").strip()
+
+    sha = "N/A"
+    diff = "clean"
+    branch = "N/A"
+    try:
+        sha = _run(["git", "rev-parse", "HEAD"])
+        subprocess.check_output(["git", "diff"], cwd=cwd)
+        diff = _run(["git", "diff-index", "HEAD"])
+        diff = "has uncommited changes" if diff else "clean"
+        branch = _run(["git", "rev-parse", "--abbrev-ref", "HEAD"])
+    except Exception:
+        pass
+    message = f"sha: {sha}, status: {diff}, branch: {branch}"
+    return message
+
+
+def collate_fn(batch):
+    # import ipdb; ipdb.set_trace()
+    batch = list(zip(*batch))
+    batch[0] = nested_tensor_from_tensor_list(batch[0])
+    return tuple(batch)
+
+
+def _max_by_axis(the_list):
+    # type: (List[List[int]]) -> List[int]
+    maxes = the_list[0]
+    for sublist in the_list[1:]:
+        for index, item in enumerate(sublist):
+            maxes[index] = max(maxes[index], item)
+    return maxes
+
+
+class NestedTensor(object):
+    def __init__(self, tensors, mask: Optional[Tensor]):
+        self.tensors = tensors
+        self.mask = mask
+        if mask == "auto":
+            self.mask = torch.zeros_like(tensors).to(tensors.device)
+            if self.mask.dim() == 3:
+                self.mask = self.mask.sum(0).to(bool)
+            elif self.mask.dim() == 4:
+                self.mask = self.mask.sum(1).to(bool)
+            else:
+                raise ValueError(
+                    "tensors dim must be 3 or 4 but {}({})".format(
+                        self.tensors.dim(), self.tensors.shape
+                    )
+                )
+
+    def imgsize(self):
+        res = []
+        for i in range(self.tensors.shape[0]):
+            mask = self.mask[i]
+            maxH = (~mask).sum(0).max()
+            maxW = (~mask).sum(1).max()
+            res.append(torch.Tensor([maxH, maxW]))
+        return res
+
+    def to(self, device):
+        # type: (Device) -> NestedTensor # noqa
+        cast_tensor = self.tensors.to(device)
+        mask = self.mask
+        if mask is not None:
+            assert mask is not None
+            cast_mask = mask.to(device)
+        else:
+            cast_mask = None
+        return NestedTensor(cast_tensor, cast_mask)
+
+    def to_img_list_single(self, tensor, mask):
+        assert tensor.dim() == 3, "dim of tensor should be 3 but {}".format(tensor.dim())
+        maxH = (~mask).sum(0).max()
+        maxW = (~mask).sum(1).max()
+        img = tensor[:, :maxH, :maxW]
+        return img
+
+    def to_img_list(self):
+        """remove the padding and convert to img list
+
+        Returns:
+            [type]: [description]
+        """
+        if self.tensors.dim() == 3:
+            return self.to_img_list_single(self.tensors, self.mask)
+        else:
+            res = []
+            for i in range(self.tensors.shape[0]):
+                tensor_i = self.tensors[i]
+                mask_i = self.mask[i]
+                res.append(self.to_img_list_single(tensor_i, mask_i))
+            return res
+
+    @property
+    def device(self):
+        return self.tensors.device
+
+    def decompose(self):
+        return self.tensors, self.mask
+
+    def __repr__(self):
+        return str(self.tensors)
+
+    @property
+    def shape(self):
+        return {"tensors.shape": self.tensors.shape, "mask.shape": self.mask.shape}
+
+
+def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
+    # TODO make this more general
+    if tensor_list[0].ndim == 3:
+        if torchvision._is_tracing():
+            # nested_tensor_from_tensor_list() does not export well to ONNX
+            # call _onnx_nested_tensor_from_tensor_list() instead
+            return _onnx_nested_tensor_from_tensor_list(tensor_list)
+
+        # TODO make it support different-sized images
+        max_size = _max_by_axis([list(img.shape) for img in tensor_list])
+        # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))
+        batch_shape = [len(tensor_list)] + max_size
+        b, c, h, w = batch_shape
+        dtype = tensor_list[0].dtype
+        device = tensor_list[0].device
+        tensor = torch.zeros(batch_shape, dtype=dtype, device=device)
+        mask = torch.ones((b, h, w), dtype=torch.bool, device=device)
+        for img, pad_img, m in zip(tensor_list, tensor, mask):
+            pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
+            m[: img.shape[1], : img.shape[2]] = False
+    else:
+        raise ValueError("not supported")
+    return NestedTensor(tensor, mask)
+
+
+# _onnx_nested_tensor_from_tensor_list() is an implementation of
+# nested_tensor_from_tensor_list() that is supported by ONNX tracing.
+@torch.jit.unused
+def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor:
+    max_size = []
+    for i in range(tensor_list[0].dim()):
+        max_size_i = torch.max(
+            torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)
+        ).to(torch.int64)
+        max_size.append(max_size_i)
+    max_size = tuple(max_size)
+
+    # work around for
+    # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
+    # m[: img.shape[1], :img.shape[2]] = False
+    # which is not yet supported in onnx
+    padded_imgs = []
+    padded_masks = []
+    for img in tensor_list:
+        padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]
+        padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))
+        padded_imgs.append(padded_img)
+
+        m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)
+        padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1)
+        padded_masks.append(padded_mask.to(torch.bool))
+
+    tensor = torch.stack(padded_imgs)
+    mask = torch.stack(padded_masks)
+
+    return NestedTensor(tensor, mask=mask)
+
+
+def setup_for_distributed(is_master):
+    """
+    This function disables printing when not in master process
+    """
+    import builtins as __builtin__
+
+    builtin_print = __builtin__.print
+
+    def print(*args, **kwargs):
+        force = kwargs.pop("force", False)
+        if is_master or force:
+            builtin_print(*args, **kwargs)
+
+    __builtin__.print = print
+
+
+def is_dist_avail_and_initialized():
+    if not dist.is_available():
+        return False
+    if not dist.is_initialized():
+        return False
+    return True
+
+
+def get_world_size():
+    if not is_dist_avail_and_initialized():
+        return 1
+    return dist.get_world_size()
+
+
+def get_rank():
+    if not is_dist_avail_and_initialized():
+        return 0
+    return dist.get_rank()
+
+
+def is_main_process():
+    return get_rank() == 0
+
+
+def save_on_master(*args, **kwargs):
+    if is_main_process():
+        torch.save(*args, **kwargs)
+
+
+def init_distributed_mode(args):
+    if "WORLD_SIZE" in os.environ and os.environ["WORLD_SIZE"] != "":  # 'RANK' in os.environ and
+        args.rank = int(os.environ["RANK"])
+        args.world_size = int(os.environ["WORLD_SIZE"])
+        args.gpu = args.local_rank = int(os.environ["LOCAL_RANK"])
+
+        # launch by torch.distributed.launch
+        # Single node
+        #   python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 1 --rank 0 ...
+        # Multi nodes
+        #   python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 2 --rank 0 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' ...
+        #   python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 2 --rank 1 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' ...
+        # args.rank = int(os.environ.get('OMPI_COMM_WORLD_RANK'))
+        # local_world_size = int(os.environ['GPU_PER_NODE_COUNT'])
+        # args.world_size = args.world_size * local_world_size
+        # args.gpu = args.local_rank = int(os.environ['LOCAL_RANK'])
+        # args.rank = args.rank * local_world_size + args.local_rank
+        print(
+            "world size: {}, rank: {}, local rank: {}".format(
+                args.world_size, args.rank, args.local_rank
+            )
+        )
+        print(json.dumps(dict(os.environ), indent=2))
+    elif "SLURM_PROCID" in os.environ:
+        args.rank = int(os.environ["SLURM_PROCID"])
+        args.gpu = args.local_rank = int(os.environ["SLURM_LOCALID"])
+        args.world_size = int(os.environ["SLURM_NPROCS"])
+
+        print(
+            "world size: {}, world rank: {}, local rank: {}, device_count: {}".format(
+                args.world_size, args.rank, args.local_rank, torch.cuda.device_count()
+            )
+        )
+    else:
+        print("Not using distributed mode")
+        args.distributed = False
+        args.world_size = 1
+        args.rank = 0
+        args.local_rank = 0
+        return
+
+    print("world_size:{} rank:{} local_rank:{}".format(args.world_size, args.rank, args.local_rank))
+    args.distributed = True
+    torch.cuda.set_device(args.local_rank)
+    args.dist_backend = "nccl"
+    print("| distributed init (rank {}): {}".format(args.rank, args.dist_url), flush=True)
+
+    torch.distributed.init_process_group(
+        backend=args.dist_backend,
+        world_size=args.world_size,
+        rank=args.rank,
+        init_method=args.dist_url,
+    )
+
+    print("Before torch.distributed.barrier()")
+    torch.distributed.barrier()
+    print("End torch.distributed.barrier()")
+    setup_for_distributed(args.rank == 0)
+
+
+@torch.no_grad()
+def accuracy(output, target, topk=(1,)):
+    """Computes the precision@k for the specified values of k"""
+    if target.numel() == 0:
+        return [torch.zeros([], device=output.device)]
+    maxk = max(topk)
+    batch_size = target.size(0)
+
+    _, pred = output.topk(maxk, 1, True, True)
+    pred = pred.t()
+    correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+    res = []
+    for k in topk:
+        correct_k = correct[:k].view(-1).float().sum(0)
+        res.append(correct_k.mul_(100.0 / batch_size))
+    return res
+
+
+@torch.no_grad()
+def accuracy_onehot(pred, gt):
+    """_summary_
+
+    Args:
+        pred (_type_): n, c
+        gt (_type_): n, c
+    """
+    tp = ((pred - gt).abs().sum(-1) < 1e-4).float().sum()
+    acc = tp / gt.shape[0] * 100
+    return acc
+
+
+def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None):
+    # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor
+    """
+    Equivalent to nn.functional.interpolate, but with support for empty batch sizes.
+    This will eventually be supported natively by PyTorch, and this
+    class can go away.
+    """
+    if __torchvision_need_compat_flag < 0.7:
+        if input.numel() > 0:
+            return torch.nn.functional.interpolate(input, size, scale_factor, mode, align_corners)
+
+        output_shape = _output_size(2, input, size, scale_factor)
+        output_shape = list(input.shape[:-2]) + list(output_shape)
+        return _new_empty_tensor(input, output_shape)
+    else:
+        return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)
+
+
+class color_sys:
+    def __init__(self, num_colors) -> None:
+        self.num_colors = num_colors
+        colors = []
+        for i in np.arange(0.0, 360.0, 360.0 / num_colors):
+            hue = i / 360.0
+            lightness = (50 + np.random.rand() * 10) / 100.0
+            saturation = (90 + np.random.rand() * 10) / 100.0
+            colors.append(
+                tuple([int(j * 255) for j in colorsys.hls_to_rgb(hue, lightness, saturation)])
+            )
+        self.colors = colors
+
+    def __call__(self, idx):
+        return self.colors[idx]
+
+
+def inverse_sigmoid(x, eps=1e-3):
+    x = x.clamp(min=0, max=1)
+    x1 = x.clamp(min=eps)
+    x2 = (1 - x).clamp(min=eps)
+    return torch.log(x1 / x2)
+
+
+def clean_state_dict(state_dict):
+    new_state_dict = OrderedDict()
+    for k, v in state_dict.items():
+        if k[:7] == "module.":
+            k = k[7:]  # remove `module.`
+        new_state_dict[k] = v
+    return new_state_dict

groundingdino/util/slconfig.py

@@ -0,0 +1,424 @@
+# ==========================================================
+# Modified from mmcv
+# ==========================================================
+import ast
+import os.path as osp
+import shutil
+import sys
+import tempfile
+from argparse import Action
+from importlib import import_module
+
+from addict import Dict
+from yapf.yapflib.yapf_api import FormatCode
+
+BASE_KEY = "_base_"
+DELETE_KEY = "_delete_"
+RESERVED_KEYS = ["filename", "text", "pretty_text", "get", "dump", "merge_from_dict"]
+
+
+def check_file_exist(filename, msg_tmpl='file "{}" does not exist'):
+    if not osp.isfile(filename):
+        raise FileNotFoundError(msg_tmpl.format(filename))
+
+
+class ConfigDict(Dict):
+    def __missing__(self, name):
+        raise KeyError(name)
+
+    def __getattr__(self, name):
+        try:
+            value = super(ConfigDict, self).__getattr__(name)
+        except KeyError:
+            ex = AttributeError(f"'{self.__class__.__name__}' object has no " f"attribute '{name}'")
+        except Exception as e:
+            ex = e
+        else:
+            return value
+        raise ex
+
+
+class SLConfig(object):
+    """
+    config files.
+    only support .py file as config now.
+
+    ref: mmcv.utils.config
+
+    Example:
+        >>> cfg = Config(dict(a=1, b=dict(b1=[0, 1])))
+        >>> cfg.a
+        1
+        >>> cfg.b
+        {'b1': [0, 1]}
+        >>> cfg.b.b1
+        [0, 1]
+        >>> cfg = Config.fromfile('tests/data/config/a.py')
+        >>> cfg.filename
+        "/home/kchen/projects/mmcv/tests/data/config/a.py"
+        >>> cfg.item4
+        'test'
+        >>> cfg
+        "Config [path: /home/kchen/projects/mmcv/tests/data/config/a.py]: "
+        "{'item1': [1, 2], 'item2': {'a': 0}, 'item3': True, 'item4': 'test'}"
+    """
+
+    @staticmethod
+    def _validate_py_syntax(filename):
+        with open(filename) as f:
+            content = f.read()
+        try:
+            ast.parse(content)
+        except SyntaxError:
+            raise SyntaxError("There are syntax errors in config " f"file {filename}")
+
+    @staticmethod
+    def _file2dict(filename):
+        filename = osp.abspath(osp.expanduser(filename))
+        check_file_exist(filename)
+        if filename.lower().endswith(".py"):
+            with tempfile.TemporaryDirectory() as temp_config_dir:
+                temp_config_file = tempfile.NamedTemporaryFile(dir=temp_config_dir, suffix=".py")
+                temp_config_name = osp.basename(temp_config_file.name)
+                shutil.copyfile(filename, osp.join(temp_config_dir, temp_config_name))
+                temp_module_name = osp.splitext(temp_config_name)[0]
+                sys.path.insert(0, temp_config_dir)
+                SLConfig._validate_py_syntax(filename)
+                mod = import_module(temp_module_name)
+                sys.path.pop(0)
+                cfg_dict = {
+                    name: value for name, value in mod.__dict__.items() if not name.startswith("__")
+                }
+                # delete imported module
+                del sys.modules[temp_module_name]
+                # close temp file
+                temp_config_file.close()
+        elif filename.lower().endswith((".yml", ".yaml", ".json")):
+            from .slio import slload
+
+            cfg_dict = slload(filename)
+        else:
+            raise IOError("Only py/yml/yaml/json type are supported now!")
+
+        cfg_text = filename + "\n"
+        with open(filename, "r") as f:
+            cfg_text += f.read()
+
+        # parse the base file
+        if BASE_KEY in cfg_dict:
+            cfg_dir = osp.dirname(filename)
+            base_filename = cfg_dict.pop(BASE_KEY)
+            base_filename = base_filename if isinstance(base_filename, list) else [base_filename]
+
+            cfg_dict_list = list()
+            cfg_text_list = list()
+            for f in base_filename:
+                _cfg_dict, _cfg_text = SLConfig._file2dict(osp.join(cfg_dir, f))
+                cfg_dict_list.append(_cfg_dict)
+                cfg_text_list.append(_cfg_text)
+
+            base_cfg_dict = dict()
+            for c in cfg_dict_list:
+                if len(base_cfg_dict.keys() & c.keys()) > 0:
+                    raise KeyError("Duplicate key is not allowed among bases")
+                    # TODO Allow the duplicate key while warnning user
+                base_cfg_dict.update(c)
+
+            base_cfg_dict = SLConfig._merge_a_into_b(cfg_dict, base_cfg_dict)
+            cfg_dict = base_cfg_dict
+
+            # merge cfg_text
+            cfg_text_list.append(cfg_text)
+            cfg_text = "\n".join(cfg_text_list)
+
+        return cfg_dict, cfg_text
+
+    @staticmethod
+    def _merge_a_into_b(a, b):
+        """merge dict `a` into dict `b` (non-inplace).
+            values in `a` will overwrite `b`.
+            copy first to avoid inplace modification
+
+        Args:
+            a ([type]): [description]
+            b ([type]): [description]
+
+        Returns:
+            [dict]: [description]
+        """
+        # import ipdb; ipdb.set_trace()
+        if not isinstance(a, dict):
+            return a
+
+        b = b.copy()
+        for k, v in a.items():
+            if isinstance(v, dict) and k in b and not v.pop(DELETE_KEY, False):
+
+                if not isinstance(b[k], dict) and not isinstance(b[k], list):
+                    # if :
+                    # import ipdb; ipdb.set_trace()
+                    raise TypeError(
+                        f"{k}={v} in child config cannot inherit from base "
+                        f"because {k} is a dict in the child config but is of "
+                        f"type {type(b[k])} in base config. You may set "
+                        f"`{DELETE_KEY}=True` to ignore the base config"
+                    )
+                b[k] = SLConfig._merge_a_into_b(v, b[k])
+            elif isinstance(b, list):
+                try:
+                    _ = int(k)
+                except:
+                    raise TypeError(
+                        f"b is a list, " f"index {k} should be an int when input but {type(k)}"
+                    )
+                b[int(k)] = SLConfig._merge_a_into_b(v, b[int(k)])
+            else:
+                b[k] = v
+
+        return b
+
+    @staticmethod
+    def fromfile(filename):
+        cfg_dict, cfg_text = SLConfig._file2dict(filename)
+        return SLConfig(cfg_dict, cfg_text=cfg_text, filename=filename)
+
+    def __init__(self, cfg_dict=None, cfg_text=None, filename=None):
+        if cfg_dict is None:
+            cfg_dict = dict()
+        elif not isinstance(cfg_dict, dict):
+            raise TypeError("cfg_dict must be a dict, but " f"got {type(cfg_dict)}")
+        for key in cfg_dict:
+            if key in RESERVED_KEYS:
+                raise KeyError(f"{key} is reserved for config file")
+
+        super(SLConfig, self).__setattr__("_cfg_dict", ConfigDict(cfg_dict))
+        super(SLConfig, self).__setattr__("_filename", filename)
+        if cfg_text:
+            text = cfg_text
+        elif filename:
+            with open(filename, "r") as f:
+                text = f.read()
+        else:
+            text = ""
+        super(SLConfig, self).__setattr__("_text", text)
+
+    @property
+    def filename(self):
+        return self._filename
+
+    @property
+    def text(self):
+        return self._text
+
+    @property
+    def pretty_text(self):
+
+        indent = 4
+
+        def _indent(s_, num_spaces):
+            s = s_.split("\n")
+            if len(s) == 1:
+                return s_
+            first = s.pop(0)
+            s = [(num_spaces * " ") + line for line in s]
+            s = "\n".join(s)
+            s = first + "\n" + s
+            return s
+
+        def _format_basic_types(k, v, use_mapping=False):
+            if isinstance(v, str):
+                v_str = f"'{v}'"
+            else:
+                v_str = str(v)
+
+            if use_mapping:
+                k_str = f"'{k}'" if isinstance(k, str) else str(k)
+                attr_str = f"{k_str}: {v_str}"
+            else:
+                attr_str = f"{str(k)}={v_str}"
+            attr_str = _indent(attr_str, indent)
+
+            return attr_str
+
+        def _format_list(k, v, use_mapping=False):
+            # check if all items in the list are dict
+            if all(isinstance(_, dict) for _ in v):
+                v_str = "[\n"
+                v_str += "\n".join(
+                    f"dict({_indent(_format_dict(v_), indent)})," for v_ in v
+                ).rstrip(",")
+                if use_mapping:
+                    k_str = f"'{k}'" if isinstance(k, str) else str(k)
+                    attr_str = f"{k_str}: {v_str}"
+                else:
+                    attr_str = f"{str(k)}={v_str}"
+                attr_str = _indent(attr_str, indent) + "]"
+            else:
+                attr_str = _format_basic_types(k, v, use_mapping)
+            return attr_str
+
+        def _contain_invalid_identifier(dict_str):
+            contain_invalid_identifier = False
+            for key_name in dict_str:
+                contain_invalid_identifier |= not str(key_name).isidentifier()
+            return contain_invalid_identifier
+
+        def _format_dict(input_dict, outest_level=False):
+            r = ""
+            s = []
+
+            use_mapping = _contain_invalid_identifier(input_dict)
+            if use_mapping:
+                r += "{"
+            for idx, (k, v) in enumerate(input_dict.items()):
+                is_last = idx >= len(input_dict) - 1
+                end = "" if outest_level or is_last else ","
+                if isinstance(v, dict):
+                    v_str = "\n" + _format_dict(v)
+                    if use_mapping:
+                        k_str = f"'{k}'" if isinstance(k, str) else str(k)
+                        attr_str = f"{k_str}: dict({v_str}"
+                    else:
+                        attr_str = f"{str(k)}=dict({v_str}"
+                    attr_str = _indent(attr_str, indent) + ")" + end
+                elif isinstance(v, list):
+                    attr_str = _format_list(k, v, use_mapping) + end
+                else:
+                    attr_str = _format_basic_types(k, v, use_mapping) + end
+
+                s.append(attr_str)
+            r += "\n".join(s)
+            if use_mapping:
+                r += "}"
+            return r
+
+        cfg_dict = self._cfg_dict.to_dict()
+        text = _format_dict(cfg_dict, outest_level=True)
+        # copied from setup.cfg
+        yapf_style = dict(
+            based_on_style="pep8",
+            blank_line_before_nested_class_or_def=True,
+            split_before_expression_after_opening_paren=True,
+        )
+        text, _ = FormatCode(text, style_config=yapf_style, verify=True)
+
+        return text
+
+    def __repr__(self):
+        return f"Config (path: {self.filename}): {self._cfg_dict.__repr__()}"
+
+    def __len__(self):
+        return len(self._cfg_dict)
+
+    def __getattr__(self, name):
+        # # debug
+        # print('+'*15)
+        # print('name=%s' % name)
+        # print("addr:", id(self))
+        # # print('type(self):', type(self))
+        # print(self.__dict__)
+        # print('+'*15)
+        # if self.__dict__ == {}:
+        #     raise ValueError
+
+        return getattr(self._cfg_dict, name)
+
+    def __getitem__(self, name):
+        return self._cfg_dict.__getitem__(name)
+
+    def __setattr__(self, name, value):
+        if isinstance(value, dict):
+            value = ConfigDict(value)
+        self._cfg_dict.__setattr__(name, value)
+
+    def __setitem__(self, name, value):
+        if isinstance(value, dict):
+            value = ConfigDict(value)
+        self._cfg_dict.__setitem__(name, value)
+
+    def __iter__(self):
+        return iter(self._cfg_dict)
+
+    def dump(self, file=None):
+        # import ipdb; ipdb.set_trace()
+        if file is None:
+            return self.pretty_text
+        else:
+            with open(file, "w") as f:
+                f.write(self.pretty_text)
+
+    def merge_from_dict(self, options):
+        """Merge list into cfg_dict
+
+        Merge the dict parsed by MultipleKVAction into this cfg.
+
+        Examples:
+            >>> options = {'model.backbone.depth': 50,
+            ...            'model.backbone.with_cp':True}
+            >>> cfg = Config(dict(model=dict(backbone=dict(type='ResNet'))))
+            >>> cfg.merge_from_dict(options)
+            >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict')
+            >>> assert cfg_dict == dict(
+            ...     model=dict(backbone=dict(depth=50, with_cp=True)))
+
+        Args:
+            options (dict): dict of configs to merge from.
+        """
+        option_cfg_dict = {}
+        for full_key, v in options.items():
+            d = option_cfg_dict
+            key_list = full_key.split(".")
+            for subkey in key_list[:-1]:
+                d.setdefault(subkey, ConfigDict())
+                d = d[subkey]
+            subkey = key_list[-1]
+            d[subkey] = v
+
+        cfg_dict = super(SLConfig, self).__getattribute__("_cfg_dict")
+        super(SLConfig, self).__setattr__(
+            "_cfg_dict", SLConfig._merge_a_into_b(option_cfg_dict, cfg_dict)
+        )
+
+    # for multiprocess
+    def __setstate__(self, state):
+        self.__init__(state)
+
+    def copy(self):
+        return SLConfig(self._cfg_dict.copy())
+
+    def deepcopy(self):
+        return SLConfig(self._cfg_dict.deepcopy())
+
+
+class DictAction(Action):
+    """
+    argparse action to split an argument into KEY=VALUE form
+    on the first = and append to a dictionary. List options should
+    be passed as comma separated values, i.e KEY=V1,V2,V3
+    """
+
+    @staticmethod
+    def _parse_int_float_bool(val):
+        try:
+            return int(val)
+        except ValueError:
+            pass
+        try:
+            return float(val)
+        except ValueError:
+            pass
+        if val.lower() in ["true", "false"]:
+            return True if val.lower() == "true" else False
+        if val.lower() in ["none", "null"]:
+            return None
+        return val
+
+    def __call__(self, parser, namespace, values, option_string=None):
+        options = {}
+        for kv in values:
+            key, val = kv.split("=", maxsplit=1)
+            val = [self._parse_int_float_bool(v) for v in val.split(",")]
+            if len(val) == 1:
+                val = val[0]
+            options[key] = val
+        setattr(namespace, self.dest, options)

groundingdino/util/slio.py

@@ -0,0 +1,177 @@
+# ==========================================================
+# Modified from mmcv
+# ==========================================================
+
+import json
+import pickle
+from abc import ABCMeta, abstractmethod
+from pathlib import Path
+
+import yaml
+
+try:
+    from yaml import CLoader as Loader, CDumper as Dumper
+except ImportError:
+    from yaml import Loader, Dumper
+
+
+# ===========================
+# Rigister handler
+# ===========================
+
+
+class BaseFileHandler(metaclass=ABCMeta):
+    @abstractmethod
+    def load_from_fileobj(self, file, **kwargs):
+        pass
+
+    @abstractmethod
+    def dump_to_fileobj(self, obj, file, **kwargs):
+        pass
+
+    @abstractmethod
+    def dump_to_str(self, obj, **kwargs):
+        pass
+
+    def load_from_path(self, filepath, mode="r", **kwargs):
+        with open(filepath, mode) as f:
+            return self.load_from_fileobj(f, **kwargs)
+
+    def dump_to_path(self, obj, filepath, mode="w", **kwargs):
+        with open(filepath, mode) as f:
+            self.dump_to_fileobj(obj, f, **kwargs)
+
+
+class JsonHandler(BaseFileHandler):
+    def load_from_fileobj(self, file):
+        return json.load(file)
+
+    def dump_to_fileobj(self, obj, file, **kwargs):
+        json.dump(obj, file, **kwargs)
+
+    def dump_to_str(self, obj, **kwargs):
+        return json.dumps(obj, **kwargs)
+
+
+class PickleHandler(BaseFileHandler):
+    def load_from_fileobj(self, file, **kwargs):
+        return pickle.load(file, **kwargs)
+
+    def load_from_path(self, filepath, **kwargs):
+        return super(PickleHandler, self).load_from_path(filepath, mode="rb", **kwargs)
+
+    def dump_to_str(self, obj, **kwargs):
+        kwargs.setdefault("protocol", 2)
+        return pickle.dumps(obj, **kwargs)
+
+    def dump_to_fileobj(self, obj, file, **kwargs):
+        kwargs.setdefault("protocol", 2)
+        pickle.dump(obj, file, **kwargs)
+
+    def dump_to_path(self, obj, filepath, **kwargs):
+        super(PickleHandler, self).dump_to_path(obj, filepath, mode="wb", **kwargs)
+
+
+class YamlHandler(BaseFileHandler):
+    def load_from_fileobj(self, file, **kwargs):
+        kwargs.setdefault("Loader", Loader)
+        return yaml.load(file, **kwargs)
+
+    def dump_to_fileobj(self, obj, file, **kwargs):
+        kwargs.setdefault("Dumper", Dumper)
+        yaml.dump(obj, file, **kwargs)
+
+    def dump_to_str(self, obj, **kwargs):
+        kwargs.setdefault("Dumper", Dumper)
+        return yaml.dump(obj, **kwargs)
+
+
+file_handlers = {
+    "json": JsonHandler(),
+    "yaml": YamlHandler(),
+    "yml": YamlHandler(),
+    "pickle": PickleHandler(),
+    "pkl": PickleHandler(),
+}
+
+# ===========================
+# load and dump
+# ===========================
+
+
+def is_str(x):
+    """Whether the input is an string instance.
+
+    Note: This method is deprecated since python 2 is no longer supported.
+    """
+    return isinstance(x, str)
+
+
+def slload(file, file_format=None, **kwargs):
+    """Load data from json/yaml/pickle files.
+
+    This method provides a unified api for loading data from serialized files.
+
+    Args:
+        file (str or :obj:`Path` or file-like object): Filename or a file-like
+            object.
+        file_format (str, optional): If not specified, the file format will be
+            inferred from the file extension, otherwise use the specified one.
+            Currently supported formats include "json", "yaml/yml" and
+            "pickle/pkl".
+
+    Returns:
+        The content from the file.
+    """
+    if isinstance(file, Path):
+        file = str(file)
+    if file_format is None and is_str(file):
+        file_format = file.split(".")[-1]
+    if file_format not in file_handlers:
+        raise TypeError(f"Unsupported format: {file_format}")
+
+    handler = file_handlers[file_format]
+    if is_str(file):
+        obj = handler.load_from_path(file, **kwargs)
+    elif hasattr(file, "read"):
+        obj = handler.load_from_fileobj(file, **kwargs)
+    else:
+        raise TypeError('"file" must be a filepath str or a file-object')
+    return obj
+
+
+def sldump(obj, file=None, file_format=None, **kwargs):
+    """Dump data to json/yaml/pickle strings or files.
+
+    This method provides a unified api for dumping data as strings or to files,
+    and also supports custom arguments for each file format.
+
+    Args:
+        obj (any): The python object to be dumped.
+        file (str or :obj:`Path` or file-like object, optional): If not
+            specified, then the object is dump to a str, otherwise to a file
+            specified by the filename or file-like object.
+        file_format (str, optional): Same as :func:`load`.
+
+    Returns:
+        bool: True for success, False otherwise.
+    """
+    if isinstance(file, Path):
+        file = str(file)
+    if file_format is None:
+        if is_str(file):
+            file_format = file.split(".")[-1]
+        elif file is None:
+            raise ValueError("file_format must be specified since file is None")
+    if file_format not in file_handlers:
+        raise TypeError(f"Unsupported format: {file_format}")
+
+    handler = file_handlers[file_format]
+    if file is None:
+        return handler.dump_to_str(obj, **kwargs)
+    elif is_str(file):
+        handler.dump_to_path(obj, file, **kwargs)
+    elif hasattr(file, "write"):
+        handler.dump_to_fileobj(obj, file, **kwargs)
+    else:
+        raise TypeError('"file" must be a filename str or a file-object')

groundingdino/util/time_counter.py

@@ -0,0 +1,62 @@
+import json
+import time
+
+
+class TimeCounter:
+    def __init__(self) -> None:
+        pass
+
+    def clear(self):
+        self.timedict = {}
+        self.basetime = time.perf_counter()
+
+    def timeit(self, name):
+        nowtime = time.perf_counter() - self.basetime
+        self.timedict[name] = nowtime
+        self.basetime = time.perf_counter()
+
+
+class TimeHolder:
+    def __init__(self) -> None:
+        self.timedict = {}
+
+    def update(self, _timedict: dict):
+        for k, v in _timedict.items():
+            if k not in self.timedict:
+                self.timedict[k] = AverageMeter(name=k, val_only=True)
+            self.timedict[k].update(val=v)
+
+    def final_res(self):
+        return {k: v.avg for k, v in self.timedict.items()}
+
+    def __str__(self):
+        return json.dumps(self.final_res(), indent=2)
+
+
+class AverageMeter(object):
+    """Computes and stores the average and current value"""
+
+    def __init__(self, name, fmt=":f", val_only=False):
+        self.name = name
+        self.fmt = fmt
+        self.val_only = val_only
+        self.reset()
+
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+    def __str__(self):
+        if self.val_only:
+            fmtstr = "{name} {val" + self.fmt + "}"
+        else:
+            fmtstr = "{name} {val" + self.fmt + "} ({avg" + self.fmt + "})"
+        return fmtstr.format(**self.__dict__)

groundingdino/util/utils.py

@@ -0,0 +1,621 @@
+import argparse
+import json
+import warnings
+from collections import OrderedDict
+from copy import deepcopy
+from typing import Any, Dict, List
+
+import numpy as np
+import torch
+
+from groundingdino.util.slconfig import SLConfig
+
+
+def slprint(x, name="x"):
+    if isinstance(x, (torch.Tensor, np.ndarray)):
+        print(f"{name}.shape:", x.shape)
+    elif isinstance(x, (tuple, list)):
+        print("type x:", type(x))
+        for i in range(min(10, len(x))):
+            slprint(x[i], f"{name}[{i}]")
+    elif isinstance(x, dict):
+        for k, v in x.items():
+            slprint(v, f"{name}[{k}]")
+    else:
+        print(f"{name}.type:", type(x))
+
+
+def clean_state_dict(state_dict):
+    new_state_dict = OrderedDict()
+    for k, v in state_dict.items():
+        if k[:7] == "module.":
+            k = k[7:]  # remove `module.`
+        new_state_dict[k] = v
+    return new_state_dict
+
+
+def renorm(
+    img: torch.FloatTensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+) -> torch.FloatTensor:
+    # img: tensor(3,H,W) or tensor(B,3,H,W)
+    # return: same as img
+    assert img.dim() == 3 or img.dim() == 4, "img.dim() should be 3 or 4 but %d" % img.dim()
+    if img.dim() == 3:
+        assert img.size(0) == 3, 'img.size(0) shoule be 3 but "%d". (%s)' % (
+            img.size(0),
+            str(img.size()),
+        )
+        img_perm = img.permute(1, 2, 0)
+        mean = torch.Tensor(mean)
+        std = torch.Tensor(std)
+        img_res = img_perm * std + mean
+        return img_res.permute(2, 0, 1)
+    else:  # img.dim() == 4
+        assert img.size(1) == 3, 'img.size(1) shoule be 3 but "%d". (%s)' % (
+            img.size(1),
+            str(img.size()),
+        )
+        img_perm = img.permute(0, 2, 3, 1)
+        mean = torch.Tensor(mean)
+        std = torch.Tensor(std)
+        img_res = img_perm * std + mean
+        return img_res.permute(0, 3, 1, 2)
+
+
+class CocoClassMapper:
+    def __init__(self) -> None:
+        self.category_map_str = {
+            "1": 1,
+            "2": 2,
+            "3": 3,
+            "4": 4,
+            "5": 5,
+            "6": 6,
+            "7": 7,
+            "8": 8,
+            "9": 9,
+            "10": 10,
+            "11": 11,
+            "13": 12,
+            "14": 13,
+            "15": 14,
+            "16": 15,
+            "17": 16,
+            "18": 17,
+            "19": 18,
+            "20": 19,
+            "21": 20,
+            "22": 21,
+            "23": 22,
+            "24": 23,
+            "25": 24,
+            "27": 25,
+            "28": 26,
+            "31": 27,
+            "32": 28,
+            "33": 29,
+            "34": 30,
+            "35": 31,
+            "36": 32,
+            "37": 33,
+            "38": 34,
+            "39": 35,
+            "40": 36,
+            "41": 37,
+            "42": 38,
+            "43": 39,
+            "44": 40,
+            "46": 41,
+            "47": 42,
+            "48": 43,
+            "49": 44,
+            "50": 45,
+            "51": 46,
+            "52": 47,
+            "53": 48,
+            "54": 49,
+            "55": 50,
+            "56": 51,
+            "57": 52,
+            "58": 53,
+            "59": 54,
+            "60": 55,
+            "61": 56,
+            "62": 57,
+            "63": 58,
+            "64": 59,
+            "65": 60,
+            "67": 61,
+            "70": 62,
+            "72": 63,
+            "73": 64,
+            "74": 65,
+            "75": 66,
+            "76": 67,
+            "77": 68,
+            "78": 69,
+            "79": 70,
+            "80": 71,
+            "81": 72,
+            "82": 73,
+            "84": 74,
+            "85": 75,
+            "86": 76,
+            "87": 77,
+            "88": 78,
+            "89": 79,
+            "90": 80,
+        }
+        self.origin2compact_mapper = {int(k): v - 1 for k, v in self.category_map_str.items()}
+        self.compact2origin_mapper = {int(v - 1): int(k) for k, v in self.category_map_str.items()}
+
+    def origin2compact(self, idx):
+        return self.origin2compact_mapper[int(idx)]
+
+    def compact2origin(self, idx):
+        return self.compact2origin_mapper[int(idx)]
+
+
+def to_device(item, device):
+    if isinstance(item, torch.Tensor):
+        return item.to(device)
+    elif isinstance(item, list):
+        return [to_device(i, device) for i in item]
+    elif isinstance(item, dict):
+        return {k: to_device(v, device) for k, v in item.items()}
+    else:
+        raise NotImplementedError(
+            "Call Shilong if you use other containers! type: {}".format(type(item))
+        )
+
+
+#
+def get_gaussian_mean(x, axis, other_axis, softmax=True):
+    """
+
+    Args:
+        x (float): Input images(BxCxHxW)
+        axis (int): The index for weighted mean
+        other_axis (int): The other index
+
+    Returns: weighted index for axis, BxC
+
+    """
+    mat2line = torch.sum(x, axis=other_axis)
+    # mat2line = mat2line / mat2line.mean() * 10
+    if softmax:
+        u = torch.softmax(mat2line, axis=2)
+    else:
+        u = mat2line / (mat2line.sum(2, keepdim=True) + 1e-6)
+    size = x.shape[axis]
+    ind = torch.linspace(0, 1, size).to(x.device)
+    batch = x.shape[0]
+    channel = x.shape[1]
+    index = ind.repeat([batch, channel, 1])
+    mean_position = torch.sum(index * u, dim=2)
+    return mean_position
+
+
+def get_expected_points_from_map(hm, softmax=True):
+    """get_gaussian_map_from_points
+        B,C,H,W -> B,N,2 float(0, 1) float(0, 1)
+        softargmax function
+
+    Args:
+        hm (float): Input images(BxCxHxW)
+
+    Returns:
+        weighted index for axis, BxCx2. float between 0 and 1.
+
+    """
+    # hm = 10*hm
+    B, C, H, W = hm.shape
+    y_mean = get_gaussian_mean(hm, 2, 3, softmax=softmax)  # B,C
+    x_mean = get_gaussian_mean(hm, 3, 2, softmax=softmax)  # B,C
+    # return torch.cat((x_mean.unsqueeze(-1), y_mean.unsqueeze(-1)), 2)
+    return torch.stack([x_mean, y_mean], dim=2)
+
+
+# Positional encoding (section 5.1)
+# borrow from nerf
+class Embedder:
+    def __init__(self, **kwargs):
+        self.kwargs = kwargs
+        self.create_embedding_fn()
+
+    def create_embedding_fn(self):
+        embed_fns = []
+        d = self.kwargs["input_dims"]
+        out_dim = 0
+        if self.kwargs["include_input"]:
+            embed_fns.append(lambda x: x)
+            out_dim += d
+
+        max_freq = self.kwargs["max_freq_log2"]
+        N_freqs = self.kwargs["num_freqs"]
+
+        if self.kwargs["log_sampling"]:
+            freq_bands = 2.0 ** torch.linspace(0.0, max_freq, steps=N_freqs)
+        else:
+            freq_bands = torch.linspace(2.0**0.0, 2.0**max_freq, steps=N_freqs)
+
+        for freq in freq_bands:
+            for p_fn in self.kwargs["periodic_fns"]:
+                embed_fns.append(lambda x, p_fn=p_fn, freq=freq: p_fn(x * freq))
+                out_dim += d
+
+        self.embed_fns = embed_fns
+        self.out_dim = out_dim
+
+    def embed(self, inputs):
+        return torch.cat([fn(inputs) for fn in self.embed_fns], -1)
+
+
+def get_embedder(multires, i=0):
+    import torch.nn as nn
+
+    if i == -1:
+        return nn.Identity(), 3
+
+    embed_kwargs = {
+        "include_input": True,
+        "input_dims": 3,
+        "max_freq_log2": multires - 1,
+        "num_freqs": multires,
+        "log_sampling": True,
+        "periodic_fns": [torch.sin, torch.cos],
+    }
+
+    embedder_obj = Embedder(**embed_kwargs)
+    embed = lambda x, eo=embedder_obj: eo.embed(x)
+    return embed, embedder_obj.out_dim
+
+
+class APOPMeter:
+    def __init__(self) -> None:
+        self.tp = 0
+        self.fp = 0
+        self.tn = 0
+        self.fn = 0
+
+    def update(self, pred, gt):
+        """
+        Input:
+            pred, gt: Tensor()
+        """
+        assert pred.shape == gt.shape
+        self.tp += torch.logical_and(pred == 1, gt == 1).sum().item()
+        self.fp += torch.logical_and(pred == 1, gt == 0).sum().item()
+        self.tn += torch.logical_and(pred == 0, gt == 0).sum().item()
+        self.tn += torch.logical_and(pred == 1, gt == 0).sum().item()
+
+    def update_cm(self, tp, fp, tn, fn):
+        self.tp += tp
+        self.fp += fp
+        self.tn += tn
+        self.tn += fn
+
+
+def inverse_sigmoid(x, eps=1e-5):
+    x = x.clamp(min=0, max=1)
+    x1 = x.clamp(min=eps)
+    x2 = (1 - x).clamp(min=eps)
+    return torch.log(x1 / x2)
+
+
+def get_raw_dict(args):
+    """
+    return the dicf contained in args.
+
+    e.g:
+        >>> with open(path, 'w') as f:
+                json.dump(get_raw_dict(args), f, indent=2)
+    """
+    if isinstance(args, argparse.Namespace):
+        return vars(args)
+    elif isinstance(args, dict):
+        return args
+    elif isinstance(args, SLConfig):
+        return args._cfg_dict
+    else:
+        raise NotImplementedError("Unknown type {}".format(type(args)))
+
+
+def stat_tensors(tensor):
+    assert tensor.dim() == 1
+    tensor_sm = tensor.softmax(0)
+    entropy = (tensor_sm * torch.log(tensor_sm + 1e-9)).sum()
+
+    return {
+        "max": tensor.max(),
+        "min": tensor.min(),
+        "mean": tensor.mean(),
+        "var": tensor.var(),
+        "std": tensor.var() ** 0.5,
+        "entropy": entropy,
+    }
+
+
+class NiceRepr:
+    """Inherit from this class and define ``__nice__`` to "nicely" print your
+    objects.
+
+    Defines ``__str__`` and ``__repr__`` in terms of ``__nice__`` function
+    Classes that inherit from :class:`NiceRepr` should redefine ``__nice__``.
+    If the inheriting class has a ``__len__``, method then the default
+    ``__nice__`` method will return its length.
+
+    Example:
+        >>> class Foo(NiceRepr):
+        ...    def __nice__(self):
+        ...        return 'info'
+        >>> foo = Foo()
+        >>> assert str(foo) == '<Foo(info)>'
+        >>> assert repr(foo).startswith('<Foo(info) at ')
+
+    Example:
+        >>> class Bar(NiceRepr):
+        ...    pass
+        >>> bar = Bar()
+        >>> import pytest
+        >>> with pytest.warns(None) as record:
+        >>>     assert 'object at' in str(bar)
+        >>>     assert 'object at' in repr(bar)
+
+    Example:
+        >>> class Baz(NiceRepr):
+        ...    def __len__(self):
+        ...        return 5
+        >>> baz = Baz()
+        >>> assert str(baz) == '<Baz(5)>'
+    """
+
+    def __nice__(self):
+        """str: a "nice" summary string describing this module"""
+        if hasattr(self, "__len__"):
+            # It is a common pattern for objects to use __len__ in __nice__
+            # As a convenience we define a default __nice__ for these objects
+            return str(len(self))
+        else:
+            # In all other cases force the subclass to overload __nice__
+            raise NotImplementedError(f"Define the __nice__ method for {self.__class__!r}")
+
+    def __repr__(self):
+        """str: the string of the module"""
+        try:
+            nice = self.__nice__()
+            classname = self.__class__.__name__
+            return f"<{classname}({nice}) at {hex(id(self))}>"
+        except NotImplementedError as ex:
+            warnings.warn(str(ex), category=RuntimeWarning)
+            return object.__repr__(self)
+
+    def __str__(self):
+        """str: the string of the module"""
+        try:
+            classname = self.__class__.__name__
+            nice = self.__nice__()
+            return f"<{classname}({nice})>"
+        except NotImplementedError as ex:
+            warnings.warn(str(ex), category=RuntimeWarning)
+            return object.__repr__(self)
+
+
+def ensure_rng(rng=None):
+    """Coerces input into a random number generator.
+
+    If the input is None, then a global random state is returned.
+
+    If the input is a numeric value, then that is used as a seed to construct a
+    random state. Otherwise the input is returned as-is.
+
+    Adapted from [1]_.
+
+    Args:
+        rng (int | numpy.random.RandomState | None):
+            if None, then defaults to the global rng. Otherwise this can be an
+            integer or a RandomState class
+    Returns:
+        (numpy.random.RandomState) : rng -
+            a numpy random number generator
+
+    References:
+        .. [1] https://gitlab.kitware.com/computer-vision/kwarray/blob/master/kwarray/util_random.py#L270  # noqa: E501
+    """
+
+    if rng is None:
+        rng = np.random.mtrand._rand
+    elif isinstance(rng, int):
+        rng = np.random.RandomState(rng)
+    else:
+        rng = rng
+    return rng
+
+
+def random_boxes(num=1, scale=1, rng=None):
+    """Simple version of ``kwimage.Boxes.random``
+
+    Returns:
+        Tensor: shape (n, 4) in x1, y1, x2, y2 format.
+
+    References:
+        https://gitlab.kitware.com/computer-vision/kwimage/blob/master/kwimage/structs/boxes.py#L1390
+
+    Example:
+        >>> num = 3
+        >>> scale = 512
+        >>> rng = 0
+        >>> boxes = random_boxes(num, scale, rng)
+        >>> print(boxes)
+        tensor([[280.9925, 278.9802, 308.6148, 366.1769],
+                [216.9113, 330.6978, 224.0446, 456.5878],
+                [405.3632, 196.3221, 493.3953, 270.7942]])
+    """
+    rng = ensure_rng(rng)
+
+    tlbr = rng.rand(num, 4).astype(np.float32)
+
+    tl_x = np.minimum(tlbr[:, 0], tlbr[:, 2])
+    tl_y = np.minimum(tlbr[:, 1], tlbr[:, 3])
+    br_x = np.maximum(tlbr[:, 0], tlbr[:, 2])
+    br_y = np.maximum(tlbr[:, 1], tlbr[:, 3])
+
+    tlbr[:, 0] = tl_x * scale
+    tlbr[:, 1] = tl_y * scale
+    tlbr[:, 2] = br_x * scale
+    tlbr[:, 3] = br_y * scale
+
+    boxes = torch.from_numpy(tlbr)
+    return boxes
+
+
+class ModelEma(torch.nn.Module):
+    def __init__(self, model, decay=0.9997, device=None):
+        super(ModelEma, self).__init__()
+        # make a copy of the model for accumulating moving average of weights
+        self.module = deepcopy(model)
+        self.module.eval()
+
+        # import ipdb; ipdb.set_trace()
+
+        self.decay = decay
+        self.device = device  # perform ema on different device from model if set
+        if self.device is not None:
+            self.module.to(device=device)
+
+    def _update(self, model, update_fn):
+        with torch.no_grad():
+            for ema_v, model_v in zip(
+                self.module.state_dict().values(), model.state_dict().values()
+            ):
+                if self.device is not None:
+                    model_v = model_v.to(device=self.device)
+                ema_v.copy_(update_fn(ema_v, model_v))
+
+    def update(self, model):
+        self._update(model, update_fn=lambda e, m: self.decay * e + (1.0 - self.decay) * m)
+
+    def set(self, model):
+        self._update(model, update_fn=lambda e, m: m)
+
+
+class BestMetricSingle:
+    def __init__(self, init_res=0.0, better="large") -> None:
+        self.init_res = init_res
+        self.best_res = init_res
+        self.best_ep = -1
+
+        self.better = better
+        assert better in ["large", "small"]
+
+    def isbetter(self, new_res, old_res):
+        if self.better == "large":
+            return new_res > old_res
+        if self.better == "small":
+            return new_res < old_res
+
+    def update(self, new_res, ep):
+        if self.isbetter(new_res, self.best_res):
+            self.best_res = new_res
+            self.best_ep = ep
+            return True
+        return False
+
+    def __str__(self) -> str:
+        return "best_res: {}\t best_ep: {}".format(self.best_res, self.best_ep)
+
+    def __repr__(self) -> str:
+        return self.__str__()
+
+    def summary(self) -> dict:
+        return {
+            "best_res": self.best_res,
+            "best_ep": self.best_ep,
+        }
+
+
+class BestMetricHolder:
+    def __init__(self, init_res=0.0, better="large", use_ema=False) -> None:
+        self.best_all = BestMetricSingle(init_res, better)
+        self.use_ema = use_ema
+        if use_ema:
+            self.best_ema = BestMetricSingle(init_res, better)
+            self.best_regular = BestMetricSingle(init_res, better)
+
+    def update(self, new_res, epoch, is_ema=False):
+        """
+        return if the results is the best.
+        """
+        if not self.use_ema:
+            return self.best_all.update(new_res, epoch)
+        else:
+            if is_ema:
+                self.best_ema.update(new_res, epoch)
+                return self.best_all.update(new_res, epoch)
+            else:
+                self.best_regular.update(new_res, epoch)
+                return self.best_all.update(new_res, epoch)
+
+    def summary(self):
+        if not self.use_ema:
+            return self.best_all.summary()
+
+        res = {}
+        res.update({f"all_{k}": v for k, v in self.best_all.summary().items()})
+        res.update({f"regular_{k}": v for k, v in self.best_regular.summary().items()})
+        res.update({f"ema_{k}": v for k, v in self.best_ema.summary().items()})
+        return res
+
+    def __repr__(self) -> str:
+        return json.dumps(self.summary(), indent=2)
+
+    def __str__(self) -> str:
+        return self.__repr__()
+
+
+def targets_to(targets: List[Dict[str, Any]], device):
+    """Moves the target dicts to the given device."""
+    excluded_keys = [
+        "questionId",
+        "tokens_positive",
+        "strings_positive",
+        "tokens",
+        "dataset_name",
+        "sentence_id",
+        "original_img_id",
+        "nb_eval",
+        "task_id",
+        "original_id",
+        "token_span",
+        "caption",
+        "dataset_type",
+    ]
+    return [
+        {k: v.to(device) if k not in excluded_keys else v for k, v in t.items()} for t in targets
+    ]
+
+
+def get_phrases_from_posmap(posmap: torch.BoolTensor, tokenlized, caption: str):
+    assert isinstance(posmap, torch.Tensor), "posmap must be torch.Tensor"
+    if posmap.dim() == 1:
+        non_zero_idx = posmap.nonzero(as_tuple=True)[0].tolist()
+        words_list = caption.split()
+
+        # build word idx list
+        words_idx_used_list = []
+        for idx in non_zero_idx:
+            word_idx = tokenlized.token_to_word(idx)
+            if word_idx is not None:
+                words_idx_used_list.append(word_idx)
+        words_idx_used_list = set(words_idx_used_list)
+
+        # build phrase
+        words_used_list = []
+        for idx, word in enumerate(words_list):
+            if idx in words_idx_used_list:
+                words_used_list.append(word)
+
+        sentence_res = " ".join(words_used_list)
+        return sentence_res
+    else:
+        raise NotImplementedError("posmap must be 1-dim")

groundingdino/util/visualizer.py

@@ -0,0 +1,318 @@
+# -*- coding: utf-8 -*-
+"""
+@File    :   visualizer.py
+@Time    :   2022/04/05 11:39:33
+@Author  :   Shilong Liu 
+@Contact :   slongliu86@gmail.com
+"""
+
+import datetime
+import os
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from matplotlib import transforms
+from matplotlib.collections import PatchCollection
+from matplotlib.patches import Polygon
+from pycocotools import mask as maskUtils
+
+
+def renorm(
+    img: torch.FloatTensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+) -> torch.FloatTensor:
+    # img: tensor(3,H,W) or tensor(B,3,H,W)
+    # return: same as img
+    assert img.dim() == 3 or img.dim() == 4, "img.dim() should be 3 or 4 but %d" % img.dim()
+    if img.dim() == 3:
+        assert img.size(0) == 3, 'img.size(0) shoule be 3 but "%d". (%s)' % (
+            img.size(0),
+            str(img.size()),
+        )
+        img_perm = img.permute(1, 2, 0)
+        mean = torch.Tensor(mean)
+        std = torch.Tensor(std)
+        img_res = img_perm * std + mean
+        return img_res.permute(2, 0, 1)
+    else:  # img.dim() == 4
+        assert img.size(1) == 3, 'img.size(1) shoule be 3 but "%d". (%s)' % (
+            img.size(1),
+            str(img.size()),
+        )
+        img_perm = img.permute(0, 2, 3, 1)
+        mean = torch.Tensor(mean)
+        std = torch.Tensor(std)
+        img_res = img_perm * std + mean
+        return img_res.permute(0, 3, 1, 2)
+
+
+class ColorMap:
+    def __init__(self, basergb=[255, 255, 0]):
+        self.basergb = np.array(basergb)
+
+    def __call__(self, attnmap):
+        # attnmap: h, w. np.uint8.
+        # return: h, w, 4. np.uint8.
+        assert attnmap.dtype == np.uint8
+        h, w = attnmap.shape
+        res = self.basergb.copy()
+        res = res[None][None].repeat(h, 0).repeat(w, 1)  # h, w, 3
+        attn1 = attnmap.copy()[..., None]  # h, w, 1
+        res = np.concatenate((res, attn1), axis=-1).astype(np.uint8)
+        return res
+
+
+def rainbow_text(x, y, ls, lc, **kw):
+    """
+    Take a list of strings ``ls`` and colors ``lc`` and place them next to each
+    other, with text ls[i] being shown in color lc[i].
+
+    This example shows how to do both vertical and horizontal text, and will
+    pass all keyword arguments to plt.text, so you can set the font size,
+    family, etc.
+    """
+    t = plt.gca().transData
+    fig = plt.gcf()
+    plt.show()
+
+    # horizontal version
+    for s, c in zip(ls, lc):
+        text = plt.text(x, y, " " + s + " ", color=c, transform=t, **kw)
+        text.draw(fig.canvas.get_renderer())
+        ex = text.get_window_extent()
+        t = transforms.offset_copy(text._transform, x=ex.width, units="dots")
+
+    # #vertical version
+    # for s,c in zip(ls,lc):
+    #     text = plt.text(x,y," "+s+" ",color=c, transform=t,
+    #             rotation=90,va='bottom',ha='center',**kw)
+    #     text.draw(fig.canvas.get_renderer())
+    #     ex = text.get_window_extent()
+    #     t = transforms.offset_copy(text._transform, y=ex.height, units='dots')
+
+
+class COCOVisualizer:
+    def __init__(self, coco=None, tokenlizer=None) -> None:
+        self.coco = coco
+
+    def visualize(self, img, tgt, caption=None, dpi=180, savedir="vis"):
+        """
+        img: tensor(3, H, W)
+        tgt: make sure they are all on cpu.
+            must have items: 'image_id', 'boxes', 'size'
+        """
+        plt.figure(dpi=dpi)
+        plt.rcParams["font.size"] = "5"
+        ax = plt.gca()
+        img = renorm(img).permute(1, 2, 0)
+        # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
+        #     import ipdb; ipdb.set_trace()
+        ax.imshow(img)
+
+        self.addtgt(tgt)
+
+        if tgt is None:
+            image_id = 0
+        elif "image_id" not in tgt:
+            image_id = 0
+        else:
+            image_id = tgt["image_id"]
+
+        if caption is None:
+            savename = "{}/{}-{}.png".format(
+                savedir, int(image_id), str(datetime.datetime.now()).replace(" ", "-")
+            )
+        else:
+            savename = "{}/{}-{}-{}.png".format(
+                savedir, caption, int(image_id), str(datetime.datetime.now()).replace(" ", "-")
+            )
+        print("savename: {}".format(savename))
+        os.makedirs(os.path.dirname(savename), exist_ok=True)
+        plt.savefig(savename)
+        plt.close()
+
+    def addtgt(self, tgt):
+        """ """
+        if tgt is None or not "boxes" in tgt:
+            ax = plt.gca()
+
+            if "caption" in tgt:
+                ax.set_title(tgt["caption"], wrap=True)
+
+            ax.set_axis_off()
+            return
+
+        ax = plt.gca()
+        H, W = tgt["size"]
+        numbox = tgt["boxes"].shape[0]
+
+        color = []
+        polygons = []
+        boxes = []
+        for box in tgt["boxes"].cpu():
+            unnormbbox = box * torch.Tensor([W, H, W, H])
+            unnormbbox[:2] -= unnormbbox[2:] / 2
+            [bbox_x, bbox_y, bbox_w, bbox_h] = unnormbbox.tolist()
+            boxes.append([bbox_x, bbox_y, bbox_w, bbox_h])
+            poly = [
+                [bbox_x, bbox_y],
+                [bbox_x, bbox_y + bbox_h],
+                [bbox_x + bbox_w, bbox_y + bbox_h],
+                [bbox_x + bbox_w, bbox_y],
+            ]
+            np_poly = np.array(poly).reshape((4, 2))
+            polygons.append(Polygon(np_poly))
+            c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]
+            color.append(c)
+
+        p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.1)
+        ax.add_collection(p)
+        p = PatchCollection(polygons, facecolor="none", edgecolors=color, linewidths=2)
+        ax.add_collection(p)
+
+        if "strings_positive" in tgt and len(tgt["strings_positive"]) > 0:
+            assert (
+                len(tgt["strings_positive"]) == numbox
+            ), f"{len(tgt['strings_positive'])} = {numbox}, "
+            for idx, strlist in enumerate(tgt["strings_positive"]):
+                cate_id = int(tgt["labels"][idx])
+                _string = str(cate_id) + ":" + " ".join(strlist)
+                bbox_x, bbox_y, bbox_w, bbox_h = boxes[idx]
+                # ax.text(bbox_x, bbox_y, _string, color='black', bbox={'facecolor': 'yellow', 'alpha': 1.0, 'pad': 1})
+                ax.text(
+                    bbox_x,
+                    bbox_y,
+                    _string,
+                    color="black",
+                    bbox={"facecolor": color[idx], "alpha": 0.6, "pad": 1},
+                )
+
+        if "box_label" in tgt:
+            assert len(tgt["box_label"]) == numbox, f"{len(tgt['box_label'])} = {numbox}, "
+            for idx, bl in enumerate(tgt["box_label"]):
+                _string = str(bl)
+                bbox_x, bbox_y, bbox_w, bbox_h = boxes[idx]
+                # ax.text(bbox_x, bbox_y, _string, color='black', bbox={'facecolor': 'yellow', 'alpha': 1.0, 'pad': 1})
+                ax.text(
+                    bbox_x,
+                    bbox_y,
+                    _string,
+                    color="black",
+                    bbox={"facecolor": color[idx], "alpha": 0.6, "pad": 1},
+                )
+
+        if "caption" in tgt:
+            ax.set_title(tgt["caption"], wrap=True)
+            # plt.figure()
+            # rainbow_text(0.0,0.0,"all unicorns poop rainbows ! ! !".split(),
+            #         ['red', 'orange', 'brown', 'green', 'blue', 'purple', 'black'])
+
+        if "attn" in tgt:
+            # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
+            #     import ipdb; ipdb.set_trace()
+            if isinstance(tgt["attn"], tuple):
+                tgt["attn"] = [tgt["attn"]]
+            for item in tgt["attn"]:
+                attn_map, basergb = item
+                attn_map = (attn_map - attn_map.min()) / (attn_map.max() - attn_map.min() + 1e-3)
+                attn_map = (attn_map * 255).astype(np.uint8)
+                cm = ColorMap(basergb)
+                heatmap = cm(attn_map)
+                ax.imshow(heatmap)
+        ax.set_axis_off()
+
+    def showAnns(self, anns, draw_bbox=False):
+        """
+        Display the specified annotations.
+        :param anns (array of object): annotations to display
+        :return: None
+        """
+        if len(anns) == 0:
+            return 0
+        if "segmentation" in anns[0] or "keypoints" in anns[0]:
+            datasetType = "instances"
+        elif "caption" in anns[0]:
+            datasetType = "captions"
+        else:
+            raise Exception("datasetType not supported")
+        if datasetType == "instances":
+            ax = plt.gca()
+            ax.set_autoscale_on(False)
+            polygons = []
+            color = []
+            for ann in anns:
+                c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]
+                if "segmentation" in ann:
+                    if type(ann["segmentation"]) == list:
+                        # polygon
+                        for seg in ann["segmentation"]:
+                            poly = np.array(seg).reshape((int(len(seg) / 2), 2))
+                            polygons.append(Polygon(poly))
+                            color.append(c)
+                    else:
+                        # mask
+                        t = self.imgs[ann["image_id"]]
+                        if type(ann["segmentation"]["counts"]) == list:
+                            rle = maskUtils.frPyObjects(
+                                [ann["segmentation"]], t["height"], t["width"]
+                            )
+                        else:
+                            rle = [ann["segmentation"]]
+                        m = maskUtils.decode(rle)
+                        img = np.ones((m.shape[0], m.shape[1], 3))
+                        if ann["iscrowd"] == 1:
+                            color_mask = np.array([2.0, 166.0, 101.0]) / 255
+                        if ann["iscrowd"] == 0:
+                            color_mask = np.random.random((1, 3)).tolist()[0]
+                        for i in range(3):
+                            img[:, :, i] = color_mask[i]
+                        ax.imshow(np.dstack((img, m * 0.5)))
+                if "keypoints" in ann and type(ann["keypoints"]) == list:
+                    # turn skeleton into zero-based index
+                    sks = np.array(self.loadCats(ann["category_id"])[0]["skeleton"]) - 1
+                    kp = np.array(ann["keypoints"])
+                    x = kp[0::3]
+                    y = kp[1::3]
+                    v = kp[2::3]
+                    for sk in sks:
+                        if np.all(v[sk] > 0):
+                            plt.plot(x[sk], y[sk], linewidth=3, color=c)
+                    plt.plot(
+                        x[v > 0],
+                        y[v > 0],
+                        "o",
+                        markersize=8,
+                        markerfacecolor=c,
+                        markeredgecolor="k",
+                        markeredgewidth=2,
+                    )
+                    plt.plot(
+                        x[v > 1],
+                        y[v > 1],
+                        "o",
+                        markersize=8,
+                        markerfacecolor=c,
+                        markeredgecolor=c,
+                        markeredgewidth=2,
+                    )
+
+                if draw_bbox:
+                    [bbox_x, bbox_y, bbox_w, bbox_h] = ann["bbox"]
+                    poly = [
+                        [bbox_x, bbox_y],
+                        [bbox_x, bbox_y + bbox_h],
+                        [bbox_x + bbox_w, bbox_y + bbox_h],
+                        [bbox_x + bbox_w, bbox_y],
+                    ]
+                    np_poly = np.array(poly).reshape((4, 2))
+                    polygons.append(Polygon(np_poly))
+                    color.append(c)
+
+            # p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4)
+            # ax.add_collection(p)
+            p = PatchCollection(polygons, facecolor="none", edgecolors=color, linewidths=2)
+            ax.add_collection(p)
+        elif datasetType == "captions":
+            for ann in anns:
+                print(ann["caption"])

groundingdino/util/vl_utils.py

@@ -0,0 +1,100 @@
+import os
+import random
+from typing import List
+
+import torch
+
+
+def create_positive_map_from_span(tokenized, token_span, max_text_len=256):
+    """construct a map such that positive_map[i,j] = True iff box i is associated to token j
+    Input:
+        - tokenized:
+            - input_ids: Tensor[1, ntokens]
+            - attention_mask: Tensor[1, ntokens]
+        - token_span: list with length num_boxes.
+            - each item: [start_idx, end_idx]
+    """
+    positive_map = torch.zeros((len(token_span), max_text_len), dtype=torch.float)
+    for j, tok_list in enumerate(token_span):
+        for (beg, end) in tok_list:
+            beg_pos = tokenized.char_to_token(beg)
+            end_pos = tokenized.char_to_token(end - 1)
+            if beg_pos is None:
+                try:
+                    beg_pos = tokenized.char_to_token(beg + 1)
+                    if beg_pos is None:
+                        beg_pos = tokenized.char_to_token(beg + 2)
+                except:
+                    beg_pos = None
+            if end_pos is None:
+                try:
+                    end_pos = tokenized.char_to_token(end - 2)
+                    if end_pos is None:
+                        end_pos = tokenized.char_to_token(end - 3)
+                except:
+                    end_pos = None
+            if beg_pos is None or end_pos is None:
+                continue
+
+            assert beg_pos is not None and end_pos is not None
+            if os.environ.get("SHILONG_DEBUG_ONLY_ONE_POS", None) == "TRUE":
+                positive_map[j, beg_pos] = 1
+                break
+            else:
+                positive_map[j, beg_pos : end_pos + 1].fill_(1)
+
+    return positive_map / (positive_map.sum(-1)[:, None] + 1e-6)
+
+
+def build_captions_and_token_span(cat_list, force_lowercase):
+    """
+    Return:
+        captions: str
+        cat2tokenspan: dict
+            {
+                'dog': [[0, 2]],
+                ...
+            }
+    """
+
+    cat2tokenspan = {}
+    captions = ""
+    for catname in cat_list:
+        class_name = catname
+        if force_lowercase:
+            class_name = class_name.lower()
+        if "/" in class_name:
+            class_name_list: List = class_name.strip().split("/")
+            class_name_list.append(class_name)
+            class_name: str = random.choice(class_name_list)
+
+        tokens_positive_i = []
+        subnamelist = [i.strip() for i in class_name.strip().split(" ")]
+        for subname in subnamelist:
+            if len(subname) == 0:
+                continue
+            if len(captions) > 0:
+                captions = captions + " "
+            strat_idx = len(captions)
+            end_idx = strat_idx + len(subname)
+            tokens_positive_i.append([strat_idx, end_idx])
+            captions = captions + subname
+
+        if len(tokens_positive_i) > 0:
+            captions = captions + " ."
+            cat2tokenspan[class_name] = tokens_positive_i
+
+    return captions, cat2tokenspan
+
+
+def build_id2posspan_and_caption(category_dict: dict):
+    """Build id2pos_span and caption from category_dict
+
+    Args:
+        category_dict (dict): category_dict
+    """
+    cat_list = [item["name"].lower() for item in category_dict]
+    id2catname = {item["id"]: item["name"].lower() for item in category_dict}
+    caption, cat2posspan = build_captions_and_token_span(cat_list, force_lowercase=True)
+    id2posspan = {catid: cat2posspan[catname] for catid, catname in id2catname.items()}
+    return id2posspan, caption

groundingdino/version.py

@@ -0,0 +1 @@
+__version__ = '0.1.0'