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15 Commits
fix/11_sim
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main
| Author | SHA1 | Date |
|---|---|---|
Shilong Liu
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9dac4c605b | 2 years ago |
SlongLiu
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3bb2c86c9a | 2 years ago |
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SlongLiu
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d3bc35fdea | 2 years ago |
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SlongLiu
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15ade007a8 | 2 years ago |
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Shilong Liu
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22292c4b78 | 2 years ago |
rentainhe
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4c8f9206b6 | 2 years ago |
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rentainhe
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97ad9935ac | 2 years ago |
George Pearse
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e93548c805 | 2 years ago |
Piotr Skalski
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e45c11c4c3 | 2 years ago |
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Piotr Skalski
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f6b1145481 | 2 years ago |
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SlongLiu
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3023d1a26f | 2 years ago |
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SlongLiu
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a02cf79301 | 2 years ago |
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SlongLiu
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67a3c1940d | 2 years ago |
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SlongLiu
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ac00bd4a36 | 2 years ago |
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Piotr Skalski
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c974f60d73 | 2 years ago |
9 changed files with 1701 additions and 29 deletions
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import argparse |
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from functools import partial |
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import cv2 |
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import requests |
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import os |
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from io import BytesIO |
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from PIL import Image |
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import numpy as np |
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from pathlib import Path |
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import warnings |
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import torch |
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# prepare the environment |
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os.system("python setup.py build develop --user") |
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os.system("pip install packaging==21.3") |
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os.system("pip install gradio") |
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warnings.filterwarnings("ignore") |
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import gradio as gr |
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from groundingdino.models import build_model |
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from groundingdino.util.slconfig import SLConfig |
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from groundingdino.util.utils import clean_state_dict |
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from groundingdino.util.inference import annotate, load_image, predict |
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import groundingdino.datasets.transforms as T |
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from huggingface_hub import hf_hub_download |
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# Use this command for evaluate the Grounding DINO model |
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config_file = "groundingdino/config/GroundingDINO_SwinT_OGC.py" |
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ckpt_repo_id = "ShilongLiu/GroundingDINO" |
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ckpt_filenmae = "groundingdino_swint_ogc.pth" |
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def load_model_hf(model_config_path, repo_id, filename, device='cpu'): |
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args = SLConfig.fromfile(model_config_path) |
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model = build_model(args) |
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args.device = device |
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cache_file = hf_hub_download(repo_id=repo_id, filename=filename) |
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checkpoint = torch.load(cache_file, map_location='cpu') |
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log = model.load_state_dict(clean_state_dict(checkpoint['model']), strict=False) |
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print("Model loaded from {} \n => {}".format(cache_file, log)) |
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_ = model.eval() |
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return model |
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def image_transform_grounding(init_image): |
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transform = T.Compose([ |
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T.RandomResize([800], max_size=1333), |
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T.ToTensor(), |
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T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) |
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]) |
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image, _ = transform(init_image, None) # 3, h, w |
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return init_image, image |
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def image_transform_grounding_for_vis(init_image): |
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transform = T.Compose([ |
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T.RandomResize([800], max_size=1333), |
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]) |
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image, _ = transform(init_image, None) # 3, h, w |
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return image |
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model = load_model_hf(config_file, ckpt_repo_id, ckpt_filenmae) |
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def run_grounding(input_image, grounding_caption, box_threshold, text_threshold): |
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init_image = input_image.convert("RGB") |
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original_size = init_image.size |
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_, image_tensor = image_transform_grounding(init_image) |
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image_pil: Image = image_transform_grounding_for_vis(init_image) |
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# run grounidng |
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boxes, logits, phrases = predict(model, image_tensor, grounding_caption, box_threshold, text_threshold, device='cpu') |
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annotated_frame = annotate(image_source=np.asarray(image_pil), boxes=boxes, logits=logits, phrases=phrases) |
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image_with_box = Image.fromarray(cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB)) |
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return image_with_box |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser("Grounding DINO demo", add_help=True) |
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parser.add_argument("--debug", action="store_true", help="using debug mode") |
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parser.add_argument("--share", action="store_true", help="share the app") |
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args = parser.parse_args() |
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block = gr.Blocks().queue() |
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with block: |
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gr.Markdown("# [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO)") |
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gr.Markdown("### Open-World Detection with Grounding DINO") |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(source='upload', type="pil") |
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grounding_caption = gr.Textbox(label="Detection Prompt") |
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run_button = gr.Button(label="Run") |
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with gr.Accordion("Advanced options", open=False): |
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box_threshold = gr.Slider( |
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label="Box Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001 |
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) |
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text_threshold = gr.Slider( |
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label="Text Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001 |
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) |
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with gr.Column(): |
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gallery = gr.outputs.Image( |
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type="pil", |
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# label="grounding results" |
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).style(full_width=True, full_height=True) |
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# gallery = gr.Gallery(label="Generated images", show_label=False).style( |
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# grid=[1], height="auto", container=True, full_width=True, full_height=True) |
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run_button.click(fn=run_grounding, inputs=[ |
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input_image, grounding_caption, box_threshold, text_threshold], outputs=[gallery]) |
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block.launch(server_name='0.0.0.0', server_port=7579, debug=args.debug, share=args.share) |
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batch_size = 1 |
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modelname = "groundingdino" |
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backbone = "swin_B_384_22k" |
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position_embedding = "sine" |
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pe_temperatureH = 20 |
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pe_temperatureW = 20 |
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return_interm_indices = [1, 2, 3] |
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backbone_freeze_keywords = None |
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enc_layers = 6 |
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dec_layers = 6 |
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pre_norm = False |
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dim_feedforward = 2048 |
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hidden_dim = 256 |
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dropout = 0.0 |
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nheads = 8 |
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num_queries = 900 |
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query_dim = 4 |
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num_patterns = 0 |
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num_feature_levels = 4 |
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enc_n_points = 4 |
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dec_n_points = 4 |
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two_stage_type = "standard" |
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two_stage_bbox_embed_share = False |
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two_stage_class_embed_share = False |
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transformer_activation = "relu" |
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dec_pred_bbox_embed_share = True |
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dn_box_noise_scale = 1.0 |
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dn_label_noise_ratio = 0.5 |
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dn_label_coef = 1.0 |
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dn_bbox_coef = 1.0 |
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embed_init_tgt = True |
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dn_labelbook_size = 2000 |
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max_text_len = 256 |
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text_encoder_type = "bert-base-uncased" |
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use_text_enhancer = True |
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use_fusion_layer = True |
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use_checkpoint = True |
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use_transformer_ckpt = True |
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use_text_cross_attention = True |
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text_dropout = 0.0 |
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fusion_dropout = 0.0 |
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fusion_droppath = 0.1 |
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sub_sentence_present = True |
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