`ultralytics 8.2.70` Segment Anything Model 2 (SAM 2) (#14813)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>test-apple-mps^2 v8.2.70
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4 months ago
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36 changed files with 3276 additions and 77 deletions
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# Ultralytics YOLO 🚀, AGPL-3.0 license |
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from .model import SAM2 |
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from .predict import SAM2Predictor |
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__all__ = "SAM2", "SAM2Predictor" # tuple or list |
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# Ultralytics YOLO 🚀, AGPL-3.0 license |
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import torch |
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from ultralytics.utils.downloads import attempt_download_asset |
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from .modules.encoders import FpnNeck, Hiera, ImageEncoder, MemoryEncoder |
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from .modules.memory_attention import MemoryAttention, MemoryAttentionLayer |
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from .modules.sam2 import SAM2Model |
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def build_sam2_t(checkpoint=None): |
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"""Build and return a Segment Anything Model (SAM2) tiny-size model with specified architecture parameters.""" |
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return _build_sam2( |
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encoder_embed_dim=96, |
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encoder_stages=[1, 2, 7, 2], |
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encoder_num_heads=1, |
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encoder_global_att_blocks=[5, 7, 9], |
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encoder_window_spec=[8, 4, 14, 7], |
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encoder_backbone_channel_list=[768, 384, 192, 96], |
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checkpoint=checkpoint, |
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) |
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def build_sam2_s(checkpoint=None): |
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"""Builds and returns a small-size Segment Anything Model (SAM2) with specified architecture parameters.""" |
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return _build_sam2( |
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encoder_embed_dim=96, |
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encoder_stages=[1, 2, 11, 2], |
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encoder_num_heads=1, |
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encoder_global_att_blocks=[7, 10, 13], |
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encoder_window_spec=[8, 4, 14, 7], |
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encoder_backbone_channel_list=[768, 384, 192, 96], |
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checkpoint=checkpoint, |
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) |
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def build_sam2_b(checkpoint=None): |
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"""Builds and returns a Segment Anything Model (SAM2) base-size model with specified architecture parameters.""" |
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return _build_sam2( |
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encoder_embed_dim=112, |
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encoder_stages=[2, 3, 16, 3], |
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encoder_num_heads=2, |
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encoder_global_att_blocks=[12, 16, 20], |
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encoder_window_spec=[8, 4, 14, 7], |
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encoder_window_spatial_size=[14, 14], |
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encoder_backbone_channel_list=[896, 448, 224, 112], |
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checkpoint=checkpoint, |
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) |
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def build_sam2_l(checkpoint=None): |
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"""Build and return a Segment Anything Model (SAM2) large-size model with specified architecture parameters.""" |
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return _build_sam2( |
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encoder_embed_dim=144, |
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encoder_stages=[2, 6, 36, 4], |
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encoder_num_heads=2, |
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encoder_global_att_blocks=[23, 33, 43], |
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encoder_window_spec=[8, 4, 16, 8], |
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encoder_backbone_channel_list=[1152, 576, 288, 144], |
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checkpoint=checkpoint, |
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) |
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def _build_sam2( |
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encoder_embed_dim=1280, |
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encoder_stages=[2, 6, 36, 4], |
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encoder_num_heads=2, |
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encoder_global_att_blocks=[7, 15, 23, 31], |
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encoder_backbone_channel_list=[1152, 576, 288, 144], |
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encoder_window_spatial_size=[7, 7], |
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encoder_window_spec=[8, 4, 16, 8], |
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checkpoint=None, |
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): |
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"""Builds a SAM2 model with specified architecture parameters and optional checkpoint loading.""" |
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image_encoder = ImageEncoder( |
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trunk=Hiera( |
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embed_dim=encoder_embed_dim, |
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num_heads=encoder_num_heads, |
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stages=encoder_stages, |
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global_att_blocks=encoder_global_att_blocks, |
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window_pos_embed_bkg_spatial_size=encoder_window_spatial_size, |
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window_spec=encoder_window_spec, |
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), |
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neck=FpnNeck( |
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d_model=256, |
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backbone_channel_list=encoder_backbone_channel_list, |
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fpn_top_down_levels=[2, 3], |
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fpn_interp_model="nearest", |
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), |
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scalp=1, |
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) |
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memory_attention = MemoryAttention(d_model=256, pos_enc_at_input=True, num_layers=4, layer=MemoryAttentionLayer()) |
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memory_encoder = MemoryEncoder(out_dim=64) |
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sam2 = SAM2Model( |
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image_encoder=image_encoder, |
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memory_attention=memory_attention, |
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memory_encoder=memory_encoder, |
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num_maskmem=7, |
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image_size=1024, |
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sigmoid_scale_for_mem_enc=20.0, |
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sigmoid_bias_for_mem_enc=-10.0, |
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use_mask_input_as_output_without_sam=True, |
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directly_add_no_mem_embed=True, |
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use_high_res_features_in_sam=True, |
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multimask_output_in_sam=True, |
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iou_prediction_use_sigmoid=True, |
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use_obj_ptrs_in_encoder=True, |
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add_tpos_enc_to_obj_ptrs=True, |
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only_obj_ptrs_in_the_past_for_eval=True, |
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pred_obj_scores=True, |
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pred_obj_scores_mlp=True, |
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fixed_no_obj_ptr=True, |
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multimask_output_for_tracking=True, |
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use_multimask_token_for_obj_ptr=True, |
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multimask_min_pt_num=0, |
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multimask_max_pt_num=1, |
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use_mlp_for_obj_ptr_proj=True, |
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compile_image_encoder=False, |
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sam_mask_decoder_extra_args=dict( |
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dynamic_multimask_via_stability=True, |
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dynamic_multimask_stability_delta=0.05, |
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dynamic_multimask_stability_thresh=0.98, |
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), |
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) |
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if checkpoint is not None: |
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checkpoint = attempt_download_asset(checkpoint) |
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with open(checkpoint, "rb") as f: |
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state_dict = torch.load(f)["model"] |
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sam2.load_state_dict(state_dict) |
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sam2.eval() |
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return sam2 |
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sam_model_map = { |
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"sam2_t.pt": build_sam2_t, |
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"sam2_s.pt": build_sam2_s, |
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"sam2_b.pt": build_sam2_b, |
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"sam2_l.pt": build_sam2_l, |
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} |
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def build_sam2(ckpt="sam_b.pt"): |
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"""Constructs a Segment Anything Model (SAM2) based on the specified checkpoint, with various size options.""" |
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model_builder = None |
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ckpt = str(ckpt) # to allow Path ckpt types |
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for k in sam_model_map.keys(): |
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if ckpt.endswith(k): |
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model_builder = sam_model_map.get(k) |
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if not model_builder: |
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raise FileNotFoundError(f"{ckpt} is not a supported SAM model. Available models are: \n {sam_model_map.keys()}") |
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return model_builder(ckpt) |
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# Ultralytics YOLO 🚀, AGPL-3.0 license |
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""" |
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SAM2 model interface. |
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This module provides an interface to the Segment Anything Model (SAM2) from Ultralytics, designed for real-time image |
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segmentation tasks. The SAM2 model allows for promptable segmentation with unparalleled versatility in image analysis, |
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and has been trained on the SA-1B dataset. It features zero-shot performance capabilities, enabling it to adapt to new |
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image distributions and tasks without prior knowledge. |
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Key Features: |
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- Promptable segmentation |
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- Real-time performance |
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- Zero-shot transfer capabilities |
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- Trained on SA-1B dataset |
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""" |
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from ultralytics.models.sam import SAM |
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from .build import build_sam2 |
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from .predict import SAM2Predictor |
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class SAM2(SAM): |
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""" |
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SAM2 class for real-time image segmentation using the Segment Anything Model (SAM2). |
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This class extends the SAM base class, providing an interface to the SAM2 model for promptable segmentation |
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tasks. It supports loading pre-trained weights and offers zero-shot performance capabilities. |
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Attributes: |
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model (torch.nn.Module): The loaded SAM2 model. |
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task_map (Dict[str, Type[SAM2Predictor]]): Mapping of 'segment' task to SAM2Predictor. |
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Methods: |
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__init__: Initializes the SAM2 model with pre-trained weights. |
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_load: Loads specified weights into the SAM2 model. |
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Examples: |
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>>> sam2 = SAM2("sam2_b.pt") |
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>>> sam2._load('path/to/sam2_weights.pt') |
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>>> task_map = sam2.task_map |
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>>> print(task_map) |
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{'segment': SAM2Predictor} |
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Notes: |
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- Supports .pt and .pth file extensions for model weights. |
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- Offers zero-shot transfer capabilities for new image distributions and tasks. |
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""" |
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def __init__(self, model="sam2_b.pt") -> None: |
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""" |
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Initializes the SAM2 model with a pre-trained model file. |
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Args: |
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model (str): Path to the pre-trained SAM2 model file. File should have a .pt or .pth extension. |
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Raises: |
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NotImplementedError: If the model file extension is not .pt or .pth. |
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Examples: |
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>>> sam2 = SAM2("sam2_b.pt") |
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""" |
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super().__init__(model=model) |
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def _load(self, weights: str, task=None): |
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""" |
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Loads the specified weights into the SAM2 model. |
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This method is responsible for loading pre-trained weights into the SAM2 model. It supports loading |
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weights from files with .pt or .pth extensions. |
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Args: |
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weights (str): Path to the weights file. Should be a file with .pt or .pth extension. |
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task (str | None): Task name. If provided, it may be used to configure model-specific settings. |
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Examples: |
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>>> sam2_model = SAM2() |
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>>> sam2_model._load('path/to/sam2_weights.pt') |
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""" |
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self.model = build_sam2(weights) |
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@property |
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def task_map(self): |
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""" |
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Provides a mapping from the 'segment' task to its corresponding 'Predictor'. |
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Returns: |
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(Dict[str, Type[SAM2Predictor]]): A dictionary mapping the 'segment' task to its corresponding |
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SAM2Predictor class. |
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Examples: |
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>>> sam2 = SAM2() |
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>>> task_map = sam2.task_map |
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>>> print(task_map) |
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{'segment': SAM2Predictor} |
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""" |
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return {"segment": {"predictor": SAM2Predictor}} |
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# Ultralytics YOLO 🚀, AGPL-3.0 license |
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# Ultralytics YOLO 🚀, AGPL-3.0 license |
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from typing import List, Optional, Tuple, Type |
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import torch |
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from torch import nn |
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from ultralytics.nn.modules import MLP, LayerNorm2d |
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class MaskDecoder(nn.Module): |
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"""Transformer-based decoder predicting instance segmentation masks from image and prompt embeddings.""" |
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def __init__( |
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self, |
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transformer_dim: int, |
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transformer: nn.Module, |
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num_multimask_outputs: int = 3, |
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activation: Type[nn.Module] = nn.GELU, |
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iou_head_depth: int = 3, |
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iou_head_hidden_dim: int = 256, |
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use_high_res_features: bool = False, |
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iou_prediction_use_sigmoid=False, |
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dynamic_multimask_via_stability=False, |
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dynamic_multimask_stability_delta=0.05, |
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dynamic_multimask_stability_thresh=0.98, |
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pred_obj_scores: bool = False, |
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pred_obj_scores_mlp: bool = False, |
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use_multimask_token_for_obj_ptr: bool = False, |
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) -> None: |
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""" |
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Initializes the MaskDecoder module for predicting instance segmentation masks. |
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Args: |
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transformer_dim (int): Channel dimension of the transformer. |
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transformer (nn.Module): Transformer used to predict masks. |
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num_multimask_outputs (int): Number of masks to predict when disambiguating masks. |
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activation (Type[nn.Module]): Type of activation to use when upscaling masks. |
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iou_head_depth (int): Depth of the MLP used to predict mask quality. |
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iou_head_hidden_dim (int): Hidden dimension of the MLP used to predict mask quality. |
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use_high_res_features (bool): Whether to use high-resolution features. |
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iou_prediction_use_sigmoid (bool): Whether to use sigmoid for IOU prediction. |
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dynamic_multimask_via_stability (bool): Whether to use dynamic multimask via stability. |
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dynamic_multimask_stability_delta (float): Delta value for dynamic multimask stability. |
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dynamic_multimask_stability_thresh (float): Threshold for dynamic multimask stability. |
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pred_obj_scores (bool): Whether to predict object scores. |
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pred_obj_scores_mlp (bool): Whether to use MLP for object score prediction. |
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use_multimask_token_for_obj_ptr (bool): Whether to use multimask token for object pointer. |
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Attributes: |
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transformer_dim (int): Channel dimension of the transformer. |
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transformer (nn.Module): Transformer used to predict masks. |
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num_multimask_outputs (int): Number of masks to predict when disambiguating masks. |
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iou_token (nn.Embedding): Embedding for IOU token. |
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num_mask_tokens (int): Total number of mask tokens. |
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mask_tokens (nn.Embedding): Embedding for mask tokens. |
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pred_obj_scores (bool): Whether to predict object scores. |
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obj_score_token (nn.Embedding): Embedding for object score token. |
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use_multimask_token_for_obj_ptr (bool): Whether to use multimask token for object pointer. |
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output_upscaling (nn.Sequential): Upscaling layers for output. |
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use_high_res_features (bool): Whether to use high-resolution features. |
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conv_s0 (nn.Conv2d): Convolutional layer for high-resolution features (s0). |
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conv_s1 (nn.Conv2d): Convolutional layer for high-resolution features (s1). |
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output_hypernetworks_mlps (nn.ModuleList): List of MLPs for output hypernetworks. |
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iou_prediction_head (MLP): MLP for IOU prediction. |
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pred_obj_score_head (nn.Linear | MLP): Linear layer or MLP for object score prediction. |
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dynamic_multimask_via_stability (bool): Whether to use dynamic multimask via stability. |
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dynamic_multimask_stability_delta (float): Delta value for dynamic multimask stability. |
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""" |
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super().__init__() |
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self.transformer_dim = transformer_dim |
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self.transformer = transformer |
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self.num_multimask_outputs = num_multimask_outputs |
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self.iou_token = nn.Embedding(1, transformer_dim) |
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self.num_mask_tokens = num_multimask_outputs + 1 |
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self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim) |
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self.pred_obj_scores = pred_obj_scores |
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if self.pred_obj_scores: |
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self.obj_score_token = nn.Embedding(1, transformer_dim) |
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self.use_multimask_token_for_obj_ptr = use_multimask_token_for_obj_ptr |
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self.output_upscaling = nn.Sequential( |
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nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2), |
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LayerNorm2d(transformer_dim // 4), |
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activation(), |
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nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2), |
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activation(), |
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) |
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self.use_high_res_features = use_high_res_features |
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if use_high_res_features: |
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self.conv_s0 = nn.Conv2d(transformer_dim, transformer_dim // 8, kernel_size=1, stride=1) |
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self.conv_s1 = nn.Conv2d(transformer_dim, transformer_dim // 4, kernel_size=1, stride=1) |
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self.output_hypernetworks_mlps = nn.ModuleList( |
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[MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3) for _ in range(self.num_mask_tokens)] |
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) |
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self.iou_prediction_head = MLP( |
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transformer_dim, |
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iou_head_hidden_dim, |
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self.num_mask_tokens, |
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iou_head_depth, |
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sigmoid=iou_prediction_use_sigmoid, |
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) |
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if self.pred_obj_scores: |
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self.pred_obj_score_head = nn.Linear(transformer_dim, 1) |
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if pred_obj_scores_mlp: |
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self.pred_obj_score_head = MLP(transformer_dim, transformer_dim, 1, 3) |
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# When outputting a single mask, optionally we can dynamically fall back to the best |
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# multimask output token if the single mask output token gives low stability scores. |
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self.dynamic_multimask_via_stability = dynamic_multimask_via_stability |
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self.dynamic_multimask_stability_delta = dynamic_multimask_stability_delta |
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self.dynamic_multimask_stability_thresh = dynamic_multimask_stability_thresh |
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def forward( |
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self, |
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image_embeddings: torch.Tensor, |
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image_pe: torch.Tensor, |
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sparse_prompt_embeddings: torch.Tensor, |
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dense_prompt_embeddings: torch.Tensor, |
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multimask_output: bool, |
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repeat_image: bool, |
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high_res_features: Optional[List[torch.Tensor]] = None, |
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) -> Tuple[torch.Tensor, torch.Tensor]: |
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""" |
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Predicts masks given image and prompt embeddings. |
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Args: |
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image_embeddings (torch.Tensor): Embeddings from the image encoder. |
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image_pe (torch.Tensor): Positional encoding with the shape of image_embeddings. |
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sparse_prompt_embeddings (torch.Tensor): Embeddings of the points and boxes. |
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dense_prompt_embeddings (torch.Tensor): Embeddings of the mask inputs. |
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multimask_output (bool): Whether to return multiple masks or a single mask. |
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repeat_image (bool): Flag to repeat the image embeddings. |
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high_res_features (List[torch.Tensor] | None): Optional high-resolution features. |
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Returns: |
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(Tuple[torch.Tensor, torch.Tensor, torch.Tensor]): A tuple containing: |
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- masks (torch.Tensor): Batched predicted masks. |
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- iou_pred (torch.Tensor): Batched predictions of mask quality. |
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- sam_tokens_out (torch.Tensor): Batched SAM token for mask output. |
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Examples: |
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>>> image_embeddings = torch.rand(1, 256, 64, 64) |
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>>> image_pe = torch.rand(1, 256, 64, 64) |
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>>> sparse_prompt_embeddings = torch.rand(1, 2, 256) |
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>>> dense_prompt_embeddings = torch.rand(1, 256, 64, 64) |
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>>> decoder = MaskDecoder(256, transformer) |
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>>> masks, iou_pred, sam_tokens_out = decoder.forward(image_embeddings, image_pe, |
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... sparse_prompt_embeddings, dense_prompt_embeddings, True, False) |
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""" |
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masks, iou_pred, mask_tokens_out, object_score_logits = self.predict_masks( |
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image_embeddings=image_embeddings, |
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image_pe=image_pe, |
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sparse_prompt_embeddings=sparse_prompt_embeddings, |
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dense_prompt_embeddings=dense_prompt_embeddings, |
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repeat_image=repeat_image, |
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high_res_features=high_res_features, |
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) |
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# Select the correct mask or masks for output |
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if multimask_output: |
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masks = masks[:, 1:, :, :] |
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iou_pred = iou_pred[:, 1:] |
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elif self.dynamic_multimask_via_stability and not self.training: |
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masks, iou_pred = self._dynamic_multimask_via_stability(masks, iou_pred) |
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else: |
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masks = masks[:, 0:1, :, :] |
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iou_pred = iou_pred[:, 0:1] |
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if multimask_output and self.use_multimask_token_for_obj_ptr: |
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sam_tokens_out = mask_tokens_out[:, 1:] # [b, 3, c] shape |
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else: |
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# Take the mask output token. Here we *always* use the token for single mask output. |
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# At test time, even if we track after 1-click (and using multimask_output=True), |
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# we still take the single mask token here. The rationale is that we always track |
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# after multiple clicks during training, so the past tokens seen during training |
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# are always the single mask token (and we'll let it be the object-memory token). |
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sam_tokens_out = mask_tokens_out[:, 0:1] # [b, 1, c] shape |
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# Prepare output |
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return masks, iou_pred, sam_tokens_out, object_score_logits |
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|
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def predict_masks( |
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self, |
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image_embeddings: torch.Tensor, |
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image_pe: torch.Tensor, |
||||
sparse_prompt_embeddings: torch.Tensor, |
||||
dense_prompt_embeddings: torch.Tensor, |
||||
repeat_image: bool, |
||||
high_res_features: Optional[List[torch.Tensor]] = None, |
||||
) -> Tuple[torch.Tensor, torch.Tensor]: |
||||
"""Predicts instance segmentation masks from image and prompt embeddings using a transformer architecture.""" |
||||
# Concatenate output tokens |
||||
s = 0 |
||||
if self.pred_obj_scores: |
||||
output_tokens = torch.cat( |
||||
[ |
||||
self.obj_score_token.weight, |
||||
self.iou_token.weight, |
||||
self.mask_tokens.weight, |
||||
], |
||||
dim=0, |
||||
) |
||||
s = 1 |
||||
else: |
||||
output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0) |
||||
output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1) |
||||
tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1) |
||||
|
||||
# Expand per-image data in batch direction to be per-mask |
||||
if repeat_image: |
||||
src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0) |
||||
else: |
||||
assert image_embeddings.shape[0] == tokens.shape[0] |
||||
src = image_embeddings |
||||
src = src + dense_prompt_embeddings |
||||
assert image_pe.size(0) == 1, "image_pe should have size 1 in batch dim (from `get_dense_pe()`)" |
||||
pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0) |
||||
b, c, h, w = src.shape |
||||
|
||||
# Run the transformer |
||||
hs, src = self.transformer(src, pos_src, tokens) |
||||
iou_token_out = hs[:, s, :] |
||||
mask_tokens_out = hs[:, s + 1 : (s + 1 + self.num_mask_tokens), :] |
||||
|
||||
# Upscale mask embeddings and predict masks using the mask tokens |
||||
src = src.transpose(1, 2).view(b, c, h, w) |
||||
if not self.use_high_res_features: |
||||
upscaled_embedding = self.output_upscaling(src) |
||||
else: |
||||
dc1, ln1, act1, dc2, act2 = self.output_upscaling |
||||
feat_s0, feat_s1 = high_res_features |
||||
upscaled_embedding = act1(ln1(dc1(src) + feat_s1)) |
||||
upscaled_embedding = act2(dc2(upscaled_embedding) + feat_s0) |
||||
|
||||
hyper_in_list: List[torch.Tensor] = [] |
||||
for i in range(self.num_mask_tokens): |
||||
hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :])) |
||||
hyper_in = torch.stack(hyper_in_list, dim=1) |
||||
b, c, h, w = upscaled_embedding.shape |
||||
masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w) |
||||
|
||||
# Generate mask quality predictions |
||||
iou_pred = self.iou_prediction_head(iou_token_out) |
||||
if self.pred_obj_scores: |
||||
assert s == 1 |
||||
object_score_logits = self.pred_obj_score_head(hs[:, 0, :]) |
||||
else: |
||||
# Obj scores logits - default to 10.0, i.e. assuming the object is present, sigmoid(10)=1 |
||||
object_score_logits = 10.0 * iou_pred.new_ones(iou_pred.shape[0], 1) |
||||
|
||||
return masks, iou_pred, mask_tokens_out, object_score_logits |
||||
|
||||
def _get_stability_scores(self, mask_logits): |
||||
"""Computes mask stability scores based on IoU between upper and lower thresholds.""" |
||||
mask_logits = mask_logits.flatten(-2) |
||||
stability_delta = self.dynamic_multimask_stability_delta |
||||
area_i = torch.sum(mask_logits > stability_delta, dim=-1).float() |
||||
area_u = torch.sum(mask_logits > -stability_delta, dim=-1).float() |
||||
stability_scores = torch.where(area_u > 0, area_i / area_u, 1.0) |
||||
return stability_scores |
||||
|
||||
def _dynamic_multimask_via_stability(self, all_mask_logits, all_iou_scores): |
||||
""" |
||||
Dynamically selects the most stable mask output based on stability scores and IoU predictions. |
||||
|
||||
When outputting a single mask, if the stability score from the current single-mask output (based on output token |
||||
0) falls below a threshold, we instead select from multi-mask outputs (based on output token 1~3) the mask with |
||||
the highest predicted IoU score. |
||||
|
||||
This is intended to ensure a valid mask for both clicking and tracking. |
||||
""" |
||||
# The best mask from multimask output tokens (1~3) |
||||
multimask_logits = all_mask_logits[:, 1:, :, :] |
||||
multimask_iou_scores = all_iou_scores[:, 1:] |
||||
best_scores_inds = torch.argmax(multimask_iou_scores, dim=-1) |
||||
batch_inds = torch.arange(multimask_iou_scores.size(0), device=all_iou_scores.device) |
||||
best_multimask_logits = multimask_logits[batch_inds, best_scores_inds] |
||||
best_multimask_logits = best_multimask_logits.unsqueeze(1) |
||||
best_multimask_iou_scores = multimask_iou_scores[batch_inds, best_scores_inds] |
||||
best_multimask_iou_scores = best_multimask_iou_scores.unsqueeze(1) |
||||
|
||||
# The mask from singlemask output token 0 and its stability score |
||||
singlemask_logits = all_mask_logits[:, 0:1, :, :] |
||||
singlemask_iou_scores = all_iou_scores[:, 0:1] |
||||
stability_scores = self._get_stability_scores(singlemask_logits) |
||||
is_stable = stability_scores >= self.dynamic_multimask_stability_thresh |
||||
|
||||
# Dynamically fall back to best multimask output upon low stability scores. |
||||
mask_logits_out = torch.where( |
||||
is_stable[..., None, None].expand_as(singlemask_logits), |
||||
singlemask_logits, |
||||
best_multimask_logits, |
||||
) |
||||
iou_scores_out = torch.where( |
||||
is_stable.expand_as(singlemask_iou_scores), |
||||
singlemask_iou_scores, |
||||
best_multimask_iou_scores, |
||||
) |
||||
return mask_logits_out, iou_scores_out |
||||
@ -0,0 +1,332 @@ |
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license |
||||
|
||||
from typing import List, Optional, Tuple |
||||
|
||||
import torch |
||||
import torch.nn as nn |
||||
import torch.nn.functional as F |
||||
|
||||
from ultralytics.models.sam.modules.encoders import PatchEmbed |
||||
|
||||
from .sam2_blocks import CXBlock, Fuser, MaskDownSampler, MultiScaleBlock, PositionEmbeddingSine |
||||
|
||||
|
||||
class MemoryEncoder(nn.Module): |
||||
"""Encodes pixel features and masks into a memory representation for efficient image segmentation.""" |
||||
|
||||
def __init__( |
||||
self, |
||||
out_dim, |
||||
in_dim=256, # in_dim of pix_feats |
||||
): |
||||
"""Initializes the MemoryEncoder module for encoding pixel features and masks in SAM-like models.""" |
||||
super().__init__() |
||||
|
||||
self.mask_downsampler = MaskDownSampler(kernel_size=3, stride=2, padding=1) |
||||
|
||||
self.pix_feat_proj = nn.Conv2d(in_dim, in_dim, kernel_size=1) |
||||
self.fuser = Fuser(CXBlock(dim=256), num_layers=2) |
||||
self.position_encoding = PositionEmbeddingSine(num_pos_feats=64) |
||||
self.out_proj = nn.Identity() |
||||
if out_dim != in_dim: |
||||
self.out_proj = nn.Conv2d(in_dim, out_dim, kernel_size=1) |
||||
|
||||
def forward( |
||||
self, |
||||
pix_feat: torch.Tensor, |
||||
masks: torch.Tensor, |
||||
skip_mask_sigmoid: bool = False, |
||||
) -> Tuple[torch.Tensor, torch.Tensor]: |
||||
"""Processes pixel features and masks, fusing them to generate encoded memory representations.""" |
||||
if not skip_mask_sigmoid: |
||||
masks = F.sigmoid(masks) |
||||
masks = self.mask_downsampler(masks) |
||||
|
||||
# Fuse pix_feats and downsampled masks, in case the visual features are on CPU, cast them to CUDA |
||||
pix_feat = pix_feat.to(masks.device) |
||||
|
||||
x = self.pix_feat_proj(pix_feat) |
||||
x = x + masks |
||||
x = self.fuser(x) |
||||
x = self.out_proj(x) |
||||
|
||||
pos = self.position_encoding(x).to(x.dtype) |
||||
|
||||
return {"vision_features": x, "vision_pos_enc": [pos]} |
||||
|
||||
|
||||
class ImageEncoder(nn.Module): |
||||
"""Encodes images using a trunk-neck architecture, producing multiscale features and positional encodings.""" |
||||
|
||||
def __init__( |
||||
self, |
||||
trunk: nn.Module, |
||||
neck: nn.Module, |
||||
scalp: int = 0, |
||||
): |
||||
"""Initializes an image encoder with a trunk, neck, and optional scalp for feature extraction.""" |
||||
super().__init__() |
||||
self.trunk = trunk |
||||
self.neck = neck |
||||
self.scalp = scalp |
||||
assert ( |
||||
self.trunk.channel_list == self.neck.backbone_channel_list |
||||
), f"Channel dims of trunk {self.trunk.channel_list} and neck {self.neck.backbone_channel_list} do not match." |
||||
|
||||
def forward(self, sample: torch.Tensor): |
||||
"""Processes image input through trunk and neck, returning features, positional encodings, and FPN outputs.""" |
||||
features, pos = self.neck(self.trunk(sample)) |
||||
if self.scalp > 0: |
||||
# Discard the lowest resolution features |
||||
features, pos = features[: -self.scalp], pos[: -self.scalp] |
||||
|
||||
src = features[-1] |
||||
output = { |
||||
"vision_features": src, |
||||
"vision_pos_enc": pos, |
||||
"backbone_fpn": features, |
||||
} |
||||
return output |
||||
|
||||
|
||||
class FpnNeck(nn.Module): |
||||
"""Feature Pyramid Network (FPN) neck variant for multiscale feature fusion in object detection models.""" |
||||
|
||||
def __init__( |
||||
self, |
||||
d_model: int, |
||||
backbone_channel_list: List[int], |
||||
kernel_size: int = 1, |
||||
stride: int = 1, |
||||
padding: int = 0, |
||||
fpn_interp_model: str = "bilinear", |
||||
fuse_type: str = "sum", |
||||
fpn_top_down_levels: Optional[List[int]] = None, |
||||
): |
||||
""" |
||||
Initializes a modified Feature Pyramid Network (FPN) neck. |
||||
|
||||
This FPN variant removes the output convolution and uses bicubic interpolation for feature resizing, |
||||
similar to ViT positional embedding interpolation. |
||||
|
||||
Args: |
||||
d_model (int): Dimension of the model. |
||||
backbone_channel_list (List[int]): List of channel dimensions from the backbone. |
||||
kernel_size (int): Kernel size for the convolutional layers. |
||||
stride (int): Stride for the convolutional layers. |
||||
padding (int): Padding for the convolutional layers. |
||||
fpn_interp_model (str): Interpolation mode for FPN feature resizing. |
||||
fuse_type (str): Type of feature fusion, either 'sum' or 'avg'. |
||||
fpn_top_down_levels (Optional[List[int]]): Levels to have top-down features in outputs. |
||||
|
||||
Attributes: |
||||
position_encoding (PositionEmbeddingSine): Sinusoidal positional encoding. |
||||
convs (nn.ModuleList): List of convolutional layers for each backbone level. |
||||
backbone_channel_list (List[int]): List of channel dimensions from the backbone. |
||||
fpn_interp_model (str): Interpolation mode for FPN feature resizing. |
||||
fuse_type (str): Type of feature fusion. |
||||
fpn_top_down_levels (List[int]): Levels with top-down feature propagation. |
||||
|
||||
Examples: |
||||
>>> backbone_channels = [64, 128, 256, 512] |
||||
>>> fpn_neck = FpnNeck(256, backbone_channels) |
||||
>>> print(fpn_neck) |
||||
""" |
||||
super().__init__() |
||||
self.position_encoding = PositionEmbeddingSine(num_pos_feats=256) |
||||
self.convs = nn.ModuleList() |
||||
self.backbone_channel_list = backbone_channel_list |
||||
for dim in backbone_channel_list: |
||||
current = nn.Sequential() |
||||
current.add_module( |
||||
"conv", |
||||
nn.Conv2d( |
||||
in_channels=dim, |
||||
out_channels=d_model, |
||||
kernel_size=kernel_size, |
||||
stride=stride, |
||||
padding=padding, |
||||
), |
||||
) |
||||
|
||||
self.convs.append(current) |
||||
self.fpn_interp_model = fpn_interp_model |
||||
assert fuse_type in ["sum", "avg"] |
||||
self.fuse_type = fuse_type |
||||
|
||||
# levels to have top-down features in its outputs |
||||
# e.g. if fpn_top_down_levels is [2, 3], then only outputs of level 2 and 3 |
||||
# have top-down propagation, while outputs of level 0 and level 1 have only |
||||
# lateral features from the same backbone level. |
||||
if fpn_top_down_levels is None: |
||||
# default is to have top-down features on all levels |
||||
fpn_top_down_levels = range(len(self.convs)) |
||||
self.fpn_top_down_levels = list(fpn_top_down_levels) |
||||
|
||||
def forward(self, xs: List[torch.Tensor]): |
||||
""" |
||||
Performs forward pass through the Feature Pyramid Network (FPN) neck. |
||||
|
||||
Args: |
||||
xs (List[torch.Tensor]): List of input tensors from the backbone, with shape (B, C, H, W) for each tensor. |
||||
|
||||
Returns: |
||||
(Tuple[List[torch.Tensor], List[torch.Tensor]]): A tuple containing two lists: |
||||
- out: List of output feature maps after FPN processing, with shape (B, d_model, H, W) for each tensor. |
||||
- pos: List of positional encodings corresponding to each output feature map. |
||||
|
||||
Examples: |
||||
>>> fpn_neck = FpnNeck(d_model=256, backbone_channel_list=[64, 128, 256, 512]) |
||||
>>> inputs = [torch.rand(1, c, 32, 32) for c in [64, 128, 256, 512]] |
||||
>>> outputs, positions = fpn_neck(inputs) |
||||
""" |
||||
out = [None] * len(self.convs) |
||||
pos = [None] * len(self.convs) |
||||
assert len(xs) == len(self.convs) |
||||
# fpn forward pass |
||||
# see https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/fpn.py |
||||
prev_features = None |
||||
# forward in top-down order (from low to high resolution) |
||||
n = len(self.convs) - 1 |
||||
for i in range(n, -1, -1): |
||||
x = xs[i] |
||||
lateral_features = self.convs[n - i](x) |
||||
if i in self.fpn_top_down_levels and prev_features is not None: |
||||
top_down_features = F.interpolate( |
||||
prev_features.to(dtype=torch.float32), |
||||
scale_factor=2.0, |
||||
mode=self.fpn_interp_model, |
||||
align_corners=(None if self.fpn_interp_model == "nearest" else False), |
||||
antialias=False, |
||||
) |
||||
prev_features = lateral_features + top_down_features |
||||
if self.fuse_type == "avg": |
||||
prev_features /= 2 |
||||
else: |
||||
prev_features = lateral_features |
||||
x_out = prev_features |
||||
out[i] = x_out |
||||
pos[i] = self.position_encoding(x_out).to(x_out.dtype) |
||||
|
||||
return out, pos |
||||
|
||||
|
||||
class Hiera(nn.Module): |
||||
"""Hierarchical vision transformer for efficient multiscale feature extraction in image processing tasks.""" |
||||
|
||||
def __init__( |
||||
self, |
||||
embed_dim: int = 96, # initial embed dim |
||||
num_heads: int = 1, # initial number of heads |
||||
drop_path_rate: float = 0.0, # stochastic depth |
||||
q_pool: int = 3, # number of q_pool stages |
||||
q_stride: Tuple[int, int] = (2, 2), # downsample stride bet. stages |
||||
stages: Tuple[int, ...] = (2, 3, 16, 3), # blocks per stage |
||||
dim_mul: float = 2.0, # dim_mul factor at stage shift |
||||
head_mul: float = 2.0, # head_mul factor at stage shift |
||||
window_pos_embed_bkg_spatial_size: Tuple[int, int] = (14, 14), |
||||
# window size per stage, when not using global att. |
||||
window_spec: Tuple[int, ...] = ( |
||||
8, |
||||
4, |
||||
14, |
||||
7, |
||||
), |
||||
# global attn in these blocks |
||||
global_att_blocks: Tuple[int, ...] = ( |
||||
12, |
||||
16, |
||||
20, |
||||
), |
||||
return_interm_layers=True, # return feats from every stage |
||||
): |
||||
"""Initializes a Hiera model with configurable architecture for hierarchical vision transformers.""" |
||||
super().__init__() |
||||
|
||||
assert len(stages) == len(window_spec) |
||||
self.window_spec = window_spec |
||||
|
||||
depth = sum(stages) |
||||
self.q_stride = q_stride |
||||
self.stage_ends = [sum(stages[:i]) - 1 for i in range(1, len(stages) + 1)] |
||||
assert 0 <= q_pool <= len(self.stage_ends[:-1]) |
||||
self.q_pool_blocks = [x + 1 for x in self.stage_ends[:-1]][:q_pool] |
||||
self.return_interm_layers = return_interm_layers |
||||
|
||||
self.patch_embed = PatchEmbed( |
||||
embed_dim=embed_dim, |
||||
kernel_size=(7, 7), |
||||
stride=(4, 4), |
||||
padding=(3, 3), |
||||
) |
||||
# Which blocks have global att? |
||||
self.global_att_blocks = global_att_blocks |
||||
|
||||
# Windowed positional embedding (https://arxiv.org/abs/2311.05613) |
||||
self.window_pos_embed_bkg_spatial_size = window_pos_embed_bkg_spatial_size |
||||
self.pos_embed = nn.Parameter(torch.zeros(1, embed_dim, *self.window_pos_embed_bkg_spatial_size)) |
||||
self.pos_embed_window = nn.Parameter(torch.zeros(1, embed_dim, self.window_spec[0], self.window_spec[0])) |
||||
|
||||
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule |
||||
|
||||
cur_stage = 1 |
||||
self.blocks = nn.ModuleList() |
||||
|
||||
for i in range(depth): |
||||
dim_out = embed_dim |
||||
# lags by a block, so first block of |
||||
# next stage uses an initial window size |
||||
# of previous stage and final window size of current stage |
||||
window_size = self.window_spec[cur_stage - 1] |
||||
|
||||
if self.global_att_blocks is not None: |
||||
window_size = 0 if i in self.global_att_blocks else window_size |
||||
|
||||
if i - 1 in self.stage_ends: |
||||
dim_out = int(embed_dim * dim_mul) |
||||
num_heads = int(num_heads * head_mul) |
||||
cur_stage += 1 |
||||
|
||||
block = MultiScaleBlock( |
||||
dim=embed_dim, |
||||
dim_out=dim_out, |
||||
num_heads=num_heads, |
||||
drop_path=dpr[i], |
||||
q_stride=self.q_stride if i in self.q_pool_blocks else None, |
||||
window_size=window_size, |
||||
) |
||||
|
||||
embed_dim = dim_out |
||||
self.blocks.append(block) |
||||
|
||||
self.channel_list = ( |
||||
[self.blocks[i].dim_out for i in self.stage_ends[::-1]] |
||||
if return_interm_layers |
||||
else [self.blocks[-1].dim_out] |
||||
) |
||||
|
||||
def _get_pos_embed(self, hw: Tuple[int, int]) -> torch.Tensor: |
||||
"""Generate positional embeddings by interpolating and combining window and background embeddings.""" |
||||
h, w = hw |
||||
window_embed = self.pos_embed_window |
||||
pos_embed = F.interpolate(self.pos_embed, size=(h, w), mode="bicubic") |
||||
pos_embed = pos_embed + window_embed.tile([x // y for x, y in zip(pos_embed.shape, window_embed.shape)]) |
||||
pos_embed = pos_embed.permute(0, 2, 3, 1) |
||||
return pos_embed |
||||
|
||||
def forward(self, x: torch.Tensor) -> List[torch.Tensor]: |
||||
"""Performs hierarchical vision transformer forward pass, returning multiscale feature maps.""" |
||||
x = self.patch_embed(x) |
||||
# x: (B, H, W, C) |
||||
|
||||
# Add pos embed |
||||
x = x + self._get_pos_embed(x.shape[1:3]) |
||||
|
||||
outputs = [] |
||||
for i, blk in enumerate(self.blocks): |
||||
x = blk(x) |
||||
if (i == self.stage_ends[-1]) or (i in self.stage_ends and self.return_interm_layers): |
||||
feats = x.permute(0, 3, 1, 2) |
||||
outputs.append(feats) |
||||
|
||||
return outputs |
||||
@ -0,0 +1,170 @@ |
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license |
||||
|
||||
import copy |
||||
from typing import Optional |
||||
|
||||
import torch |
||||
from torch import Tensor, nn |
||||
|
||||
from .sam2_blocks import RoPEAttention |
||||
|
||||
|
||||
class MemoryAttentionLayer(nn.Module): |
||||
"""Implements a memory attention layer with self-attention and cross-attention mechanisms for neural networks.""" |
||||
|
||||
def __init__( |
||||
self, |
||||
d_model: int = 256, |
||||
dim_feedforward: int = 2048, |
||||
dropout: float = 0.1, |
||||
pos_enc_at_attn: bool = False, |
||||
pos_enc_at_cross_attn_keys: bool = True, |
||||
pos_enc_at_cross_attn_queries: bool = False, |
||||
): |
||||
"""Initializes a MemoryAttentionLayer with self-attention, cross-attention, and feedforward components.""" |
||||
super().__init__() |
||||
self.d_model = d_model |
||||
self.dim_feedforward = dim_feedforward |
||||
self.dropout_value = dropout |
||||
self.self_attn = RoPEAttention(embedding_dim=256, num_heads=1, downsample_rate=1) |
||||
self.cross_attn_image = RoPEAttention( |
||||
rope_k_repeat=True, |
||||
embedding_dim=256, |
||||
num_heads=1, |
||||
downsample_rate=1, |
||||
kv_in_dim=64, |
||||
) |
||||
|
||||
# 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.norm3 = nn.LayerNorm(d_model) |
||||
self.dropout1 = nn.Dropout(dropout) |
||||
self.dropout2 = nn.Dropout(dropout) |
||||
self.dropout3 = nn.Dropout(dropout) |
||||
|
||||
self.activation = nn.ReLU() |
||||
|
||||
# Where to add pos enc |
||||
self.pos_enc_at_attn = pos_enc_at_attn |
||||
self.pos_enc_at_cross_attn_queries = pos_enc_at_cross_attn_queries |
||||
self.pos_enc_at_cross_attn_keys = pos_enc_at_cross_attn_keys |
||||
|
||||
def _forward_sa(self, tgt, query_pos): |
||||
"""Performs self-attention on input tensor using positional encoding and RoPE attention mechanism.""" |
||||
tgt2 = self.norm1(tgt) |
||||
q = k = tgt2 + query_pos if self.pos_enc_at_attn else tgt2 |
||||
tgt2 = self.self_attn(q, k, v=tgt2) |
||||
tgt = tgt + self.dropout1(tgt2) |
||||
return tgt |
||||
|
||||
def _forward_ca(self, tgt, memory, query_pos, pos, num_k_exclude_rope=0): |
||||
"""Performs cross-attention between target and memory tensors using RoPEAttention mechanism.""" |
||||
kwds = {} |
||||
if num_k_exclude_rope > 0: |
||||
assert isinstance(self.cross_attn_image, RoPEAttention) |
||||
kwds = {"num_k_exclude_rope": num_k_exclude_rope} |
||||
|
||||
# Cross-Attention |
||||
tgt2 = self.norm2(tgt) |
||||
tgt2 = self.cross_attn_image( |
||||
q=tgt2 + query_pos if self.pos_enc_at_cross_attn_queries else tgt2, |
||||
k=memory + pos if self.pos_enc_at_cross_attn_keys else memory, |
||||
v=memory, |
||||
**kwds, |
||||
) |
||||
tgt = tgt + self.dropout2(tgt2) |
||||
return tgt |
||||
|
||||
def forward( |
||||
self, |
||||
tgt, |
||||
memory, |
||||
pos: Optional[Tensor] = None, |
||||
query_pos: Optional[Tensor] = None, |
||||
num_k_exclude_rope: int = 0, |
||||
) -> torch.Tensor: |
||||
"""Performs self-attention, cross-attention, and MLP operations on input tensors for memory-based attention.""" |
||||
tgt = self._forward_sa(tgt, query_pos) |
||||
tgt = self._forward_ca(tgt, memory, query_pos, pos, num_k_exclude_rope) |
||||
# MLP |
||||
tgt2 = self.norm3(tgt) |
||||
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) |
||||
tgt = tgt + self.dropout3(tgt2) |
||||
return tgt |
||||
|
||||
|
||||
class MemoryAttention(nn.Module): |
||||
"""Memory attention module for processing sequential data with self and cross-attention mechanisms.""" |
||||
|
||||
def __init__( |
||||
self, |
||||
d_model: int, |
||||
pos_enc_at_input: bool, |
||||
layer: nn.Module, |
||||
num_layers: int, |
||||
batch_first: bool = True, # Do layers expect batch first input? |
||||
): |
||||
"""Initializes MemoryAttention module with layers and normalization for attention processing.""" |
||||
super().__init__() |
||||
self.d_model = d_model |
||||
self.layers = nn.ModuleList([copy.deepcopy(layer) for _ in range(num_layers)]) |
||||
self.num_layers = num_layers |
||||
self.norm = nn.LayerNorm(d_model) |
||||
self.pos_enc_at_input = pos_enc_at_input |
||||
self.batch_first = batch_first |
||||
|
||||
def forward( |
||||
self, |
||||
curr: torch.Tensor, # self-attention inputs |
||||
memory: torch.Tensor, # cross-attention inputs |
||||
curr_pos: Optional[Tensor] = None, # pos_enc for self-attention inputs |
||||
memory_pos: Optional[Tensor] = None, # pos_enc for cross-attention inputs |
||||
num_obj_ptr_tokens: int = 0, # number of object pointer *tokens* |
||||
): |
||||
"""Applies self-attention and cross-attention to input tensors, processing through multiple layers.""" |
||||
if isinstance(curr, list): |
||||
assert isinstance(curr_pos, list) |
||||
assert len(curr) == len(curr_pos) == 1 |
||||
curr, curr_pos = ( |
||||
curr[0], |
||||
curr_pos[0], |
||||
) |
||||
|
||||
assert curr.shape[1] == memory.shape[1], "Batch size must be the same for curr and memory" |
||||
|
||||
output = curr |
||||
if self.pos_enc_at_input and curr_pos is not None: |
||||
output = output + 0.1 * curr_pos |
||||
|
||||
if self.batch_first: |
||||
# Convert to batch first |
||||
output = output.transpose(0, 1) |
||||
curr_pos = curr_pos.transpose(0, 1) |
||||
memory = memory.transpose(0, 1) |
||||
memory_pos = memory_pos.transpose(0, 1) |
||||
|
||||
for layer in self.layers: |
||||
kwds = {} |
||||
if isinstance(layer.cross_attn_image, RoPEAttention): |
||||
kwds = {"num_k_exclude_rope": num_obj_ptr_tokens} |
||||
|
||||
output = layer( |
||||
tgt=output, |
||||
memory=memory, |
||||
pos=memory_pos, |
||||
query_pos=curr_pos, |
||||
**kwds, |
||||
) |
||||
normed_output = self.norm(output) |
||||
|
||||
if self.batch_first: |
||||
# Convert back to seq first |
||||
normed_output = normed_output.transpose(0, 1) |
||||
curr_pos = curr_pos.transpose(0, 1) |
||||
|
||||
return normed_output |
||||
@ -0,0 +1,804 @@ |
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license |
||||
|
||||
import torch |
||||
import torch.distributed |
||||
import torch.nn.functional as F |
||||
from torch.nn.init import trunc_normal_ |
||||
|
||||
from ultralytics.models.sam.modules.encoders import PromptEncoder |
||||
from ultralytics.nn.modules import MLP |
||||
|
||||
from .decoders import MaskDecoder |
||||
from .sam2_blocks import TwoWayTransformer |
||||
from .utils import get_1d_sine_pe, select_closest_cond_frames |
||||
|
||||
# a large negative value as a placeholder score for missing objects |
||||
NO_OBJ_SCORE = -1024.0 |
||||
|
||||
|
||||
class SAM2Model(torch.nn.Module): |
||||
"""SAM2Model class for Segment Anything Model 2 with memory-based video object segmentation capabilities.""" |
||||
|
||||
mask_threshold: float = 0.0 |
||||
|
||||
def __init__( |
||||
self, |
||||
image_encoder, |
||||
memory_attention, |
||||
memory_encoder, |
||||
num_maskmem=7, # default 1 input frame + 6 previous frames |
||||
image_size=512, |
||||
backbone_stride=16, # stride of the image backbone output |
||||
sigmoid_scale_for_mem_enc=1.0, # scale factor for mask sigmoid prob |
||||
sigmoid_bias_for_mem_enc=0.0, # bias factor for mask sigmoid prob |
||||
# During evaluation, whether to binarize the sigmoid mask logits on interacted frames with clicks |
||||
binarize_mask_from_pts_for_mem_enc=False, |
||||
use_mask_input_as_output_without_sam=False, # on frames with mask input, whether to directly output the input mask without using a SAM prompt encoder + mask decoder |
||||
# The maximum number of conditioning frames to participate in the memory attention (-1 means no limit; if there are more conditioning frames than this limit, |
||||
# we only cross-attend to the temporally closest `max_cond_frames_in_attn` conditioning frames in the encoder when tracking each frame). This gives the model |
||||
# a temporal locality when handling a large number of annotated frames (since closer frames should be more important) and also avoids GPU OOM. |
||||
max_cond_frames_in_attn=-1, |
||||
# on the first frame, whether to directly add the no-memory embedding to the image feature |
||||
# (instead of using the transformer encoder) |
||||
directly_add_no_mem_embed=False, |
||||
# whether to use high-resolution feature maps in the SAM mask decoder |
||||
use_high_res_features_in_sam=False, |
||||
# whether to output multiple (3) masks for the first click on initial conditioning frames |
||||
multimask_output_in_sam=False, |
||||
# the minimum and maximum number of clicks to use multimask_output_in_sam (only relevant when `multimask_output_in_sam=True`; |
||||
# default is 1 for both, meaning that only the first click gives multimask output; also note that a box counts as two points) |
||||
multimask_min_pt_num=1, |
||||
multimask_max_pt_num=1, |
||||
# whether to also use multimask output for tracking (not just for the first click on initial conditioning frames; only relevant when `multimask_output_in_sam=True`) |
||||
multimask_output_for_tracking=False, |
||||
# Whether to use multimask tokens for obj ptr; Only relevant when both |
||||
# use_obj_ptrs_in_encoder=True and multimask_output_for_tracking=True |
||||
use_multimask_token_for_obj_ptr: bool = False, |
||||
# whether to use sigmoid to restrict ious prediction to [0-1] |
||||
iou_prediction_use_sigmoid=False, |
||||
# The memory bank's temporal stride during evaluation (i.e. the `r` parameter in XMem and Cutie; XMem and Cutie use r=5). |
||||
# For r>1, the (self.num_maskmem - 1) non-conditioning memory frames consist of |
||||
# (self.num_maskmem - 2) nearest frames from every r-th frames, plus the last frame. |
||||
memory_temporal_stride_for_eval=1, |
||||
# if `add_all_frames_to_correct_as_cond` is True, we also append to the conditioning frame list any frame that receives a later correction click |
||||
# if `add_all_frames_to_correct_as_cond` is False, we conditioning frame list to only use those initial conditioning frames |
||||
add_all_frames_to_correct_as_cond=False, |
||||
# whether to apply non-overlapping constraints on the object masks in the memory encoder during evaluation (to avoid/alleviate superposing masks) |
||||
non_overlap_masks_for_mem_enc=False, |
||||
# whether to cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder |
||||
use_obj_ptrs_in_encoder=False, |
||||
# the maximum number of object pointers from other frames in encoder cross attention (only relevant when `use_obj_ptrs_in_encoder=True`) |
||||
max_obj_ptrs_in_encoder=16, |
||||
# whether to add temporal positional encoding to the object pointers in the encoder (only relevant when `use_obj_ptrs_in_encoder=True`) |
||||
add_tpos_enc_to_obj_ptrs=True, |
||||
# whether to add an extra linear projection layer for the temporal positional encoding in the object pointers to avoid potential interference |
||||
# with spatial positional encoding (only relevant when both `use_obj_ptrs_in_encoder=True` and `add_tpos_enc_to_obj_ptrs=True`) |
||||
proj_tpos_enc_in_obj_ptrs=False, |
||||
# whether to only attend to object pointers in the past (before the current frame) in the encoder during evaluation |
||||
# (only relevant when `use_obj_ptrs_in_encoder=True`; this might avoid pointer information too far in the future to distract the initial tracking) |
||||
only_obj_ptrs_in_the_past_for_eval=False, |
||||
# Whether to predict if there is an object in the frame |
||||
pred_obj_scores: bool = False, |
||||
# Whether to use an MLP to predict object scores |
||||
pred_obj_scores_mlp: bool = False, |
||||
# Only relevant if pred_obj_scores=True and use_obj_ptrs_in_encoder=True; |
||||
# Whether to have a fixed no obj pointer when there is no object present |
||||
# or to use it as an additive embedding with obj_ptr produced by decoder |
||||
fixed_no_obj_ptr: bool = False, |
||||
# Soft no object, i.e. mix in no_obj_ptr softly, |
||||
# hope to make recovery easier if there is a mistake and mitigate accumulation of errors |
||||
soft_no_obj_ptr: bool = False, |
||||
use_mlp_for_obj_ptr_proj: bool = False, |
||||
# extra arguments used to construct the SAM mask decoder; if not None, it should be a dict of kwargs to be passed into `MaskDecoder` class. |
||||
sam_mask_decoder_extra_args=None, |
||||
compile_image_encoder: bool = False, |
||||
): |
||||
"""Initializes SAM2Model model with image encoder, memory attention, and memory encoder components.""" |
||||
super().__init__() |
||||
|
||||
# Part 1: the image backbone |
||||
self.image_encoder = image_encoder |
||||
# Use level 0, 1, 2 for high-res setting, or just level 2 for the default setting |
||||
self.use_high_res_features_in_sam = use_high_res_features_in_sam |
||||
self.num_feature_levels = 3 if use_high_res_features_in_sam else 1 |
||||
self.use_obj_ptrs_in_encoder = use_obj_ptrs_in_encoder |
||||
self.max_obj_ptrs_in_encoder = max_obj_ptrs_in_encoder |
||||
if use_obj_ptrs_in_encoder: |
||||
# A conv layer to downsample the mask prompt to stride 4 (the same stride as |
||||
# low-res SAM mask logits) and to change its scales from 0~1 to SAM logit scale, |
||||
# so that it can be fed into the SAM mask decoder to generate a pointer. |
||||
self.mask_downsample = torch.nn.Conv2d(1, 1, kernel_size=4, stride=4) |
||||
self.add_tpos_enc_to_obj_ptrs = add_tpos_enc_to_obj_ptrs |
||||
if proj_tpos_enc_in_obj_ptrs: |
||||
assert add_tpos_enc_to_obj_ptrs # these options need to be used together |
||||
self.proj_tpos_enc_in_obj_ptrs = proj_tpos_enc_in_obj_ptrs |
||||
self.only_obj_ptrs_in_the_past_for_eval = only_obj_ptrs_in_the_past_for_eval |
||||
|
||||
# Part 2: memory attention to condition current frame's visual features |
||||
# with memories (and obj ptrs) from past frames |
||||
self.memory_attention = memory_attention |
||||
self.hidden_dim = memory_attention.d_model |
||||
|
||||
# Part 3: memory encoder for the previous frame's outputs |
||||
self.memory_encoder = memory_encoder |
||||
self.mem_dim = self.hidden_dim |
||||
if hasattr(self.memory_encoder, "out_proj") and hasattr(self.memory_encoder.out_proj, "weight"): |
||||
# if there is compression of memories along channel dim |
||||
self.mem_dim = self.memory_encoder.out_proj.weight.shape[0] |
||||
self.num_maskmem = num_maskmem # Number of memories accessible |
||||
# Temporal encoding of the memories |
||||
self.maskmem_tpos_enc = torch.nn.Parameter(torch.zeros(num_maskmem, 1, 1, self.mem_dim)) |
||||
trunc_normal_(self.maskmem_tpos_enc, std=0.02) |
||||
# a single token to indicate no memory embedding from previous frames |
||||
self.no_mem_embed = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim)) |
||||
self.no_mem_pos_enc = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim)) |
||||
trunc_normal_(self.no_mem_embed, std=0.02) |
||||
trunc_normal_(self.no_mem_pos_enc, std=0.02) |
||||
self.directly_add_no_mem_embed = directly_add_no_mem_embed |
||||
# Apply sigmoid to the output raw mask logits (to turn them from |
||||
# range (-inf, +inf) to range (0, 1)) before feeding them into the memory encoder |
||||
self.sigmoid_scale_for_mem_enc = sigmoid_scale_for_mem_enc |
||||
self.sigmoid_bias_for_mem_enc = sigmoid_bias_for_mem_enc |
||||
self.binarize_mask_from_pts_for_mem_enc = binarize_mask_from_pts_for_mem_enc |
||||
self.non_overlap_masks_for_mem_enc = non_overlap_masks_for_mem_enc |
||||
self.memory_temporal_stride_for_eval = memory_temporal_stride_for_eval |
||||
# On frames with mask input, whether to directly output the input mask without |
||||
# using a SAM prompt encoder + mask decoder |
||||
self.use_mask_input_as_output_without_sam = use_mask_input_as_output_without_sam |
||||
self.multimask_output_in_sam = multimask_output_in_sam |
||||
self.multimask_min_pt_num = multimask_min_pt_num |
||||
self.multimask_max_pt_num = multimask_max_pt_num |
||||
self.multimask_output_for_tracking = multimask_output_for_tracking |
||||
self.use_multimask_token_for_obj_ptr = use_multimask_token_for_obj_ptr |
||||
self.iou_prediction_use_sigmoid = iou_prediction_use_sigmoid |
||||
|
||||
# Part 4: SAM-style prompt encoder (for both mask and point inputs) |
||||
# and SAM-style mask decoder for the final mask output |
||||
self.image_size = image_size |
||||
self.backbone_stride = backbone_stride |
||||
self.sam_mask_decoder_extra_args = sam_mask_decoder_extra_args |
||||
self.pred_obj_scores = pred_obj_scores |
||||
self.pred_obj_scores_mlp = pred_obj_scores_mlp |
||||
self.fixed_no_obj_ptr = fixed_no_obj_ptr |
||||
self.soft_no_obj_ptr = soft_no_obj_ptr |
||||
if self.fixed_no_obj_ptr: |
||||
assert self.pred_obj_scores |
||||
assert self.use_obj_ptrs_in_encoder |
||||
if self.pred_obj_scores and self.use_obj_ptrs_in_encoder: |
||||
self.no_obj_ptr = torch.nn.Parameter(torch.zeros(1, self.hidden_dim)) |
||||
trunc_normal_(self.no_obj_ptr, std=0.02) |
||||
self.use_mlp_for_obj_ptr_proj = use_mlp_for_obj_ptr_proj |
||||
|
||||
self._build_sam_heads() |
||||
self.add_all_frames_to_correct_as_cond = add_all_frames_to_correct_as_cond |
||||
self.max_cond_frames_in_attn = max_cond_frames_in_attn |
||||
|
||||
# Model compilation |
||||
if compile_image_encoder: |
||||
# Compile the forward function (not the full module) to allow loading checkpoints. |
||||
print("Image encoder compilation is enabled. First forward pass will be slow.") |
||||
self.image_encoder.forward = torch.compile( |
||||
self.image_encoder.forward, |
||||
mode="max-autotune", |
||||
fullgraph=True, |
||||
dynamic=False, |
||||
) |
||||
|
||||
@property |
||||
def device(self): |
||||
"""Returns the device on which the model's parameters are stored.""" |
||||
return next(self.parameters()).device |
||||
|
||||
def forward(self, *args, **kwargs): |
||||
"""Processes input frames and prompts to generate object masks and scores in video sequences.""" |
||||
raise NotImplementedError( |
||||
"Please use the corresponding methods in SAM2VideoPredictor for inference." |
||||
"See notebooks/video_predictor_example.ipynb for an example." |
||||
) |
||||
|
||||
def _build_sam_heads(self): |
||||
"""Builds SAM-style prompt encoder and mask decoder for image segmentation tasks.""" |
||||
self.sam_prompt_embed_dim = self.hidden_dim |
||||
self.sam_image_embedding_size = self.image_size // self.backbone_stride |
||||
|
||||
# build PromptEncoder and MaskDecoder from SAM |
||||
# (their hyperparameters like `mask_in_chans=16` are from SAM code) |
||||
self.sam_prompt_encoder = PromptEncoder( |
||||
embed_dim=self.sam_prompt_embed_dim, |
||||
image_embedding_size=( |
||||
self.sam_image_embedding_size, |
||||
self.sam_image_embedding_size, |
||||
), |
||||
input_image_size=(self.image_size, self.image_size), |
||||
mask_in_chans=16, |
||||
) |
||||
self.sam_mask_decoder = MaskDecoder( |
||||
num_multimask_outputs=3, |
||||
transformer=TwoWayTransformer( |
||||
depth=2, |
||||
embedding_dim=self.sam_prompt_embed_dim, |
||||
mlp_dim=2048, |
||||
num_heads=8, |
||||
), |
||||
transformer_dim=self.sam_prompt_embed_dim, |
||||
iou_head_depth=3, |
||||
iou_head_hidden_dim=256, |
||||
use_high_res_features=self.use_high_res_features_in_sam, |
||||
iou_prediction_use_sigmoid=self.iou_prediction_use_sigmoid, |
||||
pred_obj_scores=self.pred_obj_scores, |
||||
pred_obj_scores_mlp=self.pred_obj_scores_mlp, |
||||
use_multimask_token_for_obj_ptr=self.use_multimask_token_for_obj_ptr, |
||||
**(self.sam_mask_decoder_extra_args or {}), |
||||
) |
||||
if self.use_obj_ptrs_in_encoder: |
||||
# a linear projection on SAM output tokens to turn them into object pointers |
||||
self.obj_ptr_proj = torch.nn.Linear(self.hidden_dim, self.hidden_dim) |
||||
if self.use_mlp_for_obj_ptr_proj: |
||||
self.obj_ptr_proj = MLP(self.hidden_dim, self.hidden_dim, self.hidden_dim, 3) |
||||
else: |
||||
self.obj_ptr_proj = torch.nn.Identity() |
||||
if self.proj_tpos_enc_in_obj_ptrs: |
||||
# a linear projection on temporal positional encoding in object pointers to |
||||
# avoid potential interference with spatial positional encoding |
||||
self.obj_ptr_tpos_proj = torch.nn.Linear(self.hidden_dim, self.mem_dim) |
||||
else: |
||||
self.obj_ptr_tpos_proj = torch.nn.Identity() |
||||
|
||||
def _forward_sam_heads( |
||||
self, |
||||
backbone_features, |
||||
point_inputs=None, |
||||
mask_inputs=None, |
||||
high_res_features=None, |
||||
multimask_output=False, |
||||
): |
||||
""" |
||||
Forward SAM prompt encoders and mask heads. |
||||
|
||||
Args: |
||||
backbone_features (torch.Tensor): Image features with shape (B, C, H, W). |
||||
point_inputs (Dict[str, torch.Tensor] | None): Dictionary containing point prompts. |
||||
'point_coords': Tensor of shape (B, P, 2) with float32 dtype, containing absolute |
||||
pixel-unit coordinates in (x, y) format for P input points. |
||||
'point_labels': Tensor of shape (B, P) with int32 dtype, where 1 means positive clicks, |
||||
0 means negative clicks, and -1 means padding. |
||||
mask_inputs (torch.Tensor | None): Mask of shape (B, 1, H*16, W*16), float or bool, with the |
||||
same spatial size as the image. |
||||
high_res_features (List[torch.Tensor] | None): List of two feature maps with shapes |
||||
(B, C, 4*H, 4*W) and (B, C, 2*H, 2*W) respectively, used as high-resolution feature maps |
||||
for SAM decoder. |
||||
multimask_output (bool): If True, output 3 candidate masks and their IoU estimates; if False, |
||||
output only 1 mask and its IoU estimate. |
||||
|
||||
Returns: |
||||
(Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]): |
||||
low_res_multimasks: Tensor of shape (B, M, H*4, W*4) with SAM output mask logits. |
||||
high_res_multimasks: Tensor of shape (B, M, H*16, W*16) with upsampled mask logits. |
||||
ious: Tensor of shape (B, M) with estimated IoU for each output mask. |
||||
low_res_masks: Tensor of shape (B, 1, H*4, W*4) with best low-resolution mask. |
||||
high_res_masks: Tensor of shape (B, 1, H*16, W*16) with best high-resolution mask. |
||||
obj_ptr: Tensor of shape (B, C) with object pointer vector for the output mask. |
||||
object_score_logits: Tensor of shape (B,) with object score logits. |
||||
|
||||
Where M is 3 if multimask_output=True, and 1 if multimask_output=False. |
||||
|
||||
Examples: |
||||
>>> backbone_features = torch.rand(1, 256, 32, 32) |
||||
>>> point_inputs = {"point_coords": torch.rand(1, 2, 2), "point_labels": torch.tensor([[1, 0]])} |
||||
>>> mask_inputs = torch.rand(1, 1, 512, 512) |
||||
>>> results = model._forward_sam_heads(backbone_features, point_inputs, mask_inputs) |
||||
>>> low_res_multimasks, high_res_multimasks, ious, low_res_masks, high_res_masks, obj_ptr, object_score_logits = results |
||||
""" |
||||
B = backbone_features.size(0) |
||||
device = backbone_features.device |
||||
assert backbone_features.size(1) == self.sam_prompt_embed_dim |
||||
assert backbone_features.size(2) == self.sam_image_embedding_size |
||||
assert backbone_features.size(3) == self.sam_image_embedding_size |
||||
|
||||
# a) Handle point prompts |
||||
if point_inputs is not None: |
||||
sam_point_coords = point_inputs["point_coords"] |
||||
sam_point_labels = point_inputs["point_labels"] |
||||
assert sam_point_coords.size(0) == B and sam_point_labels.size(0) == B |
||||
else: |
||||
# If no points are provide, pad with an empty point (with label -1) |
||||
sam_point_coords = torch.zeros(B, 1, 2, device=device) |
||||
sam_point_labels = -torch.ones(B, 1, dtype=torch.int32, device=device) |
||||
|
||||
# b) Handle mask prompts |
||||
if mask_inputs is not None: |
||||
# If mask_inputs is provided, downsize it into low-res mask input if needed |
||||
# and feed it as a dense mask prompt into the SAM mask encoder |
||||
assert len(mask_inputs.shape) == 4 and mask_inputs.shape[:2] == (B, 1) |
||||
if mask_inputs.shape[-2:] != self.sam_prompt_encoder.mask_input_size: |
||||
sam_mask_prompt = F.interpolate( |
||||
mask_inputs.float(), |
||||
size=self.sam_prompt_encoder.mask_input_size, |
||||
align_corners=False, |
||||
mode="bilinear", |
||||
antialias=True, # use antialias for downsampling |
||||
) |
||||
else: |
||||
sam_mask_prompt = mask_inputs |
||||
else: |
||||
# Otherwise, simply feed None (and SAM's prompt encoder will add |
||||
# a learned `no_mask_embed` to indicate no mask input in this case). |
||||
sam_mask_prompt = None |
||||
|
||||
sparse_embeddings, dense_embeddings = self.sam_prompt_encoder( |
||||
points=(sam_point_coords, sam_point_labels), |
||||
boxes=None, |
||||
masks=sam_mask_prompt, |
||||
) |
||||
( |
||||
low_res_multimasks, |
||||
ious, |
||||
sam_output_tokens, |
||||
object_score_logits, |
||||
) = self.sam_mask_decoder( |
||||
image_embeddings=backbone_features, |
||||
image_pe=self.sam_prompt_encoder.get_dense_pe(), |
||||
sparse_prompt_embeddings=sparse_embeddings, |
||||
dense_prompt_embeddings=dense_embeddings, |
||||
multimask_output=multimask_output, |
||||
repeat_image=False, # the image is already batched |
||||
high_res_features=high_res_features, |
||||
) |
||||
if self.pred_obj_scores: |
||||
is_obj_appearing = object_score_logits > 0 |
||||
|
||||
# Mask used for spatial memories is always a *hard* choice between obj and no obj, |
||||
# consistent with the actual mask prediction |
||||
low_res_multimasks = torch.where( |
||||
is_obj_appearing[:, None, None], |
||||
low_res_multimasks, |
||||
NO_OBJ_SCORE, |
||||
) |
||||
|
||||
# convert masks from possibly bfloat16 (or float16) to float32 |
||||
# (older PyTorch versions before 2.1 don't support `interpolate` on bf16) |
||||
low_res_multimasks = low_res_multimasks.float() |
||||
high_res_multimasks = F.interpolate( |
||||
low_res_multimasks, |
||||
size=(self.image_size, self.image_size), |
||||
mode="bilinear", |
||||
align_corners=False, |
||||
) |
||||
|
||||
sam_output_token = sam_output_tokens[:, 0] |
||||
if multimask_output: |
||||
# take the best mask prediction (with the highest IoU estimation) |
||||
best_iou_inds = torch.argmax(ious, dim=-1) |
||||
batch_inds = torch.arange(B, device=device) |
||||
low_res_masks = low_res_multimasks[batch_inds, best_iou_inds].unsqueeze(1) |
||||
high_res_masks = high_res_multimasks[batch_inds, best_iou_inds].unsqueeze(1) |
||||
if sam_output_tokens.size(1) > 1: |
||||
sam_output_token = sam_output_tokens[batch_inds, best_iou_inds] |
||||
else: |
||||
low_res_masks, high_res_masks = low_res_multimasks, high_res_multimasks |
||||
|
||||
# Extract object pointer from the SAM output token (with occlusion handling) |
||||
obj_ptr = self.obj_ptr_proj(sam_output_token) |
||||
if self.pred_obj_scores: |
||||
# Allow *soft* no obj ptr, unlike for masks |
||||
if self.soft_no_obj_ptr: |
||||
# Only hard possible with gt |
||||
assert not self.teacher_force_obj_scores_for_mem |
||||
lambda_is_obj_appearing = object_score_logits.sigmoid() |
||||
else: |
||||
lambda_is_obj_appearing = is_obj_appearing.float() |
||||
|
||||
if self.fixed_no_obj_ptr: |
||||
obj_ptr = lambda_is_obj_appearing * obj_ptr |
||||
obj_ptr = obj_ptr + (1 - lambda_is_obj_appearing) * self.no_obj_ptr |
||||
|
||||
return ( |
||||
low_res_multimasks, |
||||
high_res_multimasks, |
||||
ious, |
||||
low_res_masks, |
||||
high_res_masks, |
||||
obj_ptr, |
||||
object_score_logits, |
||||
) |
||||
|
||||
def _use_mask_as_output(self, backbone_features, high_res_features, mask_inputs): |
||||
"""Processes mask inputs to generate output mask logits and object pointers without using SAM.""" |
||||
# Use -10/+10 as logits for neg/pos pixels (very close to 0/1 in prob after sigmoid). |
||||
out_scale, out_bias = 20.0, -10.0 # sigmoid(-10.0)=4.5398e-05 |
||||
mask_inputs_float = mask_inputs.float() |
||||
high_res_masks = mask_inputs_float * out_scale + out_bias |
||||
low_res_masks = F.interpolate( |
||||
high_res_masks, |
||||
size=(high_res_masks.size(-2) // 4, high_res_masks.size(-1) // 4), |
||||
align_corners=False, |
||||
mode="bilinear", |
||||
antialias=True, # use antialias for downsampling |
||||
) |
||||
# a dummy IoU prediction of all 1's under mask input |
||||
ious = mask_inputs.new_ones(mask_inputs.size(0), 1).float() |
||||
if not self.use_obj_ptrs_in_encoder: |
||||
# all zeros as a dummy object pointer (of shape [B, C]) |
||||
obj_ptr = torch.zeros(mask_inputs.size(0), self.hidden_dim, device=mask_inputs.device) |
||||
else: |
||||
# produce an object pointer using the SAM decoder from the mask input |
||||
_, _, _, _, _, obj_ptr, _ = self._forward_sam_heads( |
||||
backbone_features=backbone_features, |
||||
mask_inputs=self.mask_downsample(mask_inputs_float), |
||||
high_res_features=high_res_features, |
||||
) |
||||
# In this method, we are treating mask_input as output, e.g. using it directly to create spatial mem; |
||||
# Below, we follow the same design axiom to use mask_input to decide if obj appears or not instead of relying |
||||
# on the object_scores from the SAM decoder. |
||||
is_obj_appearing = torch.any(mask_inputs.flatten(1).float() > 0.0, dim=1) |
||||
is_obj_appearing = is_obj_appearing[..., None] |
||||
lambda_is_obj_appearing = is_obj_appearing.float() |
||||
object_score_logits = out_scale * lambda_is_obj_appearing + out_bias |
||||
if self.pred_obj_scores: |
||||
if self.fixed_no_obj_ptr: |
||||
obj_ptr = lambda_is_obj_appearing * obj_ptr |
||||
obj_ptr = obj_ptr + (1 - lambda_is_obj_appearing) * self.no_obj_ptr |
||||
|
||||
return ( |
||||
low_res_masks, |
||||
high_res_masks, |
||||
ious, |
||||
low_res_masks, |
||||
high_res_masks, |
||||
obj_ptr, |
||||
object_score_logits, |
||||
) |
||||
|
||||
def forward_image(self, img_batch: torch.Tensor): |
||||
"""Process image batch through encoder to extract multi-level features for SAM model.""" |
||||
backbone_out = self.image_encoder(img_batch) |
||||
if self.use_high_res_features_in_sam: |
||||
# precompute projected level 0 and level 1 features in SAM decoder |
||||
# to avoid running it again on every SAM click |
||||
backbone_out["backbone_fpn"][0] = self.sam_mask_decoder.conv_s0(backbone_out["backbone_fpn"][0]) |
||||
backbone_out["backbone_fpn"][1] = self.sam_mask_decoder.conv_s1(backbone_out["backbone_fpn"][1]) |
||||
return backbone_out |
||||
|
||||
def _prepare_backbone_features(self, backbone_out): |
||||
"""Prepare and flatten visual features from the image backbone output.""" |
||||
backbone_out = backbone_out.copy() |
||||
assert len(backbone_out["backbone_fpn"]) == len(backbone_out["vision_pos_enc"]) |
||||
assert len(backbone_out["backbone_fpn"]) >= self.num_feature_levels |
||||
|
||||
feature_maps = backbone_out["backbone_fpn"][-self.num_feature_levels :] |
||||
vision_pos_embeds = backbone_out["vision_pos_enc"][-self.num_feature_levels :] |
||||
|
||||
feat_sizes = [(x.shape[-2], x.shape[-1]) for x in vision_pos_embeds] |
||||
# flatten NxCxHxW to HWxNxC |
||||
vision_feats = [x.flatten(2).permute(2, 0, 1) for x in feature_maps] |
||||
vision_pos_embeds = [x.flatten(2).permute(2, 0, 1) for x in vision_pos_embeds] |
||||
|
||||
return backbone_out, vision_feats, vision_pos_embeds, feat_sizes |
||||
|
||||
def _prepare_memory_conditioned_features( |
||||
self, |
||||
frame_idx, |
||||
is_init_cond_frame, |
||||
current_vision_feats, |
||||
current_vision_pos_embeds, |
||||
feat_sizes, |
||||
output_dict, |
||||
num_frames, |
||||
track_in_reverse=False, # tracking in reverse time order (for demo usage) |
||||
): |
||||
"""Prepares memory-conditioned features by fusing current frame's visual features with previous memories.""" |
||||
B = current_vision_feats[-1].size(1) # batch size on this frame |
||||
C = self.hidden_dim |
||||
H, W = feat_sizes[-1] # top-level (lowest-resolution) feature size |
||||
device = current_vision_feats[-1].device |
||||
# The case of `self.num_maskmem == 0` below is primarily used for reproducing SAM on images. |
||||
# In this case, we skip the fusion with any memory. |
||||
if self.num_maskmem == 0: # Disable memory and skip fusion |
||||
pix_feat = current_vision_feats[-1].permute(1, 2, 0).view(B, C, H, W) |
||||
return pix_feat |
||||
|
||||
num_obj_ptr_tokens = 0 |
||||
# Step 1: condition the visual features of the current frame on previous memories |
||||
if not is_init_cond_frame: |
||||
# Retrieve the memories encoded with the maskmem backbone |
||||
to_cat_memory, to_cat_memory_pos_embed = [], [] |
||||
# Add conditioning frames's output first (all cond frames have t_pos=0 for |
||||
# when getting temporal positional embedding below) |
||||
assert len(output_dict["cond_frame_outputs"]) > 0 |
||||
# Select a maximum number of temporally closest cond frames for cross attention |
||||
cond_outputs = output_dict["cond_frame_outputs"] |
||||
selected_cond_outputs, unselected_cond_outputs = select_closest_cond_frames( |
||||
frame_idx, cond_outputs, self.max_cond_frames_in_attn |
||||
) |
||||
t_pos_and_prevs = [(0, out) for out in selected_cond_outputs.values()] |
||||
# Add last (self.num_maskmem - 1) frames before current frame for non-conditioning memory |
||||
# the earliest one has t_pos=1 and the latest one has t_pos=self.num_maskmem-1 |
||||
# We also allow taking the memory frame non-consecutively (with r>1), in which case |
||||
# we take (self.num_maskmem - 2) frames among every r-th frames plus the last frame. |
||||
r = self.memory_temporal_stride_for_eval |
||||
for t_pos in range(1, self.num_maskmem): |
||||
t_rel = self.num_maskmem - t_pos # how many frames before current frame |
||||
if t_rel == 1: |
||||
# for t_rel == 1, we take the last frame (regardless of r) |
||||
if not track_in_reverse: |
||||
# the frame immediately before this frame (i.e. frame_idx - 1) |
||||
prev_frame_idx = frame_idx - t_rel |
||||
else: |
||||
# the frame immediately after this frame (i.e. frame_idx + 1) |
||||
prev_frame_idx = frame_idx + t_rel |
||||
else: |
||||
# for t_rel >= 2, we take the memory frame from every r-th frames |
||||
if not track_in_reverse: |
||||
# first find the nearest frame among every r-th frames before this frame |
||||
# for r=1, this would be (frame_idx - 2) |
||||
prev_frame_idx = ((frame_idx - 2) // r) * r |
||||
# then seek further among every r-th frames |
||||
prev_frame_idx = prev_frame_idx - (t_rel - 2) * r |
||||
else: |
||||
# first find the nearest frame among every r-th frames after this frame |
||||
# for r=1, this would be (frame_idx + 2) |
||||
prev_frame_idx = -(-(frame_idx + 2) // r) * r |
||||
# then seek further among every r-th frames |
||||
prev_frame_idx = prev_frame_idx + (t_rel - 2) * r |
||||
out = output_dict["non_cond_frame_outputs"].get(prev_frame_idx, None) |
||||
if out is None: |
||||
# If an unselected conditioning frame is among the last (self.num_maskmem - 1) |
||||
# frames, we still attend to it as if it's a non-conditioning frame. |
||||
out = unselected_cond_outputs.get(prev_frame_idx, None) |
||||
t_pos_and_prevs.append((t_pos, out)) |
||||
|
||||
for t_pos, prev in t_pos_and_prevs: |
||||
if prev is None: |
||||
continue # skip padding frames |
||||
# "maskmem_features" might have been offloaded to CPU in demo use cases, |
||||
# so we load it back to GPU (it's a no-op if it's already on GPU). |
||||
feats = prev["maskmem_features"].cuda(non_blocking=True) |
||||
to_cat_memory.append(feats.flatten(2).permute(2, 0, 1)) |
||||
# Spatial positional encoding (it might have been offloaded to CPU in eval) |
||||
maskmem_enc = prev["maskmem_pos_enc"][-1].cuda() |
||||
maskmem_enc = maskmem_enc.flatten(2).permute(2, 0, 1) |
||||
# Temporal positional encoding |
||||
maskmem_enc = maskmem_enc + self.maskmem_tpos_enc[self.num_maskmem - t_pos - 1] |
||||
to_cat_memory_pos_embed.append(maskmem_enc) |
||||
|
||||
# Construct the list of past object pointers |
||||
if self.use_obj_ptrs_in_encoder: |
||||
max_obj_ptrs_in_encoder = min(num_frames, self.max_obj_ptrs_in_encoder) |
||||
# First add those object pointers from selected conditioning frames |
||||
# (optionally, only include object pointers in the past during evaluation) |
||||
if not self.training and self.only_obj_ptrs_in_the_past_for_eval: |
||||
ptr_cond_outputs = { |
||||
t: out |
||||
for t, out in selected_cond_outputs.items() |
||||
if (t >= frame_idx if track_in_reverse else t <= frame_idx) |
||||
} |
||||
else: |
||||
ptr_cond_outputs = selected_cond_outputs |
||||
pos_and_ptrs = [ |
||||
# Temporal pos encoding contains how far away each pointer is from current frame |
||||
(abs(frame_idx - t), out["obj_ptr"]) |
||||
for t, out in ptr_cond_outputs.items() |
||||
] |
||||
# Add up to (max_obj_ptrs_in_encoder - 1) non-conditioning frames before current frame |
||||
for t_diff in range(1, max_obj_ptrs_in_encoder): |
||||
t = frame_idx + t_diff if track_in_reverse else frame_idx - t_diff |
||||
if t < 0 or (num_frames is not None and t >= num_frames): |
||||
break |
||||
out = output_dict["non_cond_frame_outputs"].get(t, unselected_cond_outputs.get(t, None)) |
||||
if out is not None: |
||||
pos_and_ptrs.append((t_diff, out["obj_ptr"])) |
||||
# If we have at least one object pointer, add them to the across attention |
||||
if len(pos_and_ptrs) > 0: |
||||
pos_list, ptrs_list = zip(*pos_and_ptrs) |
||||
# stack object pointers along dim=0 into [ptr_seq_len, B, C] shape |
||||
obj_ptrs = torch.stack(ptrs_list, dim=0) |
||||
# a temporal positional embedding based on how far each object pointer is from |
||||
# the current frame (sine embedding normalized by the max pointer num). |
||||
if self.add_tpos_enc_to_obj_ptrs: |
||||
t_diff_max = max_obj_ptrs_in_encoder - 1 |
||||
tpos_dim = C if self.proj_tpos_enc_in_obj_ptrs else self.mem_dim |
||||
obj_pos = torch.tensor(pos_list, device=device) |
||||
obj_pos = get_1d_sine_pe(obj_pos / t_diff_max, dim=tpos_dim) |
||||
obj_pos = self.obj_ptr_tpos_proj(obj_pos) |
||||
obj_pos = obj_pos.unsqueeze(1).expand(-1, B, self.mem_dim) |
||||
else: |
||||
obj_pos = obj_ptrs.new_zeros(len(pos_list), B, self.mem_dim) |
||||
if self.mem_dim < C: |
||||
# split a pointer into (C // self.mem_dim) tokens for self.mem_dim < C |
||||
obj_ptrs = obj_ptrs.reshape(-1, B, C // self.mem_dim, self.mem_dim) |
||||
obj_ptrs = obj_ptrs.permute(0, 2, 1, 3).flatten(0, 1) |
||||
obj_pos = obj_pos.repeat_interleave(C // self.mem_dim, dim=0) |
||||
to_cat_memory.append(obj_ptrs) |
||||
to_cat_memory_pos_embed.append(obj_pos) |
||||
num_obj_ptr_tokens = obj_ptrs.shape[0] |
||||
else: |
||||
num_obj_ptr_tokens = 0 |
||||
else: |
||||
# for initial conditioning frames, encode them without using any previous memory |
||||
if self.directly_add_no_mem_embed: |
||||
# directly add no-mem embedding (instead of using the transformer encoder) |
||||
pix_feat_with_mem = current_vision_feats[-1] + self.no_mem_embed |
||||
pix_feat_with_mem = pix_feat_with_mem.permute(1, 2, 0).view(B, C, H, W) |
||||
return pix_feat_with_mem |
||||
|
||||
# Use a dummy token on the first frame (to avoid empty memory input to transformer encoder) |
||||
to_cat_memory = [self.no_mem_embed.expand(1, B, self.mem_dim)] |
||||
to_cat_memory_pos_embed = [self.no_mem_pos_enc.expand(1, B, self.mem_dim)] |
||||
|
||||
# Step 2: Concatenate the memories and forward through the transformer encoder |
||||
memory = torch.cat(to_cat_memory, dim=0) |
||||
memory_pos_embed = torch.cat(to_cat_memory_pos_embed, dim=0) |
||||
|
||||
pix_feat_with_mem = self.memory_attention( |
||||
curr=current_vision_feats, |
||||
curr_pos=current_vision_pos_embeds, |
||||
memory=memory, |
||||
memory_pos=memory_pos_embed, |
||||
num_obj_ptr_tokens=num_obj_ptr_tokens, |
||||
) |
||||
# reshape the output (HW)BC => BCHW |
||||
pix_feat_with_mem = pix_feat_with_mem.permute(1, 2, 0).view(B, C, H, W) |
||||
return pix_feat_with_mem |
||||
|
||||
def _encode_new_memory( |
||||
self, |
||||
current_vision_feats, |
||||
feat_sizes, |
||||
pred_masks_high_res, |
||||
is_mask_from_pts, |
||||
): |
||||
"""Encodes the current frame's features and predicted masks into a new memory representation.""" |
||||
B = current_vision_feats[-1].size(1) # batch size on this frame |
||||
C = self.hidden_dim |
||||
H, W = feat_sizes[-1] # top-level (lowest-resolution) feature size |
||||
# top-level feature, (HW)BC => BCHW |
||||
pix_feat = current_vision_feats[-1].permute(1, 2, 0).view(B, C, H, W) |
||||
if self.non_overlap_masks_for_mem_enc and not self.training: |
||||
# optionally, apply non-overlapping constraints to the masks (it's applied |
||||
# in the batch dimension and should only be used during eval, where all |
||||
# the objects come from the same video under batch size 1). |
||||
pred_masks_high_res = self._apply_non_overlapping_constraints(pred_masks_high_res) |
||||
# scale the raw mask logits with a temperature before applying sigmoid |
||||
binarize = self.binarize_mask_from_pts_for_mem_enc and is_mask_from_pts |
||||
if binarize and not self.training: |
||||
mask_for_mem = (pred_masks_high_res > 0).float() |
||||
else: |
||||
# apply sigmoid on the raw mask logits to turn them into range (0, 1) |
||||
mask_for_mem = torch.sigmoid(pred_masks_high_res) |
||||
# apply scale and bias terms to the sigmoid probabilities |
||||
if self.sigmoid_scale_for_mem_enc != 1.0: |
||||
mask_for_mem = mask_for_mem * self.sigmoid_scale_for_mem_enc |
||||
if self.sigmoid_bias_for_mem_enc != 0.0: |
||||
mask_for_mem = mask_for_mem + self.sigmoid_bias_for_mem_enc |
||||
maskmem_out = self.memory_encoder( |
||||
pix_feat, |
||||
mask_for_mem, |
||||
skip_mask_sigmoid=True, # sigmoid already applied |
||||
) |
||||
maskmem_features = maskmem_out["vision_features"] |
||||
maskmem_pos_enc = maskmem_out["vision_pos_enc"] |
||||
|
||||
return maskmem_features, maskmem_pos_enc |
||||
|
||||
def track_step( |
||||
self, |
||||
frame_idx, |
||||
is_init_cond_frame, |
||||
current_vision_feats, |
||||
current_vision_pos_embeds, |
||||
feat_sizes, |
||||
point_inputs, |
||||
mask_inputs, |
||||
output_dict, |
||||
num_frames, |
||||
track_in_reverse=False, # tracking in reverse time order (for demo usage) |
||||
# Whether to run the memory encoder on the predicted masks. Sometimes we might want |
||||
# to skip the memory encoder with `run_mem_encoder=False`. For example, |
||||
# in demo we might call `track_step` multiple times for each user click, |
||||
# and only encode the memory when the user finalizes their clicks. And in ablation |
||||
# settings like SAM training on static images, we don't need the memory encoder. |
||||
run_mem_encoder=True, |
||||
# The previously predicted SAM mask logits (which can be fed together with new clicks in demo). |
||||
prev_sam_mask_logits=None, |
||||
): |
||||
"""Performs a single tracking step, updating object masks and memory features based on current frame inputs.""" |
||||
current_out = {"point_inputs": point_inputs, "mask_inputs": mask_inputs} |
||||
# High-resolution feature maps for the SAM head, reshape (HW)BC => BCHW |
||||
if len(current_vision_feats) > 1: |
||||
high_res_features = [ |
||||
x.permute(1, 2, 0).view(x.size(1), x.size(2), *s) |
||||
for x, s in zip(current_vision_feats[:-1], feat_sizes[:-1]) |
||||
] |
||||
else: |
||||
high_res_features = None |
||||
if mask_inputs is not None and self.use_mask_input_as_output_without_sam: |
||||
# When use_mask_input_as_output_without_sam=True, we directly output the mask input |
||||
# (see it as a GT mask) without using a SAM prompt encoder + mask decoder. |
||||
pix_feat = current_vision_feats[-1].permute(1, 2, 0) |
||||
pix_feat = pix_feat.view(-1, self.hidden_dim, *feat_sizes[-1]) |
||||
sam_outputs = self._use_mask_as_output(pix_feat, high_res_features, mask_inputs) |
||||
else: |
||||
# fused the visual feature with previous memory features in the memory bank |
||||
pix_feat_with_mem = self._prepare_memory_conditioned_features( |
||||
frame_idx=frame_idx, |
||||
is_init_cond_frame=is_init_cond_frame, |
||||
current_vision_feats=current_vision_feats[-1:], |
||||
current_vision_pos_embeds=current_vision_pos_embeds[-1:], |
||||
feat_sizes=feat_sizes[-1:], |
||||
output_dict=output_dict, |
||||
num_frames=num_frames, |
||||
track_in_reverse=track_in_reverse, |
||||
) |
||||
# apply SAM-style segmentation head |
||||
# here we might feed previously predicted low-res SAM mask logits into the SAM mask decoder, |
||||
# e.g. in demo where such logits come from earlier interaction instead of correction sampling |
||||
# (in this case, any `mask_inputs` shouldn't reach here as they are sent to _use_mask_as_output instead) |
||||
if prev_sam_mask_logits is not None: |
||||
assert point_inputs is not None and mask_inputs is None |
||||
mask_inputs = prev_sam_mask_logits |
||||
multimask_output = self._use_multimask(is_init_cond_frame, point_inputs) |
||||
sam_outputs = self._forward_sam_heads( |
||||
backbone_features=pix_feat_with_mem, |
||||
point_inputs=point_inputs, |
||||
mask_inputs=mask_inputs, |
||||
high_res_features=high_res_features, |
||||
multimask_output=multimask_output, |
||||
) |
||||
( |
||||
_, |
||||
_, |
||||
_, |
||||
low_res_masks, |
||||
high_res_masks, |
||||
obj_ptr, |
||||
_, |
||||
) = sam_outputs |
||||
|
||||
current_out["pred_masks"] = low_res_masks |
||||
current_out["pred_masks_high_res"] = high_res_masks |
||||
current_out["obj_ptr"] = obj_ptr |
||||
|
||||
# Finally run the memory encoder on the predicted mask to encode |
||||
# it into a new memory feature (that can be used in future frames) |
||||
if run_mem_encoder and self.num_maskmem > 0: |
||||
high_res_masks_for_mem_enc = high_res_masks |
||||
maskmem_features, maskmem_pos_enc = self._encode_new_memory( |
||||
current_vision_feats=current_vision_feats, |
||||
feat_sizes=feat_sizes, |
||||
pred_masks_high_res=high_res_masks_for_mem_enc, |
||||
is_mask_from_pts=(point_inputs is not None), |
||||
) |
||||
current_out["maskmem_features"] = maskmem_features |
||||
current_out["maskmem_pos_enc"] = maskmem_pos_enc |
||||
else: |
||||
current_out["maskmem_features"] = None |
||||
current_out["maskmem_pos_enc"] = None |
||||
|
||||
return current_out |
||||
|
||||
def _use_multimask(self, is_init_cond_frame, point_inputs): |
||||
"""Determines whether to use multiple mask outputs in the SAM head based on configuration and inputs.""" |
||||
num_pts = 0 if point_inputs is None else point_inputs["point_labels"].size(1) |
||||
multimask_output = ( |
||||
self.multimask_output_in_sam |
||||
and (is_init_cond_frame or self.multimask_output_for_tracking) |
||||
and (self.multimask_min_pt_num <= num_pts <= self.multimask_max_pt_num) |
||||
) |
||||
return multimask_output |
||||
|
||||
def _apply_non_overlapping_constraints(self, pred_masks): |
||||
"""Applies non-overlapping constraints to object masks, keeping highest scoring object at each location.""" |
||||
batch_size = pred_masks.size(0) |
||||
if batch_size == 1: |
||||
return pred_masks |
||||
|
||||
device = pred_masks.device |
||||
# "max_obj_inds": object index of the object with the highest score at each location |
||||
max_obj_inds = torch.argmax(pred_masks, dim=0, keepdim=True) |
||||
# "batch_obj_inds": object index of each object slice (along dim 0) in `pred_masks` |
||||
batch_obj_inds = torch.arange(batch_size, device=device)[:, None, None, None] |
||||
keep = max_obj_inds == batch_obj_inds |
||||
# suppress overlapping regions' scores below -10.0 so that the foreground regions |
||||
# don't overlap (here sigmoid(-10.0)=4.5398e-05) |
||||
pred_masks = torch.where(keep, pred_masks, torch.clamp(pred_masks, max=-10.0)) |
||||
return pred_masks |
||||
@ -0,0 +1,715 @@ |
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license |
||||
|
||||
import copy |
||||
import math |
||||
from functools import partial |
||||
from typing import Optional, Tuple, Type, Union |
||||
|
||||
import torch |
||||
import torch.nn.functional as F |
||||
from torch import Tensor, nn |
||||
|
||||
from ultralytics.models.sam.modules.transformer import ( |
||||
Attention, |
||||
) |
||||
from ultralytics.models.sam.modules.transformer import ( |
||||
TwoWayAttentionBlock as SAMTwoWayAttentionBlock, |
||||
) |
||||
from ultralytics.models.sam.modules.transformer import ( |
||||
TwoWayTransformer as SAMTwoWayTransformer, |
||||
) |
||||
from ultralytics.nn.modules import MLP, LayerNorm2d |
||||
|
||||
from .utils import apply_rotary_enc, compute_axial_cis, window_partition, window_unpartition |
||||
|
||||
|
||||
class DropPath(nn.Module): |
||||
"""Implements stochastic depth regularization for neural networks during training.""" |
||||
|
||||
def __init__(self, drop_prob=0.0, scale_by_keep=True): |
||||
"""Initialize DropPath module with specified drop probability and scaling option.""" |
||||
super(DropPath, self).__init__() |
||||
self.drop_prob = drop_prob |
||||
self.scale_by_keep = scale_by_keep |
||||
|
||||
def forward(self, x): |
||||
"""Applies stochastic depth to input tensor during training, with optional scaling.""" |
||||
if self.drop_prob == 0.0 or not self.training: |
||||
return x |
||||
keep_prob = 1 - self.drop_prob |
||||
shape = (x.shape[0],) + (1,) * (x.ndim - 1) |
||||
random_tensor = x.new_empty(shape).bernoulli_(keep_prob) |
||||
if keep_prob > 0.0 and self.scale_by_keep: |
||||
random_tensor.div_(keep_prob) |
||||
return x * random_tensor |
||||
|
||||
|
||||
class MaskDownSampler(nn.Module): |
||||
"""Downsamples and embeds masks using convolutional layers and layer normalization for efficient processing.""" |
||||
|
||||
def __init__( |
||||
self, |
||||
embed_dim=256, |
||||
kernel_size=4, |
||||
stride=4, |
||||
padding=0, |
||||
total_stride=16, |
||||
activation=nn.GELU, |
||||
): |
||||
"""Initializes a mask downsampler module for progressive downsampling and channel expansion.""" |
||||
super().__init__() |
||||
num_layers = int(math.log2(total_stride) // math.log2(stride)) |
||||
assert stride**num_layers == total_stride |
||||
self.encoder = nn.Sequential() |
||||
mask_in_chans, mask_out_chans = 1, 1 |
||||
for _ in range(num_layers): |
||||
mask_out_chans = mask_in_chans * (stride**2) |
||||
self.encoder.append( |
||||
nn.Conv2d( |
||||
mask_in_chans, |
||||
mask_out_chans, |
||||
kernel_size=kernel_size, |
||||
stride=stride, |
||||
padding=padding, |
||||
) |
||||
) |
||||
self.encoder.append(LayerNorm2d(mask_out_chans)) |
||||
self.encoder.append(activation()) |
||||
mask_in_chans = mask_out_chans |
||||
|
||||
self.encoder.append(nn.Conv2d(mask_out_chans, embed_dim, kernel_size=1)) |
||||
|
||||
def forward(self, x): |
||||
"""Downsamples and encodes input mask to embed_dim channels using convolutional layers and LayerNorm2d.""" |
||||
return self.encoder(x) |
||||
|
||||
|
||||
# Lightly adapted from ConvNext (https://github.com/facebookresearch/ConvNeXt) |
||||
class CXBlock(nn.Module): |
||||
""" |
||||
ConvNeXt Block for efficient feature extraction in convolutional neural networks. |
||||
|
||||
This block implements a modified version of the ConvNeXt architecture, offering two equivalent |
||||
implementations for improved performance and flexibility. |
||||
|
||||
Attributes: |
||||
dwconv (nn.Conv2d): Depthwise convolution layer. |
||||
norm (LayerNorm2d): Layer normalization applied to channels. |
||||
pwconv1 (nn.Linear): First pointwise convolution implemented as a linear layer. |
||||
act (nn.GELU): GELU activation function. |
||||
pwconv2 (nn.Linear): Second pointwise convolution implemented as a linear layer. |
||||
gamma (nn.Parameter | None): Learnable scale parameter for layer scaling. |
||||
drop_path (nn.Module): DropPath layer for stochastic depth regularization. |
||||
|
||||
Methods: |
||||
forward: Processes the input tensor through the ConvNeXt block. |
||||
|
||||
Examples: |
||||
>>> import torch |
||||
>>> x = torch.randn(1, 64, 56, 56) |
||||
>>> block = CXBlock(dim=64, kernel_size=7, padding=3) |
||||
>>> output = block(x) |
||||
>>> print(output.shape) |
||||
torch.Size([1, 64, 56, 56]) |
||||
""" |
||||
|
||||
def __init__( |
||||
self, |
||||
dim, |
||||
kernel_size=7, |
||||
padding=3, |
||||
drop_path=0.0, |
||||
layer_scale_init_value=1e-6, |
||||
use_dwconv=True, |
||||
): |
||||
""" |
||||
Initialize a ConvNeXt Block. |
||||
|
||||
This block implements a ConvNeXt architecture with optional depthwise convolution, layer normalization, |
||||
pointwise convolutions, and GELU activation. |
||||
|
||||
Args: |
||||
dim (int): Number of input channels. |
||||
kernel_size (int): Size of the convolutional kernel. Default is 7. |
||||
padding (int): Padding size for the convolution. Default is 3. |
||||
drop_path (float): Stochastic depth rate. Default is 0.0. |
||||
layer_scale_init_value (float): Initial value for Layer Scale. Default is 1e-6. |
||||
use_dwconv (bool): Whether to use depthwise convolution. Default is True. |
||||
|
||||
Attributes: |
||||
dwconv (nn.Conv2d): Depthwise or standard 2D convolution layer. |
||||
norm (LayerNorm2d): Layer normalization applied to the output of dwconv. |
||||
pwconv1 (nn.Linear): First pointwise convolution implemented as a linear layer. |
||||
act (nn.GELU): GELU activation function. |
||||
pwconv2 (nn.Linear): Second pointwise convolution implemented as a linear layer. |
||||
gamma (nn.Parameter | None): Learnable scale parameter for the residual path. |
||||
|
||||
Examples: |
||||
>>> block = CXBlock(dim=64, kernel_size=7, padding=3) |
||||
>>> x = torch.randn(1, 64, 32, 32) |
||||
>>> output = block(x) |
||||
>>> print(output.shape) |
||||
torch.Size([1, 64, 32, 32]) |
||||
""" |
||||
super().__init__() |
||||
self.dwconv = nn.Conv2d( |
||||
dim, |
||||
dim, |
||||
kernel_size=kernel_size, |
||||
padding=padding, |
||||
groups=dim if use_dwconv else 1, |
||||
) # depthwise conv |
||||
self.norm = LayerNorm2d(dim, eps=1e-6) |
||||
self.pwconv1 = nn.Linear(dim, 4 * dim) # pointwise/1x1 convs, implemented with linear layers |
||||
self.act = nn.GELU() |
||||
self.pwconv2 = nn.Linear(4 * dim, dim) |
||||
self.gamma = ( |
||||
nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True) |
||||
if layer_scale_init_value > 0 |
||||
else None |
||||
) |
||||
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() |
||||
|
||||
def forward(self, x): |
||||
"""Applies ConvNeXt block operations to input tensor, including convolutions and residual connection.""" |
||||
input = x |
||||
x = self.dwconv(x) |
||||
x = self.norm(x) |
||||
x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C) |
||||
x = self.pwconv1(x) |
||||
x = self.act(x) |
||||
x = self.pwconv2(x) |
||||
if self.gamma is not None: |
||||
x = self.gamma * x |
||||
x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W) |
||||
|
||||
x = input + self.drop_path(x) |
||||
return x |
||||
|
||||
|
||||
class Fuser(nn.Module): |
||||
""" |
||||
A module for fusing features through multiple layers of a neural network. |
||||
|
||||
This class applies a series of identical layers to an input tensor, optionally projecting the input first. |
||||
|
||||
Attributes: |
||||
proj (nn.Module): An optional input projection layer. Identity if no projection is needed. |
||||
layers (nn.ModuleList): A list of identical layers to be applied sequentially. |
||||
|
||||
Methods: |
||||
forward: Applies the fuser to an input tensor. |
||||
|
||||
Examples: |
||||
>>> layer = CXBlock(dim=256) |
||||
>>> fuser = Fuser(layer, num_layers=3, dim=256, input_projection=True) |
||||
>>> x = torch.randn(1, 256, 32, 32) |
||||
>>> output = fuser(x) |
||||
>>> print(output.shape) |
||||
torch.Size([1, 256, 32, 32]) |
||||
""" |
||||
|
||||
def __init__(self, layer, num_layers, dim=None, input_projection=False): |
||||
""" |
||||
Initializes the Fuser module. |
||||
|
||||
This module creates a sequence of identical layers and optionally applies an input projection. |
||||
|
||||
Args: |
||||
layer (nn.Module): The layer to be replicated in the fuser. |
||||
num_layers (int): The number of times to replicate the layer. |
||||
dim (int | None): The dimension for input projection, if used. |
||||
input_projection (bool): Whether to use input projection. |
||||
|
||||
Attributes: |
||||
proj (nn.Module): The input projection layer, or nn.Identity if not used. |
||||
layers (nn.ModuleList): A list of replicated layers. |
||||
|
||||
Examples: |
||||
>>> layer = nn.Linear(64, 64) |
||||
>>> fuser = Fuser(layer, num_layers=3, dim=64, input_projection=True) |
||||
>>> input_tensor = torch.randn(1, 64) |
||||
>>> output = fuser(input_tensor) |
||||
""" |
||||
super().__init__() |
||||
self.proj = nn.Identity() |
||||
self.layers = nn.ModuleList([copy.deepcopy(layer) for _ in range(num_layers)]) |
||||
|
||||
if input_projection: |
||||
assert dim is not None |
||||
self.proj = nn.Conv2d(dim, dim, kernel_size=1) |
||||
|
||||
def forward(self, x): |
||||
"""Applies a series of layers to the input tensor, optionally projecting it first.""" |
||||
x = self.proj(x) |
||||
for layer in self.layers: |
||||
x = layer(x) |
||||
return x |
||||
|
||||
|
||||
class TwoWayAttentionBlock(SAMTwoWayAttentionBlock): |
||||
""" |
||||
A two-way attention block for performing self-attention and cross-attention in both directions. |
||||
|
||||
This block extends the SAMTwoWayAttentionBlock and consists of four main components: self-attention on |
||||
sparse inputs, cross-attention from sparse to dense inputs, an MLP block on sparse inputs, and |
||||
cross-attention from dense to sparse inputs. |
||||
|
||||
Attributes: |
||||
self_attn (Attention): Self-attention layer for queries. |
||||
norm1 (nn.LayerNorm): Layer normalization after the first attention block. |
||||
cross_attn_token_to_image (Attention): Cross-attention layer from queries to keys. |
||||
norm2 (nn.LayerNorm): Layer normalization after the second attention block. |
||||
mlp (MLP): MLP block for transforming query embeddings. |
||||
norm3 (nn.LayerNorm): Layer normalization after the MLP block. |
||||
norm4 (nn.LayerNorm): Layer normalization after the third attention block. |
||||
cross_attn_image_to_token (Attention): Cross-attention layer from keys to queries. |
||||
skip_first_layer_pe (bool): Flag to skip positional encoding in the first layer. |
||||
|
||||
Methods: |
||||
forward: Processes input through the attention blocks and MLP. |
||||
|
||||
Examples: |
||||
>>> block = TwoWayAttentionBlock(embedding_dim=256, num_heads=8) |
||||
>>> sparse_input = torch.randn(1, 100, 256) |
||||
>>> dense_input = torch.randn(1, 256, 16, 16) |
||||
>>> sparse_output, dense_output = block(sparse_input, dense_input) |
||||
""" |
||||
|
||||
def __init__( |
||||
self, |
||||
embedding_dim: int, |
||||
num_heads: int, |
||||
mlp_dim: int = 2048, |
||||
activation: Type[nn.Module] = nn.ReLU, |
||||
attention_downsample_rate: int = 2, |
||||
skip_first_layer_pe: bool = False, |
||||
) -> None: |
||||
""" |
||||
Initializes a TwoWayAttentionBlock for performing self-attention and cross-attention in two directions. |
||||
|
||||
This block consists of four main layers: self-attention on sparse inputs, cross-attention of sparse inputs |
||||
to dense inputs, an MLP block on sparse inputs, and cross-attention of dense inputs to sparse inputs. |
||||
|
||||
Args: |
||||
embedding_dim (int): The channel dimension of the embeddings. |
||||
num_heads (int): The number of heads in the attention layers. |
||||
mlp_dim (int): The hidden dimension of the MLP block. |
||||
activation (Type[nn.Module]): The activation function of the MLP block. |
||||
attention_downsample_rate (int): The downsample rate for attention computations. |
||||
skip_first_layer_pe (bool): Whether to skip the positional encoding in the first layer. |
||||
|
||||
Attributes: |
||||
self_attn (Attention): The self-attention layer for the queries. |
||||
norm1 (nn.LayerNorm): Layer normalization following the first attention block. |
||||
cross_attn_token_to_image (Attention): Cross-attention layer from queries to keys. |
||||
norm2 (nn.LayerNorm): Layer normalization following the second attention block. |
||||
mlp (MLP): MLP block that transforms the query embeddings. |
||||
norm3 (nn.LayerNorm): Layer normalization following the MLP block. |
||||
norm4 (nn.LayerNorm): Layer normalization following the third attention block. |
||||
cross_attn_image_to_token (Attention): Cross-attention layer from keys to queries. |
||||
skip_first_layer_pe (bool): Whether to skip the positional encoding in the first layer. |
||||
|
||||
Examples: |
||||
>>> block = TwoWayAttentionBlock(embedding_dim=256, num_heads=8, mlp_dim=2048) |
||||
>>> sparse_inputs = torch.randn(1, 100, 256) |
||||
>>> dense_inputs = torch.randn(1, 256, 32, 32) |
||||
>>> sparse_outputs, dense_outputs = block(sparse_inputs, dense_inputs) |
||||
""" |
||||
super().__init__(embedding_dim, num_heads, mlp_dim, activation, attention_downsample_rate, skip_first_layer_pe) |
||||
self.mlp = MLP(embedding_dim, mlp_dim, embedding_dim, num_layers=2, act=activation) |
||||
|
||||
|
||||
class TwoWayTransformer(SAMTwoWayTransformer): |
||||
""" |
||||
A Two-Way Transformer module for simultaneous attention to image and query points. |
||||
|
||||
This class implements a specialized transformer decoder that attends to an input image using queries with |
||||
supplied positional embeddings. It is particularly useful for tasks like object detection, image |
||||
segmentation, and point cloud processing. |
||||
|
||||
Attributes: |
||||
depth (int): Number of layers in the transformer. |
||||
embedding_dim (int): Channel dimension for input embeddings. |
||||
num_heads (int): Number of heads for multihead attention. |
||||
mlp_dim (int): Internal channel dimension for the MLP block. |
||||
layers (nn.ModuleList): List of TwoWayAttentionBlock layers comprising the transformer. |
||||
final_attn_token_to_image (Attention): Final attention layer from queries to image. |
||||
norm_final_attn (nn.LayerNorm): Layer normalization applied to final queries. |
||||
|
||||
Methods: |
||||
forward: Processes input image embeddings and query embeddings through the transformer. |
||||
|
||||
Examples: |
||||
>>> transformer = TwoWayTransformer(depth=5, embedding_dim=256, num_heads=8, mlp_dim=2048) |
||||
>>> image_embedding = torch.randn(1, 256, 64, 64) |
||||
>>> query_embedding = torch.randn(1, 100, 256) |
||||
>>> output = transformer(image_embedding, query_embedding) |
||||
""" |
||||
|
||||
def __init__( |
||||
self, |
||||
depth: int, |
||||
embedding_dim: int, |
||||
num_heads: int, |
||||
mlp_dim: int, |
||||
activation: Type[nn.Module] = nn.ReLU, |
||||
attention_downsample_rate: int = 2, |
||||
) -> None: |
||||
""" |
||||
Initializes a TwoWayTransformer instance. |
||||
|
||||
This transformer decoder attends to an input image using queries with supplied positional embeddings. |
||||
It is designed for tasks like object detection, image segmentation, and point cloud processing. |
||||
|
||||
Args: |
||||
depth (int): Number of layers in the transformer. |
||||
embedding_dim (int): Channel dimension for the input embeddings. |
||||
num_heads (int): Number of heads for multihead attention. Must divide embedding_dim. |
||||
mlp_dim (int): Channel dimension internal to the MLP block. |
||||
activation (Type[nn.Module]): Activation function to use in the MLP block. |
||||
attention_downsample_rate (int): Downsampling rate for attention computations. |
||||
|
||||
Attributes: |
||||
depth (int): Number of layers in the transformer. |
||||
embedding_dim (int): Channel dimension for the input embeddings. |
||||
num_heads (int): Number of heads for multihead attention. |
||||
mlp_dim (int): Internal channel dimension for the MLP block. |
||||
layers (nn.ModuleList): List of TwoWayAttentionBlock layers comprising the transformer. |
||||
final_attn_token_to_image (Attention): Final attention layer from queries to image. |
||||
norm_final_attn (nn.LayerNorm): Layer normalization applied to the final queries. |
||||
|
||||
Examples: |
||||
>>> transformer = TwoWayTransformer(depth=5, embedding_dim=256, num_heads=8, mlp_dim=2048) |
||||
>>> transformer |
||||
TwoWayTransformer( |
||||
(layers): ModuleList( |
||||
(0-4): 5 x TwoWayAttentionBlock(...) |
||||
) |
||||
(final_attn_token_to_image): Attention(...) |
||||
(norm_final_attn): LayerNorm(...) |
||||
) |
||||
""" |
||||
super().__init__(depth, embedding_dim, num_heads, mlp_dim, activation, attention_downsample_rate) |
||||
self.layers = nn.ModuleList() |
||||
for i in range(depth): |
||||
self.layers.append( |
||||
TwoWayAttentionBlock( |
||||
embedding_dim=embedding_dim, |
||||
num_heads=num_heads, |
||||
mlp_dim=mlp_dim, |
||||
activation=activation, |
||||
attention_downsample_rate=attention_downsample_rate, |
||||
skip_first_layer_pe=(i == 0), |
||||
) |
||||
) |
||||
|
||||
|
||||
class RoPEAttention(Attention): |
||||
"""Implements rotary position encoding for attention mechanisms in transformer architectures.""" |
||||
|
||||
def __init__( |
||||
self, |
||||
*args, |
||||
rope_theta=10000.0, |
||||
# whether to repeat q rope to match k length |
||||
# this is needed for cross-attention to memories |
||||
rope_k_repeat=False, |
||||
feat_sizes=(32, 32), # [w, h] for stride 16 feats at 512 resolution |
||||
**kwargs, |
||||
): |
||||
"""Initializes RoPEAttention with rotary position encoding for attention mechanisms.""" |
||||
super().__init__(*args, **kwargs) |
||||
|
||||
self.compute_cis = partial(compute_axial_cis, dim=self.internal_dim // self.num_heads, theta=rope_theta) |
||||
freqs_cis = self.compute_cis(end_x=feat_sizes[0], end_y=feat_sizes[1]) |
||||
self.freqs_cis = freqs_cis |
||||
self.rope_k_repeat = rope_k_repeat |
||||
|
||||
def forward(self, q: Tensor, k: Tensor, v: Tensor, num_k_exclude_rope: int = 0) -> Tensor: |
||||
"""Applies rotary position encoding and computes attention between query, key, and value tensors.""" |
||||
q = self.q_proj(q) |
||||
k = self.k_proj(k) |
||||
v = self.v_proj(v) |
||||
|
||||
# Separate into heads |
||||
q = self._separate_heads(q, self.num_heads) |
||||
k = self._separate_heads(k, self.num_heads) |
||||
v = self._separate_heads(v, self.num_heads) |
||||
|
||||
# Apply rotary position encoding |
||||
w = h = math.sqrt(q.shape[-2]) |
||||
self.freqs_cis = self.freqs_cis.to(q.device) |
||||
if self.freqs_cis.shape[0] != q.shape[-2]: |
||||
self.freqs_cis = self.compute_cis(end_x=w, end_y=h).to(q.device) |
||||
if q.shape[-2] != k.shape[-2]: |
||||
assert self.rope_k_repeat |
||||
|
||||
num_k_rope = k.size(-2) - num_k_exclude_rope |
||||
q, k[:, :, :num_k_rope] = apply_rotary_enc( |
||||
q, |
||||
k[:, :, :num_k_rope], |
||||
freqs_cis=self.freqs_cis, |
||||
repeat_freqs_k=self.rope_k_repeat, |
||||
) |
||||
|
||||
# Attention |
||||
_, _, _, c_per_head = q.shape |
||||
attn = q @ k.permute(0, 1, 3, 2) # B x N_heads x N_tokens x N_tokens |
||||
attn = attn / math.sqrt(c_per_head) |
||||
attn = torch.softmax(attn, dim=-1) |
||||
|
||||
# Get output |
||||
out = attn @ v |
||||
|
||||
out = self._recombine_heads(out) |
||||
out = self.out_proj(out) |
||||
|
||||
return out |
||||
|
||||
|
||||
def do_pool(x: torch.Tensor, pool: nn.Module, norm: nn.Module = None) -> torch.Tensor: |
||||
"""Applies pooling and optional normalization to a tensor, handling permutations for spatial operations.""" |
||||
if pool is None: |
||||
return x |
||||
# (B, H, W, C) -> (B, C, H, W) |
||||
x = x.permute(0, 3, 1, 2) |
||||
x = pool(x) |
||||
# (B, C, H', W') -> (B, H', W', C) |
||||
x = x.permute(0, 2, 3, 1) |
||||
if norm: |
||||
x = norm(x) |
||||
|
||||
return x |
||||
|
||||
|
||||
class MultiScaleAttention(nn.Module): |
||||
"""Implements multi-scale self-attention with optional query pooling for efficient feature extraction.""" |
||||
|
||||
def __init__( |
||||
self, |
||||
dim: int, |
||||
dim_out: int, |
||||
num_heads: int, |
||||
q_pool: nn.Module = None, |
||||
): |
||||
"""Initializes a multi-scale attention module with configurable query pooling and linear projections.""" |
||||
super().__init__() |
||||
|
||||
self.dim = dim |
||||
self.dim_out = dim_out |
||||
|
||||
self.num_heads = num_heads |
||||
head_dim = dim_out // num_heads |
||||
self.scale = head_dim**-0.5 |
||||
|
||||
self.q_pool = q_pool |
||||
self.qkv = nn.Linear(dim, dim_out * 3) |
||||
self.proj = nn.Linear(dim_out, dim_out) |
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor: |
||||
"""Applies multi-scale attention to input tensor, optionally downsampling query features.""" |
||||
B, H, W, _ = x.shape |
||||
# qkv with shape (B, H * W, 3, nHead, C) |
||||
qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1) |
||||
# q, k, v with shape (B, H * W, nheads, C) |
||||
q, k, v = torch.unbind(qkv, 2) |
||||
|
||||
# Q pooling (for downsample at stage changes) |
||||
if self.q_pool: |
||||
q = do_pool(q.reshape(B, H, W, -1), self.q_pool) |
||||
H, W = q.shape[1:3] # downsampled shape |
||||
q = q.reshape(B, H * W, self.num_heads, -1) |
||||
|
||||
# Torch's SDPA expects [B, nheads, H*W, C] so we transpose |
||||
x = F.scaled_dot_product_attention( |
||||
q.transpose(1, 2), |
||||
k.transpose(1, 2), |
||||
v.transpose(1, 2), |
||||
) |
||||
# Transpose back |
||||
x = x.transpose(1, 2) |
||||
x = x.reshape(B, H, W, -1) |
||||
|
||||
x = self.proj(x) |
||||
|
||||
return x |
||||
|
||||
|
||||
class MultiScaleBlock(nn.Module): |
||||
"""Multiscale attention block with window partitioning and query pooling for efficient vision transformers.""" |
||||
|
||||
def __init__( |
||||
self, |
||||
dim: int, |
||||
dim_out: int, |
||||
num_heads: int, |
||||
mlp_ratio: float = 4.0, |
||||
drop_path: float = 0.0, |
||||
norm_layer: Union[nn.Module, str] = "LayerNorm", |
||||
q_stride: Tuple[int, int] = None, |
||||
act_layer: nn.Module = nn.GELU, |
||||
window_size: int = 0, |
||||
): |
||||
"""Initializes a multi-scale attention block with optional window partitioning and downsampling.""" |
||||
super().__init__() |
||||
|
||||
if isinstance(norm_layer, str): |
||||
norm_layer = partial(getattr(nn, norm_layer), eps=1e-6) |
||||
|
||||
self.dim = dim |
||||
self.dim_out = dim_out |
||||
self.norm1 = norm_layer(dim) |
||||
|
||||
self.window_size = window_size |
||||
|
||||
self.pool, self.q_stride = None, q_stride |
||||
if self.q_stride: |
||||
self.pool = nn.MaxPool2d(kernel_size=q_stride, stride=q_stride, ceil_mode=False) |
||||
|
||||
self.attn = MultiScaleAttention( |
||||
dim, |
||||
dim_out, |
||||
num_heads=num_heads, |
||||
q_pool=self.pool, |
||||
) |
||||
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() |
||||
|
||||
self.norm2 = norm_layer(dim_out) |
||||
self.mlp = MLP( |
||||
dim_out, |
||||
int(dim_out * mlp_ratio), |
||||
dim_out, |
||||
num_layers=2, |
||||
act=act_layer, |
||||
) |
||||
|
||||
if dim != dim_out: |
||||
self.proj = nn.Linear(dim, dim_out) |
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor: |
||||
"""Applies multi-scale attention and MLP processing to input tensor, with optional windowing.""" |
||||
shortcut = x # B, H, W, C |
||||
x = self.norm1(x) |
||||
|
||||
# Skip connection |
||||
if self.dim != self.dim_out: |
||||
shortcut = do_pool(self.proj(x), self.pool) |
||||
|
||||
# Window partition |
||||
window_size = self.window_size |
||||
if window_size > 0: |
||||
H, W = x.shape[1], x.shape[2] |
||||
x, pad_hw = window_partition(x, window_size) |
||||
|
||||
# Window Attention + Q Pooling (if stage change) |
||||
x = self.attn(x) |
||||
if self.q_stride: |
||||
# Shapes have changed due to Q pooling |
||||
window_size = self.window_size // self.q_stride[0] |
||||
H, W = shortcut.shape[1:3] |
||||
|
||||
pad_h = (window_size - H % window_size) % window_size |
||||
pad_w = (window_size - W % window_size) % window_size |
||||
pad_hw = (H + pad_h, W + pad_w) |
||||
|
||||
# Reverse window partition |
||||
if self.window_size > 0: |
||||
x = window_unpartition(x, window_size, pad_hw, (H, W)) |
||||
|
||||
x = shortcut + self.drop_path(x) |
||||
# MLP |
||||
x = x + self.drop_path(self.mlp(self.norm2(x))) |
||||
return x |
||||
|
||||
|
||||
class PositionEmbeddingSine(nn.Module): |
||||
"""Generates sinusoidal positional embeddings for 2D inputs like images.""" |
||||
|
||||
def __init__( |
||||
self, |
||||
num_pos_feats, |
||||
temperature: int = 10000, |
||||
normalize: bool = True, |
||||
scale: Optional[float] = None, |
||||
): |
||||
"""Initializes sinusoidal position embeddings for 2D image inputs.""" |
||||
super().__init__() |
||||
assert num_pos_feats % 2 == 0, "Expecting even model width" |
||||
self.num_pos_feats = num_pos_feats // 2 |
||||
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 |
||||
|
||||
self.cache = {} |
||||
|
||||
def _encode_xy(self, x, y): |
||||
"""Encodes 2D positions using sine and cosine functions for positional embeddings.""" |
||||
assert len(x) == len(y) and x.ndim == y.ndim == 1 |
||||
x_embed = x * self.scale |
||||
y_embed = y * 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=2).flatten(1) |
||||
pos_y = torch.stack((pos_y[:, 0::2].sin(), pos_y[:, 1::2].cos()), dim=2).flatten(1) |
||||
return pos_x, pos_y |
||||
|
||||
@torch.no_grad() |
||||
def encode_boxes(self, x, y, w, h): |
||||
"""Encodes box coordinates and dimensions into positional embeddings for object detection tasks.""" |
||||
pos_x, pos_y = self._encode_xy(x, y) |
||||
pos = torch.cat((pos_y, pos_x, h[:, None], w[:, None]), dim=1) |
||||
return pos |
||||
|
||||
encode = encode_boxes # Backwards compatibility |
||||
|
||||
@torch.no_grad() |
||||
def encode_points(self, x, y, labels): |
||||
"""Encodes 2D point coordinates with sinusoidal positional embeddings and appends labels.""" |
||||
(bx, nx), (by, ny), (bl, nl) = x.shape, y.shape, labels.shape |
||||
assert bx == by and nx == ny and bx == bl and nx == nl |
||||
pos_x, pos_y = self._encode_xy(x.flatten(), y.flatten()) |
||||
pos_x, pos_y = pos_x.reshape(bx, nx, -1), pos_y.reshape(by, ny, -1) |
||||
pos = torch.cat((pos_y, pos_x, labels[:, :, None]), dim=2) |
||||
return pos |
||||
|
||||
@torch.no_grad() |
||||
def forward(self, x: torch.Tensor): |
||||
"""Generate sinusoidal position embeddings for 2D inputs.""" |
||||
cache_key = (x.shape[-2], x.shape[-1]) |
||||
if cache_key in self.cache: |
||||
return self.cache[cache_key][None].repeat(x.shape[0], 1, 1, 1) |
||||
y_embed = ( |
||||
torch.arange(1, x.shape[-2] + 1, dtype=torch.float32, device=x.device) |
||||
.view(1, -1, 1) |
||||
.repeat(x.shape[0], 1, x.shape[-1]) |
||||
) |
||||
x_embed = ( |
||||
torch.arange(1, x.shape[-1] + 1, dtype=torch.float32, device=x.device) |
||||
.view(1, 1, -1) |
||||
.repeat(x.shape[0], x.shape[-2], 1) |
||||
) |
||||
|
||||
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_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) |
||||
self.cache[cache_key] = pos[0] |
||||
return pos |
||||
@ -0,0 +1,191 @@ |
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license |
||||
|
||||
import torch |
||||
import torch.nn.functional as F |
||||
|
||||
|
||||
def select_closest_cond_frames(frame_idx, cond_frame_outputs, max_cond_frame_num): |
||||
""" |
||||
Selects the closest conditioning frames to a given frame index. |
||||
|
||||
Args: |
||||
frame_idx (int): Current frame index. |
||||
cond_frame_outputs (Dict[int, Any]): Dictionary of conditioning frame outputs keyed by frame indices. |
||||
max_cond_frame_num (int): Maximum number of conditioning frames to select. |
||||
|
||||
Returns: |
||||
(Tuple[Dict[int, Any], Dict[int, Any]]): A tuple containing two dictionaries: |
||||
- selected_outputs: Selected items from cond_frame_outputs. |
||||
- unselected_outputs: Items not selected from cond_frame_outputs. |
||||
|
||||
Examples: |
||||
>>> frame_idx = 5 |
||||
>>> cond_frame_outputs = {1: 'a', 3: 'b', 7: 'c', 9: 'd'} |
||||
>>> max_cond_frame_num = 2 |
||||
>>> selected, unselected = select_closest_cond_frames(frame_idx, cond_frame_outputs, max_cond_frame_num) |
||||
>>> print(selected) |
||||
{3: 'b', 7: 'c'} |
||||
>>> print(unselected) |
||||
{1: 'a', 9: 'd'} |
||||
""" |
||||
if max_cond_frame_num == -1 or len(cond_frame_outputs) <= max_cond_frame_num: |
||||
selected_outputs = cond_frame_outputs |
||||
unselected_outputs = {} |
||||
else: |
||||
assert max_cond_frame_num >= 2, "we should allow using 2+ conditioning frames" |
||||
selected_outputs = {} |
||||
|
||||
# the closest conditioning frame before `frame_idx` (if any) |
||||
idx_before = max((t for t in cond_frame_outputs if t < frame_idx), default=None) |
||||
if idx_before is not None: |
||||
selected_outputs[idx_before] = cond_frame_outputs[idx_before] |
||||
|
||||
# the closest conditioning frame after `frame_idx` (if any) |
||||
idx_after = min((t for t in cond_frame_outputs if t >= frame_idx), default=None) |
||||
if idx_after is not None: |
||||
selected_outputs[idx_after] = cond_frame_outputs[idx_after] |
||||
|
||||
# add other temporally closest conditioning frames until reaching a total |
||||
# of `max_cond_frame_num` conditioning frames. |
||||
num_remain = max_cond_frame_num - len(selected_outputs) |
||||
inds_remain = sorted( |
||||
(t for t in cond_frame_outputs if t not in selected_outputs), |
||||
key=lambda x: abs(x - frame_idx), |
||||
)[:num_remain] |
||||
selected_outputs.update((t, cond_frame_outputs[t]) for t in inds_remain) |
||||
unselected_outputs = {t: v for t, v in cond_frame_outputs.items() if t not in selected_outputs} |
||||
|
||||
return selected_outputs, unselected_outputs |
||||
|
||||
|
||||
def get_1d_sine_pe(pos_inds, dim, temperature=10000): |
||||
"""Generates 1D sinusoidal positional embeddings for given positions and dimensions.""" |
||||
pe_dim = dim // 2 |
||||
dim_t = torch.arange(pe_dim, dtype=torch.float32, device=pos_inds.device) |
||||
dim_t = temperature ** (2 * (dim_t // 2) / pe_dim) |
||||
|
||||
pos_embed = pos_inds.unsqueeze(-1) / dim_t |
||||
pos_embed = torch.cat([pos_embed.sin(), pos_embed.cos()], dim=-1) |
||||
return pos_embed |
||||
|
||||
|
||||
def init_t_xy(end_x: int, end_y: int): |
||||
"""Initializes 1D and 2D coordinate tensors for a grid of size end_x by end_y.""" |
||||
t = torch.arange(end_x * end_y, dtype=torch.float32) |
||||
t_x = (t % end_x).float() |
||||
t_y = torch.div(t, end_x, rounding_mode="floor").float() |
||||
return t_x, t_y |
||||
|
||||
|
||||
def compute_axial_cis(dim: int, end_x: int, end_y: int, theta: float = 10000.0): |
||||
"""Computes axial complex exponential positional encodings for 2D spatial positions.""" |
||||
freqs_x = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim)) |
||||
freqs_y = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim)) |
||||
|
||||
t_x, t_y = init_t_xy(end_x, end_y) |
||||
freqs_x = torch.outer(t_x, freqs_x) |
||||
freqs_y = torch.outer(t_y, freqs_y) |
||||
freqs_cis_x = torch.polar(torch.ones_like(freqs_x), freqs_x) |
||||
freqs_cis_y = torch.polar(torch.ones_like(freqs_y), freqs_y) |
||||
return torch.cat([freqs_cis_x, freqs_cis_y], dim=-1) |
||||
|
||||
|
||||
def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor): |
||||
"""Reshapes frequency tensor for broadcasting, ensuring compatibility with input tensor dimensions.""" |
||||
ndim = x.ndim |
||||
assert 0 <= 1 < ndim |
||||
assert freqs_cis.shape == (x.shape[-2], x.shape[-1]) |
||||
shape = [d if i >= ndim - 2 else 1 for i, d in enumerate(x.shape)] |
||||
return freqs_cis.view(*shape) |
||||
|
||||
|
||||
def apply_rotary_enc( |
||||
xq: torch.Tensor, |
||||
xk: torch.Tensor, |
||||
freqs_cis: torch.Tensor, |
||||
repeat_freqs_k: bool = False, |
||||
): |
||||
"""Applies rotary positional encoding to query and key tensors using complex-valued frequency components.""" |
||||
xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) |
||||
xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) if xk.shape[-2] != 0 else None |
||||
freqs_cis = reshape_for_broadcast(freqs_cis, xq_) |
||||
xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3) |
||||
if xk_ is None: |
||||
# no keys to rotate, due to dropout |
||||
return xq_out.type_as(xq).to(xq.device), xk |
||||
# repeat freqs along seq_len dim to match k seq_len |
||||
if repeat_freqs_k: |
||||
r = xk_.shape[-2] // xq_.shape[-2] |
||||
freqs_cis = freqs_cis.repeat(*([1] * (freqs_cis.ndim - 2)), r, 1) |
||||
xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3) |
||||
return xq_out.type_as(xq).to(xq.device), xk_out.type_as(xk).to(xk.device) |
||||
|
||||
|
||||
def window_partition(x, window_size): |
||||
""" |
||||
Partitions input tensor into non-overlapping windows with padding if needed. |
||||
|
||||
Args: |
||||
x (torch.Tensor): Input tensor with shape (B, H, W, C). |
||||
window_size (int): Size of each window. |
||||
|
||||
Returns: |
||||
(Tuple[torch.Tensor, Tuple[int, int]]): A tuple containing: |
||||
- windows (torch.Tensor): Partitioned windows with shape (B * num_windows, window_size, window_size, C). |
||||
- (Hp, Wp) (Tuple[int, int]): Padded height and width before partition. |
||||
|
||||
Examples: |
||||
>>> x = torch.randn(1, 16, 16, 3) |
||||
>>> windows, (Hp, Wp) = window_partition(x, window_size=4) |
||||
>>> print(windows.shape, Hp, Wp) |
||||
torch.Size([16, 4, 4, 3]) 16 16 |
||||
""" |
||||
B, H, W, C = x.shape |
||||
|
||||
pad_h = (window_size - H % window_size) % window_size |
||||
pad_w = (window_size - W % window_size) % window_size |
||||
if pad_h > 0 or pad_w > 0: |
||||
x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h)) |
||||
Hp, Wp = H + pad_h, W + pad_w |
||||
|
||||
x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C) |
||||
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) |
||||
return windows, (Hp, Wp) |
||||
|
||||
|
||||
def window_unpartition(windows, window_size, pad_hw, hw): |
||||
""" |
||||
Unpartitions windowed sequences into original sequences and removes padding. |
||||
|
||||
This function reverses the windowing process, reconstructing the original input from windowed segments |
||||
and removing any padding that was added during the windowing process. |
||||
|
||||
Args: |
||||
windows (torch.Tensor): Input tensor of windowed sequences with shape (B * num_windows, window_size, |
||||
window_size, C), where B is the batch size, num_windows is the number of windows, window_size is |
||||
the size of each window, and C is the number of channels. |
||||
window_size (int): Size of each window. |
||||
pad_hw (Tuple[int, int]): Padded height and width (Hp, Wp) of the input before windowing. |
||||
hw (Tuple[int, int]): Original height and width (H, W) of the input before padding and windowing. |
||||
|
||||
Returns: |
||||
(torch.Tensor): Unpartitioned sequences with shape (B, H, W, C), where B is the batch size, H and W |
||||
are the original height and width, and C is the number of channels. |
||||
|
||||
Examples: |
||||
>>> windows = torch.rand(32, 8, 8, 64) # 32 windows of size 8x8 with 64 channels |
||||
>>> pad_hw = (16, 16) # Padded height and width |
||||
>>> hw = (15, 14) # Original height and width |
||||
>>> x = window_unpartition(windows, window_size=8, pad_hw=pad_hw, hw=hw) |
||||
>>> print(x.shape) |
||||
torch.Size([1, 15, 14, 64]) |
||||
""" |
||||
Hp, Wp = pad_hw |
||||
H, W = hw |
||||
B = windows.shape[0] // (Hp * Wp // window_size // window_size) |
||||
x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1) |
||||
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1) |
||||
|
||||
if Hp > H or Wp > W: |
||||
x = x[:, :H, :W, :].contiguous() |
||||
return x |
||||
@ -0,0 +1,182 @@ |
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license |
||||
|
||||
import torch |
||||
|
||||
from ..sam.predict import Predictor |
||||
from .build import build_sam2 |
||||
|
||||
|
||||
class SAM2Predictor(Predictor): |
||||
""" |
||||
A predictor class for the Segment Anything Model 2 (SAM2), extending the base Predictor class. |
||||
|
||||
This class provides an interface for model inference tailored to image segmentation tasks, leveraging SAM2's |
||||
advanced architecture and promptable segmentation capabilities. It facilitates flexible and real-time mask |
||||
generation, working with various types of prompts such as bounding boxes, points, and low-resolution masks. |
||||
|
||||
Attributes: |
||||
cfg (Dict): Configuration dictionary specifying model and task-related parameters. |
||||
overrides (Dict): Dictionary containing values that override the default configuration. |
||||
_callbacks (Dict): Dictionary of user-defined callback functions to augment behavior. |
||||
args (namespace): Namespace to hold command-line arguments or other operational variables. |
||||
im (torch.Tensor): Preprocessed input image tensor. |
||||
features (torch.Tensor): Extracted image features used for inference. |
||||
prompts (Dict): Collection of various prompt types, such as bounding boxes and points. |
||||
segment_all (bool): Flag to control whether to segment all objects in the image or only specified ones. |
||||
model (torch.nn.Module): The loaded SAM2 model. |
||||
device (torch.device): The device (CPU or GPU) on which the model is loaded. |
||||
_bb_feat_sizes (List[Tuple[int, int]]): List of feature sizes for different backbone levels. |
||||
|
||||
Methods: |
||||
get_model: Builds and returns the SAM2 model. |
||||
prompt_inference: Performs image segmentation inference based on various prompts. |
||||
set_image: Preprocesses and sets a single image for inference. |
||||
get_im_features: Extracts image features from the SAM2 image encoder. |
||||
|
||||
Examples: |
||||
>>> predictor = SAM2Predictor(model='sam2_l.pt') |
||||
>>> predictor.set_image('path/to/image.jpg') |
||||
>>> masks, scores = predictor.prompt_inference(im=predictor.im, points=[[500, 375]], labels=[1]) |
||||
>>> print(f"Generated {len(masks)} mask(s) with scores: {scores}") |
||||
""" |
||||
|
||||
_bb_feat_sizes = [ |
||||
(256, 256), |
||||
(128, 128), |
||||
(64, 64), |
||||
] |
||||
|
||||
def get_model(self): |
||||
"""Retrieves and initializes the Segment Anything Model (SAM) for image segmentation tasks.""" |
||||
return build_sam2(self.args.model) |
||||
|
||||
def prompt_inference( |
||||
self, |
||||
im, |
||||
bboxes=None, |
||||
points=None, |
||||
labels=None, |
||||
masks=None, |
||||
multimask_output=False, |
||||
img_idx=-1, |
||||
): |
||||
""" |
||||
Performs image segmentation inference based on various prompts using SAM2 architecture. |
||||
|
||||
Args: |
||||
im (torch.Tensor): Preprocessed input image tensor with shape (N, C, H, W). |
||||
bboxes (np.ndarray | List | None): Bounding boxes in XYXY format with shape (N, 4). |
||||
points (np.ndarray | List | None): Points indicating object locations with shape (N, 2), in pixels. |
||||
labels (np.ndarray | List | None): Labels for point prompts with shape (N,). 1 = foreground, 0 = background. |
||||
masks (np.ndarray | None): Low-resolution masks from previous predictions with shape (N, H, W). |
||||
multimask_output (bool): Flag to return multiple masks for ambiguous prompts. |
||||
img_idx (int): Index of the image in the batch to process. |
||||
|
||||
Returns: |
||||
(tuple): Tuple containing: |
||||
- np.ndarray: Output masks with shape (C, H, W), where C is the number of generated masks. |
||||
- np.ndarray: Quality scores for each mask, with length C. |
||||
- np.ndarray: Low-resolution logits with shape (C, 256, 256) for subsequent inference. |
||||
|
||||
Examples: |
||||
>>> predictor = SAM2Predictor(cfg) |
||||
>>> image = torch.rand(1, 3, 640, 640) |
||||
>>> bboxes = [[100, 100, 200, 200]] |
||||
>>> masks, scores, logits = predictor.prompt_inference(image, bboxes=bboxes) |
||||
""" |
||||
features = self.get_im_features(im) if self.features is None else self.features |
||||
|
||||
src_shape, dst_shape = self.batch[1][0].shape[:2], im.shape[2:] |
||||
r = 1.0 if self.segment_all else min(dst_shape[0] / src_shape[0], dst_shape[1] / src_shape[1]) |
||||
# Transform input prompts |
||||
if points is not None: |
||||
points = torch.as_tensor(points, dtype=torch.float32, device=self.device) |
||||
points = points[None] if points.ndim == 1 else points |
||||
# Assuming labels are all positive if users don't pass labels. |
||||
if labels is None: |
||||
labels = torch.ones(points.shape[0]) |
||||
labels = torch.as_tensor(labels, dtype=torch.int32, device=self.device) |
||||
points *= r |
||||
# (N, 2) --> (N, 1, 2), (N, ) --> (N, 1) |
||||
points, labels = points[:, None], labels[:, None] |
||||
if bboxes is not None: |
||||
bboxes = torch.as_tensor(bboxes, dtype=torch.float32, device=self.device) |
||||
bboxes = bboxes[None] if bboxes.ndim == 1 else bboxes |
||||
bboxes *= r |
||||
if masks is not None: |
||||
masks = torch.as_tensor(masks, dtype=torch.float32, device=self.device).unsqueeze(1) |
||||
|
||||
points = (points, labels) if points is not None else None |
||||
# TODO: Embed prompts |
||||
# if bboxes is not None: |
||||
# box_coords = bboxes.reshape(-1, 2, 2) |
||||
# box_labels = torch.tensor([[2, 3]], dtype=torch.int, device=bboxes.device) |
||||
# box_labels = box_labels.repeat(bboxes.size(0), 1) |
||||
# # we merge "boxes" and "points" into a single "concat_points" input (where |
||||
# # boxes are added at the beginning) to sam_prompt_encoder |
||||
# if concat_points is not None: |
||||
# concat_coords = torch.cat([box_coords, concat_points[0]], dim=1) |
||||
# concat_labels = torch.cat([box_labels, concat_points[1]], dim=1) |
||||
# concat_points = (concat_coords, concat_labels) |
||||
# else: |
||||
# concat_points = (box_coords, box_labels) |
||||
|
||||
sparse_embeddings, dense_embeddings = self.model.sam_prompt_encoder( |
||||
points=points, |
||||
boxes=bboxes, |
||||
masks=masks, |
||||
) |
||||
# Predict masks |
||||
batched_mode = points is not None and points[0].shape[0] > 1 # multi object prediction |
||||
high_res_features = [feat_level[img_idx].unsqueeze(0) for feat_level in features["high_res_feats"]] |
||||
pred_masks, pred_scores, _, _ = self.model.sam_mask_decoder( |
||||
image_embeddings=features["image_embed"][img_idx].unsqueeze(0), |
||||
image_pe=self.model.sam_prompt_encoder.get_dense_pe(), |
||||
sparse_prompt_embeddings=sparse_embeddings, |
||||
dense_prompt_embeddings=dense_embeddings, |
||||
multimask_output=multimask_output, |
||||
repeat_image=batched_mode, |
||||
high_res_features=high_res_features, |
||||
) |
||||
# (N, d, H, W) --> (N*d, H, W), (N, d) --> (N*d, ) |
||||
# `d` could be 1 or 3 depends on `multimask_output`. |
||||
return pred_masks.flatten(0, 1), pred_scores.flatten(0, 1) |
||||
|
||||
def set_image(self, image): |
||||
""" |
||||
Preprocesses and sets a single image for inference. |
||||
|
||||
This function sets up the model if not already initialized, configures the data source to the specified image, |
||||
and preprocesses the image for feature extraction. Only one image can be set at a time. |
||||
|
||||
Args: |
||||
image (str | np.ndarray): Image file path as a string, or a numpy array image read by cv2. |
||||
|
||||
Raises: |
||||
AssertionError: If more than one image is set. |
||||
|
||||
Examples: |
||||
>>> predictor = SAM2Predictor() |
||||
>>> predictor.set_image("path/to/image.jpg") |
||||
>>> predictor.set_image(np.array([...])) # Using a numpy array |
||||
""" |
||||
if self.model is None: |
||||
self.setup_model(model=None) |
||||
self.setup_source(image) |
||||
assert len(self.dataset) == 1, "`set_image` only supports setting one image!" |
||||
for batch in self.dataset: |
||||
im = self.preprocess(batch[1]) |
||||
self.features = self.get_im_features(im) |
||||
break |
||||
|
||||
def get_im_features(self, im): |
||||
"""Extracts and processes image features using SAM2's image encoder for subsequent segmentation tasks.""" |
||||
backbone_out = self.model.forward_image(im) |
||||
_, vision_feats, _, _ = self.model._prepare_backbone_features(backbone_out) |
||||
if self.model.directly_add_no_mem_embed: |
||||
vision_feats[-1] = vision_feats[-1] + self.model.no_mem_embed |
||||
feats = [ |
||||
feat.permute(1, 2, 0).view(1, -1, *feat_size) |
||||
for feat, feat_size in zip(vision_feats[::-1], self._bb_feat_sizes[::-1]) |
||||
][::-1] |
||||
return {"image_embed": feats[-1], "high_res_feats": feats[:-1]} |
||||
Loading…
Reference in new issue