{"repo_id":"Track-Anything","entity_id":"py:demo","uri":"program://Track-Anything/module/demo#L1-L87","kind":"module","name":"demo","path":"demo.py","language":"python","start_line":1,"end_line":87,"context_start_line":1,"context_end_line":87,"code":"from metaseg import SegAutoMaskPredictor, SegManualMaskPredictor, SahiAutoSegmentation, sahi_sliced_predict\n\n# For image\n\ndef automask_image_app(image_path, model_type, points_per_side, points_per_batch, min_area):\n SegAutoMaskPredictor().image_predict(\n source=image_path,\n model_type=model_type, # vit_l, vit_h, vit_b\n points_per_side=points_per_side,\n points_per_batch=points_per_batch,\n min_area=min_area,\n output_path=\"output.png\",\n show=False,\n save=True,\n )\n return \"output.png\"\n\n\n# For video\n\ndef automask_video_app(video_path, model_type, points_per_side, points_per_batch, min_area):\n SegAutoMaskPredictor().video_predict(\n source=video_path,\n model_type=model_type, # vit_l, vit_h, vit_b\n points_per_side=points_per_side,\n points_per_batch=points_per_batch,\n min_area=min_area,\n output_path=\"output.mp4\",\n )\n return \"output.mp4\"\n\n\n# For manuel box and point selection\n\ndef manual_app(image_path, model_type, input_point, input_label, input_box, multimask_output, random_color):\n SegManualMaskPredictor().image_predict(\n source=image_path,\n model_type=model_type, # vit_l, vit_h, vit_b\n input_point=input_point,\n input_label=input_label,\n input_box=input_box,\n multimask_output=multimask_output,\n random_color=random_color,\n output_path=\"output.png\",\n show=False,\n save=True,\n )\n return \"output.png\"\n\n\n# For sahi sliced prediction\n\ndef sahi_autoseg_app(\n image_path,\n sam_model_type,\n detection_model_type,\n detection_model_path,\n conf_th,\n image_size,\n slice_height,\n slice_width,\n overlap_height_ratio,\n overlap_width_ratio,\n):\n boxes = sahi_sliced_predict(\n image_path=image_path,\n detection_model_type=detection_model_type, # yolov8, detectron2, mmdetection, torchvision\n detection_model_path=detection_model_path,\n conf_th=conf_th,\n image_size=image_size,\n slice_height=slice_height,\n slice_width=slice_width,\n overlap_height_ratio=overlap_height_ratio,\n overlap_width_ratio=overlap_width_ratio,\n )\n\n SahiAutoSegmentation().predict(\n source=image_path,\n model_type=sam_model_type,\n input_box=boxes,\n multimask_output=False,\n random_color=False,\n show=False,\n save=True,\n )\n \n return \"output.png\"","source_hash":"e112615210a4a3c358dc95f368064333cbd942b56960ed8a14a913eb455b8f05","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:demo.automask_image_app","uri":"program://Track-Anything/function/demo.automask_image_app#L5-L16","kind":"function","name":"automask_image_app","path":"demo.py","language":"python","start_line":5,"end_line":16,"context_start_line":1,"context_end_line":36,"code":"from metaseg import SegAutoMaskPredictor, SegManualMaskPredictor, SahiAutoSegmentation, sahi_sliced_predict\n\n# For image\n\ndef automask_image_app(image_path, model_type, points_per_side, points_per_batch, min_area):\n SegAutoMaskPredictor().image_predict(\n source=image_path,\n model_type=model_type, # vit_l, vit_h, vit_b\n points_per_side=points_per_side,\n points_per_batch=points_per_batch,\n min_area=min_area,\n output_path=\"output.png\",\n show=False,\n save=True,\n )\n return \"output.png\"\n\n\n# For video\n\ndef automask_video_app(video_path, model_type, points_per_side, points_per_batch, min_area):\n SegAutoMaskPredictor().video_predict(\n source=video_path,\n model_type=model_type, # vit_l, vit_h, vit_b\n points_per_side=points_per_side,\n points_per_batch=points_per_batch,\n min_area=min_area,\n output_path=\"output.mp4\",\n )\n return \"output.mp4\"\n\n\n# For manuel box and point selection\n\ndef manual_app(image_path, model_type, input_point, input_label, input_box, multimask_output, random_color):\n SegManualMaskPredictor().image_predict(","source_hash":"e112615210a4a3c358dc95f368064333cbd942b56960ed8a14a913eb455b8f05","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:demo.automask_video_app","uri":"program://Track-Anything/function/demo.automask_video_app#L21-L30","kind":"function","name":"automask_video_app","path":"demo.py","language":"python","start_line":21,"end_line":30,"context_start_line":1,"context_end_line":50,"code":"from metaseg import SegAutoMaskPredictor, SegManualMaskPredictor, SahiAutoSegmentation, sahi_sliced_predict\n\n# For image\n\ndef automask_image_app(image_path, model_type, points_per_side, points_per_batch, min_area):\n SegAutoMaskPredictor().image_predict(\n source=image_path,\n model_type=model_type, # vit_l, vit_h, vit_b\n points_per_side=points_per_side,\n points_per_batch=points_per_batch,\n min_area=min_area,\n output_path=\"output.png\",\n show=False,\n save=True,\n )\n return \"output.png\"\n\n\n# For video\n\ndef automask_video_app(video_path, model_type, points_per_side, points_per_batch, min_area):\n SegAutoMaskPredictor().video_predict(\n source=video_path,\n model_type=model_type, # vit_l, vit_h, vit_b\n points_per_side=points_per_side,\n points_per_batch=points_per_batch,\n min_area=min_area,\n output_path=\"output.mp4\",\n )\n return \"output.mp4\"\n\n\n# For manuel box and point selection\n\ndef manual_app(image_path, model_type, input_point, input_label, input_box, multimask_output, random_color):\n SegManualMaskPredictor().image_predict(\n source=image_path,\n model_type=model_type, # vit_l, vit_h, vit_b\n input_point=input_point,\n input_label=input_label,\n input_box=input_box,\n multimask_output=multimask_output,\n random_color=random_color,\n output_path=\"output.png\",\n show=False,\n save=True,\n )\n return \"output.png\"\n\n","source_hash":"e112615210a4a3c358dc95f368064333cbd942b56960ed8a14a913eb455b8f05","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:demo.manual_app","uri":"program://Track-Anything/function/demo.manual_app#L35-L48","kind":"function","name":"manual_app","path":"demo.py","language":"python","start_line":35,"end_line":48,"context_start_line":15,"context_end_line":68,"code":" )\n return \"output.png\"\n\n\n# For video\n\ndef automask_video_app(video_path, model_type, points_per_side, points_per_batch, min_area):\n SegAutoMaskPredictor().video_predict(\n source=video_path,\n model_type=model_type, # vit_l, vit_h, vit_b\n points_per_side=points_per_side,\n points_per_batch=points_per_batch,\n min_area=min_area,\n output_path=\"output.mp4\",\n )\n return \"output.mp4\"\n\n\n# For manuel box and point selection\n\ndef manual_app(image_path, model_type, input_point, input_label, input_box, multimask_output, random_color):\n SegManualMaskPredictor().image_predict(\n source=image_path,\n model_type=model_type, # vit_l, vit_h, vit_b\n input_point=input_point,\n input_label=input_label,\n input_box=input_box,\n multimask_output=multimask_output,\n random_color=random_color,\n output_path=\"output.png\",\n show=False,\n save=True,\n )\n return \"output.png\"\n\n\n# For sahi sliced prediction\n\ndef sahi_autoseg_app(\n image_path,\n sam_model_type,\n detection_model_type,\n detection_model_path,\n conf_th,\n image_size,\n slice_height,\n slice_width,\n overlap_height_ratio,\n overlap_width_ratio,\n):\n boxes = sahi_sliced_predict(\n image_path=image_path,\n detection_model_type=detection_model_type, # yolov8, detectron2, mmdetection, torchvision\n detection_model_path=detection_model_path,","source_hash":"e112615210a4a3c358dc95f368064333cbd942b56960ed8a14a913eb455b8f05","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:demo.sahi_autoseg_app","uri":"program://Track-Anything/function/demo.sahi_autoseg_app#L53-L87","kind":"function","name":"sahi_autoseg_app","path":"demo.py","language":"python","start_line":53,"end_line":87,"context_start_line":33,"context_end_line":87,"code":"# For manuel box and point selection\n\ndef manual_app(image_path, model_type, input_point, input_label, input_box, multimask_output, random_color):\n SegManualMaskPredictor().image_predict(\n source=image_path,\n model_type=model_type, # vit_l, vit_h, vit_b\n input_point=input_point,\n input_label=input_label,\n input_box=input_box,\n multimask_output=multimask_output,\n random_color=random_color,\n output_path=\"output.png\",\n show=False,\n save=True,\n )\n return \"output.png\"\n\n\n# For sahi sliced prediction\n\ndef sahi_autoseg_app(\n image_path,\n sam_model_type,\n detection_model_type,\n detection_model_path,\n conf_th,\n image_size,\n slice_height,\n slice_width,\n overlap_height_ratio,\n overlap_width_ratio,\n):\n boxes = sahi_sliced_predict(\n image_path=image_path,\n detection_model_type=detection_model_type, # yolov8, detectron2, mmdetection, torchvision\n detection_model_path=detection_model_path,\n conf_th=conf_th,\n image_size=image_size,\n slice_height=slice_height,\n slice_width=slice_width,\n overlap_height_ratio=overlap_height_ratio,\n overlap_width_ratio=overlap_width_ratio,\n )\n\n SahiAutoSegmentation().predict(\n source=image_path,\n model_type=sam_model_type,\n input_box=boxes,\n multimask_output=False,\n random_color=False,\n show=False,\n save=True,\n )\n \n return \"output.png\"","source_hash":"e112615210a4a3c358dc95f368064333cbd942b56960ed8a14a913eb455b8f05","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:app","uri":"program://Track-Anything/module/app#L1-L602","kind":"module","name":"app","path":"app.py","language":"python","start_line":1,"end_line":602,"context_start_line":1,"context_end_line":602,"code":"import gradio as gr\nimport argparse\nimport gdown\nimport cv2\nimport numpy as np\nimport os\nimport sys\nsys.path.append(sys.path[0]+\"/tracker\")\nsys.path.append(sys.path[0]+\"/tracker/model\")\nfrom track_anything import TrackingAnything\nfrom track_anything import parse_augment\nimport requests\nimport json\nimport torchvision\nimport torch \nfrom tools.painter import mask_painter\nimport psutil\nimport time\ntry: \n from mmcv.cnn import ConvModule\nexcept:\n os.system(\"mim install mmcv\")\n\n# download checkpoints\ndef download_checkpoint(url, folder, filename):\n os.makedirs(folder, exist_ok=True)\n filepath = os.path.join(folder, filename)\n\n if not os.path.exists(filepath):\n print(\"download checkpoints ......\")\n response = requests.get(url, stream=True)\n with open(filepath, \"wb\") as f:\n for chunk in response.iter_content(chunk_size=8192):\n if chunk:\n f.write(chunk)\n\n print(\"download successfully!\")\n\n return filepath\n\ndef download_checkpoint_from_google_drive(file_id, folder, filename):\n os.makedirs(folder, exist_ok=True)\n filepath = os.path.join(folder, filename)\n\n if not os.path.exists(filepath):\n print(\"Downloading checkpoints from Google Drive... tips: If you cannot see the progress bar, please try to download it manuall \\\n and put it in the checkpointes directory. E2FGVI-HQ-CVPR22.pth: https://github.com/MCG-NKU/E2FGVI(E2FGVI-HQ model)\")\n url = f\"https://drive.google.com/uc?id={file_id}\"\n gdown.download(url, filepath, quiet=False)\n print(\"Downloaded successfully!\")\n\n return filepath\n\n# convert points input to prompt state\ndef get_prompt(click_state, click_input):\n inputs = json.loads(click_input)\n points = click_state[0]\n labels = click_state[1]\n for input in inputs:\n points.append(input[:2])\n labels.append(input[2])\n click_state[0] = points\n click_state[1] = labels\n prompt = {\n \"prompt_type\":[\"click\"],\n \"input_point\":click_state[0],\n \"input_label\":click_state[1],\n \"multimask_output\":\"True\",\n }\n return prompt\n\n\n# extract frames from upload video\ndef get_frames_from_video(video_input, video_state):\n \"\"\"\n Args:\n video_path:str\n timestamp:float64\n Return \n [[0:nearest_frame], [nearest_frame:], nearest_frame]\n \"\"\"\n video_path = video_input\n frames = []\n user_name = time.time()\n operation_log = [(\"\",\"\"),(\"Upload video already. Try click the image for adding targets to track and inpaint.\",\"Normal\")]\n try:\n cap = cv2.VideoCapture(video_path)\n fps = cap.get(cv2.CAP_PROP_FPS)\n while cap.isOpened():\n ret, frame = cap.read()\n if ret == True:\n current_memory_usage = psutil.virtual_memory().percent\n frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n if current_memory_usage > 90:\n operation_log = [(\"Memory usage is too high (>90%). Stop the video extraction. Please reduce the video resolution or frame rate.\", \"Error\")]\n print(\"Memory usage is too high (>90%). Please reduce the video resolution or frame rate.\")\n break\n else:\n break\n except (OSError, TypeError, ValueError, KeyError, SyntaxError) as e:\n print(\"read_frame_source:{} error. {}\\n\".format(video_path, str(e)))\n image_size = (frames[0].shape[0],frames[0].shape[1]) \n # initialize video_state\n video_state = {\n \"user_name\": user_name,\n \"video_name\": os.path.split(video_path)[-1],\n \"origin_images\": frames,\n \"painted_images\": frames.copy(),\n \"masks\": [np.zeros((frames[0].shape[0],frames[0].shape[1]), np.uint8)]*len(frames),\n \"logits\": [None]*len(frames),\n \"select_frame_number\": 0,\n \"fps\": fps\n }\n video_info = \"Video Name: {}, FPS: {}, Total Frames: {}, Image Size:{}\".format(video_state[\"video_name\"], video_state[\"fps\"], len(frames), image_size)\n model.samcontroler.sam_controler.reset_image() \n model.samcontroler.sam_controler.set_image(video_state[\"origin_images\"][0])\n return video_state, video_info, video_state[\"origin_images\"][0], gr.update(visible=True, maximum=len(frames), value=1), gr.update(visible=True, maximum=len(frames), value=len(frames)), \\\n gr.update(visible=True),\\\n gr.update(visible=True), gr.update(visible=True), \\\n gr.update(visible=True), gr.update(visible=True), \\\n gr.update(visible=True), gr.update(visible=True), \\\n gr.update(visible=True), gr.update(visible=True), \\\n gr.update(visible=True, value=operation_log)\n\ndef run_example(example):\n return video_input\n# get the select frame from gradio slider\ndef select_template(image_selection_slider, video_state, interactive_state, mask_dropdown):\n\n # images = video_state[1]\n image_selection_slider -= 1\n video_state[\"select_frame_number\"] = image_selection_slider\n\n # once select a new template frame, set the image in sam\n\n model.samcontroler.sam_controler.reset_image()\n model.samcontroler.sam_controler.set_image(video_state[\"origin_images\"][image_selection_slider])\n\n # update the masks when select a new template frame\n # if video_state[\"masks\"][image_selection_slider] is not None:\n # video_state[\"painted_images\"][image_selection_slider] = mask_painter(video_state[\"origin_images\"][image_selection_slider], video_state[\"masks\"][image_selection_slider])\n if mask_dropdown:\n print(\"ok\")\n operation_log = [(\"\",\"\"), (\"Select frame {}. Try click image and add mask for tracking.\".format(image_selection_slider),\"Normal\")]\n\n\n return video_state[\"painted_images\"][image_selection_slider], video_state, interactive_state, operation_log\n\n# set the tracking end frame\ndef get_end_number(track_pause_number_slider, video_state, interactive_state):\n interactive_state[\"track_end_number\"] = track_pause_number_slider\n operation_log = [(\"\",\"\"),(\"Set the tracking finish at frame {}\".format(track_pause_number_slider),\"Normal\")]\n\n return video_state[\"painted_images\"][track_pause_number_slider],interactive_state, operation_log\n\ndef get_resize_ratio(resize_ratio_slider, interactive_state):\n interactive_state[\"resize_ratio\"] = resize_ratio_slider\n\n return interactive_state\n\n# use sam to get the mask\ndef sam_refine(video_state, point_prompt, click_state, interactive_state, evt:gr.SelectData):\n \"\"\"\n Args:\n template_frame: PIL.Image\n point_prompt: flag for positive or negative button click\n click_state: [[points], [labels]]\n \"\"\"\n if point_prompt == \"Positive\":\n coordinate = \"[[{},{},1]]\".format(evt.index[0], evt.index[1])\n interactive_state[\"positive_click_times\"] += 1\n else:\n coordinate = \"[[{},{},0]]\".format(evt.index[0], evt.index[1])\n interactive_state[\"negative_click_times\"] += 1\n \n # prompt for sam model\n model.samcontroler.sam_controler.reset_image()\n model.samcontroler.sam_controler.set_image(video_state[\"origin_images\"][video_state[\"select_frame_number\"]])\n prompt = get_prompt(click_state=click_state, click_input=coordinate)\n\n mask, logit, painted_image = model.first_frame_click( \n image=video_state[\"origin_images\"][video_state[\"select_frame_number\"]], \n points=np.array(prompt[\"input_point\"]),\n labels=np.array(prompt[\"input_label\"]),\n multimask=prompt[\"multimask_output\"],\n )\n video_state[\"masks\"][video_state[\"select_frame_number\"]] = mask\n video_state[\"logits\"][video_state[\"select_frame_number\"]] = logit\n video_state[\"painted_images\"][video_state[\"select_frame_number\"]] = painted_image\n\n operation_log = [(\"\",\"\"), (\"Use SAM for segment. You can try add positive and negative points by clicking. Or press Clear clicks button to refresh the image. Press Add mask button when you are satisfied with the segment\",\"Normal\")]\n return painted_image, video_state, interactive_state, operation_log\n\ndef add_multi_mask(video_state, interactive_state, mask_dropdown):\n try:\n mask = video_state[\"masks\"][video_state[\"select_frame_number\"]]\n interactive_state[\"multi_mask\"][\"masks\"].append(mask)\n interactive_state[\"multi_mask\"][\"mask_names\"].append(\"mask_{:03d}\".format(len(interactive_state[\"multi_mask\"][\"masks\"])))\n mask_dropdown.append(\"mask_{:03d}\".format(len(interactive_state[\"multi_mask\"][\"masks\"])))\n select_frame, run_status = show_mask(video_state, interactive_state, mask_dropdown)\n\n operation_log = [(\"\",\"\"),(\"Added a mask, use the mask select for target tracking or inpainting.\",\"Normal\")]\n except:\n operation_log = [(\"Please click the left image to generate mask.\", \"Error\"), (\"\",\"\")]\n return interactive_state, gr.update(choices=interactive_state[\"multi_mask\"][\"mask_names\"], value=mask_dropdown), select_frame, [[],[]], operation_log\n\ndef clear_click(video_state, click_state):\n click_state = [[],[]]\n template_frame = video_state[\"origin_images\"][video_state[\"select_frame_number\"]]\n operation_log = [(\"\",\"\"), (\"Clear points history and refresh the image.\",\"Normal\")]\n return template_frame, click_state, operation_log\n\ndef remove_multi_mask(interactive_state, mask_dropdown):\n interactive_state[\"multi_mask\"][\"mask_names\"]= []\n interactive_state[\"multi_mask\"][\"masks\"] = []\n\n operation_log = [(\"\",\"\"), (\"Remove all mask, please add new masks\",\"Normal\")]\n return interactive_state, gr.update(choices=[],value=[]), operation_log\n\ndef show_mask(video_state, interactive_state, mask_dropdown):\n mask_dropdown.sort()\n select_frame = video_state[\"origin_images\"][video_state[\"select_frame_number\"]]\n for i in range(len(mask_dropdown)):\n mask_number = int(mask_dropdown[i].split(\"_\")[1]) - 1\n mask = interactive_state[\"multi_mask\"][\"masks\"][mask_number]\n select_frame = mask_painter(select_frame, mask.astype('uint8'), mask_color=mask_number+2)\n \n operation_log = [(\"\",\"\"), (\"Select {} for tracking or inpainting\".format(mask_dropdown),\"Normal\")]\n return select_frame, operation_log\n\n# tracking vos\ndef vos_tracking_video(video_state, interactive_state, mask_dropdown):\n operation_log = [(\"\",\"\"), (\"Track the selected masks, and then you can select the masks for inpainting.\",\"Normal\")]\n model.xmem.clear_memory()\n if interactive_state[\"track_end_number\"]:\n following_frames = video_state[\"origin_images\"][video_state[\"select_frame_number\"]:interactive_state[\"track_end_number\"]]\n else:\n following_frames = video_state[\"origin_images\"][video_state[\"select_frame_number\"]:]\n\n if interactive_state[\"multi_mask\"][\"masks\"]:\n if len(mask_dropdown) == 0:\n mask_dropdown = [\"mask_001\"]\n mask_dropdown.sort()\n template_mask = interactive_state[\"multi_mask\"][\"masks\"][int(mask_dropdown[0].split(\"_\")[1]) - 1] * (int(mask_dropdown[0].split(\"_\")[1]))\n for i in range(1,len(mask_dropdown)):\n mask_number = int(mask_dropdown[i].split(\"_\")[1]) - 1 \n template_mask = np.clip(template_mask+interactive_state[\"multi_mask\"][\"masks\"][mask_number]*(mask_number+1), 0, mask_number+1)\n video_state[\"masks\"][video_state[\"select_frame_number\"]]= template_mask\n else: \n template_mask = video_state[\"masks\"][video_state[\"select_frame_number\"]]\n fps = video_state[\"fps\"]\n\n # operation error\n if len(np.unique(template_mask))==1:\n template_mask[0][0]=1\n operation_log = [(\"Error! Please add at least one mask to track by clicking the left image.\",\"Error\"), (\"\",\"\")]\n # return video_output, video_state, interactive_state, operation_error\n masks, logits, painted_images = model.generator(images=following_frames, template_mask=template_mask)\n # clear GPU memory\n model.xmem.clear_memory()\n\n if interactive_state[\"track_end_number\"]: \n video_state[\"masks\"][video_state[\"select_frame_number\"]:interactive_state[\"track_end_number\"]] = masks\n video_state[\"logits\"][video_state[\"select_frame_number\"]:interactive_state[\"track_end_number\"]] = logits\n video_state[\"painted_images\"][video_state[\"select_frame_number\"]:interactive_state[\"track_end_number\"]] = painted_images\n else:\n video_state[\"masks\"][video_state[\"select_frame_number\"]:] = masks\n video_state[\"logits\"][video_state[\"select_frame_number\"]:] = logits\n video_state[\"painted_images\"][video_state[\"select_frame_number\"]:] = painted_images\n\n video_output = generate_video_from_frames(video_state[\"painted_images\"], output_path=\"./result/track/{}\".format(video_state[\"video_name\"]), fps=fps) # import video_input to name the output video\n interactive_state[\"inference_times\"] += 1\n \n print(\"For generating this tracking result, inference times: {}, click times: {}, positive: {}, negative: {}\".format(interactive_state[\"inference_times\"], \n interactive_state[\"positive_click_times\"]+interactive_state[\"negative_click_times\"],\n interactive_state[\"positive_click_times\"],\n interactive_state[\"negative_click_times\"]))\n\n #### shanggao code for mask save\n if interactive_state[\"mask_save\"]:\n if not os.path.exists('./result/mask/{}'.format(video_state[\"video_name\"].split('.')[0])):\n os.makedirs('./result/mask/{}'.format(video_state[\"video_name\"].split('.')[0]))\n i = 0\n print(\"save mask\")\n for mask in video_state[\"masks\"]:\n np.save(os.path.join('./result/mask/{}'.format(video_state[\"video_name\"].split('.')[0]), '{:05d}.npy'.format(i)), mask)\n i+=1\n # save_mask(video_state[\"masks\"], video_state[\"video_name\"])\n #### shanggao code for mask save\n return video_output, video_state, interactive_state, operation_log\n\n# extracting masks from mask_dropdown\n# def extract_sole_mask(video_state, mask_dropdown):\n# combined_masks = \n# unique_masks = np.unique(combined_masks)\n# return 0 \n\n# inpaint \ndef inpaint_video(video_state, interactive_state, mask_dropdown):\n operation_log = [(\"\",\"\"), (\"Removed the selected masks.\",\"Normal\")]\n\n frames = np.asarray(video_state[\"origin_images\"])\n fps = video_state[\"fps\"]\n inpaint_masks = np.asarray(video_state[\"masks\"])\n if len(mask_dropdown) == 0:\n mask_dropdown = [\"mask_001\"]\n mask_dropdown.sort()\n # convert mask_dropdown to mask numbers\n inpaint_mask_numbers = [int(mask_dropdown[i].split(\"_\")[1]) for i in range(len(mask_dropdown))]\n # interate through all masks and remove the masks that are not in mask_dropdown\n unique_masks = np.unique(inpaint_masks)\n num_masks = len(unique_masks) - 1\n for i in range(1, num_masks + 1):\n if i in inpaint_mask_numbers:\n continue\n inpaint_masks[inpaint_masks==i] = 0\n # inpaint for videos\n\n try:\n inpainted_frames = model.baseinpainter.inpaint(frames, inpaint_masks, ratio=interactive_state[\"resize_ratio\"]) # numpy array, T, H, W, 3\n except:\n operation_log = [(\"Error! You are trying to inpaint without masks input. Please track the selected mask first, and then press inpaint. If VRAM exceeded, please use the resize ratio to scaling down the image size.\",\"Error\"), (\"\",\"\")]\n inpainted_frames = video_state[\"origin_images\"]\n video_output = generate_video_from_frames(inpainted_frames, output_path=\"./result/inpaint/{}\".format(video_state[\"video_name\"]), fps=fps) # import video_input to name the output video\n\n return video_output, operation_log\n\n\n# generate video after vos inference\ndef generate_video_from_frames(frames, output_path, fps=30):\n \"\"\"\n Generates a video from a list of frames.\n \n Args:\n frames (list of numpy arrays): The frames to include in the video.\n output_path (str): The path to save the generated video.\n fps (int, optional): The frame rate of the output video. Defaults to 30.\n \"\"\"\n # height, width, layers = frames[0].shape\n # fourcc = cv2.VideoWriter_fourcc(*\"mp4v\")\n # video = cv2.VideoWriter(output_path, fourcc, fps, (width, height))\n # print(output_path)\n # for frame in frames:\n # video.write(frame)\n \n # video.release()\n frames = torch.from_numpy(np.asarray(frames))\n if not os.path.exists(os.path.dirname(output_path)):\n os.makedirs(os.path.dirname(output_path))\n torchvision.io.write_video(output_path, frames, fps=fps, video_codec=\"libx264\")\n return output_path\n\n\n# args, defined in track_anything.py\nargs = parse_augment()\n\n# check and download checkpoints if needed\nSAM_checkpoint_dict = {\n 'vit_h': \"sam_vit_h_4b8939.pth\",\n 'vit_l': \"sam_vit_l_0b3195.pth\", \n \"vit_b\": \"sam_vit_b_01ec64.pth\"\n}\nSAM_checkpoint_url_dict = {\n 'vit_h': \"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\",\n 'vit_l': \"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth\",\n 'vit_b': \"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth\"\n}\nsam_checkpoint = SAM_checkpoint_dict[args.sam_model_type] \nsam_checkpoint_url = SAM_checkpoint_url_dict[args.sam_model_type] \nxmem_checkpoint = \"XMem-s012.pth\"\nxmem_checkpoint_url = \"https://github.com/hkchengrex/XMem/releases/download/v1.0/XMem-s012.pth\"\ne2fgvi_checkpoint = \"E2FGVI-HQ-CVPR22.pth\"\ne2fgvi_checkpoint_id = \"10wGdKSUOie0XmCr8SQ2A2FeDe-mfn5w3\"\n\n\nfolder =\"./checkpoints\"\nSAM_checkpoint = download_checkpoint(sam_checkpoint_url, folder, sam_checkpoint)\nxmem_checkpoint = download_checkpoint(xmem_checkpoint_url, folder, xmem_checkpoint)\ne2fgvi_checkpoint = download_checkpoint_from_google_drive(e2fgvi_checkpoint_id, folder, e2fgvi_checkpoint)\nargs.port = 12212\nargs.device = \"cuda:3\"\n# args.mask_save = True\n\n# initialize sam, xmem, e2fgvi models\nmodel = TrackingAnything(SAM_checkpoint, xmem_checkpoint, e2fgvi_checkpoint,args)\n\n\ntitle = \"\"\"

Track-Anything

\n \"\"\"\ndescription = \"\"\"

Gradio demo for Track Anything, a flexible and interactive tool for video object tracking, segmentation, and inpainting. I To use it, simply upload your video, or click one of the examples to load them. Code: https://github.com/gaomingqi/Track-Anything \"Duplicate

\"\"\"\n\n\nwith gr.Blocks() as iface:\n \"\"\"\n state for \n \"\"\"\n click_state = gr.State([[],[]])\n interactive_state = gr.State({\n \"inference_times\": 0,\n \"negative_click_times\" : 0,\n \"positive_click_times\": 0,\n \"mask_save\": args.mask_save,\n \"multi_mask\": {\n \"mask_names\": [],\n \"masks\": []\n },\n \"track_end_number\": None,\n \"resize_ratio\": 1\n }\n )\n\n video_state = gr.State(\n {\n \"user_name\": \"\",\n \"video_name\": \"\",\n \"origin_images\": None,\n \"painted_images\": None,\n \"masks\": None,\n \"inpaint_masks\": None,\n \"logits\": None,\n \"select_frame_number\": 0,\n \"fps\": 30\n }\n )\n gr.Markdown(title)\n gr.Markdown(description)\n with gr.Row():\n# ... truncated ...","source_hash":"21d2c55f3fcfa74fc1eff2135fa6381b8f8470dd6c58c999e5adef16e4064eea","truncated":true} {"repo_id":"Track-Anything","entity_id":"py:app.download_checkpoint","uri":"program://Track-Anything/function/app.download_checkpoint#L25-L39","kind":"function","name":"download_checkpoint","path":"app.py","language":"python","start_line":25,"end_line":39,"context_start_line":5,"context_end_line":59,"code":"import numpy as np\nimport os\nimport sys\nsys.path.append(sys.path[0]+\"/tracker\")\nsys.path.append(sys.path[0]+\"/tracker/model\")\nfrom track_anything import TrackingAnything\nfrom track_anything import parse_augment\nimport requests\nimport json\nimport torchvision\nimport torch \nfrom tools.painter import mask_painter\nimport psutil\nimport time\ntry: \n from mmcv.cnn import ConvModule\nexcept:\n os.system(\"mim install mmcv\")\n\n# download checkpoints\ndef download_checkpoint(url, folder, filename):\n os.makedirs(folder, exist_ok=True)\n filepath = os.path.join(folder, filename)\n\n if not os.path.exists(filepath):\n print(\"download checkpoints ......\")\n response = requests.get(url, stream=True)\n with open(filepath, \"wb\") as f:\n for chunk in response.iter_content(chunk_size=8192):\n if chunk:\n f.write(chunk)\n\n print(\"download successfully!\")\n\n return filepath\n\ndef download_checkpoint_from_google_drive(file_id, folder, filename):\n os.makedirs(folder, exist_ok=True)\n filepath = os.path.join(folder, filename)\n\n if not os.path.exists(filepath):\n print(\"Downloading checkpoints from Google Drive... tips: If you cannot see the progress bar, please try to download it manuall \\\n and put it in the checkpointes directory. E2FGVI-HQ-CVPR22.pth: https://github.com/MCG-NKU/E2FGVI(E2FGVI-HQ model)\")\n url = f\"https://drive.google.com/uc?id={file_id}\"\n gdown.download(url, filepath, quiet=False)\n print(\"Downloaded successfully!\")\n\n return filepath\n\n# convert points input to prompt state\ndef get_prompt(click_state, click_input):\n inputs = json.loads(click_input)\n points = click_state[0]\n labels = click_state[1]\n for input in inputs:","source_hash":"21d2c55f3fcfa74fc1eff2135fa6381b8f8470dd6c58c999e5adef16e4064eea","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:app.download_checkpoint_from_google_drive","uri":"program://Track-Anything/function/app.download_checkpoint_from_google_drive#L41-L52","kind":"function","name":"download_checkpoint_from_google_drive","path":"app.py","language":"python","start_line":41,"end_line":52,"context_start_line":21,"context_end_line":72,"code":"except:\n os.system(\"mim install mmcv\")\n\n# download checkpoints\ndef download_checkpoint(url, folder, filename):\n os.makedirs(folder, exist_ok=True)\n filepath = os.path.join(folder, filename)\n\n if not os.path.exists(filepath):\n print(\"download checkpoints ......\")\n response = requests.get(url, stream=True)\n with open(filepath, \"wb\") as f:\n for chunk in response.iter_content(chunk_size=8192):\n if chunk:\n f.write(chunk)\n\n print(\"download successfully!\")\n\n return filepath\n\ndef download_checkpoint_from_google_drive(file_id, folder, filename):\n os.makedirs(folder, exist_ok=True)\n filepath = os.path.join(folder, filename)\n\n if not os.path.exists(filepath):\n print(\"Downloading checkpoints from Google Drive... tips: If you cannot see the progress bar, please try to download it manuall \\\n and put it in the checkpointes directory. E2FGVI-HQ-CVPR22.pth: https://github.com/MCG-NKU/E2FGVI(E2FGVI-HQ model)\")\n url = f\"https://drive.google.com/uc?id={file_id}\"\n gdown.download(url, filepath, quiet=False)\n print(\"Downloaded successfully!\")\n\n return filepath\n\n# convert points input to prompt state\ndef get_prompt(click_state, click_input):\n inputs = json.loads(click_input)\n points = click_state[0]\n labels = click_state[1]\n for input in inputs:\n points.append(input[:2])\n labels.append(input[2])\n click_state[0] = points\n click_state[1] = labels\n prompt = {\n \"prompt_type\":[\"click\"],\n \"input_point\":click_state[0],\n \"input_label\":click_state[1],\n \"multimask_output\":\"True\",\n }\n return prompt\n\n","source_hash":"21d2c55f3fcfa74fc1eff2135fa6381b8f8470dd6c58c999e5adef16e4064eea","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:app.get_prompt","uri":"program://Track-Anything/function/app.get_prompt#L55-L70","kind":"function","name":"get_prompt","path":"app.py","language":"python","start_line":55,"end_line":70,"context_start_line":35,"context_end_line":90,"code":" f.write(chunk)\n\n print(\"download successfully!\")\n\n return filepath\n\ndef download_checkpoint_from_google_drive(file_id, folder, filename):\n os.makedirs(folder, exist_ok=True)\n filepath = os.path.join(folder, filename)\n\n if not os.path.exists(filepath):\n print(\"Downloading checkpoints from Google Drive... tips: If you cannot see the progress bar, please try to download it manuall \\\n and put it in the checkpointes directory. E2FGVI-HQ-CVPR22.pth: https://github.com/MCG-NKU/E2FGVI(E2FGVI-HQ model)\")\n url = f\"https://drive.google.com/uc?id={file_id}\"\n gdown.download(url, filepath, quiet=False)\n print(\"Downloaded successfully!\")\n\n return filepath\n\n# convert points input to prompt state\ndef get_prompt(click_state, click_input):\n inputs = json.loads(click_input)\n points = click_state[0]\n labels = click_state[1]\n for input in inputs:\n points.append(input[:2])\n labels.append(input[2])\n click_state[0] = points\n click_state[1] = labels\n prompt = {\n \"prompt_type\":[\"click\"],\n \"input_point\":click_state[0],\n \"input_label\":click_state[1],\n \"multimask_output\":\"True\",\n }\n return prompt\n\n\n# extract frames from upload video\ndef get_frames_from_video(video_input, video_state):\n \"\"\"\n Args:\n video_path:str\n timestamp:float64\n Return \n [[0:nearest_frame], [nearest_frame:], nearest_frame]\n \"\"\"\n video_path = video_input\n frames = []\n user_name = time.time()\n operation_log = [(\"\",\"\"),(\"Upload video already. Try click the image for adding targets to track and inpaint.\",\"Normal\")]\n try:\n cap = cv2.VideoCapture(video_path)\n fps = cap.get(cv2.CAP_PROP_FPS)\n while cap.isOpened():\n ret, frame = cap.read()","source_hash":"21d2c55f3fcfa74fc1eff2135fa6381b8f8470dd6c58c999e5adef16e4064eea","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:app.get_frames_from_video","uri":"program://Track-Anything/function/app.get_frames_from_video#L74-L123","kind":"function","name":"get_frames_from_video","path":"app.py","language":"python","start_line":74,"end_line":123,"context_start_line":54,"context_end_line":143,"code":"# convert points input to prompt state\ndef get_prompt(click_state, click_input):\n inputs = json.loads(click_input)\n points = click_state[0]\n labels = click_state[1]\n for input in inputs:\n points.append(input[:2])\n labels.append(input[2])\n click_state[0] = points\n click_state[1] = labels\n prompt = {\n \"prompt_type\":[\"click\"],\n \"input_point\":click_state[0],\n \"input_label\":click_state[1],\n \"multimask_output\":\"True\",\n }\n return prompt\n\n\n# extract frames from upload video\ndef get_frames_from_video(video_input, video_state):\n \"\"\"\n Args:\n video_path:str\n timestamp:float64\n Return \n [[0:nearest_frame], [nearest_frame:], nearest_frame]\n \"\"\"\n video_path = video_input\n frames = []\n user_name = time.time()\n operation_log = [(\"\",\"\"),(\"Upload video already. Try click the image for adding targets to track and inpaint.\",\"Normal\")]\n try:\n cap = cv2.VideoCapture(video_path)\n fps = cap.get(cv2.CAP_PROP_FPS)\n while cap.isOpened():\n ret, frame = cap.read()\n if ret == True:\n current_memory_usage = psutil.virtual_memory().percent\n frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n if current_memory_usage > 90:\n operation_log = [(\"Memory usage is too high (>90%). Stop the video extraction. Please reduce the video resolution or frame rate.\", \"Error\")]\n print(\"Memory usage is too high (>90%). Please reduce the video resolution or frame rate.\")\n break\n else:\n break\n except (OSError, TypeError, ValueError, KeyError, SyntaxError) as e:\n print(\"read_frame_source:{} error. {}\\n\".format(video_path, str(e)))\n image_size = (frames[0].shape[0],frames[0].shape[1]) \n # initialize video_state\n video_state = {\n \"user_name\": user_name,\n \"video_name\": os.path.split(video_path)[-1],\n \"origin_images\": frames,\n \"painted_images\": frames.copy(),\n \"masks\": [np.zeros((frames[0].shape[0],frames[0].shape[1]), np.uint8)]*len(frames),\n \"logits\": [None]*len(frames),\n \"select_frame_number\": 0,\n \"fps\": fps\n }\n video_info = \"Video Name: {}, FPS: {}, Total Frames: {}, Image Size:{}\".format(video_state[\"video_name\"], video_state[\"fps\"], len(frames), image_size)\n model.samcontroler.sam_controler.reset_image() \n model.samcontroler.sam_controler.set_image(video_state[\"origin_images\"][0])\n return video_state, video_info, video_state[\"origin_images\"][0], gr.update(visible=True, maximum=len(frames), value=1), gr.update(visible=True, maximum=len(frames), value=len(frames)), \\\n gr.update(visible=True),\\\n gr.update(visible=True), gr.update(visible=True), \\\n gr.update(visible=True), gr.update(visible=True), \\\n gr.update(visible=True), gr.update(visible=True), \\\n gr.update(visible=True), gr.update(visible=True), \\\n gr.update(visible=True, value=operation_log)\n\ndef run_example(example):\n return video_input\n# get the select frame from gradio slider\ndef select_template(image_selection_slider, video_state, interactive_state, mask_dropdown):\n\n # images = video_state[1]\n image_selection_slider -= 1\n video_state[\"select_frame_number\"] = image_selection_slider\n\n # once select a new template frame, set the image in sam\n\n model.samcontroler.sam_controler.reset_image()\n model.samcontroler.sam_controler.set_image(video_state[\"origin_images\"][image_selection_slider])\n\n # update the masks when select a new template frame\n # if video_state[\"masks\"][image_selection_slider] is not None:\n # video_state[\"painted_images\"][image_selection_slider] = mask_painter(video_state[\"origin_images\"][image_selection_slider], video_state[\"masks\"][image_selection_slider])\n if mask_dropdown:\n print(\"ok\")","source_hash":"21d2c55f3fcfa74fc1eff2135fa6381b8f8470dd6c58c999e5adef16e4064eea","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:app.run_example","uri":"program://Track-Anything/function/app.run_example#L125-L126","kind":"function","name":"run_example","path":"app.py","language":"python","start_line":125,"end_line":126,"context_start_line":105,"context_end_line":146,"code":" \"user_name\": user_name,\n \"video_name\": os.path.split(video_path)[-1],\n \"origin_images\": frames,\n \"painted_images\": frames.copy(),\n \"masks\": [np.zeros((frames[0].shape[0],frames[0].shape[1]), np.uint8)]*len(frames),\n \"logits\": [None]*len(frames),\n \"select_frame_number\": 0,\n \"fps\": fps\n }\n video_info = \"Video Name: {}, FPS: {}, Total Frames: {}, Image Size:{}\".format(video_state[\"video_name\"], video_state[\"fps\"], len(frames), image_size)\n model.samcontroler.sam_controler.reset_image() \n model.samcontroler.sam_controler.set_image(video_state[\"origin_images\"][0])\n return video_state, video_info, video_state[\"origin_images\"][0], gr.update(visible=True, maximum=len(frames), value=1), gr.update(visible=True, maximum=len(frames), value=len(frames)), \\\n gr.update(visible=True),\\\n gr.update(visible=True), gr.update(visible=True), \\\n gr.update(visible=True), gr.update(visible=True), \\\n gr.update(visible=True), gr.update(visible=True), \\\n gr.update(visible=True), gr.update(visible=True), \\\n gr.update(visible=True, value=operation_log)\n\ndef run_example(example):\n return video_input\n# get the select frame from gradio slider\ndef select_template(image_selection_slider, video_state, interactive_state, mask_dropdown):\n\n # images = video_state[1]\n image_selection_slider -= 1\n video_state[\"select_frame_number\"] = image_selection_slider\n\n # once select a new template frame, set the image in sam\n\n model.samcontroler.sam_controler.reset_image()\n model.samcontroler.sam_controler.set_image(video_state[\"origin_images\"][image_selection_slider])\n\n # update the masks when select a new template frame\n # if video_state[\"masks\"][image_selection_slider] is not None:\n # video_state[\"painted_images\"][image_selection_slider] = mask_painter(video_state[\"origin_images\"][image_selection_slider], video_state[\"masks\"][image_selection_slider])\n if mask_dropdown:\n print(\"ok\")\n operation_log = [(\"\",\"\"), (\"Select frame {}. Try click image and add mask for tracking.\".format(image_selection_slider),\"Normal\")]\n\n","source_hash":"21d2c55f3fcfa74fc1eff2135fa6381b8f8470dd6c58c999e5adef16e4064eea","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:app.select_template","uri":"program://Track-Anything/function/app.select_template#L128-L147","kind":"function","name":"select_template","path":"app.py","language":"python","start_line":128,"end_line":147,"context_start_line":108,"context_end_line":167,"code":" \"painted_images\": frames.copy(),\n \"masks\": [np.zeros((frames[0].shape[0],frames[0].shape[1]), np.uint8)]*len(frames),\n \"logits\": [None]*len(frames),\n \"select_frame_number\": 0,\n \"fps\": fps\n }\n video_info = \"Video Name: {}, FPS: {}, Total Frames: {}, Image Size:{}\".format(video_state[\"video_name\"], video_state[\"fps\"], len(frames), image_size)\n model.samcontroler.sam_controler.reset_image() \n model.samcontroler.sam_controler.set_image(video_state[\"origin_images\"][0])\n return video_state, video_info, video_state[\"origin_images\"][0], gr.update(visible=True, maximum=len(frames), value=1), gr.update(visible=True, maximum=len(frames), value=len(frames)), \\\n gr.update(visible=True),\\\n gr.update(visible=True), gr.update(visible=True), \\\n gr.update(visible=True), gr.update(visible=True), \\\n gr.update(visible=True), gr.update(visible=True), \\\n gr.update(visible=True), gr.update(visible=True), \\\n gr.update(visible=True, value=operation_log)\n\ndef run_example(example):\n return video_input\n# get the select frame from gradio slider\ndef select_template(image_selection_slider, video_state, interactive_state, mask_dropdown):\n\n # images = video_state[1]\n image_selection_slider -= 1\n video_state[\"select_frame_number\"] = image_selection_slider\n\n # once select a new template frame, set the image in sam\n\n model.samcontroler.sam_controler.reset_image()\n model.samcontroler.sam_controler.set_image(video_state[\"origin_images\"][image_selection_slider])\n\n # update the masks when select a new template frame\n # if video_state[\"masks\"][image_selection_slider] is not None:\n # video_state[\"painted_images\"][image_selection_slider] = mask_painter(video_state[\"origin_images\"][image_selection_slider], video_state[\"masks\"][image_selection_slider])\n if mask_dropdown:\n print(\"ok\")\n operation_log = [(\"\",\"\"), (\"Select frame {}. Try click image and add mask for tracking.\".format(image_selection_slider),\"Normal\")]\n\n\n return video_state[\"painted_images\"][image_selection_slider], video_state, interactive_state, operation_log\n\n# set the tracking end frame\ndef get_end_number(track_pause_number_slider, video_state, interactive_state):\n interactive_state[\"track_end_number\"] = track_pause_number_slider\n operation_log = [(\"\",\"\"),(\"Set the tracking finish at frame {}\".format(track_pause_number_slider),\"Normal\")]\n\n return video_state[\"painted_images\"][track_pause_number_slider],interactive_state, operation_log\n\ndef get_resize_ratio(resize_ratio_slider, interactive_state):\n interactive_state[\"resize_ratio\"] = resize_ratio_slider\n\n return interactive_state\n\n# use sam to get the mask\ndef sam_refine(video_state, point_prompt, click_state, interactive_state, evt:gr.SelectData):\n \"\"\"\n Args:\n template_frame: PIL.Image\n point_prompt: flag for positive or negative button click\n click_state: [[points], [labels]]","source_hash":"21d2c55f3fcfa74fc1eff2135fa6381b8f8470dd6c58c999e5adef16e4064eea","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:app.get_end_number","uri":"program://Track-Anything/function/app.get_end_number#L150-L154","kind":"function","name":"get_end_number","path":"app.py","language":"python","start_line":150,"end_line":154,"context_start_line":130,"context_end_line":174,"code":" # images = video_state[1]\n image_selection_slider -= 1\n video_state[\"select_frame_number\"] = image_selection_slider\n\n # once select a new template frame, set the image in sam\n\n model.samcontroler.sam_controler.reset_image()\n model.samcontroler.sam_controler.set_image(video_state[\"origin_images\"][image_selection_slider])\n\n # update the masks when select a new template frame\n # if video_state[\"masks\"][image_selection_slider] is not None:\n # video_state[\"painted_images\"][image_selection_slider] = mask_painter(video_state[\"origin_images\"][image_selection_slider], video_state[\"masks\"][image_selection_slider])\n if mask_dropdown:\n print(\"ok\")\n operation_log = [(\"\",\"\"), (\"Select frame {}. Try click image and add mask for tracking.\".format(image_selection_slider),\"Normal\")]\n\n\n return video_state[\"painted_images\"][image_selection_slider], video_state, interactive_state, operation_log\n\n# set the tracking end frame\ndef get_end_number(track_pause_number_slider, video_state, interactive_state):\n interactive_state[\"track_end_number\"] = track_pause_number_slider\n operation_log = [(\"\",\"\"),(\"Set the tracking finish at frame {}\".format(track_pause_number_slider),\"Normal\")]\n\n return video_state[\"painted_images\"][track_pause_number_slider],interactive_state, operation_log\n\ndef get_resize_ratio(resize_ratio_slider, interactive_state):\n interactive_state[\"resize_ratio\"] = resize_ratio_slider\n\n return interactive_state\n\n# use sam to get the mask\ndef sam_refine(video_state, point_prompt, click_state, interactive_state, evt:gr.SelectData):\n \"\"\"\n Args:\n template_frame: PIL.Image\n point_prompt: flag for positive or negative button click\n click_state: [[points], [labels]]\n \"\"\"\n if point_prompt == \"Positive\":\n coordinate = \"[[{},{},1]]\".format(evt.index[0], evt.index[1])\n interactive_state[\"positive_click_times\"] += 1\n else:\n coordinate = \"[[{},{},0]]\".format(evt.index[0], evt.index[1])\n interactive_state[\"negative_click_times\"] += 1","source_hash":"21d2c55f3fcfa74fc1eff2135fa6381b8f8470dd6c58c999e5adef16e4064eea","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:app.get_resize_ratio","uri":"program://Track-Anything/function/app.get_resize_ratio#L156-L159","kind":"function","name":"get_resize_ratio","path":"app.py","language":"python","start_line":156,"end_line":159,"context_start_line":136,"context_end_line":179,"code":" model.samcontroler.sam_controler.reset_image()\n model.samcontroler.sam_controler.set_image(video_state[\"origin_images\"][image_selection_slider])\n\n # update the masks when select a new template frame\n # if video_state[\"masks\"][image_selection_slider] is not None:\n # video_state[\"painted_images\"][image_selection_slider] = mask_painter(video_state[\"origin_images\"][image_selection_slider], video_state[\"masks\"][image_selection_slider])\n if mask_dropdown:\n print(\"ok\")\n operation_log = [(\"\",\"\"), (\"Select frame {}. Try click image and add mask for tracking.\".format(image_selection_slider),\"Normal\")]\n\n\n return video_state[\"painted_images\"][image_selection_slider], video_state, interactive_state, operation_log\n\n# set the tracking end frame\ndef get_end_number(track_pause_number_slider, video_state, interactive_state):\n interactive_state[\"track_end_number\"] = track_pause_number_slider\n operation_log = [(\"\",\"\"),(\"Set the tracking finish at frame {}\".format(track_pause_number_slider),\"Normal\")]\n\n return video_state[\"painted_images\"][track_pause_number_slider],interactive_state, operation_log\n\ndef get_resize_ratio(resize_ratio_slider, interactive_state):\n interactive_state[\"resize_ratio\"] = resize_ratio_slider\n\n return interactive_state\n\n# use sam to get the mask\ndef sam_refine(video_state, point_prompt, click_state, interactive_state, evt:gr.SelectData):\n \"\"\"\n Args:\n template_frame: PIL.Image\n point_prompt: flag for positive or negative button click\n click_state: [[points], [labels]]\n \"\"\"\n if point_prompt == \"Positive\":\n coordinate = \"[[{},{},1]]\".format(evt.index[0], evt.index[1])\n interactive_state[\"positive_click_times\"] += 1\n else:\n coordinate = \"[[{},{},0]]\".format(evt.index[0], evt.index[1])\n interactive_state[\"negative_click_times\"] += 1\n \n # prompt for sam model\n model.samcontroler.sam_controler.reset_image()\n model.samcontroler.sam_controler.set_image(video_state[\"origin_images\"][video_state[\"select_frame_number\"]])\n prompt = get_prompt(click_state=click_state, click_input=coordinate)","source_hash":"21d2c55f3fcfa74fc1eff2135fa6381b8f8470dd6c58c999e5adef16e4064eea","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:app.sam_refine","uri":"program://Track-Anything/function/app.sam_refine#L162-L192","kind":"function","name":"sam_refine","path":"app.py","language":"python","start_line":162,"end_line":192,"context_start_line":142,"context_end_line":212,"code":" if mask_dropdown:\n print(\"ok\")\n operation_log = [(\"\",\"\"), (\"Select frame {}. Try click image and add mask for tracking.\".format(image_selection_slider),\"Normal\")]\n\n\n return video_state[\"painted_images\"][image_selection_slider], video_state, interactive_state, operation_log\n\n# set the tracking end frame\ndef get_end_number(track_pause_number_slider, video_state, interactive_state):\n interactive_state[\"track_end_number\"] = track_pause_number_slider\n operation_log = [(\"\",\"\"),(\"Set the tracking finish at frame {}\".format(track_pause_number_slider),\"Normal\")]\n\n return video_state[\"painted_images\"][track_pause_number_slider],interactive_state, operation_log\n\ndef get_resize_ratio(resize_ratio_slider, interactive_state):\n interactive_state[\"resize_ratio\"] = resize_ratio_slider\n\n return interactive_state\n\n# use sam to get the mask\ndef sam_refine(video_state, point_prompt, click_state, interactive_state, evt:gr.SelectData):\n \"\"\"\n Args:\n template_frame: PIL.Image\n point_prompt: flag for positive or negative button click\n click_state: [[points], [labels]]\n \"\"\"\n if point_prompt == \"Positive\":\n coordinate = \"[[{},{},1]]\".format(evt.index[0], evt.index[1])\n interactive_state[\"positive_click_times\"] += 1\n else:\n coordinate = \"[[{},{},0]]\".format(evt.index[0], evt.index[1])\n interactive_state[\"negative_click_times\"] += 1\n \n # prompt for sam model\n model.samcontroler.sam_controler.reset_image()\n model.samcontroler.sam_controler.set_image(video_state[\"origin_images\"][video_state[\"select_frame_number\"]])\n prompt = get_prompt(click_state=click_state, click_input=coordinate)\n\n mask, logit, painted_image = model.first_frame_click( \n image=video_state[\"origin_images\"][video_state[\"select_frame_number\"]], \n points=np.array(prompt[\"input_point\"]),\n labels=np.array(prompt[\"input_label\"]),\n multimask=prompt[\"multimask_output\"],\n )\n video_state[\"masks\"][video_state[\"select_frame_number\"]] = mask\n video_state[\"logits\"][video_state[\"select_frame_number\"]] = logit\n video_state[\"painted_images\"][video_state[\"select_frame_number\"]] = painted_image\n\n operation_log = [(\"\",\"\"), (\"Use SAM for segment. You can try add positive and negative points by clicking. Or press Clear clicks button to refresh the image. Press Add mask button when you are satisfied with the segment\",\"Normal\")]\n return painted_image, video_state, interactive_state, operation_log\n\ndef add_multi_mask(video_state, interactive_state, mask_dropdown):\n try:\n mask = video_state[\"masks\"][video_state[\"select_frame_number\"]]\n interactive_state[\"multi_mask\"][\"masks\"].append(mask)\n interactive_state[\"multi_mask\"][\"mask_names\"].append(\"mask_{:03d}\".format(len(interactive_state[\"multi_mask\"][\"masks\"])))\n mask_dropdown.append(\"mask_{:03d}\".format(len(interactive_state[\"multi_mask\"][\"masks\"])))\n select_frame, run_status = show_mask(video_state, interactive_state, mask_dropdown)\n\n operation_log = [(\"\",\"\"),(\"Added a mask, use the mask select for target tracking or inpainting.\",\"Normal\")]\n except:\n operation_log = [(\"Please click the left image to generate mask.\", \"Error\"), (\"\",\"\")]\n return interactive_state, gr.update(choices=interactive_state[\"multi_mask\"][\"mask_names\"], value=mask_dropdown), select_frame, [[],[]], operation_log\n\ndef clear_click(video_state, click_state):\n click_state = [[],[]]\n template_frame = video_state[\"origin_images\"][video_state[\"select_frame_number\"]]\n operation_log = [(\"\",\"\"), (\"Clear points history and refresh the image.\",\"Normal\")]\n return template_frame, click_state, operation_log\n","source_hash":"21d2c55f3fcfa74fc1eff2135fa6381b8f8470dd6c58c999e5adef16e4064eea","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:app.add_multi_mask","uri":"program://Track-Anything/function/app.add_multi_mask#L194-L205","kind":"function","name":"add_multi_mask","path":"app.py","language":"python","start_line":194,"end_line":205,"context_start_line":174,"context_end_line":225,"code":" interactive_state[\"negative_click_times\"] += 1\n \n # prompt for sam model\n model.samcontroler.sam_controler.reset_image()\n model.samcontroler.sam_controler.set_image(video_state[\"origin_images\"][video_state[\"select_frame_number\"]])\n prompt = get_prompt(click_state=click_state, click_input=coordinate)\n\n mask, logit, painted_image = model.first_frame_click( \n image=video_state[\"origin_images\"][video_state[\"select_frame_number\"]], \n points=np.array(prompt[\"input_point\"]),\n labels=np.array(prompt[\"input_label\"]),\n multimask=prompt[\"multimask_output\"],\n )\n video_state[\"masks\"][video_state[\"select_frame_number\"]] = mask\n video_state[\"logits\"][video_state[\"select_frame_number\"]] = logit\n video_state[\"painted_images\"][video_state[\"select_frame_number\"]] = painted_image\n\n operation_log = [(\"\",\"\"), (\"Use SAM for segment. You can try add positive and negative points by clicking. Or press Clear clicks button to refresh the image. Press Add mask button when you are satisfied with the segment\",\"Normal\")]\n return painted_image, video_state, interactive_state, operation_log\n\ndef add_multi_mask(video_state, interactive_state, mask_dropdown):\n try:\n mask = video_state[\"masks\"][video_state[\"select_frame_number\"]]\n interactive_state[\"multi_mask\"][\"masks\"].append(mask)\n interactive_state[\"multi_mask\"][\"mask_names\"].append(\"mask_{:03d}\".format(len(interactive_state[\"multi_mask\"][\"masks\"])))\n mask_dropdown.append(\"mask_{:03d}\".format(len(interactive_state[\"multi_mask\"][\"masks\"])))\n select_frame, run_status = show_mask(video_state, interactive_state, mask_dropdown)\n\n operation_log = [(\"\",\"\"),(\"Added a mask, use the mask select for target tracking or inpainting.\",\"Normal\")]\n except:\n operation_log = [(\"Please click the left image to generate mask.\", \"Error\"), (\"\",\"\")]\n return interactive_state, gr.update(choices=interactive_state[\"multi_mask\"][\"mask_names\"], value=mask_dropdown), select_frame, [[],[]], operation_log\n\ndef clear_click(video_state, click_state):\n click_state = [[],[]]\n template_frame = video_state[\"origin_images\"][video_state[\"select_frame_number\"]]\n operation_log = [(\"\",\"\"), (\"Clear points history and refresh the image.\",\"Normal\")]\n return template_frame, click_state, operation_log\n\ndef remove_multi_mask(interactive_state, mask_dropdown):\n interactive_state[\"multi_mask\"][\"mask_names\"]= []\n interactive_state[\"multi_mask\"][\"masks\"] = []\n\n operation_log = [(\"\",\"\"), (\"Remove all mask, please add new masks\",\"Normal\")]\n return interactive_state, gr.update(choices=[],value=[]), operation_log\n\ndef show_mask(video_state, interactive_state, mask_dropdown):\n mask_dropdown.sort()\n select_frame = video_state[\"origin_images\"][video_state[\"select_frame_number\"]]\n for i in range(len(mask_dropdown)):\n mask_number = int(mask_dropdown[i].split(\"_\")[1]) - 1\n mask = interactive_state[\"multi_mask\"][\"masks\"][mask_number]","source_hash":"21d2c55f3fcfa74fc1eff2135fa6381b8f8470dd6c58c999e5adef16e4064eea","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:app.clear_click","uri":"program://Track-Anything/function/app.clear_click#L207-L211","kind":"function","name":"clear_click","path":"app.py","language":"python","start_line":207,"end_line":211,"context_start_line":187,"context_end_line":231,"code":" video_state[\"masks\"][video_state[\"select_frame_number\"]] = mask\n video_state[\"logits\"][video_state[\"select_frame_number\"]] = logit\n video_state[\"painted_images\"][video_state[\"select_frame_number\"]] = painted_image\n\n operation_log = [(\"\",\"\"), (\"Use SAM for segment. You can try add positive and negative points by clicking. Or press Clear clicks button to refresh the image. Press Add mask button when you are satisfied with the segment\",\"Normal\")]\n return painted_image, video_state, interactive_state, operation_log\n\ndef add_multi_mask(video_state, interactive_state, mask_dropdown):\n try:\n mask = video_state[\"masks\"][video_state[\"select_frame_number\"]]\n interactive_state[\"multi_mask\"][\"masks\"].append(mask)\n interactive_state[\"multi_mask\"][\"mask_names\"].append(\"mask_{:03d}\".format(len(interactive_state[\"multi_mask\"][\"masks\"])))\n mask_dropdown.append(\"mask_{:03d}\".format(len(interactive_state[\"multi_mask\"][\"masks\"])))\n select_frame, run_status = show_mask(video_state, interactive_state, mask_dropdown)\n\n operation_log = [(\"\",\"\"),(\"Added a mask, use the mask select for target tracking or inpainting.\",\"Normal\")]\n except:\n operation_log = [(\"Please click the left image to generate mask.\", \"Error\"), (\"\",\"\")]\n return interactive_state, gr.update(choices=interactive_state[\"multi_mask\"][\"mask_names\"], value=mask_dropdown), select_frame, [[],[]], operation_log\n\ndef clear_click(video_state, click_state):\n click_state = [[],[]]\n template_frame = video_state[\"origin_images\"][video_state[\"select_frame_number\"]]\n operation_log = [(\"\",\"\"), (\"Clear points history and refresh the image.\",\"Normal\")]\n return template_frame, click_state, operation_log\n\ndef remove_multi_mask(interactive_state, mask_dropdown):\n interactive_state[\"multi_mask\"][\"mask_names\"]= []\n interactive_state[\"multi_mask\"][\"masks\"] = []\n\n operation_log = [(\"\",\"\"), (\"Remove all mask, please add new masks\",\"Normal\")]\n return interactive_state, gr.update(choices=[],value=[]), operation_log\n\ndef show_mask(video_state, interactive_state, mask_dropdown):\n mask_dropdown.sort()\n select_frame = video_state[\"origin_images\"][video_state[\"select_frame_number\"]]\n for i in range(len(mask_dropdown)):\n mask_number = int(mask_dropdown[i].split(\"_\")[1]) - 1\n mask = interactive_state[\"multi_mask\"][\"masks\"][mask_number]\n select_frame = mask_painter(select_frame, mask.astype('uint8'), mask_color=mask_number+2)\n \n operation_log = [(\"\",\"\"), (\"Select {} for tracking or inpainting\".format(mask_dropdown),\"Normal\")]\n return select_frame, operation_log\n\n# tracking vos","source_hash":"21d2c55f3fcfa74fc1eff2135fa6381b8f8470dd6c58c999e5adef16e4064eea","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:app.remove_multi_mask","uri":"program://Track-Anything/function/app.remove_multi_mask#L213-L218","kind":"function","name":"remove_multi_mask","path":"app.py","language":"python","start_line":213,"end_line":218,"context_start_line":193,"context_end_line":238,"code":"\ndef add_multi_mask(video_state, interactive_state, mask_dropdown):\n try:\n mask = video_state[\"masks\"][video_state[\"select_frame_number\"]]\n interactive_state[\"multi_mask\"][\"masks\"].append(mask)\n interactive_state[\"multi_mask\"][\"mask_names\"].append(\"mask_{:03d}\".format(len(interactive_state[\"multi_mask\"][\"masks\"])))\n mask_dropdown.append(\"mask_{:03d}\".format(len(interactive_state[\"multi_mask\"][\"masks\"])))\n select_frame, run_status = show_mask(video_state, interactive_state, mask_dropdown)\n\n operation_log = [(\"\",\"\"),(\"Added a mask, use the mask select for target tracking or inpainting.\",\"Normal\")]\n except:\n operation_log = [(\"Please click the left image to generate mask.\", \"Error\"), (\"\",\"\")]\n return interactive_state, gr.update(choices=interactive_state[\"multi_mask\"][\"mask_names\"], value=mask_dropdown), select_frame, [[],[]], operation_log\n\ndef clear_click(video_state, click_state):\n click_state = [[],[]]\n template_frame = video_state[\"origin_images\"][video_state[\"select_frame_number\"]]\n operation_log = [(\"\",\"\"), (\"Clear points history and refresh the image.\",\"Normal\")]\n return template_frame, click_state, operation_log\n\ndef remove_multi_mask(interactive_state, mask_dropdown):\n interactive_state[\"multi_mask\"][\"mask_names\"]= []\n interactive_state[\"multi_mask\"][\"masks\"] = []\n\n operation_log = [(\"\",\"\"), (\"Remove all mask, please add new masks\",\"Normal\")]\n return interactive_state, gr.update(choices=[],value=[]), operation_log\n\ndef show_mask(video_state, interactive_state, mask_dropdown):\n mask_dropdown.sort()\n select_frame = video_state[\"origin_images\"][video_state[\"select_frame_number\"]]\n for i in range(len(mask_dropdown)):\n mask_number = int(mask_dropdown[i].split(\"_\")[1]) - 1\n mask = interactive_state[\"multi_mask\"][\"masks\"][mask_number]\n select_frame = mask_painter(select_frame, mask.astype('uint8'), mask_color=mask_number+2)\n \n operation_log = [(\"\",\"\"), (\"Select {} for tracking or inpainting\".format(mask_dropdown),\"Normal\")]\n return select_frame, operation_log\n\n# tracking vos\ndef vos_tracking_video(video_state, interactive_state, mask_dropdown):\n operation_log = [(\"\",\"\"), (\"Track the selected masks, and then you can select the masks for inpainting.\",\"Normal\")]\n model.xmem.clear_memory()\n if interactive_state[\"track_end_number\"]:\n following_frames = video_state[\"origin_images\"][video_state[\"select_frame_number\"]:interactive_state[\"track_end_number\"]]\n else:\n following_frames = video_state[\"origin_images\"][video_state[\"select_frame_number\"]:]","source_hash":"21d2c55f3fcfa74fc1eff2135fa6381b8f8470dd6c58c999e5adef16e4064eea","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:app.show_mask","uri":"program://Track-Anything/function/app.show_mask#L220-L229","kind":"function","name":"show_mask","path":"app.py","language":"python","start_line":220,"end_line":229,"context_start_line":200,"context_end_line":249,"code":" select_frame, run_status = show_mask(video_state, interactive_state, mask_dropdown)\n\n operation_log = [(\"\",\"\"),(\"Added a mask, use the mask select for target tracking or inpainting.\",\"Normal\")]\n except:\n operation_log = [(\"Please click the left image to generate mask.\", \"Error\"), (\"\",\"\")]\n return interactive_state, gr.update(choices=interactive_state[\"multi_mask\"][\"mask_names\"], value=mask_dropdown), select_frame, [[],[]], operation_log\n\ndef clear_click(video_state, click_state):\n click_state = [[],[]]\n template_frame = video_state[\"origin_images\"][video_state[\"select_frame_number\"]]\n operation_log = [(\"\",\"\"), (\"Clear points history and refresh the image.\",\"Normal\")]\n return template_frame, click_state, operation_log\n\ndef remove_multi_mask(interactive_state, mask_dropdown):\n interactive_state[\"multi_mask\"][\"mask_names\"]= []\n interactive_state[\"multi_mask\"][\"masks\"] = []\n\n operation_log = [(\"\",\"\"), (\"Remove all mask, please add new masks\",\"Normal\")]\n return interactive_state, gr.update(choices=[],value=[]), operation_log\n\ndef show_mask(video_state, interactive_state, mask_dropdown):\n mask_dropdown.sort()\n select_frame = video_state[\"origin_images\"][video_state[\"select_frame_number\"]]\n for i in range(len(mask_dropdown)):\n mask_number = int(mask_dropdown[i].split(\"_\")[1]) - 1\n mask = interactive_state[\"multi_mask\"][\"masks\"][mask_number]\n select_frame = mask_painter(select_frame, mask.astype('uint8'), mask_color=mask_number+2)\n \n operation_log = [(\"\",\"\"), (\"Select {} for tracking or inpainting\".format(mask_dropdown),\"Normal\")]\n return select_frame, operation_log\n\n# tracking vos\ndef vos_tracking_video(video_state, interactive_state, mask_dropdown):\n operation_log = [(\"\",\"\"), (\"Track the selected masks, and then you can select the masks for inpainting.\",\"Normal\")]\n model.xmem.clear_memory()\n if interactive_state[\"track_end_number\"]:\n following_frames = video_state[\"origin_images\"][video_state[\"select_frame_number\"]:interactive_state[\"track_end_number\"]]\n else:\n following_frames = video_state[\"origin_images\"][video_state[\"select_frame_number\"]:]\n\n if interactive_state[\"multi_mask\"][\"masks\"]:\n if len(mask_dropdown) == 0:\n mask_dropdown = [\"mask_001\"]\n mask_dropdown.sort()\n template_mask = interactive_state[\"multi_mask\"][\"masks\"][int(mask_dropdown[0].split(\"_\")[1]) - 1] * (int(mask_dropdown[0].split(\"_\")[1]))\n for i in range(1,len(mask_dropdown)):\n mask_number = int(mask_dropdown[i].split(\"_\")[1]) - 1 \n template_mask = np.clip(template_mask+interactive_state[\"multi_mask\"][\"masks\"][mask_number]*(mask_number+1), 0, mask_number+1)\n video_state[\"masks\"][video_state[\"select_frame_number\"]]= template_mask\n else: ","source_hash":"21d2c55f3fcfa74fc1eff2135fa6381b8f8470dd6c58c999e5adef16e4064eea","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:app.vos_tracking_video","uri":"program://Track-Anything/function/app.vos_tracking_video#L232-L290","kind":"function","name":"vos_tracking_video","path":"app.py","language":"python","start_line":232,"end_line":290,"context_start_line":212,"context_end_line":310,"code":"\ndef remove_multi_mask(interactive_state, mask_dropdown):\n interactive_state[\"multi_mask\"][\"mask_names\"]= []\n interactive_state[\"multi_mask\"][\"masks\"] = []\n\n operation_log = [(\"\",\"\"), (\"Remove all mask, please add new masks\",\"Normal\")]\n return interactive_state, gr.update(choices=[],value=[]), operation_log\n\ndef show_mask(video_state, interactive_state, mask_dropdown):\n mask_dropdown.sort()\n select_frame = video_state[\"origin_images\"][video_state[\"select_frame_number\"]]\n for i in range(len(mask_dropdown)):\n mask_number = int(mask_dropdown[i].split(\"_\")[1]) - 1\n mask = interactive_state[\"multi_mask\"][\"masks\"][mask_number]\n select_frame = mask_painter(select_frame, mask.astype('uint8'), mask_color=mask_number+2)\n \n operation_log = [(\"\",\"\"), (\"Select {} for tracking or inpainting\".format(mask_dropdown),\"Normal\")]\n return select_frame, operation_log\n\n# tracking vos\ndef vos_tracking_video(video_state, interactive_state, mask_dropdown):\n operation_log = [(\"\",\"\"), (\"Track the selected masks, and then you can select the masks for inpainting.\",\"Normal\")]\n model.xmem.clear_memory()\n if interactive_state[\"track_end_number\"]:\n following_frames = video_state[\"origin_images\"][video_state[\"select_frame_number\"]:interactive_state[\"track_end_number\"]]\n else:\n following_frames = video_state[\"origin_images\"][video_state[\"select_frame_number\"]:]\n\n if interactive_state[\"multi_mask\"][\"masks\"]:\n if len(mask_dropdown) == 0:\n mask_dropdown = [\"mask_001\"]\n mask_dropdown.sort()\n template_mask = interactive_state[\"multi_mask\"][\"masks\"][int(mask_dropdown[0].split(\"_\")[1]) - 1] * (int(mask_dropdown[0].split(\"_\")[1]))\n for i in range(1,len(mask_dropdown)):\n mask_number = int(mask_dropdown[i].split(\"_\")[1]) - 1 \n template_mask = np.clip(template_mask+interactive_state[\"multi_mask\"][\"masks\"][mask_number]*(mask_number+1), 0, mask_number+1)\n video_state[\"masks\"][video_state[\"select_frame_number\"]]= template_mask\n else: \n template_mask = video_state[\"masks\"][video_state[\"select_frame_number\"]]\n fps = video_state[\"fps\"]\n\n # operation error\n if len(np.unique(template_mask))==1:\n template_mask[0][0]=1\n operation_log = [(\"Error! Please add at least one mask to track by clicking the left image.\",\"Error\"), (\"\",\"\")]\n # return video_output, video_state, interactive_state, operation_error\n masks, logits, painted_images = model.generator(images=following_frames, template_mask=template_mask)\n # clear GPU memory\n model.xmem.clear_memory()\n\n if interactive_state[\"track_end_number\"]: \n video_state[\"masks\"][video_state[\"select_frame_number\"]:interactive_state[\"track_end_number\"]] = masks\n video_state[\"logits\"][video_state[\"select_frame_number\"]:interactive_state[\"track_end_number\"]] = logits\n video_state[\"painted_images\"][video_state[\"select_frame_number\"]:interactive_state[\"track_end_number\"]] = painted_images\n else:\n video_state[\"masks\"][video_state[\"select_frame_number\"]:] = masks\n video_state[\"logits\"][video_state[\"select_frame_number\"]:] = logits\n video_state[\"painted_images\"][video_state[\"select_frame_number\"]:] = painted_images\n\n video_output = generate_video_from_frames(video_state[\"painted_images\"], output_path=\"./result/track/{}\".format(video_state[\"video_name\"]), fps=fps) # import video_input to name the output video\n interactive_state[\"inference_times\"] += 1\n \n print(\"For generating this tracking result, inference times: {}, click times: {}, positive: {}, negative: {}\".format(interactive_state[\"inference_times\"], \n interactive_state[\"positive_click_times\"]+interactive_state[\"negative_click_times\"],\n interactive_state[\"positive_click_times\"],\n interactive_state[\"negative_click_times\"]))\n\n #### shanggao code for mask save\n if interactive_state[\"mask_save\"]:\n if not os.path.exists('./result/mask/{}'.format(video_state[\"video_name\"].split('.')[0])):\n os.makedirs('./result/mask/{}'.format(video_state[\"video_name\"].split('.')[0]))\n i = 0\n print(\"save mask\")\n for mask in video_state[\"masks\"]:\n np.save(os.path.join('./result/mask/{}'.format(video_state[\"video_name\"].split('.')[0]), '{:05d}.npy'.format(i)), mask)\n i+=1\n # save_mask(video_state[\"masks\"], video_state[\"video_name\"])\n #### shanggao code for mask save\n return video_output, video_state, interactive_state, operation_log\n\n# extracting masks from mask_dropdown\n# def extract_sole_mask(video_state, mask_dropdown):\n# combined_masks = \n# unique_masks = np.unique(combined_masks)\n# return 0 \n\n# inpaint \ndef inpaint_video(video_state, interactive_state, mask_dropdown):\n operation_log = [(\"\",\"\"), (\"Removed the selected masks.\",\"Normal\")]\n\n frames = np.asarray(video_state[\"origin_images\"])\n fps = video_state[\"fps\"]\n inpaint_masks = np.asarray(video_state[\"masks\"])\n if len(mask_dropdown) == 0:\n mask_dropdown = [\"mask_001\"]\n mask_dropdown.sort()\n # convert mask_dropdown to mask numbers\n inpaint_mask_numbers = [int(mask_dropdown[i].split(\"_\")[1]) for i in range(len(mask_dropdown))]\n # interate through all masks and remove the masks that are not in mask_dropdown","source_hash":"21d2c55f3fcfa74fc1eff2135fa6381b8f8470dd6c58c999e5adef16e4064eea","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:app.inpaint_video","uri":"program://Track-Anything/function/app.inpaint_video#L299-L326","kind":"function","name":"inpaint_video","path":"app.py","language":"python","start_line":299,"end_line":326,"context_start_line":279,"context_end_line":346,"code":" #### shanggao code for mask save\n if interactive_state[\"mask_save\"]:\n if not os.path.exists('./result/mask/{}'.format(video_state[\"video_name\"].split('.')[0])):\n os.makedirs('./result/mask/{}'.format(video_state[\"video_name\"].split('.')[0]))\n i = 0\n print(\"save mask\")\n for mask in video_state[\"masks\"]:\n np.save(os.path.join('./result/mask/{}'.format(video_state[\"video_name\"].split('.')[0]), '{:05d}.npy'.format(i)), mask)\n i+=1\n # save_mask(video_state[\"masks\"], video_state[\"video_name\"])\n #### shanggao code for mask save\n return video_output, video_state, interactive_state, operation_log\n\n# extracting masks from mask_dropdown\n# def extract_sole_mask(video_state, mask_dropdown):\n# combined_masks = \n# unique_masks = np.unique(combined_masks)\n# return 0 \n\n# inpaint \ndef inpaint_video(video_state, interactive_state, mask_dropdown):\n operation_log = [(\"\",\"\"), (\"Removed the selected masks.\",\"Normal\")]\n\n frames = np.asarray(video_state[\"origin_images\"])\n fps = video_state[\"fps\"]\n inpaint_masks = np.asarray(video_state[\"masks\"])\n if len(mask_dropdown) == 0:\n mask_dropdown = [\"mask_001\"]\n mask_dropdown.sort()\n # convert mask_dropdown to mask numbers\n inpaint_mask_numbers = [int(mask_dropdown[i].split(\"_\")[1]) for i in range(len(mask_dropdown))]\n # interate through all masks and remove the masks that are not in mask_dropdown\n unique_masks = np.unique(inpaint_masks)\n num_masks = len(unique_masks) - 1\n for i in range(1, num_masks + 1):\n if i in inpaint_mask_numbers:\n continue\n inpaint_masks[inpaint_masks==i] = 0\n # inpaint for videos\n\n try:\n inpainted_frames = model.baseinpainter.inpaint(frames, inpaint_masks, ratio=interactive_state[\"resize_ratio\"]) # numpy array, T, H, W, 3\n except:\n operation_log = [(\"Error! You are trying to inpaint without masks input. Please track the selected mask first, and then press inpaint. If VRAM exceeded, please use the resize ratio to scaling down the image size.\",\"Error\"), (\"\",\"\")]\n inpainted_frames = video_state[\"origin_images\"]\n video_output = generate_video_from_frames(inpainted_frames, output_path=\"./result/inpaint/{}\".format(video_state[\"video_name\"]), fps=fps) # import video_input to name the output video\n\n return video_output, operation_log\n\n\n# generate video after vos inference\ndef generate_video_from_frames(frames, output_path, fps=30):\n \"\"\"\n Generates a video from a list of frames.\n \n Args:\n frames (list of numpy arrays): The frames to include in the video.\n output_path (str): The path to save the generated video.\n fps (int, optional): The frame rate of the output video. Defaults to 30.\n \"\"\"\n # height, width, layers = frames[0].shape\n # fourcc = cv2.VideoWriter_fourcc(*\"mp4v\")\n # video = cv2.VideoWriter(output_path, fourcc, fps, (width, height))\n # print(output_path)\n # for frame in frames:\n # video.write(frame)\n \n # video.release()","source_hash":"21d2c55f3fcfa74fc1eff2135fa6381b8f8470dd6c58c999e5adef16e4064eea","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:app.generate_video_from_frames","uri":"program://Track-Anything/function/app.generate_video_from_frames#L330-L351","kind":"function","name":"generate_video_from_frames","path":"app.py","language":"python","start_line":330,"end_line":351,"context_start_line":310,"context_end_line":371,"code":" # interate through all masks and remove the masks that are not in mask_dropdown\n unique_masks = np.unique(inpaint_masks)\n num_masks = len(unique_masks) - 1\n for i in range(1, num_masks + 1):\n if i in inpaint_mask_numbers:\n continue\n inpaint_masks[inpaint_masks==i] = 0\n # inpaint for videos\n\n try:\n inpainted_frames = model.baseinpainter.inpaint(frames, inpaint_masks, ratio=interactive_state[\"resize_ratio\"]) # numpy array, T, H, W, 3\n except:\n operation_log = [(\"Error! You are trying to inpaint without masks input. Please track the selected mask first, and then press inpaint. If VRAM exceeded, please use the resize ratio to scaling down the image size.\",\"Error\"), (\"\",\"\")]\n inpainted_frames = video_state[\"origin_images\"]\n video_output = generate_video_from_frames(inpainted_frames, output_path=\"./result/inpaint/{}\".format(video_state[\"video_name\"]), fps=fps) # import video_input to name the output video\n\n return video_output, operation_log\n\n\n# generate video after vos inference\ndef generate_video_from_frames(frames, output_path, fps=30):\n \"\"\"\n Generates a video from a list of frames.\n \n Args:\n frames (list of numpy arrays): The frames to include in the video.\n output_path (str): The path to save the generated video.\n fps (int, optional): The frame rate of the output video. Defaults to 30.\n \"\"\"\n # height, width, layers = frames[0].shape\n # fourcc = cv2.VideoWriter_fourcc(*\"mp4v\")\n # video = cv2.VideoWriter(output_path, fourcc, fps, (width, height))\n # print(output_path)\n # for frame in frames:\n # video.write(frame)\n \n # video.release()\n frames = torch.from_numpy(np.asarray(frames))\n if not os.path.exists(os.path.dirname(output_path)):\n os.makedirs(os.path.dirname(output_path))\n torchvision.io.write_video(output_path, frames, fps=fps, video_codec=\"libx264\")\n return output_path\n\n\n# args, defined in track_anything.py\nargs = parse_augment()\n\n# check and download checkpoints if needed\nSAM_checkpoint_dict = {\n 'vit_h': \"sam_vit_h_4b8939.pth\",\n 'vit_l': \"sam_vit_l_0b3195.pth\", \n \"vit_b\": \"sam_vit_b_01ec64.pth\"\n}\nSAM_checkpoint_url_dict = {\n 'vit_h': \"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\",\n 'vit_l': \"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth\",\n 'vit_b': \"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth\"\n}\nsam_checkpoint = SAM_checkpoint_dict[args.sam_model_type] \nsam_checkpoint_url = SAM_checkpoint_url_dict[args.sam_model_type] \nxmem_checkpoint = \"XMem-s012.pth\"\nxmem_checkpoint_url = \"https://github.com/hkchengrex/XMem/releases/download/v1.0/XMem-s012.pth\"","source_hash":"21d2c55f3fcfa74fc1eff2135fa6381b8f8470dd6c58c999e5adef16e4064eea","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:text_server","uri":"program://Track-Anything/module/text_server#L1-L72","kind":"module","name":"text_server","path":"text_server.py","language":"python","start_line":1,"end_line":72,"context_start_line":1,"context_end_line":72,"code":"import os\nimport sys\nimport cv2\nimport time\nimport json\nimport queue\nimport numpy as np\nimport requests\nimport concurrent.futures\nfrom PIL import Image\nfrom flask import Flask, render_template, request, jsonify, send_file\nimport torchvision\nimport torch\n\nfrom demo import automask_image_app, automask_video_app, sahi_autoseg_app\nsys.path.append(sys.path[0] + \"/tracker\")\nsys.path.append(sys.path[0] + \"/tracker/model\")\nfrom track_anything import TrackingAnything\nfrom track_anything import parse_augment\n\n# ... (all the functions defined in the original code except the Gradio part)\n\napp = Flask(__name__)\napp.config['UPLOAD_FOLDER'] = './uploaded_videos'\napp.config['ALLOWED_EXTENSIONS'] = {'mp4', 'avi', 'mov', 'mkv'}\n\n\ndef allowed_file(filename):\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in app.config['ALLOWED_EXTENSIONS']\n\n@app.route(\"/\")\ndef index():\n return render_template(\"index.html\")\n\n@app.route(\"/upload_video\", methods=[\"POST\"])\ndef upload_video():\n # ... (handle video upload and processing)\n return jsonify(status=\"success\", data=video_data)\n\n@app.route(\"/template_select\", methods=[\"POST\"])\ndef template_select():\n # ... (handle template selection and processing)\n return jsonify(status=\"success\", data=template_data)\n\n@app.route(\"/sam_refine\", methods=[\"POST\"])\ndef sam_refine_request():\n # ... (handle sam refine and processing)\n return jsonify(status=\"success\", data=sam_data)\n\n@app.route(\"/track_video\", methods=[\"POST\"])\ndef track_video():\n # ... (handle video tracking and processing)\n return jsonify(status=\"success\", data=tracking_data)\n\n@app.route(\"/track_image\", methods=[\"POST\"])\ndef track_image():\n # ... (handle image tracking and processing)\n return jsonify(status=\"success\", data=tracking_data)\n\n@app.route(\"/download_video\", methods=[\"GET\"])\ndef download_video():\n try:\n return send_file(\"output.mp4\", attachment_filename=\"output.mp4\")\n except Exception as e:\n return str(e)\n\nif __name__ == \"__main__\":\n app.run(debug=True, host=\"0.0.0.0\", port=args.port)\n\n\nif __name__ == '__main__':\n app.run(host=\"0.0.0.0\",port=12212, debug=True)","source_hash":"50b2875c0c4d91501b5ecebc15a34ca02e93fc817a951600f3c8ba95d2d36d24","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:text_server.allowed_file","uri":"program://Track-Anything/function/text_server.allowed_file#L28-L29","kind":"function","name":"allowed_file","path":"text_server.py","language":"python","start_line":28,"end_line":29,"context_start_line":8,"context_end_line":49,"code":"import requests\nimport concurrent.futures\nfrom PIL import Image\nfrom flask import Flask, render_template, request, jsonify, send_file\nimport torchvision\nimport torch\n\nfrom demo import automask_image_app, automask_video_app, sahi_autoseg_app\nsys.path.append(sys.path[0] + \"/tracker\")\nsys.path.append(sys.path[0] + \"/tracker/model\")\nfrom track_anything import TrackingAnything\nfrom track_anything import parse_augment\n\n# ... (all the functions defined in the original code except the Gradio part)\n\napp = Flask(__name__)\napp.config['UPLOAD_FOLDER'] = './uploaded_videos'\napp.config['ALLOWED_EXTENSIONS'] = {'mp4', 'avi', 'mov', 'mkv'}\n\n\ndef allowed_file(filename):\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in app.config['ALLOWED_EXTENSIONS']\n\n@app.route(\"/\")\ndef index():\n return render_template(\"index.html\")\n\n@app.route(\"/upload_video\", methods=[\"POST\"])\ndef upload_video():\n # ... (handle video upload and processing)\n return jsonify(status=\"success\", data=video_data)\n\n@app.route(\"/template_select\", methods=[\"POST\"])\ndef template_select():\n # ... (handle template selection and processing)\n return jsonify(status=\"success\", data=template_data)\n\n@app.route(\"/sam_refine\", methods=[\"POST\"])\ndef sam_refine_request():\n # ... (handle sam refine and processing)\n return jsonify(status=\"success\", data=sam_data)\n","source_hash":"50b2875c0c4d91501b5ecebc15a34ca02e93fc817a951600f3c8ba95d2d36d24","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:text_server.index","uri":"program://Track-Anything/function/text_server.index#L32-L33","kind":"function","name":"index","path":"text_server.py","language":"python","start_line":32,"end_line":33,"context_start_line":12,"context_end_line":53,"code":"import torchvision\nimport torch\n\nfrom demo import automask_image_app, automask_video_app, sahi_autoseg_app\nsys.path.append(sys.path[0] + \"/tracker\")\nsys.path.append(sys.path[0] + \"/tracker/model\")\nfrom track_anything import TrackingAnything\nfrom track_anything import parse_augment\n\n# ... (all the functions defined in the original code except the Gradio part)\n\napp = Flask(__name__)\napp.config['UPLOAD_FOLDER'] = './uploaded_videos'\napp.config['ALLOWED_EXTENSIONS'] = {'mp4', 'avi', 'mov', 'mkv'}\n\n\ndef allowed_file(filename):\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in app.config['ALLOWED_EXTENSIONS']\n\n@app.route(\"/\")\ndef index():\n return render_template(\"index.html\")\n\n@app.route(\"/upload_video\", methods=[\"POST\"])\ndef upload_video():\n # ... (handle video upload and processing)\n return jsonify(status=\"success\", data=video_data)\n\n@app.route(\"/template_select\", methods=[\"POST\"])\ndef template_select():\n # ... (handle template selection and processing)\n return jsonify(status=\"success\", data=template_data)\n\n@app.route(\"/sam_refine\", methods=[\"POST\"])\ndef sam_refine_request():\n # ... (handle sam refine and processing)\n return jsonify(status=\"success\", data=sam_data)\n\n@app.route(\"/track_video\", methods=[\"POST\"])\ndef track_video():\n # ... (handle video tracking and processing)\n return jsonify(status=\"success\", data=tracking_data)","source_hash":"50b2875c0c4d91501b5ecebc15a34ca02e93fc817a951600f3c8ba95d2d36d24","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:text_server.upload_video","uri":"program://Track-Anything/function/text_server.upload_video#L36-L38","kind":"function","name":"upload_video","path":"text_server.py","language":"python","start_line":36,"end_line":38,"context_start_line":16,"context_end_line":58,"code":"sys.path.append(sys.path[0] + \"/tracker\")\nsys.path.append(sys.path[0] + \"/tracker/model\")\nfrom track_anything import TrackingAnything\nfrom track_anything import parse_augment\n\n# ... (all the functions defined in the original code except the Gradio part)\n\napp = Flask(__name__)\napp.config['UPLOAD_FOLDER'] = './uploaded_videos'\napp.config['ALLOWED_EXTENSIONS'] = {'mp4', 'avi', 'mov', 'mkv'}\n\n\ndef allowed_file(filename):\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in app.config['ALLOWED_EXTENSIONS']\n\n@app.route(\"/\")\ndef index():\n return render_template(\"index.html\")\n\n@app.route(\"/upload_video\", methods=[\"POST\"])\ndef upload_video():\n # ... (handle video upload and processing)\n return jsonify(status=\"success\", data=video_data)\n\n@app.route(\"/template_select\", methods=[\"POST\"])\ndef template_select():\n # ... (handle template selection and processing)\n return jsonify(status=\"success\", data=template_data)\n\n@app.route(\"/sam_refine\", methods=[\"POST\"])\ndef sam_refine_request():\n # ... (handle sam refine and processing)\n return jsonify(status=\"success\", data=sam_data)\n\n@app.route(\"/track_video\", methods=[\"POST\"])\ndef track_video():\n # ... (handle video tracking and processing)\n return jsonify(status=\"success\", data=tracking_data)\n\n@app.route(\"/track_image\", methods=[\"POST\"])\ndef track_image():\n # ... (handle image tracking and processing)\n return jsonify(status=\"success\", data=tracking_data)","source_hash":"50b2875c0c4d91501b5ecebc15a34ca02e93fc817a951600f3c8ba95d2d36d24","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:text_server.template_select","uri":"program://Track-Anything/function/text_server.template_select#L41-L43","kind":"function","name":"template_select","path":"text_server.py","language":"python","start_line":41,"end_line":43,"context_start_line":21,"context_end_line":63,"code":"# ... (all the functions defined in the original code except the Gradio part)\n\napp = Flask(__name__)\napp.config['UPLOAD_FOLDER'] = './uploaded_videos'\napp.config['ALLOWED_EXTENSIONS'] = {'mp4', 'avi', 'mov', 'mkv'}\n\n\ndef allowed_file(filename):\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in app.config['ALLOWED_EXTENSIONS']\n\n@app.route(\"/\")\ndef index():\n return render_template(\"index.html\")\n\n@app.route(\"/upload_video\", methods=[\"POST\"])\ndef upload_video():\n # ... (handle video upload and processing)\n return jsonify(status=\"success\", data=video_data)\n\n@app.route(\"/template_select\", methods=[\"POST\"])\ndef template_select():\n # ... (handle template selection and processing)\n return jsonify(status=\"success\", data=template_data)\n\n@app.route(\"/sam_refine\", methods=[\"POST\"])\ndef sam_refine_request():\n # ... (handle sam refine and processing)\n return jsonify(status=\"success\", data=sam_data)\n\n@app.route(\"/track_video\", methods=[\"POST\"])\ndef track_video():\n # ... (handle video tracking and processing)\n return jsonify(status=\"success\", data=tracking_data)\n\n@app.route(\"/track_image\", methods=[\"POST\"])\ndef track_image():\n # ... (handle image tracking and processing)\n return jsonify(status=\"success\", data=tracking_data)\n\n@app.route(\"/download_video\", methods=[\"GET\"])\ndef download_video():\n try:\n return send_file(\"output.mp4\", attachment_filename=\"output.mp4\")","source_hash":"50b2875c0c4d91501b5ecebc15a34ca02e93fc817a951600f3c8ba95d2d36d24","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:text_server.sam_refine_request","uri":"program://Track-Anything/function/text_server.sam_refine_request#L46-L48","kind":"function","name":"sam_refine_request","path":"text_server.py","language":"python","start_line":46,"end_line":48,"context_start_line":26,"context_end_line":68,"code":"\n\ndef allowed_file(filename):\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in app.config['ALLOWED_EXTENSIONS']\n\n@app.route(\"/\")\ndef index():\n return render_template(\"index.html\")\n\n@app.route(\"/upload_video\", methods=[\"POST\"])\ndef upload_video():\n # ... (handle video upload and processing)\n return jsonify(status=\"success\", data=video_data)\n\n@app.route(\"/template_select\", methods=[\"POST\"])\ndef template_select():\n # ... (handle template selection and processing)\n return jsonify(status=\"success\", data=template_data)\n\n@app.route(\"/sam_refine\", methods=[\"POST\"])\ndef sam_refine_request():\n # ... (handle sam refine and processing)\n return jsonify(status=\"success\", data=sam_data)\n\n@app.route(\"/track_video\", methods=[\"POST\"])\ndef track_video():\n # ... (handle video tracking and processing)\n return jsonify(status=\"success\", data=tracking_data)\n\n@app.route(\"/track_image\", methods=[\"POST\"])\ndef track_image():\n # ... (handle image tracking and processing)\n return jsonify(status=\"success\", data=tracking_data)\n\n@app.route(\"/download_video\", methods=[\"GET\"])\ndef download_video():\n try:\n return send_file(\"output.mp4\", attachment_filename=\"output.mp4\")\n except Exception as e:\n return str(e)\n\nif __name__ == \"__main__\":\n app.run(debug=True, host=\"0.0.0.0\", port=args.port)","source_hash":"50b2875c0c4d91501b5ecebc15a34ca02e93fc817a951600f3c8ba95d2d36d24","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:text_server.track_video","uri":"program://Track-Anything/function/text_server.track_video#L51-L53","kind":"function","name":"track_video","path":"text_server.py","language":"python","start_line":51,"end_line":53,"context_start_line":31,"context_end_line":72,"code":"@app.route(\"/\")\ndef index():\n return render_template(\"index.html\")\n\n@app.route(\"/upload_video\", methods=[\"POST\"])\ndef upload_video():\n # ... (handle video upload and processing)\n return jsonify(status=\"success\", data=video_data)\n\n@app.route(\"/template_select\", methods=[\"POST\"])\ndef template_select():\n # ... (handle template selection and processing)\n return jsonify(status=\"success\", data=template_data)\n\n@app.route(\"/sam_refine\", methods=[\"POST\"])\ndef sam_refine_request():\n # ... (handle sam refine and processing)\n return jsonify(status=\"success\", data=sam_data)\n\n@app.route(\"/track_video\", methods=[\"POST\"])\ndef track_video():\n # ... (handle video tracking and processing)\n return jsonify(status=\"success\", data=tracking_data)\n\n@app.route(\"/track_image\", methods=[\"POST\"])\ndef track_image():\n # ... (handle image tracking and processing)\n return jsonify(status=\"success\", data=tracking_data)\n\n@app.route(\"/download_video\", methods=[\"GET\"])\ndef download_video():\n try:\n return send_file(\"output.mp4\", attachment_filename=\"output.mp4\")\n except Exception as e:\n return str(e)\n\nif __name__ == \"__main__\":\n app.run(debug=True, host=\"0.0.0.0\", port=args.port)\n\n\nif __name__ == '__main__':\n app.run(host=\"0.0.0.0\",port=12212, debug=True)","source_hash":"50b2875c0c4d91501b5ecebc15a34ca02e93fc817a951600f3c8ba95d2d36d24","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:text_server.track_image","uri":"program://Track-Anything/function/text_server.track_image#L56-L58","kind":"function","name":"track_image","path":"text_server.py","language":"python","start_line":56,"end_line":58,"context_start_line":36,"context_end_line":72,"code":"def upload_video():\n # ... (handle video upload and processing)\n return jsonify(status=\"success\", data=video_data)\n\n@app.route(\"/template_select\", methods=[\"POST\"])\ndef template_select():\n # ... (handle template selection and processing)\n return jsonify(status=\"success\", data=template_data)\n\n@app.route(\"/sam_refine\", methods=[\"POST\"])\ndef sam_refine_request():\n # ... (handle sam refine and processing)\n return jsonify(status=\"success\", data=sam_data)\n\n@app.route(\"/track_video\", methods=[\"POST\"])\ndef track_video():\n # ... (handle video tracking and processing)\n return jsonify(status=\"success\", data=tracking_data)\n\n@app.route(\"/track_image\", methods=[\"POST\"])\ndef track_image():\n # ... (handle image tracking and processing)\n return jsonify(status=\"success\", data=tracking_data)\n\n@app.route(\"/download_video\", methods=[\"GET\"])\ndef download_video():\n try:\n return send_file(\"output.mp4\", attachment_filename=\"output.mp4\")\n except Exception as e:\n return str(e)\n\nif __name__ == \"__main__\":\n app.run(debug=True, host=\"0.0.0.0\", port=args.port)\n\n\nif __name__ == '__main__':\n app.run(host=\"0.0.0.0\",port=12212, debug=True)","source_hash":"50b2875c0c4d91501b5ecebc15a34ca02e93fc817a951600f3c8ba95d2d36d24","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:text_server.download_video","uri":"program://Track-Anything/function/text_server.download_video#L61-L65","kind":"function","name":"download_video","path":"text_server.py","language":"python","start_line":61,"end_line":65,"context_start_line":41,"context_end_line":72,"code":"def template_select():\n # ... (handle template selection and processing)\n return jsonify(status=\"success\", data=template_data)\n\n@app.route(\"/sam_refine\", methods=[\"POST\"])\ndef sam_refine_request():\n # ... (handle sam refine and processing)\n return jsonify(status=\"success\", data=sam_data)\n\n@app.route(\"/track_video\", methods=[\"POST\"])\ndef track_video():\n # ... (handle video tracking and processing)\n return jsonify(status=\"success\", data=tracking_data)\n\n@app.route(\"/track_image\", methods=[\"POST\"])\ndef track_image():\n # ... (handle image tracking and processing)\n return jsonify(status=\"success\", data=tracking_data)\n\n@app.route(\"/download_video\", methods=[\"GET\"])\ndef download_video():\n try:\n return send_file(\"output.mp4\", attachment_filename=\"output.mp4\")\n except Exception as e:\n return str(e)\n\nif __name__ == \"__main__\":\n app.run(debug=True, host=\"0.0.0.0\", port=args.port)\n\n\nif __name__ == '__main__':\n app.run(host=\"0.0.0.0\",port=12212, debug=True)","source_hash":"50b2875c0c4d91501b5ecebc15a34ca02e93fc817a951600f3c8ba95d2d36d24","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:track_anything","uri":"program://Track-Anything/module/track_anything#L1-L96","kind":"module","name":"track_anything","path":"track_anything.py","language":"python","start_line":1,"end_line":96,"context_start_line":1,"context_end_line":96,"code":"import PIL\nfrom tqdm import tqdm\n\nfrom tools.interact_tools import SamControler\nfrom tracker.base_tracker import BaseTracker\nfrom inpainter.base_inpainter import BaseInpainter\nimport numpy as np\nimport argparse\n\n\n\nclass TrackingAnything():\n def __init__(self, sam_checkpoint, xmem_checkpoint, e2fgvi_checkpoint, args):\n self.args = args\n self.sam_checkpoint = sam_checkpoint\n self.xmem_checkpoint = xmem_checkpoint\n self.e2fgvi_checkpoint = e2fgvi_checkpoint\n self.samcontroler = SamControler(self.sam_checkpoint, args.sam_model_type, args.device)\n self.xmem = BaseTracker(self.xmem_checkpoint, device=args.device)\n self.baseinpainter = BaseInpainter(self.e2fgvi_checkpoint, args.device) \n # def inference_step(self, first_flag: bool, interact_flag: bool, image: np.ndarray, \n # same_image_flag: bool, points:np.ndarray, labels: np.ndarray, logits: np.ndarray=None, multimask=True):\n # if first_flag:\n # mask, logit, painted_image = self.samcontroler.first_frame_click(image, points, labels, multimask)\n # return mask, logit, painted_image\n \n # if interact_flag:\n # mask, logit, painted_image = self.samcontroler.interact_loop(image, same_image_flag, points, labels, logits, multimask)\n # return mask, logit, painted_image\n \n # mask, logit, painted_image = self.xmem.track(image, logit)\n # return mask, logit, painted_image\n \n def first_frame_click(self, image: np.ndarray, points:np.ndarray, labels: np.ndarray, multimask=True):\n mask, logit, painted_image = self.samcontroler.first_frame_click(image, points, labels, multimask)\n return mask, logit, painted_image\n \n # def interact(self, image: np.ndarray, same_image_flag: bool, points:np.ndarray, labels: np.ndarray, logits: np.ndarray=None, multimask=True):\n # mask, logit, painted_image = self.samcontroler.interact_loop(image, same_image_flag, points, labels, logits, multimask)\n # return mask, logit, painted_image\n\n def generator(self, images: list, template_mask:np.ndarray):\n \n masks = []\n logits = []\n painted_images = []\n for i in tqdm(range(len(images)), desc=\"Tracking image\"):\n if i ==0: \n mask, logit, painted_image = self.xmem.track(images[i], template_mask)\n masks.append(mask)\n logits.append(logit)\n painted_images.append(painted_image)\n \n else:\n mask, logit, painted_image = self.xmem.track(images[i])\n masks.append(mask)\n logits.append(logit)\n painted_images.append(painted_image)\n return masks, logits, painted_images\n \n \ndef parse_augment():\n parser = argparse.ArgumentParser()\n parser.add_argument('--device', type=str, default=\"cuda:0\")\n parser.add_argument('--sam_model_type', type=str, default=\"vit_h\")\n parser.add_argument('--port', type=int, default=6080, help=\"only useful when running gradio applications\") \n parser.add_argument('--debug', action=\"store_true\")\n parser.add_argument('--mask_save', default=False)\n args = parser.parse_args()\n\n if args.debug:\n print(args)\n return args \n\n\nif __name__ == \"__main__\":\n masks = None\n logits = None\n painted_images = None\n images = []\n image = np.array(PIL.Image.open('/hhd3/gaoshang/truck.jpg'))\n args = parse_augment()\n # images.append(np.ones((20,20,3)).astype('uint8'))\n # images.append(np.ones((20,20,3)).astype('uint8'))\n images.append(image)\n images.append(image)\n\n mask = np.zeros_like(image)[:,:,0]\n mask[0,0]= 1\n trackany = TrackingAnything('/ssd1/gaomingqi/checkpoints/sam_vit_h_4b8939.pth','/ssd1/gaomingqi/checkpoints/XMem-s012.pth', args)\n masks, logits ,painted_images= trackany.generator(images, mask)\n \n \n \n \n ","source_hash":"4a5b0b9e1d9dccdc700b21a496a6f29574916434416975019848dc19540bce6a","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:track_anything.TrackingAnything","uri":"program://Track-Anything/class/track_anything.TrackingAnything#L12-L59","kind":"class","name":"TrackingAnything","path":"track_anything.py","language":"python","start_line":12,"end_line":59,"context_start_line":1,"context_end_line":79,"code":"import PIL\nfrom tqdm import tqdm\n\nfrom tools.interact_tools import SamControler\nfrom tracker.base_tracker import BaseTracker\nfrom inpainter.base_inpainter import BaseInpainter\nimport numpy as np\nimport argparse\n\n\n\nclass TrackingAnything():\n def __init__(self, sam_checkpoint, xmem_checkpoint, e2fgvi_checkpoint, args):\n self.args = args\n self.sam_checkpoint = sam_checkpoint\n self.xmem_checkpoint = xmem_checkpoint\n self.e2fgvi_checkpoint = e2fgvi_checkpoint\n self.samcontroler = SamControler(self.sam_checkpoint, args.sam_model_type, args.device)\n self.xmem = BaseTracker(self.xmem_checkpoint, device=args.device)\n self.baseinpainter = BaseInpainter(self.e2fgvi_checkpoint, args.device) \n # def inference_step(self, first_flag: bool, interact_flag: bool, image: np.ndarray, \n # same_image_flag: bool, points:np.ndarray, labels: np.ndarray, logits: np.ndarray=None, multimask=True):\n # if first_flag:\n # mask, logit, painted_image = self.samcontroler.first_frame_click(image, points, labels, multimask)\n # return mask, logit, painted_image\n \n # if interact_flag:\n # mask, logit, painted_image = self.samcontroler.interact_loop(image, same_image_flag, points, labels, logits, multimask)\n # return mask, logit, painted_image\n \n # mask, logit, painted_image = self.xmem.track(image, logit)\n # return mask, logit, painted_image\n \n def first_frame_click(self, image: np.ndarray, points:np.ndarray, labels: np.ndarray, multimask=True):\n mask, logit, painted_image = self.samcontroler.first_frame_click(image, points, labels, multimask)\n return mask, logit, painted_image\n \n # def interact(self, image: np.ndarray, same_image_flag: bool, points:np.ndarray, labels: np.ndarray, logits: np.ndarray=None, multimask=True):\n # mask, logit, painted_image = self.samcontroler.interact_loop(image, same_image_flag, points, labels, logits, multimask)\n # return mask, logit, painted_image\n\n def generator(self, images: list, template_mask:np.ndarray):\n \n masks = []\n logits = []\n painted_images = []\n for i in tqdm(range(len(images)), desc=\"Tracking image\"):\n if i ==0: \n mask, logit, painted_image = self.xmem.track(images[i], template_mask)\n masks.append(mask)\n logits.append(logit)\n painted_images.append(painted_image)\n \n else:\n mask, logit, painted_image = self.xmem.track(images[i])\n masks.append(mask)\n logits.append(logit)\n painted_images.append(painted_image)\n return masks, logits, painted_images\n \n \ndef parse_augment():\n parser = argparse.ArgumentParser()\n parser.add_argument('--device', type=str, default=\"cuda:0\")\n parser.add_argument('--sam_model_type', type=str, default=\"vit_h\")\n parser.add_argument('--port', type=int, default=6080, help=\"only useful when running gradio applications\") \n parser.add_argument('--debug', action=\"store_true\")\n parser.add_argument('--mask_save', default=False)\n args = parser.parse_args()\n\n if args.debug:\n print(args)\n return args \n\n\nif __name__ == \"__main__\":\n masks = None\n logits = None\n painted_images = None","source_hash":"4a5b0b9e1d9dccdc700b21a496a6f29574916434416975019848dc19540bce6a","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:track_anything.parse_augment","uri":"program://Track-Anything/function/track_anything.parse_augment#L62-L73","kind":"function","name":"parse_augment","path":"track_anything.py","language":"python","start_line":62,"end_line":73,"context_start_line":42,"context_end_line":93,"code":" def generator(self, images: list, template_mask:np.ndarray):\n \n masks = []\n logits = []\n painted_images = []\n for i in tqdm(range(len(images)), desc=\"Tracking image\"):\n if i ==0: \n mask, logit, painted_image = self.xmem.track(images[i], template_mask)\n masks.append(mask)\n logits.append(logit)\n painted_images.append(painted_image)\n \n else:\n mask, logit, painted_image = self.xmem.track(images[i])\n masks.append(mask)\n logits.append(logit)\n painted_images.append(painted_image)\n return masks, logits, painted_images\n \n \ndef parse_augment():\n parser = argparse.ArgumentParser()\n parser.add_argument('--device', type=str, default=\"cuda:0\")\n parser.add_argument('--sam_model_type', type=str, default=\"vit_h\")\n parser.add_argument('--port', type=int, default=6080, help=\"only useful when running gradio applications\") \n parser.add_argument('--debug', action=\"store_true\")\n parser.add_argument('--mask_save', default=False)\n args = parser.parse_args()\n\n if args.debug:\n print(args)\n return args \n\n\nif __name__ == \"__main__\":\n masks = None\n logits = None\n painted_images = None\n images = []\n image = np.array(PIL.Image.open('/hhd3/gaoshang/truck.jpg'))\n args = parse_augment()\n # images.append(np.ones((20,20,3)).astype('uint8'))\n # images.append(np.ones((20,20,3)).astype('uint8'))\n images.append(image)\n images.append(image)\n\n mask = np.zeros_like(image)[:,:,0]\n mask[0,0]= 1\n trackany = TrackingAnything('/ssd1/gaomingqi/checkpoints/sam_vit_h_4b8939.pth','/ssd1/gaomingqi/checkpoints/XMem-s012.pth', args)\n masks, logits ,painted_images= trackany.generator(images, mask)\n \n ","source_hash":"4a5b0b9e1d9dccdc700b21a496a6f29574916434416975019848dc19540bce6a","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:track_anything.__init__","uri":"program://Track-Anything/function/track_anything.__init__#L13-L20","kind":"function","name":"__init__","path":"track_anything.py","language":"python","start_line":13,"end_line":20,"context_start_line":1,"context_end_line":40,"code":"import PIL\nfrom tqdm import tqdm\n\nfrom tools.interact_tools import SamControler\nfrom tracker.base_tracker import BaseTracker\nfrom inpainter.base_inpainter import BaseInpainter\nimport numpy as np\nimport argparse\n\n\n\nclass TrackingAnything():\n def __init__(self, sam_checkpoint, xmem_checkpoint, e2fgvi_checkpoint, args):\n self.args = args\n self.sam_checkpoint = sam_checkpoint\n self.xmem_checkpoint = xmem_checkpoint\n self.e2fgvi_checkpoint = e2fgvi_checkpoint\n self.samcontroler = SamControler(self.sam_checkpoint, args.sam_model_type, args.device)\n self.xmem = BaseTracker(self.xmem_checkpoint, device=args.device)\n self.baseinpainter = BaseInpainter(self.e2fgvi_checkpoint, args.device) \n # def inference_step(self, first_flag: bool, interact_flag: bool, image: np.ndarray, \n # same_image_flag: bool, points:np.ndarray, labels: np.ndarray, logits: np.ndarray=None, multimask=True):\n # if first_flag:\n # mask, logit, painted_image = self.samcontroler.first_frame_click(image, points, labels, multimask)\n # return mask, logit, painted_image\n \n # if interact_flag:\n # mask, logit, painted_image = self.samcontroler.interact_loop(image, same_image_flag, points, labels, logits, multimask)\n # return mask, logit, painted_image\n \n # mask, logit, painted_image = self.xmem.track(image, logit)\n # return mask, logit, painted_image\n \n def first_frame_click(self, image: np.ndarray, points:np.ndarray, labels: np.ndarray, multimask=True):\n mask, logit, painted_image = self.samcontroler.first_frame_click(image, points, labels, multimask)\n return mask, logit, painted_image\n \n # def interact(self, image: np.ndarray, same_image_flag: bool, points:np.ndarray, labels: np.ndarray, logits: np.ndarray=None, multimask=True):\n # mask, logit, painted_image = self.samcontroler.interact_loop(image, same_image_flag, points, labels, logits, multimask)\n # return mask, logit, painted_image","source_hash":"4a5b0b9e1d9dccdc700b21a496a6f29574916434416975019848dc19540bce6a","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:track_anything.first_frame_click","uri":"program://Track-Anything/function/track_anything.first_frame_click#L34-L36","kind":"function","name":"first_frame_click","path":"track_anything.py","language":"python","start_line":34,"end_line":36,"context_start_line":14,"context_end_line":56,"code":" self.args = args\n self.sam_checkpoint = sam_checkpoint\n self.xmem_checkpoint = xmem_checkpoint\n self.e2fgvi_checkpoint = e2fgvi_checkpoint\n self.samcontroler = SamControler(self.sam_checkpoint, args.sam_model_type, args.device)\n self.xmem = BaseTracker(self.xmem_checkpoint, device=args.device)\n self.baseinpainter = BaseInpainter(self.e2fgvi_checkpoint, args.device) \n # def inference_step(self, first_flag: bool, interact_flag: bool, image: np.ndarray, \n # same_image_flag: bool, points:np.ndarray, labels: np.ndarray, logits: np.ndarray=None, multimask=True):\n # if first_flag:\n # mask, logit, painted_image = self.samcontroler.first_frame_click(image, points, labels, multimask)\n # return mask, logit, painted_image\n \n # if interact_flag:\n # mask, logit, painted_image = self.samcontroler.interact_loop(image, same_image_flag, points, labels, logits, multimask)\n # return mask, logit, painted_image\n \n # mask, logit, painted_image = self.xmem.track(image, logit)\n # return mask, logit, painted_image\n \n def first_frame_click(self, image: np.ndarray, points:np.ndarray, labels: np.ndarray, multimask=True):\n mask, logit, painted_image = self.samcontroler.first_frame_click(image, points, labels, multimask)\n return mask, logit, painted_image\n \n # def interact(self, image: np.ndarray, same_image_flag: bool, points:np.ndarray, labels: np.ndarray, logits: np.ndarray=None, multimask=True):\n # mask, logit, painted_image = self.samcontroler.interact_loop(image, same_image_flag, points, labels, logits, multimask)\n # return mask, logit, painted_image\n\n def generator(self, images: list, template_mask:np.ndarray):\n \n masks = []\n logits = []\n painted_images = []\n for i in tqdm(range(len(images)), desc=\"Tracking image\"):\n if i ==0: \n mask, logit, painted_image = self.xmem.track(images[i], template_mask)\n masks.append(mask)\n logits.append(logit)\n painted_images.append(painted_image)\n \n else:\n mask, logit, painted_image = self.xmem.track(images[i])\n masks.append(mask)","source_hash":"4a5b0b9e1d9dccdc700b21a496a6f29574916434416975019848dc19540bce6a","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:track_anything.generator","uri":"program://Track-Anything/function/track_anything.generator#L42-L59","kind":"function","name":"generator","path":"track_anything.py","language":"python","start_line":42,"end_line":59,"context_start_line":22,"context_end_line":79,"code":" # same_image_flag: bool, points:np.ndarray, labels: np.ndarray, logits: np.ndarray=None, multimask=True):\n # if first_flag:\n # mask, logit, painted_image = self.samcontroler.first_frame_click(image, points, labels, multimask)\n # return mask, logit, painted_image\n \n # if interact_flag:\n # mask, logit, painted_image = self.samcontroler.interact_loop(image, same_image_flag, points, labels, logits, multimask)\n # return mask, logit, painted_image\n \n # mask, logit, painted_image = self.xmem.track(image, logit)\n # return mask, logit, painted_image\n \n def first_frame_click(self, image: np.ndarray, points:np.ndarray, labels: np.ndarray, multimask=True):\n mask, logit, painted_image = self.samcontroler.first_frame_click(image, points, labels, multimask)\n return mask, logit, painted_image\n \n # def interact(self, image: np.ndarray, same_image_flag: bool, points:np.ndarray, labels: np.ndarray, logits: np.ndarray=None, multimask=True):\n # mask, logit, painted_image = self.samcontroler.interact_loop(image, same_image_flag, points, labels, logits, multimask)\n # return mask, logit, painted_image\n\n def generator(self, images: list, template_mask:np.ndarray):\n \n masks = []\n logits = []\n painted_images = []\n for i in tqdm(range(len(images)), desc=\"Tracking image\"):\n if i ==0: \n mask, logit, painted_image = self.xmem.track(images[i], template_mask)\n masks.append(mask)\n logits.append(logit)\n painted_images.append(painted_image)\n \n else:\n mask, logit, painted_image = self.xmem.track(images[i])\n masks.append(mask)\n logits.append(logit)\n painted_images.append(painted_image)\n return masks, logits, painted_images\n \n \ndef parse_augment():\n parser = argparse.ArgumentParser()\n parser.add_argument('--device', type=str, default=\"cuda:0\")\n parser.add_argument('--sam_model_type', type=str, default=\"vit_h\")\n parser.add_argument('--port', type=int, default=6080, help=\"only useful when running gradio applications\") \n parser.add_argument('--debug', action=\"store_true\")\n parser.add_argument('--mask_save', default=False)\n args = parser.parse_args()\n\n if args.debug:\n print(args)\n return args \n\n\nif __name__ == \"__main__\":\n masks = None\n logits = None\n painted_images = None","source_hash":"4a5b0b9e1d9dccdc700b21a496a6f29574916434416975019848dc19540bce6a","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.base_inpainter","uri":"program://Track-Anything/module/inpainter.base_inpainter#L1-L384","kind":"module","name":"inpainter.base_inpainter","path":"inpainter/base_inpainter.py","language":"python","start_line":1,"end_line":384,"context_start_line":1,"context_end_line":384,"code":"import os\nimport glob\nfrom PIL import Image\n\nimport torch\nimport yaml\nimport cv2\nimport importlib\nimport numpy as np\nfrom tqdm import tqdm\n\nfrom inpainter.util.tensor_util import resize_frames, resize_masks\n\n\nclass BaseInpainter:\n\tdef __init__(self, E2FGVI_checkpoint, device) -> None:\n\t\t\"\"\"\n\t\tE2FGVI_checkpoint: checkpoint of inpainter (version hq, with multi-resolution support)\n\t\t\"\"\"\n\t\tnet = importlib.import_module('inpainter.model.e2fgvi_hq')\n\t\tself.model = net.InpaintGenerator().to(device)\n\t\tself.model.load_state_dict(torch.load(E2FGVI_checkpoint, map_location=device))\n\t\tself.model.eval()\n\t\tself.device = device\n\t\t# load configurations\n\t\twith open(\"inpainter/config/config.yaml\", 'r') as stream: \n\t\t\tconfig = yaml.safe_load(stream) \n\t\tself.neighbor_stride = config['neighbor_stride']\n\t\tself.num_ref = config['num_ref']\n\t\tself.step = config['step']\n\t\t# config for E2FGVI with splits\n\t\tself.num_subset_frames = config['num_subset_frames']\n\t\tself.num_external_ref = config['num_external_ref']\n\n\t# sample reference frames from the whole video\n\tdef get_ref_index(self, f, neighbor_ids, length):\n\t\tref_index = []\n\t\tif self.num_ref == -1:\n\t\t\tfor i in range(0, length, self.step):\n\t\t\t\tif i not in neighbor_ids:\n\t\t\t\t\tref_index.append(i)\n\t\telse:\n\t\t\tstart_idx = max(0, f - self.step * (self.num_ref // 2))\n\t\t\tend_idx = min(length, f + self.step * (self.num_ref // 2))\n\t\t\tfor i in range(start_idx, end_idx + 1, self.step):\n\t\t\t\tif i not in neighbor_ids:\n\t\t\t\t\tif len(ref_index) > self.num_ref:\n\t\t\t\t\t\tbreak\n\t\t\t\t\tref_index.append(i)\n\t\treturn ref_index\n\n\tdef inpaint_efficient(self, frames, masks, num_tcb, num_tca, dilate_radius=15, ratio=1):\n\t\t\"\"\"\n\t\tPerform Inpainting for video subsets\n\t\tframes: numpy array, T, H, W, 3\n\t\tmasks: numpy array, T, H, W\n\t\tnum_tcb: constant, number of temporal context before, frames\n\t\tnum_tca: constant, number of temporal context after, frames\n\t\tdilate_radius: radius when applying dilation on masks\n\t\tratio: down-sample ratio\n\n\t\tOutput:\n\t\tinpainted_frames: numpy array, T, H, W, 3\n\t\t\"\"\"\n\t\tassert frames.shape[:3] == masks.shape, 'different size between frames and masks'\n\t\tassert ratio > 0 and ratio <= 1, 'ratio must in (0, 1]'\n\t\t\n\t\t# --------------------\n\t\t# pre-processing\n\t\t# --------------------\n\t\tmasks = masks.copy()\n\t\tmasks = np.clip(masks, 0, 1)\n\t\tkernel = cv2.getStructuringElement(2, (dilate_radius, dilate_radius))\n\t\tmasks = np.stack([cv2.dilate(mask, kernel) for mask in masks], 0)\n\t\tT, H, W = masks.shape\n\t\tmasks = np.expand_dims(masks, axis=3) # expand to T, H, W, 1\n\t\t# size: (w, h)\n\t\tif ratio == 1:\n\t\t\tsize = None\n\t\t\tbinary_masks = masks\n\t\telse:\n\t\t\tsize = [int(W*ratio), int(H*ratio)]\n\t\t\tsize = [si+1 if si%2>0 else si for si in size] # only consider even values\n\t\t\t# shortest side should be larger than 50\n\t\t\tif min(size) < 50:\n\t\t\t\tratio = 50. / min(H, W)\n\t\t\t\tsize = [int(W*ratio), int(H*ratio)]\n\t\t\tbinary_masks = resize_masks(masks, tuple(size))\n\t\t\tframes = resize_frames(frames, tuple(size)) # T, H, W, 3\n\t\t# frames and binary_masks are numpy arrays\n\t\th, w = frames.shape[1:3]\n\t\tvideo_length = T - (num_tca + num_tcb) # real video length\n\t\t# convert to tensor\n\t\timgs = (torch.from_numpy(frames).permute(0, 3, 1, 2).contiguous().unsqueeze(0).float().div(255)) * 2 - 1\n\t\tmasks = torch.from_numpy(binary_masks).permute(0, 3, 1, 2).contiguous().unsqueeze(0)\n\t\timgs, masks = imgs.to(self.device), masks.to(self.device)\n\t\tcomp_frames = [None] * video_length\n\t\ttcb_imgs = None\n\t\ttca_imgs = None\n\t\ttcb_masks = None\n\t\ttca_masks = None\n\t\t# --------------------\n\t\t# end of pre-processing\n\t\t# --------------------\n\n\t\t# separate tc frames/masks from imgs and masks\n\t\tif num_tcb > 0:\n\t\t\ttcb_imgs = imgs[:, :num_tcb]\n\t\t\ttcb_masks = masks[:, :num_tcb]\n\t\tif num_tca > 0:\n\t\t\ttca_imgs = imgs[:, -num_tca:]\n\t\t\ttca_masks = masks[:, -num_tca:]\n\t\t\tend_idx = -num_tca\n\t\telse:\n\t\t\tend_idx = T\n\n\t\timgs = imgs[:, num_tcb:end_idx]\n\t\tmasks = masks[:, num_tcb:end_idx]\n\t\tbinary_masks = binary_masks[num_tcb:end_idx]\t# only neighbor area are involved\n\t\tframes = frames[num_tcb:end_idx]\t\t\t\t# only neighbor area are involved\n\n\t\tfor f in tqdm(range(0, video_length, self.neighbor_stride), desc='Inpainting image'):\n\t\t\tneighbor_ids = [\n\t\t\t\ti for i in range(max(0, f - self.neighbor_stride),\n\t\t\t\t\t\t\t\tmin(video_length, f + self.neighbor_stride + 1))\n\t\t\t]\n\t\t\tref_ids = self.get_ref_index(f, neighbor_ids, video_length)\n\n\t\t\t# selected_imgs = imgs[:1, neighbor_ids + ref_ids, :, :, :]\n\t\t\t# selected_masks = masks[:1, neighbor_ids + ref_ids, :, :, :]\n\t\t\t\n\t\t\tselected_imgs = imgs[:, neighbor_ids]\n\t\t\tselected_masks = masks[:, neighbor_ids]\n\t\t\t# pad before\n\t\t\tif tcb_imgs is not None:\n\t\t\t\tselected_imgs = torch.concat([selected_imgs, tcb_imgs], dim=1)\n\t\t\t\tselected_masks = torch.concat([selected_masks, tcb_masks], dim=1)\n\t\t\t# integrate ref frames\n\t\t\tselected_imgs = torch.concat([selected_imgs, imgs[:, ref_ids]], dim=1)\n\t\t\tselected_masks = torch.concat([selected_masks, masks[:, ref_ids]], dim=1)\n\t\t\t# pad after\n\t\t\tif tca_imgs is not None:\n\t\t\t\tselected_imgs = torch.concat([selected_imgs, tca_imgs], dim=1)\n\t\t\t\tselected_masks = torch.concat([selected_masks, tca_masks], dim=1)\n\n\t\t\twith torch.no_grad():\n\t\t\t\tmasked_imgs = selected_imgs * (1 - selected_masks)\n\t\t\t\tmod_size_h = 60\n\t\t\t\tmod_size_w = 108\n\t\t\t\th_pad = (mod_size_h - h % mod_size_h) % mod_size_h\n\t\t\t\tw_pad = (mod_size_w - w % mod_size_w) % mod_size_w\n\t\t\t\tmasked_imgs = torch.cat(\n\t\t\t\t\t[masked_imgs, torch.flip(masked_imgs, [3])],\n\t\t\t\t\t3)[:, :, :, :h + h_pad, :]\n\t\t\t\tmasked_imgs = torch.cat(\n\t\t\t\t\t[masked_imgs, torch.flip(masked_imgs, [4])],\n\t\t\t\t\t4)[:, :, :, :, :w + w_pad]\n\t\t\t\tpred_imgs, _ = self.model(masked_imgs, len(neighbor_ids))\n\t\t\t\tpred_imgs = pred_imgs[:, :, :h, :w]\n\t\t\t\tpred_imgs = (pred_imgs + 1) / 2\n\t\t\t\tpred_imgs = pred_imgs.cpu().permute(0, 2, 3, 1).numpy() * 255\n\t\t\t\tfor i in range(len(neighbor_ids)):\n\t\t\t\t\tidx = neighbor_ids[i]\n\t\t\t\t\timg = pred_imgs[i].astype(np.uint8) * binary_masks[idx] + frames[idx] * (\n\t\t\t\t\t\t\t1 - binary_masks[idx])\n\t\t\t\t\tif comp_frames[idx] is None:\n\t\t\t\t\t\tcomp_frames[idx] = img\n\t\t\t\t\telse:\n\t\t\t\t\t\tcomp_frames[idx] = comp_frames[idx].astype(\n\t\t\t\t\t\t\tnp.float32) * 0.5 + img.astype(np.float32) * 0.5\n\t\t\ttorch.cuda.empty_cache()\n\t\tinpainted_frames = np.stack(comp_frames, 0)\n\t\treturn inpainted_frames.astype(np.uint8)\n\n\tdef inpaint(self, frames, masks, dilate_radius=15, ratio=1):\n\t\t\"\"\"\n\t\tPerform Inpainting for video subsets\n\t\tframes: numpy array, T, H, W, 3\n\t\tmasks: numpy array, T, H, W\n\t\tdilate_radius: radius when applying dilation on masks\n\t\tratio: down-sample ratio\n\n\t\tOutput:\n\t\tinpainted_frames: numpy array, T, H, W, 3\n\t\t\"\"\"\n\t\tassert frames.shape[:3] == masks.shape, 'different size between frames and masks'\n\t\tassert ratio > 0 and ratio <= 1, 'ratio must in (0, 1]'\n\t\t\n\t\t# set num_subset_frames\n\t\tnum_subset_frames = self.num_subset_frames\n\t\t# split frames into subsets\n\t\tvideo_length = len(frames)\n\t\tnum_splits = video_length // num_subset_frames\n\t\tid_splits = [[i*num_subset_frames, (i+1)*num_subset_frames] for i in range(num_splits)] # id splits\n\t\t\n\t\tif num_splits == 0:\n\t\t\tid_splits = [[0, video_length]]\n\t\t\n\t\t# if remaining split > num_subset_frames/2, add a new split, else, append to the last split\n\t\tif video_length - id_splits[-1][-1] > num_subset_frames / 3:\n\t\t\tid_splits.append([num_splits*num_subset_frames, video_length])\n\t\telse:\n\t\t\tdiff = video_length - id_splits[-1][-1]\n\t\t\tid_splits = [[ids[0]+diff, ids[1]+diff] for ids in id_splits]\n\t\t\tid_splits[0][0] = 0\t\t# if OOM, let it happen at the begining :D\n\n\t\t# if appending, convert the appended split to the FIRST one, avoiding OOM at last\n\n\t\t# perform inpainting for each split\n\t\tinpainted_splits = []\n\t\tfor id_split in id_splits:\n\t\t\tvideo_split = frames[id_split[0]:id_split[1]]\n\t\t\tmask_split = masks[id_split[0]:id_split[1]]\n\n\t\t\t# | id_before | ----- | id_split[0] | ----- | id_split[1] | ----- | id_after |\n\t\t\t# for each split, consider its temporal context [-context_range] frames and [context_range] frames\n\t\t\tid_before = max(0, id_split[0] - self.step * self.num_external_ref)\n\t\t\ttry:\n\t\t\t\ttcb_frames = np.stack([frames[idb] for idb in range(id_before, (id_split[0]-self.step) + 1, self.step)], 0)\n\t\t\t\ttcb_masks = np.stack([masks[idb] for idb in range(id_before, (id_split[0]-self.step) + 1, self.step)], 0)\n\t\t\t\tnum_tcb = len(tcb_frames)\n\t\t\texcept:\n\t\t\t\tnum_tcb = 0\n\t\t\tid_after = min(video_length, id_split[1] + self.step * self.num_external_ref + 1)\n\t\t\ttry:\n\t\t\t\ttca_frames = np.stack([frames[ida] for ida in range(id_split[1]+self.step, id_after, self.step)], 0)\n\t\t\t\ttca_masks = np.stack([masks[ida] for ida in range(id_split[1]+self.step, id_after, self.step)], 0)\n\t\t\t\tnum_tca = len(tca_frames)\n\t\t\texcept:\n\t\t\t\tnum_tca = 0\n\n\t\t\t# concatenate temporal context frames/masks with input frames/masks (for parallel pre-processing)\n\t\t\tif num_tcb > 0:\n\t\t\t\tvideo_split = np.concatenate([tcb_frames, video_split], 0)\n\t\t\t\tmask_split = np.concatenate([tcb_masks, mask_split], 0)\n\t\t\tif num_tca > 0:\n\t\t\t\tvideo_split = np.concatenate([video_split, tca_frames], 0)\n\t\t\t\tmask_split = np.concatenate([mask_split, tca_masks], 0)\n\t\t\t\n\t\t\ttorch.cuda.empty_cache()\n\t\t\t# inpaint each split\n\t\t\tinpainted_splits.append(self.inpaint_efficient(video_split, mask_split, num_tcb, num_tca, dilate_radius, ratio))\n\t\t\ttorch.cuda.empty_cache()\n\t\tinpainted_frames = np.concatenate(inpainted_splits, 0)\n\n\t\treturn inpainted_frames.astype(np.uint8)\n\n\tdef inpaint_ori(self, frames, masks, dilate_radius=15, ratio=1):\n\t\t\"\"\"\n\t\tframes: numpy array, T, H, W, 3\n\t\tmasks: numpy array, T, H, W\n\t\tdilate_radius: radius when applying dilation on masks\n\t\tratio: down-sample ratio\n\n\t\tOutput:\n\t\tinpainted_frames: numpy array, T, H, W, 3\n\t\t\"\"\"\n\t\tassert frames.shape[:3] == masks.shape, 'different size between frames and masks'\n\t\tassert ratio > 0 and ratio <= 1, 'ratio must in (0, 1]'\n\t\tmasks = masks.copy()\n\t\tmasks = np.clip(masks, 0, 1)\n\t\tkernel = cv2.getStructuringElement(2, (dilate_radius, dilate_radius))\n\t\tmasks = np.stack([cv2.dilate(mask, kernel) for mask in masks], 0)\n\n\t\tT, H, W = masks.shape\n\t\tmasks = np.expand_dims(masks, axis=3) # expand to T, H, W, 1\n\t\t# size: (w, h)\n\t\tif ratio == 1:\n\t\t\tsize = None\n\t\t\tbinary_masks = masks\n\t\telse:\n\t\t\tsize = [int(W*ratio), int(H*ratio)]\n\t\t\tsize = [si+1 if si%2>0 else si for si in size] # only consider even values\n\t\t\t# shortest side should be larger than 50\n\t\t\tif min(size) < 50:\n\t\t\t\tratio = 50. / min(H, W)\n\t\t\t\tsize = [int(W*ratio), int(H*ratio)]\n\n\t\t\tsize = [160, 120]\n\t\t\tbinary_masks = resize_masks(masks, tuple(size))\n\t\t\tframes = resize_frames(frames, tuple(size)) # T, H, W, 3\n\t\t# frames and binary_masks are numpy arrays\n\t\th, w = frames.shape[1:3]\n\t\tvideo_length = T\n\n\t\t# convert to tensor\n\t\timgs = (torch.from_numpy(frames).permute(0, 3, 1, 2).contiguous().unsqueeze(0).float().div(255)) * 2 - 1\n\t\tmasks = torch.from_numpy(binary_masks).permute(0, 3, 1, 2).contiguous().unsqueeze(0)\n\n\t\timgs, masks = imgs.to(self.device), masks.to(self.device)\n\t\tcomp_frames = [None] * video_length\n\n\t\tfor f in tqdm(range(0, video_length, self.neighbor_stride), desc='Inpainting image'):\n\t\t\tneighbor_ids = [\n\t\t\t\ti for i in range(max(0, f - self.neighbor_stride),\n\t\t\t\t\t\t\t\tmin(video_length, f + self.neighbor_stride + 1))\n\t\t\t]\n\t\t\tref_ids = self.get_ref_index(f, neighbor_ids, video_length)\n\t\t\tselected_imgs = imgs[:1, neighbor_ids + ref_ids, :, :, :]\n\t\t\tselected_masks = masks[:1, neighbor_ids + ref_ids, :, :, :]\n\t\t\twith torch.no_grad():\n\t\t\t\tmasked_imgs = selected_imgs * (1 - selected_masks)\n\t\t\t\tmod_size_h = 60\n\t\t\t\tmod_size_w = 108\n\t\t\t\th_pad = (mod_size_h - h % mod_size_h) % mod_size_h\n\t\t\t\tw_pad = (mod_size_w - w % mod_size_w) % mod_size_w\n\t\t\t\tmasked_imgs = torch.cat(\n\t\t\t\t\t[masked_imgs, torch.flip(masked_imgs, [3])],\n\t\t\t\t\t3)[:, :, :, :h + h_pad, :]\n\t\t\t\tmasked_imgs = torch.cat(\n\t\t\t\t\t[masked_imgs, torch.flip(masked_imgs, [4])],\n\t\t\t\t\t4)[:, :, :, :, :w + w_pad]\n\t\t\t\tpred_imgs, _ = self.model(masked_imgs, len(neighbor_ids))\n\t\t\t\tpred_imgs = pred_imgs[:, :, :h, :w]\n\t\t\t\tpred_imgs = (pred_imgs + 1) / 2\n\t\t\t\tpred_imgs = pred_imgs.cpu().permute(0, 2, 3, 1).numpy() * 255\n\t\t\t\tfor i in range(len(neighbor_ids)):\n\t\t\t\t\tidx = neighbor_ids[i]\n\t\t\t\t\timg = pred_imgs[i].astype(np.uint8) * binary_masks[idx] + frames[idx] * (\n\t\t\t\t\t\t\t1 - binary_masks[idx])\n\t\t\t\t\tif comp_frames[idx] is None:\n\t\t\t\t\t\tcomp_frames[idx] = img\n\t\t\t\t\telse:\n\t\t\t\t\t\tcomp_frames[idx] = comp_frames[idx].astype(\n\t\t\t\t\t\t\tnp.float32) * 0.5 + img.astype(np.float32) * 0.5\n\t\t\ttorch.cuda.empty_cache()\n\t\tinpainted_frames = np.stack(comp_frames, 0)\n\t\treturn inpainted_frames.astype(np.uint8)\n\n\nif __name__ == '__main__':\n\n\t# # davis-2017\n\t# frame_path = glob.glob(os.path.join('/ssd1/gaomingqi/datasets/davis/JPEGImages/480p/parkour', '*.jpg'))\n\t# frame_path.sort()\n\t# mask_path = glob.glob(os.path.join('/ssd1/gaomingqi/datasets/davis/Annotations/480p/parkour', \"*.png\"))\n\t# mask_path.sort()\n\n\t# long and large video\n\tmask_path = glob.glob(os.path.join('/ssd1/gaomingqi/test-sample13', '*.npy'))\n\tmask_path.sort()\n\tframes = np.load('/ssd1/gaomingqi/revenger.npy')\n\tsave_path = '/ssd1/gaomingqi/results/inpainting/avengers_split'\n\n\tif not os.path.exists(save_path):\n\t\tos.mkdir(save_path)\n\n\tmasks = []\n\tfor ti, mid in enumerate(mask_path):\n\t\tmasks.append(np.load(mid, allow_pickle=True))\n\t\tif ti > 1122:\n\t\t\tbreak\n\n\tmasks = np.stack(masks[:len(frames)], 0)\n\n\t# ----------------------------------------------\n\t# how to use\n\t# ----------------------------------------------\n\t# 1/3: set checkpoint and device\n\tcheckpoint = '/ssd1/gaomingqi/checkpoints/E2FGVI-HQ-CVPR22.pth'\n\tdevice = 'cuda:4'\n\t# 2/3: initialise inpainter\n\tbase_inpainter = BaseInpainter(checkpoint, device)\n\t# 3/3: inpainting (frames: numpy array, T, H, W, 3; masks: numpy array, T, H, W)\n\t# ratio: (0, 1], ratio for down sample, default value is 1\n\tinpainted_frames = base_inpainter.inpaint(frames[:300], masks[:300], ratio=0.6) # numpy array, T, H, W, 3\n\n\t# save\n\tfor ti, inpainted_frame in enumerate(inpainted_frames):\n\t\tframe = Image.fromarray(inpainted_frame).convert('RGB')\n\t\tframe.save(os.path.join(save_path, f'{ti:05d}.jpg'))\n\n\ttorch.cuda.empty_cache()\n\tprint('switch to ori')\n\n\t# inpainted_frames = base_inpainter.inpaint_ori(frames[:50], masks[:50], ratio=0.1)\n\t# save_path = '/ssd1/gaomingqi/results/inpainting/avengers'\n\t# # ----------------------------------------------\n\t# # end\n\t# # ----------------------------------------------\n\t# # save\n\t# for ti, inpainted_frame in enumerate(inpainted_frames):\n\t# \tframe = Image.fromarray(inpainted_frame).convert('RGB')\n\t# \tframe.save(os.path.join(save_path, f'{ti:05d}.jpg'))","source_hash":"f7bbf56a57382aa2181be0c78b8aaff516bf820569cf9724ee7a01a3ed7b916c","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.base_inpainter.BaseInpainter","uri":"program://Track-Anything/class/inpainter.base_inpainter.BaseInpainter#L15-L328","kind":"class","name":"BaseInpainter","path":"inpainter/base_inpainter.py","language":"python","start_line":15,"end_line":328,"context_start_line":1,"context_end_line":348,"code":"import os\nimport glob\nfrom PIL import Image\n\nimport torch\nimport yaml\nimport cv2\nimport importlib\nimport numpy as np\nfrom tqdm import tqdm\n\nfrom inpainter.util.tensor_util import resize_frames, resize_masks\n\n\nclass BaseInpainter:\n\tdef __init__(self, E2FGVI_checkpoint, device) -> None:\n\t\t\"\"\"\n\t\tE2FGVI_checkpoint: checkpoint of inpainter (version hq, with multi-resolution support)\n\t\t\"\"\"\n\t\tnet = importlib.import_module('inpainter.model.e2fgvi_hq')\n\t\tself.model = net.InpaintGenerator().to(device)\n\t\tself.model.load_state_dict(torch.load(E2FGVI_checkpoint, map_location=device))\n\t\tself.model.eval()\n\t\tself.device = device\n\t\t# load configurations\n\t\twith open(\"inpainter/config/config.yaml\", 'r') as stream: \n\t\t\tconfig = yaml.safe_load(stream) \n\t\tself.neighbor_stride = config['neighbor_stride']\n\t\tself.num_ref = config['num_ref']\n\t\tself.step = config['step']\n\t\t# config for E2FGVI with splits\n\t\tself.num_subset_frames = config['num_subset_frames']\n\t\tself.num_external_ref = config['num_external_ref']\n\n\t# sample reference frames from the whole video\n\tdef get_ref_index(self, f, neighbor_ids, length):\n\t\tref_index = []\n\t\tif self.num_ref == -1:\n\t\t\tfor i in range(0, length, self.step):\n\t\t\t\tif i not in neighbor_ids:\n\t\t\t\t\tref_index.append(i)\n\t\telse:\n\t\t\tstart_idx = max(0, f - self.step * (self.num_ref // 2))\n\t\t\tend_idx = min(length, f + self.step * (self.num_ref // 2))\n\t\t\tfor i in range(start_idx, end_idx + 1, self.step):\n\t\t\t\tif i not in neighbor_ids:\n\t\t\t\t\tif len(ref_index) > self.num_ref:\n\t\t\t\t\t\tbreak\n\t\t\t\t\tref_index.append(i)\n\t\treturn ref_index\n\n\tdef inpaint_efficient(self, frames, masks, num_tcb, num_tca, dilate_radius=15, ratio=1):\n\t\t\"\"\"\n\t\tPerform Inpainting for video subsets\n\t\tframes: numpy array, T, H, W, 3\n\t\tmasks: numpy array, T, H, W\n\t\tnum_tcb: constant, number of temporal context before, frames\n\t\tnum_tca: constant, number of temporal context after, frames\n\t\tdilate_radius: radius when applying dilation on masks\n\t\tratio: down-sample ratio\n\n\t\tOutput:\n\t\tinpainted_frames: numpy array, T, H, W, 3\n\t\t\"\"\"\n\t\tassert frames.shape[:3] == masks.shape, 'different size between frames and masks'\n\t\tassert ratio > 0 and ratio <= 1, 'ratio must in (0, 1]'\n\t\t\n\t\t# --------------------\n\t\t# pre-processing\n\t\t# --------------------\n\t\tmasks = masks.copy()\n\t\tmasks = np.clip(masks, 0, 1)\n\t\tkernel = cv2.getStructuringElement(2, (dilate_radius, dilate_radius))\n\t\tmasks = np.stack([cv2.dilate(mask, kernel) for mask in masks], 0)\n\t\tT, H, W = masks.shape\n\t\tmasks = np.expand_dims(masks, axis=3) # expand to T, H, W, 1\n\t\t# size: (w, h)\n\t\tif ratio == 1:\n\t\t\tsize = None\n\t\t\tbinary_masks = masks\n\t\telse:\n\t\t\tsize = [int(W*ratio), int(H*ratio)]\n\t\t\tsize = [si+1 if si%2>0 else si for si in size] # only consider even values\n\t\t\t# shortest side should be larger than 50\n\t\t\tif min(size) < 50:\n\t\t\t\tratio = 50. / min(H, W)\n\t\t\t\tsize = [int(W*ratio), int(H*ratio)]\n\t\t\tbinary_masks = resize_masks(masks, tuple(size))\n\t\t\tframes = resize_frames(frames, tuple(size)) # T, H, W, 3\n\t\t# frames and binary_masks are numpy arrays\n\t\th, w = frames.shape[1:3]\n\t\tvideo_length = T - (num_tca + num_tcb) # real video length\n\t\t# convert to tensor\n\t\timgs = (torch.from_numpy(frames).permute(0, 3, 1, 2).contiguous().unsqueeze(0).float().div(255)) * 2 - 1\n\t\tmasks = torch.from_numpy(binary_masks).permute(0, 3, 1, 2).contiguous().unsqueeze(0)\n\t\timgs, masks = imgs.to(self.device), masks.to(self.device)\n\t\tcomp_frames = [None] * video_length\n\t\ttcb_imgs = None\n\t\ttca_imgs = None\n\t\ttcb_masks = None\n\t\ttca_masks = None\n\t\t# --------------------\n\t\t# end of pre-processing\n\t\t# --------------------\n\n\t\t# separate tc frames/masks from imgs and masks\n\t\tif num_tcb > 0:\n\t\t\ttcb_imgs = imgs[:, :num_tcb]\n\t\t\ttcb_masks = masks[:, :num_tcb]\n\t\tif num_tca > 0:\n\t\t\ttca_imgs = imgs[:, -num_tca:]\n\t\t\ttca_masks = masks[:, -num_tca:]\n\t\t\tend_idx = -num_tca\n\t\telse:\n\t\t\tend_idx = T\n\n\t\timgs = imgs[:, num_tcb:end_idx]\n\t\tmasks = masks[:, num_tcb:end_idx]\n\t\tbinary_masks = binary_masks[num_tcb:end_idx]\t# only neighbor area are involved\n\t\tframes = frames[num_tcb:end_idx]\t\t\t\t# only neighbor area are involved\n\n\t\tfor f in tqdm(range(0, video_length, self.neighbor_stride), desc='Inpainting image'):\n\t\t\tneighbor_ids = [\n\t\t\t\ti for i in range(max(0, f - self.neighbor_stride),\n\t\t\t\t\t\t\t\tmin(video_length, f + self.neighbor_stride + 1))\n\t\t\t]\n\t\t\tref_ids = self.get_ref_index(f, neighbor_ids, video_length)\n\n\t\t\t# selected_imgs = imgs[:1, neighbor_ids + ref_ids, :, :, :]\n\t\t\t# selected_masks = masks[:1, neighbor_ids + ref_ids, :, :, :]\n\t\t\t\n\t\t\tselected_imgs = imgs[:, neighbor_ids]\n\t\t\tselected_masks = masks[:, neighbor_ids]\n\t\t\t# pad before\n\t\t\tif tcb_imgs is not None:\n\t\t\t\tselected_imgs = torch.concat([selected_imgs, tcb_imgs], dim=1)\n\t\t\t\tselected_masks = torch.concat([selected_masks, tcb_masks], dim=1)\n\t\t\t# integrate ref frames\n\t\t\tselected_imgs = torch.concat([selected_imgs, imgs[:, ref_ids]], dim=1)\n\t\t\tselected_masks = torch.concat([selected_masks, masks[:, ref_ids]], dim=1)\n\t\t\t# pad after\n\t\t\tif tca_imgs is not None:\n\t\t\t\tselected_imgs = torch.concat([selected_imgs, tca_imgs], dim=1)\n\t\t\t\tselected_masks = torch.concat([selected_masks, tca_masks], dim=1)\n\n\t\t\twith torch.no_grad():\n\t\t\t\tmasked_imgs = selected_imgs * (1 - selected_masks)\n\t\t\t\tmod_size_h = 60\n\t\t\t\tmod_size_w = 108\n\t\t\t\th_pad = (mod_size_h - h % mod_size_h) % mod_size_h\n\t\t\t\tw_pad = (mod_size_w - w % mod_size_w) % mod_size_w\n\t\t\t\tmasked_imgs = torch.cat(\n\t\t\t\t\t[masked_imgs, torch.flip(masked_imgs, [3])],\n\t\t\t\t\t3)[:, :, :, :h + h_pad, :]\n\t\t\t\tmasked_imgs = torch.cat(\n\t\t\t\t\t[masked_imgs, torch.flip(masked_imgs, [4])],\n\t\t\t\t\t4)[:, :, :, :, :w + w_pad]\n\t\t\t\tpred_imgs, _ = self.model(masked_imgs, len(neighbor_ids))\n\t\t\t\tpred_imgs = pred_imgs[:, :, :h, :w]\n\t\t\t\tpred_imgs = (pred_imgs + 1) / 2\n\t\t\t\tpred_imgs = pred_imgs.cpu().permute(0, 2, 3, 1).numpy() * 255\n\t\t\t\tfor i in range(len(neighbor_ids)):\n\t\t\t\t\tidx = neighbor_ids[i]\n\t\t\t\t\timg = pred_imgs[i].astype(np.uint8) * binary_masks[idx] + frames[idx] * (\n\t\t\t\t\t\t\t1 - binary_masks[idx])\n\t\t\t\t\tif comp_frames[idx] is None:\n\t\t\t\t\t\tcomp_frames[idx] = img\n\t\t\t\t\telse:\n\t\t\t\t\t\tcomp_frames[idx] = comp_frames[idx].astype(\n\t\t\t\t\t\t\tnp.float32) * 0.5 + img.astype(np.float32) * 0.5\n\t\t\ttorch.cuda.empty_cache()\n\t\tinpainted_frames = np.stack(comp_frames, 0)\n\t\treturn inpainted_frames.astype(np.uint8)\n\n\tdef inpaint(self, frames, masks, dilate_radius=15, ratio=1):\n\t\t\"\"\"\n\t\tPerform Inpainting for video subsets\n\t\tframes: numpy array, T, H, W, 3\n\t\tmasks: numpy array, T, H, W\n\t\tdilate_radius: radius when applying dilation on masks\n\t\tratio: down-sample ratio\n\n\t\tOutput:\n\t\tinpainted_frames: numpy array, T, H, W, 3\n\t\t\"\"\"\n\t\tassert frames.shape[:3] == masks.shape, 'different size between frames and masks'\n\t\tassert ratio > 0 and ratio <= 1, 'ratio must in (0, 1]'\n\t\t\n\t\t# set num_subset_frames\n\t\tnum_subset_frames = self.num_subset_frames\n\t\t# split frames into subsets\n\t\tvideo_length = len(frames)\n\t\tnum_splits = video_length // num_subset_frames\n\t\tid_splits = [[i*num_subset_frames, (i+1)*num_subset_frames] for i in range(num_splits)] # id splits\n\t\t\n\t\tif num_splits == 0:\n\t\t\tid_splits = [[0, video_length]]\n\t\t\n\t\t# if remaining split > num_subset_frames/2, add a new split, else, append to the last split\n\t\tif video_length - id_splits[-1][-1] > num_subset_frames / 3:\n\t\t\tid_splits.append([num_splits*num_subset_frames, video_length])\n\t\telse:\n\t\t\tdiff = video_length - id_splits[-1][-1]\n\t\t\tid_splits = [[ids[0]+diff, ids[1]+diff] for ids in id_splits]\n\t\t\tid_splits[0][0] = 0\t\t# if OOM, let it happen at the begining :D\n\n\t\t# if appending, convert the appended split to the FIRST one, avoiding OOM at last\n\n\t\t# perform inpainting for each split\n\t\tinpainted_splits = []\n\t\tfor id_split in id_splits:\n\t\t\tvideo_split = frames[id_split[0]:id_split[1]]\n\t\t\tmask_split = masks[id_split[0]:id_split[1]]\n\n\t\t\t# | id_before | ----- | id_split[0] | ----- | id_split[1] | ----- | id_after |\n\t\t\t# for each split, consider its temporal context [-context_range] frames and [context_range] frames\n\t\t\tid_before = max(0, id_split[0] - self.step * self.num_external_ref)\n\t\t\ttry:\n\t\t\t\ttcb_frames = np.stack([frames[idb] for idb in range(id_before, (id_split[0]-self.step) + 1, self.step)], 0)\n\t\t\t\ttcb_masks = np.stack([masks[idb] for idb in range(id_before, (id_split[0]-self.step) + 1, self.step)], 0)\n\t\t\t\tnum_tcb = len(tcb_frames)\n\t\t\texcept:\n\t\t\t\tnum_tcb = 0\n\t\t\tid_after = min(video_length, id_split[1] + self.step * self.num_external_ref + 1)\n\t\t\ttry:\n\t\t\t\ttca_frames = np.stack([frames[ida] for ida in range(id_split[1]+self.step, id_after, self.step)], 0)\n\t\t\t\ttca_masks = np.stack([masks[ida] for ida in range(id_split[1]+self.step, id_after, self.step)], 0)\n\t\t\t\tnum_tca = len(tca_frames)\n\t\t\texcept:\n\t\t\t\tnum_tca = 0\n\n\t\t\t# concatenate temporal context frames/masks with input frames/masks (for parallel pre-processing)\n\t\t\tif num_tcb > 0:\n\t\t\t\tvideo_split = np.concatenate([tcb_frames, video_split], 0)\n\t\t\t\tmask_split = np.concatenate([tcb_masks, mask_split], 0)\n\t\t\tif num_tca > 0:\n\t\t\t\tvideo_split = np.concatenate([video_split, tca_frames], 0)\n\t\t\t\tmask_split = np.concatenate([mask_split, tca_masks], 0)\n\t\t\t\n\t\t\ttorch.cuda.empty_cache()\n\t\t\t# inpaint each split\n\t\t\tinpainted_splits.append(self.inpaint_efficient(video_split, mask_split, num_tcb, num_tca, dilate_radius, ratio))\n\t\t\ttorch.cuda.empty_cache()\n\t\tinpainted_frames = np.concatenate(inpainted_splits, 0)\n\n\t\treturn inpainted_frames.astype(np.uint8)\n\n\tdef inpaint_ori(self, frames, masks, dilate_radius=15, ratio=1):\n\t\t\"\"\"\n\t\tframes: numpy array, T, H, W, 3\n\t\tmasks: numpy array, T, H, W\n\t\tdilate_radius: radius when applying dilation on masks\n\t\tratio: down-sample ratio\n\n\t\tOutput:\n\t\tinpainted_frames: numpy array, T, H, W, 3\n\t\t\"\"\"\n\t\tassert frames.shape[:3] == masks.shape, 'different size between frames and masks'\n\t\tassert ratio > 0 and ratio <= 1, 'ratio must in (0, 1]'\n\t\tmasks = masks.copy()\n\t\tmasks = np.clip(masks, 0, 1)\n\t\tkernel = cv2.getStructuringElement(2, (dilate_radius, dilate_radius))\n\t\tmasks = np.stack([cv2.dilate(mask, kernel) for mask in masks], 0)\n\n\t\tT, H, W = masks.shape\n\t\tmasks = np.expand_dims(masks, axis=3) # expand to T, H, W, 1\n\t\t# size: (w, h)\n\t\tif ratio == 1:\n\t\t\tsize = None\n\t\t\tbinary_masks = masks\n\t\telse:\n\t\t\tsize = [int(W*ratio), int(H*ratio)]\n\t\t\tsize = [si+1 if si%2>0 else si for si in size] # only consider even values\n\t\t\t# shortest side should be larger than 50\n\t\t\tif min(size) < 50:\n\t\t\t\tratio = 50. / min(H, W)\n\t\t\t\tsize = [int(W*ratio), int(H*ratio)]\n\n\t\t\tsize = [160, 120]\n\t\t\tbinary_masks = resize_masks(masks, tuple(size))\n\t\t\tframes = resize_frames(frames, tuple(size)) # T, H, W, 3\n\t\t# frames and binary_masks are numpy arrays\n\t\th, w = frames.shape[1:3]\n\t\tvideo_length = T\n\n\t\t# convert to tensor\n\t\timgs = (torch.from_numpy(frames).permute(0, 3, 1, 2).contiguous().unsqueeze(0).float().div(255)) * 2 - 1\n\t\tmasks = torch.from_numpy(binary_masks).permute(0, 3, 1, 2).contiguous().unsqueeze(0)\n\n\t\timgs, masks = imgs.to(self.device), masks.to(self.device)\n\t\tcomp_frames = [None] * video_length\n\n\t\tfor f in tqdm(range(0, video_length, self.neighbor_stride), desc='Inpainting image'):\n\t\t\tneighbor_ids = [\n\t\t\t\ti for i in range(max(0, f - self.neighbor_stride),\n\t\t\t\t\t\t\t\tmin(video_length, f + self.neighbor_stride + 1))\n\t\t\t]\n\t\t\tref_ids = self.get_ref_index(f, neighbor_ids, video_length)\n\t\t\tselected_imgs = imgs[:1, neighbor_ids + ref_ids, :, :, :]\n\t\t\tselected_masks = masks[:1, neighbor_ids + ref_ids, :, :, :]\n\t\t\twith torch.no_grad():\n\t\t\t\tmasked_imgs = selected_imgs * (1 - selected_masks)\n\t\t\t\tmod_size_h = 60\n\t\t\t\tmod_size_w = 108\n\t\t\t\th_pad = (mod_size_h - h % mod_size_h) % mod_size_h\n\t\t\t\tw_pad = (mod_size_w - w % mod_size_w) % mod_size_w\n\t\t\t\tmasked_imgs = torch.cat(\n\t\t\t\t\t[masked_imgs, torch.flip(masked_imgs, [3])],\n\t\t\t\t\t3)[:, :, :, :h + h_pad, :]\n\t\t\t\tmasked_imgs = torch.cat(\n\t\t\t\t\t[masked_imgs, torch.flip(masked_imgs, [4])],\n\t\t\t\t\t4)[:, :, :, :, :w + w_pad]\n\t\t\t\tpred_imgs, _ = self.model(masked_imgs, len(neighbor_ids))\n\t\t\t\tpred_imgs = pred_imgs[:, :, :h, :w]\n\t\t\t\tpred_imgs = (pred_imgs + 1) / 2\n\t\t\t\tpred_imgs = pred_imgs.cpu().permute(0, 2, 3, 1).numpy() * 255\n\t\t\t\tfor i in range(len(neighbor_ids)):\n\t\t\t\t\tidx = neighbor_ids[i]\n\t\t\t\t\timg = pred_imgs[i].astype(np.uint8) * binary_masks[idx] + frames[idx] * (\n\t\t\t\t\t\t\t1 - binary_masks[idx])\n\t\t\t\t\tif comp_frames[idx] is None:\n\t\t\t\t\t\tcomp_frames[idx] = img\n\t\t\t\t\telse:\n\t\t\t\t\t\tcomp_frames[idx] = comp_frames[idx].astype(\n\t\t\t\t\t\t\tnp.float32) * 0.5 + img.astype(np.float32) * 0.5\n\t\t\ttorch.cuda.empty_cache()\n\t\tinpainted_frames = np.stack(comp_frames, 0)\n\t\treturn inpainted_frames.astype(np.uint8)\n\n\nif __name__ == '__main__':\n\n\t# # davis-2017\n\t# frame_path = glob.glob(os.path.join('/ssd1/gaomingqi/datasets/davis/JPEGImages/480p/parkour', '*.jpg'))\n\t# frame_path.sort()\n\t# mask_path = glob.glob(os.path.join('/ssd1/gaomingqi/datasets/davis/Annotations/480p/parkour', \"*.png\"))\n\t# mask_path.sort()\n\n\t# long and large video\n\tmask_path = glob.glob(os.path.join('/ssd1/gaomingqi/test-sample13', '*.npy'))\n\tmask_path.sort()\n\tframes = np.load('/ssd1/gaomingqi/revenger.npy')\n\tsave_path = '/ssd1/gaomingqi/results/inpainting/avengers_split'\n\n\tif not os.path.exists(save_path):\n\t\tos.mkdir(save_path)\n\n\tmasks = []","source_hash":"f7bbf56a57382aa2181be0c78b8aaff516bf820569cf9724ee7a01a3ed7b916c","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.base_inpainter.__init__","uri":"program://Track-Anything/function/inpainter.base_inpainter.__init__#L16-L33","kind":"function","name":"__init__","path":"inpainter/base_inpainter.py","language":"python","start_line":16,"end_line":33,"context_start_line":1,"context_end_line":53,"code":"import os\nimport glob\nfrom PIL import Image\n\nimport torch\nimport yaml\nimport cv2\nimport importlib\nimport numpy as np\nfrom tqdm import tqdm\n\nfrom inpainter.util.tensor_util import resize_frames, resize_masks\n\n\nclass BaseInpainter:\n\tdef __init__(self, E2FGVI_checkpoint, device) -> None:\n\t\t\"\"\"\n\t\tE2FGVI_checkpoint: checkpoint of inpainter (version hq, with multi-resolution support)\n\t\t\"\"\"\n\t\tnet = importlib.import_module('inpainter.model.e2fgvi_hq')\n\t\tself.model = net.InpaintGenerator().to(device)\n\t\tself.model.load_state_dict(torch.load(E2FGVI_checkpoint, map_location=device))\n\t\tself.model.eval()\n\t\tself.device = device\n\t\t# load configurations\n\t\twith open(\"inpainter/config/config.yaml\", 'r') as stream: \n\t\t\tconfig = yaml.safe_load(stream) \n\t\tself.neighbor_stride = config['neighbor_stride']\n\t\tself.num_ref = config['num_ref']\n\t\tself.step = config['step']\n\t\t# config for E2FGVI with splits\n\t\tself.num_subset_frames = config['num_subset_frames']\n\t\tself.num_external_ref = config['num_external_ref']\n\n\t# sample reference frames from the whole video\n\tdef get_ref_index(self, f, neighbor_ids, length):\n\t\tref_index = []\n\t\tif self.num_ref == -1:\n\t\t\tfor i in range(0, length, self.step):\n\t\t\t\tif i not in neighbor_ids:\n\t\t\t\t\tref_index.append(i)\n\t\telse:\n\t\t\tstart_idx = max(0, f - self.step * (self.num_ref // 2))\n\t\t\tend_idx = min(length, f + self.step * (self.num_ref // 2))\n\t\t\tfor i in range(start_idx, end_idx + 1, self.step):\n\t\t\t\tif i not in neighbor_ids:\n\t\t\t\t\tif len(ref_index) > self.num_ref:\n\t\t\t\t\t\tbreak\n\t\t\t\t\tref_index.append(i)\n\t\treturn ref_index\n\n\tdef inpaint_efficient(self, frames, masks, num_tcb, num_tca, dilate_radius=15, ratio=1):\n\t\t\"\"\"","source_hash":"f7bbf56a57382aa2181be0c78b8aaff516bf820569cf9724ee7a01a3ed7b916c","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.base_inpainter.get_ref_index","uri":"program://Track-Anything/function/inpainter.base_inpainter.get_ref_index#L36-L50","kind":"function","name":"get_ref_index","path":"inpainter/base_inpainter.py","language":"python","start_line":36,"end_line":50,"context_start_line":16,"context_end_line":70,"code":"\tdef __init__(self, E2FGVI_checkpoint, device) -> None:\n\t\t\"\"\"\n\t\tE2FGVI_checkpoint: checkpoint of inpainter (version hq, with multi-resolution support)\n\t\t\"\"\"\n\t\tnet = importlib.import_module('inpainter.model.e2fgvi_hq')\n\t\tself.model = net.InpaintGenerator().to(device)\n\t\tself.model.load_state_dict(torch.load(E2FGVI_checkpoint, map_location=device))\n\t\tself.model.eval()\n\t\tself.device = device\n\t\t# load configurations\n\t\twith open(\"inpainter/config/config.yaml\", 'r') as stream: \n\t\t\tconfig = yaml.safe_load(stream) \n\t\tself.neighbor_stride = config['neighbor_stride']\n\t\tself.num_ref = config['num_ref']\n\t\tself.step = config['step']\n\t\t# config for E2FGVI with splits\n\t\tself.num_subset_frames = config['num_subset_frames']\n\t\tself.num_external_ref = config['num_external_ref']\n\n\t# sample reference frames from the whole video\n\tdef get_ref_index(self, f, neighbor_ids, length):\n\t\tref_index = []\n\t\tif self.num_ref == -1:\n\t\t\tfor i in range(0, length, self.step):\n\t\t\t\tif i not in neighbor_ids:\n\t\t\t\t\tref_index.append(i)\n\t\telse:\n\t\t\tstart_idx = max(0, f - self.step * (self.num_ref // 2))\n\t\t\tend_idx = min(length, f + self.step * (self.num_ref // 2))\n\t\t\tfor i in range(start_idx, end_idx + 1, self.step):\n\t\t\t\tif i not in neighbor_ids:\n\t\t\t\t\tif len(ref_index) > self.num_ref:\n\t\t\t\t\t\tbreak\n\t\t\t\t\tref_index.append(i)\n\t\treturn ref_index\n\n\tdef inpaint_efficient(self, frames, masks, num_tcb, num_tca, dilate_radius=15, ratio=1):\n\t\t\"\"\"\n\t\tPerform Inpainting for video subsets\n\t\tframes: numpy array, T, H, W, 3\n\t\tmasks: numpy array, T, H, W\n\t\tnum_tcb: constant, number of temporal context before, frames\n\t\tnum_tca: constant, number of temporal context after, frames\n\t\tdilate_radius: radius when applying dilation on masks\n\t\tratio: down-sample ratio\n\n\t\tOutput:\n\t\tinpainted_frames: numpy array, T, H, W, 3\n\t\t\"\"\"\n\t\tassert frames.shape[:3] == masks.shape, 'different size between frames and masks'\n\t\tassert ratio > 0 and ratio <= 1, 'ratio must in (0, 1]'\n\t\t\n\t\t# --------------------\n\t\t# pre-processing\n\t\t# --------------------","source_hash":"f7bbf56a57382aa2181be0c78b8aaff516bf820569cf9724ee7a01a3ed7b916c","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.base_inpainter.inpaint_efficient","uri":"program://Track-Anything/function/inpainter.base_inpainter.inpaint_efficient#L52-L173","kind":"function","name":"inpaint_efficient","path":"inpainter/base_inpainter.py","language":"python","start_line":52,"end_line":173,"context_start_line":32,"context_end_line":193,"code":"\t\tself.num_subset_frames = config['num_subset_frames']\n\t\tself.num_external_ref = config['num_external_ref']\n\n\t# sample reference frames from the whole video\n\tdef get_ref_index(self, f, neighbor_ids, length):\n\t\tref_index = []\n\t\tif self.num_ref == -1:\n\t\t\tfor i in range(0, length, self.step):\n\t\t\t\tif i not in neighbor_ids:\n\t\t\t\t\tref_index.append(i)\n\t\telse:\n\t\t\tstart_idx = max(0, f - self.step * (self.num_ref // 2))\n\t\t\tend_idx = min(length, f + self.step * (self.num_ref // 2))\n\t\t\tfor i in range(start_idx, end_idx + 1, self.step):\n\t\t\t\tif i not in neighbor_ids:\n\t\t\t\t\tif len(ref_index) > self.num_ref:\n\t\t\t\t\t\tbreak\n\t\t\t\t\tref_index.append(i)\n\t\treturn ref_index\n\n\tdef inpaint_efficient(self, frames, masks, num_tcb, num_tca, dilate_radius=15, ratio=1):\n\t\t\"\"\"\n\t\tPerform Inpainting for video subsets\n\t\tframes: numpy array, T, H, W, 3\n\t\tmasks: numpy array, T, H, W\n\t\tnum_tcb: constant, number of temporal context before, frames\n\t\tnum_tca: constant, number of temporal context after, frames\n\t\tdilate_radius: radius when applying dilation on masks\n\t\tratio: down-sample ratio\n\n\t\tOutput:\n\t\tinpainted_frames: numpy array, T, H, W, 3\n\t\t\"\"\"\n\t\tassert frames.shape[:3] == masks.shape, 'different size between frames and masks'\n\t\tassert ratio > 0 and ratio <= 1, 'ratio must in (0, 1]'\n\t\t\n\t\t# --------------------\n\t\t# pre-processing\n\t\t# --------------------\n\t\tmasks = masks.copy()\n\t\tmasks = np.clip(masks, 0, 1)\n\t\tkernel = cv2.getStructuringElement(2, (dilate_radius, dilate_radius))\n\t\tmasks = np.stack([cv2.dilate(mask, kernel) for mask in masks], 0)\n\t\tT, H, W = masks.shape\n\t\tmasks = np.expand_dims(masks, axis=3) # expand to T, H, W, 1\n\t\t# size: (w, h)\n\t\tif ratio == 1:\n\t\t\tsize = None\n\t\t\tbinary_masks = masks\n\t\telse:\n\t\t\tsize = [int(W*ratio), int(H*ratio)]\n\t\t\tsize = [si+1 if si%2>0 else si for si in size] # only consider even values\n\t\t\t# shortest side should be larger than 50\n\t\t\tif min(size) < 50:\n\t\t\t\tratio = 50. / min(H, W)\n\t\t\t\tsize = [int(W*ratio), int(H*ratio)]\n\t\t\tbinary_masks = resize_masks(masks, tuple(size))\n\t\t\tframes = resize_frames(frames, tuple(size)) # T, H, W, 3\n\t\t# frames and binary_masks are numpy arrays\n\t\th, w = frames.shape[1:3]\n\t\tvideo_length = T - (num_tca + num_tcb) # real video length\n\t\t# convert to tensor\n\t\timgs = (torch.from_numpy(frames).permute(0, 3, 1, 2).contiguous().unsqueeze(0).float().div(255)) * 2 - 1\n\t\tmasks = torch.from_numpy(binary_masks).permute(0, 3, 1, 2).contiguous().unsqueeze(0)\n\t\timgs, masks = imgs.to(self.device), masks.to(self.device)\n\t\tcomp_frames = [None] * video_length\n\t\ttcb_imgs = None\n\t\ttca_imgs = None\n\t\ttcb_masks = None\n\t\ttca_masks = None\n\t\t# --------------------\n\t\t# end of pre-processing\n\t\t# --------------------\n\n\t\t# separate tc frames/masks from imgs and masks\n\t\tif num_tcb > 0:\n\t\t\ttcb_imgs = imgs[:, :num_tcb]\n\t\t\ttcb_masks = masks[:, :num_tcb]\n\t\tif num_tca > 0:\n\t\t\ttca_imgs = imgs[:, -num_tca:]\n\t\t\ttca_masks = masks[:, -num_tca:]\n\t\t\tend_idx = -num_tca\n\t\telse:\n\t\t\tend_idx = T\n\n\t\timgs = imgs[:, num_tcb:end_idx]\n\t\tmasks = masks[:, num_tcb:end_idx]\n\t\tbinary_masks = binary_masks[num_tcb:end_idx]\t# only neighbor area are involved\n\t\tframes = frames[num_tcb:end_idx]\t\t\t\t# only neighbor area are involved\n\n\t\tfor f in tqdm(range(0, video_length, self.neighbor_stride), desc='Inpainting image'):\n\t\t\tneighbor_ids = [\n\t\t\t\ti for i in range(max(0, f - self.neighbor_stride),\n\t\t\t\t\t\t\t\tmin(video_length, f + self.neighbor_stride + 1))\n\t\t\t]\n\t\t\tref_ids = self.get_ref_index(f, neighbor_ids, video_length)\n\n\t\t\t# selected_imgs = imgs[:1, neighbor_ids + ref_ids, :, :, :]\n\t\t\t# selected_masks = masks[:1, neighbor_ids + ref_ids, :, :, :]\n\t\t\t\n\t\t\tselected_imgs = imgs[:, neighbor_ids]\n\t\t\tselected_masks = masks[:, neighbor_ids]\n\t\t\t# pad before\n\t\t\tif tcb_imgs is not None:\n\t\t\t\tselected_imgs = torch.concat([selected_imgs, tcb_imgs], dim=1)\n\t\t\t\tselected_masks = torch.concat([selected_masks, tcb_masks], dim=1)\n\t\t\t# integrate ref frames\n\t\t\tselected_imgs = torch.concat([selected_imgs, imgs[:, ref_ids]], dim=1)\n\t\t\tselected_masks = torch.concat([selected_masks, masks[:, ref_ids]], dim=1)\n\t\t\t# pad after\n\t\t\tif tca_imgs is not None:\n\t\t\t\tselected_imgs = torch.concat([selected_imgs, tca_imgs], dim=1)\n\t\t\t\tselected_masks = torch.concat([selected_masks, tca_masks], dim=1)\n\n\t\t\twith torch.no_grad():\n\t\t\t\tmasked_imgs = selected_imgs * (1 - selected_masks)\n\t\t\t\tmod_size_h = 60\n\t\t\t\tmod_size_w = 108\n\t\t\t\th_pad = (mod_size_h - h % mod_size_h) % mod_size_h\n\t\t\t\tw_pad = (mod_size_w - w % mod_size_w) % mod_size_w\n\t\t\t\tmasked_imgs = torch.cat(\n\t\t\t\t\t[masked_imgs, torch.flip(masked_imgs, [3])],\n\t\t\t\t\t3)[:, :, :, :h + h_pad, :]\n\t\t\t\tmasked_imgs = torch.cat(\n\t\t\t\t\t[masked_imgs, torch.flip(masked_imgs, [4])],\n\t\t\t\t\t4)[:, :, :, :, :w + w_pad]\n\t\t\t\tpred_imgs, _ = self.model(masked_imgs, len(neighbor_ids))\n\t\t\t\tpred_imgs = pred_imgs[:, :, :h, :w]\n\t\t\t\tpred_imgs = (pred_imgs + 1) / 2\n\t\t\t\tpred_imgs = pred_imgs.cpu().permute(0, 2, 3, 1).numpy() * 255\n\t\t\t\tfor i in range(len(neighbor_ids)):\n\t\t\t\t\tidx = neighbor_ids[i]\n\t\t\t\t\timg = pred_imgs[i].astype(np.uint8) * binary_masks[idx] + frames[idx] * (\n\t\t\t\t\t\t\t1 - binary_masks[idx])\n\t\t\t\t\tif comp_frames[idx] is None:\n\t\t\t\t\t\tcomp_frames[idx] = img\n\t\t\t\t\telse:\n\t\t\t\t\t\tcomp_frames[idx] = comp_frames[idx].astype(\n\t\t\t\t\t\t\tnp.float32) * 0.5 + img.astype(np.float32) * 0.5\n\t\t\ttorch.cuda.empty_cache()\n\t\tinpainted_frames = np.stack(comp_frames, 0)\n\t\treturn inpainted_frames.astype(np.uint8)\n\n\tdef inpaint(self, frames, masks, dilate_radius=15, ratio=1):\n\t\t\"\"\"\n\t\tPerform Inpainting for video subsets\n\t\tframes: numpy array, T, H, W, 3\n\t\tmasks: numpy array, T, H, W\n\t\tdilate_radius: radius when applying dilation on masks\n\t\tratio: down-sample ratio\n\n\t\tOutput:\n\t\tinpainted_frames: numpy array, T, H, W, 3\n\t\t\"\"\"\n\t\tassert frames.shape[:3] == masks.shape, 'different size between frames and masks'\n\t\tassert ratio > 0 and ratio <= 1, 'ratio must in (0, 1]'\n\t\t\n\t\t# set num_subset_frames\n\t\tnum_subset_frames = self.num_subset_frames\n\t\t# split frames into subsets\n\t\tvideo_length = len(frames)\n\t\tnum_splits = video_length // num_subset_frames","source_hash":"f7bbf56a57382aa2181be0c78b8aaff516bf820569cf9724ee7a01a3ed7b916c","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.base_inpainter.inpaint","uri":"program://Track-Anything/function/inpainter.base_inpainter.inpaint#L175-L246","kind":"function","name":"inpaint","path":"inpainter/base_inpainter.py","language":"python","start_line":175,"end_line":246,"context_start_line":155,"context_end_line":266,"code":"\t\t\t\tmasked_imgs = torch.cat(\n\t\t\t\t\t[masked_imgs, torch.flip(masked_imgs, [4])],\n\t\t\t\t\t4)[:, :, :, :, :w + w_pad]\n\t\t\t\tpred_imgs, _ = self.model(masked_imgs, len(neighbor_ids))\n\t\t\t\tpred_imgs = pred_imgs[:, :, :h, :w]\n\t\t\t\tpred_imgs = (pred_imgs + 1) / 2\n\t\t\t\tpred_imgs = pred_imgs.cpu().permute(0, 2, 3, 1).numpy() * 255\n\t\t\t\tfor i in range(len(neighbor_ids)):\n\t\t\t\t\tidx = neighbor_ids[i]\n\t\t\t\t\timg = pred_imgs[i].astype(np.uint8) * binary_masks[idx] + frames[idx] * (\n\t\t\t\t\t\t\t1 - binary_masks[idx])\n\t\t\t\t\tif comp_frames[idx] is None:\n\t\t\t\t\t\tcomp_frames[idx] = img\n\t\t\t\t\telse:\n\t\t\t\t\t\tcomp_frames[idx] = comp_frames[idx].astype(\n\t\t\t\t\t\t\tnp.float32) * 0.5 + img.astype(np.float32) * 0.5\n\t\t\ttorch.cuda.empty_cache()\n\t\tinpainted_frames = np.stack(comp_frames, 0)\n\t\treturn inpainted_frames.astype(np.uint8)\n\n\tdef inpaint(self, frames, masks, dilate_radius=15, ratio=1):\n\t\t\"\"\"\n\t\tPerform Inpainting for video subsets\n\t\tframes: numpy array, T, H, W, 3\n\t\tmasks: numpy array, T, H, W\n\t\tdilate_radius: radius when applying dilation on masks\n\t\tratio: down-sample ratio\n\n\t\tOutput:\n\t\tinpainted_frames: numpy array, T, H, W, 3\n\t\t\"\"\"\n\t\tassert frames.shape[:3] == masks.shape, 'different size between frames and masks'\n\t\tassert ratio > 0 and ratio <= 1, 'ratio must in (0, 1]'\n\t\t\n\t\t# set num_subset_frames\n\t\tnum_subset_frames = self.num_subset_frames\n\t\t# split frames into subsets\n\t\tvideo_length = len(frames)\n\t\tnum_splits = video_length // num_subset_frames\n\t\tid_splits = [[i*num_subset_frames, (i+1)*num_subset_frames] for i in range(num_splits)] # id splits\n\t\t\n\t\tif num_splits == 0:\n\t\t\tid_splits = [[0, video_length]]\n\t\t\n\t\t# if remaining split > num_subset_frames/2, add a new split, else, append to the last split\n\t\tif video_length - id_splits[-1][-1] > num_subset_frames / 3:\n\t\t\tid_splits.append([num_splits*num_subset_frames, video_length])\n\t\telse:\n\t\t\tdiff = video_length - id_splits[-1][-1]\n\t\t\tid_splits = [[ids[0]+diff, ids[1]+diff] for ids in id_splits]\n\t\t\tid_splits[0][0] = 0\t\t# if OOM, let it happen at the begining :D\n\n\t\t# if appending, convert the appended split to the FIRST one, avoiding OOM at last\n\n\t\t# perform inpainting for each split\n\t\tinpainted_splits = []\n\t\tfor id_split in id_splits:\n\t\t\tvideo_split = frames[id_split[0]:id_split[1]]\n\t\t\tmask_split = masks[id_split[0]:id_split[1]]\n\n\t\t\t# | id_before | ----- | id_split[0] | ----- | id_split[1] | ----- | id_after |\n\t\t\t# for each split, consider its temporal context [-context_range] frames and [context_range] frames\n\t\t\tid_before = max(0, id_split[0] - self.step * self.num_external_ref)\n\t\t\ttry:\n\t\t\t\ttcb_frames = np.stack([frames[idb] for idb in range(id_before, (id_split[0]-self.step) + 1, self.step)], 0)\n\t\t\t\ttcb_masks = np.stack([masks[idb] for idb in range(id_before, (id_split[0]-self.step) + 1, self.step)], 0)\n\t\t\t\tnum_tcb = len(tcb_frames)\n\t\t\texcept:\n\t\t\t\tnum_tcb = 0\n\t\t\tid_after = min(video_length, id_split[1] + self.step * self.num_external_ref + 1)\n\t\t\ttry:\n\t\t\t\ttca_frames = np.stack([frames[ida] for ida in range(id_split[1]+self.step, id_after, self.step)], 0)\n\t\t\t\ttca_masks = np.stack([masks[ida] for ida in range(id_split[1]+self.step, id_after, self.step)], 0)\n\t\t\t\tnum_tca = len(tca_frames)\n\t\t\texcept:\n\t\t\t\tnum_tca = 0\n\n\t\t\t# concatenate temporal context frames/masks with input frames/masks (for parallel pre-processing)\n\t\t\tif num_tcb > 0:\n\t\t\t\tvideo_split = np.concatenate([tcb_frames, video_split], 0)\n\t\t\t\tmask_split = np.concatenate([tcb_masks, mask_split], 0)\n\t\t\tif num_tca > 0:\n\t\t\t\tvideo_split = np.concatenate([video_split, tca_frames], 0)\n\t\t\t\tmask_split = np.concatenate([mask_split, tca_masks], 0)\n\t\t\t\n\t\t\ttorch.cuda.empty_cache()\n\t\t\t# inpaint each split\n\t\t\tinpainted_splits.append(self.inpaint_efficient(video_split, mask_split, num_tcb, num_tca, dilate_radius, ratio))\n\t\t\ttorch.cuda.empty_cache()\n\t\tinpainted_frames = np.concatenate(inpainted_splits, 0)\n\n\t\treturn inpainted_frames.astype(np.uint8)\n\n\tdef inpaint_ori(self, frames, masks, dilate_radius=15, ratio=1):\n\t\t\"\"\"\n\t\tframes: numpy array, T, H, W, 3\n\t\tmasks: numpy array, T, H, W\n\t\tdilate_radius: radius when applying dilation on masks\n\t\tratio: down-sample ratio\n\n\t\tOutput:\n\t\tinpainted_frames: numpy array, T, H, W, 3\n\t\t\"\"\"\n\t\tassert frames.shape[:3] == masks.shape, 'different size between frames and masks'\n\t\tassert ratio > 0 and ratio <= 1, 'ratio must in (0, 1]'\n\t\tmasks = masks.copy()\n\t\tmasks = np.clip(masks, 0, 1)\n\t\tkernel = cv2.getStructuringElement(2, (dilate_radius, dilate_radius))\n\t\tmasks = np.stack([cv2.dilate(mask, kernel) for mask in masks], 0)\n\n\t\tT, H, W = masks.shape\n\t\tmasks = np.expand_dims(masks, axis=3) # expand to T, H, W, 1","source_hash":"f7bbf56a57382aa2181be0c78b8aaff516bf820569cf9724ee7a01a3ed7b916c","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.base_inpainter.inpaint_ori","uri":"program://Track-Anything/function/inpainter.base_inpainter.inpaint_ori#L248-L328","kind":"function","name":"inpaint_ori","path":"inpainter/base_inpainter.py","language":"python","start_line":248,"end_line":328,"context_start_line":228,"context_end_line":348,"code":"\t\t\t\tnum_tca = len(tca_frames)\n\t\t\texcept:\n\t\t\t\tnum_tca = 0\n\n\t\t\t# concatenate temporal context frames/masks with input frames/masks (for parallel pre-processing)\n\t\t\tif num_tcb > 0:\n\t\t\t\tvideo_split = np.concatenate([tcb_frames, video_split], 0)\n\t\t\t\tmask_split = np.concatenate([tcb_masks, mask_split], 0)\n\t\t\tif num_tca > 0:\n\t\t\t\tvideo_split = np.concatenate([video_split, tca_frames], 0)\n\t\t\t\tmask_split = np.concatenate([mask_split, tca_masks], 0)\n\t\t\t\n\t\t\ttorch.cuda.empty_cache()\n\t\t\t# inpaint each split\n\t\t\tinpainted_splits.append(self.inpaint_efficient(video_split, mask_split, num_tcb, num_tca, dilate_radius, ratio))\n\t\t\ttorch.cuda.empty_cache()\n\t\tinpainted_frames = np.concatenate(inpainted_splits, 0)\n\n\t\treturn inpainted_frames.astype(np.uint8)\n\n\tdef inpaint_ori(self, frames, masks, dilate_radius=15, ratio=1):\n\t\t\"\"\"\n\t\tframes: numpy array, T, H, W, 3\n\t\tmasks: numpy array, T, H, W\n\t\tdilate_radius: radius when applying dilation on masks\n\t\tratio: down-sample ratio\n\n\t\tOutput:\n\t\tinpainted_frames: numpy array, T, H, W, 3\n\t\t\"\"\"\n\t\tassert frames.shape[:3] == masks.shape, 'different size between frames and masks'\n\t\tassert ratio > 0 and ratio <= 1, 'ratio must in (0, 1]'\n\t\tmasks = masks.copy()\n\t\tmasks = np.clip(masks, 0, 1)\n\t\tkernel = cv2.getStructuringElement(2, (dilate_radius, dilate_radius))\n\t\tmasks = np.stack([cv2.dilate(mask, kernel) for mask in masks], 0)\n\n\t\tT, H, W = masks.shape\n\t\tmasks = np.expand_dims(masks, axis=3) # expand to T, H, W, 1\n\t\t# size: (w, h)\n\t\tif ratio == 1:\n\t\t\tsize = None\n\t\t\tbinary_masks = masks\n\t\telse:\n\t\t\tsize = [int(W*ratio), int(H*ratio)]\n\t\t\tsize = [si+1 if si%2>0 else si for si in size] # only consider even values\n\t\t\t# shortest side should be larger than 50\n\t\t\tif min(size) < 50:\n\t\t\t\tratio = 50. / min(H, W)\n\t\t\t\tsize = [int(W*ratio), int(H*ratio)]\n\n\t\t\tsize = [160, 120]\n\t\t\tbinary_masks = resize_masks(masks, tuple(size))\n\t\t\tframes = resize_frames(frames, tuple(size)) # T, H, W, 3\n\t\t# frames and binary_masks are numpy arrays\n\t\th, w = frames.shape[1:3]\n\t\tvideo_length = T\n\n\t\t# convert to tensor\n\t\timgs = (torch.from_numpy(frames).permute(0, 3, 1, 2).contiguous().unsqueeze(0).float().div(255)) * 2 - 1\n\t\tmasks = torch.from_numpy(binary_masks).permute(0, 3, 1, 2).contiguous().unsqueeze(0)\n\n\t\timgs, masks = imgs.to(self.device), masks.to(self.device)\n\t\tcomp_frames = [None] * video_length\n\n\t\tfor f in tqdm(range(0, video_length, self.neighbor_stride), desc='Inpainting image'):\n\t\t\tneighbor_ids = [\n\t\t\t\ti for i in range(max(0, f - self.neighbor_stride),\n\t\t\t\t\t\t\t\tmin(video_length, f + self.neighbor_stride + 1))\n\t\t\t]\n\t\t\tref_ids = self.get_ref_index(f, neighbor_ids, video_length)\n\t\t\tselected_imgs = imgs[:1, neighbor_ids + ref_ids, :, :, :]\n\t\t\tselected_masks = masks[:1, neighbor_ids + ref_ids, :, :, :]\n\t\t\twith torch.no_grad():\n\t\t\t\tmasked_imgs = selected_imgs * (1 - selected_masks)\n\t\t\t\tmod_size_h = 60\n\t\t\t\tmod_size_w = 108\n\t\t\t\th_pad = (mod_size_h - h % mod_size_h) % mod_size_h\n\t\t\t\tw_pad = (mod_size_w - w % mod_size_w) % mod_size_w\n\t\t\t\tmasked_imgs = torch.cat(\n\t\t\t\t\t[masked_imgs, torch.flip(masked_imgs, [3])],\n\t\t\t\t\t3)[:, :, :, :h + h_pad, :]\n\t\t\t\tmasked_imgs = torch.cat(\n\t\t\t\t\t[masked_imgs, torch.flip(masked_imgs, [4])],\n\t\t\t\t\t4)[:, :, :, :, :w + w_pad]\n\t\t\t\tpred_imgs, _ = self.model(masked_imgs, len(neighbor_ids))\n\t\t\t\tpred_imgs = pred_imgs[:, :, :h, :w]\n\t\t\t\tpred_imgs = (pred_imgs + 1) / 2\n\t\t\t\tpred_imgs = pred_imgs.cpu().permute(0, 2, 3, 1).numpy() * 255\n\t\t\t\tfor i in range(len(neighbor_ids)):\n\t\t\t\t\tidx = neighbor_ids[i]\n\t\t\t\t\timg = pred_imgs[i].astype(np.uint8) * binary_masks[idx] + frames[idx] * (\n\t\t\t\t\t\t\t1 - binary_masks[idx])\n\t\t\t\t\tif comp_frames[idx] is None:\n\t\t\t\t\t\tcomp_frames[idx] = img\n\t\t\t\t\telse:\n\t\t\t\t\t\tcomp_frames[idx] = comp_frames[idx].astype(\n\t\t\t\t\t\t\tnp.float32) * 0.5 + img.astype(np.float32) * 0.5\n\t\t\ttorch.cuda.empty_cache()\n\t\tinpainted_frames = np.stack(comp_frames, 0)\n\t\treturn inpainted_frames.astype(np.uint8)\n\n\nif __name__ == '__main__':\n\n\t# # davis-2017\n\t# frame_path = glob.glob(os.path.join('/ssd1/gaomingqi/datasets/davis/JPEGImages/480p/parkour', '*.jpg'))\n\t# frame_path.sort()\n\t# mask_path = glob.glob(os.path.join('/ssd1/gaomingqi/datasets/davis/Annotations/480p/parkour', \"*.png\"))\n\t# mask_path.sort()\n\n\t# long and large video\n\tmask_path = glob.glob(os.path.join('/ssd1/gaomingqi/test-sample13', '*.npy'))\n\tmask_path.sort()\n\tframes = np.load('/ssd1/gaomingqi/revenger.npy')\n\tsave_path = '/ssd1/gaomingqi/results/inpainting/avengers_split'\n\n\tif not os.path.exists(save_path):\n\t\tos.mkdir(save_path)\n\n\tmasks = []","source_hash":"f7bbf56a57382aa2181be0c78b8aaff516bf820569cf9724ee7a01a3ed7b916c","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.util.tensor_util","uri":"program://Track-Anything/module/inpainter.util.tensor_util#L1-L24","kind":"module","name":"inpainter.util.tensor_util","path":"inpainter/util/tensor_util.py","language":"python","start_line":1,"end_line":24,"context_start_line":1,"context_end_line":24,"code":"import cv2\nimport numpy as np\n\n# resize frames\ndef resize_frames(frames, size=None):\n \"\"\"\n size: (w, h)\n \"\"\"\n if size is not None:\n frames = [cv2.resize(f, size) for f in frames]\n frames = np.stack(frames, 0)\n\n return frames\n\n# resize frames\ndef resize_masks(masks, size=None):\n \"\"\"\n size: (w, h)\n \"\"\"\n if size is not None:\n masks = [np.expand_dims(cv2.resize(m, size), 2) for m in masks]\n masks = np.stack(masks, 0)\n\n return masks","source_hash":"226d60eef4a77f4ff9ee821b50bed556839d762adb0320c7c2c3af252e3ec9be","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.util.tensor_util.resize_frames","uri":"program://Track-Anything/function/inpainter.util.tensor_util.resize_frames#L5-L13","kind":"function","name":"resize_frames","path":"inpainter/util/tensor_util.py","language":"python","start_line":5,"end_line":13,"context_start_line":1,"context_end_line":24,"code":"import cv2\nimport numpy as np\n\n# resize frames\ndef resize_frames(frames, size=None):\n \"\"\"\n size: (w, h)\n \"\"\"\n if size is not None:\n frames = [cv2.resize(f, size) for f in frames]\n frames = np.stack(frames, 0)\n\n return frames\n\n# resize frames\ndef resize_masks(masks, size=None):\n \"\"\"\n size: (w, h)\n \"\"\"\n if size is not None:\n masks = [np.expand_dims(cv2.resize(m, size), 2) for m in masks]\n masks = np.stack(masks, 0)\n\n return masks","source_hash":"226d60eef4a77f4ff9ee821b50bed556839d762adb0320c7c2c3af252e3ec9be","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.util.tensor_util.resize_masks","uri":"program://Track-Anything/function/inpainter.util.tensor_util.resize_masks#L16-L24","kind":"function","name":"resize_masks","path":"inpainter/util/tensor_util.py","language":"python","start_line":16,"end_line":24,"context_start_line":1,"context_end_line":24,"code":"import cv2\nimport numpy as np\n\n# resize frames\ndef resize_frames(frames, size=None):\n \"\"\"\n size: (w, h)\n \"\"\"\n if size is not None:\n frames = [cv2.resize(f, size) for f in frames]\n frames = np.stack(frames, 0)\n\n return frames\n\n# resize frames\ndef resize_masks(masks, size=None):\n \"\"\"\n size: (w, h)\n \"\"\"\n if size is not None:\n masks = [np.expand_dims(cv2.resize(m, size), 2) for m in masks]\n masks = np.stack(masks, 0)\n\n return masks","source_hash":"226d60eef4a77f4ff9ee821b50bed556839d762adb0320c7c2c3af252e3ec9be","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.e2fgvi_hq","uri":"program://Track-Anything/module/inpainter.model.e2fgvi_hq#L1-L350","kind":"module","name":"inpainter.model.e2fgvi_hq","path":"inpainter/model/e2fgvi_hq.py","language":"python","start_line":1,"end_line":350,"context_start_line":1,"context_end_line":350,"code":"''' Towards An End-to-End Framework for Video Inpainting\n'''\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom inpainter.model.modules.flow_comp import SPyNet\nfrom inpainter.model.modules.feat_prop import BidirectionalPropagation, SecondOrderDeformableAlignment\nfrom inpainter.model.modules.tfocal_transformer_hq import TemporalFocalTransformerBlock, SoftSplit, SoftComp\nfrom inpainter.model.modules.spectral_norm import spectral_norm as _spectral_norm\n\n\nclass BaseNetwork(nn.Module):\n def __init__(self):\n super(BaseNetwork, self).__init__()\n\n def print_network(self):\n if isinstance(self, list):\n self = self[0]\n num_params = 0\n for param in self.parameters():\n num_params += param.numel()\n print(\n 'Network [%s] was created. Total number of parameters: %.1f million. '\n 'To see the architecture, do print(network).' %\n (type(self).__name__, num_params / 1000000))\n\n def init_weights(self, init_type='normal', gain=0.02):\n '''\n initialize network's weights\n init_type: normal | xavier | kaiming | orthogonal\n https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/9451e70673400885567d08a9e97ade2524c700d0/models/networks.py#L39\n '''\n def init_func(m):\n classname = m.__class__.__name__\n if classname.find('InstanceNorm2d') != -1:\n if hasattr(m, 'weight') and m.weight is not None:\n nn.init.constant_(m.weight.data, 1.0)\n if hasattr(m, 'bias') and m.bias is not None:\n nn.init.constant_(m.bias.data, 0.0)\n elif hasattr(m, 'weight') and (classname.find('Conv') != -1\n or classname.find('Linear') != -1):\n if init_type == 'normal':\n nn.init.normal_(m.weight.data, 0.0, gain)\n elif init_type == 'xavier':\n nn.init.xavier_normal_(m.weight.data, gain=gain)\n elif init_type == 'xavier_uniform':\n nn.init.xavier_uniform_(m.weight.data, gain=1.0)\n elif init_type == 'kaiming':\n nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')\n elif init_type == 'orthogonal':\n nn.init.orthogonal_(m.weight.data, gain=gain)\n elif init_type == 'none': # uses pytorch's default init method\n m.reset_parameters()\n else:\n raise NotImplementedError(\n 'initialization method [%s] is not implemented' %\n init_type)\n if hasattr(m, 'bias') and m.bias is not None:\n nn.init.constant_(m.bias.data, 0.0)\n\n self.apply(init_func)\n\n # propagate to children\n for m in self.children():\n if hasattr(m, 'init_weights'):\n m.init_weights(init_type, gain)\n\n\nclass Encoder(nn.Module):\n def __init__(self):\n super(Encoder, self).__init__()\n self.group = [1, 2, 4, 8, 1]\n self.layers = nn.ModuleList([\n nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(256, 384, kernel_size=3, stride=1, padding=1, groups=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(640, 512, kernel_size=3, stride=1, padding=1, groups=2),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(768, 384, kernel_size=3, stride=1, padding=1, groups=4),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(640, 256, kernel_size=3, stride=1, padding=1, groups=8),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(512, 128, kernel_size=3, stride=1, padding=1, groups=1),\n nn.LeakyReLU(0.2, inplace=True)\n ])\n\n def forward(self, x):\n bt, c, _, _ = x.size()\n # h, w = h//4, w//4\n out = x\n for i, layer in enumerate(self.layers):\n if i == 8:\n x0 = out\n _, _, h, w = x0.size()\n if i > 8 and i % 2 == 0:\n g = self.group[(i - 8) // 2]\n x = x0.view(bt, g, -1, h, w)\n o = out.view(bt, g, -1, h, w)\n out = torch.cat([x, o], 2).view(bt, -1, h, w)\n out = layer(out)\n return out\n\n\nclass deconv(nn.Module):\n def __init__(self,\n input_channel,\n output_channel,\n kernel_size=3,\n padding=0):\n super().__init__()\n self.conv = nn.Conv2d(input_channel,\n output_channel,\n kernel_size=kernel_size,\n stride=1,\n padding=padding)\n\n def forward(self, x):\n x = F.interpolate(x,\n scale_factor=2,\n mode='bilinear',\n align_corners=True)\n return self.conv(x)\n\n\nclass InpaintGenerator(BaseNetwork):\n def __init__(self, init_weights=True):\n super(InpaintGenerator, self).__init__()\n channel = 256\n hidden = 512\n\n # encoder\n self.encoder = Encoder()\n\n # decoder\n self.decoder = nn.Sequential(\n deconv(channel // 2, 128, kernel_size=3, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n deconv(64, 64, kernel_size=3, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 3, kernel_size=3, stride=1, padding=1))\n\n # feature propagation module\n self.feat_prop_module = BidirectionalPropagation(channel // 2)\n\n # soft split and soft composition\n kernel_size = (7, 7)\n padding = (3, 3)\n stride = (3, 3)\n output_size = (60, 108)\n t2t_params = {\n 'kernel_size': kernel_size,\n 'stride': stride,\n 'padding': padding\n }\n self.ss = SoftSplit(channel // 2,\n hidden,\n kernel_size,\n stride,\n padding,\n t2t_param=t2t_params)\n self.sc = SoftComp(channel // 2, hidden, kernel_size, stride, padding)\n\n n_vecs = 1\n for i, d in enumerate(kernel_size):\n n_vecs *= int((output_size[i] + 2 * padding[i] -\n (d - 1) - 1) / stride[i] + 1)\n\n blocks = []\n depths = 8\n num_heads = [4] * depths\n window_size = [(5, 9)] * depths\n focal_windows = [(5, 9)] * depths\n focal_levels = [2] * depths\n pool_method = \"fc\"\n\n for i in range(depths):\n blocks.append(\n TemporalFocalTransformerBlock(dim=hidden,\n num_heads=num_heads[i],\n window_size=window_size[i],\n focal_level=focal_levels[i],\n focal_window=focal_windows[i],\n n_vecs=n_vecs,\n t2t_params=t2t_params,\n pool_method=pool_method))\n self.transformer = nn.Sequential(*blocks)\n\n if init_weights:\n self.init_weights()\n # Need to initial the weights of MSDeformAttn specifically\n for m in self.modules():\n if isinstance(m, SecondOrderDeformableAlignment):\n m.init_offset()\n\n # flow completion network\n self.update_spynet = SPyNet()\n\n def forward_bidirect_flow(self, masked_local_frames):\n b, l_t, c, h, w = masked_local_frames.size()\n\n # compute forward and backward flows of masked frames\n masked_local_frames = F.interpolate(masked_local_frames.view(\n -1, c, h, w),\n scale_factor=1 / 4,\n mode='bilinear',\n align_corners=True,\n recompute_scale_factor=True)\n masked_local_frames = masked_local_frames.view(b, l_t, c, h // 4,\n w // 4)\n mlf_1 = masked_local_frames[:, :-1, :, :, :].reshape(\n -1, c, h // 4, w // 4)\n mlf_2 = masked_local_frames[:, 1:, :, :, :].reshape(\n -1, c, h // 4, w // 4)\n pred_flows_forward = self.update_spynet(mlf_1, mlf_2)\n pred_flows_backward = self.update_spynet(mlf_2, mlf_1)\n\n pred_flows_forward = pred_flows_forward.view(b, l_t - 1, 2, h // 4,\n w // 4)\n pred_flows_backward = pred_flows_backward.view(b, l_t - 1, 2, h // 4,\n w // 4)\n\n return pred_flows_forward, pred_flows_backward\n\n def forward(self, masked_frames, num_local_frames):\n l_t = num_local_frames\n b, t, ori_c, ori_h, ori_w = masked_frames.size()\n\n # normalization before feeding into the flow completion module\n masked_local_frames = (masked_frames[:, :l_t, ...] + 1) / 2\n pred_flows = self.forward_bidirect_flow(masked_local_frames)\n\n # extracting features and performing the feature propagation on local features\n enc_feat = self.encoder(masked_frames.view(b * t, ori_c, ori_h, ori_w))\n _, c, h, w = enc_feat.size()\n fold_output_size = (h, w)\n local_feat = enc_feat.view(b, t, c, h, w)[:, :l_t, ...]\n ref_feat = enc_feat.view(b, t, c, h, w)[:, l_t:, ...]\n local_feat = self.feat_prop_module(local_feat, pred_flows[0],\n pred_flows[1])\n enc_feat = torch.cat((local_feat, ref_feat), dim=1)\n\n # content hallucination through stacking multiple temporal focal transformer blocks\n trans_feat = self.ss(enc_feat.view(-1, c, h, w), b, fold_output_size)\n trans_feat = self.transformer([trans_feat, fold_output_size])\n trans_feat = self.sc(trans_feat[0], t, fold_output_size)\n trans_feat = trans_feat.view(b, t, -1, h, w)\n enc_feat = enc_feat + trans_feat\n\n # decode frames from features\n output = self.decoder(enc_feat.view(b * t, c, h, w))\n output = torch.tanh(output)\n return output, pred_flows\n\n\n# ######################################################################\n# Discriminator for Temporal Patch GAN\n# ######################################################################\n\n\nclass Discriminator(BaseNetwork):\n def __init__(self,\n in_channels=3,\n use_sigmoid=False,\n use_spectral_norm=True,\n init_weights=True):\n super(Discriminator, self).__init__()\n self.use_sigmoid = use_sigmoid\n nf = 32\n\n self.conv = nn.Sequential(\n spectral_norm(\n nn.Conv3d(in_channels=in_channels,\n out_channels=nf * 1,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=1,\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(64, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n spectral_norm(\n nn.Conv3d(nf * 1,\n nf * 2,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2),\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(128, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n spectral_norm(\n nn.Conv3d(nf * 2,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2),\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(256, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n spectral_norm(\n nn.Conv3d(nf * 4,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2),\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(256, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n spectral_norm(\n nn.Conv3d(nf * 4,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2),\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(256, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv3d(nf * 4,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2)))\n\n if init_weights:\n self.init_weights()\n\n def forward(self, xs):\n # T, C, H, W = xs.shape (old)\n # B, T, C, H, W (new)\n xs_t = torch.transpose(xs, 1, 2)\n feat = self.conv(xs_t)\n if self.use_sigmoid:\n feat = torch.sigmoid(feat)\n out = torch.transpose(feat, 1, 2) # B, T, C, H, W\n return out\n\n\ndef spectral_norm(module, mode=True):\n if mode:\n return _spectral_norm(module)\n return module","source_hash":"f81e75e68e498f851b09f5019288e3cb6bf57f664c0f205e6ef5a9f2259e18d4","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.e2fgvi_hq.BaseNetwork","uri":"program://Track-Anything/class/inpainter.model.e2fgvi_hq.BaseNetwork#L14-L68","kind":"class","name":"BaseNetwork","path":"inpainter/model/e2fgvi_hq.py","language":"python","start_line":14,"end_line":68,"context_start_line":1,"context_end_line":88,"code":"''' Towards An End-to-End Framework for Video Inpainting\n'''\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom inpainter.model.modules.flow_comp import SPyNet\nfrom inpainter.model.modules.feat_prop import BidirectionalPropagation, SecondOrderDeformableAlignment\nfrom inpainter.model.modules.tfocal_transformer_hq import TemporalFocalTransformerBlock, SoftSplit, SoftComp\nfrom inpainter.model.modules.spectral_norm import spectral_norm as _spectral_norm\n\n\nclass BaseNetwork(nn.Module):\n def __init__(self):\n super(BaseNetwork, self).__init__()\n\n def print_network(self):\n if isinstance(self, list):\n self = self[0]\n num_params = 0\n for param in self.parameters():\n num_params += param.numel()\n print(\n 'Network [%s] was created. Total number of parameters: %.1f million. '\n 'To see the architecture, do print(network).' %\n (type(self).__name__, num_params / 1000000))\n\n def init_weights(self, init_type='normal', gain=0.02):\n '''\n initialize network's weights\n init_type: normal | xavier | kaiming | orthogonal\n https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/9451e70673400885567d08a9e97ade2524c700d0/models/networks.py#L39\n '''\n def init_func(m):\n classname = m.__class__.__name__\n if classname.find('InstanceNorm2d') != -1:\n if hasattr(m, 'weight') and m.weight is not None:\n nn.init.constant_(m.weight.data, 1.0)\n if hasattr(m, 'bias') and m.bias is not None:\n nn.init.constant_(m.bias.data, 0.0)\n elif hasattr(m, 'weight') and (classname.find('Conv') != -1\n or classname.find('Linear') != -1):\n if init_type == 'normal':\n nn.init.normal_(m.weight.data, 0.0, gain)\n elif init_type == 'xavier':\n nn.init.xavier_normal_(m.weight.data, gain=gain)\n elif init_type == 'xavier_uniform':\n nn.init.xavier_uniform_(m.weight.data, gain=1.0)\n elif init_type == 'kaiming':\n nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')\n elif init_type == 'orthogonal':\n nn.init.orthogonal_(m.weight.data, gain=gain)\n elif init_type == 'none': # uses pytorch's default init method\n m.reset_parameters()\n else:\n raise NotImplementedError(\n 'initialization method [%s] is not implemented' %\n init_type)\n if hasattr(m, 'bias') and m.bias is not None:\n nn.init.constant_(m.bias.data, 0.0)\n\n self.apply(init_func)\n\n # propagate to children\n for m in self.children():\n if hasattr(m, 'init_weights'):\n m.init_weights(init_type, gain)\n\n\nclass Encoder(nn.Module):\n def __init__(self):\n super(Encoder, self).__init__()\n self.group = [1, 2, 4, 8, 1]\n self.layers = nn.ModuleList([\n nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(256, 384, kernel_size=3, stride=1, padding=1, groups=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(640, 512, kernel_size=3, stride=1, padding=1, groups=2),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(768, 384, kernel_size=3, stride=1, padding=1, groups=4),","source_hash":"f81e75e68e498f851b09f5019288e3cb6bf57f664c0f205e6ef5a9f2259e18d4","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.e2fgvi_hq.Encoder","uri":"program://Track-Anything/class/inpainter.model.e2fgvi_hq.Encoder#L71-L110","kind":"class","name":"Encoder","path":"inpainter/model/e2fgvi_hq.py","language":"python","start_line":71,"end_line":110,"context_start_line":51,"context_end_line":130,"code":" nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')\n elif init_type == 'orthogonal':\n nn.init.orthogonal_(m.weight.data, gain=gain)\n elif init_type == 'none': # uses pytorch's default init method\n m.reset_parameters()\n else:\n raise NotImplementedError(\n 'initialization method [%s] is not implemented' %\n init_type)\n if hasattr(m, 'bias') and m.bias is not None:\n nn.init.constant_(m.bias.data, 0.0)\n\n self.apply(init_func)\n\n # propagate to children\n for m in self.children():\n if hasattr(m, 'init_weights'):\n m.init_weights(init_type, gain)\n\n\nclass Encoder(nn.Module):\n def __init__(self):\n super(Encoder, self).__init__()\n self.group = [1, 2, 4, 8, 1]\n self.layers = nn.ModuleList([\n nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(256, 384, kernel_size=3, stride=1, padding=1, groups=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(640, 512, kernel_size=3, stride=1, padding=1, groups=2),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(768, 384, kernel_size=3, stride=1, padding=1, groups=4),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(640, 256, kernel_size=3, stride=1, padding=1, groups=8),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(512, 128, kernel_size=3, stride=1, padding=1, groups=1),\n nn.LeakyReLU(0.2, inplace=True)\n ])\n\n def forward(self, x):\n bt, c, _, _ = x.size()\n # h, w = h//4, w//4\n out = x\n for i, layer in enumerate(self.layers):\n if i == 8:\n x0 = out\n _, _, h, w = x0.size()\n if i > 8 and i % 2 == 0:\n g = self.group[(i - 8) // 2]\n x = x0.view(bt, g, -1, h, w)\n o = out.view(bt, g, -1, h, w)\n out = torch.cat([x, o], 2).view(bt, -1, h, w)\n out = layer(out)\n return out\n\n\nclass deconv(nn.Module):\n def __init__(self,\n input_channel,\n output_channel,\n kernel_size=3,\n padding=0):\n super().__init__()\n self.conv = nn.Conv2d(input_channel,\n output_channel,\n kernel_size=kernel_size,\n stride=1,\n padding=padding)\n\n def forward(self, x):\n x = F.interpolate(x,\n scale_factor=2,\n mode='bilinear',\n align_corners=True)","source_hash":"f81e75e68e498f851b09f5019288e3cb6bf57f664c0f205e6ef5a9f2259e18d4","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.e2fgvi_hq.deconv","uri":"program://Track-Anything/class/inpainter.model.e2fgvi_hq.deconv#L113-L131","kind":"class","name":"deconv","path":"inpainter/model/e2fgvi_hq.py","language":"python","start_line":113,"end_line":131,"context_start_line":93,"context_end_line":151,"code":" nn.LeakyReLU(0.2, inplace=True)\n ])\n\n def forward(self, x):\n bt, c, _, _ = x.size()\n # h, w = h//4, w//4\n out = x\n for i, layer in enumerate(self.layers):\n if i == 8:\n x0 = out\n _, _, h, w = x0.size()\n if i > 8 and i % 2 == 0:\n g = self.group[(i - 8) // 2]\n x = x0.view(bt, g, -1, h, w)\n o = out.view(bt, g, -1, h, w)\n out = torch.cat([x, o], 2).view(bt, -1, h, w)\n out = layer(out)\n return out\n\n\nclass deconv(nn.Module):\n def __init__(self,\n input_channel,\n output_channel,\n kernel_size=3,\n padding=0):\n super().__init__()\n self.conv = nn.Conv2d(input_channel,\n output_channel,\n kernel_size=kernel_size,\n stride=1,\n padding=padding)\n\n def forward(self, x):\n x = F.interpolate(x,\n scale_factor=2,\n mode='bilinear',\n align_corners=True)\n return self.conv(x)\n\n\nclass InpaintGenerator(BaseNetwork):\n def __init__(self, init_weights=True):\n super(InpaintGenerator, self).__init__()\n channel = 256\n hidden = 512\n\n # encoder\n self.encoder = Encoder()\n\n # decoder\n self.decoder = nn.Sequential(\n deconv(channel // 2, 128, kernel_size=3, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n deconv(64, 64, kernel_size=3, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 3, kernel_size=3, stride=1, padding=1))","source_hash":"f81e75e68e498f851b09f5019288e3cb6bf57f664c0f205e6ef5a9f2259e18d4","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.e2fgvi_hq.InpaintGenerator","uri":"program://Track-Anything/class/inpainter.model.e2fgvi_hq.InpaintGenerator#L134-L263","kind":"class","name":"InpaintGenerator","path":"inpainter/model/e2fgvi_hq.py","language":"python","start_line":134,"end_line":263,"context_start_line":114,"context_end_line":283,"code":" def __init__(self,\n input_channel,\n output_channel,\n kernel_size=3,\n padding=0):\n super().__init__()\n self.conv = nn.Conv2d(input_channel,\n output_channel,\n kernel_size=kernel_size,\n stride=1,\n padding=padding)\n\n def forward(self, x):\n x = F.interpolate(x,\n scale_factor=2,\n mode='bilinear',\n align_corners=True)\n return self.conv(x)\n\n\nclass InpaintGenerator(BaseNetwork):\n def __init__(self, init_weights=True):\n super(InpaintGenerator, self).__init__()\n channel = 256\n hidden = 512\n\n # encoder\n self.encoder = Encoder()\n\n # decoder\n self.decoder = nn.Sequential(\n deconv(channel // 2, 128, kernel_size=3, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n deconv(64, 64, kernel_size=3, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 3, kernel_size=3, stride=1, padding=1))\n\n # feature propagation module\n self.feat_prop_module = BidirectionalPropagation(channel // 2)\n\n # soft split and soft composition\n kernel_size = (7, 7)\n padding = (3, 3)\n stride = (3, 3)\n output_size = (60, 108)\n t2t_params = {\n 'kernel_size': kernel_size,\n 'stride': stride,\n 'padding': padding\n }\n self.ss = SoftSplit(channel // 2,\n hidden,\n kernel_size,\n stride,\n padding,\n t2t_param=t2t_params)\n self.sc = SoftComp(channel // 2, hidden, kernel_size, stride, padding)\n\n n_vecs = 1\n for i, d in enumerate(kernel_size):\n n_vecs *= int((output_size[i] + 2 * padding[i] -\n (d - 1) - 1) / stride[i] + 1)\n\n blocks = []\n depths = 8\n num_heads = [4] * depths\n window_size = [(5, 9)] * depths\n focal_windows = [(5, 9)] * depths\n focal_levels = [2] * depths\n pool_method = \"fc\"\n\n for i in range(depths):\n blocks.append(\n TemporalFocalTransformerBlock(dim=hidden,\n num_heads=num_heads[i],\n window_size=window_size[i],\n focal_level=focal_levels[i],\n focal_window=focal_windows[i],\n n_vecs=n_vecs,\n t2t_params=t2t_params,\n pool_method=pool_method))\n self.transformer = nn.Sequential(*blocks)\n\n if init_weights:\n self.init_weights()\n # Need to initial the weights of MSDeformAttn specifically\n for m in self.modules():\n if isinstance(m, SecondOrderDeformableAlignment):\n m.init_offset()\n\n # flow completion network\n self.update_spynet = SPyNet()\n\n def forward_bidirect_flow(self, masked_local_frames):\n b, l_t, c, h, w = masked_local_frames.size()\n\n # compute forward and backward flows of masked frames\n masked_local_frames = F.interpolate(masked_local_frames.view(\n -1, c, h, w),\n scale_factor=1 / 4,\n mode='bilinear',\n align_corners=True,\n recompute_scale_factor=True)\n masked_local_frames = masked_local_frames.view(b, l_t, c, h // 4,\n w // 4)\n mlf_1 = masked_local_frames[:, :-1, :, :, :].reshape(\n -1, c, h // 4, w // 4)\n mlf_2 = masked_local_frames[:, 1:, :, :, :].reshape(\n -1, c, h // 4, w // 4)\n pred_flows_forward = self.update_spynet(mlf_1, mlf_2)\n pred_flows_backward = self.update_spynet(mlf_2, mlf_1)\n\n pred_flows_forward = pred_flows_forward.view(b, l_t - 1, 2, h // 4,\n w // 4)\n pred_flows_backward = pred_flows_backward.view(b, l_t - 1, 2, h // 4,\n w // 4)\n\n return pred_flows_forward, pred_flows_backward\n\n def forward(self, masked_frames, num_local_frames):\n l_t = num_local_frames\n b, t, ori_c, ori_h, ori_w = masked_frames.size()\n\n # normalization before feeding into the flow completion module\n masked_local_frames = (masked_frames[:, :l_t, ...] + 1) / 2\n pred_flows = self.forward_bidirect_flow(masked_local_frames)\n\n # extracting features and performing the feature propagation on local features\n enc_feat = self.encoder(masked_frames.view(b * t, ori_c, ori_h, ori_w))\n _, c, h, w = enc_feat.size()\n fold_output_size = (h, w)\n local_feat = enc_feat.view(b, t, c, h, w)[:, :l_t, ...]\n ref_feat = enc_feat.view(b, t, c, h, w)[:, l_t:, ...]\n local_feat = self.feat_prop_module(local_feat, pred_flows[0],\n pred_flows[1])\n enc_feat = torch.cat((local_feat, ref_feat), dim=1)\n\n # content hallucination through stacking multiple temporal focal transformer blocks\n trans_feat = self.ss(enc_feat.view(-1, c, h, w), b, fold_output_size)\n trans_feat = self.transformer([trans_feat, fold_output_size])\n trans_feat = self.sc(trans_feat[0], t, fold_output_size)\n trans_feat = trans_feat.view(b, t, -1, h, w)\n enc_feat = enc_feat + trans_feat\n\n # decode frames from features\n output = self.decoder(enc_feat.view(b * t, c, h, w))\n output = torch.tanh(output)\n return output, pred_flows\n\n\n# ######################################################################\n# Discriminator for Temporal Patch GAN\n# ######################################################################\n\n\nclass Discriminator(BaseNetwork):\n def __init__(self,\n in_channels=3,\n use_sigmoid=False,\n use_spectral_norm=True,\n init_weights=True):\n super(Discriminator, self).__init__()\n self.use_sigmoid = use_sigmoid\n nf = 32\n\n self.conv = nn.Sequential(\n spectral_norm(\n nn.Conv3d(in_channels=in_channels,","source_hash":"f81e75e68e498f851b09f5019288e3cb6bf57f664c0f205e6ef5a9f2259e18d4","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.e2fgvi_hq.Discriminator","uri":"program://Track-Anything/class/inpainter.model.e2fgvi_hq.Discriminator#L271-L344","kind":"class","name":"Discriminator","path":"inpainter/model/e2fgvi_hq.py","language":"python","start_line":271,"end_line":344,"context_start_line":251,"context_end_line":350,"code":" enc_feat = torch.cat((local_feat, ref_feat), dim=1)\n\n # content hallucination through stacking multiple temporal focal transformer blocks\n trans_feat = self.ss(enc_feat.view(-1, c, h, w), b, fold_output_size)\n trans_feat = self.transformer([trans_feat, fold_output_size])\n trans_feat = self.sc(trans_feat[0], t, fold_output_size)\n trans_feat = trans_feat.view(b, t, -1, h, w)\n enc_feat = enc_feat + trans_feat\n\n # decode frames from features\n output = self.decoder(enc_feat.view(b * t, c, h, w))\n output = torch.tanh(output)\n return output, pred_flows\n\n\n# ######################################################################\n# Discriminator for Temporal Patch GAN\n# ######################################################################\n\n\nclass Discriminator(BaseNetwork):\n def __init__(self,\n in_channels=3,\n use_sigmoid=False,\n use_spectral_norm=True,\n init_weights=True):\n super(Discriminator, self).__init__()\n self.use_sigmoid = use_sigmoid\n nf = 32\n\n self.conv = nn.Sequential(\n spectral_norm(\n nn.Conv3d(in_channels=in_channels,\n out_channels=nf * 1,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=1,\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(64, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n spectral_norm(\n nn.Conv3d(nf * 1,\n nf * 2,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2),\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(128, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n spectral_norm(\n nn.Conv3d(nf * 2,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2),\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(256, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n spectral_norm(\n nn.Conv3d(nf * 4,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2),\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(256, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n spectral_norm(\n nn.Conv3d(nf * 4,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2),\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(256, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv3d(nf * 4,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2)))\n\n if init_weights:\n self.init_weights()\n\n def forward(self, xs):\n # T, C, H, W = xs.shape (old)\n # B, T, C, H, W (new)\n xs_t = torch.transpose(xs, 1, 2)\n feat = self.conv(xs_t)\n if self.use_sigmoid:\n feat = torch.sigmoid(feat)\n out = torch.transpose(feat, 1, 2) # B, T, C, H, W\n return out\n\n\ndef spectral_norm(module, mode=True):\n if mode:\n return _spectral_norm(module)\n return module","source_hash":"f81e75e68e498f851b09f5019288e3cb6bf57f664c0f205e6ef5a9f2259e18d4","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.e2fgvi_hq.spectral_norm","uri":"program://Track-Anything/function/inpainter.model.e2fgvi_hq.spectral_norm#L347-L350","kind":"function","name":"spectral_norm","path":"inpainter/model/e2fgvi_hq.py","language":"python","start_line":347,"end_line":350,"context_start_line":327,"context_end_line":350,"code":" nn.Conv3d(nf * 4,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2)))\n\n if init_weights:\n self.init_weights()\n\n def forward(self, xs):\n # T, C, H, W = xs.shape (old)\n # B, T, C, H, W (new)\n xs_t = torch.transpose(xs, 1, 2)\n feat = self.conv(xs_t)\n if self.use_sigmoid:\n feat = torch.sigmoid(feat)\n out = torch.transpose(feat, 1, 2) # B, T, C, H, W\n return out\n\n\ndef spectral_norm(module, mode=True):\n if mode:\n return _spectral_norm(module)\n return module","source_hash":"f81e75e68e498f851b09f5019288e3cb6bf57f664c0f205e6ef5a9f2259e18d4","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.e2fgvi_hq.__init__","uri":"program://Track-Anything/function/inpainter.model.e2fgvi_hq.__init__#L272-L334","kind":"function","name":"__init__","path":"inpainter/model/e2fgvi_hq.py","language":"python","start_line":272,"end_line":334,"context_start_line":252,"context_end_line":350,"code":"\n # content hallucination through stacking multiple temporal focal transformer blocks\n trans_feat = self.ss(enc_feat.view(-1, c, h, w), b, fold_output_size)\n trans_feat = self.transformer([trans_feat, fold_output_size])\n trans_feat = self.sc(trans_feat[0], t, fold_output_size)\n trans_feat = trans_feat.view(b, t, -1, h, w)\n enc_feat = enc_feat + trans_feat\n\n # decode frames from features\n output = self.decoder(enc_feat.view(b * t, c, h, w))\n output = torch.tanh(output)\n return output, pred_flows\n\n\n# ######################################################################\n# Discriminator for Temporal Patch GAN\n# ######################################################################\n\n\nclass Discriminator(BaseNetwork):\n def __init__(self,\n in_channels=3,\n use_sigmoid=False,\n use_spectral_norm=True,\n init_weights=True):\n super(Discriminator, self).__init__()\n self.use_sigmoid = use_sigmoid\n nf = 32\n\n self.conv = nn.Sequential(\n spectral_norm(\n nn.Conv3d(in_channels=in_channels,\n out_channels=nf * 1,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=1,\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(64, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n spectral_norm(\n nn.Conv3d(nf * 1,\n nf * 2,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2),\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(128, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n spectral_norm(\n nn.Conv3d(nf * 2,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2),\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(256, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n spectral_norm(\n nn.Conv3d(nf * 4,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2),\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(256, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n spectral_norm(\n nn.Conv3d(nf * 4,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2),\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(256, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv3d(nf * 4,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2)))\n\n if init_weights:\n self.init_weights()\n\n def forward(self, xs):\n # T, C, H, W = xs.shape (old)\n # B, T, C, H, W (new)\n xs_t = torch.transpose(xs, 1, 2)\n feat = self.conv(xs_t)\n if self.use_sigmoid:\n feat = torch.sigmoid(feat)\n out = torch.transpose(feat, 1, 2) # B, T, C, H, W\n return out\n\n\ndef spectral_norm(module, mode=True):\n if mode:\n return _spectral_norm(module)\n return module","source_hash":"f81e75e68e498f851b09f5019288e3cb6bf57f664c0f205e6ef5a9f2259e18d4","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.e2fgvi_hq.print_network","uri":"program://Track-Anything/function/inpainter.model.e2fgvi_hq.print_network#L18-L27","kind":"function","name":"print_network","path":"inpainter/model/e2fgvi_hq.py","language":"python","start_line":18,"end_line":27,"context_start_line":1,"context_end_line":47,"code":"''' Towards An End-to-End Framework for Video Inpainting\n'''\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom inpainter.model.modules.flow_comp import SPyNet\nfrom inpainter.model.modules.feat_prop import BidirectionalPropagation, SecondOrderDeformableAlignment\nfrom inpainter.model.modules.tfocal_transformer_hq import TemporalFocalTransformerBlock, SoftSplit, SoftComp\nfrom inpainter.model.modules.spectral_norm import spectral_norm as _spectral_norm\n\n\nclass BaseNetwork(nn.Module):\n def __init__(self):\n super(BaseNetwork, self).__init__()\n\n def print_network(self):\n if isinstance(self, list):\n self = self[0]\n num_params = 0\n for param in self.parameters():\n num_params += param.numel()\n print(\n 'Network [%s] was created. Total number of parameters: %.1f million. '\n 'To see the architecture, do print(network).' %\n (type(self).__name__, num_params / 1000000))\n\n def init_weights(self, init_type='normal', gain=0.02):\n '''\n initialize network's weights\n init_type: normal | xavier | kaiming | orthogonal\n https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/9451e70673400885567d08a9e97ade2524c700d0/models/networks.py#L39\n '''\n def init_func(m):\n classname = m.__class__.__name__\n if classname.find('InstanceNorm2d') != -1:\n if hasattr(m, 'weight') and m.weight is not None:\n nn.init.constant_(m.weight.data, 1.0)\n if hasattr(m, 'bias') and m.bias is not None:\n nn.init.constant_(m.bias.data, 0.0)\n elif hasattr(m, 'weight') and (classname.find('Conv') != -1\n or classname.find('Linear') != -1):\n if init_type == 'normal':\n nn.init.normal_(m.weight.data, 0.0, gain)\n elif init_type == 'xavier':\n nn.init.xavier_normal_(m.weight.data, gain=gain)","source_hash":"f81e75e68e498f851b09f5019288e3cb6bf57f664c0f205e6ef5a9f2259e18d4","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.e2fgvi_hq.init_weights","uri":"program://Track-Anything/function/inpainter.model.e2fgvi_hq.init_weights#L29-L68","kind":"function","name":"init_weights","path":"inpainter/model/e2fgvi_hq.py","language":"python","start_line":29,"end_line":68,"context_start_line":9,"context_end_line":88,"code":"from inpainter.model.modules.feat_prop import BidirectionalPropagation, SecondOrderDeformableAlignment\nfrom inpainter.model.modules.tfocal_transformer_hq import TemporalFocalTransformerBlock, SoftSplit, SoftComp\nfrom inpainter.model.modules.spectral_norm import spectral_norm as _spectral_norm\n\n\nclass BaseNetwork(nn.Module):\n def __init__(self):\n super(BaseNetwork, self).__init__()\n\n def print_network(self):\n if isinstance(self, list):\n self = self[0]\n num_params = 0\n for param in self.parameters():\n num_params += param.numel()\n print(\n 'Network [%s] was created. Total number of parameters: %.1f million. '\n 'To see the architecture, do print(network).' %\n (type(self).__name__, num_params / 1000000))\n\n def init_weights(self, init_type='normal', gain=0.02):\n '''\n initialize network's weights\n init_type: normal | xavier | kaiming | orthogonal\n https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/9451e70673400885567d08a9e97ade2524c700d0/models/networks.py#L39\n '''\n def init_func(m):\n classname = m.__class__.__name__\n if classname.find('InstanceNorm2d') != -1:\n if hasattr(m, 'weight') and m.weight is not None:\n nn.init.constant_(m.weight.data, 1.0)\n if hasattr(m, 'bias') and m.bias is not None:\n nn.init.constant_(m.bias.data, 0.0)\n elif hasattr(m, 'weight') and (classname.find('Conv') != -1\n or classname.find('Linear') != -1):\n if init_type == 'normal':\n nn.init.normal_(m.weight.data, 0.0, gain)\n elif init_type == 'xavier':\n nn.init.xavier_normal_(m.weight.data, gain=gain)\n elif init_type == 'xavier_uniform':\n nn.init.xavier_uniform_(m.weight.data, gain=1.0)\n elif init_type == 'kaiming':\n nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')\n elif init_type == 'orthogonal':\n nn.init.orthogonal_(m.weight.data, gain=gain)\n elif init_type == 'none': # uses pytorch's default init method\n m.reset_parameters()\n else:\n raise NotImplementedError(\n 'initialization method [%s] is not implemented' %\n init_type)\n if hasattr(m, 'bias') and m.bias is not None:\n nn.init.constant_(m.bias.data, 0.0)\n\n self.apply(init_func)\n\n # propagate to children\n for m in self.children():\n if hasattr(m, 'init_weights'):\n m.init_weights(init_type, gain)\n\n\nclass Encoder(nn.Module):\n def __init__(self):\n super(Encoder, self).__init__()\n self.group = [1, 2, 4, 8, 1]\n self.layers = nn.ModuleList([\n nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(256, 384, kernel_size=3, stride=1, padding=1, groups=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(640, 512, kernel_size=3, stride=1, padding=1, groups=2),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(768, 384, kernel_size=3, stride=1, padding=1, groups=4),","source_hash":"f81e75e68e498f851b09f5019288e3cb6bf57f664c0f205e6ef5a9f2259e18d4","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.e2fgvi_hq.forward","uri":"program://Track-Anything/function/inpainter.model.e2fgvi_hq.forward#L336-L344","kind":"function","name":"forward","path":"inpainter/model/e2fgvi_hq.py","language":"python","start_line":336,"end_line":344,"context_start_line":316,"context_end_line":350,"code":" # nn.InstanceNorm2d(256, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n spectral_norm(\n nn.Conv3d(nf * 4,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2),\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(256, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv3d(nf * 4,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2)))\n\n if init_weights:\n self.init_weights()\n\n def forward(self, xs):\n # T, C, H, W = xs.shape (old)\n # B, T, C, H, W (new)\n xs_t = torch.transpose(xs, 1, 2)\n feat = self.conv(xs_t)\n if self.use_sigmoid:\n feat = torch.sigmoid(feat)\n out = torch.transpose(feat, 1, 2) # B, T, C, H, W\n return out\n\n\ndef spectral_norm(module, mode=True):\n if mode:\n return _spectral_norm(module)\n return module","source_hash":"f81e75e68e498f851b09f5019288e3cb6bf57f664c0f205e6ef5a9f2259e18d4","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.e2fgvi_hq.forward_bidirect_flow","uri":"program://Track-Anything/function/inpainter.model.e2fgvi_hq.forward_bidirect_flow#L209-L233","kind":"function","name":"forward_bidirect_flow","path":"inpainter/model/e2fgvi_hq.py","language":"python","start_line":209,"end_line":233,"context_start_line":189,"context_end_line":253,"code":" TemporalFocalTransformerBlock(dim=hidden,\n num_heads=num_heads[i],\n window_size=window_size[i],\n focal_level=focal_levels[i],\n focal_window=focal_windows[i],\n n_vecs=n_vecs,\n t2t_params=t2t_params,\n pool_method=pool_method))\n self.transformer = nn.Sequential(*blocks)\n\n if init_weights:\n self.init_weights()\n # Need to initial the weights of MSDeformAttn specifically\n for m in self.modules():\n if isinstance(m, SecondOrderDeformableAlignment):\n m.init_offset()\n\n # flow completion network\n self.update_spynet = SPyNet()\n\n def forward_bidirect_flow(self, masked_local_frames):\n b, l_t, c, h, w = masked_local_frames.size()\n\n # compute forward and backward flows of masked frames\n masked_local_frames = F.interpolate(masked_local_frames.view(\n -1, c, h, w),\n scale_factor=1 / 4,\n mode='bilinear',\n align_corners=True,\n recompute_scale_factor=True)\n masked_local_frames = masked_local_frames.view(b, l_t, c, h // 4,\n w // 4)\n mlf_1 = masked_local_frames[:, :-1, :, :, :].reshape(\n -1, c, h // 4, w // 4)\n mlf_2 = masked_local_frames[:, 1:, :, :, :].reshape(\n -1, c, h // 4, w // 4)\n pred_flows_forward = self.update_spynet(mlf_1, mlf_2)\n pred_flows_backward = self.update_spynet(mlf_2, mlf_1)\n\n pred_flows_forward = pred_flows_forward.view(b, l_t - 1, 2, h // 4,\n w // 4)\n pred_flows_backward = pred_flows_backward.view(b, l_t - 1, 2, h // 4,\n w // 4)\n\n return pred_flows_forward, pred_flows_backward\n\n def forward(self, masked_frames, num_local_frames):\n l_t = num_local_frames\n b, t, ori_c, ori_h, ori_w = masked_frames.size()\n\n # normalization before feeding into the flow completion module\n masked_local_frames = (masked_frames[:, :l_t, ...] + 1) / 2\n pred_flows = self.forward_bidirect_flow(masked_local_frames)\n\n # extracting features and performing the feature propagation on local features\n enc_feat = self.encoder(masked_frames.view(b * t, ori_c, ori_h, ori_w))\n _, c, h, w = enc_feat.size()\n fold_output_size = (h, w)\n local_feat = enc_feat.view(b, t, c, h, w)[:, :l_t, ...]\n ref_feat = enc_feat.view(b, t, c, h, w)[:, l_t:, ...]\n local_feat = self.feat_prop_module(local_feat, pred_flows[0],\n pred_flows[1])\n enc_feat = torch.cat((local_feat, ref_feat), dim=1)\n\n # content hallucination through stacking multiple temporal focal transformer blocks","source_hash":"f81e75e68e498f851b09f5019288e3cb6bf57f664c0f205e6ef5a9f2259e18d4","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.e2fgvi_hq.init_func","uri":"program://Track-Anything/function/inpainter.model.e2fgvi_hq.init_func#L35-L61","kind":"function","name":"init_func","path":"inpainter/model/e2fgvi_hq.py","language":"python","start_line":35,"end_line":61,"context_start_line":15,"context_end_line":81,"code":" def __init__(self):\n super(BaseNetwork, self).__init__()\n\n def print_network(self):\n if isinstance(self, list):\n self = self[0]\n num_params = 0\n for param in self.parameters():\n num_params += param.numel()\n print(\n 'Network [%s] was created. Total number of parameters: %.1f million. '\n 'To see the architecture, do print(network).' %\n (type(self).__name__, num_params / 1000000))\n\n def init_weights(self, init_type='normal', gain=0.02):\n '''\n initialize network's weights\n init_type: normal | xavier | kaiming | orthogonal\n https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/9451e70673400885567d08a9e97ade2524c700d0/models/networks.py#L39\n '''\n def init_func(m):\n classname = m.__class__.__name__\n if classname.find('InstanceNorm2d') != -1:\n if hasattr(m, 'weight') and m.weight is not None:\n nn.init.constant_(m.weight.data, 1.0)\n if hasattr(m, 'bias') and m.bias is not None:\n nn.init.constant_(m.bias.data, 0.0)\n elif hasattr(m, 'weight') and (classname.find('Conv') != -1\n or classname.find('Linear') != -1):\n if init_type == 'normal':\n nn.init.normal_(m.weight.data, 0.0, gain)\n elif init_type == 'xavier':\n nn.init.xavier_normal_(m.weight.data, gain=gain)\n elif init_type == 'xavier_uniform':\n nn.init.xavier_uniform_(m.weight.data, gain=1.0)\n elif init_type == 'kaiming':\n nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')\n elif init_type == 'orthogonal':\n nn.init.orthogonal_(m.weight.data, gain=gain)\n elif init_type == 'none': # uses pytorch's default init method\n m.reset_parameters()\n else:\n raise NotImplementedError(\n 'initialization method [%s] is not implemented' %\n init_type)\n if hasattr(m, 'bias') and m.bias is not None:\n nn.init.constant_(m.bias.data, 0.0)\n\n self.apply(init_func)\n\n # propagate to children\n for m in self.children():\n if hasattr(m, 'init_weights'):\n m.init_weights(init_type, gain)\n\n\nclass Encoder(nn.Module):\n def __init__(self):\n super(Encoder, self).__init__()\n self.group = [1, 2, 4, 8, 1]\n self.layers = nn.ModuleList([\n nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),\n nn.LeakyReLU(0.2, inplace=True),","source_hash":"f81e75e68e498f851b09f5019288e3cb6bf57f664c0f205e6ef5a9f2259e18d4","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.e2fgvi","uri":"program://Track-Anything/module/inpainter.model.e2fgvi#L1-L350","kind":"module","name":"inpainter.model.e2fgvi","path":"inpainter/model/e2fgvi.py","language":"python","start_line":1,"end_line":350,"context_start_line":1,"context_end_line":350,"code":"''' Towards An End-to-End Framework for Video Inpainting\n'''\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom model.modules.flow_comp import SPyNet\nfrom model.modules.feat_prop import BidirectionalPropagation, SecondOrderDeformableAlignment\nfrom model.modules.tfocal_transformer import TemporalFocalTransformerBlock, SoftSplit, SoftComp\nfrom model.modules.spectral_norm import spectral_norm as _spectral_norm\n\n\nclass BaseNetwork(nn.Module):\n def __init__(self):\n super(BaseNetwork, self).__init__()\n\n def print_network(self):\n if isinstance(self, list):\n self = self[0]\n num_params = 0\n for param in self.parameters():\n num_params += param.numel()\n print(\n 'Network [%s] was created. Total number of parameters: %.1f million. '\n 'To see the architecture, do print(network).' %\n (type(self).__name__, num_params / 1000000))\n\n def init_weights(self, init_type='normal', gain=0.02):\n '''\n initialize network's weights\n init_type: normal | xavier | kaiming | orthogonal\n https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/9451e70673400885567d08a9e97ade2524c700d0/models/networks.py#L39\n '''\n def init_func(m):\n classname = m.__class__.__name__\n if classname.find('InstanceNorm2d') != -1:\n if hasattr(m, 'weight') and m.weight is not None:\n nn.init.constant_(m.weight.data, 1.0)\n if hasattr(m, 'bias') and m.bias is not None:\n nn.init.constant_(m.bias.data, 0.0)\n elif hasattr(m, 'weight') and (classname.find('Conv') != -1\n or classname.find('Linear') != -1):\n if init_type == 'normal':\n nn.init.normal_(m.weight.data, 0.0, gain)\n elif init_type == 'xavier':\n nn.init.xavier_normal_(m.weight.data, gain=gain)\n elif init_type == 'xavier_uniform':\n nn.init.xavier_uniform_(m.weight.data, gain=1.0)\n elif init_type == 'kaiming':\n nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')\n elif init_type == 'orthogonal':\n nn.init.orthogonal_(m.weight.data, gain=gain)\n elif init_type == 'none': # uses pytorch's default init method\n m.reset_parameters()\n else:\n raise NotImplementedError(\n 'initialization method [%s] is not implemented' %\n init_type)\n if hasattr(m, 'bias') and m.bias is not None:\n nn.init.constant_(m.bias.data, 0.0)\n\n self.apply(init_func)\n\n # propagate to children\n for m in self.children():\n if hasattr(m, 'init_weights'):\n m.init_weights(init_type, gain)\n\n\nclass Encoder(nn.Module):\n def __init__(self):\n super(Encoder, self).__init__()\n self.group = [1, 2, 4, 8, 1]\n self.layers = nn.ModuleList([\n nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(256, 384, kernel_size=3, stride=1, padding=1, groups=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(640, 512, kernel_size=3, stride=1, padding=1, groups=2),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(768, 384, kernel_size=3, stride=1, padding=1, groups=4),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(640, 256, kernel_size=3, stride=1, padding=1, groups=8),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(512, 128, kernel_size=3, stride=1, padding=1, groups=1),\n nn.LeakyReLU(0.2, inplace=True)\n ])\n\n def forward(self, x):\n bt, c, h, w = x.size()\n h, w = h // 4, w // 4\n out = x\n for i, layer in enumerate(self.layers):\n if i == 8:\n x0 = out\n if i > 8 and i % 2 == 0:\n g = self.group[(i - 8) // 2]\n x = x0.view(bt, g, -1, h, w)\n o = out.view(bt, g, -1, h, w)\n out = torch.cat([x, o], 2).view(bt, -1, h, w)\n out = layer(out)\n return out\n\n\nclass deconv(nn.Module):\n def __init__(self,\n input_channel,\n output_channel,\n kernel_size=3,\n padding=0):\n super().__init__()\n self.conv = nn.Conv2d(input_channel,\n output_channel,\n kernel_size=kernel_size,\n stride=1,\n padding=padding)\n\n def forward(self, x):\n x = F.interpolate(x,\n scale_factor=2,\n mode='bilinear',\n align_corners=True)\n return self.conv(x)\n\n\nclass InpaintGenerator(BaseNetwork):\n def __init__(self, init_weights=True):\n super(InpaintGenerator, self).__init__()\n channel = 256\n hidden = 512\n\n # encoder\n self.encoder = Encoder()\n\n # decoder\n self.decoder = nn.Sequential(\n deconv(channel // 2, 128, kernel_size=3, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n deconv(64, 64, kernel_size=3, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 3, kernel_size=3, stride=1, padding=1))\n\n # feature propagation module\n self.feat_prop_module = BidirectionalPropagation(channel // 2)\n\n # soft split and soft composition\n kernel_size = (7, 7)\n padding = (3, 3)\n stride = (3, 3)\n output_size = (60, 108)\n t2t_params = {\n 'kernel_size': kernel_size,\n 'stride': stride,\n 'padding': padding,\n 'output_size': output_size\n }\n self.ss = SoftSplit(channel // 2,\n hidden,\n kernel_size,\n stride,\n padding,\n t2t_param=t2t_params)\n self.sc = SoftComp(channel // 2, hidden, output_size, kernel_size,\n stride, padding)\n\n n_vecs = 1\n for i, d in enumerate(kernel_size):\n n_vecs *= int((output_size[i] + 2 * padding[i] -\n (d - 1) - 1) / stride[i] + 1)\n\n blocks = []\n depths = 8\n num_heads = [4] * depths\n window_size = [(5, 9)] * depths\n focal_windows = [(5, 9)] * depths\n focal_levels = [2] * depths\n pool_method = \"fc\"\n\n for i in range(depths):\n blocks.append(\n TemporalFocalTransformerBlock(dim=hidden,\n num_heads=num_heads[i],\n window_size=window_size[i],\n focal_level=focal_levels[i],\n focal_window=focal_windows[i],\n n_vecs=n_vecs,\n t2t_params=t2t_params,\n pool_method=pool_method))\n self.transformer = nn.Sequential(*blocks)\n\n if init_weights:\n self.init_weights()\n # Need to initial the weights of MSDeformAttn specifically\n for m in self.modules():\n if isinstance(m, SecondOrderDeformableAlignment):\n m.init_offset()\n\n # flow completion network\n self.update_spynet = SPyNet()\n\n def forward_bidirect_flow(self, masked_local_frames):\n b, l_t, c, h, w = masked_local_frames.size()\n\n # compute forward and backward flows of masked frames\n masked_local_frames = F.interpolate(masked_local_frames.view(\n -1, c, h, w),\n scale_factor=1 / 4,\n mode='bilinear',\n align_corners=True,\n recompute_scale_factor=True)\n masked_local_frames = masked_local_frames.view(b, l_t, c, h // 4,\n w // 4)\n mlf_1 = masked_local_frames[:, :-1, :, :, :].reshape(\n -1, c, h // 4, w // 4)\n mlf_2 = masked_local_frames[:, 1:, :, :, :].reshape(\n -1, c, h // 4, w // 4)\n pred_flows_forward = self.update_spynet(mlf_1, mlf_2)\n pred_flows_backward = self.update_spynet(mlf_2, mlf_1)\n\n pred_flows_forward = pred_flows_forward.view(b, l_t - 1, 2, h // 4,\n w // 4)\n pred_flows_backward = pred_flows_backward.view(b, l_t - 1, 2, h // 4,\n w // 4)\n\n return pred_flows_forward, pred_flows_backward\n\n def forward(self, masked_frames, num_local_frames):\n l_t = num_local_frames\n b, t, ori_c, ori_h, ori_w = masked_frames.size()\n\n # normalization before feeding into the flow completion module\n masked_local_frames = (masked_frames[:, :l_t, ...] + 1) / 2\n pred_flows = self.forward_bidirect_flow(masked_local_frames)\n\n # extracting features and performing the feature propagation on local features\n enc_feat = self.encoder(masked_frames.view(b * t, ori_c, ori_h, ori_w))\n _, c, h, w = enc_feat.size()\n local_feat = enc_feat.view(b, t, c, h, w)[:, :l_t, ...]\n ref_feat = enc_feat.view(b, t, c, h, w)[:, l_t:, ...]\n local_feat = self.feat_prop_module(local_feat, pred_flows[0],\n pred_flows[1])\n enc_feat = torch.cat((local_feat, ref_feat), dim=1)\n\n # content hallucination through stacking multiple temporal focal transformer blocks\n trans_feat = self.ss(enc_feat.view(-1, c, h, w), b)\n trans_feat = self.transformer(trans_feat)\n trans_feat = self.sc(trans_feat, t)\n trans_feat = trans_feat.view(b, t, -1, h, w)\n enc_feat = enc_feat + trans_feat\n\n # decode frames from features\n output = self.decoder(enc_feat.view(b * t, c, h, w))\n output = torch.tanh(output)\n return output, pred_flows\n\n\n# ######################################################################\n# Discriminator for Temporal Patch GAN\n# ######################################################################\n\n\nclass Discriminator(BaseNetwork):\n def __init__(self,\n in_channels=3,\n use_sigmoid=False,\n use_spectral_norm=True,\n init_weights=True):\n super(Discriminator, self).__init__()\n self.use_sigmoid = use_sigmoid\n nf = 32\n\n self.conv = nn.Sequential(\n spectral_norm(\n nn.Conv3d(in_channels=in_channels,\n out_channels=nf * 1,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=1,\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(64, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n spectral_norm(\n nn.Conv3d(nf * 1,\n nf * 2,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2),\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(128, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n spectral_norm(\n nn.Conv3d(nf * 2,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2),\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(256, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n spectral_norm(\n nn.Conv3d(nf * 4,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2),\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(256, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n spectral_norm(\n nn.Conv3d(nf * 4,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2),\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(256, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv3d(nf * 4,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2)))\n\n if init_weights:\n self.init_weights()\n\n def forward(self, xs):\n # T, C, H, W = xs.shape (old)\n # B, T, C, H, W (new)\n xs_t = torch.transpose(xs, 1, 2)\n feat = self.conv(xs_t)\n if self.use_sigmoid:\n feat = torch.sigmoid(feat)\n out = torch.transpose(feat, 1, 2) # B, T, C, H, W\n return out\n\n\ndef spectral_norm(module, mode=True):\n if mode:\n return _spectral_norm(module)\n return module","source_hash":"e9eb1165951e88d199c5f0879247c7b928e6e3272407a49b548f8acc981278c6","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.e2fgvi.BaseNetwork","uri":"program://Track-Anything/class/inpainter.model.e2fgvi.BaseNetwork#L14-L68","kind":"class","name":"BaseNetwork","path":"inpainter/model/e2fgvi.py","language":"python","start_line":14,"end_line":68,"context_start_line":1,"context_end_line":88,"code":"''' Towards An End-to-End Framework for Video Inpainting\n'''\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom model.modules.flow_comp import SPyNet\nfrom model.modules.feat_prop import BidirectionalPropagation, SecondOrderDeformableAlignment\nfrom model.modules.tfocal_transformer import TemporalFocalTransformerBlock, SoftSplit, SoftComp\nfrom model.modules.spectral_norm import spectral_norm as _spectral_norm\n\n\nclass BaseNetwork(nn.Module):\n def __init__(self):\n super(BaseNetwork, self).__init__()\n\n def print_network(self):\n if isinstance(self, list):\n self = self[0]\n num_params = 0\n for param in self.parameters():\n num_params += param.numel()\n print(\n 'Network [%s] was created. Total number of parameters: %.1f million. '\n 'To see the architecture, do print(network).' %\n (type(self).__name__, num_params / 1000000))\n\n def init_weights(self, init_type='normal', gain=0.02):\n '''\n initialize network's weights\n init_type: normal | xavier | kaiming | orthogonal\n https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/9451e70673400885567d08a9e97ade2524c700d0/models/networks.py#L39\n '''\n def init_func(m):\n classname = m.__class__.__name__\n if classname.find('InstanceNorm2d') != -1:\n if hasattr(m, 'weight') and m.weight is not None:\n nn.init.constant_(m.weight.data, 1.0)\n if hasattr(m, 'bias') and m.bias is not None:\n nn.init.constant_(m.bias.data, 0.0)\n elif hasattr(m, 'weight') and (classname.find('Conv') != -1\n or classname.find('Linear') != -1):\n if init_type == 'normal':\n nn.init.normal_(m.weight.data, 0.0, gain)\n elif init_type == 'xavier':\n nn.init.xavier_normal_(m.weight.data, gain=gain)\n elif init_type == 'xavier_uniform':\n nn.init.xavier_uniform_(m.weight.data, gain=1.0)\n elif init_type == 'kaiming':\n nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')\n elif init_type == 'orthogonal':\n nn.init.orthogonal_(m.weight.data, gain=gain)\n elif init_type == 'none': # uses pytorch's default init method\n m.reset_parameters()\n else:\n raise NotImplementedError(\n 'initialization method [%s] is not implemented' %\n init_type)\n if hasattr(m, 'bias') and m.bias is not None:\n nn.init.constant_(m.bias.data, 0.0)\n\n self.apply(init_func)\n\n # propagate to children\n for m in self.children():\n if hasattr(m, 'init_weights'):\n m.init_weights(init_type, gain)\n\n\nclass Encoder(nn.Module):\n def __init__(self):\n super(Encoder, self).__init__()\n self.group = [1, 2, 4, 8, 1]\n self.layers = nn.ModuleList([\n nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(256, 384, kernel_size=3, stride=1, padding=1, groups=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(640, 512, kernel_size=3, stride=1, padding=1, groups=2),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(768, 384, kernel_size=3, stride=1, padding=1, groups=4),","source_hash":"e9eb1165951e88d199c5f0879247c7b928e6e3272407a49b548f8acc981278c6","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.e2fgvi.Encoder","uri":"program://Track-Anything/class/inpainter.model.e2fgvi.Encoder#L71-L109","kind":"class","name":"Encoder","path":"inpainter/model/e2fgvi.py","language":"python","start_line":71,"end_line":109,"context_start_line":51,"context_end_line":129,"code":" nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')\n elif init_type == 'orthogonal':\n nn.init.orthogonal_(m.weight.data, gain=gain)\n elif init_type == 'none': # uses pytorch's default init method\n m.reset_parameters()\n else:\n raise NotImplementedError(\n 'initialization method [%s] is not implemented' %\n init_type)\n if hasattr(m, 'bias') and m.bias is not None:\n nn.init.constant_(m.bias.data, 0.0)\n\n self.apply(init_func)\n\n # propagate to children\n for m in self.children():\n if hasattr(m, 'init_weights'):\n m.init_weights(init_type, gain)\n\n\nclass Encoder(nn.Module):\n def __init__(self):\n super(Encoder, self).__init__()\n self.group = [1, 2, 4, 8, 1]\n self.layers = nn.ModuleList([\n nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(256, 384, kernel_size=3, stride=1, padding=1, groups=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(640, 512, kernel_size=3, stride=1, padding=1, groups=2),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(768, 384, kernel_size=3, stride=1, padding=1, groups=4),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(640, 256, kernel_size=3, stride=1, padding=1, groups=8),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(512, 128, kernel_size=3, stride=1, padding=1, groups=1),\n nn.LeakyReLU(0.2, inplace=True)\n ])\n\n def forward(self, x):\n bt, c, h, w = x.size()\n h, w = h // 4, w // 4\n out = x\n for i, layer in enumerate(self.layers):\n if i == 8:\n x0 = out\n if i > 8 and i % 2 == 0:\n g = self.group[(i - 8) // 2]\n x = x0.view(bt, g, -1, h, w)\n o = out.view(bt, g, -1, h, w)\n out = torch.cat([x, o], 2).view(bt, -1, h, w)\n out = layer(out)\n return out\n\n\nclass deconv(nn.Module):\n def __init__(self,\n input_channel,\n output_channel,\n kernel_size=3,\n padding=0):\n super().__init__()\n self.conv = nn.Conv2d(input_channel,\n output_channel,\n kernel_size=kernel_size,\n stride=1,\n padding=padding)\n\n def forward(self, x):\n x = F.interpolate(x,\n scale_factor=2,\n mode='bilinear',\n align_corners=True)","source_hash":"e9eb1165951e88d199c5f0879247c7b928e6e3272407a49b548f8acc981278c6","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.e2fgvi.deconv","uri":"program://Track-Anything/class/inpainter.model.e2fgvi.deconv#L112-L130","kind":"class","name":"deconv","path":"inpainter/model/e2fgvi.py","language":"python","start_line":112,"end_line":130,"context_start_line":92,"context_end_line":150,"code":" nn.Conv2d(512, 128, kernel_size=3, stride=1, padding=1, groups=1),\n nn.LeakyReLU(0.2, inplace=True)\n ])\n\n def forward(self, x):\n bt, c, h, w = x.size()\n h, w = h // 4, w // 4\n out = x\n for i, layer in enumerate(self.layers):\n if i == 8:\n x0 = out\n if i > 8 and i % 2 == 0:\n g = self.group[(i - 8) // 2]\n x = x0.view(bt, g, -1, h, w)\n o = out.view(bt, g, -1, h, w)\n out = torch.cat([x, o], 2).view(bt, -1, h, w)\n out = layer(out)\n return out\n\n\nclass deconv(nn.Module):\n def __init__(self,\n input_channel,\n output_channel,\n kernel_size=3,\n padding=0):\n super().__init__()\n self.conv = nn.Conv2d(input_channel,\n output_channel,\n kernel_size=kernel_size,\n stride=1,\n padding=padding)\n\n def forward(self, x):\n x = F.interpolate(x,\n scale_factor=2,\n mode='bilinear',\n align_corners=True)\n return self.conv(x)\n\n\nclass InpaintGenerator(BaseNetwork):\n def __init__(self, init_weights=True):\n super(InpaintGenerator, self).__init__()\n channel = 256\n hidden = 512\n\n # encoder\n self.encoder = Encoder()\n\n # decoder\n self.decoder = nn.Sequential(\n deconv(channel // 2, 128, kernel_size=3, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n deconv(64, 64, kernel_size=3, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 3, kernel_size=3, stride=1, padding=1))","source_hash":"e9eb1165951e88d199c5f0879247c7b928e6e3272407a49b548f8acc981278c6","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.e2fgvi.InpaintGenerator","uri":"program://Track-Anything/class/inpainter.model.e2fgvi.InpaintGenerator#L133-L263","kind":"class","name":"InpaintGenerator","path":"inpainter/model/e2fgvi.py","language":"python","start_line":133,"end_line":263,"context_start_line":113,"context_end_line":283,"code":" def __init__(self,\n input_channel,\n output_channel,\n kernel_size=3,\n padding=0):\n super().__init__()\n self.conv = nn.Conv2d(input_channel,\n output_channel,\n kernel_size=kernel_size,\n stride=1,\n padding=padding)\n\n def forward(self, x):\n x = F.interpolate(x,\n scale_factor=2,\n mode='bilinear',\n align_corners=True)\n return self.conv(x)\n\n\nclass InpaintGenerator(BaseNetwork):\n def __init__(self, init_weights=True):\n super(InpaintGenerator, self).__init__()\n channel = 256\n hidden = 512\n\n # encoder\n self.encoder = Encoder()\n\n # decoder\n self.decoder = nn.Sequential(\n deconv(channel // 2, 128, kernel_size=3, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n deconv(64, 64, kernel_size=3, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 3, kernel_size=3, stride=1, padding=1))\n\n # feature propagation module\n self.feat_prop_module = BidirectionalPropagation(channel // 2)\n\n # soft split and soft composition\n kernel_size = (7, 7)\n padding = (3, 3)\n stride = (3, 3)\n output_size = (60, 108)\n t2t_params = {\n 'kernel_size': kernel_size,\n 'stride': stride,\n 'padding': padding,\n 'output_size': output_size\n }\n self.ss = SoftSplit(channel // 2,\n hidden,\n kernel_size,\n stride,\n padding,\n t2t_param=t2t_params)\n self.sc = SoftComp(channel // 2, hidden, output_size, kernel_size,\n stride, padding)\n\n n_vecs = 1\n for i, d in enumerate(kernel_size):\n n_vecs *= int((output_size[i] + 2 * padding[i] -\n (d - 1) - 1) / stride[i] + 1)\n\n blocks = []\n depths = 8\n num_heads = [4] * depths\n window_size = [(5, 9)] * depths\n focal_windows = [(5, 9)] * depths\n focal_levels = [2] * depths\n pool_method = \"fc\"\n\n for i in range(depths):\n blocks.append(\n TemporalFocalTransformerBlock(dim=hidden,\n num_heads=num_heads[i],\n window_size=window_size[i],\n focal_level=focal_levels[i],\n focal_window=focal_windows[i],\n n_vecs=n_vecs,\n t2t_params=t2t_params,\n pool_method=pool_method))\n self.transformer = nn.Sequential(*blocks)\n\n if init_weights:\n self.init_weights()\n # Need to initial the weights of MSDeformAttn specifically\n for m in self.modules():\n if isinstance(m, SecondOrderDeformableAlignment):\n m.init_offset()\n\n # flow completion network\n self.update_spynet = SPyNet()\n\n def forward_bidirect_flow(self, masked_local_frames):\n b, l_t, c, h, w = masked_local_frames.size()\n\n # compute forward and backward flows of masked frames\n masked_local_frames = F.interpolate(masked_local_frames.view(\n -1, c, h, w),\n scale_factor=1 / 4,\n mode='bilinear',\n align_corners=True,\n recompute_scale_factor=True)\n masked_local_frames = masked_local_frames.view(b, l_t, c, h // 4,\n w // 4)\n mlf_1 = masked_local_frames[:, :-1, :, :, :].reshape(\n -1, c, h // 4, w // 4)\n mlf_2 = masked_local_frames[:, 1:, :, :, :].reshape(\n -1, c, h // 4, w // 4)\n pred_flows_forward = self.update_spynet(mlf_1, mlf_2)\n pred_flows_backward = self.update_spynet(mlf_2, mlf_1)\n\n pred_flows_forward = pred_flows_forward.view(b, l_t - 1, 2, h // 4,\n w // 4)\n pred_flows_backward = pred_flows_backward.view(b, l_t - 1, 2, h // 4,\n w // 4)\n\n return pred_flows_forward, pred_flows_backward\n\n def forward(self, masked_frames, num_local_frames):\n l_t = num_local_frames\n b, t, ori_c, ori_h, ori_w = masked_frames.size()\n\n # normalization before feeding into the flow completion module\n masked_local_frames = (masked_frames[:, :l_t, ...] + 1) / 2\n pred_flows = self.forward_bidirect_flow(masked_local_frames)\n\n # extracting features and performing the feature propagation on local features\n enc_feat = self.encoder(masked_frames.view(b * t, ori_c, ori_h, ori_w))\n _, c, h, w = enc_feat.size()\n local_feat = enc_feat.view(b, t, c, h, w)[:, :l_t, ...]\n ref_feat = enc_feat.view(b, t, c, h, w)[:, l_t:, ...]\n local_feat = self.feat_prop_module(local_feat, pred_flows[0],\n pred_flows[1])\n enc_feat = torch.cat((local_feat, ref_feat), dim=1)\n\n # content hallucination through stacking multiple temporal focal transformer blocks\n trans_feat = self.ss(enc_feat.view(-1, c, h, w), b)\n trans_feat = self.transformer(trans_feat)\n trans_feat = self.sc(trans_feat, t)\n trans_feat = trans_feat.view(b, t, -1, h, w)\n enc_feat = enc_feat + trans_feat\n\n # decode frames from features\n output = self.decoder(enc_feat.view(b * t, c, h, w))\n output = torch.tanh(output)\n return output, pred_flows\n\n\n# ######################################################################\n# Discriminator for Temporal Patch GAN\n# ######################################################################\n\n\nclass Discriminator(BaseNetwork):\n def __init__(self,\n in_channels=3,\n use_sigmoid=False,\n use_spectral_norm=True,\n init_weights=True):\n super(Discriminator, self).__init__()\n self.use_sigmoid = use_sigmoid\n nf = 32\n\n self.conv = nn.Sequential(\n spectral_norm(\n nn.Conv3d(in_channels=in_channels,","source_hash":"e9eb1165951e88d199c5f0879247c7b928e6e3272407a49b548f8acc981278c6","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.e2fgvi.Discriminator","uri":"program://Track-Anything/class/inpainter.model.e2fgvi.Discriminator#L271-L344","kind":"class","name":"Discriminator","path":"inpainter/model/e2fgvi.py","language":"python","start_line":271,"end_line":344,"context_start_line":251,"context_end_line":350,"code":" enc_feat = torch.cat((local_feat, ref_feat), dim=1)\n\n # content hallucination through stacking multiple temporal focal transformer blocks\n trans_feat = self.ss(enc_feat.view(-1, c, h, w), b)\n trans_feat = self.transformer(trans_feat)\n trans_feat = self.sc(trans_feat, t)\n trans_feat = trans_feat.view(b, t, -1, h, w)\n enc_feat = enc_feat + trans_feat\n\n # decode frames from features\n output = self.decoder(enc_feat.view(b * t, c, h, w))\n output = torch.tanh(output)\n return output, pred_flows\n\n\n# ######################################################################\n# Discriminator for Temporal Patch GAN\n# ######################################################################\n\n\nclass Discriminator(BaseNetwork):\n def __init__(self,\n in_channels=3,\n use_sigmoid=False,\n use_spectral_norm=True,\n init_weights=True):\n super(Discriminator, self).__init__()\n self.use_sigmoid = use_sigmoid\n nf = 32\n\n self.conv = nn.Sequential(\n spectral_norm(\n nn.Conv3d(in_channels=in_channels,\n out_channels=nf * 1,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=1,\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(64, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n spectral_norm(\n nn.Conv3d(nf * 1,\n nf * 2,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2),\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(128, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n spectral_norm(\n nn.Conv3d(nf * 2,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2),\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(256, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n spectral_norm(\n nn.Conv3d(nf * 4,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2),\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(256, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n spectral_norm(\n nn.Conv3d(nf * 4,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2),\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(256, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv3d(nf * 4,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2)))\n\n if init_weights:\n self.init_weights()\n\n def forward(self, xs):\n # T, C, H, W = xs.shape (old)\n # B, T, C, H, W (new)\n xs_t = torch.transpose(xs, 1, 2)\n feat = self.conv(xs_t)\n if self.use_sigmoid:\n feat = torch.sigmoid(feat)\n out = torch.transpose(feat, 1, 2) # B, T, C, H, W\n return out\n\n\ndef spectral_norm(module, mode=True):\n if mode:\n return _spectral_norm(module)\n return module","source_hash":"e9eb1165951e88d199c5f0879247c7b928e6e3272407a49b548f8acc981278c6","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.e2fgvi.spectral_norm","uri":"program://Track-Anything/function/inpainter.model.e2fgvi.spectral_norm#L347-L350","kind":"function","name":"spectral_norm","path":"inpainter/model/e2fgvi.py","language":"python","start_line":347,"end_line":350,"context_start_line":327,"context_end_line":350,"code":" nn.Conv3d(nf * 4,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2)))\n\n if init_weights:\n self.init_weights()\n\n def forward(self, xs):\n # T, C, H, W = xs.shape (old)\n # B, T, C, H, W (new)\n xs_t = torch.transpose(xs, 1, 2)\n feat = self.conv(xs_t)\n if self.use_sigmoid:\n feat = torch.sigmoid(feat)\n out = torch.transpose(feat, 1, 2) # B, T, C, H, W\n return out\n\n\ndef spectral_norm(module, mode=True):\n if mode:\n return _spectral_norm(module)\n return module","source_hash":"e9eb1165951e88d199c5f0879247c7b928e6e3272407a49b548f8acc981278c6","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.e2fgvi.__init__","uri":"program://Track-Anything/function/inpainter.model.e2fgvi.__init__#L272-L334","kind":"function","name":"__init__","path":"inpainter/model/e2fgvi.py","language":"python","start_line":272,"end_line":334,"context_start_line":252,"context_end_line":350,"code":"\n # content hallucination through stacking multiple temporal focal transformer blocks\n trans_feat = self.ss(enc_feat.view(-1, c, h, w), b)\n trans_feat = self.transformer(trans_feat)\n trans_feat = self.sc(trans_feat, t)\n trans_feat = trans_feat.view(b, t, -1, h, w)\n enc_feat = enc_feat + trans_feat\n\n # decode frames from features\n output = self.decoder(enc_feat.view(b * t, c, h, w))\n output = torch.tanh(output)\n return output, pred_flows\n\n\n# ######################################################################\n# Discriminator for Temporal Patch GAN\n# ######################################################################\n\n\nclass Discriminator(BaseNetwork):\n def __init__(self,\n in_channels=3,\n use_sigmoid=False,\n use_spectral_norm=True,\n init_weights=True):\n super(Discriminator, self).__init__()\n self.use_sigmoid = use_sigmoid\n nf = 32\n\n self.conv = nn.Sequential(\n spectral_norm(\n nn.Conv3d(in_channels=in_channels,\n out_channels=nf * 1,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=1,\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(64, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n spectral_norm(\n nn.Conv3d(nf * 1,\n nf * 2,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2),\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(128, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n spectral_norm(\n nn.Conv3d(nf * 2,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2),\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(256, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n spectral_norm(\n nn.Conv3d(nf * 4,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2),\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(256, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n spectral_norm(\n nn.Conv3d(nf * 4,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2),\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(256, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv3d(nf * 4,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2)))\n\n if init_weights:\n self.init_weights()\n\n def forward(self, xs):\n # T, C, H, W = xs.shape (old)\n # B, T, C, H, W (new)\n xs_t = torch.transpose(xs, 1, 2)\n feat = self.conv(xs_t)\n if self.use_sigmoid:\n feat = torch.sigmoid(feat)\n out = torch.transpose(feat, 1, 2) # B, T, C, H, W\n return out\n\n\ndef spectral_norm(module, mode=True):\n if mode:\n return _spectral_norm(module)\n return module","source_hash":"e9eb1165951e88d199c5f0879247c7b928e6e3272407a49b548f8acc981278c6","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.e2fgvi.print_network","uri":"program://Track-Anything/function/inpainter.model.e2fgvi.print_network#L18-L27","kind":"function","name":"print_network","path":"inpainter/model/e2fgvi.py","language":"python","start_line":18,"end_line":27,"context_start_line":1,"context_end_line":47,"code":"''' Towards An End-to-End Framework for Video Inpainting\n'''\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom model.modules.flow_comp import SPyNet\nfrom model.modules.feat_prop import BidirectionalPropagation, SecondOrderDeformableAlignment\nfrom model.modules.tfocal_transformer import TemporalFocalTransformerBlock, SoftSplit, SoftComp\nfrom model.modules.spectral_norm import spectral_norm as _spectral_norm\n\n\nclass BaseNetwork(nn.Module):\n def __init__(self):\n super(BaseNetwork, self).__init__()\n\n def print_network(self):\n if isinstance(self, list):\n self = self[0]\n num_params = 0\n for param in self.parameters():\n num_params += param.numel()\n print(\n 'Network [%s] was created. Total number of parameters: %.1f million. '\n 'To see the architecture, do print(network).' %\n (type(self).__name__, num_params / 1000000))\n\n def init_weights(self, init_type='normal', gain=0.02):\n '''\n initialize network's weights\n init_type: normal | xavier | kaiming | orthogonal\n https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/9451e70673400885567d08a9e97ade2524c700d0/models/networks.py#L39\n '''\n def init_func(m):\n classname = m.__class__.__name__\n if classname.find('InstanceNorm2d') != -1:\n if hasattr(m, 'weight') and m.weight is not None:\n nn.init.constant_(m.weight.data, 1.0)\n if hasattr(m, 'bias') and m.bias is not None:\n nn.init.constant_(m.bias.data, 0.0)\n elif hasattr(m, 'weight') and (classname.find('Conv') != -1\n or classname.find('Linear') != -1):\n if init_type == 'normal':\n nn.init.normal_(m.weight.data, 0.0, gain)\n elif init_type == 'xavier':\n nn.init.xavier_normal_(m.weight.data, gain=gain)","source_hash":"e9eb1165951e88d199c5f0879247c7b928e6e3272407a49b548f8acc981278c6","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.e2fgvi.init_weights","uri":"program://Track-Anything/function/inpainter.model.e2fgvi.init_weights#L29-L68","kind":"function","name":"init_weights","path":"inpainter/model/e2fgvi.py","language":"python","start_line":29,"end_line":68,"context_start_line":9,"context_end_line":88,"code":"from model.modules.feat_prop import BidirectionalPropagation, SecondOrderDeformableAlignment\nfrom model.modules.tfocal_transformer import TemporalFocalTransformerBlock, SoftSplit, SoftComp\nfrom model.modules.spectral_norm import spectral_norm as _spectral_norm\n\n\nclass BaseNetwork(nn.Module):\n def __init__(self):\n super(BaseNetwork, self).__init__()\n\n def print_network(self):\n if isinstance(self, list):\n self = self[0]\n num_params = 0\n for param in self.parameters():\n num_params += param.numel()\n print(\n 'Network [%s] was created. Total number of parameters: %.1f million. '\n 'To see the architecture, do print(network).' %\n (type(self).__name__, num_params / 1000000))\n\n def init_weights(self, init_type='normal', gain=0.02):\n '''\n initialize network's weights\n init_type: normal | xavier | kaiming | orthogonal\n https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/9451e70673400885567d08a9e97ade2524c700d0/models/networks.py#L39\n '''\n def init_func(m):\n classname = m.__class__.__name__\n if classname.find('InstanceNorm2d') != -1:\n if hasattr(m, 'weight') and m.weight is not None:\n nn.init.constant_(m.weight.data, 1.0)\n if hasattr(m, 'bias') and m.bias is not None:\n nn.init.constant_(m.bias.data, 0.0)\n elif hasattr(m, 'weight') and (classname.find('Conv') != -1\n or classname.find('Linear') != -1):\n if init_type == 'normal':\n nn.init.normal_(m.weight.data, 0.0, gain)\n elif init_type == 'xavier':\n nn.init.xavier_normal_(m.weight.data, gain=gain)\n elif init_type == 'xavier_uniform':\n nn.init.xavier_uniform_(m.weight.data, gain=1.0)\n elif init_type == 'kaiming':\n nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')\n elif init_type == 'orthogonal':\n nn.init.orthogonal_(m.weight.data, gain=gain)\n elif init_type == 'none': # uses pytorch's default init method\n m.reset_parameters()\n else:\n raise NotImplementedError(\n 'initialization method [%s] is not implemented' %\n init_type)\n if hasattr(m, 'bias') and m.bias is not None:\n nn.init.constant_(m.bias.data, 0.0)\n\n self.apply(init_func)\n\n # propagate to children\n for m in self.children():\n if hasattr(m, 'init_weights'):\n m.init_weights(init_type, gain)\n\n\nclass Encoder(nn.Module):\n def __init__(self):\n super(Encoder, self).__init__()\n self.group = [1, 2, 4, 8, 1]\n self.layers = nn.ModuleList([\n nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(256, 384, kernel_size=3, stride=1, padding=1, groups=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(640, 512, kernel_size=3, stride=1, padding=1, groups=2),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(768, 384, kernel_size=3, stride=1, padding=1, groups=4),","source_hash":"e9eb1165951e88d199c5f0879247c7b928e6e3272407a49b548f8acc981278c6","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.e2fgvi.forward","uri":"program://Track-Anything/function/inpainter.model.e2fgvi.forward#L336-L344","kind":"function","name":"forward","path":"inpainter/model/e2fgvi.py","language":"python","start_line":336,"end_line":344,"context_start_line":316,"context_end_line":350,"code":" # nn.InstanceNorm2d(256, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n spectral_norm(\n nn.Conv3d(nf * 4,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2),\n bias=not use_spectral_norm), use_spectral_norm),\n # nn.InstanceNorm2d(256, track_running_stats=False),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv3d(nf * 4,\n nf * 4,\n kernel_size=(3, 5, 5),\n stride=(1, 2, 2),\n padding=(1, 2, 2)))\n\n if init_weights:\n self.init_weights()\n\n def forward(self, xs):\n # T, C, H, W = xs.shape (old)\n # B, T, C, H, W (new)\n xs_t = torch.transpose(xs, 1, 2)\n feat = self.conv(xs_t)\n if self.use_sigmoid:\n feat = torch.sigmoid(feat)\n out = torch.transpose(feat, 1, 2) # B, T, C, H, W\n return out\n\n\ndef spectral_norm(module, mode=True):\n if mode:\n return _spectral_norm(module)\n return module","source_hash":"e9eb1165951e88d199c5f0879247c7b928e6e3272407a49b548f8acc981278c6","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.e2fgvi.forward_bidirect_flow","uri":"program://Track-Anything/function/inpainter.model.e2fgvi.forward_bidirect_flow#L210-L234","kind":"function","name":"forward_bidirect_flow","path":"inpainter/model/e2fgvi.py","language":"python","start_line":210,"end_line":234,"context_start_line":190,"context_end_line":254,"code":" TemporalFocalTransformerBlock(dim=hidden,\n num_heads=num_heads[i],\n window_size=window_size[i],\n focal_level=focal_levels[i],\n focal_window=focal_windows[i],\n n_vecs=n_vecs,\n t2t_params=t2t_params,\n pool_method=pool_method))\n self.transformer = nn.Sequential(*blocks)\n\n if init_weights:\n self.init_weights()\n # Need to initial the weights of MSDeformAttn specifically\n for m in self.modules():\n if isinstance(m, SecondOrderDeformableAlignment):\n m.init_offset()\n\n # flow completion network\n self.update_spynet = SPyNet()\n\n def forward_bidirect_flow(self, masked_local_frames):\n b, l_t, c, h, w = masked_local_frames.size()\n\n # compute forward and backward flows of masked frames\n masked_local_frames = F.interpolate(masked_local_frames.view(\n -1, c, h, w),\n scale_factor=1 / 4,\n mode='bilinear',\n align_corners=True,\n recompute_scale_factor=True)\n masked_local_frames = masked_local_frames.view(b, l_t, c, h // 4,\n w // 4)\n mlf_1 = masked_local_frames[:, :-1, :, :, :].reshape(\n -1, c, h // 4, w // 4)\n mlf_2 = masked_local_frames[:, 1:, :, :, :].reshape(\n -1, c, h // 4, w // 4)\n pred_flows_forward = self.update_spynet(mlf_1, mlf_2)\n pred_flows_backward = self.update_spynet(mlf_2, mlf_1)\n\n pred_flows_forward = pred_flows_forward.view(b, l_t - 1, 2, h // 4,\n w // 4)\n pred_flows_backward = pred_flows_backward.view(b, l_t - 1, 2, h // 4,\n w // 4)\n\n return pred_flows_forward, pred_flows_backward\n\n def forward(self, masked_frames, num_local_frames):\n l_t = num_local_frames\n b, t, ori_c, ori_h, ori_w = masked_frames.size()\n\n # normalization before feeding into the flow completion module\n masked_local_frames = (masked_frames[:, :l_t, ...] + 1) / 2\n pred_flows = self.forward_bidirect_flow(masked_local_frames)\n\n # extracting features and performing the feature propagation on local features\n enc_feat = self.encoder(masked_frames.view(b * t, ori_c, ori_h, ori_w))\n _, c, h, w = enc_feat.size()\n local_feat = enc_feat.view(b, t, c, h, w)[:, :l_t, ...]\n ref_feat = enc_feat.view(b, t, c, h, w)[:, l_t:, ...]\n local_feat = self.feat_prop_module(local_feat, pred_flows[0],\n pred_flows[1])\n enc_feat = torch.cat((local_feat, ref_feat), dim=1)\n\n # content hallucination through stacking multiple temporal focal transformer blocks\n trans_feat = self.ss(enc_feat.view(-1, c, h, w), b)","source_hash":"e9eb1165951e88d199c5f0879247c7b928e6e3272407a49b548f8acc981278c6","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.e2fgvi.init_func","uri":"program://Track-Anything/function/inpainter.model.e2fgvi.init_func#L35-L61","kind":"function","name":"init_func","path":"inpainter/model/e2fgvi.py","language":"python","start_line":35,"end_line":61,"context_start_line":15,"context_end_line":81,"code":" def __init__(self):\n super(BaseNetwork, self).__init__()\n\n def print_network(self):\n if isinstance(self, list):\n self = self[0]\n num_params = 0\n for param in self.parameters():\n num_params += param.numel()\n print(\n 'Network [%s] was created. Total number of parameters: %.1f million. '\n 'To see the architecture, do print(network).' %\n (type(self).__name__, num_params / 1000000))\n\n def init_weights(self, init_type='normal', gain=0.02):\n '''\n initialize network's weights\n init_type: normal | xavier | kaiming | orthogonal\n https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/9451e70673400885567d08a9e97ade2524c700d0/models/networks.py#L39\n '''\n def init_func(m):\n classname = m.__class__.__name__\n if classname.find('InstanceNorm2d') != -1:\n if hasattr(m, 'weight') and m.weight is not None:\n nn.init.constant_(m.weight.data, 1.0)\n if hasattr(m, 'bias') and m.bias is not None:\n nn.init.constant_(m.bias.data, 0.0)\n elif hasattr(m, 'weight') and (classname.find('Conv') != -1\n or classname.find('Linear') != -1):\n if init_type == 'normal':\n nn.init.normal_(m.weight.data, 0.0, gain)\n elif init_type == 'xavier':\n nn.init.xavier_normal_(m.weight.data, gain=gain)\n elif init_type == 'xavier_uniform':\n nn.init.xavier_uniform_(m.weight.data, gain=1.0)\n elif init_type == 'kaiming':\n nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')\n elif init_type == 'orthogonal':\n nn.init.orthogonal_(m.weight.data, gain=gain)\n elif init_type == 'none': # uses pytorch's default init method\n m.reset_parameters()\n else:\n raise NotImplementedError(\n 'initialization method [%s] is not implemented' %\n init_type)\n if hasattr(m, 'bias') and m.bias is not None:\n nn.init.constant_(m.bias.data, 0.0)\n\n self.apply(init_func)\n\n # propagate to children\n for m in self.children():\n if hasattr(m, 'init_weights'):\n m.init_weights(init_type, gain)\n\n\nclass Encoder(nn.Module):\n def __init__(self):\n super(Encoder, self).__init__()\n self.group = [1, 2, 4, 8, 1]\n self.layers = nn.ModuleList([\n nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),\n nn.LeakyReLU(0.2, inplace=True),","source_hash":"e9eb1165951e88d199c5f0879247c7b928e6e3272407a49b548f8acc981278c6","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.tfocal_transformer_hq","uri":"program://Track-Anything/module/inpainter.model.modules.tfocal_transformer_hq#L1-L567","kind":"module","name":"inpainter.model.modules.tfocal_transformer_hq","path":"inpainter/model/modules/tfocal_transformer_hq.py","language":"python","start_line":1,"end_line":567,"context_start_line":1,"context_end_line":567,"code":"\"\"\"\n This code is based on:\n [1] FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting, ICCV 2021\n https://github.com/ruiliu-ai/FuseFormer\n [2] Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet, ICCV 2021\n https://github.com/yitu-opensource/T2T-ViT\n [3] Focal Self-attention for Local-Global Interactions in Vision Transformers, NeurIPS 2021\n https://github.com/microsoft/Focal-Transformer \n\"\"\"\n\nimport math\nfrom functools import reduce\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass SoftSplit(nn.Module):\n def __init__(self, channel, hidden, kernel_size, stride, padding,\n t2t_param):\n super(SoftSplit, self).__init__()\n self.kernel_size = kernel_size\n self.t2t = nn.Unfold(kernel_size=kernel_size,\n stride=stride,\n padding=padding)\n c_in = reduce((lambda x, y: x * y), kernel_size) * channel\n self.embedding = nn.Linear(c_in, hidden)\n\n self.t2t_param = t2t_param\n\n def forward(self, x, b, output_size):\n f_h = int((output_size[0] + 2 * self.t2t_param['padding'][0] -\n (self.t2t_param['kernel_size'][0] - 1) - 1) /\n self.t2t_param['stride'][0] + 1)\n f_w = int((output_size[1] + 2 * self.t2t_param['padding'][1] -\n (self.t2t_param['kernel_size'][1] - 1) - 1) /\n self.t2t_param['stride'][1] + 1)\n\n feat = self.t2t(x)\n feat = feat.permute(0, 2, 1)\n # feat shape [b*t, num_vec, ks*ks*c]\n feat = self.embedding(feat)\n # feat shape after embedding [b, t*num_vec, hidden]\n feat = feat.view(b, -1, f_h, f_w, feat.size(2))\n return feat\n\n\nclass SoftComp(nn.Module):\n def __init__(self, channel, hidden, kernel_size, stride, padding):\n super(SoftComp, self).__init__()\n self.relu = nn.LeakyReLU(0.2, inplace=True)\n c_out = reduce((lambda x, y: x * y), kernel_size) * channel\n self.embedding = nn.Linear(hidden, c_out)\n self.kernel_size = kernel_size\n self.stride = stride\n self.padding = padding\n self.bias_conv = nn.Conv2d(channel,\n channel,\n kernel_size=3,\n stride=1,\n padding=1)\n # TODO upsample conv\n # self.bias_conv = nn.Conv2d()\n # self.bias = nn.Parameter(torch.zeros((channel, h, w), dtype=torch.float32), requires_grad=True)\n\n def forward(self, x, t, output_size):\n b_, _, _, _, c_ = x.shape\n x = x.view(b_, -1, c_)\n feat = self.embedding(x)\n b, _, c = feat.size()\n feat = feat.view(b * t, -1, c).permute(0, 2, 1)\n feat = F.fold(feat,\n output_size=output_size,\n kernel_size=self.kernel_size,\n stride=self.stride,\n padding=self.padding)\n feat = self.bias_conv(feat)\n return feat\n\n\nclass FusionFeedForward(nn.Module):\n def __init__(self, d_model, n_vecs=None, t2t_params=None):\n super(FusionFeedForward, self).__init__()\n # We set d_ff as a default to 1960\n hd = 1960\n self.conv1 = nn.Sequential(nn.Linear(d_model, hd))\n self.conv2 = nn.Sequential(nn.GELU(), nn.Linear(hd, d_model))\n assert t2t_params is not None and n_vecs is not None\n self.t2t_params = t2t_params\n\n def forward(self, x, output_size):\n n_vecs = 1\n for i, d in enumerate(self.t2t_params['kernel_size']):\n n_vecs *= int((output_size[i] + 2 * self.t2t_params['padding'][i] -\n (d - 1) - 1) / self.t2t_params['stride'][i] + 1)\n\n x = self.conv1(x)\n b, n, c = x.size()\n normalizer = x.new_ones(b, n, 49).view(-1, n_vecs, 49).permute(0, 2, 1)\n normalizer = F.fold(normalizer,\n output_size=output_size,\n kernel_size=self.t2t_params['kernel_size'],\n padding=self.t2t_params['padding'],\n stride=self.t2t_params['stride'])\n\n x = F.fold(x.view(-1, n_vecs, c).permute(0, 2, 1),\n output_size=output_size,\n kernel_size=self.t2t_params['kernel_size'],\n padding=self.t2t_params['padding'],\n stride=self.t2t_params['stride'])\n\n x = F.unfold(x / normalizer,\n kernel_size=self.t2t_params['kernel_size'],\n padding=self.t2t_params['padding'],\n stride=self.t2t_params['stride']).permute(\n 0, 2, 1).contiguous().view(b, n, c)\n x = self.conv2(x)\n return x\n\n\ndef window_partition(x, window_size):\n \"\"\"\n Args:\n x: shape is (B, T, H, W, C)\n window_size (tuple[int]): window size\n Returns:\n windows: (B*num_windows, T*window_size*window_size, C)\n \"\"\"\n B, T, H, W, C = x.shape\n\n x = x.view(B, T, H // window_size[0], window_size[0], W // window_size[1],\n window_size[1], C)\n\n windows = x.permute(0, 2, 4, 1, 3, 5, 6).contiguous().view(\n -1, T * window_size[0] * window_size[1], C)\n return windows\n\n\ndef window_partition_noreshape(x, window_size):\n \"\"\"\n Args:\n x: shape is (B, T, H, W, C)\n window_size (tuple[int]): window size\n Returns:\n windows: (B, num_windows_h, num_windows_w, T, window_size, window_size, C)\n \"\"\"\n B, T, H, W, C = x.shape\n x = x.view(B, T, H // window_size[0], window_size[0], W // window_size[1],\n window_size[1], C)\n windows = x.permute(0, 2, 4, 1, 3, 5, 6).contiguous()\n return windows\n\n\ndef window_reverse(windows, window_size, T, H, W):\n \"\"\"\n Args:\n windows: shape is (num_windows*B, T, window_size, window_size, C)\n window_size (tuple[int]): Window size\n T (int): Temporal length of video\n H (int): Height of image\n W (int): Width of image\n Returns:\n x: (B, T, H, W, C)\n \"\"\"\n B = int(windows.shape[0] / (H * W / window_size[0] / window_size[1]))\n x = windows.view(B, H // window_size[0], W // window_size[1], T,\n window_size[0], window_size[1], -1)\n x = x.permute(0, 3, 1, 4, 2, 5, 6).contiguous().view(B, T, H, W, -1)\n return x\n\n\nclass WindowAttention(nn.Module):\n \"\"\"Temporal focal window attention\n \"\"\"\n def __init__(self, dim, expand_size, window_size, focal_window,\n focal_level, num_heads, qkv_bias, pool_method):\n\n super().__init__()\n self.dim = dim\n self.expand_size = expand_size\n self.window_size = window_size # Wh, Ww\n self.pool_method = pool_method\n self.num_heads = num_heads\n head_dim = dim // num_heads\n self.scale = head_dim**-0.5\n self.focal_level = focal_level\n self.focal_window = focal_window\n\n if any(i > 0 for i in self.expand_size) and focal_level > 0:\n # get mask for rolled k and rolled v\n mask_tl = torch.ones(self.window_size[0], self.window_size[1])\n mask_tl[:-self.expand_size[0], :-self.expand_size[1]] = 0\n mask_tr = torch.ones(self.window_size[0], self.window_size[1])\n mask_tr[:-self.expand_size[0], self.expand_size[1]:] = 0\n mask_bl = torch.ones(self.window_size[0], self.window_size[1])\n mask_bl[self.expand_size[0]:, :-self.expand_size[1]] = 0\n mask_br = torch.ones(self.window_size[0], self.window_size[1])\n mask_br[self.expand_size[0]:, self.expand_size[1]:] = 0\n mask_rolled = torch.stack((mask_tl, mask_tr, mask_bl, mask_br),\n 0).flatten(0)\n self.register_buffer(\"valid_ind_rolled\",\n mask_rolled.nonzero(as_tuple=False).view(-1))\n\n if pool_method != \"none\" and focal_level > 1:\n self.unfolds = nn.ModuleList()\n\n # build relative position bias between local patch and pooled windows\n for k in range(focal_level - 1):\n stride = 2**k\n kernel_size = tuple(2 * (i // 2) + 2**k + (2**k - 1)\n for i in self.focal_window)\n # define unfolding operations\n self.unfolds += [\n nn.Unfold(kernel_size=kernel_size,\n stride=stride,\n padding=tuple(i // 2 for i in kernel_size))\n ]\n\n # define unfolding index for focal_level > 0\n if k > 0:\n mask = torch.zeros(kernel_size)\n mask[(2**k) - 1:, (2**k) - 1:] = 1\n self.register_buffer(\n \"valid_ind_unfold_{}\".format(k),\n mask.flatten(0).nonzero(as_tuple=False).view(-1))\n\n self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n self.proj = nn.Linear(dim, dim)\n\n self.softmax = nn.Softmax(dim=-1)\n\n def forward(self, x_all, mask_all=None):\n \"\"\"\n Args:\n x: input features with shape of (B, T, Wh, Ww, C)\n mask: (0/-inf) mask with shape of (num_windows, T*Wh*Ww, T*Wh*Ww) or None\n\n output: (nW*B, Wh*Ww, C)\n \"\"\"\n x = x_all[0]\n\n B, T, nH, nW, C = x.shape\n qkv = self.qkv(x).reshape(B, T, nH, nW, 3,\n C).permute(4, 0, 1, 2, 3, 5).contiguous()\n q, k, v = qkv[0], qkv[1], qkv[2] # B, T, nH, nW, C\n\n # partition q map\n (q_windows, k_windows, v_windows) = map(\n lambda t: window_partition(t, self.window_size).view(\n -1, T, self.window_size[0] * self.window_size[1], self.\n num_heads, C // self.num_heads).permute(0, 3, 1, 2, 4).\n contiguous().view(-1, self.num_heads, T * self.window_size[\n 0] * self.window_size[1], C // self.num_heads), (q, k, v))\n # q(k/v)_windows shape : [16, 4, 225, 128]\n\n if any(i > 0 for i in self.expand_size) and self.focal_level > 0:\n (k_tl, v_tl) = map(\n lambda t: torch.roll(t,\n shifts=(-self.expand_size[0], -self.\n expand_size[1]),\n dims=(2, 3)), (k, v))\n (k_tr, v_tr) = map(\n lambda t: torch.roll(t,\n shifts=(-self.expand_size[0], self.\n expand_size[1]),\n dims=(2, 3)), (k, v))\n (k_bl, v_bl) = map(\n lambda t: torch.roll(t,\n shifts=(self.expand_size[0], -self.\n expand_size[1]),\n dims=(2, 3)), (k, v))\n (k_br, v_br) = map(\n lambda t: torch.roll(t,\n shifts=(self.expand_size[0], self.\n expand_size[1]),\n dims=(2, 3)), (k, v))\n\n (k_tl_windows, k_tr_windows, k_bl_windows, k_br_windows) = map(\n lambda t: window_partition(t, self.window_size).view(\n -1, T, self.window_size[0] * self.window_size[1], self.\n num_heads, C // self.num_heads), (k_tl, k_tr, k_bl, k_br))\n (v_tl_windows, v_tr_windows, v_bl_windows, v_br_windows) = map(\n lambda t: window_partition(t, self.window_size).view(\n -1, T, self.window_size[0] * self.window_size[1], self.\n num_heads, C // self.num_heads), (v_tl, v_tr, v_bl, v_br))\n k_rolled = torch.cat(\n (k_tl_windows, k_tr_windows, k_bl_windows, k_br_windows),\n 2).permute(0, 3, 1, 2, 4).contiguous()\n v_rolled = torch.cat(\n (v_tl_windows, v_tr_windows, v_bl_windows, v_br_windows),\n 2).permute(0, 3, 1, 2, 4).contiguous()\n\n # mask out tokens in current window\n k_rolled = k_rolled[:, :, :, self.valid_ind_rolled]\n v_rolled = v_rolled[:, :, :, self.valid_ind_rolled]\n temp_N = k_rolled.shape[3]\n k_rolled = k_rolled.view(-1, self.num_heads, T * temp_N,\n C // self.num_heads)\n v_rolled = v_rolled.view(-1, self.num_heads, T * temp_N,\n C // self.num_heads)\n k_rolled = torch.cat((k_windows, k_rolled), 2)\n v_rolled = torch.cat((v_windows, v_rolled), 2)\n else:\n k_rolled = k_windows\n v_rolled = v_windows\n\n # q(k/v)_windows shape : [16, 4, 225, 128]\n # k_rolled.shape : [16, 4, 5, 165, 128]\n # ideal expanded window size 153 ((5+2*2)*(9+2*4))\n # k_windows=45 expand_window=108 overlap_window=12 (since expand_size < window_size / 2)\n\n if self.pool_method != \"none\" and self.focal_level > 1:\n k_pooled = []\n v_pooled = []\n for k in range(self.focal_level - 1):\n stride = 2**k\n # B, T, nWh, nWw, C\n x_window_pooled = x_all[k + 1].permute(0, 3, 1, 2,\n 4).contiguous()\n\n nWh, nWw = x_window_pooled.shape[2:4]\n\n # generate mask for pooled windows\n mask = x_window_pooled.new(T, nWh, nWw).fill_(1)\n # unfold mask: [nWh*nWw//s//s, k*k, 1]\n unfolded_mask = self.unfolds[k](mask.unsqueeze(1)).view(\n 1, T, self.unfolds[k].kernel_size[0], self.unfolds[k].kernel_size[1], -1).permute(4, 1, 2, 3, 0).contiguous().\\\n view(nWh*nWw // stride // stride, -1, 1)\n\n if k > 0:\n valid_ind_unfold_k = getattr(\n self, \"valid_ind_unfold_{}\".format(k))\n unfolded_mask = unfolded_mask[:, valid_ind_unfold_k]\n\n x_window_masks = unfolded_mask.flatten(1).unsqueeze(0)\n x_window_masks = x_window_masks.masked_fill(\n x_window_masks == 0,\n float(-100.0)).masked_fill(x_window_masks > 0, float(0.0))\n mask_all[k + 1] = x_window_masks\n\n # generate k and v for pooled windows\n qkv_pooled = self.qkv(x_window_pooled).reshape(\n B, T, nWh, nWw, 3, C).permute(4, 0, 1, 5, 2,\n 3).view(3, -1, C, nWh,\n nWw).contiguous()\n # B*T, C, nWh, nWw\n k_pooled_k, v_pooled_k = qkv_pooled[1], qkv_pooled[2]\n # k_pooled_k shape: [5, 512, 4, 4]\n # self.unfolds[k](k_pooled_k) shape: [5, 23040 (512 * 5 * 9 ), 16]\n\n (k_pooled_k, v_pooled_k) = map(\n lambda t: self.unfolds[k]\n (t).view(B, T, C, self.unfolds[k].kernel_size[0], self.\n unfolds[k].kernel_size[1], -1)\n .permute(0, 5, 1, 3, 4, 2).contiguous().view(\n -1, T, self.unfolds[k].kernel_size[0] * self.unfolds[\n k].kernel_size[1], self.num_heads, C // self.\n num_heads).permute(0, 3, 1, 2, 4).contiguous(),\n # (B x (nH*nW)) x nHeads x T x (unfold_wsize x unfold_wsize) x head_dim\n (k_pooled_k, v_pooled_k))\n # k_pooled_k shape : [16, 4, 5, 45, 128]\n\n # select valid unfolding index\n if k > 0:\n (k_pooled_k, v_pooled_k) = map(\n lambda t: t[:, :, :, valid_ind_unfold_k],\n (k_pooled_k, v_pooled_k))\n\n k_pooled_k = k_pooled_k.view(\n -1, self.num_heads, T * self.unfolds[k].kernel_size[0] *\n self.unfolds[k].kernel_size[1], C // self.num_heads)\n v_pooled_k = v_pooled_k.view(\n -1, self.num_heads, T * self.unfolds[k].kernel_size[0] *\n self.unfolds[k].kernel_size[1], C // self.num_heads)\n\n k_pooled += [k_pooled_k]\n v_pooled += [v_pooled_k]\n\n # k_all (v_all) shape : [16, 4, 5 * 210, 128]\n k_all = torch.cat([k_rolled] + k_pooled, 2)\n v_all = torch.cat([v_rolled] + v_pooled, 2)\n else:\n k_all = k_rolled\n v_all = v_rolled\n\n N = k_all.shape[-2]\n q_windows = q_windows * self.scale\n # B*nW, nHead, T*window_size*window_size, T*focal_window_size*focal_window_size\n attn = (q_windows @ k_all.transpose(-2, -1))\n # T * 45\n window_area = T * self.window_size[0] * self.window_size[1]\n # T * 165\n window_area_rolled = k_rolled.shape[2]\n\n if self.pool_method != \"none\" and self.focal_level > 1:\n offset = window_area_rolled\n for k in range(self.focal_level - 1):\n # add attentional mask\n # mask_all[1] shape [1, 16, T * 45]\n\n bias = tuple((i + 2**k - 1) for i in self.focal_window)\n\n if mask_all[k + 1] is not None:\n attn[:, :, :window_area, offset:(offset + (T*bias[0]*bias[1]))] = \\\n attn[:, :, :window_area, offset:(offset + (T*bias[0]*bias[1]))] + \\\n mask_all[k+1][:, :, None, None, :].repeat(\n attn.shape[0] // mask_all[k+1].shape[1], 1, 1, 1, 1).view(-1, 1, 1, mask_all[k+1].shape[-1])\n\n offset += T * bias[0] * bias[1]\n\n if mask_all[0] is not None:\n nW = mask_all[0].shape[0]\n attn = attn.view(attn.shape[0] // nW, nW, self.num_heads,\n window_area, N)\n attn[:, :, :, :, :\n window_area] = attn[:, :, :, :, :window_area] + mask_all[0][\n None, :, None, :, :]\n attn = attn.view(-1, self.num_heads, window_area, N)\n attn = self.softmax(attn)\n else:\n attn = self.softmax(attn)\n\n x = (attn @ v_all).transpose(1, 2).reshape(attn.shape[0], window_area,\n C)\n x = self.proj(x)\n return x\n\n\nclass TemporalFocalTransformerBlock(nn.Module):\n r\"\"\" Temporal Focal Transformer Block.\n Args:\n dim (int): Number of input channels.\n num_heads (int): Number of attention heads.\n window_size (tuple[int]): Window size.\n shift_size (int): Shift size for SW-MSA.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n focal_level (int): The number level of focal window.\n focal_window (int): Window size of each focal window.\n n_vecs (int): Required for F3N.\n t2t_params (int): T2T parameters for F3N.\n \"\"\"\n def __init__(self,\n dim,\n num_heads,\n window_size=(5, 9),\n mlp_ratio=4.,\n qkv_bias=True,\n pool_method=\"fc\",\n focal_level=2,\n focal_window=(5, 9),\n norm_layer=nn.LayerNorm,\n n_vecs=None,\n t2t_params=None):\n super().__init__()\n self.dim = dim\n self.num_heads = num_heads\n self.window_size = window_size\n self.expand_size = tuple(i // 2 for i in window_size) # TODO\n self.mlp_ratio = mlp_ratio\n# ... truncated ...","source_hash":"ac19b36ce0b778ac79c37bf9cbce9dd45b0a5c7cb224f047c9c4e7c29725e882","truncated":true} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.tfocal_transformer_hq.SoftSplit","uri":"program://Track-Anything/class/inpainter.model.modules.tfocal_transformer_hq.SoftSplit#L19-L46","kind":"class","name":"SoftSplit","path":"inpainter/model/modules/tfocal_transformer_hq.py","language":"python","start_line":19,"end_line":46,"context_start_line":1,"context_end_line":66,"code":"\"\"\"\n This code is based on:\n [1] FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting, ICCV 2021\n https://github.com/ruiliu-ai/FuseFormer\n [2] Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet, ICCV 2021\n https://github.com/yitu-opensource/T2T-ViT\n [3] Focal Self-attention for Local-Global Interactions in Vision Transformers, NeurIPS 2021\n https://github.com/microsoft/Focal-Transformer \n\"\"\"\n\nimport math\nfrom functools import reduce\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass SoftSplit(nn.Module):\n def __init__(self, channel, hidden, kernel_size, stride, padding,\n t2t_param):\n super(SoftSplit, self).__init__()\n self.kernel_size = kernel_size\n self.t2t = nn.Unfold(kernel_size=kernel_size,\n stride=stride,\n padding=padding)\n c_in = reduce((lambda x, y: x * y), kernel_size) * channel\n self.embedding = nn.Linear(c_in, hidden)\n\n self.t2t_param = t2t_param\n\n def forward(self, x, b, output_size):\n f_h = int((output_size[0] + 2 * self.t2t_param['padding'][0] -\n (self.t2t_param['kernel_size'][0] - 1) - 1) /\n self.t2t_param['stride'][0] + 1)\n f_w = int((output_size[1] + 2 * self.t2t_param['padding'][1] -\n (self.t2t_param['kernel_size'][1] - 1) - 1) /\n self.t2t_param['stride'][1] + 1)\n\n feat = self.t2t(x)\n feat = feat.permute(0, 2, 1)\n # feat shape [b*t, num_vec, ks*ks*c]\n feat = self.embedding(feat)\n # feat shape after embedding [b, t*num_vec, hidden]\n feat = feat.view(b, -1, f_h, f_w, feat.size(2))\n return feat\n\n\nclass SoftComp(nn.Module):\n def __init__(self, channel, hidden, kernel_size, stride, padding):\n super(SoftComp, self).__init__()\n self.relu = nn.LeakyReLU(0.2, inplace=True)\n c_out = reduce((lambda x, y: x * y), kernel_size) * channel\n self.embedding = nn.Linear(hidden, c_out)\n self.kernel_size = kernel_size\n self.stride = stride\n self.padding = padding\n self.bias_conv = nn.Conv2d(channel,\n channel,\n kernel_size=3,\n stride=1,\n padding=1)\n # TODO upsample conv\n # self.bias_conv = nn.Conv2d()\n # self.bias = nn.Parameter(torch.zeros((channel, h, w), dtype=torch.float32), requires_grad=True)\n","source_hash":"ac19b36ce0b778ac79c37bf9cbce9dd45b0a5c7cb224f047c9c4e7c29725e882","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.tfocal_transformer_hq.SoftComp","uri":"program://Track-Anything/class/inpainter.model.modules.tfocal_transformer_hq.SoftComp#L49-L79","kind":"class","name":"SoftComp","path":"inpainter/model/modules/tfocal_transformer_hq.py","language":"python","start_line":49,"end_line":79,"context_start_line":29,"context_end_line":99,"code":"\n self.t2t_param = t2t_param\n\n def forward(self, x, b, output_size):\n f_h = int((output_size[0] + 2 * self.t2t_param['padding'][0] -\n (self.t2t_param['kernel_size'][0] - 1) - 1) /\n self.t2t_param['stride'][0] + 1)\n f_w = int((output_size[1] + 2 * self.t2t_param['padding'][1] -\n (self.t2t_param['kernel_size'][1] - 1) - 1) /\n self.t2t_param['stride'][1] + 1)\n\n feat = self.t2t(x)\n feat = feat.permute(0, 2, 1)\n # feat shape [b*t, num_vec, ks*ks*c]\n feat = self.embedding(feat)\n # feat shape after embedding [b, t*num_vec, hidden]\n feat = feat.view(b, -1, f_h, f_w, feat.size(2))\n return feat\n\n\nclass SoftComp(nn.Module):\n def __init__(self, channel, hidden, kernel_size, stride, padding):\n super(SoftComp, self).__init__()\n self.relu = nn.LeakyReLU(0.2, inplace=True)\n c_out = reduce((lambda x, y: x * y), kernel_size) * channel\n self.embedding = nn.Linear(hidden, c_out)\n self.kernel_size = kernel_size\n self.stride = stride\n self.padding = padding\n self.bias_conv = nn.Conv2d(channel,\n channel,\n kernel_size=3,\n stride=1,\n padding=1)\n # TODO upsample conv\n # self.bias_conv = nn.Conv2d()\n # self.bias = nn.Parameter(torch.zeros((channel, h, w), dtype=torch.float32), requires_grad=True)\n\n def forward(self, x, t, output_size):\n b_, _, _, _, c_ = x.shape\n x = x.view(b_, -1, c_)\n feat = self.embedding(x)\n b, _, c = feat.size()\n feat = feat.view(b * t, -1, c).permute(0, 2, 1)\n feat = F.fold(feat,\n output_size=output_size,\n kernel_size=self.kernel_size,\n stride=self.stride,\n padding=self.padding)\n feat = self.bias_conv(feat)\n return feat\n\n\nclass FusionFeedForward(nn.Module):\n def __init__(self, d_model, n_vecs=None, t2t_params=None):\n super(FusionFeedForward, self).__init__()\n # We set d_ff as a default to 1960\n hd = 1960\n self.conv1 = nn.Sequential(nn.Linear(d_model, hd))\n self.conv2 = nn.Sequential(nn.GELU(), nn.Linear(hd, d_model))\n assert t2t_params is not None and n_vecs is not None\n self.t2t_params = t2t_params\n\n def forward(self, x, output_size):\n n_vecs = 1\n for i, d in enumerate(self.t2t_params['kernel_size']):\n n_vecs *= int((output_size[i] + 2 * self.t2t_params['padding'][i] -\n (d - 1) - 1) / self.t2t_params['stride'][i] + 1)\n\n x = self.conv1(x)\n b, n, c = x.size()","source_hash":"ac19b36ce0b778ac79c37bf9cbce9dd45b0a5c7cb224f047c9c4e7c29725e882","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.tfocal_transformer_hq.FusionFeedForward","uri":"program://Track-Anything/class/inpainter.model.modules.tfocal_transformer_hq.FusionFeedForward#L82-L119","kind":"class","name":"FusionFeedForward","path":"inpainter/model/modules/tfocal_transformer_hq.py","language":"python","start_line":82,"end_line":119,"context_start_line":62,"context_end_line":139,"code":" padding=1)\n # TODO upsample conv\n # self.bias_conv = nn.Conv2d()\n # self.bias = nn.Parameter(torch.zeros((channel, h, w), dtype=torch.float32), requires_grad=True)\n\n def forward(self, x, t, output_size):\n b_, _, _, _, c_ = x.shape\n x = x.view(b_, -1, c_)\n feat = self.embedding(x)\n b, _, c = feat.size()\n feat = feat.view(b * t, -1, c).permute(0, 2, 1)\n feat = F.fold(feat,\n output_size=output_size,\n kernel_size=self.kernel_size,\n stride=self.stride,\n padding=self.padding)\n feat = self.bias_conv(feat)\n return feat\n\n\nclass FusionFeedForward(nn.Module):\n def __init__(self, d_model, n_vecs=None, t2t_params=None):\n super(FusionFeedForward, self).__init__()\n # We set d_ff as a default to 1960\n hd = 1960\n self.conv1 = nn.Sequential(nn.Linear(d_model, hd))\n self.conv2 = nn.Sequential(nn.GELU(), nn.Linear(hd, d_model))\n assert t2t_params is not None and n_vecs is not None\n self.t2t_params = t2t_params\n\n def forward(self, x, output_size):\n n_vecs = 1\n for i, d in enumerate(self.t2t_params['kernel_size']):\n n_vecs *= int((output_size[i] + 2 * self.t2t_params['padding'][i] -\n (d - 1) - 1) / self.t2t_params['stride'][i] + 1)\n\n x = self.conv1(x)\n b, n, c = x.size()\n normalizer = x.new_ones(b, n, 49).view(-1, n_vecs, 49).permute(0, 2, 1)\n normalizer = F.fold(normalizer,\n output_size=output_size,\n kernel_size=self.t2t_params['kernel_size'],\n padding=self.t2t_params['padding'],\n stride=self.t2t_params['stride'])\n\n x = F.fold(x.view(-1, n_vecs, c).permute(0, 2, 1),\n output_size=output_size,\n kernel_size=self.t2t_params['kernel_size'],\n padding=self.t2t_params['padding'],\n stride=self.t2t_params['stride'])\n\n x = F.unfold(x / normalizer,\n kernel_size=self.t2t_params['kernel_size'],\n padding=self.t2t_params['padding'],\n stride=self.t2t_params['stride']).permute(\n 0, 2, 1).contiguous().view(b, n, c)\n x = self.conv2(x)\n return x\n\n\ndef window_partition(x, window_size):\n \"\"\"\n Args:\n x: shape is (B, T, H, W, C)\n window_size (tuple[int]): window size\n Returns:\n windows: (B*num_windows, T*window_size*window_size, C)\n \"\"\"\n B, T, H, W, C = x.shape\n\n x = x.view(B, T, H // window_size[0], window_size[0], W // window_size[1],\n window_size[1], C)\n\n windows = x.permute(0, 2, 4, 1, 3, 5, 6).contiguous().view(\n -1, T * window_size[0] * window_size[1], C)\n return windows\n\n","source_hash":"ac19b36ce0b778ac79c37bf9cbce9dd45b0a5c7cb224f047c9c4e7c29725e882","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.tfocal_transformer_hq.window_partition","uri":"program://Track-Anything/function/inpainter.model.modules.tfocal_transformer_hq.window_partition#L122-L137","kind":"function","name":"window_partition","path":"inpainter/model/modules/tfocal_transformer_hq.py","language":"python","start_line":122,"end_line":137,"context_start_line":102,"context_end_line":157,"code":" output_size=output_size,\n kernel_size=self.t2t_params['kernel_size'],\n padding=self.t2t_params['padding'],\n stride=self.t2t_params['stride'])\n\n x = F.fold(x.view(-1, n_vecs, c).permute(0, 2, 1),\n output_size=output_size,\n kernel_size=self.t2t_params['kernel_size'],\n padding=self.t2t_params['padding'],\n stride=self.t2t_params['stride'])\n\n x = F.unfold(x / normalizer,\n kernel_size=self.t2t_params['kernel_size'],\n padding=self.t2t_params['padding'],\n stride=self.t2t_params['stride']).permute(\n 0, 2, 1).contiguous().view(b, n, c)\n x = self.conv2(x)\n return x\n\n\ndef window_partition(x, window_size):\n \"\"\"\n Args:\n x: shape is (B, T, H, W, C)\n window_size (tuple[int]): window size\n Returns:\n windows: (B*num_windows, T*window_size*window_size, C)\n \"\"\"\n B, T, H, W, C = x.shape\n\n x = x.view(B, T, H // window_size[0], window_size[0], W // window_size[1],\n window_size[1], C)\n\n windows = x.permute(0, 2, 4, 1, 3, 5, 6).contiguous().view(\n -1, T * window_size[0] * window_size[1], C)\n return windows\n\n\ndef window_partition_noreshape(x, window_size):\n \"\"\"\n Args:\n x: shape is (B, T, H, W, C)\n window_size (tuple[int]): window size\n Returns:\n windows: (B, num_windows_h, num_windows_w, T, window_size, window_size, C)\n \"\"\"\n B, T, H, W, C = x.shape\n x = x.view(B, T, H // window_size[0], window_size[0], W // window_size[1],\n window_size[1], C)\n windows = x.permute(0, 2, 4, 1, 3, 5, 6).contiguous()\n return windows\n\n\ndef window_reverse(windows, window_size, T, H, W):\n \"\"\"\n Args:","source_hash":"ac19b36ce0b778ac79c37bf9cbce9dd45b0a5c7cb224f047c9c4e7c29725e882","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.tfocal_transformer_hq.window_partition_noreshape","uri":"program://Track-Anything/function/inpainter.model.modules.tfocal_transformer_hq.window_partition_noreshape#L140-L152","kind":"function","name":"window_partition_noreshape","path":"inpainter/model/modules/tfocal_transformer_hq.py","language":"python","start_line":140,"end_line":152,"context_start_line":120,"context_end_line":172,"code":"\n\ndef window_partition(x, window_size):\n \"\"\"\n Args:\n x: shape is (B, T, H, W, C)\n window_size (tuple[int]): window size\n Returns:\n windows: (B*num_windows, T*window_size*window_size, C)\n \"\"\"\n B, T, H, W, C = x.shape\n\n x = x.view(B, T, H // window_size[0], window_size[0], W // window_size[1],\n window_size[1], C)\n\n windows = x.permute(0, 2, 4, 1, 3, 5, 6).contiguous().view(\n -1, T * window_size[0] * window_size[1], C)\n return windows\n\n\ndef window_partition_noreshape(x, window_size):\n \"\"\"\n Args:\n x: shape is (B, T, H, W, C)\n window_size (tuple[int]): window size\n Returns:\n windows: (B, num_windows_h, num_windows_w, T, window_size, window_size, C)\n \"\"\"\n B, T, H, W, C = x.shape\n x = x.view(B, T, H // window_size[0], window_size[0], W // window_size[1],\n window_size[1], C)\n windows = x.permute(0, 2, 4, 1, 3, 5, 6).contiguous()\n return windows\n\n\ndef window_reverse(windows, window_size, T, H, W):\n \"\"\"\n Args:\n windows: shape is (num_windows*B, T, window_size, window_size, C)\n window_size (tuple[int]): Window size\n T (int): Temporal length of video\n H (int): Height of image\n W (int): Width of image\n Returns:\n x: (B, T, H, W, C)\n \"\"\"\n B = int(windows.shape[0] / (H * W / window_size[0] / window_size[1]))\n x = windows.view(B, H // window_size[0], W // window_size[1], T,\n window_size[0], window_size[1], -1)\n x = x.permute(0, 3, 1, 4, 2, 5, 6).contiguous().view(B, T, H, W, -1)\n return x\n\n","source_hash":"ac19b36ce0b778ac79c37bf9cbce9dd45b0a5c7cb224f047c9c4e7c29725e882","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.tfocal_transformer_hq.window_reverse","uri":"program://Track-Anything/function/inpainter.model.modules.tfocal_transformer_hq.window_reverse#L155-L170","kind":"function","name":"window_reverse","path":"inpainter/model/modules/tfocal_transformer_hq.py","language":"python","start_line":155,"end_line":170,"context_start_line":135,"context_end_line":190,"code":" windows = x.permute(0, 2, 4, 1, 3, 5, 6).contiguous().view(\n -1, T * window_size[0] * window_size[1], C)\n return windows\n\n\ndef window_partition_noreshape(x, window_size):\n \"\"\"\n Args:\n x: shape is (B, T, H, W, C)\n window_size (tuple[int]): window size\n Returns:\n windows: (B, num_windows_h, num_windows_w, T, window_size, window_size, C)\n \"\"\"\n B, T, H, W, C = x.shape\n x = x.view(B, T, H // window_size[0], window_size[0], W // window_size[1],\n window_size[1], C)\n windows = x.permute(0, 2, 4, 1, 3, 5, 6).contiguous()\n return windows\n\n\ndef window_reverse(windows, window_size, T, H, W):\n \"\"\"\n Args:\n windows: shape is (num_windows*B, T, window_size, window_size, C)\n window_size (tuple[int]): Window size\n T (int): Temporal length of video\n H (int): Height of image\n W (int): Width of image\n Returns:\n x: (B, T, H, W, C)\n \"\"\"\n B = int(windows.shape[0] / (H * W / window_size[0] / window_size[1]))\n x = windows.view(B, H // window_size[0], W // window_size[1], T,\n window_size[0], window_size[1], -1)\n x = x.permute(0, 3, 1, 4, 2, 5, 6).contiguous().view(B, T, H, W, -1)\n return x\n\n\nclass WindowAttention(nn.Module):\n \"\"\"Temporal focal window attention\n \"\"\"\n def __init__(self, dim, expand_size, window_size, focal_window,\n focal_level, num_heads, qkv_bias, pool_method):\n\n super().__init__()\n self.dim = dim\n self.expand_size = expand_size\n self.window_size = window_size # Wh, Ww\n self.pool_method = pool_method\n self.num_heads = num_heads\n head_dim = dim // num_heads\n self.scale = head_dim**-0.5\n self.focal_level = focal_level\n self.focal_window = focal_window\n\n if any(i > 0 for i in self.expand_size) and focal_level > 0:","source_hash":"ac19b36ce0b778ac79c37bf9cbce9dd45b0a5c7cb224f047c9c4e7c29725e882","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.tfocal_transformer_hq.WindowAttention","uri":"program://Track-Anything/class/inpainter.model.modules.tfocal_transformer_hq.WindowAttention#L173-L427","kind":"class","name":"WindowAttention","path":"inpainter/model/modules/tfocal_transformer_hq.py","language":"python","start_line":173,"end_line":427,"context_start_line":153,"context_end_line":447,"code":"\n\ndef window_reverse(windows, window_size, T, H, W):\n \"\"\"\n Args:\n windows: shape is (num_windows*B, T, window_size, window_size, C)\n window_size (tuple[int]): Window size\n T (int): Temporal length of video\n H (int): Height of image\n W (int): Width of image\n Returns:\n x: (B, T, H, W, C)\n \"\"\"\n B = int(windows.shape[0] / (H * W / window_size[0] / window_size[1]))\n x = windows.view(B, H // window_size[0], W // window_size[1], T,\n window_size[0], window_size[1], -1)\n x = x.permute(0, 3, 1, 4, 2, 5, 6).contiguous().view(B, T, H, W, -1)\n return x\n\n\nclass WindowAttention(nn.Module):\n \"\"\"Temporal focal window attention\n \"\"\"\n def __init__(self, dim, expand_size, window_size, focal_window,\n focal_level, num_heads, qkv_bias, pool_method):\n\n super().__init__()\n self.dim = dim\n self.expand_size = expand_size\n self.window_size = window_size # Wh, Ww\n self.pool_method = pool_method\n self.num_heads = num_heads\n head_dim = dim // num_heads\n self.scale = head_dim**-0.5\n self.focal_level = focal_level\n self.focal_window = focal_window\n\n if any(i > 0 for i in self.expand_size) and focal_level > 0:\n # get mask for rolled k and rolled v\n mask_tl = torch.ones(self.window_size[0], self.window_size[1])\n mask_tl[:-self.expand_size[0], :-self.expand_size[1]] = 0\n mask_tr = torch.ones(self.window_size[0], self.window_size[1])\n mask_tr[:-self.expand_size[0], self.expand_size[1]:] = 0\n mask_bl = torch.ones(self.window_size[0], self.window_size[1])\n mask_bl[self.expand_size[0]:, :-self.expand_size[1]] = 0\n mask_br = torch.ones(self.window_size[0], self.window_size[1])\n mask_br[self.expand_size[0]:, self.expand_size[1]:] = 0\n mask_rolled = torch.stack((mask_tl, mask_tr, mask_bl, mask_br),\n 0).flatten(0)\n self.register_buffer(\"valid_ind_rolled\",\n mask_rolled.nonzero(as_tuple=False).view(-1))\n\n if pool_method != \"none\" and focal_level > 1:\n self.unfolds = nn.ModuleList()\n\n # build relative position bias between local patch and pooled windows\n for k in range(focal_level - 1):\n stride = 2**k\n kernel_size = tuple(2 * (i // 2) + 2**k + (2**k - 1)\n for i in self.focal_window)\n # define unfolding operations\n self.unfolds += [\n nn.Unfold(kernel_size=kernel_size,\n stride=stride,\n padding=tuple(i // 2 for i in kernel_size))\n ]\n\n # define unfolding index for focal_level > 0\n if k > 0:\n mask = torch.zeros(kernel_size)\n mask[(2**k) - 1:, (2**k) - 1:] = 1\n self.register_buffer(\n \"valid_ind_unfold_{}\".format(k),\n mask.flatten(0).nonzero(as_tuple=False).view(-1))\n\n self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n self.proj = nn.Linear(dim, dim)\n\n self.softmax = nn.Softmax(dim=-1)\n\n def forward(self, x_all, mask_all=None):\n \"\"\"\n Args:\n x: input features with shape of (B, T, Wh, Ww, C)\n mask: (0/-inf) mask with shape of (num_windows, T*Wh*Ww, T*Wh*Ww) or None\n\n output: (nW*B, Wh*Ww, C)\n \"\"\"\n x = x_all[0]\n\n B, T, nH, nW, C = x.shape\n qkv = self.qkv(x).reshape(B, T, nH, nW, 3,\n C).permute(4, 0, 1, 2, 3, 5).contiguous()\n q, k, v = qkv[0], qkv[1], qkv[2] # B, T, nH, nW, C\n\n # partition q map\n (q_windows, k_windows, v_windows) = map(\n lambda t: window_partition(t, self.window_size).view(\n -1, T, self.window_size[0] * self.window_size[1], self.\n num_heads, C // self.num_heads).permute(0, 3, 1, 2, 4).\n contiguous().view(-1, self.num_heads, T * self.window_size[\n 0] * self.window_size[1], C // self.num_heads), (q, k, v))\n # q(k/v)_windows shape : [16, 4, 225, 128]\n\n if any(i > 0 for i in self.expand_size) and self.focal_level > 0:\n (k_tl, v_tl) = map(\n lambda t: torch.roll(t,\n shifts=(-self.expand_size[0], -self.\n expand_size[1]),\n dims=(2, 3)), (k, v))\n (k_tr, v_tr) = map(\n lambda t: torch.roll(t,\n shifts=(-self.expand_size[0], self.\n expand_size[1]),\n dims=(2, 3)), (k, v))\n (k_bl, v_bl) = map(\n lambda t: torch.roll(t,\n shifts=(self.expand_size[0], -self.\n expand_size[1]),\n dims=(2, 3)), (k, v))\n (k_br, v_br) = map(\n lambda t: torch.roll(t,\n shifts=(self.expand_size[0], self.\n expand_size[1]),\n dims=(2, 3)), (k, v))\n\n (k_tl_windows, k_tr_windows, k_bl_windows, k_br_windows) = map(\n lambda t: window_partition(t, self.window_size).view(\n -1, T, self.window_size[0] * self.window_size[1], self.\n num_heads, C // self.num_heads), (k_tl, k_tr, k_bl, k_br))\n (v_tl_windows, v_tr_windows, v_bl_windows, v_br_windows) = map(\n lambda t: window_partition(t, self.window_size).view(\n -1, T, self.window_size[0] * self.window_size[1], self.\n num_heads, C // self.num_heads), (v_tl, v_tr, v_bl, v_br))\n k_rolled = torch.cat(\n (k_tl_windows, k_tr_windows, k_bl_windows, k_br_windows),\n 2).permute(0, 3, 1, 2, 4).contiguous()\n v_rolled = torch.cat(\n (v_tl_windows, v_tr_windows, v_bl_windows, v_br_windows),\n 2).permute(0, 3, 1, 2, 4).contiguous()\n\n # mask out tokens in current window\n k_rolled = k_rolled[:, :, :, self.valid_ind_rolled]\n v_rolled = v_rolled[:, :, :, self.valid_ind_rolled]\n temp_N = k_rolled.shape[3]\n k_rolled = k_rolled.view(-1, self.num_heads, T * temp_N,\n C // self.num_heads)\n v_rolled = v_rolled.view(-1, self.num_heads, T * temp_N,\n C // self.num_heads)\n k_rolled = torch.cat((k_windows, k_rolled), 2)\n v_rolled = torch.cat((v_windows, v_rolled), 2)\n else:\n k_rolled = k_windows\n v_rolled = v_windows\n\n # q(k/v)_windows shape : [16, 4, 225, 128]\n # k_rolled.shape : [16, 4, 5, 165, 128]\n # ideal expanded window size 153 ((5+2*2)*(9+2*4))\n # k_windows=45 expand_window=108 overlap_window=12 (since expand_size < window_size / 2)\n\n if self.pool_method != \"none\" and self.focal_level > 1:\n k_pooled = []\n v_pooled = []\n for k in range(self.focal_level - 1):\n stride = 2**k\n # B, T, nWh, nWw, C\n x_window_pooled = x_all[k + 1].permute(0, 3, 1, 2,\n 4).contiguous()\n\n nWh, nWw = x_window_pooled.shape[2:4]\n\n # generate mask for pooled windows\n mask = x_window_pooled.new(T, nWh, nWw).fill_(1)\n # unfold mask: [nWh*nWw//s//s, k*k, 1]\n unfolded_mask = self.unfolds[k](mask.unsqueeze(1)).view(\n 1, T, self.unfolds[k].kernel_size[0], self.unfolds[k].kernel_size[1], -1).permute(4, 1, 2, 3, 0).contiguous().\\\n view(nWh*nWw // stride // stride, -1, 1)\n\n if k > 0:\n valid_ind_unfold_k = getattr(\n self, \"valid_ind_unfold_{}\".format(k))\n unfolded_mask = unfolded_mask[:, valid_ind_unfold_k]\n\n x_window_masks = unfolded_mask.flatten(1).unsqueeze(0)\n x_window_masks = x_window_masks.masked_fill(\n x_window_masks == 0,\n float(-100.0)).masked_fill(x_window_masks > 0, float(0.0))\n mask_all[k + 1] = x_window_masks\n\n # generate k and v for pooled windows\n qkv_pooled = self.qkv(x_window_pooled).reshape(\n B, T, nWh, nWw, 3, C).permute(4, 0, 1, 5, 2,\n 3).view(3, -1, C, nWh,\n nWw).contiguous()\n # B*T, C, nWh, nWw\n k_pooled_k, v_pooled_k = qkv_pooled[1], qkv_pooled[2]\n # k_pooled_k shape: [5, 512, 4, 4]\n # self.unfolds[k](k_pooled_k) shape: [5, 23040 (512 * 5 * 9 ), 16]\n\n (k_pooled_k, v_pooled_k) = map(\n lambda t: self.unfolds[k]\n (t).view(B, T, C, self.unfolds[k].kernel_size[0], self.\n unfolds[k].kernel_size[1], -1)\n .permute(0, 5, 1, 3, 4, 2).contiguous().view(\n -1, T, self.unfolds[k].kernel_size[0] * self.unfolds[\n k].kernel_size[1], self.num_heads, C // self.\n num_heads).permute(0, 3, 1, 2, 4).contiguous(),\n # (B x (nH*nW)) x nHeads x T x (unfold_wsize x unfold_wsize) x head_dim\n (k_pooled_k, v_pooled_k))\n # k_pooled_k shape : [16, 4, 5, 45, 128]\n\n # select valid unfolding index\n if k > 0:\n (k_pooled_k, v_pooled_k) = map(\n lambda t: t[:, :, :, valid_ind_unfold_k],\n (k_pooled_k, v_pooled_k))\n\n k_pooled_k = k_pooled_k.view(\n -1, self.num_heads, T * self.unfolds[k].kernel_size[0] *\n self.unfolds[k].kernel_size[1], C // self.num_heads)\n v_pooled_k = v_pooled_k.view(\n -1, self.num_heads, T * self.unfolds[k].kernel_size[0] *\n self.unfolds[k].kernel_size[1], C // self.num_heads)\n\n k_pooled += [k_pooled_k]\n v_pooled += [v_pooled_k]\n\n # k_all (v_all) shape : [16, 4, 5 * 210, 128]\n k_all = torch.cat([k_rolled] + k_pooled, 2)\n v_all = torch.cat([v_rolled] + v_pooled, 2)\n else:\n k_all = k_rolled\n v_all = v_rolled\n\n N = k_all.shape[-2]\n q_windows = q_windows * self.scale\n # B*nW, nHead, T*window_size*window_size, T*focal_window_size*focal_window_size\n attn = (q_windows @ k_all.transpose(-2, -1))\n # T * 45\n window_area = T * self.window_size[0] * self.window_size[1]\n # T * 165\n window_area_rolled = k_rolled.shape[2]\n\n if self.pool_method != \"none\" and self.focal_level > 1:\n offset = window_area_rolled\n for k in range(self.focal_level - 1):\n # add attentional mask\n # mask_all[1] shape [1, 16, T * 45]\n\n bias = tuple((i + 2**k - 1) for i in self.focal_window)\n\n if mask_all[k + 1] is not None:\n attn[:, :, :window_area, offset:(offset + (T*bias[0]*bias[1]))] = \\\n attn[:, :, :window_area, offset:(offset + (T*bias[0]*bias[1]))] + \\\n mask_all[k+1][:, :, None, None, :].repeat(\n attn.shape[0] // mask_all[k+1].shape[1], 1, 1, 1, 1).view(-1, 1, 1, mask_all[k+1].shape[-1])\n\n offset += T * bias[0] * bias[1]\n\n if mask_all[0] is not None:\n nW = mask_all[0].shape[0]\n attn = attn.view(attn.shape[0] // nW, nW, self.num_heads,\n window_area, N)\n attn[:, :, :, :, :\n window_area] = attn[:, :, :, :, :window_area] + mask_all[0][\n None, :, None, :, :]\n attn = attn.view(-1, self.num_heads, window_area, N)\n attn = self.softmax(attn)\n else:\n attn = self.softmax(attn)\n\n x = (attn @ v_all).transpose(1, 2).reshape(attn.shape[0], window_area,\n C)\n x = self.proj(x)\n return x\n\n\nclass TemporalFocalTransformerBlock(nn.Module):\n r\"\"\" Temporal Focal Transformer Block.\n Args:\n dim (int): Number of input channels.\n num_heads (int): Number of attention heads.\n window_size (tuple[int]): Window size.\n shift_size (int): Shift size for SW-MSA.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n focal_level (int): The number level of focal window.\n focal_window (int): Window size of each focal window.\n n_vecs (int): Required for F3N.\n t2t_params (int): T2T parameters for F3N.\n \"\"\"\n def __init__(self,\n dim,\n num_heads,","source_hash":"ac19b36ce0b778ac79c37bf9cbce9dd45b0a5c7cb224f047c9c4e7c29725e882","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.tfocal_transformer_hq.TemporalFocalTransformerBlock","uri":"program://Track-Anything/class/inpainter.model.modules.tfocal_transformer_hq.TemporalFocalTransformerBlock#L430-L567","kind":"class","name":"TemporalFocalTransformerBlock","path":"inpainter/model/modules/tfocal_transformer_hq.py","language":"python","start_line":430,"end_line":567,"context_start_line":410,"context_end_line":567,"code":" offset += T * bias[0] * bias[1]\n\n if mask_all[0] is not None:\n nW = mask_all[0].shape[0]\n attn = attn.view(attn.shape[0] // nW, nW, self.num_heads,\n window_area, N)\n attn[:, :, :, :, :\n window_area] = attn[:, :, :, :, :window_area] + mask_all[0][\n None, :, None, :, :]\n attn = attn.view(-1, self.num_heads, window_area, N)\n attn = self.softmax(attn)\n else:\n attn = self.softmax(attn)\n\n x = (attn @ v_all).transpose(1, 2).reshape(attn.shape[0], window_area,\n C)\n x = self.proj(x)\n return x\n\n\nclass TemporalFocalTransformerBlock(nn.Module):\n r\"\"\" Temporal Focal Transformer Block.\n Args:\n dim (int): Number of input channels.\n num_heads (int): Number of attention heads.\n window_size (tuple[int]): Window size.\n shift_size (int): Shift size for SW-MSA.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n focal_level (int): The number level of focal window.\n focal_window (int): Window size of each focal window.\n n_vecs (int): Required for F3N.\n t2t_params (int): T2T parameters for F3N.\n \"\"\"\n def __init__(self,\n dim,\n num_heads,\n window_size=(5, 9),\n mlp_ratio=4.,\n qkv_bias=True,\n pool_method=\"fc\",\n focal_level=2,\n focal_window=(5, 9),\n norm_layer=nn.LayerNorm,\n n_vecs=None,\n t2t_params=None):\n super().__init__()\n self.dim = dim\n self.num_heads = num_heads\n self.window_size = window_size\n self.expand_size = tuple(i // 2 for i in window_size) # TODO\n self.mlp_ratio = mlp_ratio\n self.pool_method = pool_method\n self.focal_level = focal_level\n self.focal_window = focal_window\n\n self.window_size_glo = self.window_size\n\n self.pool_layers = nn.ModuleList()\n if self.pool_method != \"none\":\n for k in range(self.focal_level - 1):\n window_size_glo = tuple(\n math.floor(i / (2**k)) for i in self.window_size_glo)\n self.pool_layers.append(\n nn.Linear(window_size_glo[0] * window_size_glo[1], 1))\n self.pool_layers[-1].weight.data.fill_(\n 1. / (window_size_glo[0] * window_size_glo[1]))\n self.pool_layers[-1].bias.data.fill_(0)\n\n self.norm1 = norm_layer(dim)\n\n self.attn = WindowAttention(dim,\n expand_size=self.expand_size,\n window_size=self.window_size,\n focal_window=focal_window,\n focal_level=focal_level,\n num_heads=num_heads,\n qkv_bias=qkv_bias,\n pool_method=pool_method)\n\n self.norm2 = norm_layer(dim)\n self.mlp = FusionFeedForward(dim, n_vecs=n_vecs, t2t_params=t2t_params)\n\n def forward(self, x):\n output_size = x[1]\n x = x[0]\n\n B, T, H, W, C = x.shape\n\n shortcut = x\n x = self.norm1(x)\n\n shifted_x = x\n\n x_windows_all = [shifted_x]\n x_window_masks_all = [None]\n\n # partition windows tuple(i // 2 for i in window_size)\n if self.focal_level > 1 and self.pool_method != \"none\":\n # if we add coarser granularity and the pool method is not none\n for k in range(self.focal_level - 1):\n window_size_glo = tuple(\n math.floor(i / (2**k)) for i in self.window_size_glo)\n pooled_h = math.ceil(H / window_size_glo[0]) * (2**k)\n pooled_w = math.ceil(W / window_size_glo[1]) * (2**k)\n H_pool = pooled_h * window_size_glo[0]\n W_pool = pooled_w * window_size_glo[1]\n\n x_level_k = shifted_x\n # trim or pad shifted_x depending on the required size\n if H > H_pool:\n trim_t = (H - H_pool) // 2\n trim_b = H - H_pool - trim_t\n x_level_k = x_level_k[:, :, trim_t:-trim_b]\n elif H < H_pool:\n pad_t = (H_pool - H) // 2\n pad_b = H_pool - H - pad_t\n x_level_k = F.pad(x_level_k, (0, 0, 0, 0, pad_t, pad_b))\n\n if W > W_pool:\n trim_l = (W - W_pool) // 2\n trim_r = W - W_pool - trim_l\n x_level_k = x_level_k[:, :, :, trim_l:-trim_r]\n elif W < W_pool:\n pad_l = (W_pool - W) // 2\n pad_r = W_pool - W - pad_l\n x_level_k = F.pad(x_level_k, (0, 0, pad_l, pad_r))\n\n x_windows_noreshape = window_partition_noreshape(\n x_level_k.contiguous(), window_size_glo\n ) # B, nw, nw, T, window_size, window_size, C\n nWh, nWw = x_windows_noreshape.shape[1:3]\n x_windows_noreshape = x_windows_noreshape.view(\n B, nWh, nWw, T, window_size_glo[0] * window_size_glo[1],\n C).transpose(4, 5) # B, nWh, nWw, T, C, wsize**2\n x_windows_pooled = self.pool_layers[k](\n x_windows_noreshape).flatten(-2) # B, nWh, nWw, T, C\n\n x_windows_all += [x_windows_pooled]\n x_window_masks_all += [None]\n\n # nW*B, T*window_size*window_size, C\n attn_windows = self.attn(x_windows_all, mask_all=x_window_masks_all)\n\n # merge windows\n attn_windows = attn_windows.view(-1, T, self.window_size[0],\n self.window_size[1], C)\n shifted_x = window_reverse(attn_windows, self.window_size, T, H,\n W) # B T H' W' C\n\n # FFN\n x = shortcut + shifted_x\n y = self.norm2(x)\n x = x + self.mlp(y.view(B, T * H * W, C), output_size).view(\n B, T, H, W, C)\n\n return x, output_size","source_hash":"ac19b36ce0b778ac79c37bf9cbce9dd45b0a5c7cb224f047c9c4e7c29725e882","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.tfocal_transformer_hq.__init__","uri":"program://Track-Anything/function/inpainter.model.modules.tfocal_transformer_hq.__init__#L445-L492","kind":"function","name":"__init__","path":"inpainter/model/modules/tfocal_transformer_hq.py","language":"python","start_line":445,"end_line":492,"context_start_line":425,"context_end_line":512,"code":" C)\n x = self.proj(x)\n return x\n\n\nclass TemporalFocalTransformerBlock(nn.Module):\n r\"\"\" Temporal Focal Transformer Block.\n Args:\n dim (int): Number of input channels.\n num_heads (int): Number of attention heads.\n window_size (tuple[int]): Window size.\n shift_size (int): Shift size for SW-MSA.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n focal_level (int): The number level of focal window.\n focal_window (int): Window size of each focal window.\n n_vecs (int): Required for F3N.\n t2t_params (int): T2T parameters for F3N.\n \"\"\"\n def __init__(self,\n dim,\n num_heads,\n window_size=(5, 9),\n mlp_ratio=4.,\n qkv_bias=True,\n pool_method=\"fc\",\n focal_level=2,\n focal_window=(5, 9),\n norm_layer=nn.LayerNorm,\n n_vecs=None,\n t2t_params=None):\n super().__init__()\n self.dim = dim\n self.num_heads = num_heads\n self.window_size = window_size\n self.expand_size = tuple(i // 2 for i in window_size) # TODO\n self.mlp_ratio = mlp_ratio\n self.pool_method = pool_method\n self.focal_level = focal_level\n self.focal_window = focal_window\n\n self.window_size_glo = self.window_size\n\n self.pool_layers = nn.ModuleList()\n if self.pool_method != \"none\":\n for k in range(self.focal_level - 1):\n window_size_glo = tuple(\n math.floor(i / (2**k)) for i in self.window_size_glo)\n self.pool_layers.append(\n nn.Linear(window_size_glo[0] * window_size_glo[1], 1))\n self.pool_layers[-1].weight.data.fill_(\n 1. / (window_size_glo[0] * window_size_glo[1]))\n self.pool_layers[-1].bias.data.fill_(0)\n\n self.norm1 = norm_layer(dim)\n\n self.attn = WindowAttention(dim,\n expand_size=self.expand_size,\n window_size=self.window_size,\n focal_window=focal_window,\n focal_level=focal_level,\n num_heads=num_heads,\n qkv_bias=qkv_bias,\n pool_method=pool_method)\n\n self.norm2 = norm_layer(dim)\n self.mlp = FusionFeedForward(dim, n_vecs=n_vecs, t2t_params=t2t_params)\n\n def forward(self, x):\n output_size = x[1]\n x = x[0]\n\n B, T, H, W, C = x.shape\n\n shortcut = x\n x = self.norm1(x)\n\n shifted_x = x\n\n x_windows_all = [shifted_x]\n x_window_masks_all = [None]\n\n # partition windows tuple(i // 2 for i in window_size)\n if self.focal_level > 1 and self.pool_method != \"none\":\n # if we add coarser granularity and the pool method is not none\n for k in range(self.focal_level - 1):\n window_size_glo = tuple(","source_hash":"ac19b36ce0b778ac79c37bf9cbce9dd45b0a5c7cb224f047c9c4e7c29725e882","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.tfocal_transformer_hq.forward","uri":"program://Track-Anything/function/inpainter.model.modules.tfocal_transformer_hq.forward#L494-L567","kind":"function","name":"forward","path":"inpainter/model/modules/tfocal_transformer_hq.py","language":"python","start_line":494,"end_line":567,"context_start_line":474,"context_end_line":567,"code":" self.pool_layers.append(\n nn.Linear(window_size_glo[0] * window_size_glo[1], 1))\n self.pool_layers[-1].weight.data.fill_(\n 1. / (window_size_glo[0] * window_size_glo[1]))\n self.pool_layers[-1].bias.data.fill_(0)\n\n self.norm1 = norm_layer(dim)\n\n self.attn = WindowAttention(dim,\n expand_size=self.expand_size,\n window_size=self.window_size,\n focal_window=focal_window,\n focal_level=focal_level,\n num_heads=num_heads,\n qkv_bias=qkv_bias,\n pool_method=pool_method)\n\n self.norm2 = norm_layer(dim)\n self.mlp = FusionFeedForward(dim, n_vecs=n_vecs, t2t_params=t2t_params)\n\n def forward(self, x):\n output_size = x[1]\n x = x[0]\n\n B, T, H, W, C = x.shape\n\n shortcut = x\n x = self.norm1(x)\n\n shifted_x = x\n\n x_windows_all = [shifted_x]\n x_window_masks_all = [None]\n\n # partition windows tuple(i // 2 for i in window_size)\n if self.focal_level > 1 and self.pool_method != \"none\":\n # if we add coarser granularity and the pool method is not none\n for k in range(self.focal_level - 1):\n window_size_glo = tuple(\n math.floor(i / (2**k)) for i in self.window_size_glo)\n pooled_h = math.ceil(H / window_size_glo[0]) * (2**k)\n pooled_w = math.ceil(W / window_size_glo[1]) * (2**k)\n H_pool = pooled_h * window_size_glo[0]\n W_pool = pooled_w * window_size_glo[1]\n\n x_level_k = shifted_x\n # trim or pad shifted_x depending on the required size\n if H > H_pool:\n trim_t = (H - H_pool) // 2\n trim_b = H - H_pool - trim_t\n x_level_k = x_level_k[:, :, trim_t:-trim_b]\n elif H < H_pool:\n pad_t = (H_pool - H) // 2\n pad_b = H_pool - H - pad_t\n x_level_k = F.pad(x_level_k, (0, 0, 0, 0, pad_t, pad_b))\n\n if W > W_pool:\n trim_l = (W - W_pool) // 2\n trim_r = W - W_pool - trim_l\n x_level_k = x_level_k[:, :, :, trim_l:-trim_r]\n elif W < W_pool:\n pad_l = (W_pool - W) // 2\n pad_r = W_pool - W - pad_l\n x_level_k = F.pad(x_level_k, (0, 0, pad_l, pad_r))\n\n x_windows_noreshape = window_partition_noreshape(\n x_level_k.contiguous(), window_size_glo\n ) # B, nw, nw, T, window_size, window_size, C\n nWh, nWw = x_windows_noreshape.shape[1:3]\n x_windows_noreshape = x_windows_noreshape.view(\n B, nWh, nWw, T, window_size_glo[0] * window_size_glo[1],\n C).transpose(4, 5) # B, nWh, nWw, T, C, wsize**2\n x_windows_pooled = self.pool_layers[k](\n x_windows_noreshape).flatten(-2) # B, nWh, nWw, T, C\n\n x_windows_all += [x_windows_pooled]\n x_window_masks_all += [None]\n\n # nW*B, T*window_size*window_size, C\n attn_windows = self.attn(x_windows_all, mask_all=x_window_masks_all)\n\n # merge windows\n attn_windows = attn_windows.view(-1, T, self.window_size[0],\n self.window_size[1], C)\n shifted_x = window_reverse(attn_windows, self.window_size, T, H,\n W) # B T H' W' C\n\n # FFN\n x = shortcut + shifted_x\n y = self.norm2(x)\n x = x + self.mlp(y.view(B, T * H * W, C), output_size).view(\n B, T, H, W, C)\n\n return x, output_size","source_hash":"ac19b36ce0b778ac79c37bf9cbce9dd45b0a5c7cb224f047c9c4e7c29725e882","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.flow_comp","uri":"program://Track-Anything/module/inpainter.model.modules.flow_comp#L1-L450","kind":"module","name":"inpainter.model.modules.flow_comp","path":"inpainter/model/modules/flow_comp.py","language":"python","start_line":1,"end_line":450,"context_start_line":1,"context_end_line":450,"code":"import numpy as np\n\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch\n\nfrom mmcv.cnn import ConvModule\nfrom mmengine.runner import load_checkpoint\n\n\nclass FlowCompletionLoss(nn.Module):\n \"\"\"Flow completion loss\"\"\"\n def __init__(self):\n super().__init__()\n self.fix_spynet = SPyNet()\n for p in self.fix_spynet.parameters():\n p.requires_grad = False\n\n self.l1_criterion = nn.L1Loss()\n\n def forward(self, pred_flows, gt_local_frames):\n b, l_t, c, h, w = gt_local_frames.size()\n\n with torch.no_grad():\n # compute gt forward and backward flows\n gt_local_frames = F.interpolate(gt_local_frames.view(-1, c, h, w),\n scale_factor=1 / 4,\n mode='bilinear',\n align_corners=True,\n recompute_scale_factor=True)\n gt_local_frames = gt_local_frames.view(b, l_t, c, h // 4, w // 4)\n gtlf_1 = gt_local_frames[:, :-1, :, :, :].reshape(\n -1, c, h // 4, w // 4)\n gtlf_2 = gt_local_frames[:, 1:, :, :, :].reshape(\n -1, c, h // 4, w // 4)\n gt_flows_forward = self.fix_spynet(gtlf_1, gtlf_2)\n gt_flows_backward = self.fix_spynet(gtlf_2, gtlf_1)\n\n # calculate loss for flow completion\n forward_flow_loss = self.l1_criterion(\n pred_flows[0].view(-1, 2, h // 4, w // 4), gt_flows_forward)\n backward_flow_loss = self.l1_criterion(\n pred_flows[1].view(-1, 2, h // 4, w // 4), gt_flows_backward)\n flow_loss = forward_flow_loss + backward_flow_loss\n\n return flow_loss\n\n\nclass SPyNet(nn.Module):\n \"\"\"SPyNet network structure.\n The difference to the SPyNet in [tof.py] is that\n 1. more SPyNetBasicModule is used in this version, and\n 2. no batch normalization is used in this version.\n Paper:\n Optical Flow Estimation using a Spatial Pyramid Network, CVPR, 2017\n Args:\n pretrained (str): path for pre-trained SPyNet. Default: None.\n \"\"\"\n def __init__(\n self,\n use_pretrain=True,\n pretrained='https://download.openmmlab.com/mmediting/restorers/basicvsr/spynet_20210409-c6c1bd09.pth'\n ):\n super().__init__()\n\n self.basic_module = nn.ModuleList(\n [SPyNetBasicModule() for _ in range(6)])\n\n if use_pretrain:\n if isinstance(pretrained, str):\n print(\"load pretrained SPyNet...\")\n load_checkpoint(self, pretrained, strict=True)\n elif pretrained is not None:\n raise TypeError('[pretrained] should be str or None, '\n f'but got {type(pretrained)}.')\n\n self.register_buffer(\n 'mean',\n torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))\n self.register_buffer(\n 'std',\n torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))\n\n def compute_flow(self, ref, supp):\n \"\"\"Compute flow from ref to supp.\n Note that in this function, the images are already resized to a\n multiple of 32.\n Args:\n ref (Tensor): Reference image with shape of (n, 3, h, w).\n supp (Tensor): Supporting image with shape of (n, 3, h, w).\n Returns:\n Tensor: Estimated optical flow: (n, 2, h, w).\n \"\"\"\n n, _, h, w = ref.size()\n\n # normalize the input images\n ref = [(ref - self.mean) / self.std]\n supp = [(supp - self.mean) / self.std]\n\n # generate downsampled frames\n for level in range(5):\n ref.append(\n F.avg_pool2d(input=ref[-1],\n kernel_size=2,\n stride=2,\n count_include_pad=False))\n supp.append(\n F.avg_pool2d(input=supp[-1],\n kernel_size=2,\n stride=2,\n count_include_pad=False))\n ref = ref[::-1]\n supp = supp[::-1]\n\n # flow computation\n flow = ref[0].new_zeros(n, 2, h // 32, w // 32)\n for level in range(len(ref)):\n if level == 0:\n flow_up = flow\n else:\n flow_up = F.interpolate(input=flow,\n scale_factor=2,\n mode='bilinear',\n align_corners=True) * 2.0\n\n # add the residue to the upsampled flow\n flow = flow_up + self.basic_module[level](torch.cat([\n ref[level],\n flow_warp(supp[level],\n flow_up.permute(0, 2, 3, 1).contiguous(),\n padding_mode='border'), flow_up\n ], 1))\n\n return flow\n\n def forward(self, ref, supp):\n \"\"\"Forward function of SPyNet.\n This function computes the optical flow from ref to supp.\n Args:\n ref (Tensor): Reference image with shape of (n, 3, h, w).\n supp (Tensor): Supporting image with shape of (n, 3, h, w).\n Returns:\n Tensor: Estimated optical flow: (n, 2, h, w).\n \"\"\"\n\n # upsize to a multiple of 32\n h, w = ref.shape[2:4]\n w_up = w if (w % 32) == 0 else 32 * (w // 32 + 1)\n h_up = h if (h % 32) == 0 else 32 * (h // 32 + 1)\n ref = F.interpolate(input=ref,\n size=(h_up, w_up),\n mode='bilinear',\n align_corners=False)\n supp = F.interpolate(input=supp,\n size=(h_up, w_up),\n mode='bilinear',\n align_corners=False)\n\n # compute flow, and resize back to the original resolution\n flow = F.interpolate(input=self.compute_flow(ref, supp),\n size=(h, w),\n mode='bilinear',\n align_corners=False)\n\n # adjust the flow values\n flow[:, 0, :, :] *= float(w) / float(w_up)\n flow[:, 1, :, :] *= float(h) / float(h_up)\n\n return flow\n\n\nclass SPyNetBasicModule(nn.Module):\n \"\"\"Basic Module for SPyNet.\n Paper:\n Optical Flow Estimation using a Spatial Pyramid Network, CVPR, 2017\n \"\"\"\n def __init__(self):\n super().__init__()\n\n self.basic_module = nn.Sequential(\n ConvModule(in_channels=8,\n out_channels=32,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=dict(type='ReLU')),\n ConvModule(in_channels=32,\n out_channels=64,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=dict(type='ReLU')),\n ConvModule(in_channels=64,\n out_channels=32,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=dict(type='ReLU')),\n ConvModule(in_channels=32,\n out_channels=16,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=dict(type='ReLU')),\n ConvModule(in_channels=16,\n out_channels=2,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=None))\n\n def forward(self, tensor_input):\n \"\"\"\n Args:\n tensor_input (Tensor): Input tensor with shape (b, 8, h, w).\n 8 channels contain:\n [reference image (3), neighbor image (3), initial flow (2)].\n Returns:\n Tensor: Refined flow with shape (b, 2, h, w)\n \"\"\"\n return self.basic_module(tensor_input)\n\n\n# Flow visualization code used from https://github.com/tomrunia/OpticalFlow_Visualization\ndef make_colorwheel():\n \"\"\"\n Generates a color wheel for optical flow visualization as presented in:\n Baker et al. \"A Database and Evaluation Methodology for Optical Flow\" (ICCV, 2007)\n URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf\n\n Code follows the original C++ source code of Daniel Scharstein.\n Code follows the the Matlab source code of Deqing Sun.\n\n Returns:\n np.ndarray: Color wheel\n \"\"\"\n\n RY = 15\n YG = 6\n GC = 4\n CB = 11\n BM = 13\n MR = 6\n\n ncols = RY + YG + GC + CB + BM + MR\n colorwheel = np.zeros((ncols, 3))\n col = 0\n\n # RY\n colorwheel[0:RY, 0] = 255\n colorwheel[0:RY, 1] = np.floor(255 * np.arange(0, RY) / RY)\n col = col + RY\n # YG\n colorwheel[col:col + YG, 0] = 255 - np.floor(255 * np.arange(0, YG) / YG)\n colorwheel[col:col + YG, 1] = 255\n col = col + YG\n # GC\n colorwheel[col:col + GC, 1] = 255\n colorwheel[col:col + GC, 2] = np.floor(255 * np.arange(0, GC) / GC)\n col = col + GC\n # CB\n colorwheel[col:col + CB, 1] = 255 - np.floor(255 * np.arange(CB) / CB)\n colorwheel[col:col + CB, 2] = 255\n col = col + CB\n # BM\n colorwheel[col:col + BM, 2] = 255\n colorwheel[col:col + BM, 0] = np.floor(255 * np.arange(0, BM) / BM)\n col = col + BM\n # MR\n colorwheel[col:col + MR, 2] = 255 - np.floor(255 * np.arange(MR) / MR)\n colorwheel[col:col + MR, 0] = 255\n return colorwheel\n\n\ndef flow_uv_to_colors(u, v, convert_to_bgr=False):\n \"\"\"\n Applies the flow color wheel to (possibly clipped) flow components u and v.\n\n According to the C++ source code of Daniel Scharstein\n According to the Matlab source code of Deqing Sun\n\n Args:\n u (np.ndarray): Input horizontal flow of shape [H,W]\n v (np.ndarray): Input vertical flow of shape [H,W]\n convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.\n\n Returns:\n np.ndarray: Flow visualization image of shape [H,W,3]\n \"\"\"\n flow_image = np.zeros((u.shape[0], u.shape[1], 3), np.uint8)\n colorwheel = make_colorwheel() # shape [55x3]\n ncols = colorwheel.shape[0]\n rad = np.sqrt(np.square(u) + np.square(v))\n a = np.arctan2(-v, -u) / np.pi\n fk = (a + 1) / 2 * (ncols - 1)\n k0 = np.floor(fk).astype(np.int32)\n k1 = k0 + 1\n k1[k1 == ncols] = 0\n f = fk - k0\n for i in range(colorwheel.shape[1]):\n tmp = colorwheel[:, i]\n col0 = tmp[k0] / 255.0\n col1 = tmp[k1] / 255.0\n col = (1 - f) * col0 + f * col1\n idx = (rad <= 1)\n col[idx] = 1 - rad[idx] * (1 - col[idx])\n col[~idx] = col[~idx] * 0.75 # out of range\n # Note the 2-i => BGR instead of RGB\n ch_idx = 2 - i if convert_to_bgr else i\n flow_image[:, :, ch_idx] = np.floor(255 * col)\n return flow_image\n\n\ndef flow_to_image(flow_uv, clip_flow=None, convert_to_bgr=False):\n \"\"\"\n Expects a two dimensional flow image of shape.\n\n Args:\n flow_uv (np.ndarray): Flow UV image of shape [H,W,2]\n clip_flow (float, optional): Clip maximum of flow values. Defaults to None.\n convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.\n\n Returns:\n np.ndarray: Flow visualization image of shape [H,W,3]\n \"\"\"\n assert flow_uv.ndim == 3, 'input flow must have three dimensions'\n assert flow_uv.shape[2] == 2, 'input flow must have shape [H,W,2]'\n if clip_flow is not None:\n flow_uv = np.clip(flow_uv, 0, clip_flow)\n u = flow_uv[:, :, 0]\n v = flow_uv[:, :, 1]\n rad = np.sqrt(np.square(u) + np.square(v))\n rad_max = np.max(rad)\n epsilon = 1e-5\n u = u / (rad_max + epsilon)\n v = v / (rad_max + epsilon)\n return flow_uv_to_colors(u, v, convert_to_bgr)\n\n\ndef flow_warp(x,\n flow,\n interpolation='bilinear',\n padding_mode='zeros',\n align_corners=True):\n \"\"\"Warp an image or a feature map with optical flow.\n Args:\n x (Tensor): Tensor with size (n, c, h, w).\n flow (Tensor): Tensor with size (n, h, w, 2). The last dimension is\n a two-channel, denoting the width and height relative offsets.\n Note that the values are not normalized to [-1, 1].\n interpolation (str): Interpolation mode: 'nearest' or 'bilinear'.\n Default: 'bilinear'.\n padding_mode (str): Padding mode: 'zeros' or 'border' or 'reflection'.\n Default: 'zeros'.\n align_corners (bool): Whether align corners. Default: True.\n Returns:\n Tensor: Warped image or feature map.\n \"\"\"\n if x.size()[-2:] != flow.size()[1:3]:\n raise ValueError(f'The spatial sizes of input ({x.size()[-2:]}) and '\n f'flow ({flow.size()[1:3]}) are not the same.')\n _, _, h, w = x.size()\n # create mesh grid\n grid_y, grid_x = torch.meshgrid(torch.arange(0, h), torch.arange(0, w))\n grid = torch.stack((grid_x, grid_y), 2).type_as(x) # (w, h, 2)\n grid.requires_grad = False\n\n grid_flow = grid + flow\n # scale grid_flow to [-1,1]\n grid_flow_x = 2.0 * grid_flow[:, :, :, 0] / max(w - 1, 1) - 1.0\n grid_flow_y = 2.0 * grid_flow[:, :, :, 1] / max(h - 1, 1) - 1.0\n grid_flow = torch.stack((grid_flow_x, grid_flow_y), dim=3)\n output = F.grid_sample(x,\n grid_flow,\n mode=interpolation,\n padding_mode=padding_mode,\n align_corners=align_corners)\n return output\n\n\ndef initial_mask_flow(mask):\n \"\"\"\n mask 1 indicates valid pixel 0 indicates unknown pixel\n \"\"\"\n B, T, C, H, W = mask.shape\n\n # calculate relative position\n grid_y, grid_x = torch.meshgrid(torch.arange(0, H), torch.arange(0, W))\n\n grid_y, grid_x = grid_y.type_as(mask), grid_x.type_as(mask)\n abs_relative_pos_y = H - torch.abs(grid_y[None, :, :] - grid_y[:, None, :])\n relative_pos_y = H - (grid_y[None, :, :] - grid_y[:, None, :])\n\n abs_relative_pos_x = W - torch.abs(grid_x[:, None, :] - grid_x[:, :, None])\n relative_pos_x = W - (grid_x[:, None, :] - grid_x[:, :, None])\n\n # calculate the nearest indices\n pos_up = mask.unsqueeze(3).repeat(\n 1, 1, 1, H, 1, 1).flip(4) * abs_relative_pos_y[None, None, None] * (\n relative_pos_y <= H)[None, None, None]\n nearest_indice_up = pos_up.max(dim=4)[1]\n\n pos_down = mask.unsqueeze(3).repeat(1, 1, 1, H, 1, 1) * abs_relative_pos_y[\n None, None, None] * (relative_pos_y <= H)[None, None, None]\n nearest_indice_down = (pos_down).max(dim=4)[1]\n\n pos_left = mask.unsqueeze(4).repeat(\n 1, 1, 1, 1, W, 1).flip(5) * abs_relative_pos_x[None, None, None] * (\n relative_pos_x <= W)[None, None, None]\n nearest_indice_left = (pos_left).max(dim=5)[1]\n\n pos_right = mask.unsqueeze(4).repeat(\n 1, 1, 1, 1, W, 1) * abs_relative_pos_x[None, None, None] * (\n relative_pos_x <= W)[None, None, None]\n nearest_indice_right = (pos_right).max(dim=5)[1]\n\n # NOTE: IMPORTANT !!! depending on how to use this offset\n initial_offset_up = -(nearest_indice_up - grid_y[None, None, None]).flip(3)\n initial_offset_down = nearest_indice_down - grid_y[None, None, None]\n\n initial_offset_left = -(nearest_indice_left -\n grid_x[None, None, None]).flip(4)\n initial_offset_right = nearest_indice_right - grid_x[None, None, None]\n\n # nearest_indice_x = (mask.unsqueeze(1).repeat(1, img_width, 1) * relative_pos_x).max(dim=2)[1]\n # initial_offset_x = nearest_indice_x - grid_x\n\n # handle the boundary cases\n final_offset_down = (initial_offset_down < 0) * initial_offset_up + (\n initial_offset_down > 0) * initial_offset_down\n final_offset_up = (initial_offset_up > 0) * initial_offset_down + (\n initial_offset_up < 0) * initial_offset_up\n final_offset_right = (initial_offset_right < 0) * initial_offset_left + (\n initial_offset_right > 0) * initial_offset_right\n final_offset_left = (initial_offset_left > 0) * initial_offset_right + (\n initial_offset_left < 0) * initial_offset_left\n zero_offset = torch.zeros_like(final_offset_down)\n # out = torch.cat([final_offset_left, zero_offset, final_offset_right, zero_offset, zero_offset, final_offset_up, zero_offset, final_offset_down], dim=2)\n out = torch.cat([\n zero_offset, final_offset_left, zero_offset, final_offset_right,\n final_offset_up, zero_offset, final_offset_down, zero_offset\n ],\n dim=2)\n\n return out","source_hash":"b7598bbabf8f6af1a5f6ab2d6af3783f28fdbfe9d8bb6698620a6f77223b382b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.flow_comp.FlowCompletionLoss","uri":"program://Track-Anything/class/inpainter.model.modules.flow_comp.FlowCompletionLoss#L11-L46","kind":"class","name":"FlowCompletionLoss","path":"inpainter/model/modules/flow_comp.py","language":"python","start_line":11,"end_line":46,"context_start_line":1,"context_end_line":66,"code":"import numpy as np\n\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch\n\nfrom mmcv.cnn import ConvModule\nfrom mmengine.runner import load_checkpoint\n\n\nclass FlowCompletionLoss(nn.Module):\n \"\"\"Flow completion loss\"\"\"\n def __init__(self):\n super().__init__()\n self.fix_spynet = SPyNet()\n for p in self.fix_spynet.parameters():\n p.requires_grad = False\n\n self.l1_criterion = nn.L1Loss()\n\n def forward(self, pred_flows, gt_local_frames):\n b, l_t, c, h, w = gt_local_frames.size()\n\n with torch.no_grad():\n # compute gt forward and backward flows\n gt_local_frames = F.interpolate(gt_local_frames.view(-1, c, h, w),\n scale_factor=1 / 4,\n mode='bilinear',\n align_corners=True,\n recompute_scale_factor=True)\n gt_local_frames = gt_local_frames.view(b, l_t, c, h // 4, w // 4)\n gtlf_1 = gt_local_frames[:, :-1, :, :, :].reshape(\n -1, c, h // 4, w // 4)\n gtlf_2 = gt_local_frames[:, 1:, :, :, :].reshape(\n -1, c, h // 4, w // 4)\n gt_flows_forward = self.fix_spynet(gtlf_1, gtlf_2)\n gt_flows_backward = self.fix_spynet(gtlf_2, gtlf_1)\n\n # calculate loss for flow completion\n forward_flow_loss = self.l1_criterion(\n pred_flows[0].view(-1, 2, h // 4, w // 4), gt_flows_forward)\n backward_flow_loss = self.l1_criterion(\n pred_flows[1].view(-1, 2, h // 4, w // 4), gt_flows_backward)\n flow_loss = forward_flow_loss + backward_flow_loss\n\n return flow_loss\n\n\nclass SPyNet(nn.Module):\n \"\"\"SPyNet network structure.\n The difference to the SPyNet in [tof.py] is that\n 1. more SPyNetBasicModule is used in this version, and\n 2. no batch normalization is used in this version.\n Paper:\n Optical Flow Estimation using a Spatial Pyramid Network, CVPR, 2017\n Args:\n pretrained (str): path for pre-trained SPyNet. Default: None.\n \"\"\"\n def __init__(\n self,\n use_pretrain=True,\n pretrained='https://download.openmmlab.com/mmediting/restorers/basicvsr/spynet_20210409-c6c1bd09.pth'\n ):\n super().__init__()\n\n self.basic_module = nn.ModuleList(","source_hash":"b7598bbabf8f6af1a5f6ab2d6af3783f28fdbfe9d8bb6698620a6f77223b382b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.flow_comp.SPyNet","uri":"program://Track-Anything/class/inpainter.model.modules.flow_comp.SPyNet#L49-L169","kind":"class","name":"SPyNet","path":"inpainter/model/modules/flow_comp.py","language":"python","start_line":49,"end_line":169,"context_start_line":29,"context_end_line":189,"code":" align_corners=True,\n recompute_scale_factor=True)\n gt_local_frames = gt_local_frames.view(b, l_t, c, h // 4, w // 4)\n gtlf_1 = gt_local_frames[:, :-1, :, :, :].reshape(\n -1, c, h // 4, w // 4)\n gtlf_2 = gt_local_frames[:, 1:, :, :, :].reshape(\n -1, c, h // 4, w // 4)\n gt_flows_forward = self.fix_spynet(gtlf_1, gtlf_2)\n gt_flows_backward = self.fix_spynet(gtlf_2, gtlf_1)\n\n # calculate loss for flow completion\n forward_flow_loss = self.l1_criterion(\n pred_flows[0].view(-1, 2, h // 4, w // 4), gt_flows_forward)\n backward_flow_loss = self.l1_criterion(\n pred_flows[1].view(-1, 2, h // 4, w // 4), gt_flows_backward)\n flow_loss = forward_flow_loss + backward_flow_loss\n\n return flow_loss\n\n\nclass SPyNet(nn.Module):\n \"\"\"SPyNet network structure.\n The difference to the SPyNet in [tof.py] is that\n 1. more SPyNetBasicModule is used in this version, and\n 2. no batch normalization is used in this version.\n Paper:\n Optical Flow Estimation using a Spatial Pyramid Network, CVPR, 2017\n Args:\n pretrained (str): path for pre-trained SPyNet. Default: None.\n \"\"\"\n def __init__(\n self,\n use_pretrain=True,\n pretrained='https://download.openmmlab.com/mmediting/restorers/basicvsr/spynet_20210409-c6c1bd09.pth'\n ):\n super().__init__()\n\n self.basic_module = nn.ModuleList(\n [SPyNetBasicModule() for _ in range(6)])\n\n if use_pretrain:\n if isinstance(pretrained, str):\n print(\"load pretrained SPyNet...\")\n load_checkpoint(self, pretrained, strict=True)\n elif pretrained is not None:\n raise TypeError('[pretrained] should be str or None, '\n f'but got {type(pretrained)}.')\n\n self.register_buffer(\n 'mean',\n torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))\n self.register_buffer(\n 'std',\n torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))\n\n def compute_flow(self, ref, supp):\n \"\"\"Compute flow from ref to supp.\n Note that in this function, the images are already resized to a\n multiple of 32.\n Args:\n ref (Tensor): Reference image with shape of (n, 3, h, w).\n supp (Tensor): Supporting image with shape of (n, 3, h, w).\n Returns:\n Tensor: Estimated optical flow: (n, 2, h, w).\n \"\"\"\n n, _, h, w = ref.size()\n\n # normalize the input images\n ref = [(ref - self.mean) / self.std]\n supp = [(supp - self.mean) / self.std]\n\n # generate downsampled frames\n for level in range(5):\n ref.append(\n F.avg_pool2d(input=ref[-1],\n kernel_size=2,\n stride=2,\n count_include_pad=False))\n supp.append(\n F.avg_pool2d(input=supp[-1],\n kernel_size=2,\n stride=2,\n count_include_pad=False))\n ref = ref[::-1]\n supp = supp[::-1]\n\n # flow computation\n flow = ref[0].new_zeros(n, 2, h // 32, w // 32)\n for level in range(len(ref)):\n if level == 0:\n flow_up = flow\n else:\n flow_up = F.interpolate(input=flow,\n scale_factor=2,\n mode='bilinear',\n align_corners=True) * 2.0\n\n # add the residue to the upsampled flow\n flow = flow_up + self.basic_module[level](torch.cat([\n ref[level],\n flow_warp(supp[level],\n flow_up.permute(0, 2, 3, 1).contiguous(),\n padding_mode='border'), flow_up\n ], 1))\n\n return flow\n\n def forward(self, ref, supp):\n \"\"\"Forward function of SPyNet.\n This function computes the optical flow from ref to supp.\n Args:\n ref (Tensor): Reference image with shape of (n, 3, h, w).\n supp (Tensor): Supporting image with shape of (n, 3, h, w).\n Returns:\n Tensor: Estimated optical flow: (n, 2, h, w).\n \"\"\"\n\n # upsize to a multiple of 32\n h, w = ref.shape[2:4]\n w_up = w if (w % 32) == 0 else 32 * (w // 32 + 1)\n h_up = h if (h % 32) == 0 else 32 * (h // 32 + 1)\n ref = F.interpolate(input=ref,\n size=(h_up, w_up),\n mode='bilinear',\n align_corners=False)\n supp = F.interpolate(input=supp,\n size=(h_up, w_up),\n mode='bilinear',\n align_corners=False)\n\n # compute flow, and resize back to the original resolution\n flow = F.interpolate(input=self.compute_flow(ref, supp),\n size=(h, w),\n mode='bilinear',\n align_corners=False)\n\n # adjust the flow values\n flow[:, 0, :, :] *= float(w) / float(w_up)\n flow[:, 1, :, :] *= float(h) / float(h_up)\n\n return flow\n\n\nclass SPyNetBasicModule(nn.Module):\n \"\"\"Basic Module for SPyNet.\n Paper:\n Optical Flow Estimation using a Spatial Pyramid Network, CVPR, 2017\n \"\"\"\n def __init__(self):\n super().__init__()\n\n self.basic_module = nn.Sequential(\n ConvModule(in_channels=8,\n out_channels=32,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=dict(type='ReLU')),\n ConvModule(in_channels=32,\n out_channels=64,","source_hash":"b7598bbabf8f6af1a5f6ab2d6af3783f28fdbfe9d8bb6698620a6f77223b382b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.flow_comp.SPyNetBasicModule","uri":"program://Track-Anything/class/inpainter.model.modules.flow_comp.SPyNetBasicModule#L172-L226","kind":"class","name":"SPyNetBasicModule","path":"inpainter/model/modules/flow_comp.py","language":"python","start_line":172,"end_line":226,"context_start_line":152,"context_end_line":246,"code":" mode='bilinear',\n align_corners=False)\n supp = F.interpolate(input=supp,\n size=(h_up, w_up),\n mode='bilinear',\n align_corners=False)\n\n # compute flow, and resize back to the original resolution\n flow = F.interpolate(input=self.compute_flow(ref, supp),\n size=(h, w),\n mode='bilinear',\n align_corners=False)\n\n # adjust the flow values\n flow[:, 0, :, :] *= float(w) / float(w_up)\n flow[:, 1, :, :] *= float(h) / float(h_up)\n\n return flow\n\n\nclass SPyNetBasicModule(nn.Module):\n \"\"\"Basic Module for SPyNet.\n Paper:\n Optical Flow Estimation using a Spatial Pyramid Network, CVPR, 2017\n \"\"\"\n def __init__(self):\n super().__init__()\n\n self.basic_module = nn.Sequential(\n ConvModule(in_channels=8,\n out_channels=32,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=dict(type='ReLU')),\n ConvModule(in_channels=32,\n out_channels=64,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=dict(type='ReLU')),\n ConvModule(in_channels=64,\n out_channels=32,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=dict(type='ReLU')),\n ConvModule(in_channels=32,\n out_channels=16,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=dict(type='ReLU')),\n ConvModule(in_channels=16,\n out_channels=2,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=None))\n\n def forward(self, tensor_input):\n \"\"\"\n Args:\n tensor_input (Tensor): Input tensor with shape (b, 8, h, w).\n 8 channels contain:\n [reference image (3), neighbor image (3), initial flow (2)].\n Returns:\n Tensor: Refined flow with shape (b, 2, h, w)\n \"\"\"\n return self.basic_module(tensor_input)\n\n\n# Flow visualization code used from https://github.com/tomrunia/OpticalFlow_Visualization\ndef make_colorwheel():\n \"\"\"\n Generates a color wheel for optical flow visualization as presented in:\n Baker et al. \"A Database and Evaluation Methodology for Optical Flow\" (ICCV, 2007)\n URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf\n\n Code follows the original C++ source code of Daniel Scharstein.\n Code follows the the Matlab source code of Deqing Sun.\n\n Returns:\n np.ndarray: Color wheel\n \"\"\"\n\n RY = 15\n YG = 6\n GC = 4\n CB = 11","source_hash":"b7598bbabf8f6af1a5f6ab2d6af3783f28fdbfe9d8bb6698620a6f77223b382b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.flow_comp.make_colorwheel","uri":"program://Track-Anything/function/inpainter.model.modules.flow_comp.make_colorwheel#L230-L277","kind":"function","name":"make_colorwheel","path":"inpainter/model/modules/flow_comp.py","language":"python","start_line":230,"end_line":277,"context_start_line":210,"context_end_line":297,"code":" out_channels=2,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=None))\n\n def forward(self, tensor_input):\n \"\"\"\n Args:\n tensor_input (Tensor): Input tensor with shape (b, 8, h, w).\n 8 channels contain:\n [reference image (3), neighbor image (3), initial flow (2)].\n Returns:\n Tensor: Refined flow with shape (b, 2, h, w)\n \"\"\"\n return self.basic_module(tensor_input)\n\n\n# Flow visualization code used from https://github.com/tomrunia/OpticalFlow_Visualization\ndef make_colorwheel():\n \"\"\"\n Generates a color wheel for optical flow visualization as presented in:\n Baker et al. \"A Database and Evaluation Methodology for Optical Flow\" (ICCV, 2007)\n URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf\n\n Code follows the original C++ source code of Daniel Scharstein.\n Code follows the the Matlab source code of Deqing Sun.\n\n Returns:\n np.ndarray: Color wheel\n \"\"\"\n\n RY = 15\n YG = 6\n GC = 4\n CB = 11\n BM = 13\n MR = 6\n\n ncols = RY + YG + GC + CB + BM + MR\n colorwheel = np.zeros((ncols, 3))\n col = 0\n\n # RY\n colorwheel[0:RY, 0] = 255\n colorwheel[0:RY, 1] = np.floor(255 * np.arange(0, RY) / RY)\n col = col + RY\n # YG\n colorwheel[col:col + YG, 0] = 255 - np.floor(255 * np.arange(0, YG) / YG)\n colorwheel[col:col + YG, 1] = 255\n col = col + YG\n # GC\n colorwheel[col:col + GC, 1] = 255\n colorwheel[col:col + GC, 2] = np.floor(255 * np.arange(0, GC) / GC)\n col = col + GC\n # CB\n colorwheel[col:col + CB, 1] = 255 - np.floor(255 * np.arange(CB) / CB)\n colorwheel[col:col + CB, 2] = 255\n col = col + CB\n # BM\n colorwheel[col:col + BM, 2] = 255\n colorwheel[col:col + BM, 0] = np.floor(255 * np.arange(0, BM) / BM)\n col = col + BM\n # MR\n colorwheel[col:col + MR, 2] = 255 - np.floor(255 * np.arange(MR) / MR)\n colorwheel[col:col + MR, 0] = 255\n return colorwheel\n\n\ndef flow_uv_to_colors(u, v, convert_to_bgr=False):\n \"\"\"\n Applies the flow color wheel to (possibly clipped) flow components u and v.\n\n According to the C++ source code of Daniel Scharstein\n According to the Matlab source code of Deqing Sun\n\n Args:\n u (np.ndarray): Input horizontal flow of shape [H,W]\n v (np.ndarray): Input vertical flow of shape [H,W]\n convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.\n\n Returns:\n np.ndarray: Flow visualization image of shape [H,W,3]\n \"\"\"\n flow_image = np.zeros((u.shape[0], u.shape[1], 3), np.uint8)\n colorwheel = make_colorwheel() # shape [55x3]\n ncols = colorwheel.shape[0]","source_hash":"b7598bbabf8f6af1a5f6ab2d6af3783f28fdbfe9d8bb6698620a6f77223b382b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.flow_comp.flow_uv_to_colors","uri":"program://Track-Anything/function/inpainter.model.modules.flow_comp.flow_uv_to_colors#L280-L316","kind":"function","name":"flow_uv_to_colors","path":"inpainter/model/modules/flow_comp.py","language":"python","start_line":280,"end_line":316,"context_start_line":260,"context_end_line":336,"code":" colorwheel[col:col + YG, 1] = 255\n col = col + YG\n # GC\n colorwheel[col:col + GC, 1] = 255\n colorwheel[col:col + GC, 2] = np.floor(255 * np.arange(0, GC) / GC)\n col = col + GC\n # CB\n colorwheel[col:col + CB, 1] = 255 - np.floor(255 * np.arange(CB) / CB)\n colorwheel[col:col + CB, 2] = 255\n col = col + CB\n # BM\n colorwheel[col:col + BM, 2] = 255\n colorwheel[col:col + BM, 0] = np.floor(255 * np.arange(0, BM) / BM)\n col = col + BM\n # MR\n colorwheel[col:col + MR, 2] = 255 - np.floor(255 * np.arange(MR) / MR)\n colorwheel[col:col + MR, 0] = 255\n return colorwheel\n\n\ndef flow_uv_to_colors(u, v, convert_to_bgr=False):\n \"\"\"\n Applies the flow color wheel to (possibly clipped) flow components u and v.\n\n According to the C++ source code of Daniel Scharstein\n According to the Matlab source code of Deqing Sun\n\n Args:\n u (np.ndarray): Input horizontal flow of shape [H,W]\n v (np.ndarray): Input vertical flow of shape [H,W]\n convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.\n\n Returns:\n np.ndarray: Flow visualization image of shape [H,W,3]\n \"\"\"\n flow_image = np.zeros((u.shape[0], u.shape[1], 3), np.uint8)\n colorwheel = make_colorwheel() # shape [55x3]\n ncols = colorwheel.shape[0]\n rad = np.sqrt(np.square(u) + np.square(v))\n a = np.arctan2(-v, -u) / np.pi\n fk = (a + 1) / 2 * (ncols - 1)\n k0 = np.floor(fk).astype(np.int32)\n k1 = k0 + 1\n k1[k1 == ncols] = 0\n f = fk - k0\n for i in range(colorwheel.shape[1]):\n tmp = colorwheel[:, i]\n col0 = tmp[k0] / 255.0\n col1 = tmp[k1] / 255.0\n col = (1 - f) * col0 + f * col1\n idx = (rad <= 1)\n col[idx] = 1 - rad[idx] * (1 - col[idx])\n col[~idx] = col[~idx] * 0.75 # out of range\n # Note the 2-i => BGR instead of RGB\n ch_idx = 2 - i if convert_to_bgr else i\n flow_image[:, :, ch_idx] = np.floor(255 * col)\n return flow_image\n\n\ndef flow_to_image(flow_uv, clip_flow=None, convert_to_bgr=False):\n \"\"\"\n Expects a two dimensional flow image of shape.\n\n Args:\n flow_uv (np.ndarray): Flow UV image of shape [H,W,2]\n clip_flow (float, optional): Clip maximum of flow values. Defaults to None.\n convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.\n\n Returns:\n np.ndarray: Flow visualization image of shape [H,W,3]\n \"\"\"\n assert flow_uv.ndim == 3, 'input flow must have three dimensions'\n assert flow_uv.shape[2] == 2, 'input flow must have shape [H,W,2]'\n if clip_flow is not None:\n flow_uv = np.clip(flow_uv, 0, clip_flow)\n u = flow_uv[:, :, 0]\n v = flow_uv[:, :, 1]","source_hash":"b7598bbabf8f6af1a5f6ab2d6af3783f28fdbfe9d8bb6698620a6f77223b382b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.flow_comp.flow_to_image","uri":"program://Track-Anything/function/inpainter.model.modules.flow_comp.flow_to_image#L319-L342","kind":"function","name":"flow_to_image","path":"inpainter/model/modules/flow_comp.py","language":"python","start_line":319,"end_line":342,"context_start_line":299,"context_end_line":362,"code":" a = np.arctan2(-v, -u) / np.pi\n fk = (a + 1) / 2 * (ncols - 1)\n k0 = np.floor(fk).astype(np.int32)\n k1 = k0 + 1\n k1[k1 == ncols] = 0\n f = fk - k0\n for i in range(colorwheel.shape[1]):\n tmp = colorwheel[:, i]\n col0 = tmp[k0] / 255.0\n col1 = tmp[k1] / 255.0\n col = (1 - f) * col0 + f * col1\n idx = (rad <= 1)\n col[idx] = 1 - rad[idx] * (1 - col[idx])\n col[~idx] = col[~idx] * 0.75 # out of range\n # Note the 2-i => BGR instead of RGB\n ch_idx = 2 - i if convert_to_bgr else i\n flow_image[:, :, ch_idx] = np.floor(255 * col)\n return flow_image\n\n\ndef flow_to_image(flow_uv, clip_flow=None, convert_to_bgr=False):\n \"\"\"\n Expects a two dimensional flow image of shape.\n\n Args:\n flow_uv (np.ndarray): Flow UV image of shape [H,W,2]\n clip_flow (float, optional): Clip maximum of flow values. Defaults to None.\n convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.\n\n Returns:\n np.ndarray: Flow visualization image of shape [H,W,3]\n \"\"\"\n assert flow_uv.ndim == 3, 'input flow must have three dimensions'\n assert flow_uv.shape[2] == 2, 'input flow must have shape [H,W,2]'\n if clip_flow is not None:\n flow_uv = np.clip(flow_uv, 0, clip_flow)\n u = flow_uv[:, :, 0]\n v = flow_uv[:, :, 1]\n rad = np.sqrt(np.square(u) + np.square(v))\n rad_max = np.max(rad)\n epsilon = 1e-5\n u = u / (rad_max + epsilon)\n v = v / (rad_max + epsilon)\n return flow_uv_to_colors(u, v, convert_to_bgr)\n\n\ndef flow_warp(x,\n flow,\n interpolation='bilinear',\n padding_mode='zeros',\n align_corners=True):\n \"\"\"Warp an image or a feature map with optical flow.\n Args:\n x (Tensor): Tensor with size (n, c, h, w).\n flow (Tensor): Tensor with size (n, h, w, 2). The last dimension is\n a two-channel, denoting the width and height relative offsets.\n Note that the values are not normalized to [-1, 1].\n interpolation (str): Interpolation mode: 'nearest' or 'bilinear'.\n Default: 'bilinear'.\n padding_mode (str): Padding mode: 'zeros' or 'border' or 'reflection'.\n Default: 'zeros'.\n align_corners (bool): Whether align corners. Default: True.\n Returns:\n Tensor: Warped image or feature map.","source_hash":"b7598bbabf8f6af1a5f6ab2d6af3783f28fdbfe9d8bb6698620a6f77223b382b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.flow_comp.flow_warp","uri":"program://Track-Anything/function/inpainter.model.modules.flow_comp.flow_warp#L345-L383","kind":"function","name":"flow_warp","path":"inpainter/model/modules/flow_comp.py","language":"python","start_line":345,"end_line":383,"context_start_line":325,"context_end_line":403,"code":" clip_flow (float, optional): Clip maximum of flow values. Defaults to None.\n convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.\n\n Returns:\n np.ndarray: Flow visualization image of shape [H,W,3]\n \"\"\"\n assert flow_uv.ndim == 3, 'input flow must have three dimensions'\n assert flow_uv.shape[2] == 2, 'input flow must have shape [H,W,2]'\n if clip_flow is not None:\n flow_uv = np.clip(flow_uv, 0, clip_flow)\n u = flow_uv[:, :, 0]\n v = flow_uv[:, :, 1]\n rad = np.sqrt(np.square(u) + np.square(v))\n rad_max = np.max(rad)\n epsilon = 1e-5\n u = u / (rad_max + epsilon)\n v = v / (rad_max + epsilon)\n return flow_uv_to_colors(u, v, convert_to_bgr)\n\n\ndef flow_warp(x,\n flow,\n interpolation='bilinear',\n padding_mode='zeros',\n align_corners=True):\n \"\"\"Warp an image or a feature map with optical flow.\n Args:\n x (Tensor): Tensor with size (n, c, h, w).\n flow (Tensor): Tensor with size (n, h, w, 2). The last dimension is\n a two-channel, denoting the width and height relative offsets.\n Note that the values are not normalized to [-1, 1].\n interpolation (str): Interpolation mode: 'nearest' or 'bilinear'.\n Default: 'bilinear'.\n padding_mode (str): Padding mode: 'zeros' or 'border' or 'reflection'.\n Default: 'zeros'.\n align_corners (bool): Whether align corners. Default: True.\n Returns:\n Tensor: Warped image or feature map.\n \"\"\"\n if x.size()[-2:] != flow.size()[1:3]:\n raise ValueError(f'The spatial sizes of input ({x.size()[-2:]}) and '\n f'flow ({flow.size()[1:3]}) are not the same.')\n _, _, h, w = x.size()\n # create mesh grid\n grid_y, grid_x = torch.meshgrid(torch.arange(0, h), torch.arange(0, w))\n grid = torch.stack((grid_x, grid_y), 2).type_as(x) # (w, h, 2)\n grid.requires_grad = False\n\n grid_flow = grid + flow\n # scale grid_flow to [-1,1]\n grid_flow_x = 2.0 * grid_flow[:, :, :, 0] / max(w - 1, 1) - 1.0\n grid_flow_y = 2.0 * grid_flow[:, :, :, 1] / max(h - 1, 1) - 1.0\n grid_flow = torch.stack((grid_flow_x, grid_flow_y), dim=3)\n output = F.grid_sample(x,\n grid_flow,\n mode=interpolation,\n padding_mode=padding_mode,\n align_corners=align_corners)\n return output\n\n\ndef initial_mask_flow(mask):\n \"\"\"\n mask 1 indicates valid pixel 0 indicates unknown pixel\n \"\"\"\n B, T, C, H, W = mask.shape\n\n # calculate relative position\n grid_y, grid_x = torch.meshgrid(torch.arange(0, H), torch.arange(0, W))\n\n grid_y, grid_x = grid_y.type_as(mask), grid_x.type_as(mask)\n abs_relative_pos_y = H - torch.abs(grid_y[None, :, :] - grid_y[:, None, :])\n relative_pos_y = H - (grid_y[None, :, :] - grid_y[:, None, :])\n\n abs_relative_pos_x = W - torch.abs(grid_x[:, None, :] - grid_x[:, :, None])\n relative_pos_x = W - (grid_x[:, None, :] - grid_x[:, :, None])\n\n # calculate the nearest indices\n pos_up = mask.unsqueeze(3).repeat(","source_hash":"b7598bbabf8f6af1a5f6ab2d6af3783f28fdbfe9d8bb6698620a6f77223b382b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.flow_comp.initial_mask_flow","uri":"program://Track-Anything/function/inpainter.model.modules.flow_comp.initial_mask_flow#L386-L450","kind":"function","name":"initial_mask_flow","path":"inpainter/model/modules/flow_comp.py","language":"python","start_line":386,"end_line":450,"context_start_line":366,"context_end_line":450,"code":" f'flow ({flow.size()[1:3]}) are not the same.')\n _, _, h, w = x.size()\n # create mesh grid\n grid_y, grid_x = torch.meshgrid(torch.arange(0, h), torch.arange(0, w))\n grid = torch.stack((grid_x, grid_y), 2).type_as(x) # (w, h, 2)\n grid.requires_grad = False\n\n grid_flow = grid + flow\n # scale grid_flow to [-1,1]\n grid_flow_x = 2.0 * grid_flow[:, :, :, 0] / max(w - 1, 1) - 1.0\n grid_flow_y = 2.0 * grid_flow[:, :, :, 1] / max(h - 1, 1) - 1.0\n grid_flow = torch.stack((grid_flow_x, grid_flow_y), dim=3)\n output = F.grid_sample(x,\n grid_flow,\n mode=interpolation,\n padding_mode=padding_mode,\n align_corners=align_corners)\n return output\n\n\ndef initial_mask_flow(mask):\n \"\"\"\n mask 1 indicates valid pixel 0 indicates unknown pixel\n \"\"\"\n B, T, C, H, W = mask.shape\n\n # calculate relative position\n grid_y, grid_x = torch.meshgrid(torch.arange(0, H), torch.arange(0, W))\n\n grid_y, grid_x = grid_y.type_as(mask), grid_x.type_as(mask)\n abs_relative_pos_y = H - torch.abs(grid_y[None, :, :] - grid_y[:, None, :])\n relative_pos_y = H - (grid_y[None, :, :] - grid_y[:, None, :])\n\n abs_relative_pos_x = W - torch.abs(grid_x[:, None, :] - grid_x[:, :, None])\n relative_pos_x = W - (grid_x[:, None, :] - grid_x[:, :, None])\n\n # calculate the nearest indices\n pos_up = mask.unsqueeze(3).repeat(\n 1, 1, 1, H, 1, 1).flip(4) * abs_relative_pos_y[None, None, None] * (\n relative_pos_y <= H)[None, None, None]\n nearest_indice_up = pos_up.max(dim=4)[1]\n\n pos_down = mask.unsqueeze(3).repeat(1, 1, 1, H, 1, 1) * abs_relative_pos_y[\n None, None, None] * (relative_pos_y <= H)[None, None, None]\n nearest_indice_down = (pos_down).max(dim=4)[1]\n\n pos_left = mask.unsqueeze(4).repeat(\n 1, 1, 1, 1, W, 1).flip(5) * abs_relative_pos_x[None, None, None] * (\n relative_pos_x <= W)[None, None, None]\n nearest_indice_left = (pos_left).max(dim=5)[1]\n\n pos_right = mask.unsqueeze(4).repeat(\n 1, 1, 1, 1, W, 1) * abs_relative_pos_x[None, None, None] * (\n relative_pos_x <= W)[None, None, None]\n nearest_indice_right = (pos_right).max(dim=5)[1]\n\n # NOTE: IMPORTANT !!! depending on how to use this offset\n initial_offset_up = -(nearest_indice_up - grid_y[None, None, None]).flip(3)\n initial_offset_down = nearest_indice_down - grid_y[None, None, None]\n\n initial_offset_left = -(nearest_indice_left -\n grid_x[None, None, None]).flip(4)\n initial_offset_right = nearest_indice_right - grid_x[None, None, None]\n\n # nearest_indice_x = (mask.unsqueeze(1).repeat(1, img_width, 1) * relative_pos_x).max(dim=2)[1]\n # initial_offset_x = nearest_indice_x - grid_x\n\n # handle the boundary cases\n final_offset_down = (initial_offset_down < 0) * initial_offset_up + (\n initial_offset_down > 0) * initial_offset_down\n final_offset_up = (initial_offset_up > 0) * initial_offset_down + (\n initial_offset_up < 0) * initial_offset_up\n final_offset_right = (initial_offset_right < 0) * initial_offset_left + (\n initial_offset_right > 0) * initial_offset_right\n final_offset_left = (initial_offset_left > 0) * initial_offset_right + (\n initial_offset_left < 0) * initial_offset_left\n zero_offset = torch.zeros_like(final_offset_down)\n # out = torch.cat([final_offset_left, zero_offset, final_offset_right, zero_offset, zero_offset, final_offset_up, zero_offset, final_offset_down], dim=2)\n out = torch.cat([\n zero_offset, final_offset_left, zero_offset, final_offset_right,\n final_offset_up, zero_offset, final_offset_down, zero_offset\n ],\n dim=2)\n\n return out","source_hash":"b7598bbabf8f6af1a5f6ab2d6af3783f28fdbfe9d8bb6698620a6f77223b382b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.flow_comp.__init__","uri":"program://Track-Anything/function/inpainter.model.modules.flow_comp.__init__#L177-L215","kind":"function","name":"__init__","path":"inpainter/model/modules/flow_comp.py","language":"python","start_line":177,"end_line":215,"context_start_line":157,"context_end_line":235,"code":" align_corners=False)\n\n # compute flow, and resize back to the original resolution\n flow = F.interpolate(input=self.compute_flow(ref, supp),\n size=(h, w),\n mode='bilinear',\n align_corners=False)\n\n # adjust the flow values\n flow[:, 0, :, :] *= float(w) / float(w_up)\n flow[:, 1, :, :] *= float(h) / float(h_up)\n\n return flow\n\n\nclass SPyNetBasicModule(nn.Module):\n \"\"\"Basic Module for SPyNet.\n Paper:\n Optical Flow Estimation using a Spatial Pyramid Network, CVPR, 2017\n \"\"\"\n def __init__(self):\n super().__init__()\n\n self.basic_module = nn.Sequential(\n ConvModule(in_channels=8,\n out_channels=32,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=dict(type='ReLU')),\n ConvModule(in_channels=32,\n out_channels=64,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=dict(type='ReLU')),\n ConvModule(in_channels=64,\n out_channels=32,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=dict(type='ReLU')),\n ConvModule(in_channels=32,\n out_channels=16,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=dict(type='ReLU')),\n ConvModule(in_channels=16,\n out_channels=2,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=None))\n\n def forward(self, tensor_input):\n \"\"\"\n Args:\n tensor_input (Tensor): Input tensor with shape (b, 8, h, w).\n 8 channels contain:\n [reference image (3), neighbor image (3), initial flow (2)].\n Returns:\n Tensor: Refined flow with shape (b, 2, h, w)\n \"\"\"\n return self.basic_module(tensor_input)\n\n\n# Flow visualization code used from https://github.com/tomrunia/OpticalFlow_Visualization\ndef make_colorwheel():\n \"\"\"\n Generates a color wheel for optical flow visualization as presented in:\n Baker et al. \"A Database and Evaluation Methodology for Optical Flow\" (ICCV, 2007)\n URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf\n","source_hash":"b7598bbabf8f6af1a5f6ab2d6af3783f28fdbfe9d8bb6698620a6f77223b382b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.flow_comp.forward","uri":"program://Track-Anything/function/inpainter.model.modules.flow_comp.forward#L217-L226","kind":"function","name":"forward","path":"inpainter/model/modules/flow_comp.py","language":"python","start_line":217,"end_line":226,"context_start_line":197,"context_end_line":246,"code":" kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=dict(type='ReLU')),\n ConvModule(in_channels=32,\n out_channels=16,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=dict(type='ReLU')),\n ConvModule(in_channels=16,\n out_channels=2,\n kernel_size=7,\n stride=1,\n padding=3,\n norm_cfg=None,\n act_cfg=None))\n\n def forward(self, tensor_input):\n \"\"\"\n Args:\n tensor_input (Tensor): Input tensor with shape (b, 8, h, w).\n 8 channels contain:\n [reference image (3), neighbor image (3), initial flow (2)].\n Returns:\n Tensor: Refined flow with shape (b, 2, h, w)\n \"\"\"\n return self.basic_module(tensor_input)\n\n\n# Flow visualization code used from https://github.com/tomrunia/OpticalFlow_Visualization\ndef make_colorwheel():\n \"\"\"\n Generates a color wheel for optical flow visualization as presented in:\n Baker et al. \"A Database and Evaluation Methodology for Optical Flow\" (ICCV, 2007)\n URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf\n\n Code follows the original C++ source code of Daniel Scharstein.\n Code follows the the Matlab source code of Deqing Sun.\n\n Returns:\n np.ndarray: Color wheel\n \"\"\"\n\n RY = 15\n YG = 6\n GC = 4\n CB = 11","source_hash":"b7598bbabf8f6af1a5f6ab2d6af3783f28fdbfe9d8bb6698620a6f77223b382b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.flow_comp.compute_flow","uri":"program://Track-Anything/function/inpainter.model.modules.flow_comp.compute_flow#L84-L134","kind":"function","name":"compute_flow","path":"inpainter/model/modules/flow_comp.py","language":"python","start_line":84,"end_line":134,"context_start_line":64,"context_end_line":154,"code":" super().__init__()\n\n self.basic_module = nn.ModuleList(\n [SPyNetBasicModule() for _ in range(6)])\n\n if use_pretrain:\n if isinstance(pretrained, str):\n print(\"load pretrained SPyNet...\")\n load_checkpoint(self, pretrained, strict=True)\n elif pretrained is not None:\n raise TypeError('[pretrained] should be str or None, '\n f'but got {type(pretrained)}.')\n\n self.register_buffer(\n 'mean',\n torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))\n self.register_buffer(\n 'std',\n torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))\n\n def compute_flow(self, ref, supp):\n \"\"\"Compute flow from ref to supp.\n Note that in this function, the images are already resized to a\n multiple of 32.\n Args:\n ref (Tensor): Reference image with shape of (n, 3, h, w).\n supp (Tensor): Supporting image with shape of (n, 3, h, w).\n Returns:\n Tensor: Estimated optical flow: (n, 2, h, w).\n \"\"\"\n n, _, h, w = ref.size()\n\n # normalize the input images\n ref = [(ref - self.mean) / self.std]\n supp = [(supp - self.mean) / self.std]\n\n # generate downsampled frames\n for level in range(5):\n ref.append(\n F.avg_pool2d(input=ref[-1],\n kernel_size=2,\n stride=2,\n count_include_pad=False))\n supp.append(\n F.avg_pool2d(input=supp[-1],\n kernel_size=2,\n stride=2,\n count_include_pad=False))\n ref = ref[::-1]\n supp = supp[::-1]\n\n # flow computation\n flow = ref[0].new_zeros(n, 2, h // 32, w // 32)\n for level in range(len(ref)):\n if level == 0:\n flow_up = flow\n else:\n flow_up = F.interpolate(input=flow,\n scale_factor=2,\n mode='bilinear',\n align_corners=True) * 2.0\n\n # add the residue to the upsampled flow\n flow = flow_up + self.basic_module[level](torch.cat([\n ref[level],\n flow_warp(supp[level],\n flow_up.permute(0, 2, 3, 1).contiguous(),\n padding_mode='border'), flow_up\n ], 1))\n\n return flow\n\n def forward(self, ref, supp):\n \"\"\"Forward function of SPyNet.\n This function computes the optical flow from ref to supp.\n Args:\n ref (Tensor): Reference image with shape of (n, 3, h, w).\n supp (Tensor): Supporting image with shape of (n, 3, h, w).\n Returns:\n Tensor: Estimated optical flow: (n, 2, h, w).\n \"\"\"\n\n # upsize to a multiple of 32\n h, w = ref.shape[2:4]\n w_up = w if (w % 32) == 0 else 32 * (w // 32 + 1)\n h_up = h if (h % 32) == 0 else 32 * (h // 32 + 1)\n ref = F.interpolate(input=ref,\n size=(h_up, w_up),\n mode='bilinear',\n align_corners=False)\n supp = F.interpolate(input=supp,","source_hash":"b7598bbabf8f6af1a5f6ab2d6af3783f28fdbfe9d8bb6698620a6f77223b382b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.feat_prop","uri":"program://Track-Anything/module/inpainter.model.modules.feat_prop#L1-L149","kind":"module","name":"inpainter.model.modules.feat_prop","path":"inpainter/model/modules/feat_prop.py","language":"python","start_line":1,"end_line":149,"context_start_line":1,"context_end_line":149,"code":"\"\"\"\n BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment, CVPR 2022\n\"\"\"\nimport torch\nimport torch.nn as nn\n\nfrom mmcv.ops import ModulatedDeformConv2d, modulated_deform_conv2d\nfrom mmengine.model import constant_init\n\nfrom inpainter.model.modules.flow_comp import flow_warp\n\n\nclass SecondOrderDeformableAlignment(ModulatedDeformConv2d):\n \"\"\"Second-order deformable alignment module.\"\"\"\n def __init__(self, *args, **kwargs):\n self.max_residue_magnitude = kwargs.pop('max_residue_magnitude', 10)\n\n super(SecondOrderDeformableAlignment, self).__init__(*args, **kwargs)\n\n self.conv_offset = nn.Sequential(\n nn.Conv2d(3 * self.out_channels + 4, self.out_channels, 3, 1, 1),\n nn.LeakyReLU(negative_slope=0.1, inplace=True),\n nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1),\n nn.LeakyReLU(negative_slope=0.1, inplace=True),\n nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1),\n nn.LeakyReLU(negative_slope=0.1, inplace=True),\n nn.Conv2d(self.out_channels, 27 * self.deform_groups, 3, 1, 1),\n )\n\n self.init_offset()\n\n def init_offset(self):\n constant_init(self.conv_offset[-1], val=0, bias=0)\n\n def forward(self, x, extra_feat, flow_1, flow_2):\n extra_feat = torch.cat([extra_feat, flow_1, flow_2], dim=1)\n out = self.conv_offset(extra_feat)\n o1, o2, mask = torch.chunk(out, 3, dim=1)\n\n # offset\n offset = self.max_residue_magnitude * torch.tanh(\n torch.cat((o1, o2), dim=1))\n offset_1, offset_2 = torch.chunk(offset, 2, dim=1)\n offset_1 = offset_1 + flow_1.flip(1).repeat(1,\n offset_1.size(1) // 2, 1,\n 1)\n offset_2 = offset_2 + flow_2.flip(1).repeat(1,\n offset_2.size(1) // 2, 1,\n 1)\n offset = torch.cat([offset_1, offset_2], dim=1)\n\n # mask\n mask = torch.sigmoid(mask)\n\n return modulated_deform_conv2d(x, offset, mask, self.weight, self.bias,\n self.stride, self.padding,\n self.dilation, self.groups,\n self.deform_groups)\n\n\nclass BidirectionalPropagation(nn.Module):\n def __init__(self, channel):\n super(BidirectionalPropagation, self).__init__()\n modules = ['backward_', 'forward_']\n self.deform_align = nn.ModuleDict()\n self.backbone = nn.ModuleDict()\n self.channel = channel\n\n for i, module in enumerate(modules):\n self.deform_align[module] = SecondOrderDeformableAlignment(\n 2 * channel, channel, 3, padding=1, deform_groups=16)\n\n self.backbone[module] = nn.Sequential(\n nn.Conv2d((2 + i) * channel, channel, 3, 1, 1),\n nn.LeakyReLU(negative_slope=0.1, inplace=True),\n nn.Conv2d(channel, channel, 3, 1, 1),\n )\n\n self.fusion = nn.Conv2d(2 * channel, channel, 1, 1, 0)\n\n def forward(self, x, flows_backward, flows_forward):\n \"\"\"\n x shape : [b, t, c, h, w]\n return [b, t, c, h, w]\n \"\"\"\n b, t, c, h, w = x.shape\n feats = {}\n feats['spatial'] = [x[:, i, :, :, :] for i in range(0, t)]\n\n for module_name in ['backward_', 'forward_']:\n\n feats[module_name] = []\n\n frame_idx = range(0, t)\n flow_idx = range(-1, t - 1)\n mapping_idx = list(range(0, len(feats['spatial'])))\n mapping_idx += mapping_idx[::-1]\n\n if 'backward' in module_name:\n frame_idx = frame_idx[::-1]\n flows = flows_backward\n else:\n flows = flows_forward\n\n feat_prop = x.new_zeros(b, self.channel, h, w)\n for i, idx in enumerate(frame_idx):\n feat_current = feats['spatial'][mapping_idx[idx]]\n\n if i > 0:\n flow_n1 = flows[:, flow_idx[i], :, :, :]\n cond_n1 = flow_warp(feat_prop, flow_n1.permute(0, 2, 3, 1))\n\n # initialize second-order features\n feat_n2 = torch.zeros_like(feat_prop)\n flow_n2 = torch.zeros_like(flow_n1)\n cond_n2 = torch.zeros_like(cond_n1)\n if i > 1:\n feat_n2 = feats[module_name][-2]\n flow_n2 = flows[:, flow_idx[i - 1], :, :, :]\n flow_n2 = flow_n1 + flow_warp(\n flow_n2, flow_n1.permute(0, 2, 3, 1))\n cond_n2 = flow_warp(feat_n2,\n flow_n2.permute(0, 2, 3, 1))\n\n cond = torch.cat([cond_n1, feat_current, cond_n2], dim=1)\n feat_prop = torch.cat([feat_prop, feat_n2], dim=1)\n feat_prop = self.deform_align[module_name](feat_prop, cond,\n flow_n1,\n flow_n2)\n\n feat = [feat_current] + [\n feats[k][idx]\n for k in feats if k not in ['spatial', module_name]\n ] + [feat_prop]\n\n feat = torch.cat(feat, dim=1)\n feat_prop = feat_prop + self.backbone[module_name](feat)\n feats[module_name].append(feat_prop)\n\n if 'backward' in module_name:\n feats[module_name] = feats[module_name][::-1]\n\n outputs = []\n for i in range(0, t):\n align_feats = [feats[k].pop(0) for k in feats if k != 'spatial']\n align_feats = torch.cat(align_feats, dim=1)\n outputs.append(self.fusion(align_feats))\n\n return torch.stack(outputs, dim=1) + x","source_hash":"7f226418d038f218389a9267c4a4d6489a625289367728b09d2c7443ce19391b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.feat_prop.SecondOrderDeformableAlignment","uri":"program://Track-Anything/class/inpainter.model.modules.feat_prop.SecondOrderDeformableAlignment#L13-L58","kind":"class","name":"SecondOrderDeformableAlignment","path":"inpainter/model/modules/feat_prop.py","language":"python","start_line":13,"end_line":58,"context_start_line":1,"context_end_line":78,"code":"\"\"\"\n BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment, CVPR 2022\n\"\"\"\nimport torch\nimport torch.nn as nn\n\nfrom mmcv.ops import ModulatedDeformConv2d, modulated_deform_conv2d\nfrom mmengine.model import constant_init\n\nfrom inpainter.model.modules.flow_comp import flow_warp\n\n\nclass SecondOrderDeformableAlignment(ModulatedDeformConv2d):\n \"\"\"Second-order deformable alignment module.\"\"\"\n def __init__(self, *args, **kwargs):\n self.max_residue_magnitude = kwargs.pop('max_residue_magnitude', 10)\n\n super(SecondOrderDeformableAlignment, self).__init__(*args, **kwargs)\n\n self.conv_offset = nn.Sequential(\n nn.Conv2d(3 * self.out_channels + 4, self.out_channels, 3, 1, 1),\n nn.LeakyReLU(negative_slope=0.1, inplace=True),\n nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1),\n nn.LeakyReLU(negative_slope=0.1, inplace=True),\n nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1),\n nn.LeakyReLU(negative_slope=0.1, inplace=True),\n nn.Conv2d(self.out_channels, 27 * self.deform_groups, 3, 1, 1),\n )\n\n self.init_offset()\n\n def init_offset(self):\n constant_init(self.conv_offset[-1], val=0, bias=0)\n\n def forward(self, x, extra_feat, flow_1, flow_2):\n extra_feat = torch.cat([extra_feat, flow_1, flow_2], dim=1)\n out = self.conv_offset(extra_feat)\n o1, o2, mask = torch.chunk(out, 3, dim=1)\n\n # offset\n offset = self.max_residue_magnitude * torch.tanh(\n torch.cat((o1, o2), dim=1))\n offset_1, offset_2 = torch.chunk(offset, 2, dim=1)\n offset_1 = offset_1 + flow_1.flip(1).repeat(1,\n offset_1.size(1) // 2, 1,\n 1)\n offset_2 = offset_2 + flow_2.flip(1).repeat(1,\n offset_2.size(1) // 2, 1,\n 1)\n offset = torch.cat([offset_1, offset_2], dim=1)\n\n # mask\n mask = torch.sigmoid(mask)\n\n return modulated_deform_conv2d(x, offset, mask, self.weight, self.bias,\n self.stride, self.padding,\n self.dilation, self.groups,\n self.deform_groups)\n\n\nclass BidirectionalPropagation(nn.Module):\n def __init__(self, channel):\n super(BidirectionalPropagation, self).__init__()\n modules = ['backward_', 'forward_']\n self.deform_align = nn.ModuleDict()\n self.backbone = nn.ModuleDict()\n self.channel = channel\n\n for i, module in enumerate(modules):\n self.deform_align[module] = SecondOrderDeformableAlignment(\n 2 * channel, channel, 3, padding=1, deform_groups=16)\n\n self.backbone[module] = nn.Sequential(\n nn.Conv2d((2 + i) * channel, channel, 3, 1, 1),\n nn.LeakyReLU(negative_slope=0.1, inplace=True),\n nn.Conv2d(channel, channel, 3, 1, 1),\n )\n","source_hash":"7f226418d038f218389a9267c4a4d6489a625289367728b09d2c7443ce19391b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.feat_prop.BidirectionalPropagation","uri":"program://Track-Anything/class/inpainter.model.modules.feat_prop.BidirectionalPropagation#L61-L149","kind":"class","name":"BidirectionalPropagation","path":"inpainter/model/modules/feat_prop.py","language":"python","start_line":61,"end_line":149,"context_start_line":41,"context_end_line":149,"code":" offset = self.max_residue_magnitude * torch.tanh(\n torch.cat((o1, o2), dim=1))\n offset_1, offset_2 = torch.chunk(offset, 2, dim=1)\n offset_1 = offset_1 + flow_1.flip(1).repeat(1,\n offset_1.size(1) // 2, 1,\n 1)\n offset_2 = offset_2 + flow_2.flip(1).repeat(1,\n offset_2.size(1) // 2, 1,\n 1)\n offset = torch.cat([offset_1, offset_2], dim=1)\n\n # mask\n mask = torch.sigmoid(mask)\n\n return modulated_deform_conv2d(x, offset, mask, self.weight, self.bias,\n self.stride, self.padding,\n self.dilation, self.groups,\n self.deform_groups)\n\n\nclass BidirectionalPropagation(nn.Module):\n def __init__(self, channel):\n super(BidirectionalPropagation, self).__init__()\n modules = ['backward_', 'forward_']\n self.deform_align = nn.ModuleDict()\n self.backbone = nn.ModuleDict()\n self.channel = channel\n\n for i, module in enumerate(modules):\n self.deform_align[module] = SecondOrderDeformableAlignment(\n 2 * channel, channel, 3, padding=1, deform_groups=16)\n\n self.backbone[module] = nn.Sequential(\n nn.Conv2d((2 + i) * channel, channel, 3, 1, 1),\n nn.LeakyReLU(negative_slope=0.1, inplace=True),\n nn.Conv2d(channel, channel, 3, 1, 1),\n )\n\n self.fusion = nn.Conv2d(2 * channel, channel, 1, 1, 0)\n\n def forward(self, x, flows_backward, flows_forward):\n \"\"\"\n x shape : [b, t, c, h, w]\n return [b, t, c, h, w]\n \"\"\"\n b, t, c, h, w = x.shape\n feats = {}\n feats['spatial'] = [x[:, i, :, :, :] for i in range(0, t)]\n\n for module_name in ['backward_', 'forward_']:\n\n feats[module_name] = []\n\n frame_idx = range(0, t)\n flow_idx = range(-1, t - 1)\n mapping_idx = list(range(0, len(feats['spatial'])))\n mapping_idx += mapping_idx[::-1]\n\n if 'backward' in module_name:\n frame_idx = frame_idx[::-1]\n flows = flows_backward\n else:\n flows = flows_forward\n\n feat_prop = x.new_zeros(b, self.channel, h, w)\n for i, idx in enumerate(frame_idx):\n feat_current = feats['spatial'][mapping_idx[idx]]\n\n if i > 0:\n flow_n1 = flows[:, flow_idx[i], :, :, :]\n cond_n1 = flow_warp(feat_prop, flow_n1.permute(0, 2, 3, 1))\n\n # initialize second-order features\n feat_n2 = torch.zeros_like(feat_prop)\n flow_n2 = torch.zeros_like(flow_n1)\n cond_n2 = torch.zeros_like(cond_n1)\n if i > 1:\n feat_n2 = feats[module_name][-2]\n flow_n2 = flows[:, flow_idx[i - 1], :, :, :]\n flow_n2 = flow_n1 + flow_warp(\n flow_n2, flow_n1.permute(0, 2, 3, 1))\n cond_n2 = flow_warp(feat_n2,\n flow_n2.permute(0, 2, 3, 1))\n\n cond = torch.cat([cond_n1, feat_current, cond_n2], dim=1)\n feat_prop = torch.cat([feat_prop, feat_n2], dim=1)\n feat_prop = self.deform_align[module_name](feat_prop, cond,\n flow_n1,\n flow_n2)\n\n feat = [feat_current] + [\n feats[k][idx]\n for k in feats if k not in ['spatial', module_name]\n ] + [feat_prop]\n\n feat = torch.cat(feat, dim=1)\n feat_prop = feat_prop + self.backbone[module_name](feat)\n feats[module_name].append(feat_prop)\n\n if 'backward' in module_name:\n feats[module_name] = feats[module_name][::-1]\n\n outputs = []\n for i in range(0, t):\n align_feats = [feats[k].pop(0) for k in feats if k != 'spatial']\n align_feats = torch.cat(align_feats, dim=1)\n outputs.append(self.fusion(align_feats))\n\n return torch.stack(outputs, dim=1) + x","source_hash":"7f226418d038f218389a9267c4a4d6489a625289367728b09d2c7443ce19391b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.feat_prop.__init__","uri":"program://Track-Anything/function/inpainter.model.modules.feat_prop.__init__#L62-L79","kind":"function","name":"__init__","path":"inpainter/model/modules/feat_prop.py","language":"python","start_line":62,"end_line":79,"context_start_line":42,"context_end_line":99,"code":" torch.cat((o1, o2), dim=1))\n offset_1, offset_2 = torch.chunk(offset, 2, dim=1)\n offset_1 = offset_1 + flow_1.flip(1).repeat(1,\n offset_1.size(1) // 2, 1,\n 1)\n offset_2 = offset_2 + flow_2.flip(1).repeat(1,\n offset_2.size(1) // 2, 1,\n 1)\n offset = torch.cat([offset_1, offset_2], dim=1)\n\n # mask\n mask = torch.sigmoid(mask)\n\n return modulated_deform_conv2d(x, offset, mask, self.weight, self.bias,\n self.stride, self.padding,\n self.dilation, self.groups,\n self.deform_groups)\n\n\nclass BidirectionalPropagation(nn.Module):\n def __init__(self, channel):\n super(BidirectionalPropagation, self).__init__()\n modules = ['backward_', 'forward_']\n self.deform_align = nn.ModuleDict()\n self.backbone = nn.ModuleDict()\n self.channel = channel\n\n for i, module in enumerate(modules):\n self.deform_align[module] = SecondOrderDeformableAlignment(\n 2 * channel, channel, 3, padding=1, deform_groups=16)\n\n self.backbone[module] = nn.Sequential(\n nn.Conv2d((2 + i) * channel, channel, 3, 1, 1),\n nn.LeakyReLU(negative_slope=0.1, inplace=True),\n nn.Conv2d(channel, channel, 3, 1, 1),\n )\n\n self.fusion = nn.Conv2d(2 * channel, channel, 1, 1, 0)\n\n def forward(self, x, flows_backward, flows_forward):\n \"\"\"\n x shape : [b, t, c, h, w]\n return [b, t, c, h, w]\n \"\"\"\n b, t, c, h, w = x.shape\n feats = {}\n feats['spatial'] = [x[:, i, :, :, :] for i in range(0, t)]\n\n for module_name in ['backward_', 'forward_']:\n\n feats[module_name] = []\n\n frame_idx = range(0, t)\n flow_idx = range(-1, t - 1)\n mapping_idx = list(range(0, len(feats['spatial'])))\n mapping_idx += mapping_idx[::-1]\n\n if 'backward' in module_name:","source_hash":"7f226418d038f218389a9267c4a4d6489a625289367728b09d2c7443ce19391b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.feat_prop.init_offset","uri":"program://Track-Anything/function/inpainter.model.modules.feat_prop.init_offset#L32-L33","kind":"function","name":"init_offset","path":"inpainter/model/modules/feat_prop.py","language":"python","start_line":32,"end_line":33,"context_start_line":12,"context_end_line":53,"code":"\nclass SecondOrderDeformableAlignment(ModulatedDeformConv2d):\n \"\"\"Second-order deformable alignment module.\"\"\"\n def __init__(self, *args, **kwargs):\n self.max_residue_magnitude = kwargs.pop('max_residue_magnitude', 10)\n\n super(SecondOrderDeformableAlignment, self).__init__(*args, **kwargs)\n\n self.conv_offset = nn.Sequential(\n nn.Conv2d(3 * self.out_channels + 4, self.out_channels, 3, 1, 1),\n nn.LeakyReLU(negative_slope=0.1, inplace=True),\n nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1),\n nn.LeakyReLU(negative_slope=0.1, inplace=True),\n nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1),\n nn.LeakyReLU(negative_slope=0.1, inplace=True),\n nn.Conv2d(self.out_channels, 27 * self.deform_groups, 3, 1, 1),\n )\n\n self.init_offset()\n\n def init_offset(self):\n constant_init(self.conv_offset[-1], val=0, bias=0)\n\n def forward(self, x, extra_feat, flow_1, flow_2):\n extra_feat = torch.cat([extra_feat, flow_1, flow_2], dim=1)\n out = self.conv_offset(extra_feat)\n o1, o2, mask = torch.chunk(out, 3, dim=1)\n\n # offset\n offset = self.max_residue_magnitude * torch.tanh(\n torch.cat((o1, o2), dim=1))\n offset_1, offset_2 = torch.chunk(offset, 2, dim=1)\n offset_1 = offset_1 + flow_1.flip(1).repeat(1,\n offset_1.size(1) // 2, 1,\n 1)\n offset_2 = offset_2 + flow_2.flip(1).repeat(1,\n offset_2.size(1) // 2, 1,\n 1)\n offset = torch.cat([offset_1, offset_2], dim=1)\n\n # mask\n mask = torch.sigmoid(mask)","source_hash":"7f226418d038f218389a9267c4a4d6489a625289367728b09d2c7443ce19391b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.feat_prop.forward","uri":"program://Track-Anything/function/inpainter.model.modules.feat_prop.forward#L81-L149","kind":"function","name":"forward","path":"inpainter/model/modules/feat_prop.py","language":"python","start_line":81,"end_line":149,"context_start_line":61,"context_end_line":149,"code":"class BidirectionalPropagation(nn.Module):\n def __init__(self, channel):\n super(BidirectionalPropagation, self).__init__()\n modules = ['backward_', 'forward_']\n self.deform_align = nn.ModuleDict()\n self.backbone = nn.ModuleDict()\n self.channel = channel\n\n for i, module in enumerate(modules):\n self.deform_align[module] = SecondOrderDeformableAlignment(\n 2 * channel, channel, 3, padding=1, deform_groups=16)\n\n self.backbone[module] = nn.Sequential(\n nn.Conv2d((2 + i) * channel, channel, 3, 1, 1),\n nn.LeakyReLU(negative_slope=0.1, inplace=True),\n nn.Conv2d(channel, channel, 3, 1, 1),\n )\n\n self.fusion = nn.Conv2d(2 * channel, channel, 1, 1, 0)\n\n def forward(self, x, flows_backward, flows_forward):\n \"\"\"\n x shape : [b, t, c, h, w]\n return [b, t, c, h, w]\n \"\"\"\n b, t, c, h, w = x.shape\n feats = {}\n feats['spatial'] = [x[:, i, :, :, :] for i in range(0, t)]\n\n for module_name in ['backward_', 'forward_']:\n\n feats[module_name] = []\n\n frame_idx = range(0, t)\n flow_idx = range(-1, t - 1)\n mapping_idx = list(range(0, len(feats['spatial'])))\n mapping_idx += mapping_idx[::-1]\n\n if 'backward' in module_name:\n frame_idx = frame_idx[::-1]\n flows = flows_backward\n else:\n flows = flows_forward\n\n feat_prop = x.new_zeros(b, self.channel, h, w)\n for i, idx in enumerate(frame_idx):\n feat_current = feats['spatial'][mapping_idx[idx]]\n\n if i > 0:\n flow_n1 = flows[:, flow_idx[i], :, :, :]\n cond_n1 = flow_warp(feat_prop, flow_n1.permute(0, 2, 3, 1))\n\n # initialize second-order features\n feat_n2 = torch.zeros_like(feat_prop)\n flow_n2 = torch.zeros_like(flow_n1)\n cond_n2 = torch.zeros_like(cond_n1)\n if i > 1:\n feat_n2 = feats[module_name][-2]\n flow_n2 = flows[:, flow_idx[i - 1], :, :, :]\n flow_n2 = flow_n1 + flow_warp(\n flow_n2, flow_n1.permute(0, 2, 3, 1))\n cond_n2 = flow_warp(feat_n2,\n flow_n2.permute(0, 2, 3, 1))\n\n cond = torch.cat([cond_n1, feat_current, cond_n2], dim=1)\n feat_prop = torch.cat([feat_prop, feat_n2], dim=1)\n feat_prop = self.deform_align[module_name](feat_prop, cond,\n flow_n1,\n flow_n2)\n\n feat = [feat_current] + [\n feats[k][idx]\n for k in feats if k not in ['spatial', module_name]\n ] + [feat_prop]\n\n feat = torch.cat(feat, dim=1)\n feat_prop = feat_prop + self.backbone[module_name](feat)\n feats[module_name].append(feat_prop)\n\n if 'backward' in module_name:\n feats[module_name] = feats[module_name][::-1]\n\n outputs = []\n for i in range(0, t):\n align_feats = [feats[k].pop(0) for k in feats if k != 'spatial']\n align_feats = torch.cat(align_feats, dim=1)\n outputs.append(self.fusion(align_feats))\n\n return torch.stack(outputs, dim=1) + x","source_hash":"7f226418d038f218389a9267c4a4d6489a625289367728b09d2c7443ce19391b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.tfocal_transformer","uri":"program://Track-Anything/module/inpainter.model.modules.tfocal_transformer#L1-L536","kind":"module","name":"inpainter.model.modules.tfocal_transformer","path":"inpainter/model/modules/tfocal_transformer.py","language":"python","start_line":1,"end_line":536,"context_start_line":1,"context_end_line":536,"code":"\"\"\"\n This code is based on:\n [1] FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting, ICCV 2021\n https://github.com/ruiliu-ai/FuseFormer\n [2] Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet, ICCV 2021\n https://github.com/yitu-opensource/T2T-ViT\n [3] Focal Self-attention for Local-Global Interactions in Vision Transformers, NeurIPS 2021\n https://github.com/microsoft/Focal-Transformer \n\"\"\"\n\nimport math\nfrom functools import reduce\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass SoftSplit(nn.Module):\n def __init__(self, channel, hidden, kernel_size, stride, padding,\n t2t_param):\n super(SoftSplit, self).__init__()\n self.kernel_size = kernel_size\n self.t2t = nn.Unfold(kernel_size=kernel_size,\n stride=stride,\n padding=padding)\n c_in = reduce((lambda x, y: x * y), kernel_size) * channel\n self.embedding = nn.Linear(c_in, hidden)\n\n self.f_h = int(\n (t2t_param['output_size'][0] + 2 * t2t_param['padding'][0] -\n (t2t_param['kernel_size'][0] - 1) - 1) / t2t_param['stride'][0] +\n 1)\n self.f_w = int(\n (t2t_param['output_size'][1] + 2 * t2t_param['padding'][1] -\n (t2t_param['kernel_size'][1] - 1) - 1) / t2t_param['stride'][1] +\n 1)\n\n def forward(self, x, b):\n feat = self.t2t(x)\n feat = feat.permute(0, 2, 1)\n # feat shape [b*t, num_vec, ks*ks*c]\n feat = self.embedding(feat)\n # feat shape after embedding [b, t*num_vec, hidden]\n feat = feat.view(b, -1, self.f_h, self.f_w, feat.size(2))\n return feat\n\n\nclass SoftComp(nn.Module):\n def __init__(self, channel, hidden, output_size, kernel_size, stride,\n padding):\n super(SoftComp, self).__init__()\n self.relu = nn.LeakyReLU(0.2, inplace=True)\n c_out = reduce((lambda x, y: x * y), kernel_size) * channel\n self.embedding = nn.Linear(hidden, c_out)\n self.t2t = torch.nn.Fold(output_size=output_size,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding)\n h, w = output_size\n self.bias = nn.Parameter(torch.zeros((channel, h, w),\n dtype=torch.float32),\n requires_grad=True)\n\n def forward(self, x, t):\n b_, _, _, _, c_ = x.shape\n x = x.view(b_, -1, c_)\n feat = self.embedding(x)\n b, _, c = feat.size()\n feat = feat.view(b * t, -1, c).permute(0, 2, 1)\n feat = self.t2t(feat) + self.bias[None]\n return feat\n\n\nclass FusionFeedForward(nn.Module):\n def __init__(self, d_model, n_vecs=None, t2t_params=None):\n super(FusionFeedForward, self).__init__()\n # We set d_ff as a default to 1960\n hd = 1960\n self.conv1 = nn.Sequential(nn.Linear(d_model, hd))\n self.conv2 = nn.Sequential(nn.GELU(), nn.Linear(hd, d_model))\n assert t2t_params is not None and n_vecs is not None\n tp = t2t_params.copy()\n self.fold = nn.Fold(**tp)\n del tp['output_size']\n self.unfold = nn.Unfold(**tp)\n self.n_vecs = n_vecs\n\n def forward(self, x):\n x = self.conv1(x)\n b, n, c = x.size()\n normalizer = x.new_ones(b, n, 49).view(-1, self.n_vecs,\n 49).permute(0, 2, 1)\n x = self.unfold(\n self.fold(x.view(-1, self.n_vecs, c).permute(0, 2, 1)) /\n self.fold(normalizer)).permute(0, 2, 1).contiguous().view(b, n, c)\n x = self.conv2(x)\n return x\n\n\ndef window_partition(x, window_size):\n \"\"\"\n Args:\n x: shape is (B, T, H, W, C)\n window_size (tuple[int]): window size\n Returns:\n windows: (B*num_windows, T*window_size*window_size, C)\n \"\"\"\n B, T, H, W, C = x.shape\n x = x.view(B, T, H // window_size[0], window_size[0], W // window_size[1],\n window_size[1], C)\n windows = x.permute(0, 2, 4, 1, 3, 5, 6).contiguous().view(\n -1, T * window_size[0] * window_size[1], C)\n return windows\n\n\ndef window_partition_noreshape(x, window_size):\n \"\"\"\n Args:\n x: shape is (B, T, H, W, C)\n window_size (tuple[int]): window size\n Returns:\n windows: (B, num_windows_h, num_windows_w, T, window_size, window_size, C)\n \"\"\"\n B, T, H, W, C = x.shape\n x = x.view(B, T, H // window_size[0], window_size[0], W // window_size[1],\n window_size[1], C)\n windows = x.permute(0, 2, 4, 1, 3, 5, 6).contiguous()\n return windows\n\n\ndef window_reverse(windows, window_size, T, H, W):\n \"\"\"\n Args:\n windows: shape is (num_windows*B, T, window_size, window_size, C)\n window_size (tuple[int]): Window size\n T (int): Temporal length of video\n H (int): Height of image\n W (int): Width of image\n Returns:\n x: (B, T, H, W, C)\n \"\"\"\n B = int(windows.shape[0] / (H * W / window_size[0] / window_size[1]))\n x = windows.view(B, H // window_size[0], W // window_size[1], T,\n window_size[0], window_size[1], -1)\n x = x.permute(0, 3, 1, 4, 2, 5, 6).contiguous().view(B, T, H, W, -1)\n return x\n\n\nclass WindowAttention(nn.Module):\n \"\"\"Temporal focal window attention\n \"\"\"\n def __init__(self, dim, expand_size, window_size, focal_window,\n focal_level, num_heads, qkv_bias, pool_method):\n\n super().__init__()\n self.dim = dim\n self.expand_size = expand_size\n self.window_size = window_size # Wh, Ww\n self.pool_method = pool_method\n self.num_heads = num_heads\n head_dim = dim // num_heads\n self.scale = head_dim**-0.5\n self.focal_level = focal_level\n self.focal_window = focal_window\n\n if any(i > 0 for i in self.expand_size) and focal_level > 0:\n # get mask for rolled k and rolled v\n mask_tl = torch.ones(self.window_size[0], self.window_size[1])\n mask_tl[:-self.expand_size[0], :-self.expand_size[1]] = 0\n mask_tr = torch.ones(self.window_size[0], self.window_size[1])\n mask_tr[:-self.expand_size[0], self.expand_size[1]:] = 0\n mask_bl = torch.ones(self.window_size[0], self.window_size[1])\n mask_bl[self.expand_size[0]:, :-self.expand_size[1]] = 0\n mask_br = torch.ones(self.window_size[0], self.window_size[1])\n mask_br[self.expand_size[0]:, self.expand_size[1]:] = 0\n mask_rolled = torch.stack((mask_tl, mask_tr, mask_bl, mask_br),\n 0).flatten(0)\n self.register_buffer(\"valid_ind_rolled\",\n mask_rolled.nonzero(as_tuple=False).view(-1))\n\n if pool_method != \"none\" and focal_level > 1:\n self.unfolds = nn.ModuleList()\n\n # build relative position bias between local patch and pooled windows\n for k in range(focal_level - 1):\n stride = 2**k\n kernel_size = tuple(2 * (i // 2) + 2**k + (2**k - 1)\n for i in self.focal_window)\n # define unfolding operations\n self.unfolds += [\n nn.Unfold(kernel_size=kernel_size,\n stride=stride,\n padding=tuple(i // 2 for i in kernel_size))\n ]\n\n # define unfolding index for focal_level > 0\n if k > 0:\n mask = torch.zeros(kernel_size)\n mask[(2**k) - 1:, (2**k) - 1:] = 1\n self.register_buffer(\n \"valid_ind_unfold_{}\".format(k),\n mask.flatten(0).nonzero(as_tuple=False).view(-1))\n\n self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n self.proj = nn.Linear(dim, dim)\n\n self.softmax = nn.Softmax(dim=-1)\n\n def forward(self, x_all, mask_all=None):\n \"\"\"\n Args:\n x: input features with shape of (B, T, Wh, Ww, C)\n mask: (0/-inf) mask with shape of (num_windows, T*Wh*Ww, T*Wh*Ww) or None\n\n output: (nW*B, Wh*Ww, C)\n \"\"\"\n x = x_all[0]\n\n B, T, nH, nW, C = x.shape\n qkv = self.qkv(x).reshape(B, T, nH, nW, 3,\n C).permute(4, 0, 1, 2, 3, 5).contiguous()\n q, k, v = qkv[0], qkv[1], qkv[2] # B, T, nH, nW, C\n\n # partition q map\n (q_windows, k_windows, v_windows) = map(\n lambda t: window_partition(t, self.window_size).view(\n -1, T, self.window_size[0] * self.window_size[1], self.\n num_heads, C // self.num_heads).permute(0, 3, 1, 2, 4).\n contiguous().view(-1, self.num_heads, T * self.window_size[\n 0] * self.window_size[1], C // self.num_heads), (q, k, v))\n # q(k/v)_windows shape : [16, 4, 225, 128]\n\n if any(i > 0 for i in self.expand_size) and self.focal_level > 0:\n (k_tl, v_tl) = map(\n lambda t: torch.roll(t,\n shifts=(-self.expand_size[0], -self.\n expand_size[1]),\n dims=(2, 3)), (k, v))\n (k_tr, v_tr) = map(\n lambda t: torch.roll(t,\n shifts=(-self.expand_size[0], self.\n expand_size[1]),\n dims=(2, 3)), (k, v))\n (k_bl, v_bl) = map(\n lambda t: torch.roll(t,\n shifts=(self.expand_size[0], -self.\n expand_size[1]),\n dims=(2, 3)), (k, v))\n (k_br, v_br) = map(\n lambda t: torch.roll(t,\n shifts=(self.expand_size[0], self.\n expand_size[1]),\n dims=(2, 3)), (k, v))\n\n (k_tl_windows, k_tr_windows, k_bl_windows, k_br_windows) = map(\n lambda t: window_partition(t, self.window_size).view(\n -1, T, self.window_size[0] * self.window_size[1], self.\n num_heads, C // self.num_heads), (k_tl, k_tr, k_bl, k_br))\n (v_tl_windows, v_tr_windows, v_bl_windows, v_br_windows) = map(\n lambda t: window_partition(t, self.window_size).view(\n -1, T, self.window_size[0] * self.window_size[1], self.\n num_heads, C // self.num_heads), (v_tl, v_tr, v_bl, v_br))\n k_rolled = torch.cat(\n (k_tl_windows, k_tr_windows, k_bl_windows, k_br_windows),\n 2).permute(0, 3, 1, 2, 4).contiguous()\n v_rolled = torch.cat(\n (v_tl_windows, v_tr_windows, v_bl_windows, v_br_windows),\n 2).permute(0, 3, 1, 2, 4).contiguous()\n\n # mask out tokens in current window\n k_rolled = k_rolled[:, :, :, self.valid_ind_rolled]\n v_rolled = v_rolled[:, :, :, self.valid_ind_rolled]\n temp_N = k_rolled.shape[3]\n k_rolled = k_rolled.view(-1, self.num_heads, T * temp_N,\n C // self.num_heads)\n v_rolled = v_rolled.view(-1, self.num_heads, T * temp_N,\n C // self.num_heads)\n k_rolled = torch.cat((k_windows, k_rolled), 2)\n v_rolled = torch.cat((v_windows, v_rolled), 2)\n else:\n k_rolled = k_windows\n v_rolled = v_windows\n\n # q(k/v)_windows shape : [16, 4, 225, 128]\n # k_rolled.shape : [16, 4, 5, 165, 128]\n # ideal expanded window size 153 ((5+2*2)*(9+2*4))\n # k_windows=45 expand_window=108 overlap_window=12 (since expand_size < window_size / 2)\n\n if self.pool_method != \"none\" and self.focal_level > 1:\n k_pooled = []\n v_pooled = []\n for k in range(self.focal_level - 1):\n stride = 2**k\n x_window_pooled = x_all[k + 1].permute(\n 0, 3, 1, 2, 4).contiguous() # B, T, nWh, nWw, C\n\n nWh, nWw = x_window_pooled.shape[2:4]\n\n # generate mask for pooled windows\n mask = x_window_pooled.new(T, nWh, nWw).fill_(1)\n # unfold mask: [nWh*nWw//s//s, k*k, 1]\n unfolded_mask = self.unfolds[k](mask.unsqueeze(1)).view(\n 1, T, self.unfolds[k].kernel_size[0], self.unfolds[k].kernel_size[1], -1).permute(4, 1, 2, 3, 0).contiguous().\\\n view(nWh*nWw // stride // stride, -1, 1)\n\n if k > 0:\n valid_ind_unfold_k = getattr(\n self, \"valid_ind_unfold_{}\".format(k))\n unfolded_mask = unfolded_mask[:, valid_ind_unfold_k]\n\n x_window_masks = unfolded_mask.flatten(1).unsqueeze(0)\n x_window_masks = x_window_masks.masked_fill(\n x_window_masks == 0,\n float(-100.0)).masked_fill(x_window_masks > 0, float(0.0))\n mask_all[k + 1] = x_window_masks\n\n # generate k and v for pooled windows\n qkv_pooled = self.qkv(x_window_pooled).reshape(\n B, T, nWh, nWw, 3, C).permute(4, 0, 1, 5, 2,\n 3).view(3, -1, C, nWh,\n nWw).contiguous()\n k_pooled_k, v_pooled_k = qkv_pooled[1], qkv_pooled[\n 2] # B*T, C, nWh, nWw\n # k_pooled_k shape: [5, 512, 4, 4]\n # self.unfolds[k](k_pooled_k) shape: [5, 23040 (512 * 5 * 9 ), 16]\n\n (k_pooled_k, v_pooled_k) = map(\n lambda t: self.unfolds[k](t).view(\n B, T, C, self.unfolds[k].kernel_size[0], self.unfolds[k].kernel_size[1], -1).permute(0, 5, 1, 3, 4, 2).contiguous().\\\n view(-1, T, self.unfolds[k].kernel_size[0]*self.unfolds[k].kernel_size[1], self.num_heads, C // self.num_heads).permute(0, 3, 1, 2, 4).contiguous(),\n (k_pooled_k, v_pooled_k) # (B x (nH*nW)) x nHeads x T x (unfold_wsize x unfold_wsize) x head_dim\n )\n # k_pooled_k shape : [16, 4, 5, 45, 128]\n\n # select valid unfolding index\n if k > 0:\n (k_pooled_k, v_pooled_k) = map(\n lambda t: t[:, :, :, valid_ind_unfold_k],\n (k_pooled_k, v_pooled_k))\n\n k_pooled_k = k_pooled_k.view(\n -1, self.num_heads, T * self.unfolds[k].kernel_size[0] *\n self.unfolds[k].kernel_size[1], C // self.num_heads)\n v_pooled_k = v_pooled_k.view(\n -1, self.num_heads, T * self.unfolds[k].kernel_size[0] *\n self.unfolds[k].kernel_size[1], C // self.num_heads)\n\n k_pooled += [k_pooled_k]\n v_pooled += [v_pooled_k]\n\n # k_all (v_all) shape : [16, 4, 5 * 210, 128]\n k_all = torch.cat([k_rolled] + k_pooled, 2)\n v_all = torch.cat([v_rolled] + v_pooled, 2)\n else:\n k_all = k_rolled\n v_all = v_rolled\n\n N = k_all.shape[-2]\n q_windows = q_windows * self.scale\n attn = (\n q_windows @ k_all.transpose(-2, -1)\n ) # B*nW, nHead, T*window_size*window_size, T*focal_window_size*focal_window_size\n # T * 45\n window_area = T * self.window_size[0] * self.window_size[1]\n # T * 165\n window_area_rolled = k_rolled.shape[2]\n\n if self.pool_method != \"none\" and self.focal_level > 1:\n offset = window_area_rolled\n for k in range(self.focal_level - 1):\n # add attentional mask\n # mask_all[1] shape [1, 16, T * 45]\n\n bias = tuple((i + 2**k - 1) for i in self.focal_window)\n\n if mask_all[k + 1] is not None:\n attn[:, :, :window_area, offset:(offset + (T*bias[0]*bias[1]))] = \\\n attn[:, :, :window_area, offset:(offset + (T*bias[0]*bias[1]))] + \\\n mask_all[k+1][:, :, None, None, :].repeat(attn.shape[0] // mask_all[k+1].shape[1], 1, 1, 1, 1).view(-1, 1, 1, mask_all[k+1].shape[-1])\n\n offset += T * bias[0] * bias[1]\n\n if mask_all[0] is not None:\n nW = mask_all[0].shape[0]\n attn = attn.view(attn.shape[0] // nW, nW, self.num_heads,\n window_area, N)\n attn[:, :, :, :, :\n window_area] = attn[:, :, :, :, :window_area] + mask_all[0][\n None, :, None, :, :]\n attn = attn.view(-1, self.num_heads, window_area, N)\n attn = self.softmax(attn)\n else:\n attn = self.softmax(attn)\n\n x = (attn @ v_all).transpose(1, 2).reshape(attn.shape[0], window_area,\n C)\n x = self.proj(x)\n return x\n\n\nclass TemporalFocalTransformerBlock(nn.Module):\n r\"\"\" Temporal Focal Transformer Block.\n Args:\n dim (int): Number of input channels.\n num_heads (int): Number of attention heads.\n window_size (tuple[int]): Window size.\n shift_size (int): Shift size for SW-MSA.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n focal_level (int): The number level of focal window.\n focal_window (int): Window size of each focal window.\n n_vecs (int): Required for F3N.\n t2t_params (int): T2T parameters for F3N.\n \"\"\"\n def __init__(self,\n dim,\n num_heads,\n window_size=(5, 9),\n mlp_ratio=4.,\n qkv_bias=True,\n pool_method=\"fc\",\n focal_level=2,\n focal_window=(5, 9),\n norm_layer=nn.LayerNorm,\n n_vecs=None,\n t2t_params=None):\n super().__init__()\n self.dim = dim\n self.num_heads = num_heads\n self.window_size = window_size\n self.expand_size = tuple(i // 2 for i in window_size) # TODO\n self.mlp_ratio = mlp_ratio\n self.pool_method = pool_method\n self.focal_level = focal_level\n self.focal_window = focal_window\n\n self.window_size_glo = self.window_size\n\n self.pool_layers = nn.ModuleList()\n if self.pool_method != \"none\":\n for k in range(self.focal_level - 1):\n window_size_glo = tuple(\n math.floor(i / (2**k)) for i in self.window_size_glo)\n self.pool_layers.append(\n nn.Linear(window_size_glo[0] * window_size_glo[1], 1))\n self.pool_layers[-1].weight.data.fill_(\n 1. / (window_size_glo[0] * window_size_glo[1]))\n self.pool_layers[-1].bias.data.fill_(0)\n\n self.norm1 = norm_layer(dim)\n\n self.attn = WindowAttention(dim,\n expand_size=self.expand_size,\n window_size=self.window_size,\n focal_window=focal_window,\n focal_level=focal_level,\n num_heads=num_heads,\n qkv_bias=qkv_bias,\n pool_method=pool_method)\n# ... truncated ...","source_hash":"55c9167ddc8b6b05c864d7dd8d1ae1ba4a24fc60e905e5be4912ac93397d4e89","truncated":true} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.tfocal_transformer.SoftSplit","uri":"program://Track-Anything/class/inpainter.model.modules.tfocal_transformer.SoftSplit#L19-L46","kind":"class","name":"SoftSplit","path":"inpainter/model/modules/tfocal_transformer.py","language":"python","start_line":19,"end_line":46,"context_start_line":1,"context_end_line":66,"code":"\"\"\"\n This code is based on:\n [1] FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting, ICCV 2021\n https://github.com/ruiliu-ai/FuseFormer\n [2] Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet, ICCV 2021\n https://github.com/yitu-opensource/T2T-ViT\n [3] Focal Self-attention for Local-Global Interactions in Vision Transformers, NeurIPS 2021\n https://github.com/microsoft/Focal-Transformer \n\"\"\"\n\nimport math\nfrom functools import reduce\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass SoftSplit(nn.Module):\n def __init__(self, channel, hidden, kernel_size, stride, padding,\n t2t_param):\n super(SoftSplit, self).__init__()\n self.kernel_size = kernel_size\n self.t2t = nn.Unfold(kernel_size=kernel_size,\n stride=stride,\n padding=padding)\n c_in = reduce((lambda x, y: x * y), kernel_size) * channel\n self.embedding = nn.Linear(c_in, hidden)\n\n self.f_h = int(\n (t2t_param['output_size'][0] + 2 * t2t_param['padding'][0] -\n (t2t_param['kernel_size'][0] - 1) - 1) / t2t_param['stride'][0] +\n 1)\n self.f_w = int(\n (t2t_param['output_size'][1] + 2 * t2t_param['padding'][1] -\n (t2t_param['kernel_size'][1] - 1) - 1) / t2t_param['stride'][1] +\n 1)\n\n def forward(self, x, b):\n feat = self.t2t(x)\n feat = feat.permute(0, 2, 1)\n # feat shape [b*t, num_vec, ks*ks*c]\n feat = self.embedding(feat)\n # feat shape after embedding [b, t*num_vec, hidden]\n feat = feat.view(b, -1, self.f_h, self.f_w, feat.size(2))\n return feat\n\n\nclass SoftComp(nn.Module):\n def __init__(self, channel, hidden, output_size, kernel_size, stride,\n padding):\n super(SoftComp, self).__init__()\n self.relu = nn.LeakyReLU(0.2, inplace=True)\n c_out = reduce((lambda x, y: x * y), kernel_size) * channel\n self.embedding = nn.Linear(hidden, c_out)\n self.t2t = torch.nn.Fold(output_size=output_size,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding)\n h, w = output_size\n self.bias = nn.Parameter(torch.zeros((channel, h, w),\n dtype=torch.float32),\n requires_grad=True)\n\n def forward(self, x, t):\n b_, _, _, _, c_ = x.shape","source_hash":"55c9167ddc8b6b05c864d7dd8d1ae1ba4a24fc60e905e5be4912ac93397d4e89","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.tfocal_transformer.SoftComp","uri":"program://Track-Anything/class/inpainter.model.modules.tfocal_transformer.SoftComp#L49-L72","kind":"class","name":"SoftComp","path":"inpainter/model/modules/tfocal_transformer.py","language":"python","start_line":49,"end_line":72,"context_start_line":29,"context_end_line":92,"code":"\n self.f_h = int(\n (t2t_param['output_size'][0] + 2 * t2t_param['padding'][0] -\n (t2t_param['kernel_size'][0] - 1) - 1) / t2t_param['stride'][0] +\n 1)\n self.f_w = int(\n (t2t_param['output_size'][1] + 2 * t2t_param['padding'][1] -\n (t2t_param['kernel_size'][1] - 1) - 1) / t2t_param['stride'][1] +\n 1)\n\n def forward(self, x, b):\n feat = self.t2t(x)\n feat = feat.permute(0, 2, 1)\n # feat shape [b*t, num_vec, ks*ks*c]\n feat = self.embedding(feat)\n # feat shape after embedding [b, t*num_vec, hidden]\n feat = feat.view(b, -1, self.f_h, self.f_w, feat.size(2))\n return feat\n\n\nclass SoftComp(nn.Module):\n def __init__(self, channel, hidden, output_size, kernel_size, stride,\n padding):\n super(SoftComp, self).__init__()\n self.relu = nn.LeakyReLU(0.2, inplace=True)\n c_out = reduce((lambda x, y: x * y), kernel_size) * channel\n self.embedding = nn.Linear(hidden, c_out)\n self.t2t = torch.nn.Fold(output_size=output_size,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding)\n h, w = output_size\n self.bias = nn.Parameter(torch.zeros((channel, h, w),\n dtype=torch.float32),\n requires_grad=True)\n\n def forward(self, x, t):\n b_, _, _, _, c_ = x.shape\n x = x.view(b_, -1, c_)\n feat = self.embedding(x)\n b, _, c = feat.size()\n feat = feat.view(b * t, -1, c).permute(0, 2, 1)\n feat = self.t2t(feat) + self.bias[None]\n return feat\n\n\nclass FusionFeedForward(nn.Module):\n def __init__(self, d_model, n_vecs=None, t2t_params=None):\n super(FusionFeedForward, self).__init__()\n # We set d_ff as a default to 1960\n hd = 1960\n self.conv1 = nn.Sequential(nn.Linear(d_model, hd))\n self.conv2 = nn.Sequential(nn.GELU(), nn.Linear(hd, d_model))\n assert t2t_params is not None and n_vecs is not None\n tp = t2t_params.copy()\n self.fold = nn.Fold(**tp)\n del tp['output_size']\n self.unfold = nn.Unfold(**tp)\n self.n_vecs = n_vecs\n\n def forward(self, x):\n x = self.conv1(x)\n b, n, c = x.size()\n normalizer = x.new_ones(b, n, 49).view(-1, self.n_vecs,","source_hash":"55c9167ddc8b6b05c864d7dd8d1ae1ba4a24fc60e905e5be4912ac93397d4e89","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.tfocal_transformer.FusionFeedForward","uri":"program://Track-Anything/class/inpainter.model.modules.tfocal_transformer.FusionFeedForward#L75-L98","kind":"class","name":"FusionFeedForward","path":"inpainter/model/modules/tfocal_transformer.py","language":"python","start_line":75,"end_line":98,"context_start_line":55,"context_end_line":118,"code":" self.embedding = nn.Linear(hidden, c_out)\n self.t2t = torch.nn.Fold(output_size=output_size,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding)\n h, w = output_size\n self.bias = nn.Parameter(torch.zeros((channel, h, w),\n dtype=torch.float32),\n requires_grad=True)\n\n def forward(self, x, t):\n b_, _, _, _, c_ = x.shape\n x = x.view(b_, -1, c_)\n feat = self.embedding(x)\n b, _, c = feat.size()\n feat = feat.view(b * t, -1, c).permute(0, 2, 1)\n feat = self.t2t(feat) + self.bias[None]\n return feat\n\n\nclass FusionFeedForward(nn.Module):\n def __init__(self, d_model, n_vecs=None, t2t_params=None):\n super(FusionFeedForward, self).__init__()\n # We set d_ff as a default to 1960\n hd = 1960\n self.conv1 = nn.Sequential(nn.Linear(d_model, hd))\n self.conv2 = nn.Sequential(nn.GELU(), nn.Linear(hd, d_model))\n assert t2t_params is not None and n_vecs is not None\n tp = t2t_params.copy()\n self.fold = nn.Fold(**tp)\n del tp['output_size']\n self.unfold = nn.Unfold(**tp)\n self.n_vecs = n_vecs\n\n def forward(self, x):\n x = self.conv1(x)\n b, n, c = x.size()\n normalizer = x.new_ones(b, n, 49).view(-1, self.n_vecs,\n 49).permute(0, 2, 1)\n x = self.unfold(\n self.fold(x.view(-1, self.n_vecs, c).permute(0, 2, 1)) /\n self.fold(normalizer)).permute(0, 2, 1).contiguous().view(b, n, c)\n x = self.conv2(x)\n return x\n\n\ndef window_partition(x, window_size):\n \"\"\"\n Args:\n x: shape is (B, T, H, W, C)\n window_size (tuple[int]): window size\n Returns:\n windows: (B*num_windows, T*window_size*window_size, C)\n \"\"\"\n B, T, H, W, C = x.shape\n x = x.view(B, T, H // window_size[0], window_size[0], W // window_size[1],\n window_size[1], C)\n windows = x.permute(0, 2, 4, 1, 3, 5, 6).contiguous().view(\n -1, T * window_size[0] * window_size[1], C)\n return windows\n\n\ndef window_partition_noreshape(x, window_size):\n \"\"\"","source_hash":"55c9167ddc8b6b05c864d7dd8d1ae1ba4a24fc60e905e5be4912ac93397d4e89","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.tfocal_transformer.window_partition","uri":"program://Track-Anything/function/inpainter.model.modules.tfocal_transformer.window_partition#L101-L114","kind":"function","name":"window_partition","path":"inpainter/model/modules/tfocal_transformer.py","language":"python","start_line":101,"end_line":114,"context_start_line":81,"context_end_line":134,"code":" self.conv2 = nn.Sequential(nn.GELU(), nn.Linear(hd, d_model))\n assert t2t_params is not None and n_vecs is not None\n tp = t2t_params.copy()\n self.fold = nn.Fold(**tp)\n del tp['output_size']\n self.unfold = nn.Unfold(**tp)\n self.n_vecs = n_vecs\n\n def forward(self, x):\n x = self.conv1(x)\n b, n, c = x.size()\n normalizer = x.new_ones(b, n, 49).view(-1, self.n_vecs,\n 49).permute(0, 2, 1)\n x = self.unfold(\n self.fold(x.view(-1, self.n_vecs, c).permute(0, 2, 1)) /\n self.fold(normalizer)).permute(0, 2, 1).contiguous().view(b, n, c)\n x = self.conv2(x)\n return x\n\n\ndef window_partition(x, window_size):\n \"\"\"\n Args:\n x: shape is (B, T, H, W, C)\n window_size (tuple[int]): window size\n Returns:\n windows: (B*num_windows, T*window_size*window_size, C)\n \"\"\"\n B, T, H, W, C = x.shape\n x = x.view(B, T, H // window_size[0], window_size[0], W // window_size[1],\n window_size[1], C)\n windows = x.permute(0, 2, 4, 1, 3, 5, 6).contiguous().view(\n -1, T * window_size[0] * window_size[1], C)\n return windows\n\n\ndef window_partition_noreshape(x, window_size):\n \"\"\"\n Args:\n x: shape is (B, T, H, W, C)\n window_size (tuple[int]): window size\n Returns:\n windows: (B, num_windows_h, num_windows_w, T, window_size, window_size, C)\n \"\"\"\n B, T, H, W, C = x.shape\n x = x.view(B, T, H // window_size[0], window_size[0], W // window_size[1],\n window_size[1], C)\n windows = x.permute(0, 2, 4, 1, 3, 5, 6).contiguous()\n return windows\n\n\ndef window_reverse(windows, window_size, T, H, W):\n \"\"\"\n Args:","source_hash":"55c9167ddc8b6b05c864d7dd8d1ae1ba4a24fc60e905e5be4912ac93397d4e89","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.tfocal_transformer.window_partition_noreshape","uri":"program://Track-Anything/function/inpainter.model.modules.tfocal_transformer.window_partition_noreshape#L117-L129","kind":"function","name":"window_partition_noreshape","path":"inpainter/model/modules/tfocal_transformer.py","language":"python","start_line":117,"end_line":129,"context_start_line":97,"context_end_line":149,"code":" x = self.conv2(x)\n return x\n\n\ndef window_partition(x, window_size):\n \"\"\"\n Args:\n x: shape is (B, T, H, W, C)\n window_size (tuple[int]): window size\n Returns:\n windows: (B*num_windows, T*window_size*window_size, C)\n \"\"\"\n B, T, H, W, C = x.shape\n x = x.view(B, T, H // window_size[0], window_size[0], W // window_size[1],\n window_size[1], C)\n windows = x.permute(0, 2, 4, 1, 3, 5, 6).contiguous().view(\n -1, T * window_size[0] * window_size[1], C)\n return windows\n\n\ndef window_partition_noreshape(x, window_size):\n \"\"\"\n Args:\n x: shape is (B, T, H, W, C)\n window_size (tuple[int]): window size\n Returns:\n windows: (B, num_windows_h, num_windows_w, T, window_size, window_size, C)\n \"\"\"\n B, T, H, W, C = x.shape\n x = x.view(B, T, H // window_size[0], window_size[0], W // window_size[1],\n window_size[1], C)\n windows = x.permute(0, 2, 4, 1, 3, 5, 6).contiguous()\n return windows\n\n\ndef window_reverse(windows, window_size, T, H, W):\n \"\"\"\n Args:\n windows: shape is (num_windows*B, T, window_size, window_size, C)\n window_size (tuple[int]): Window size\n T (int): Temporal length of video\n H (int): Height of image\n W (int): Width of image\n Returns:\n x: (B, T, H, W, C)\n \"\"\"\n B = int(windows.shape[0] / (H * W / window_size[0] / window_size[1]))\n x = windows.view(B, H // window_size[0], W // window_size[1], T,\n window_size[0], window_size[1], -1)\n x = x.permute(0, 3, 1, 4, 2, 5, 6).contiguous().view(B, T, H, W, -1)\n return x\n\n","source_hash":"55c9167ddc8b6b05c864d7dd8d1ae1ba4a24fc60e905e5be4912ac93397d4e89","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.tfocal_transformer.window_reverse","uri":"program://Track-Anything/function/inpainter.model.modules.tfocal_transformer.window_reverse#L132-L147","kind":"function","name":"window_reverse","path":"inpainter/model/modules/tfocal_transformer.py","language":"python","start_line":132,"end_line":147,"context_start_line":112,"context_end_line":167,"code":" windows = x.permute(0, 2, 4, 1, 3, 5, 6).contiguous().view(\n -1, T * window_size[0] * window_size[1], C)\n return windows\n\n\ndef window_partition_noreshape(x, window_size):\n \"\"\"\n Args:\n x: shape is (B, T, H, W, C)\n window_size (tuple[int]): window size\n Returns:\n windows: (B, num_windows_h, num_windows_w, T, window_size, window_size, C)\n \"\"\"\n B, T, H, W, C = x.shape\n x = x.view(B, T, H // window_size[0], window_size[0], W // window_size[1],\n window_size[1], C)\n windows = x.permute(0, 2, 4, 1, 3, 5, 6).contiguous()\n return windows\n\n\ndef window_reverse(windows, window_size, T, H, W):\n \"\"\"\n Args:\n windows: shape is (num_windows*B, T, window_size, window_size, C)\n window_size (tuple[int]): Window size\n T (int): Temporal length of video\n H (int): Height of image\n W (int): Width of image\n Returns:\n x: (B, T, H, W, C)\n \"\"\"\n B = int(windows.shape[0] / (H * W / window_size[0] / window_size[1]))\n x = windows.view(B, H // window_size[0], W // window_size[1], T,\n window_size[0], window_size[1], -1)\n x = x.permute(0, 3, 1, 4, 2, 5, 6).contiguous().view(B, T, H, W, -1)\n return x\n\n\nclass WindowAttention(nn.Module):\n \"\"\"Temporal focal window attention\n \"\"\"\n def __init__(self, dim, expand_size, window_size, focal_window,\n focal_level, num_heads, qkv_bias, pool_method):\n\n super().__init__()\n self.dim = dim\n self.expand_size = expand_size\n self.window_size = window_size # Wh, Ww\n self.pool_method = pool_method\n self.num_heads = num_heads\n head_dim = dim // num_heads\n self.scale = head_dim**-0.5\n self.focal_level = focal_level\n self.focal_window = focal_window\n\n if any(i > 0 for i in self.expand_size) and focal_level > 0:","source_hash":"55c9167ddc8b6b05c864d7dd8d1ae1ba4a24fc60e905e5be4912ac93397d4e89","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.tfocal_transformer.WindowAttention","uri":"program://Track-Anything/class/inpainter.model.modules.tfocal_transformer.WindowAttention#L150-L399","kind":"class","name":"WindowAttention","path":"inpainter/model/modules/tfocal_transformer.py","language":"python","start_line":150,"end_line":399,"context_start_line":130,"context_end_line":419,"code":"\n\ndef window_reverse(windows, window_size, T, H, W):\n \"\"\"\n Args:\n windows: shape is (num_windows*B, T, window_size, window_size, C)\n window_size (tuple[int]): Window size\n T (int): Temporal length of video\n H (int): Height of image\n W (int): Width of image\n Returns:\n x: (B, T, H, W, C)\n \"\"\"\n B = int(windows.shape[0] / (H * W / window_size[0] / window_size[1]))\n x = windows.view(B, H // window_size[0], W // window_size[1], T,\n window_size[0], window_size[1], -1)\n x = x.permute(0, 3, 1, 4, 2, 5, 6).contiguous().view(B, T, H, W, -1)\n return x\n\n\nclass WindowAttention(nn.Module):\n \"\"\"Temporal focal window attention\n \"\"\"\n def __init__(self, dim, expand_size, window_size, focal_window,\n focal_level, num_heads, qkv_bias, pool_method):\n\n super().__init__()\n self.dim = dim\n self.expand_size = expand_size\n self.window_size = window_size # Wh, Ww\n self.pool_method = pool_method\n self.num_heads = num_heads\n head_dim = dim // num_heads\n self.scale = head_dim**-0.5\n self.focal_level = focal_level\n self.focal_window = focal_window\n\n if any(i > 0 for i in self.expand_size) and focal_level > 0:\n # get mask for rolled k and rolled v\n mask_tl = torch.ones(self.window_size[0], self.window_size[1])\n mask_tl[:-self.expand_size[0], :-self.expand_size[1]] = 0\n mask_tr = torch.ones(self.window_size[0], self.window_size[1])\n mask_tr[:-self.expand_size[0], self.expand_size[1]:] = 0\n mask_bl = torch.ones(self.window_size[0], self.window_size[1])\n mask_bl[self.expand_size[0]:, :-self.expand_size[1]] = 0\n mask_br = torch.ones(self.window_size[0], self.window_size[1])\n mask_br[self.expand_size[0]:, self.expand_size[1]:] = 0\n mask_rolled = torch.stack((mask_tl, mask_tr, mask_bl, mask_br),\n 0).flatten(0)\n self.register_buffer(\"valid_ind_rolled\",\n mask_rolled.nonzero(as_tuple=False).view(-1))\n\n if pool_method != \"none\" and focal_level > 1:\n self.unfolds = nn.ModuleList()\n\n # build relative position bias between local patch and pooled windows\n for k in range(focal_level - 1):\n stride = 2**k\n kernel_size = tuple(2 * (i // 2) + 2**k + (2**k - 1)\n for i in self.focal_window)\n # define unfolding operations\n self.unfolds += [\n nn.Unfold(kernel_size=kernel_size,\n stride=stride,\n padding=tuple(i // 2 for i in kernel_size))\n ]\n\n # define unfolding index for focal_level > 0\n if k > 0:\n mask = torch.zeros(kernel_size)\n mask[(2**k) - 1:, (2**k) - 1:] = 1\n self.register_buffer(\n \"valid_ind_unfold_{}\".format(k),\n mask.flatten(0).nonzero(as_tuple=False).view(-1))\n\n self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n self.proj = nn.Linear(dim, dim)\n\n self.softmax = nn.Softmax(dim=-1)\n\n def forward(self, x_all, mask_all=None):\n \"\"\"\n Args:\n x: input features with shape of (B, T, Wh, Ww, C)\n mask: (0/-inf) mask with shape of (num_windows, T*Wh*Ww, T*Wh*Ww) or None\n\n output: (nW*B, Wh*Ww, C)\n \"\"\"\n x = x_all[0]\n\n B, T, nH, nW, C = x.shape\n qkv = self.qkv(x).reshape(B, T, nH, nW, 3,\n C).permute(4, 0, 1, 2, 3, 5).contiguous()\n q, k, v = qkv[0], qkv[1], qkv[2] # B, T, nH, nW, C\n\n # partition q map\n (q_windows, k_windows, v_windows) = map(\n lambda t: window_partition(t, self.window_size).view(\n -1, T, self.window_size[0] * self.window_size[1], self.\n num_heads, C // self.num_heads).permute(0, 3, 1, 2, 4).\n contiguous().view(-1, self.num_heads, T * self.window_size[\n 0] * self.window_size[1], C // self.num_heads), (q, k, v))\n # q(k/v)_windows shape : [16, 4, 225, 128]\n\n if any(i > 0 for i in self.expand_size) and self.focal_level > 0:\n (k_tl, v_tl) = map(\n lambda t: torch.roll(t,\n shifts=(-self.expand_size[0], -self.\n expand_size[1]),\n dims=(2, 3)), (k, v))\n (k_tr, v_tr) = map(\n lambda t: torch.roll(t,\n shifts=(-self.expand_size[0], self.\n expand_size[1]),\n dims=(2, 3)), (k, v))\n (k_bl, v_bl) = map(\n lambda t: torch.roll(t,\n shifts=(self.expand_size[0], -self.\n expand_size[1]),\n dims=(2, 3)), (k, v))\n (k_br, v_br) = map(\n lambda t: torch.roll(t,\n shifts=(self.expand_size[0], self.\n expand_size[1]),\n dims=(2, 3)), (k, v))\n\n (k_tl_windows, k_tr_windows, k_bl_windows, k_br_windows) = map(\n lambda t: window_partition(t, self.window_size).view(\n -1, T, self.window_size[0] * self.window_size[1], self.\n num_heads, C // self.num_heads), (k_tl, k_tr, k_bl, k_br))\n (v_tl_windows, v_tr_windows, v_bl_windows, v_br_windows) = map(\n lambda t: window_partition(t, self.window_size).view(\n -1, T, self.window_size[0] * self.window_size[1], self.\n num_heads, C // self.num_heads), (v_tl, v_tr, v_bl, v_br))\n k_rolled = torch.cat(\n (k_tl_windows, k_tr_windows, k_bl_windows, k_br_windows),\n 2).permute(0, 3, 1, 2, 4).contiguous()\n v_rolled = torch.cat(\n (v_tl_windows, v_tr_windows, v_bl_windows, v_br_windows),\n 2).permute(0, 3, 1, 2, 4).contiguous()\n\n # mask out tokens in current window\n k_rolled = k_rolled[:, :, :, self.valid_ind_rolled]\n v_rolled = v_rolled[:, :, :, self.valid_ind_rolled]\n temp_N = k_rolled.shape[3]\n k_rolled = k_rolled.view(-1, self.num_heads, T * temp_N,\n C // self.num_heads)\n v_rolled = v_rolled.view(-1, self.num_heads, T * temp_N,\n C // self.num_heads)\n k_rolled = torch.cat((k_windows, k_rolled), 2)\n v_rolled = torch.cat((v_windows, v_rolled), 2)\n else:\n k_rolled = k_windows\n v_rolled = v_windows\n\n # q(k/v)_windows shape : [16, 4, 225, 128]\n # k_rolled.shape : [16, 4, 5, 165, 128]\n # ideal expanded window size 153 ((5+2*2)*(9+2*4))\n # k_windows=45 expand_window=108 overlap_window=12 (since expand_size < window_size / 2)\n\n if self.pool_method != \"none\" and self.focal_level > 1:\n k_pooled = []\n v_pooled = []\n for k in range(self.focal_level - 1):\n stride = 2**k\n x_window_pooled = x_all[k + 1].permute(\n 0, 3, 1, 2, 4).contiguous() # B, T, nWh, nWw, C\n\n nWh, nWw = x_window_pooled.shape[2:4]\n\n # generate mask for pooled windows\n mask = x_window_pooled.new(T, nWh, nWw).fill_(1)\n # unfold mask: [nWh*nWw//s//s, k*k, 1]\n unfolded_mask = self.unfolds[k](mask.unsqueeze(1)).view(\n 1, T, self.unfolds[k].kernel_size[0], self.unfolds[k].kernel_size[1], -1).permute(4, 1, 2, 3, 0).contiguous().\\\n view(nWh*nWw // stride // stride, -1, 1)\n\n if k > 0:\n valid_ind_unfold_k = getattr(\n self, \"valid_ind_unfold_{}\".format(k))\n unfolded_mask = unfolded_mask[:, valid_ind_unfold_k]\n\n x_window_masks = unfolded_mask.flatten(1).unsqueeze(0)\n x_window_masks = x_window_masks.masked_fill(\n x_window_masks == 0,\n float(-100.0)).masked_fill(x_window_masks > 0, float(0.0))\n mask_all[k + 1] = x_window_masks\n\n # generate k and v for pooled windows\n qkv_pooled = self.qkv(x_window_pooled).reshape(\n B, T, nWh, nWw, 3, C).permute(4, 0, 1, 5, 2,\n 3).view(3, -1, C, nWh,\n nWw).contiguous()\n k_pooled_k, v_pooled_k = qkv_pooled[1], qkv_pooled[\n 2] # B*T, C, nWh, nWw\n # k_pooled_k shape: [5, 512, 4, 4]\n # self.unfolds[k](k_pooled_k) shape: [5, 23040 (512 * 5 * 9 ), 16]\n\n (k_pooled_k, v_pooled_k) = map(\n lambda t: self.unfolds[k](t).view(\n B, T, C, self.unfolds[k].kernel_size[0], self.unfolds[k].kernel_size[1], -1).permute(0, 5, 1, 3, 4, 2).contiguous().\\\n view(-1, T, self.unfolds[k].kernel_size[0]*self.unfolds[k].kernel_size[1], self.num_heads, C // self.num_heads).permute(0, 3, 1, 2, 4).contiguous(),\n (k_pooled_k, v_pooled_k) # (B x (nH*nW)) x nHeads x T x (unfold_wsize x unfold_wsize) x head_dim\n )\n # k_pooled_k shape : [16, 4, 5, 45, 128]\n\n # select valid unfolding index\n if k > 0:\n (k_pooled_k, v_pooled_k) = map(\n lambda t: t[:, :, :, valid_ind_unfold_k],\n (k_pooled_k, v_pooled_k))\n\n k_pooled_k = k_pooled_k.view(\n -1, self.num_heads, T * self.unfolds[k].kernel_size[0] *\n self.unfolds[k].kernel_size[1], C // self.num_heads)\n v_pooled_k = v_pooled_k.view(\n -1, self.num_heads, T * self.unfolds[k].kernel_size[0] *\n self.unfolds[k].kernel_size[1], C // self.num_heads)\n\n k_pooled += [k_pooled_k]\n v_pooled += [v_pooled_k]\n\n # k_all (v_all) shape : [16, 4, 5 * 210, 128]\n k_all = torch.cat([k_rolled] + k_pooled, 2)\n v_all = torch.cat([v_rolled] + v_pooled, 2)\n else:\n k_all = k_rolled\n v_all = v_rolled\n\n N = k_all.shape[-2]\n q_windows = q_windows * self.scale\n attn = (\n q_windows @ k_all.transpose(-2, -1)\n ) # B*nW, nHead, T*window_size*window_size, T*focal_window_size*focal_window_size\n # T * 45\n window_area = T * self.window_size[0] * self.window_size[1]\n # T * 165\n window_area_rolled = k_rolled.shape[2]\n\n if self.pool_method != \"none\" and self.focal_level > 1:\n offset = window_area_rolled\n for k in range(self.focal_level - 1):\n # add attentional mask\n # mask_all[1] shape [1, 16, T * 45]\n\n bias = tuple((i + 2**k - 1) for i in self.focal_window)\n\n if mask_all[k + 1] is not None:\n attn[:, :, :window_area, offset:(offset + (T*bias[0]*bias[1]))] = \\\n attn[:, :, :window_area, offset:(offset + (T*bias[0]*bias[1]))] + \\\n mask_all[k+1][:, :, None, None, :].repeat(attn.shape[0] // mask_all[k+1].shape[1], 1, 1, 1, 1).view(-1, 1, 1, mask_all[k+1].shape[-1])\n\n offset += T * bias[0] * bias[1]\n\n if mask_all[0] is not None:\n nW = mask_all[0].shape[0]\n attn = attn.view(attn.shape[0] // nW, nW, self.num_heads,\n window_area, N)\n attn[:, :, :, :, :\n window_area] = attn[:, :, :, :, :window_area] + mask_all[0][\n None, :, None, :, :]\n attn = attn.view(-1, self.num_heads, window_area, N)\n attn = self.softmax(attn)\n else:\n attn = self.softmax(attn)\n\n x = (attn @ v_all).transpose(1, 2).reshape(attn.shape[0], window_area,\n C)\n x = self.proj(x)\n return x\n\n\nclass TemporalFocalTransformerBlock(nn.Module):\n r\"\"\" Temporal Focal Transformer Block.\n Args:\n dim (int): Number of input channels.\n num_heads (int): Number of attention heads.\n window_size (tuple[int]): Window size.\n shift_size (int): Shift size for SW-MSA.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n focal_level (int): The number level of focal window.\n focal_window (int): Window size of each focal window.\n n_vecs (int): Required for F3N.\n t2t_params (int): T2T parameters for F3N.\n \"\"\"\n def __init__(self,\n dim,\n num_heads,","source_hash":"55c9167ddc8b6b05c864d7dd8d1ae1ba4a24fc60e905e5be4912ac93397d4e89","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.tfocal_transformer.TemporalFocalTransformerBlock","uri":"program://Track-Anything/class/inpainter.model.modules.tfocal_transformer.TemporalFocalTransformerBlock#L402-L536","kind":"class","name":"TemporalFocalTransformerBlock","path":"inpainter/model/modules/tfocal_transformer.py","language":"python","start_line":402,"end_line":536,"context_start_line":382,"context_end_line":536,"code":" offset += T * bias[0] * bias[1]\n\n if mask_all[0] is not None:\n nW = mask_all[0].shape[0]\n attn = attn.view(attn.shape[0] // nW, nW, self.num_heads,\n window_area, N)\n attn[:, :, :, :, :\n window_area] = attn[:, :, :, :, :window_area] + mask_all[0][\n None, :, None, :, :]\n attn = attn.view(-1, self.num_heads, window_area, N)\n attn = self.softmax(attn)\n else:\n attn = self.softmax(attn)\n\n x = (attn @ v_all).transpose(1, 2).reshape(attn.shape[0], window_area,\n C)\n x = self.proj(x)\n return x\n\n\nclass TemporalFocalTransformerBlock(nn.Module):\n r\"\"\" Temporal Focal Transformer Block.\n Args:\n dim (int): Number of input channels.\n num_heads (int): Number of attention heads.\n window_size (tuple[int]): Window size.\n shift_size (int): Shift size for SW-MSA.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n focal_level (int): The number level of focal window.\n focal_window (int): Window size of each focal window.\n n_vecs (int): Required for F3N.\n t2t_params (int): T2T parameters for F3N.\n \"\"\"\n def __init__(self,\n dim,\n num_heads,\n window_size=(5, 9),\n mlp_ratio=4.,\n qkv_bias=True,\n pool_method=\"fc\",\n focal_level=2,\n focal_window=(5, 9),\n norm_layer=nn.LayerNorm,\n n_vecs=None,\n t2t_params=None):\n super().__init__()\n self.dim = dim\n self.num_heads = num_heads\n self.window_size = window_size\n self.expand_size = tuple(i // 2 for i in window_size) # TODO\n self.mlp_ratio = mlp_ratio\n self.pool_method = pool_method\n self.focal_level = focal_level\n self.focal_window = focal_window\n\n self.window_size_glo = self.window_size\n\n self.pool_layers = nn.ModuleList()\n if self.pool_method != \"none\":\n for k in range(self.focal_level - 1):\n window_size_glo = tuple(\n math.floor(i / (2**k)) for i in self.window_size_glo)\n self.pool_layers.append(\n nn.Linear(window_size_glo[0] * window_size_glo[1], 1))\n self.pool_layers[-1].weight.data.fill_(\n 1. / (window_size_glo[0] * window_size_glo[1]))\n self.pool_layers[-1].bias.data.fill_(0)\n\n self.norm1 = norm_layer(dim)\n\n self.attn = WindowAttention(dim,\n expand_size=self.expand_size,\n window_size=self.window_size,\n focal_window=focal_window,\n focal_level=focal_level,\n num_heads=num_heads,\n qkv_bias=qkv_bias,\n pool_method=pool_method)\n\n self.norm2 = norm_layer(dim)\n self.mlp = FusionFeedForward(dim, n_vecs=n_vecs, t2t_params=t2t_params)\n\n def forward(self, x):\n B, T, H, W, C = x.shape\n\n shortcut = x\n x = self.norm1(x)\n\n shifted_x = x\n\n x_windows_all = [shifted_x]\n x_window_masks_all = [None]\n\n # partition windows tuple(i // 2 for i in window_size)\n if self.focal_level > 1 and self.pool_method != \"none\":\n # if we add coarser granularity and the pool method is not none\n for k in range(self.focal_level - 1):\n window_size_glo = tuple(\n math.floor(i / (2**k)) for i in self.window_size_glo)\n pooled_h = math.ceil(H / window_size_glo[0]) * (2**k)\n pooled_w = math.ceil(W / window_size_glo[1]) * (2**k)\n H_pool = pooled_h * window_size_glo[0]\n W_pool = pooled_w * window_size_glo[1]\n\n x_level_k = shifted_x\n # trim or pad shifted_x depending on the required size\n if H > H_pool:\n trim_t = (H - H_pool) // 2\n trim_b = H - H_pool - trim_t\n x_level_k = x_level_k[:, :, trim_t:-trim_b]\n elif H < H_pool:\n pad_t = (H_pool - H) // 2\n pad_b = H_pool - H - pad_t\n x_level_k = F.pad(x_level_k, (0, 0, 0, 0, pad_t, pad_b))\n\n if W > W_pool:\n trim_l = (W - W_pool) // 2\n trim_r = W - W_pool - trim_l\n x_level_k = x_level_k[:, :, :, trim_l:-trim_r]\n elif W < W_pool:\n pad_l = (W_pool - W) // 2\n pad_r = W_pool - W - pad_l\n x_level_k = F.pad(x_level_k, (0, 0, pad_l, pad_r))\n\n x_windows_noreshape = window_partition_noreshape(\n x_level_k.contiguous(), window_size_glo\n ) # B, nw, nw, T, window_size, window_size, C\n nWh, nWw = x_windows_noreshape.shape[1:3]\n x_windows_noreshape = x_windows_noreshape.view(\n B, nWh, nWw, T, window_size_glo[0] * window_size_glo[1],\n C).transpose(4, 5) # B, nWh, nWw, T, C, wsize**2\n x_windows_pooled = self.pool_layers[k](\n x_windows_noreshape).flatten(-2) # B, nWh, nWw, T, C\n\n x_windows_all += [x_windows_pooled]\n x_window_masks_all += [None]\n\n attn_windows = self.attn(\n x_windows_all,\n mask_all=x_window_masks_all) # nW*B, T*window_size*window_size, C\n\n # merge windows\n attn_windows = attn_windows.view(-1, T, self.window_size[0],\n self.window_size[1], C)\n shifted_x = window_reverse(attn_windows, self.window_size, T, H,\n W) # B T H' W' C\n\n # FFN\n x = shortcut + shifted_x\n y = self.norm2(x)\n x = x + self.mlp(y.view(B, T * H * W, C)).view(B, T, H, W, C)\n\n return x","source_hash":"55c9167ddc8b6b05c864d7dd8d1ae1ba4a24fc60e905e5be4912ac93397d4e89","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.tfocal_transformer.__init__","uri":"program://Track-Anything/function/inpainter.model.modules.tfocal_transformer.__init__#L417-L464","kind":"function","name":"__init__","path":"inpainter/model/modules/tfocal_transformer.py","language":"python","start_line":417,"end_line":464,"context_start_line":397,"context_end_line":484,"code":" C)\n x = self.proj(x)\n return x\n\n\nclass TemporalFocalTransformerBlock(nn.Module):\n r\"\"\" Temporal Focal Transformer Block.\n Args:\n dim (int): Number of input channels.\n num_heads (int): Number of attention heads.\n window_size (tuple[int]): Window size.\n shift_size (int): Shift size for SW-MSA.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n focal_level (int): The number level of focal window.\n focal_window (int): Window size of each focal window.\n n_vecs (int): Required for F3N.\n t2t_params (int): T2T parameters for F3N.\n \"\"\"\n def __init__(self,\n dim,\n num_heads,\n window_size=(5, 9),\n mlp_ratio=4.,\n qkv_bias=True,\n pool_method=\"fc\",\n focal_level=2,\n focal_window=(5, 9),\n norm_layer=nn.LayerNorm,\n n_vecs=None,\n t2t_params=None):\n super().__init__()\n self.dim = dim\n self.num_heads = num_heads\n self.window_size = window_size\n self.expand_size = tuple(i // 2 for i in window_size) # TODO\n self.mlp_ratio = mlp_ratio\n self.pool_method = pool_method\n self.focal_level = focal_level\n self.focal_window = focal_window\n\n self.window_size_glo = self.window_size\n\n self.pool_layers = nn.ModuleList()\n if self.pool_method != \"none\":\n for k in range(self.focal_level - 1):\n window_size_glo = tuple(\n math.floor(i / (2**k)) for i in self.window_size_glo)\n self.pool_layers.append(\n nn.Linear(window_size_glo[0] * window_size_glo[1], 1))\n self.pool_layers[-1].weight.data.fill_(\n 1. / (window_size_glo[0] * window_size_glo[1]))\n self.pool_layers[-1].bias.data.fill_(0)\n\n self.norm1 = norm_layer(dim)\n\n self.attn = WindowAttention(dim,\n expand_size=self.expand_size,\n window_size=self.window_size,\n focal_window=focal_window,\n focal_level=focal_level,\n num_heads=num_heads,\n qkv_bias=qkv_bias,\n pool_method=pool_method)\n\n self.norm2 = norm_layer(dim)\n self.mlp = FusionFeedForward(dim, n_vecs=n_vecs, t2t_params=t2t_params)\n\n def forward(self, x):\n B, T, H, W, C = x.shape\n\n shortcut = x\n x = self.norm1(x)\n\n shifted_x = x\n\n x_windows_all = [shifted_x]\n x_window_masks_all = [None]\n\n # partition windows tuple(i // 2 for i in window_size)\n if self.focal_level > 1 and self.pool_method != \"none\":\n # if we add coarser granularity and the pool method is not none\n for k in range(self.focal_level - 1):\n window_size_glo = tuple(\n math.floor(i / (2**k)) for i in self.window_size_glo)\n pooled_h = math.ceil(H / window_size_glo[0]) * (2**k)\n pooled_w = math.ceil(W / window_size_glo[1]) * (2**k)","source_hash":"55c9167ddc8b6b05c864d7dd8d1ae1ba4a24fc60e905e5be4912ac93397d4e89","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.tfocal_transformer.forward","uri":"program://Track-Anything/function/inpainter.model.modules.tfocal_transformer.forward#L466-L536","kind":"function","name":"forward","path":"inpainter/model/modules/tfocal_transformer.py","language":"python","start_line":466,"end_line":536,"context_start_line":446,"context_end_line":536,"code":" self.pool_layers.append(\n nn.Linear(window_size_glo[0] * window_size_glo[1], 1))\n self.pool_layers[-1].weight.data.fill_(\n 1. / (window_size_glo[0] * window_size_glo[1]))\n self.pool_layers[-1].bias.data.fill_(0)\n\n self.norm1 = norm_layer(dim)\n\n self.attn = WindowAttention(dim,\n expand_size=self.expand_size,\n window_size=self.window_size,\n focal_window=focal_window,\n focal_level=focal_level,\n num_heads=num_heads,\n qkv_bias=qkv_bias,\n pool_method=pool_method)\n\n self.norm2 = norm_layer(dim)\n self.mlp = FusionFeedForward(dim, n_vecs=n_vecs, t2t_params=t2t_params)\n\n def forward(self, x):\n B, T, H, W, C = x.shape\n\n shortcut = x\n x = self.norm1(x)\n\n shifted_x = x\n\n x_windows_all = [shifted_x]\n x_window_masks_all = [None]\n\n # partition windows tuple(i // 2 for i in window_size)\n if self.focal_level > 1 and self.pool_method != \"none\":\n # if we add coarser granularity and the pool method is not none\n for k in range(self.focal_level - 1):\n window_size_glo = tuple(\n math.floor(i / (2**k)) for i in self.window_size_glo)\n pooled_h = math.ceil(H / window_size_glo[0]) * (2**k)\n pooled_w = math.ceil(W / window_size_glo[1]) * (2**k)\n H_pool = pooled_h * window_size_glo[0]\n W_pool = pooled_w * window_size_glo[1]\n\n x_level_k = shifted_x\n # trim or pad shifted_x depending on the required size\n if H > H_pool:\n trim_t = (H - H_pool) // 2\n trim_b = H - H_pool - trim_t\n x_level_k = x_level_k[:, :, trim_t:-trim_b]\n elif H < H_pool:\n pad_t = (H_pool - H) // 2\n pad_b = H_pool - H - pad_t\n x_level_k = F.pad(x_level_k, (0, 0, 0, 0, pad_t, pad_b))\n\n if W > W_pool:\n trim_l = (W - W_pool) // 2\n trim_r = W - W_pool - trim_l\n x_level_k = x_level_k[:, :, :, trim_l:-trim_r]\n elif W < W_pool:\n pad_l = (W_pool - W) // 2\n pad_r = W_pool - W - pad_l\n x_level_k = F.pad(x_level_k, (0, 0, pad_l, pad_r))\n\n x_windows_noreshape = window_partition_noreshape(\n x_level_k.contiguous(), window_size_glo\n ) # B, nw, nw, T, window_size, window_size, C\n nWh, nWw = x_windows_noreshape.shape[1:3]\n x_windows_noreshape = x_windows_noreshape.view(\n B, nWh, nWw, T, window_size_glo[0] * window_size_glo[1],\n C).transpose(4, 5) # B, nWh, nWw, T, C, wsize**2\n x_windows_pooled = self.pool_layers[k](\n x_windows_noreshape).flatten(-2) # B, nWh, nWw, T, C\n\n x_windows_all += [x_windows_pooled]\n x_window_masks_all += [None]\n\n attn_windows = self.attn(\n x_windows_all,\n mask_all=x_window_masks_all) # nW*B, T*window_size*window_size, C\n\n # merge windows\n attn_windows = attn_windows.view(-1, T, self.window_size[0],\n self.window_size[1], C)\n shifted_x = window_reverse(attn_windows, self.window_size, T, H,\n W) # B T H' W' C\n\n # FFN\n x = shortcut + shifted_x\n y = self.norm2(x)\n x = x + self.mlp(y.view(B, T * H * W, C)).view(B, T, H, W, C)\n\n return x","source_hash":"55c9167ddc8b6b05c864d7dd8d1ae1ba4a24fc60e905e5be4912ac93397d4e89","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.spectral_norm","uri":"program://Track-Anything/module/inpainter.model.modules.spectral_norm#L1-L288","kind":"module","name":"inpainter.model.modules.spectral_norm","path":"inpainter/model/modules/spectral_norm.py","language":"python","start_line":1,"end_line":288,"context_start_line":1,"context_end_line":288,"code":"\"\"\"\nSpectral Normalization from https://arxiv.org/abs/1802.05957\n\"\"\"\nimport torch\nfrom torch.nn.functional import normalize\n\n\nclass SpectralNorm(object):\n # Invariant before and after each forward call:\n # u = normalize(W @ v)\n # NB: At initialization, this invariant is not enforced\n\n _version = 1\n\n # At version 1:\n # made `W` not a buffer,\n # added `v` as a buffer, and\n # made eval mode use `W = u @ W_orig @ v` rather than the stored `W`.\n\n def __init__(self, name='weight', n_power_iterations=1, dim=0, eps=1e-12):\n self.name = name\n self.dim = dim\n if n_power_iterations <= 0:\n raise ValueError(\n 'Expected n_power_iterations to be positive, but '\n 'got n_power_iterations={}'.format(n_power_iterations))\n self.n_power_iterations = n_power_iterations\n self.eps = eps\n\n def reshape_weight_to_matrix(self, weight):\n weight_mat = weight\n if self.dim != 0:\n # permute dim to front\n weight_mat = weight_mat.permute(\n self.dim,\n *[d for d in range(weight_mat.dim()) if d != self.dim])\n height = weight_mat.size(0)\n return weight_mat.reshape(height, -1)\n\n def compute_weight(self, module, do_power_iteration):\n # NB: If `do_power_iteration` is set, the `u` and `v` vectors are\n # updated in power iteration **in-place**. This is very important\n # because in `DataParallel` forward, the vectors (being buffers) are\n # broadcast from the parallelized module to each module replica,\n # which is a new module object created on the fly. And each replica\n # runs its own spectral norm power iteration. So simply assigning\n # the updated vectors to the module this function runs on will cause\n # the update to be lost forever. And the next time the parallelized\n # module is replicated, the same randomly initialized vectors are\n # broadcast and used!\n #\n # Therefore, to make the change propagate back, we rely on two\n # important behaviors (also enforced via tests):\n # 1. `DataParallel` doesn't clone storage if the broadcast tensor\n # is already on correct device; and it makes sure that the\n # parallelized module is already on `device[0]`.\n # 2. If the out tensor in `out=` kwarg has correct shape, it will\n # just fill in the values.\n # Therefore, since the same power iteration is performed on all\n # devices, simply updating the tensors in-place will make sure that\n # the module replica on `device[0]` will update the _u vector on the\n # parallized module (by shared storage).\n #\n # However, after we update `u` and `v` in-place, we need to **clone**\n # them before using them to normalize the weight. This is to support\n # backproping through two forward passes, e.g., the common pattern in\n # GAN training: loss = D(real) - D(fake). Otherwise, engine will\n # complain that variables needed to do backward for the first forward\n # (i.e., the `u` and `v` vectors) are changed in the second forward.\n weight = getattr(module, self.name + '_orig')\n u = getattr(module, self.name + '_u')\n v = getattr(module, self.name + '_v')\n weight_mat = self.reshape_weight_to_matrix(weight)\n\n if do_power_iteration:\n with torch.no_grad():\n for _ in range(self.n_power_iterations):\n # Spectral norm of weight equals to `u^T W v`, where `u` and `v`\n # are the first left and right singular vectors.\n # This power iteration produces approximations of `u` and `v`.\n v = normalize(torch.mv(weight_mat.t(), u),\n dim=0,\n eps=self.eps,\n out=v)\n u = normalize(torch.mv(weight_mat, v),\n dim=0,\n eps=self.eps,\n out=u)\n if self.n_power_iterations > 0:\n # See above on why we need to clone\n u = u.clone()\n v = v.clone()\n\n sigma = torch.dot(u, torch.mv(weight_mat, v))\n weight = weight / sigma\n return weight\n\n def remove(self, module):\n with torch.no_grad():\n weight = self.compute_weight(module, do_power_iteration=False)\n delattr(module, self.name)\n delattr(module, self.name + '_u')\n delattr(module, self.name + '_v')\n delattr(module, self.name + '_orig')\n module.register_parameter(self.name,\n torch.nn.Parameter(weight.detach()))\n\n def __call__(self, module, inputs):\n setattr(\n module, self.name,\n self.compute_weight(module, do_power_iteration=module.training))\n\n def _solve_v_and_rescale(self, weight_mat, u, target_sigma):\n # Tries to returns a vector `v` s.t. `u = normalize(W @ v)`\n # (the invariant at top of this class) and `u @ W @ v = sigma`.\n # This uses pinverse in case W^T W is not invertible.\n v = torch.chain_matmul(weight_mat.t().mm(weight_mat).pinverse(),\n weight_mat.t(), u.unsqueeze(1)).squeeze(1)\n return v.mul_(target_sigma / torch.dot(u, torch.mv(weight_mat, v)))\n\n @staticmethod\n def apply(module, name, n_power_iterations, dim, eps):\n for k, hook in module._forward_pre_hooks.items():\n if isinstance(hook, SpectralNorm) and hook.name == name:\n raise RuntimeError(\n \"Cannot register two spectral_norm hooks on \"\n \"the same parameter {}\".format(name))\n\n fn = SpectralNorm(name, n_power_iterations, dim, eps)\n weight = module._parameters[name]\n\n with torch.no_grad():\n weight_mat = fn.reshape_weight_to_matrix(weight)\n\n h, w = weight_mat.size()\n # randomly initialize `u` and `v`\n u = normalize(weight.new_empty(h).normal_(0, 1), dim=0, eps=fn.eps)\n v = normalize(weight.new_empty(w).normal_(0, 1), dim=0, eps=fn.eps)\n\n delattr(module, fn.name)\n module.register_parameter(fn.name + \"_orig\", weight)\n # We still need to assign weight back as fn.name because all sorts of\n # things may assume that it exists, e.g., when initializing weights.\n # However, we can't directly assign as it could be an nn.Parameter and\n # gets added as a parameter. Instead, we register weight.data as a plain\n # attribute.\n setattr(module, fn.name, weight.data)\n module.register_buffer(fn.name + \"_u\", u)\n module.register_buffer(fn.name + \"_v\", v)\n\n module.register_forward_pre_hook(fn)\n\n module._register_state_dict_hook(SpectralNormStateDictHook(fn))\n module._register_load_state_dict_pre_hook(\n SpectralNormLoadStateDictPreHook(fn))\n return fn\n\n\n# This is a top level class because Py2 pickle doesn't like inner class nor an\n# instancemethod.\nclass SpectralNormLoadStateDictPreHook(object):\n # See docstring of SpectralNorm._version on the changes to spectral_norm.\n def __init__(self, fn):\n self.fn = fn\n\n # For state_dict with version None, (assuming that it has gone through at\n # least one training forward), we have\n #\n # u = normalize(W_orig @ v)\n # W = W_orig / sigma, where sigma = u @ W_orig @ v\n #\n # To compute `v`, we solve `W_orig @ x = u`, and let\n # v = x / (u @ W_orig @ x) * (W / W_orig).\n def __call__(self, state_dict, prefix, local_metadata, strict,\n missing_keys, unexpected_keys, error_msgs):\n fn = self.fn\n version = local_metadata.get('spectral_norm',\n {}).get(fn.name + '.version', None)\n if version is None or version < 1:\n with torch.no_grad():\n weight_orig = state_dict[prefix + fn.name + '_orig']\n # weight = state_dict.pop(prefix + fn.name)\n # sigma = (weight_orig / weight).mean()\n weight_mat = fn.reshape_weight_to_matrix(weight_orig)\n u = state_dict[prefix + fn.name + '_u']\n # v = fn._solve_v_and_rescale(weight_mat, u, sigma)\n # state_dict[prefix + fn.name + '_v'] = v\n\n\n# This is a top level class because Py2 pickle doesn't like inner class nor an\n# instancemethod.\nclass SpectralNormStateDictHook(object):\n # See docstring of SpectralNorm._version on the changes to spectral_norm.\n def __init__(self, fn):\n self.fn = fn\n\n def __call__(self, module, state_dict, prefix, local_metadata):\n if 'spectral_norm' not in local_metadata:\n local_metadata['spectral_norm'] = {}\n key = self.fn.name + '.version'\n if key in local_metadata['spectral_norm']:\n raise RuntimeError(\n \"Unexpected key in metadata['spectral_norm']: {}\".format(key))\n local_metadata['spectral_norm'][key] = self.fn._version\n\n\ndef spectral_norm(module,\n name='weight',\n n_power_iterations=1,\n eps=1e-12,\n dim=None):\n r\"\"\"Applies spectral normalization to a parameter in the given module.\n\n .. math::\n \\mathbf{W}_{SN} = \\dfrac{\\mathbf{W}}{\\sigma(\\mathbf{W})},\n \\sigma(\\mathbf{W}) = \\max_{\\mathbf{h}: \\mathbf{h} \\ne 0} \\dfrac{\\|\\mathbf{W} \\mathbf{h}\\|_2}{\\|\\mathbf{h}\\|_2}\n\n Spectral normalization stabilizes the training of discriminators (critics)\n in Generative Adversarial Networks (GANs) by rescaling the weight tensor\n with spectral norm :math:`\\sigma` of the weight matrix calculated using\n power iteration method. If the dimension of the weight tensor is greater\n than 2, it is reshaped to 2D in power iteration method to get spectral\n norm. This is implemented via a hook that calculates spectral norm and\n rescales weight before every :meth:`~Module.forward` call.\n\n See `Spectral Normalization for Generative Adversarial Networks`_ .\n\n .. _`Spectral Normalization for Generative Adversarial Networks`: https://arxiv.org/abs/1802.05957\n\n Args:\n module (nn.Module): containing module\n name (str, optional): name of weight parameter\n n_power_iterations (int, optional): number of power iterations to\n calculate spectral norm\n eps (float, optional): epsilon for numerical stability in\n calculating norms\n dim (int, optional): dimension corresponding to number of outputs,\n the default is ``0``, except for modules that are instances of\n ConvTranspose{1,2,3}d, when it is ``1``\n\n Returns:\n The original module with the spectral norm hook\n\n Example::\n\n >>> m = spectral_norm(nn.Linear(20, 40))\n >>> m\n Linear(in_features=20, out_features=40, bias=True)\n >>> m.weight_u.size()\n torch.Size([40])\n\n \"\"\"\n if dim is None:\n if isinstance(module,\n (torch.nn.ConvTranspose1d, torch.nn.ConvTranspose2d,\n torch.nn.ConvTranspose3d)):\n dim = 1\n else:\n dim = 0\n SpectralNorm.apply(module, name, n_power_iterations, dim, eps)\n return module\n\n\ndef remove_spectral_norm(module, name='weight'):\n r\"\"\"Removes the spectral normalization reparameterization from a module.\n\n Args:\n module (Module): containing module\n name (str, optional): name of weight parameter\n\n Example:\n >>> m = spectral_norm(nn.Linear(40, 10))\n >>> remove_spectral_norm(m)\n \"\"\"\n for k, hook in module._forward_pre_hooks.items():\n if isinstance(hook, SpectralNorm) and hook.name == name:\n hook.remove(module)\n del module._forward_pre_hooks[k]\n return module\n\n raise ValueError(\"spectral_norm of '{}' not found in {}\".format(\n name, module))\n\n\ndef use_spectral_norm(module, use_sn=False):\n if use_sn:\n return spectral_norm(module)\n return module","source_hash":"1ce8754d2e8fe34b3898a1f9e40c8b2e196da1596e468f0cd8167cace625028f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.spectral_norm.SpectralNorm","uri":"program://Track-Anything/class/inpainter.model.modules.spectral_norm.SpectralNorm#L8-L156","kind":"class","name":"SpectralNorm","path":"inpainter/model/modules/spectral_norm.py","language":"python","start_line":8,"end_line":156,"context_start_line":1,"context_end_line":176,"code":"\"\"\"\nSpectral Normalization from https://arxiv.org/abs/1802.05957\n\"\"\"\nimport torch\nfrom torch.nn.functional import normalize\n\n\nclass SpectralNorm(object):\n # Invariant before and after each forward call:\n # u = normalize(W @ v)\n # NB: At initialization, this invariant is not enforced\n\n _version = 1\n\n # At version 1:\n # made `W` not a buffer,\n # added `v` as a buffer, and\n # made eval mode use `W = u @ W_orig @ v` rather than the stored `W`.\n\n def __init__(self, name='weight', n_power_iterations=1, dim=0, eps=1e-12):\n self.name = name\n self.dim = dim\n if n_power_iterations <= 0:\n raise ValueError(\n 'Expected n_power_iterations to be positive, but '\n 'got n_power_iterations={}'.format(n_power_iterations))\n self.n_power_iterations = n_power_iterations\n self.eps = eps\n\n def reshape_weight_to_matrix(self, weight):\n weight_mat = weight\n if self.dim != 0:\n # permute dim to front\n weight_mat = weight_mat.permute(\n self.dim,\n *[d for d in range(weight_mat.dim()) if d != self.dim])\n height = weight_mat.size(0)\n return weight_mat.reshape(height, -1)\n\n def compute_weight(self, module, do_power_iteration):\n # NB: If `do_power_iteration` is set, the `u` and `v` vectors are\n # updated in power iteration **in-place**. This is very important\n # because in `DataParallel` forward, the vectors (being buffers) are\n # broadcast from the parallelized module to each module replica,\n # which is a new module object created on the fly. And each replica\n # runs its own spectral norm power iteration. So simply assigning\n # the updated vectors to the module this function runs on will cause\n # the update to be lost forever. And the next time the parallelized\n # module is replicated, the same randomly initialized vectors are\n # broadcast and used!\n #\n # Therefore, to make the change propagate back, we rely on two\n # important behaviors (also enforced via tests):\n # 1. `DataParallel` doesn't clone storage if the broadcast tensor\n # is already on correct device; and it makes sure that the\n # parallelized module is already on `device[0]`.\n # 2. If the out tensor in `out=` kwarg has correct shape, it will\n # just fill in the values.\n # Therefore, since the same power iteration is performed on all\n # devices, simply updating the tensors in-place will make sure that\n # the module replica on `device[0]` will update the _u vector on the\n # parallized module (by shared storage).\n #\n # However, after we update `u` and `v` in-place, we need to **clone**\n # them before using them to normalize the weight. This is to support\n # backproping through two forward passes, e.g., the common pattern in\n # GAN training: loss = D(real) - D(fake). Otherwise, engine will\n # complain that variables needed to do backward for the first forward\n # (i.e., the `u` and `v` vectors) are changed in the second forward.\n weight = getattr(module, self.name + '_orig')\n u = getattr(module, self.name + '_u')\n v = getattr(module, self.name + '_v')\n weight_mat = self.reshape_weight_to_matrix(weight)\n\n if do_power_iteration:\n with torch.no_grad():\n for _ in range(self.n_power_iterations):\n # Spectral norm of weight equals to `u^T W v`, where `u` and `v`\n # are the first left and right singular vectors.\n # This power iteration produces approximations of `u` and `v`.\n v = normalize(torch.mv(weight_mat.t(), u),\n dim=0,\n eps=self.eps,\n out=v)\n u = normalize(torch.mv(weight_mat, v),\n dim=0,\n eps=self.eps,\n out=u)\n if self.n_power_iterations > 0:\n # See above on why we need to clone\n u = u.clone()\n v = v.clone()\n\n sigma = torch.dot(u, torch.mv(weight_mat, v))\n weight = weight / sigma\n return weight\n\n def remove(self, module):\n with torch.no_grad():\n weight = self.compute_weight(module, do_power_iteration=False)\n delattr(module, self.name)\n delattr(module, self.name + '_u')\n delattr(module, self.name + '_v')\n delattr(module, self.name + '_orig')\n module.register_parameter(self.name,\n torch.nn.Parameter(weight.detach()))\n\n def __call__(self, module, inputs):\n setattr(\n module, self.name,\n self.compute_weight(module, do_power_iteration=module.training))\n\n def _solve_v_and_rescale(self, weight_mat, u, target_sigma):\n # Tries to returns a vector `v` s.t. `u = normalize(W @ v)`\n # (the invariant at top of this class) and `u @ W @ v = sigma`.\n # This uses pinverse in case W^T W is not invertible.\n v = torch.chain_matmul(weight_mat.t().mm(weight_mat).pinverse(),\n weight_mat.t(), u.unsqueeze(1)).squeeze(1)\n return v.mul_(target_sigma / torch.dot(u, torch.mv(weight_mat, v)))\n\n @staticmethod\n def apply(module, name, n_power_iterations, dim, eps):\n for k, hook in module._forward_pre_hooks.items():\n if isinstance(hook, SpectralNorm) and hook.name == name:\n raise RuntimeError(\n \"Cannot register two spectral_norm hooks on \"\n \"the same parameter {}\".format(name))\n\n fn = SpectralNorm(name, n_power_iterations, dim, eps)\n weight = module._parameters[name]\n\n with torch.no_grad():\n weight_mat = fn.reshape_weight_to_matrix(weight)\n\n h, w = weight_mat.size()\n # randomly initialize `u` and `v`\n u = normalize(weight.new_empty(h).normal_(0, 1), dim=0, eps=fn.eps)\n v = normalize(weight.new_empty(w).normal_(0, 1), dim=0, eps=fn.eps)\n\n delattr(module, fn.name)\n module.register_parameter(fn.name + \"_orig\", weight)\n # We still need to assign weight back as fn.name because all sorts of\n # things may assume that it exists, e.g., when initializing weights.\n # However, we can't directly assign as it could be an nn.Parameter and\n # gets added as a parameter. Instead, we register weight.data as a plain\n # attribute.\n setattr(module, fn.name, weight.data)\n module.register_buffer(fn.name + \"_u\", u)\n module.register_buffer(fn.name + \"_v\", v)\n\n module.register_forward_pre_hook(fn)\n\n module._register_state_dict_hook(SpectralNormStateDictHook(fn))\n module._register_load_state_dict_pre_hook(\n SpectralNormLoadStateDictPreHook(fn))\n return fn\n\n\n# This is a top level class because Py2 pickle doesn't like inner class nor an\n# instancemethod.\nclass SpectralNormLoadStateDictPreHook(object):\n # See docstring of SpectralNorm._version on the changes to spectral_norm.\n def __init__(self, fn):\n self.fn = fn\n\n # For state_dict with version None, (assuming that it has gone through at\n # least one training forward), we have\n #\n # u = normalize(W_orig @ v)\n # W = W_orig / sigma, where sigma = u @ W_orig @ v\n #\n # To compute `v`, we solve `W_orig @ x = u`, and let\n # v = x / (u @ W_orig @ x) * (W / W_orig).\n def __call__(self, state_dict, prefix, local_metadata, strict,\n missing_keys, unexpected_keys, error_msgs):\n fn = self.fn","source_hash":"1ce8754d2e8fe34b3898a1f9e40c8b2e196da1596e468f0cd8167cace625028f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.spectral_norm.SpectralNormLoadStateDictPreHook","uri":"program://Track-Anything/class/inpainter.model.modules.spectral_norm.SpectralNormLoadStateDictPreHook#L161-L185","kind":"class","name":"SpectralNormLoadStateDictPreHook","path":"inpainter/model/modules/spectral_norm.py","language":"python","start_line":161,"end_line":185,"context_start_line":141,"context_end_line":205,"code":" module.register_parameter(fn.name + \"_orig\", weight)\n # We still need to assign weight back as fn.name because all sorts of\n # things may assume that it exists, e.g., when initializing weights.\n # However, we can't directly assign as it could be an nn.Parameter and\n # gets added as a parameter. Instead, we register weight.data as a plain\n # attribute.\n setattr(module, fn.name, weight.data)\n module.register_buffer(fn.name + \"_u\", u)\n module.register_buffer(fn.name + \"_v\", v)\n\n module.register_forward_pre_hook(fn)\n\n module._register_state_dict_hook(SpectralNormStateDictHook(fn))\n module._register_load_state_dict_pre_hook(\n SpectralNormLoadStateDictPreHook(fn))\n return fn\n\n\n# This is a top level class because Py2 pickle doesn't like inner class nor an\n# instancemethod.\nclass SpectralNormLoadStateDictPreHook(object):\n # See docstring of SpectralNorm._version on the changes to spectral_norm.\n def __init__(self, fn):\n self.fn = fn\n\n # For state_dict with version None, (assuming that it has gone through at\n # least one training forward), we have\n #\n # u = normalize(W_orig @ v)\n # W = W_orig / sigma, where sigma = u @ W_orig @ v\n #\n # To compute `v`, we solve `W_orig @ x = u`, and let\n # v = x / (u @ W_orig @ x) * (W / W_orig).\n def __call__(self, state_dict, prefix, local_metadata, strict,\n missing_keys, unexpected_keys, error_msgs):\n fn = self.fn\n version = local_metadata.get('spectral_norm',\n {}).get(fn.name + '.version', None)\n if version is None or version < 1:\n with torch.no_grad():\n weight_orig = state_dict[prefix + fn.name + '_orig']\n # weight = state_dict.pop(prefix + fn.name)\n # sigma = (weight_orig / weight).mean()\n weight_mat = fn.reshape_weight_to_matrix(weight_orig)\n u = state_dict[prefix + fn.name + '_u']\n # v = fn._solve_v_and_rescale(weight_mat, u, sigma)\n # state_dict[prefix + fn.name + '_v'] = v\n\n\n# This is a top level class because Py2 pickle doesn't like inner class nor an\n# instancemethod.\nclass SpectralNormStateDictHook(object):\n # See docstring of SpectralNorm._version on the changes to spectral_norm.\n def __init__(self, fn):\n self.fn = fn\n\n def __call__(self, module, state_dict, prefix, local_metadata):\n if 'spectral_norm' not in local_metadata:\n local_metadata['spectral_norm'] = {}\n key = self.fn.name + '.version'\n if key in local_metadata['spectral_norm']:\n raise RuntimeError(\n \"Unexpected key in metadata['spectral_norm']: {}\".format(key))\n local_metadata['spectral_norm'][key] = self.fn._version\n","source_hash":"1ce8754d2e8fe34b3898a1f9e40c8b2e196da1596e468f0cd8167cace625028f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.spectral_norm.SpectralNormStateDictHook","uri":"program://Track-Anything/class/inpainter.model.modules.spectral_norm.SpectralNormStateDictHook#L192-L204","kind":"class","name":"SpectralNormStateDictHook","path":"inpainter/model/modules/spectral_norm.py","language":"python","start_line":192,"end_line":204,"context_start_line":172,"context_end_line":224,"code":" # To compute `v`, we solve `W_orig @ x = u`, and let\n # v = x / (u @ W_orig @ x) * (W / W_orig).\n def __call__(self, state_dict, prefix, local_metadata, strict,\n missing_keys, unexpected_keys, error_msgs):\n fn = self.fn\n version = local_metadata.get('spectral_norm',\n {}).get(fn.name + '.version', None)\n if version is None or version < 1:\n with torch.no_grad():\n weight_orig = state_dict[prefix + fn.name + '_orig']\n # weight = state_dict.pop(prefix + fn.name)\n # sigma = (weight_orig / weight).mean()\n weight_mat = fn.reshape_weight_to_matrix(weight_orig)\n u = state_dict[prefix + fn.name + '_u']\n # v = fn._solve_v_and_rescale(weight_mat, u, sigma)\n # state_dict[prefix + fn.name + '_v'] = v\n\n\n# This is a top level class because Py2 pickle doesn't like inner class nor an\n# instancemethod.\nclass SpectralNormStateDictHook(object):\n # See docstring of SpectralNorm._version on the changes to spectral_norm.\n def __init__(self, fn):\n self.fn = fn\n\n def __call__(self, module, state_dict, prefix, local_metadata):\n if 'spectral_norm' not in local_metadata:\n local_metadata['spectral_norm'] = {}\n key = self.fn.name + '.version'\n if key in local_metadata['spectral_norm']:\n raise RuntimeError(\n \"Unexpected key in metadata['spectral_norm']: {}\".format(key))\n local_metadata['spectral_norm'][key] = self.fn._version\n\n\ndef spectral_norm(module,\n name='weight',\n n_power_iterations=1,\n eps=1e-12,\n dim=None):\n r\"\"\"Applies spectral normalization to a parameter in the given module.\n\n .. math::\n \\mathbf{W}_{SN} = \\dfrac{\\mathbf{W}}{\\sigma(\\mathbf{W})},\n \\sigma(\\mathbf{W}) = \\max_{\\mathbf{h}: \\mathbf{h} \\ne 0} \\dfrac{\\|\\mathbf{W} \\mathbf{h}\\|_2}{\\|\\mathbf{h}\\|_2}\n\n Spectral normalization stabilizes the training of discriminators (critics)\n in Generative Adversarial Networks (GANs) by rescaling the weight tensor\n with spectral norm :math:`\\sigma` of the weight matrix calculated using\n power iteration method. If the dimension of the weight tensor is greater\n than 2, it is reshaped to 2D in power iteration method to get spectral\n norm. This is implemented via a hook that calculates spectral norm and\n rescales weight before every :meth:`~Module.forward` call.","source_hash":"1ce8754d2e8fe34b3898a1f9e40c8b2e196da1596e468f0cd8167cace625028f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.spectral_norm.spectral_norm","uri":"program://Track-Anything/function/inpainter.model.modules.spectral_norm.spectral_norm#L207-L261","kind":"function","name":"spectral_norm","path":"inpainter/model/modules/spectral_norm.py","language":"python","start_line":207,"end_line":261,"context_start_line":187,"context_end_line":281,"code":" # state_dict[prefix + fn.name + '_v'] = v\n\n\n# This is a top level class because Py2 pickle doesn't like inner class nor an\n# instancemethod.\nclass SpectralNormStateDictHook(object):\n # See docstring of SpectralNorm._version on the changes to spectral_norm.\n def __init__(self, fn):\n self.fn = fn\n\n def __call__(self, module, state_dict, prefix, local_metadata):\n if 'spectral_norm' not in local_metadata:\n local_metadata['spectral_norm'] = {}\n key = self.fn.name + '.version'\n if key in local_metadata['spectral_norm']:\n raise RuntimeError(\n \"Unexpected key in metadata['spectral_norm']: {}\".format(key))\n local_metadata['spectral_norm'][key] = self.fn._version\n\n\ndef spectral_norm(module,\n name='weight',\n n_power_iterations=1,\n eps=1e-12,\n dim=None):\n r\"\"\"Applies spectral normalization to a parameter in the given module.\n\n .. math::\n \\mathbf{W}_{SN} = \\dfrac{\\mathbf{W}}{\\sigma(\\mathbf{W})},\n \\sigma(\\mathbf{W}) = \\max_{\\mathbf{h}: \\mathbf{h} \\ne 0} \\dfrac{\\|\\mathbf{W} \\mathbf{h}\\|_2}{\\|\\mathbf{h}\\|_2}\n\n Spectral normalization stabilizes the training of discriminators (critics)\n in Generative Adversarial Networks (GANs) by rescaling the weight tensor\n with spectral norm :math:`\\sigma` of the weight matrix calculated using\n power iteration method. If the dimension of the weight tensor is greater\n than 2, it is reshaped to 2D in power iteration method to get spectral\n norm. This is implemented via a hook that calculates spectral norm and\n rescales weight before every :meth:`~Module.forward` call.\n\n See `Spectral Normalization for Generative Adversarial Networks`_ .\n\n .. _`Spectral Normalization for Generative Adversarial Networks`: https://arxiv.org/abs/1802.05957\n\n Args:\n module (nn.Module): containing module\n name (str, optional): name of weight parameter\n n_power_iterations (int, optional): number of power iterations to\n calculate spectral norm\n eps (float, optional): epsilon for numerical stability in\n calculating norms\n dim (int, optional): dimension corresponding to number of outputs,\n the default is ``0``, except for modules that are instances of\n ConvTranspose{1,2,3}d, when it is ``1``\n\n Returns:\n The original module with the spectral norm hook\n\n Example::\n\n >>> m = spectral_norm(nn.Linear(20, 40))\n >>> m\n Linear(in_features=20, out_features=40, bias=True)\n >>> m.weight_u.size()\n torch.Size([40])\n\n \"\"\"\n if dim is None:\n if isinstance(module,\n (torch.nn.ConvTranspose1d, torch.nn.ConvTranspose2d,\n torch.nn.ConvTranspose3d)):\n dim = 1\n else:\n dim = 0\n SpectralNorm.apply(module, name, n_power_iterations, dim, eps)\n return module\n\n\ndef remove_spectral_norm(module, name='weight'):\n r\"\"\"Removes the spectral normalization reparameterization from a module.\n\n Args:\n module (Module): containing module\n name (str, optional): name of weight parameter\n\n Example:\n >>> m = spectral_norm(nn.Linear(40, 10))\n >>> remove_spectral_norm(m)\n \"\"\"\n for k, hook in module._forward_pre_hooks.items():\n if isinstance(hook, SpectralNorm) and hook.name == name:\n hook.remove(module)\n del module._forward_pre_hooks[k]\n return module\n\n raise ValueError(\"spectral_norm of '{}' not found in {}\".format(","source_hash":"1ce8754d2e8fe34b3898a1f9e40c8b2e196da1596e468f0cd8167cace625028f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.spectral_norm.remove_spectral_norm","uri":"program://Track-Anything/function/inpainter.model.modules.spectral_norm.remove_spectral_norm#L264-L282","kind":"function","name":"remove_spectral_norm","path":"inpainter/model/modules/spectral_norm.py","language":"python","start_line":264,"end_line":282,"context_start_line":244,"context_end_line":288,"code":" Example::\n\n >>> m = spectral_norm(nn.Linear(20, 40))\n >>> m\n Linear(in_features=20, out_features=40, bias=True)\n >>> m.weight_u.size()\n torch.Size([40])\n\n \"\"\"\n if dim is None:\n if isinstance(module,\n (torch.nn.ConvTranspose1d, torch.nn.ConvTranspose2d,\n torch.nn.ConvTranspose3d)):\n dim = 1\n else:\n dim = 0\n SpectralNorm.apply(module, name, n_power_iterations, dim, eps)\n return module\n\n\ndef remove_spectral_norm(module, name='weight'):\n r\"\"\"Removes the spectral normalization reparameterization from a module.\n\n Args:\n module (Module): containing module\n name (str, optional): name of weight parameter\n\n Example:\n >>> m = spectral_norm(nn.Linear(40, 10))\n >>> remove_spectral_norm(m)\n \"\"\"\n for k, hook in module._forward_pre_hooks.items():\n if isinstance(hook, SpectralNorm) and hook.name == name:\n hook.remove(module)\n del module._forward_pre_hooks[k]\n return module\n\n raise ValueError(\"spectral_norm of '{}' not found in {}\".format(\n name, module))\n\n\ndef use_spectral_norm(module, use_sn=False):\n if use_sn:\n return spectral_norm(module)\n return module","source_hash":"1ce8754d2e8fe34b3898a1f9e40c8b2e196da1596e468f0cd8167cace625028f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.spectral_norm.use_spectral_norm","uri":"program://Track-Anything/function/inpainter.model.modules.spectral_norm.use_spectral_norm#L285-L288","kind":"function","name":"use_spectral_norm","path":"inpainter/model/modules/spectral_norm.py","language":"python","start_line":285,"end_line":288,"context_start_line":265,"context_end_line":288,"code":" r\"\"\"Removes the spectral normalization reparameterization from a module.\n\n Args:\n module (Module): containing module\n name (str, optional): name of weight parameter\n\n Example:\n >>> m = spectral_norm(nn.Linear(40, 10))\n >>> remove_spectral_norm(m)\n \"\"\"\n for k, hook in module._forward_pre_hooks.items():\n if isinstance(hook, SpectralNorm) and hook.name == name:\n hook.remove(module)\n del module._forward_pre_hooks[k]\n return module\n\n raise ValueError(\"spectral_norm of '{}' not found in {}\".format(\n name, module))\n\n\ndef use_spectral_norm(module, use_sn=False):\n if use_sn:\n return spectral_norm(module)\n return module","source_hash":"1ce8754d2e8fe34b3898a1f9e40c8b2e196da1596e468f0cd8167cace625028f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.spectral_norm.__init__","uri":"program://Track-Anything/function/inpainter.model.modules.spectral_norm.__init__#L194-L195","kind":"function","name":"__init__","path":"inpainter/model/modules/spectral_norm.py","language":"python","start_line":194,"end_line":195,"context_start_line":174,"context_end_line":215,"code":" def __call__(self, state_dict, prefix, local_metadata, strict,\n missing_keys, unexpected_keys, error_msgs):\n fn = self.fn\n version = local_metadata.get('spectral_norm',\n {}).get(fn.name + '.version', None)\n if version is None or version < 1:\n with torch.no_grad():\n weight_orig = state_dict[prefix + fn.name + '_orig']\n # weight = state_dict.pop(prefix + fn.name)\n # sigma = (weight_orig / weight).mean()\n weight_mat = fn.reshape_weight_to_matrix(weight_orig)\n u = state_dict[prefix + fn.name + '_u']\n # v = fn._solve_v_and_rescale(weight_mat, u, sigma)\n # state_dict[prefix + fn.name + '_v'] = v\n\n\n# This is a top level class because Py2 pickle doesn't like inner class nor an\n# instancemethod.\nclass SpectralNormStateDictHook(object):\n # See docstring of SpectralNorm._version on the changes to spectral_norm.\n def __init__(self, fn):\n self.fn = fn\n\n def __call__(self, module, state_dict, prefix, local_metadata):\n if 'spectral_norm' not in local_metadata:\n local_metadata['spectral_norm'] = {}\n key = self.fn.name + '.version'\n if key in local_metadata['spectral_norm']:\n raise RuntimeError(\n \"Unexpected key in metadata['spectral_norm']: {}\".format(key))\n local_metadata['spectral_norm'][key] = self.fn._version\n\n\ndef spectral_norm(module,\n name='weight',\n n_power_iterations=1,\n eps=1e-12,\n dim=None):\n r\"\"\"Applies spectral normalization to a parameter in the given module.\n\n .. math::\n \\mathbf{W}_{SN} = \\dfrac{\\mathbf{W}}{\\sigma(\\mathbf{W})},","source_hash":"1ce8754d2e8fe34b3898a1f9e40c8b2e196da1596e468f0cd8167cace625028f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.spectral_norm.reshape_weight_to_matrix","uri":"program://Track-Anything/function/inpainter.model.modules.spectral_norm.reshape_weight_to_matrix#L30-L38","kind":"function","name":"reshape_weight_to_matrix","path":"inpainter/model/modules/spectral_norm.py","language":"python","start_line":30,"end_line":38,"context_start_line":10,"context_end_line":58,"code":" # u = normalize(W @ v)\n # NB: At initialization, this invariant is not enforced\n\n _version = 1\n\n # At version 1:\n # made `W` not a buffer,\n # added `v` as a buffer, and\n # made eval mode use `W = u @ W_orig @ v` rather than the stored `W`.\n\n def __init__(self, name='weight', n_power_iterations=1, dim=0, eps=1e-12):\n self.name = name\n self.dim = dim\n if n_power_iterations <= 0:\n raise ValueError(\n 'Expected n_power_iterations to be positive, but '\n 'got n_power_iterations={}'.format(n_power_iterations))\n self.n_power_iterations = n_power_iterations\n self.eps = eps\n\n def reshape_weight_to_matrix(self, weight):\n weight_mat = weight\n if self.dim != 0:\n # permute dim to front\n weight_mat = weight_mat.permute(\n self.dim,\n *[d for d in range(weight_mat.dim()) if d != self.dim])\n height = weight_mat.size(0)\n return weight_mat.reshape(height, -1)\n\n def compute_weight(self, module, do_power_iteration):\n # NB: If `do_power_iteration` is set, the `u` and `v` vectors are\n # updated in power iteration **in-place**. This is very important\n # because in `DataParallel` forward, the vectors (being buffers) are\n # broadcast from the parallelized module to each module replica,\n # which is a new module object created on the fly. And each replica\n # runs its own spectral norm power iteration. So simply assigning\n # the updated vectors to the module this function runs on will cause\n # the update to be lost forever. And the next time the parallelized\n # module is replicated, the same randomly initialized vectors are\n # broadcast and used!\n #\n # Therefore, to make the change propagate back, we rely on two\n # important behaviors (also enforced via tests):\n # 1. `DataParallel` doesn't clone storage if the broadcast tensor\n # is already on correct device; and it makes sure that the\n # parallelized module is already on `device[0]`.\n # 2. If the out tensor in `out=` kwarg has correct shape, it will\n # just fill in the values.","source_hash":"1ce8754d2e8fe34b3898a1f9e40c8b2e196da1596e468f0cd8167cace625028f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.spectral_norm.compute_weight","uri":"program://Track-Anything/function/inpainter.model.modules.spectral_norm.compute_weight#L40-L96","kind":"function","name":"compute_weight","path":"inpainter/model/modules/spectral_norm.py","language":"python","start_line":40,"end_line":96,"context_start_line":20,"context_end_line":116,"code":" def __init__(self, name='weight', n_power_iterations=1, dim=0, eps=1e-12):\n self.name = name\n self.dim = dim\n if n_power_iterations <= 0:\n raise ValueError(\n 'Expected n_power_iterations to be positive, but '\n 'got n_power_iterations={}'.format(n_power_iterations))\n self.n_power_iterations = n_power_iterations\n self.eps = eps\n\n def reshape_weight_to_matrix(self, weight):\n weight_mat = weight\n if self.dim != 0:\n # permute dim to front\n weight_mat = weight_mat.permute(\n self.dim,\n *[d for d in range(weight_mat.dim()) if d != self.dim])\n height = weight_mat.size(0)\n return weight_mat.reshape(height, -1)\n\n def compute_weight(self, module, do_power_iteration):\n # NB: If `do_power_iteration` is set, the `u` and `v` vectors are\n # updated in power iteration **in-place**. This is very important\n # because in `DataParallel` forward, the vectors (being buffers) are\n # broadcast from the parallelized module to each module replica,\n # which is a new module object created on the fly. And each replica\n # runs its own spectral norm power iteration. So simply assigning\n # the updated vectors to the module this function runs on will cause\n # the update to be lost forever. And the next time the parallelized\n # module is replicated, the same randomly initialized vectors are\n # broadcast and used!\n #\n # Therefore, to make the change propagate back, we rely on two\n # important behaviors (also enforced via tests):\n # 1. `DataParallel` doesn't clone storage if the broadcast tensor\n # is already on correct device; and it makes sure that the\n # parallelized module is already on `device[0]`.\n # 2. If the out tensor in `out=` kwarg has correct shape, it will\n # just fill in the values.\n # Therefore, since the same power iteration is performed on all\n # devices, simply updating the tensors in-place will make sure that\n # the module replica on `device[0]` will update the _u vector on the\n # parallized module (by shared storage).\n #\n # However, after we update `u` and `v` in-place, we need to **clone**\n # them before using them to normalize the weight. This is to support\n # backproping through two forward passes, e.g., the common pattern in\n # GAN training: loss = D(real) - D(fake). Otherwise, engine will\n # complain that variables needed to do backward for the first forward\n # (i.e., the `u` and `v` vectors) are changed in the second forward.\n weight = getattr(module, self.name + '_orig')\n u = getattr(module, self.name + '_u')\n v = getattr(module, self.name + '_v')\n weight_mat = self.reshape_weight_to_matrix(weight)\n\n if do_power_iteration:\n with torch.no_grad():\n for _ in range(self.n_power_iterations):\n # Spectral norm of weight equals to `u^T W v`, where `u` and `v`\n # are the first left and right singular vectors.\n # This power iteration produces approximations of `u` and `v`.\n v = normalize(torch.mv(weight_mat.t(), u),\n dim=0,\n eps=self.eps,\n out=v)\n u = normalize(torch.mv(weight_mat, v),\n dim=0,\n eps=self.eps,\n out=u)\n if self.n_power_iterations > 0:\n # See above on why we need to clone\n u = u.clone()\n v = v.clone()\n\n sigma = torch.dot(u, torch.mv(weight_mat, v))\n weight = weight / sigma\n return weight\n\n def remove(self, module):\n with torch.no_grad():\n weight = self.compute_weight(module, do_power_iteration=False)\n delattr(module, self.name)\n delattr(module, self.name + '_u')\n delattr(module, self.name + '_v')\n delattr(module, self.name + '_orig')\n module.register_parameter(self.name,\n torch.nn.Parameter(weight.detach()))\n\n def __call__(self, module, inputs):\n setattr(\n module, self.name,\n self.compute_weight(module, do_power_iteration=module.training))\n\n def _solve_v_and_rescale(self, weight_mat, u, target_sigma):\n # Tries to returns a vector `v` s.t. `u = normalize(W @ v)`\n # (the invariant at top of this class) and `u @ W @ v = sigma`.\n # This uses pinverse in case W^T W is not invertible.","source_hash":"1ce8754d2e8fe34b3898a1f9e40c8b2e196da1596e468f0cd8167cace625028f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.spectral_norm.remove","uri":"program://Track-Anything/function/inpainter.model.modules.spectral_norm.remove#L98-L106","kind":"function","name":"remove","path":"inpainter/model/modules/spectral_norm.py","language":"python","start_line":98,"end_line":106,"context_start_line":78,"context_end_line":126,"code":" # Spectral norm of weight equals to `u^T W v`, where `u` and `v`\n # are the first left and right singular vectors.\n # This power iteration produces approximations of `u` and `v`.\n v = normalize(torch.mv(weight_mat.t(), u),\n dim=0,\n eps=self.eps,\n out=v)\n u = normalize(torch.mv(weight_mat, v),\n dim=0,\n eps=self.eps,\n out=u)\n if self.n_power_iterations > 0:\n # See above on why we need to clone\n u = u.clone()\n v = v.clone()\n\n sigma = torch.dot(u, torch.mv(weight_mat, v))\n weight = weight / sigma\n return weight\n\n def remove(self, module):\n with torch.no_grad():\n weight = self.compute_weight(module, do_power_iteration=False)\n delattr(module, self.name)\n delattr(module, self.name + '_u')\n delattr(module, self.name + '_v')\n delattr(module, self.name + '_orig')\n module.register_parameter(self.name,\n torch.nn.Parameter(weight.detach()))\n\n def __call__(self, module, inputs):\n setattr(\n module, self.name,\n self.compute_weight(module, do_power_iteration=module.training))\n\n def _solve_v_and_rescale(self, weight_mat, u, target_sigma):\n # Tries to returns a vector `v` s.t. `u = normalize(W @ v)`\n # (the invariant at top of this class) and `u @ W @ v = sigma`.\n # This uses pinverse in case W^T W is not invertible.\n v = torch.chain_matmul(weight_mat.t().mm(weight_mat).pinverse(),\n weight_mat.t(), u.unsqueeze(1)).squeeze(1)\n return v.mul_(target_sigma / torch.dot(u, torch.mv(weight_mat, v)))\n\n @staticmethod\n def apply(module, name, n_power_iterations, dim, eps):\n for k, hook in module._forward_pre_hooks.items():\n if isinstance(hook, SpectralNorm) and hook.name == name:\n raise RuntimeError(\n \"Cannot register two spectral_norm hooks on \"","source_hash":"1ce8754d2e8fe34b3898a1f9e40c8b2e196da1596e468f0cd8167cace625028f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.spectral_norm.__call__","uri":"program://Track-Anything/function/inpainter.model.modules.spectral_norm.__call__#L197-L204","kind":"function","name":"__call__","path":"inpainter/model/modules/spectral_norm.py","language":"python","start_line":197,"end_line":204,"context_start_line":177,"context_end_line":224,"code":" version = local_metadata.get('spectral_norm',\n {}).get(fn.name + '.version', None)\n if version is None or version < 1:\n with torch.no_grad():\n weight_orig = state_dict[prefix + fn.name + '_orig']\n # weight = state_dict.pop(prefix + fn.name)\n # sigma = (weight_orig / weight).mean()\n weight_mat = fn.reshape_weight_to_matrix(weight_orig)\n u = state_dict[prefix + fn.name + '_u']\n # v = fn._solve_v_and_rescale(weight_mat, u, sigma)\n # state_dict[prefix + fn.name + '_v'] = v\n\n\n# This is a top level class because Py2 pickle doesn't like inner class nor an\n# instancemethod.\nclass SpectralNormStateDictHook(object):\n # See docstring of SpectralNorm._version on the changes to spectral_norm.\n def __init__(self, fn):\n self.fn = fn\n\n def __call__(self, module, state_dict, prefix, local_metadata):\n if 'spectral_norm' not in local_metadata:\n local_metadata['spectral_norm'] = {}\n key = self.fn.name + '.version'\n if key in local_metadata['spectral_norm']:\n raise RuntimeError(\n \"Unexpected key in metadata['spectral_norm']: {}\".format(key))\n local_metadata['spectral_norm'][key] = self.fn._version\n\n\ndef spectral_norm(module,\n name='weight',\n n_power_iterations=1,\n eps=1e-12,\n dim=None):\n r\"\"\"Applies spectral normalization to a parameter in the given module.\n\n .. math::\n \\mathbf{W}_{SN} = \\dfrac{\\mathbf{W}}{\\sigma(\\mathbf{W})},\n \\sigma(\\mathbf{W}) = \\max_{\\mathbf{h}: \\mathbf{h} \\ne 0} \\dfrac{\\|\\mathbf{W} \\mathbf{h}\\|_2}{\\|\\mathbf{h}\\|_2}\n\n Spectral normalization stabilizes the training of discriminators (critics)\n in Generative Adversarial Networks (GANs) by rescaling the weight tensor\n with spectral norm :math:`\\sigma` of the weight matrix calculated using\n power iteration method. If the dimension of the weight tensor is greater\n than 2, it is reshaped to 2D in power iteration method to get spectral\n norm. This is implemented via a hook that calculates spectral norm and\n rescales weight before every :meth:`~Module.forward` call.","source_hash":"1ce8754d2e8fe34b3898a1f9e40c8b2e196da1596e468f0cd8167cace625028f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.spectral_norm._solve_v_and_rescale","uri":"program://Track-Anything/function/inpainter.model.modules.spectral_norm._solve_v_and_rescale#L113-L119","kind":"function","name":"_solve_v_and_rescale","path":"inpainter/model/modules/spectral_norm.py","language":"python","start_line":113,"end_line":119,"context_start_line":93,"context_end_line":139,"code":"\n sigma = torch.dot(u, torch.mv(weight_mat, v))\n weight = weight / sigma\n return weight\n\n def remove(self, module):\n with torch.no_grad():\n weight = self.compute_weight(module, do_power_iteration=False)\n delattr(module, self.name)\n delattr(module, self.name + '_u')\n delattr(module, self.name + '_v')\n delattr(module, self.name + '_orig')\n module.register_parameter(self.name,\n torch.nn.Parameter(weight.detach()))\n\n def __call__(self, module, inputs):\n setattr(\n module, self.name,\n self.compute_weight(module, do_power_iteration=module.training))\n\n def _solve_v_and_rescale(self, weight_mat, u, target_sigma):\n # Tries to returns a vector `v` s.t. `u = normalize(W @ v)`\n # (the invariant at top of this class) and `u @ W @ v = sigma`.\n # This uses pinverse in case W^T W is not invertible.\n v = torch.chain_matmul(weight_mat.t().mm(weight_mat).pinverse(),\n weight_mat.t(), u.unsqueeze(1)).squeeze(1)\n return v.mul_(target_sigma / torch.dot(u, torch.mv(weight_mat, v)))\n\n @staticmethod\n def apply(module, name, n_power_iterations, dim, eps):\n for k, hook in module._forward_pre_hooks.items():\n if isinstance(hook, SpectralNorm) and hook.name == name:\n raise RuntimeError(\n \"Cannot register two spectral_norm hooks on \"\n \"the same parameter {}\".format(name))\n\n fn = SpectralNorm(name, n_power_iterations, dim, eps)\n weight = module._parameters[name]\n\n with torch.no_grad():\n weight_mat = fn.reshape_weight_to_matrix(weight)\n\n h, w = weight_mat.size()\n # randomly initialize `u` and `v`\n u = normalize(weight.new_empty(h).normal_(0, 1), dim=0, eps=fn.eps)\n v = normalize(weight.new_empty(w).normal_(0, 1), dim=0, eps=fn.eps)\n","source_hash":"1ce8754d2e8fe34b3898a1f9e40c8b2e196da1596e468f0cd8167cace625028f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:inpainter.model.modules.spectral_norm.apply","uri":"program://Track-Anything/function/inpainter.model.modules.spectral_norm.apply#L122-L156","kind":"function","name":"apply","path":"inpainter/model/modules/spectral_norm.py","language":"python","start_line":122,"end_line":156,"context_start_line":102,"context_end_line":176,"code":" delattr(module, self.name + '_u')\n delattr(module, self.name + '_v')\n delattr(module, self.name + '_orig')\n module.register_parameter(self.name,\n torch.nn.Parameter(weight.detach()))\n\n def __call__(self, module, inputs):\n setattr(\n module, self.name,\n self.compute_weight(module, do_power_iteration=module.training))\n\n def _solve_v_and_rescale(self, weight_mat, u, target_sigma):\n # Tries to returns a vector `v` s.t. `u = normalize(W @ v)`\n # (the invariant at top of this class) and `u @ W @ v = sigma`.\n # This uses pinverse in case W^T W is not invertible.\n v = torch.chain_matmul(weight_mat.t().mm(weight_mat).pinverse(),\n weight_mat.t(), u.unsqueeze(1)).squeeze(1)\n return v.mul_(target_sigma / torch.dot(u, torch.mv(weight_mat, v)))\n\n @staticmethod\n def apply(module, name, n_power_iterations, dim, eps):\n for k, hook in module._forward_pre_hooks.items():\n if isinstance(hook, SpectralNorm) and hook.name == name:\n raise RuntimeError(\n \"Cannot register two spectral_norm hooks on \"\n \"the same parameter {}\".format(name))\n\n fn = SpectralNorm(name, n_power_iterations, dim, eps)\n weight = module._parameters[name]\n\n with torch.no_grad():\n weight_mat = fn.reshape_weight_to_matrix(weight)\n\n h, w = weight_mat.size()\n # randomly initialize `u` and `v`\n u = normalize(weight.new_empty(h).normal_(0, 1), dim=0, eps=fn.eps)\n v = normalize(weight.new_empty(w).normal_(0, 1), dim=0, eps=fn.eps)\n\n delattr(module, fn.name)\n module.register_parameter(fn.name + \"_orig\", weight)\n # We still need to assign weight back as fn.name because all sorts of\n # things may assume that it exists, e.g., when initializing weights.\n # However, we can't directly assign as it could be an nn.Parameter and\n # gets added as a parameter. Instead, we register weight.data as a plain\n # attribute.\n setattr(module, fn.name, weight.data)\n module.register_buffer(fn.name + \"_u\", u)\n module.register_buffer(fn.name + \"_v\", v)\n\n module.register_forward_pre_hook(fn)\n\n module._register_state_dict_hook(SpectralNormStateDictHook(fn))\n module._register_load_state_dict_pre_hook(\n SpectralNormLoadStateDictPreHook(fn))\n return fn\n\n\n# This is a top level class because Py2 pickle doesn't like inner class nor an\n# instancemethod.\nclass SpectralNormLoadStateDictPreHook(object):\n # See docstring of SpectralNorm._version on the changes to spectral_norm.\n def __init__(self, fn):\n self.fn = fn\n\n # For state_dict with version None, (assuming that it has gone through at\n # least one training forward), we have\n #\n # u = normalize(W_orig @ v)\n # W = W_orig / sigma, where sigma = u @ W_orig @ v\n #\n # To compute `v`, we solve `W_orig @ x = u`, and let\n # v = x / (u @ W_orig @ x) * (W / W_orig).\n def __call__(self, state_dict, prefix, local_metadata, strict,\n missing_keys, unexpected_keys, error_msgs):\n fn = self.fn","source_hash":"1ce8754d2e8fe34b3898a1f9e40c8b2e196da1596e468f0cd8167cace625028f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tools.mask_painter","uri":"program://Track-Anything/module/tools.mask_painter#L1-L288","kind":"module","name":"tools.mask_painter","path":"tools/mask_painter.py","language":"python","start_line":1,"end_line":288,"context_start_line":1,"context_end_line":288,"code":"import cv2\nimport torch\nimport numpy as np\nfrom PIL import Image\nimport copy\nimport time\n\n\ndef colormap(rgb=True):\n\tcolor_list = np.array(\n\t\t[\n\t\t\t0.000, 0.000, 0.000,\n\t\t\t1.000, 1.000, 1.000,\n\t\t\t1.000, 0.498, 0.313,\n\t\t\t0.392, 0.581, 0.929,\n\t\t\t0.000, 0.447, 0.741,\n\t\t\t0.850, 0.325, 0.098,\n\t\t\t0.929, 0.694, 0.125,\n\t\t\t0.494, 0.184, 0.556,\n\t\t\t0.466, 0.674, 0.188,\n\t\t\t0.301, 0.745, 0.933,\n\t\t\t0.635, 0.078, 0.184,\n\t\t\t0.300, 0.300, 0.300,\n\t\t\t0.600, 0.600, 0.600,\n\t\t\t1.000, 0.000, 0.000,\n\t\t\t1.000, 0.500, 0.000,\n\t\t\t0.749, 0.749, 0.000,\n\t\t\t0.000, 1.000, 0.000,\n\t\t\t0.000, 0.000, 1.000,\n\t\t\t0.667, 0.000, 1.000,\n\t\t\t0.333, 0.333, 0.000,\n\t\t\t0.333, 0.667, 0.000,\n\t\t\t0.333, 1.000, 0.000,\n\t\t\t0.667, 0.333, 0.000,\n\t\t\t0.667, 0.667, 0.000,\n\t\t\t0.667, 1.000, 0.000,\n\t\t\t1.000, 0.333, 0.000,\n\t\t\t1.000, 0.667, 0.000,\n\t\t\t1.000, 1.000, 0.000,\n\t\t\t0.000, 0.333, 0.500,\n\t\t\t0.000, 0.667, 0.500,\n\t\t\t0.000, 1.000, 0.500,\n\t\t\t0.333, 0.000, 0.500,\n\t\t\t0.333, 0.333, 0.500,\n\t\t\t0.333, 0.667, 0.500,\n\t\t\t0.333, 1.000, 0.500,\n\t\t\t0.667, 0.000, 0.500,\n\t\t\t0.667, 0.333, 0.500,\n\t\t\t0.667, 0.667, 0.500,\n\t\t\t0.667, 1.000, 0.500,\n\t\t\t1.000, 0.000, 0.500,\n\t\t\t1.000, 0.333, 0.500,\n\t\t\t1.000, 0.667, 0.500,\n\t\t\t1.000, 1.000, 0.500,\n\t\t\t0.000, 0.333, 1.000,\n\t\t\t0.000, 0.667, 1.000,\n\t\t\t0.000, 1.000, 1.000,\n\t\t\t0.333, 0.000, 1.000,\n\t\t\t0.333, 0.333, 1.000,\n\t\t\t0.333, 0.667, 1.000,\n\t\t\t0.333, 1.000, 1.000,\n\t\t\t0.667, 0.000, 1.000,\n\t\t\t0.667, 0.333, 1.000,\n\t\t\t0.667, 0.667, 1.000,\n\t\t\t0.667, 1.000, 1.000,\n\t\t\t1.000, 0.000, 1.000,\n\t\t\t1.000, 0.333, 1.000,\n\t\t\t1.000, 0.667, 1.000,\n\t\t\t0.167, 0.000, 0.000,\n\t\t\t0.333, 0.000, 0.000,\n\t\t\t0.500, 0.000, 0.000,\n\t\t\t0.667, 0.000, 0.000,\n\t\t\t0.833, 0.000, 0.000,\n\t\t\t1.000, 0.000, 0.000,\n\t\t\t0.000, 0.167, 0.000,\n\t\t\t0.000, 0.333, 0.000,\n\t\t\t0.000, 0.500, 0.000,\n\t\t\t0.000, 0.667, 0.000,\n\t\t\t0.000, 0.833, 0.000,\n\t\t\t0.000, 1.000, 0.000,\n\t\t\t0.000, 0.000, 0.167,\n\t\t\t0.000, 0.000, 0.333,\n\t\t\t0.000, 0.000, 0.500,\n\t\t\t0.000, 0.000, 0.667,\n\t\t\t0.000, 0.000, 0.833,\n\t\t\t0.000, 0.000, 1.000,\n\t\t\t0.143, 0.143, 0.143,\n\t\t\t0.286, 0.286, 0.286,\n\t\t\t0.429, 0.429, 0.429,\n\t\t\t0.571, 0.571, 0.571,\n\t\t\t0.714, 0.714, 0.714,\n\t\t\t0.857, 0.857, 0.857\n\t\t]\n\t).astype(np.float32)\n\tcolor_list = color_list.reshape((-1, 3)) * 255\n\tif not rgb:\n\t\tcolor_list = color_list[:, ::-1]\n\treturn color_list\n\n\ncolor_list = colormap()\ncolor_list = color_list.astype('uint8').tolist()\n\n\ndef vis_add_mask(image, background_mask, contour_mask, background_color, contour_color, background_alpha, contour_alpha):\n\tbackground_color = np.array(background_color)\n\tcontour_color = np.array(contour_color)\n\n\t# background_mask = 1 - background_mask\n\t# contour_mask = 1 - contour_mask\n\n\tfor i in range(3):\n\t\timage[:, :, i] = image[:, :, i] * (1-background_alpha+background_mask*background_alpha) \\\n\t\t\t+ background_color[i] * (background_alpha-background_mask*background_alpha)\n\t\t\n\t\timage[:, :, i] = image[:, :, i] * (1-contour_alpha+contour_mask*contour_alpha) \\\n\t\t\t+ contour_color[i] * (contour_alpha-contour_mask*contour_alpha)\n\n\treturn image.astype('uint8')\n\n\ndef mask_generator_00(mask, background_radius, contour_radius):\n\t# no background width when '00'\n\t# distance map\n\tdist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)\n\tdist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3)\n\tdist_map = dist_transform_fore - dist_transform_back\n\t# ...:::!!!:::...\n\tcontour_radius += 2\n\tcontour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))\n\tcontour_mask = contour_mask / np.max(contour_mask)\n\tcontour_mask[contour_mask>0.5] = 1.\n\n\treturn mask, contour_mask\n\n\ndef mask_generator_01(mask, background_radius, contour_radius):\n\t# no background width when '00'\n\t# distance map\n\tdist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)\n\tdist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3)\n\tdist_map = dist_transform_fore - dist_transform_back\n\t# ...:::!!!:::...\n\tcontour_radius += 2\n\tcontour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))\n\tcontour_mask = contour_mask / np.max(contour_mask)\n\treturn mask, contour_mask\n\n\ndef mask_generator_10(mask, background_radius, contour_radius):\n\t# distance map\n\tdist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)\n\tdist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3)\n\tdist_map = dist_transform_fore - dist_transform_back\n\t# .....:::::!!!!!\n\tbackground_mask = np.clip(dist_map, -background_radius, background_radius)\n\tbackground_mask = (background_mask - np.min(background_mask))\n\tbackground_mask = background_mask / np.max(background_mask)\n\t# ...:::!!!:::...\n\tcontour_radius += 2\n\tcontour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))\n\tcontour_mask = contour_mask / np.max(contour_mask)\n\tcontour_mask[contour_mask>0.5] = 1.\n\treturn background_mask, contour_mask\n\n\ndef mask_generator_11(mask, background_radius, contour_radius):\n\t# distance map\n\tdist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)\n\tdist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3)\n\tdist_map = dist_transform_fore - dist_transform_back\n\t# .....:::::!!!!!\n\tbackground_mask = np.clip(dist_map, -background_radius, background_radius)\n\tbackground_mask = (background_mask - np.min(background_mask))\n\tbackground_mask = background_mask / np.max(background_mask)\n\t# ...:::!!!:::...\n\tcontour_radius += 2\n\tcontour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))\n\tcontour_mask = contour_mask / np.max(contour_mask)\n\treturn background_mask, contour_mask\n\n\ndef mask_painter(input_image, input_mask, background_alpha=0.5, background_blur_radius=7, contour_width=3, contour_color=3, contour_alpha=1, mode='11'):\n\t\"\"\"\n\tInput:\n\tinput_image: numpy array\n\tinput_mask: numpy array\n\tbackground_alpha: transparency of background, [0, 1], 1: all black, 0: do nothing\n\tbackground_blur_radius: radius of background blur, must be odd number\n\tcontour_width: width of mask contour, must be odd number\n\tcontour_color: color index (in color map) of mask contour, 0: black, 1: white, >1: others\n\tcontour_alpha: transparency of mask contour, [0, 1], if 0: no contour highlighted\n\tmode: painting mode, '00', no blur, '01' only blur contour, '10' only blur background, '11' blur both\n\n\tOutput:\n\tpainted_image: numpy array\n\t\"\"\"\n\tassert input_image.shape[:2] == input_mask.shape, 'different shape'\n\tassert background_blur_radius % 2 * contour_width % 2 > 0, 'background_blur_radius and contour_width must be ODD'\n\tassert mode in ['00', '01', '10', '11'], 'mode should be 00, 01, 10, or 11'\n\n\t# downsample input image and mask\n\twidth, height = input_image.shape[0], input_image.shape[1]\n\tres = 1024\n\tratio = min(1.0 * res / max(width, height), 1.0) \n\tinput_image = cv2.resize(input_image, (int(height*ratio), int(width*ratio)))\n\tinput_mask = cv2.resize(input_mask, (int(height*ratio), int(width*ratio)))\n\t\n\t# 0: background, 1: foreground\n\tmsk = np.clip(input_mask, 0, 1)\n\n\t# generate masks for background and contour pixels\n\tbackground_radius = (background_blur_radius - 1) // 2\n\tcontour_radius = (contour_width - 1) // 2\n\tgenerator_dict = {'00':mask_generator_00, '01':mask_generator_01, '10':mask_generator_10, '11':mask_generator_11}\n\tbackground_mask, contour_mask = generator_dict[mode](msk, background_radius, contour_radius)\n\n\t# paint\n\tpainted_image = vis_add_mask\\\n\t\t(input_image, background_mask, contour_mask, color_list[0], color_list[contour_color], background_alpha, contour_alpha)\t# black for background\n\n\treturn painted_image\n\n\nif __name__ == '__main__':\n\t\n\tbackground_alpha = 0.7 \t# transparency of background 1: all black, 0: do nothing\n\tbackground_blur_radius = 31\t# radius of background blur, must be odd number\n\tcontour_width = 11 \t# contour width, must be odd number\n\tcontour_color = 3 \t\t# id in color map, 0: black, 1: white, >1: others\n\tcontour_alpha = 1 \t# transparency of background, 0: no contour highlighted\n\n\t# load input image and mask\n\tinput_image = np.array(Image.open('./test_img/painter_input_image.jpg').convert('RGB'))\n\tinput_mask = np.array(Image.open('./test_img/painter_input_mask.jpg').convert('P'))\n\t\n\t# paint\n\toverall_time_1 = 0\n\toverall_time_2 = 0\n\toverall_time_3 = 0\n\toverall_time_4 = 0\n\toverall_time_5 = 0\n\t\n\tfor i in range(50):\n\t\tt2 = time.time()\n\t\tpainted_image_00 = mask_painter(input_image, input_mask, background_alpha, background_blur_radius, contour_width, contour_color, contour_alpha, mode='00')\n\t\te2 = time.time()\n\n\t\tt3 = time.time()\n\t\tpainted_image_10 = mask_painter(input_image, input_mask, background_alpha, background_blur_radius, contour_width, contour_color, contour_alpha, mode='10')\n\t\te3 = time.time()\n\n\t\tt1 = time.time()\n\t\tpainted_image = mask_painter(input_image, input_mask, background_alpha, background_blur_radius, contour_width, contour_color, contour_alpha)\n\t\te1 = time.time()\n\n\t\tt4 = time.time()\n\t\tpainted_image_01 = mask_painter(input_image, input_mask, background_alpha, background_blur_radius, contour_width, contour_color, contour_alpha, mode='01')\n\t\te4 = time.time()\n\n\t\tt5 = time.time()\n\t\tpainted_image_11 = mask_painter(input_image, input_mask, background_alpha, background_blur_radius, contour_width, contour_color, contour_alpha, mode='11')\n\t\te5 = time.time()\n\n\t\toverall_time_1 += (e1 - t1)\n\t\toverall_time_2 += (e2 - t2)\n\t\toverall_time_3 += (e3 - t3)\n\t\toverall_time_4 += (e4 - t4)\n\t\toverall_time_5 += (e5 - t5)\n\n\tprint(f'average time w gaussian: {overall_time_1/50}')\n\tprint(f'average time w/o gaussian00: {overall_time_2/50}')\n\tprint(f'average time w/o gaussian10: {overall_time_3/50}')\n\tprint(f'average time w/o gaussian01: {overall_time_4/50}')\n\tprint(f'average time w/o gaussian11: {overall_time_5/50}')\n\n\t# save\n\tpainted_image_00 = Image.fromarray(painted_image_00)\n\tpainted_image_00.save('./test_img/painter_output_image_00.png')\n\n\tpainted_image_10 = Image.fromarray(painted_image_10)\n\tpainted_image_10.save('./test_img/painter_output_image_10.png')\n\n\tpainted_image_01 = Image.fromarray(painted_image_01)\n\tpainted_image_01.save('./test_img/painter_output_image_01.png')\n\n\tpainted_image_11 = Image.fromarray(painted_image_11)\n\tpainted_image_11.save('./test_img/painter_output_image_11.png')","source_hash":"23f33d25d706a5a2bfabe9bd2a393d01df2b4ebd1232498659dc9a9e18a1d848","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tools.mask_painter.colormap","uri":"program://Track-Anything/function/tools.mask_painter.colormap#L9-L98","kind":"function","name":"colormap","path":"tools/mask_painter.py","language":"python","start_line":9,"end_line":98,"context_start_line":1,"context_end_line":118,"code":"import cv2\nimport torch\nimport numpy as np\nfrom PIL import Image\nimport copy\nimport time\n\n\ndef colormap(rgb=True):\n\tcolor_list = np.array(\n\t\t[\n\t\t\t0.000, 0.000, 0.000,\n\t\t\t1.000, 1.000, 1.000,\n\t\t\t1.000, 0.498, 0.313,\n\t\t\t0.392, 0.581, 0.929,\n\t\t\t0.000, 0.447, 0.741,\n\t\t\t0.850, 0.325, 0.098,\n\t\t\t0.929, 0.694, 0.125,\n\t\t\t0.494, 0.184, 0.556,\n\t\t\t0.466, 0.674, 0.188,\n\t\t\t0.301, 0.745, 0.933,\n\t\t\t0.635, 0.078, 0.184,\n\t\t\t0.300, 0.300, 0.300,\n\t\t\t0.600, 0.600, 0.600,\n\t\t\t1.000, 0.000, 0.000,\n\t\t\t1.000, 0.500, 0.000,\n\t\t\t0.749, 0.749, 0.000,\n\t\t\t0.000, 1.000, 0.000,\n\t\t\t0.000, 0.000, 1.000,\n\t\t\t0.667, 0.000, 1.000,\n\t\t\t0.333, 0.333, 0.000,\n\t\t\t0.333, 0.667, 0.000,\n\t\t\t0.333, 1.000, 0.000,\n\t\t\t0.667, 0.333, 0.000,\n\t\t\t0.667, 0.667, 0.000,\n\t\t\t0.667, 1.000, 0.000,\n\t\t\t1.000, 0.333, 0.000,\n\t\t\t1.000, 0.667, 0.000,\n\t\t\t1.000, 1.000, 0.000,\n\t\t\t0.000, 0.333, 0.500,\n\t\t\t0.000, 0.667, 0.500,\n\t\t\t0.000, 1.000, 0.500,\n\t\t\t0.333, 0.000, 0.500,\n\t\t\t0.333, 0.333, 0.500,\n\t\t\t0.333, 0.667, 0.500,\n\t\t\t0.333, 1.000, 0.500,\n\t\t\t0.667, 0.000, 0.500,\n\t\t\t0.667, 0.333, 0.500,\n\t\t\t0.667, 0.667, 0.500,\n\t\t\t0.667, 1.000, 0.500,\n\t\t\t1.000, 0.000, 0.500,\n\t\t\t1.000, 0.333, 0.500,\n\t\t\t1.000, 0.667, 0.500,\n\t\t\t1.000, 1.000, 0.500,\n\t\t\t0.000, 0.333, 1.000,\n\t\t\t0.000, 0.667, 1.000,\n\t\t\t0.000, 1.000, 1.000,\n\t\t\t0.333, 0.000, 1.000,\n\t\t\t0.333, 0.333, 1.000,\n\t\t\t0.333, 0.667, 1.000,\n\t\t\t0.333, 1.000, 1.000,\n\t\t\t0.667, 0.000, 1.000,\n\t\t\t0.667, 0.333, 1.000,\n\t\t\t0.667, 0.667, 1.000,\n\t\t\t0.667, 1.000, 1.000,\n\t\t\t1.000, 0.000, 1.000,\n\t\t\t1.000, 0.333, 1.000,\n\t\t\t1.000, 0.667, 1.000,\n\t\t\t0.167, 0.000, 0.000,\n\t\t\t0.333, 0.000, 0.000,\n\t\t\t0.500, 0.000, 0.000,\n\t\t\t0.667, 0.000, 0.000,\n\t\t\t0.833, 0.000, 0.000,\n\t\t\t1.000, 0.000, 0.000,\n\t\t\t0.000, 0.167, 0.000,\n\t\t\t0.000, 0.333, 0.000,\n\t\t\t0.000, 0.500, 0.000,\n\t\t\t0.000, 0.667, 0.000,\n\t\t\t0.000, 0.833, 0.000,\n\t\t\t0.000, 1.000, 0.000,\n\t\t\t0.000, 0.000, 0.167,\n\t\t\t0.000, 0.000, 0.333,\n\t\t\t0.000, 0.000, 0.500,\n\t\t\t0.000, 0.000, 0.667,\n\t\t\t0.000, 0.000, 0.833,\n\t\t\t0.000, 0.000, 1.000,\n\t\t\t0.143, 0.143, 0.143,\n\t\t\t0.286, 0.286, 0.286,\n\t\t\t0.429, 0.429, 0.429,\n\t\t\t0.571, 0.571, 0.571,\n\t\t\t0.714, 0.714, 0.714,\n\t\t\t0.857, 0.857, 0.857\n\t\t]\n\t).astype(np.float32)\n\tcolor_list = color_list.reshape((-1, 3)) * 255\n\tif not rgb:\n\t\tcolor_list = color_list[:, ::-1]\n\treturn color_list\n\n\ncolor_list = colormap()\ncolor_list = color_list.astype('uint8').tolist()\n\n\ndef vis_add_mask(image, background_mask, contour_mask, background_color, contour_color, background_alpha, contour_alpha):\n\tbackground_color = np.array(background_color)\n\tcontour_color = np.array(contour_color)\n\n\t# background_mask = 1 - background_mask\n\t# contour_mask = 1 - contour_mask\n\n\tfor i in range(3):\n\t\timage[:, :, i] = image[:, :, i] * (1-background_alpha+background_mask*background_alpha) \\\n\t\t\t+ background_color[i] * (background_alpha-background_mask*background_alpha)\n\t\t\n\t\timage[:, :, i] = image[:, :, i] * (1-contour_alpha+contour_mask*contour_alpha) \\\n\t\t\t+ contour_color[i] * (contour_alpha-contour_mask*contour_alpha)\n","source_hash":"23f33d25d706a5a2bfabe9bd2a393d01df2b4ebd1232498659dc9a9e18a1d848","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tools.mask_painter.vis_add_mask","uri":"program://Track-Anything/function/tools.mask_painter.vis_add_mask#L105-L119","kind":"function","name":"vis_add_mask","path":"tools/mask_painter.py","language":"python","start_line":105,"end_line":119,"context_start_line":85,"context_end_line":139,"code":"\t\t\t0.000, 0.000, 0.833,\n\t\t\t0.000, 0.000, 1.000,\n\t\t\t0.143, 0.143, 0.143,\n\t\t\t0.286, 0.286, 0.286,\n\t\t\t0.429, 0.429, 0.429,\n\t\t\t0.571, 0.571, 0.571,\n\t\t\t0.714, 0.714, 0.714,\n\t\t\t0.857, 0.857, 0.857\n\t\t]\n\t).astype(np.float32)\n\tcolor_list = color_list.reshape((-1, 3)) * 255\n\tif not rgb:\n\t\tcolor_list = color_list[:, ::-1]\n\treturn color_list\n\n\ncolor_list = colormap()\ncolor_list = color_list.astype('uint8').tolist()\n\n\ndef vis_add_mask(image, background_mask, contour_mask, background_color, contour_color, background_alpha, contour_alpha):\n\tbackground_color = np.array(background_color)\n\tcontour_color = np.array(contour_color)\n\n\t# background_mask = 1 - background_mask\n\t# contour_mask = 1 - contour_mask\n\n\tfor i in range(3):\n\t\timage[:, :, i] = image[:, :, i] * (1-background_alpha+background_mask*background_alpha) \\\n\t\t\t+ background_color[i] * (background_alpha-background_mask*background_alpha)\n\t\t\n\t\timage[:, :, i] = image[:, :, i] * (1-contour_alpha+contour_mask*contour_alpha) \\\n\t\t\t+ contour_color[i] * (contour_alpha-contour_mask*contour_alpha)\n\n\treturn image.astype('uint8')\n\n\ndef mask_generator_00(mask, background_radius, contour_radius):\n\t# no background width when '00'\n\t# distance map\n\tdist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)\n\tdist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3)\n\tdist_map = dist_transform_fore - dist_transform_back\n\t# ...:::!!!:::...\n\tcontour_radius += 2\n\tcontour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))\n\tcontour_mask = contour_mask / np.max(contour_mask)\n\tcontour_mask[contour_mask>0.5] = 1.\n\n\treturn mask, contour_mask\n\n\ndef mask_generator_01(mask, background_radius, contour_radius):\n\t# no background width when '00'\n\t# distance map","source_hash":"23f33d25d706a5a2bfabe9bd2a393d01df2b4ebd1232498659dc9a9e18a1d848","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tools.mask_painter.mask_generator_00","uri":"program://Track-Anything/function/tools.mask_painter.mask_generator_00#L122-L134","kind":"function","name":"mask_generator_00","path":"tools/mask_painter.py","language":"python","start_line":122,"end_line":134,"context_start_line":102,"context_end_line":154,"code":"color_list = color_list.astype('uint8').tolist()\n\n\ndef vis_add_mask(image, background_mask, contour_mask, background_color, contour_color, background_alpha, contour_alpha):\n\tbackground_color = np.array(background_color)\n\tcontour_color = np.array(contour_color)\n\n\t# background_mask = 1 - background_mask\n\t# contour_mask = 1 - contour_mask\n\n\tfor i in range(3):\n\t\timage[:, :, i] = image[:, :, i] * (1-background_alpha+background_mask*background_alpha) \\\n\t\t\t+ background_color[i] * (background_alpha-background_mask*background_alpha)\n\t\t\n\t\timage[:, :, i] = image[:, :, i] * (1-contour_alpha+contour_mask*contour_alpha) \\\n\t\t\t+ contour_color[i] * (contour_alpha-contour_mask*contour_alpha)\n\n\treturn image.astype('uint8')\n\n\ndef mask_generator_00(mask, background_radius, contour_radius):\n\t# no background width when '00'\n\t# distance map\n\tdist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)\n\tdist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3)\n\tdist_map = dist_transform_fore - dist_transform_back\n\t# ...:::!!!:::...\n\tcontour_radius += 2\n\tcontour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))\n\tcontour_mask = contour_mask / np.max(contour_mask)\n\tcontour_mask[contour_mask>0.5] = 1.\n\n\treturn mask, contour_mask\n\n\ndef mask_generator_01(mask, background_radius, contour_radius):\n\t# no background width when '00'\n\t# distance map\n\tdist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)\n\tdist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3)\n\tdist_map = dist_transform_fore - dist_transform_back\n\t# ...:::!!!:::...\n\tcontour_radius += 2\n\tcontour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))\n\tcontour_mask = contour_mask / np.max(contour_mask)\n\treturn mask, contour_mask\n\n\ndef mask_generator_10(mask, background_radius, contour_radius):\n\t# distance map\n\tdist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)\n\tdist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3)\n\tdist_map = dist_transform_fore - dist_transform_back","source_hash":"23f33d25d706a5a2bfabe9bd2a393d01df2b4ebd1232498659dc9a9e18a1d848","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tools.mask_painter.mask_generator_01","uri":"program://Track-Anything/function/tools.mask_painter.mask_generator_01#L137-L147","kind":"function","name":"mask_generator_01","path":"tools/mask_painter.py","language":"python","start_line":137,"end_line":147,"context_start_line":117,"context_end_line":167,"code":"\t\t\t+ contour_color[i] * (contour_alpha-contour_mask*contour_alpha)\n\n\treturn image.astype('uint8')\n\n\ndef mask_generator_00(mask, background_radius, contour_radius):\n\t# no background width when '00'\n\t# distance map\n\tdist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)\n\tdist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3)\n\tdist_map = dist_transform_fore - dist_transform_back\n\t# ...:::!!!:::...\n\tcontour_radius += 2\n\tcontour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))\n\tcontour_mask = contour_mask / np.max(contour_mask)\n\tcontour_mask[contour_mask>0.5] = 1.\n\n\treturn mask, contour_mask\n\n\ndef mask_generator_01(mask, background_radius, contour_radius):\n\t# no background width when '00'\n\t# distance map\n\tdist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)\n\tdist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3)\n\tdist_map = dist_transform_fore - dist_transform_back\n\t# ...:::!!!:::...\n\tcontour_radius += 2\n\tcontour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))\n\tcontour_mask = contour_mask / np.max(contour_mask)\n\treturn mask, contour_mask\n\n\ndef mask_generator_10(mask, background_radius, contour_radius):\n\t# distance map\n\tdist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)\n\tdist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3)\n\tdist_map = dist_transform_fore - dist_transform_back\n\t# .....:::::!!!!!\n\tbackground_mask = np.clip(dist_map, -background_radius, background_radius)\n\tbackground_mask = (background_mask - np.min(background_mask))\n\tbackground_mask = background_mask / np.max(background_mask)\n\t# ...:::!!!:::...\n\tcontour_radius += 2\n\tcontour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))\n\tcontour_mask = contour_mask / np.max(contour_mask)\n\tcontour_mask[contour_mask>0.5] = 1.\n\treturn background_mask, contour_mask\n\n\ndef mask_generator_11(mask, background_radius, contour_radius):","source_hash":"23f33d25d706a5a2bfabe9bd2a393d01df2b4ebd1232498659dc9a9e18a1d848","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tools.mask_painter.mask_generator_10","uri":"program://Track-Anything/function/tools.mask_painter.mask_generator_10#L150-L164","kind":"function","name":"mask_generator_10","path":"tools/mask_painter.py","language":"python","start_line":150,"end_line":164,"context_start_line":130,"context_end_line":184,"code":"\tcontour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))\n\tcontour_mask = contour_mask / np.max(contour_mask)\n\tcontour_mask[contour_mask>0.5] = 1.\n\n\treturn mask, contour_mask\n\n\ndef mask_generator_01(mask, background_radius, contour_radius):\n\t# no background width when '00'\n\t# distance map\n\tdist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)\n\tdist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3)\n\tdist_map = dist_transform_fore - dist_transform_back\n\t# ...:::!!!:::...\n\tcontour_radius += 2\n\tcontour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))\n\tcontour_mask = contour_mask / np.max(contour_mask)\n\treturn mask, contour_mask\n\n\ndef mask_generator_10(mask, background_radius, contour_radius):\n\t# distance map\n\tdist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)\n\tdist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3)\n\tdist_map = dist_transform_fore - dist_transform_back\n\t# .....:::::!!!!!\n\tbackground_mask = np.clip(dist_map, -background_radius, background_radius)\n\tbackground_mask = (background_mask - np.min(background_mask))\n\tbackground_mask = background_mask / np.max(background_mask)\n\t# ...:::!!!:::...\n\tcontour_radius += 2\n\tcontour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))\n\tcontour_mask = contour_mask / np.max(contour_mask)\n\tcontour_mask[contour_mask>0.5] = 1.\n\treturn background_mask, contour_mask\n\n\ndef mask_generator_11(mask, background_radius, contour_radius):\n\t# distance map\n\tdist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)\n\tdist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3)\n\tdist_map = dist_transform_fore - dist_transform_back\n\t# .....:::::!!!!!\n\tbackground_mask = np.clip(dist_map, -background_radius, background_radius)\n\tbackground_mask = (background_mask - np.min(background_mask))\n\tbackground_mask = background_mask / np.max(background_mask)\n\t# ...:::!!!:::...\n\tcontour_radius += 2\n\tcontour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))\n\tcontour_mask = contour_mask / np.max(contour_mask)\n\treturn background_mask, contour_mask\n\n\ndef mask_painter(input_image, input_mask, background_alpha=0.5, background_blur_radius=7, contour_width=3, contour_color=3, contour_alpha=1, mode='11'):\n\t\"\"\"","source_hash":"23f33d25d706a5a2bfabe9bd2a393d01df2b4ebd1232498659dc9a9e18a1d848","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tools.mask_painter.mask_generator_11","uri":"program://Track-Anything/function/tools.mask_painter.mask_generator_11#L167-L180","kind":"function","name":"mask_generator_11","path":"tools/mask_painter.py","language":"python","start_line":167,"end_line":180,"context_start_line":147,"context_end_line":200,"code":"\treturn mask, contour_mask\n\n\ndef mask_generator_10(mask, background_radius, contour_radius):\n\t# distance map\n\tdist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)\n\tdist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3)\n\tdist_map = dist_transform_fore - dist_transform_back\n\t# .....:::::!!!!!\n\tbackground_mask = np.clip(dist_map, -background_radius, background_radius)\n\tbackground_mask = (background_mask - np.min(background_mask))\n\tbackground_mask = background_mask / np.max(background_mask)\n\t# ...:::!!!:::...\n\tcontour_radius += 2\n\tcontour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))\n\tcontour_mask = contour_mask / np.max(contour_mask)\n\tcontour_mask[contour_mask>0.5] = 1.\n\treturn background_mask, contour_mask\n\n\ndef mask_generator_11(mask, background_radius, contour_radius):\n\t# distance map\n\tdist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)\n\tdist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3)\n\tdist_map = dist_transform_fore - dist_transform_back\n\t# .....:::::!!!!!\n\tbackground_mask = np.clip(dist_map, -background_radius, background_radius)\n\tbackground_mask = (background_mask - np.min(background_mask))\n\tbackground_mask = background_mask / np.max(background_mask)\n\t# ...:::!!!:::...\n\tcontour_radius += 2\n\tcontour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))\n\tcontour_mask = contour_mask / np.max(contour_mask)\n\treturn background_mask, contour_mask\n\n\ndef mask_painter(input_image, input_mask, background_alpha=0.5, background_blur_radius=7, contour_width=3, contour_color=3, contour_alpha=1, mode='11'):\n\t\"\"\"\n\tInput:\n\tinput_image: numpy array\n\tinput_mask: numpy array\n\tbackground_alpha: transparency of background, [0, 1], 1: all black, 0: do nothing\n\tbackground_blur_radius: radius of background blur, must be odd number\n\tcontour_width: width of mask contour, must be odd number\n\tcontour_color: color index (in color map) of mask contour, 0: black, 1: white, >1: others\n\tcontour_alpha: transparency of mask contour, [0, 1], if 0: no contour highlighted\n\tmode: painting mode, '00', no blur, '01' only blur contour, '10' only blur background, '11' blur both\n\n\tOutput:\n\tpainted_image: numpy array\n\t\"\"\"\n\tassert input_image.shape[:2] == input_mask.shape, 'different shape'\n\tassert background_blur_radius % 2 * contour_width % 2 > 0, 'background_blur_radius and contour_width must be ODD'\n\tassert mode in ['00', '01', '10', '11'], 'mode should be 00, 01, 10, or 11'","source_hash":"23f33d25d706a5a2bfabe9bd2a393d01df2b4ebd1232498659dc9a9e18a1d848","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tools.mask_painter.mask_painter","uri":"program://Track-Anything/function/tools.mask_painter.mask_painter#L183-L222","kind":"function","name":"mask_painter","path":"tools/mask_painter.py","language":"python","start_line":183,"end_line":222,"context_start_line":163,"context_end_line":242,"code":"\tcontour_mask[contour_mask>0.5] = 1.\n\treturn background_mask, contour_mask\n\n\ndef mask_generator_11(mask, background_radius, contour_radius):\n\t# distance map\n\tdist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)\n\tdist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3)\n\tdist_map = dist_transform_fore - dist_transform_back\n\t# .....:::::!!!!!\n\tbackground_mask = np.clip(dist_map, -background_radius, background_radius)\n\tbackground_mask = (background_mask - np.min(background_mask))\n\tbackground_mask = background_mask / np.max(background_mask)\n\t# ...:::!!!:::...\n\tcontour_radius += 2\n\tcontour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))\n\tcontour_mask = contour_mask / np.max(contour_mask)\n\treturn background_mask, contour_mask\n\n\ndef mask_painter(input_image, input_mask, background_alpha=0.5, background_blur_radius=7, contour_width=3, contour_color=3, contour_alpha=1, mode='11'):\n\t\"\"\"\n\tInput:\n\tinput_image: numpy array\n\tinput_mask: numpy array\n\tbackground_alpha: transparency of background, [0, 1], 1: all black, 0: do nothing\n\tbackground_blur_radius: radius of background blur, must be odd number\n\tcontour_width: width of mask contour, must be odd number\n\tcontour_color: color index (in color map) of mask contour, 0: black, 1: white, >1: others\n\tcontour_alpha: transparency of mask contour, [0, 1], if 0: no contour highlighted\n\tmode: painting mode, '00', no blur, '01' only blur contour, '10' only blur background, '11' blur both\n\n\tOutput:\n\tpainted_image: numpy array\n\t\"\"\"\n\tassert input_image.shape[:2] == input_mask.shape, 'different shape'\n\tassert background_blur_radius % 2 * contour_width % 2 > 0, 'background_blur_radius and contour_width must be ODD'\n\tassert mode in ['00', '01', '10', '11'], 'mode should be 00, 01, 10, or 11'\n\n\t# downsample input image and mask\n\twidth, height = input_image.shape[0], input_image.shape[1]\n\tres = 1024\n\tratio = min(1.0 * res / max(width, height), 1.0) \n\tinput_image = cv2.resize(input_image, (int(height*ratio), int(width*ratio)))\n\tinput_mask = cv2.resize(input_mask, (int(height*ratio), int(width*ratio)))\n\t\n\t# 0: background, 1: foreground\n\tmsk = np.clip(input_mask, 0, 1)\n\n\t# generate masks for background and contour pixels\n\tbackground_radius = (background_blur_radius - 1) // 2\n\tcontour_radius = (contour_width - 1) // 2\n\tgenerator_dict = {'00':mask_generator_00, '01':mask_generator_01, '10':mask_generator_10, '11':mask_generator_11}\n\tbackground_mask, contour_mask = generator_dict[mode](msk, background_radius, contour_radius)\n\n\t# paint\n\tpainted_image = vis_add_mask\\\n\t\t(input_image, background_mask, contour_mask, color_list[0], color_list[contour_color], background_alpha, contour_alpha)\t# black for background\n\n\treturn painted_image\n\n\nif __name__ == '__main__':\n\t\n\tbackground_alpha = 0.7 \t# transparency of background 1: all black, 0: do nothing\n\tbackground_blur_radius = 31\t# radius of background blur, must be odd number\n\tcontour_width = 11 \t# contour width, must be odd number\n\tcontour_color = 3 \t\t# id in color map, 0: black, 1: white, >1: others\n\tcontour_alpha = 1 \t# transparency of background, 0: no contour highlighted\n\n\t# load input image and mask\n\tinput_image = np.array(Image.open('./test_img/painter_input_image.jpg').convert('RGB'))\n\tinput_mask = np.array(Image.open('./test_img/painter_input_mask.jpg').convert('P'))\n\t\n\t# paint\n\toverall_time_1 = 0\n\toverall_time_2 = 0\n\toverall_time_3 = 0\n\toverall_time_4 = 0\n\toverall_time_5 = 0","source_hash":"23f33d25d706a5a2bfabe9bd2a393d01df2b4ebd1232498659dc9a9e18a1d848","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tools.painter","uri":"program://Track-Anything/module/tools.painter#L1-L215","kind":"module","name":"tools.painter","path":"tools/painter.py","language":"python","start_line":1,"end_line":215,"context_start_line":1,"context_end_line":215,"code":"# paint masks, contours, or points on images, with specified colors\nimport cv2\nimport torch\nimport numpy as np\nfrom PIL import Image\nimport copy\nimport time\n\n\ndef colormap(rgb=True):\n\tcolor_list = np.array(\n\t\t[\n\t\t\t0.000, 0.000, 0.000,\n\t\t\t1.000, 1.000, 1.000,\n\t\t\t1.000, 0.498, 0.313,\n\t\t\t0.392, 0.581, 0.929,\n\t\t\t0.000, 0.447, 0.741,\n\t\t\t0.850, 0.325, 0.098,\n\t\t\t0.929, 0.694, 0.125,\n\t\t\t0.494, 0.184, 0.556,\n\t\t\t0.466, 0.674, 0.188,\n\t\t\t0.301, 0.745, 0.933,\n\t\t\t0.635, 0.078, 0.184,\n\t\t\t0.300, 0.300, 0.300,\n\t\t\t0.600, 0.600, 0.600,\n\t\t\t1.000, 0.000, 0.000,\n\t\t\t1.000, 0.500, 0.000,\n\t\t\t0.749, 0.749, 0.000,\n\t\t\t0.000, 1.000, 0.000,\n\t\t\t0.000, 0.000, 1.000,\n\t\t\t0.667, 0.000, 1.000,\n\t\t\t0.333, 0.333, 0.000,\n\t\t\t0.333, 0.667, 0.000,\n\t\t\t0.333, 1.000, 0.000,\n\t\t\t0.667, 0.333, 0.000,\n\t\t\t0.667, 0.667, 0.000,\n\t\t\t0.667, 1.000, 0.000,\n\t\t\t1.000, 0.333, 0.000,\n\t\t\t1.000, 0.667, 0.000,\n\t\t\t1.000, 1.000, 0.000,\n\t\t\t0.000, 0.333, 0.500,\n\t\t\t0.000, 0.667, 0.500,\n\t\t\t0.000, 1.000, 0.500,\n\t\t\t0.333, 0.000, 0.500,\n\t\t\t0.333, 0.333, 0.500,\n\t\t\t0.333, 0.667, 0.500,\n\t\t\t0.333, 1.000, 0.500,\n\t\t\t0.667, 0.000, 0.500,\n\t\t\t0.667, 0.333, 0.500,\n\t\t\t0.667, 0.667, 0.500,\n\t\t\t0.667, 1.000, 0.500,\n\t\t\t1.000, 0.000, 0.500,\n\t\t\t1.000, 0.333, 0.500,\n\t\t\t1.000, 0.667, 0.500,\n\t\t\t1.000, 1.000, 0.500,\n\t\t\t0.000, 0.333, 1.000,\n\t\t\t0.000, 0.667, 1.000,\n\t\t\t0.000, 1.000, 1.000,\n\t\t\t0.333, 0.000, 1.000,\n\t\t\t0.333, 0.333, 1.000,\n\t\t\t0.333, 0.667, 1.000,\n\t\t\t0.333, 1.000, 1.000,\n\t\t\t0.667, 0.000, 1.000,\n\t\t\t0.667, 0.333, 1.000,\n\t\t\t0.667, 0.667, 1.000,\n\t\t\t0.667, 1.000, 1.000,\n\t\t\t1.000, 0.000, 1.000,\n\t\t\t1.000, 0.333, 1.000,\n\t\t\t1.000, 0.667, 1.000,\n\t\t\t0.167, 0.000, 0.000,\n\t\t\t0.333, 0.000, 0.000,\n\t\t\t0.500, 0.000, 0.000,\n\t\t\t0.667, 0.000, 0.000,\n\t\t\t0.833, 0.000, 0.000,\n\t\t\t1.000, 0.000, 0.000,\n\t\t\t0.000, 0.167, 0.000,\n\t\t\t0.000, 0.333, 0.000,\n\t\t\t0.000, 0.500, 0.000,\n\t\t\t0.000, 0.667, 0.000,\n\t\t\t0.000, 0.833, 0.000,\n\t\t\t0.000, 1.000, 0.000,\n\t\t\t0.000, 0.000, 0.167,\n\t\t\t0.000, 0.000, 0.333,\n\t\t\t0.000, 0.000, 0.500,\n\t\t\t0.000, 0.000, 0.667,\n\t\t\t0.000, 0.000, 0.833,\n\t\t\t0.000, 0.000, 1.000,\n\t\t\t0.143, 0.143, 0.143,\n\t\t\t0.286, 0.286, 0.286,\n\t\t\t0.429, 0.429, 0.429,\n\t\t\t0.571, 0.571, 0.571,\n\t\t\t0.714, 0.714, 0.714,\n\t\t\t0.857, 0.857, 0.857\n\t\t]\n\t).astype(np.float32)\n\tcolor_list = color_list.reshape((-1, 3)) * 255\n\tif not rgb:\n\t\tcolor_list = color_list[:, ::-1]\n\treturn color_list\n\n\ncolor_list = colormap()\ncolor_list = color_list.astype('uint8').tolist()\n\n\ndef vis_add_mask(image, mask, color, alpha):\n\tcolor = np.array(color_list[color])\n\tmask = mask > 0.5\n\timage[mask] = image[mask] * (1-alpha) + color * alpha\n\treturn image.astype('uint8')\n\ndef point_painter(input_image, input_points, point_color=5, point_alpha=0.9, point_radius=15, contour_color=2, contour_width=5):\n\th, w = input_image.shape[:2]\n\tpoint_mask = np.zeros((h, w)).astype('uint8')\n\tfor point in input_points:\n\t\tpoint_mask[point[1], point[0]] = 1\n\n\tkernel = cv2.getStructuringElement(2, (point_radius, point_radius))\n\tpoint_mask = cv2.dilate(point_mask, kernel)\n\n\tcontour_radius = (contour_width - 1) // 2\n\tdist_transform_fore = cv2.distanceTransform(point_mask, cv2.DIST_L2, 3)\n\tdist_transform_back = cv2.distanceTransform(1-point_mask, cv2.DIST_L2, 3)\n\tdist_map = dist_transform_fore - dist_transform_back\n\t# ...:::!!!:::...\n\tcontour_radius += 2\n\tcontour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))\n\tcontour_mask = contour_mask / np.max(contour_mask)\n\tcontour_mask[contour_mask>0.5] = 1.\n\n\t# paint mask\n\tpainted_image = vis_add_mask(input_image.copy(), point_mask, point_color, point_alpha)\n\t# paint contour\n\tpainted_image = vis_add_mask(painted_image.copy(), 1-contour_mask, contour_color, 1)\n\treturn painted_image\n\ndef mask_painter(input_image, input_mask, mask_color=5, mask_alpha=0.7, contour_color=1, contour_width=3):\n\tassert input_image.shape[:2] == input_mask.shape, 'different shape between image and mask'\n\t# 0: background, 1: foreground\n\tmask = np.clip(input_mask, 0, 1)\n\tcontour_radius = (contour_width - 1) // 2\n\n\tdist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)\n\tdist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3)\n\tdist_map = dist_transform_fore - dist_transform_back\n\t# ...:::!!!:::...\n\tcontour_radius += 2\n\tcontour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))\n\tcontour_mask = contour_mask / np.max(contour_mask)\n\tcontour_mask[contour_mask>0.5] = 1.\n\n\t# paint mask\n\tpainted_image = vis_add_mask(input_image.copy(), mask.copy(), mask_color, mask_alpha)\n\t# paint contour\n\tpainted_image = vis_add_mask(painted_image.copy(), 1-contour_mask, contour_color, 1)\n\n\treturn painted_image\n\ndef background_remover(input_image, input_mask):\n\t\"\"\"\n\tinput_image: H, W, 3, np.array\n\tinput_mask: H, W, np.array\n\n\timage_wo_background: PIL.Image\t\n\t\"\"\"\n\tassert input_image.shape[:2] == input_mask.shape, 'different shape between image and mask'\n\t# 0: background, 1: foreground\n\tmask = np.expand_dims(np.clip(input_mask, 0, 1), axis=2)*255\n\timage_wo_background = np.concatenate([input_image, mask], axis=2)\t\t# H, W, 4\n\timage_wo_background = Image.fromarray(image_wo_background).convert('RGBA')\n\n\treturn image_wo_background\n\nif __name__ == '__main__':\n\tinput_image = np.array(Image.open('images/painter_input_image.jpg').convert('RGB'))\n\tinput_mask = np.array(Image.open('images/painter_input_mask.jpg').convert('P'))\n\n\t# example of mask painter\n\tmask_color = 3\n\tmask_alpha = 0.7\n\tcontour_color = 1\n\tcontour_width = 5\n\n\t# save\n\tpainted_image = Image.fromarray(input_image)\n\tpainted_image.save('images/original.png')\n\n\tpainted_image = mask_painter(input_image, input_mask, mask_color, mask_alpha, contour_color, contour_width)\n\t# save\n\tpainted_image = Image.fromarray(input_image)\n\tpainted_image.save('images/original1.png')\n\n\t# example of point painter\n\tinput_image = np.array(Image.open('images/painter_input_image.jpg').convert('RGB'))\n\tinput_points = np.array([[500, 375], [70, 600]])\t# x, y\n\tpoint_color = 5\n\tpoint_alpha = 0.9\n\tpoint_radius = 15\n\tcontour_color = 2\n\tcontour_width = 5\n\tpainted_image_1 = point_painter(input_image, input_points, point_color, point_alpha, point_radius, contour_color, contour_width)\n\t# save\n\tpainted_image = Image.fromarray(painted_image_1)\n\tpainted_image.save('images/point_painter_1.png')\n\n\tinput_image = np.array(Image.open('images/painter_input_image.jpg').convert('RGB'))\n\tpainted_image_2 = point_painter(input_image, input_points, point_color=9, point_radius=20, contour_color=29)\n\t# save\n\tpainted_image = Image.fromarray(painted_image_2)\n\tpainted_image.save('images/point_painter_2.png')\n\n\t# example of background remover\n\tinput_image = np.array(Image.open('images/original.png').convert('RGB'))\n\timage_wo_background = background_remover(input_image, input_mask)\t# return PIL.Image\n\timage_wo_background.save('images/image_wo_background.png')","source_hash":"c6b8ffea015704b7504f1c87e5e6d1f4d936e85eca1361f6beaa030296fd2aca","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tools.painter.colormap","uri":"program://Track-Anything/function/tools.painter.colormap#L10-L99","kind":"function","name":"colormap","path":"tools/painter.py","language":"python","start_line":10,"end_line":99,"context_start_line":1,"context_end_line":119,"code":"# paint masks, contours, or points on images, with specified colors\nimport cv2\nimport torch\nimport numpy as np\nfrom PIL import Image\nimport copy\nimport time\n\n\ndef colormap(rgb=True):\n\tcolor_list = np.array(\n\t\t[\n\t\t\t0.000, 0.000, 0.000,\n\t\t\t1.000, 1.000, 1.000,\n\t\t\t1.000, 0.498, 0.313,\n\t\t\t0.392, 0.581, 0.929,\n\t\t\t0.000, 0.447, 0.741,\n\t\t\t0.850, 0.325, 0.098,\n\t\t\t0.929, 0.694, 0.125,\n\t\t\t0.494, 0.184, 0.556,\n\t\t\t0.466, 0.674, 0.188,\n\t\t\t0.301, 0.745, 0.933,\n\t\t\t0.635, 0.078, 0.184,\n\t\t\t0.300, 0.300, 0.300,\n\t\t\t0.600, 0.600, 0.600,\n\t\t\t1.000, 0.000, 0.000,\n\t\t\t1.000, 0.500, 0.000,\n\t\t\t0.749, 0.749, 0.000,\n\t\t\t0.000, 1.000, 0.000,\n\t\t\t0.000, 0.000, 1.000,\n\t\t\t0.667, 0.000, 1.000,\n\t\t\t0.333, 0.333, 0.000,\n\t\t\t0.333, 0.667, 0.000,\n\t\t\t0.333, 1.000, 0.000,\n\t\t\t0.667, 0.333, 0.000,\n\t\t\t0.667, 0.667, 0.000,\n\t\t\t0.667, 1.000, 0.000,\n\t\t\t1.000, 0.333, 0.000,\n\t\t\t1.000, 0.667, 0.000,\n\t\t\t1.000, 1.000, 0.000,\n\t\t\t0.000, 0.333, 0.500,\n\t\t\t0.000, 0.667, 0.500,\n\t\t\t0.000, 1.000, 0.500,\n\t\t\t0.333, 0.000, 0.500,\n\t\t\t0.333, 0.333, 0.500,\n\t\t\t0.333, 0.667, 0.500,\n\t\t\t0.333, 1.000, 0.500,\n\t\t\t0.667, 0.000, 0.500,\n\t\t\t0.667, 0.333, 0.500,\n\t\t\t0.667, 0.667, 0.500,\n\t\t\t0.667, 1.000, 0.500,\n\t\t\t1.000, 0.000, 0.500,\n\t\t\t1.000, 0.333, 0.500,\n\t\t\t1.000, 0.667, 0.500,\n\t\t\t1.000, 1.000, 0.500,\n\t\t\t0.000, 0.333, 1.000,\n\t\t\t0.000, 0.667, 1.000,\n\t\t\t0.000, 1.000, 1.000,\n\t\t\t0.333, 0.000, 1.000,\n\t\t\t0.333, 0.333, 1.000,\n\t\t\t0.333, 0.667, 1.000,\n\t\t\t0.333, 1.000, 1.000,\n\t\t\t0.667, 0.000, 1.000,\n\t\t\t0.667, 0.333, 1.000,\n\t\t\t0.667, 0.667, 1.000,\n\t\t\t0.667, 1.000, 1.000,\n\t\t\t1.000, 0.000, 1.000,\n\t\t\t1.000, 0.333, 1.000,\n\t\t\t1.000, 0.667, 1.000,\n\t\t\t0.167, 0.000, 0.000,\n\t\t\t0.333, 0.000, 0.000,\n\t\t\t0.500, 0.000, 0.000,\n\t\t\t0.667, 0.000, 0.000,\n\t\t\t0.833, 0.000, 0.000,\n\t\t\t1.000, 0.000, 0.000,\n\t\t\t0.000, 0.167, 0.000,\n\t\t\t0.000, 0.333, 0.000,\n\t\t\t0.000, 0.500, 0.000,\n\t\t\t0.000, 0.667, 0.000,\n\t\t\t0.000, 0.833, 0.000,\n\t\t\t0.000, 1.000, 0.000,\n\t\t\t0.000, 0.000, 0.167,\n\t\t\t0.000, 0.000, 0.333,\n\t\t\t0.000, 0.000, 0.500,\n\t\t\t0.000, 0.000, 0.667,\n\t\t\t0.000, 0.000, 0.833,\n\t\t\t0.000, 0.000, 1.000,\n\t\t\t0.143, 0.143, 0.143,\n\t\t\t0.286, 0.286, 0.286,\n\t\t\t0.429, 0.429, 0.429,\n\t\t\t0.571, 0.571, 0.571,\n\t\t\t0.714, 0.714, 0.714,\n\t\t\t0.857, 0.857, 0.857\n\t\t]\n\t).astype(np.float32)\n\tcolor_list = color_list.reshape((-1, 3)) * 255\n\tif not rgb:\n\t\tcolor_list = color_list[:, ::-1]\n\treturn color_list\n\n\ncolor_list = colormap()\ncolor_list = color_list.astype('uint8').tolist()\n\n\ndef vis_add_mask(image, mask, color, alpha):\n\tcolor = np.array(color_list[color])\n\tmask = mask > 0.5\n\timage[mask] = image[mask] * (1-alpha) + color * alpha\n\treturn image.astype('uint8')\n\ndef point_painter(input_image, input_points, point_color=5, point_alpha=0.9, point_radius=15, contour_color=2, contour_width=5):\n\th, w = input_image.shape[:2]\n\tpoint_mask = np.zeros((h, w)).astype('uint8')\n\tfor point in input_points:\n\t\tpoint_mask[point[1], point[0]] = 1\n\n\tkernel = cv2.getStructuringElement(2, (point_radius, point_radius))\n\tpoint_mask = cv2.dilate(point_mask, kernel)","source_hash":"c6b8ffea015704b7504f1c87e5e6d1f4d936e85eca1361f6beaa030296fd2aca","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tools.painter.vis_add_mask","uri":"program://Track-Anything/function/tools.painter.vis_add_mask#L106-L110","kind":"function","name":"vis_add_mask","path":"tools/painter.py","language":"python","start_line":106,"end_line":110,"context_start_line":86,"context_end_line":130,"code":"\t\t\t0.000, 0.000, 0.833,\n\t\t\t0.000, 0.000, 1.000,\n\t\t\t0.143, 0.143, 0.143,\n\t\t\t0.286, 0.286, 0.286,\n\t\t\t0.429, 0.429, 0.429,\n\t\t\t0.571, 0.571, 0.571,\n\t\t\t0.714, 0.714, 0.714,\n\t\t\t0.857, 0.857, 0.857\n\t\t]\n\t).astype(np.float32)\n\tcolor_list = color_list.reshape((-1, 3)) * 255\n\tif not rgb:\n\t\tcolor_list = color_list[:, ::-1]\n\treturn color_list\n\n\ncolor_list = colormap()\ncolor_list = color_list.astype('uint8').tolist()\n\n\ndef vis_add_mask(image, mask, color, alpha):\n\tcolor = np.array(color_list[color])\n\tmask = mask > 0.5\n\timage[mask] = image[mask] * (1-alpha) + color * alpha\n\treturn image.astype('uint8')\n\ndef point_painter(input_image, input_points, point_color=5, point_alpha=0.9, point_radius=15, contour_color=2, contour_width=5):\n\th, w = input_image.shape[:2]\n\tpoint_mask = np.zeros((h, w)).astype('uint8')\n\tfor point in input_points:\n\t\tpoint_mask[point[1], point[0]] = 1\n\n\tkernel = cv2.getStructuringElement(2, (point_radius, point_radius))\n\tpoint_mask = cv2.dilate(point_mask, kernel)\n\n\tcontour_radius = (contour_width - 1) // 2\n\tdist_transform_fore = cv2.distanceTransform(point_mask, cv2.DIST_L2, 3)\n\tdist_transform_back = cv2.distanceTransform(1-point_mask, cv2.DIST_L2, 3)\n\tdist_map = dist_transform_fore - dist_transform_back\n\t# ...:::!!!:::...\n\tcontour_radius += 2\n\tcontour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))\n\tcontour_mask = contour_mask / np.max(contour_mask)\n\tcontour_mask[contour_mask>0.5] = 1.\n","source_hash":"c6b8ffea015704b7504f1c87e5e6d1f4d936e85eca1361f6beaa030296fd2aca","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tools.painter.point_painter","uri":"program://Track-Anything/function/tools.painter.point_painter#L112-L135","kind":"function","name":"point_painter","path":"tools/painter.py","language":"python","start_line":112,"end_line":135,"context_start_line":92,"context_end_line":155,"code":"\t\t\t0.714, 0.714, 0.714,\n\t\t\t0.857, 0.857, 0.857\n\t\t]\n\t).astype(np.float32)\n\tcolor_list = color_list.reshape((-1, 3)) * 255\n\tif not rgb:\n\t\tcolor_list = color_list[:, ::-1]\n\treturn color_list\n\n\ncolor_list = colormap()\ncolor_list = color_list.astype('uint8').tolist()\n\n\ndef vis_add_mask(image, mask, color, alpha):\n\tcolor = np.array(color_list[color])\n\tmask = mask > 0.5\n\timage[mask] = image[mask] * (1-alpha) + color * alpha\n\treturn image.astype('uint8')\n\ndef point_painter(input_image, input_points, point_color=5, point_alpha=0.9, point_radius=15, contour_color=2, contour_width=5):\n\th, w = input_image.shape[:2]\n\tpoint_mask = np.zeros((h, w)).astype('uint8')\n\tfor point in input_points:\n\t\tpoint_mask[point[1], point[0]] = 1\n\n\tkernel = cv2.getStructuringElement(2, (point_radius, point_radius))\n\tpoint_mask = cv2.dilate(point_mask, kernel)\n\n\tcontour_radius = (contour_width - 1) // 2\n\tdist_transform_fore = cv2.distanceTransform(point_mask, cv2.DIST_L2, 3)\n\tdist_transform_back = cv2.distanceTransform(1-point_mask, cv2.DIST_L2, 3)\n\tdist_map = dist_transform_fore - dist_transform_back\n\t# ...:::!!!:::...\n\tcontour_radius += 2\n\tcontour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))\n\tcontour_mask = contour_mask / np.max(contour_mask)\n\tcontour_mask[contour_mask>0.5] = 1.\n\n\t# paint mask\n\tpainted_image = vis_add_mask(input_image.copy(), point_mask, point_color, point_alpha)\n\t# paint contour\n\tpainted_image = vis_add_mask(painted_image.copy(), 1-contour_mask, contour_color, 1)\n\treturn painted_image\n\ndef mask_painter(input_image, input_mask, mask_color=5, mask_alpha=0.7, contour_color=1, contour_width=3):\n\tassert input_image.shape[:2] == input_mask.shape, 'different shape between image and mask'\n\t# 0: background, 1: foreground\n\tmask = np.clip(input_mask, 0, 1)\n\tcontour_radius = (contour_width - 1) // 2\n\n\tdist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)\n\tdist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3)\n\tdist_map = dist_transform_fore - dist_transform_back\n\t# ...:::!!!:::...\n\tcontour_radius += 2\n\tcontour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))\n\tcontour_mask = contour_mask / np.max(contour_mask)\n\tcontour_mask[contour_mask>0.5] = 1.\n\n\t# paint mask\n\tpainted_image = vis_add_mask(input_image.copy(), mask.copy(), mask_color, mask_alpha)\n\t# paint contour\n\tpainted_image = vis_add_mask(painted_image.copy(), 1-contour_mask, contour_color, 1)","source_hash":"c6b8ffea015704b7504f1c87e5e6d1f4d936e85eca1361f6beaa030296fd2aca","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tools.painter.mask_painter","uri":"program://Track-Anything/function/tools.painter.mask_painter#L137-L157","kind":"function","name":"mask_painter","path":"tools/painter.py","language":"python","start_line":137,"end_line":157,"context_start_line":117,"context_end_line":177,"code":"\n\tkernel = cv2.getStructuringElement(2, (point_radius, point_radius))\n\tpoint_mask = cv2.dilate(point_mask, kernel)\n\n\tcontour_radius = (contour_width - 1) // 2\n\tdist_transform_fore = cv2.distanceTransform(point_mask, cv2.DIST_L2, 3)\n\tdist_transform_back = cv2.distanceTransform(1-point_mask, cv2.DIST_L2, 3)\n\tdist_map = dist_transform_fore - dist_transform_back\n\t# ...:::!!!:::...\n\tcontour_radius += 2\n\tcontour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))\n\tcontour_mask = contour_mask / np.max(contour_mask)\n\tcontour_mask[contour_mask>0.5] = 1.\n\n\t# paint mask\n\tpainted_image = vis_add_mask(input_image.copy(), point_mask, point_color, point_alpha)\n\t# paint contour\n\tpainted_image = vis_add_mask(painted_image.copy(), 1-contour_mask, contour_color, 1)\n\treturn painted_image\n\ndef mask_painter(input_image, input_mask, mask_color=5, mask_alpha=0.7, contour_color=1, contour_width=3):\n\tassert input_image.shape[:2] == input_mask.shape, 'different shape between image and mask'\n\t# 0: background, 1: foreground\n\tmask = np.clip(input_mask, 0, 1)\n\tcontour_radius = (contour_width - 1) // 2\n\n\tdist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)\n\tdist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3)\n\tdist_map = dist_transform_fore - dist_transform_back\n\t# ...:::!!!:::...\n\tcontour_radius += 2\n\tcontour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))\n\tcontour_mask = contour_mask / np.max(contour_mask)\n\tcontour_mask[contour_mask>0.5] = 1.\n\n\t# paint mask\n\tpainted_image = vis_add_mask(input_image.copy(), mask.copy(), mask_color, mask_alpha)\n\t# paint contour\n\tpainted_image = vis_add_mask(painted_image.copy(), 1-contour_mask, contour_color, 1)\n\n\treturn painted_image\n\ndef background_remover(input_image, input_mask):\n\t\"\"\"\n\tinput_image: H, W, 3, np.array\n\tinput_mask: H, W, np.array\n\n\timage_wo_background: PIL.Image\t\n\t\"\"\"\n\tassert input_image.shape[:2] == input_mask.shape, 'different shape between image and mask'\n\t# 0: background, 1: foreground\n\tmask = np.expand_dims(np.clip(input_mask, 0, 1), axis=2)*255\n\timage_wo_background = np.concatenate([input_image, mask], axis=2)\t\t# H, W, 4\n\timage_wo_background = Image.fromarray(image_wo_background).convert('RGBA')\n\n\treturn image_wo_background\n\nif __name__ == '__main__':\n\tinput_image = np.array(Image.open('images/painter_input_image.jpg').convert('RGB'))\n\tinput_mask = np.array(Image.open('images/painter_input_mask.jpg').convert('P'))\n","source_hash":"c6b8ffea015704b7504f1c87e5e6d1f4d936e85eca1361f6beaa030296fd2aca","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tools.painter.background_remover","uri":"program://Track-Anything/function/tools.painter.background_remover#L159-L172","kind":"function","name":"background_remover","path":"tools/painter.py","language":"python","start_line":159,"end_line":172,"context_start_line":139,"context_end_line":192,"code":"\t# 0: background, 1: foreground\n\tmask = np.clip(input_mask, 0, 1)\n\tcontour_radius = (contour_width - 1) // 2\n\n\tdist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)\n\tdist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3)\n\tdist_map = dist_transform_fore - dist_transform_back\n\t# ...:::!!!:::...\n\tcontour_radius += 2\n\tcontour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))\n\tcontour_mask = contour_mask / np.max(contour_mask)\n\tcontour_mask[contour_mask>0.5] = 1.\n\n\t# paint mask\n\tpainted_image = vis_add_mask(input_image.copy(), mask.copy(), mask_color, mask_alpha)\n\t# paint contour\n\tpainted_image = vis_add_mask(painted_image.copy(), 1-contour_mask, contour_color, 1)\n\n\treturn painted_image\n\ndef background_remover(input_image, input_mask):\n\t\"\"\"\n\tinput_image: H, W, 3, np.array\n\tinput_mask: H, W, np.array\n\n\timage_wo_background: PIL.Image\t\n\t\"\"\"\n\tassert input_image.shape[:2] == input_mask.shape, 'different shape between image and mask'\n\t# 0: background, 1: foreground\n\tmask = np.expand_dims(np.clip(input_mask, 0, 1), axis=2)*255\n\timage_wo_background = np.concatenate([input_image, mask], axis=2)\t\t# H, W, 4\n\timage_wo_background = Image.fromarray(image_wo_background).convert('RGBA')\n\n\treturn image_wo_background\n\nif __name__ == '__main__':\n\tinput_image = np.array(Image.open('images/painter_input_image.jpg').convert('RGB'))\n\tinput_mask = np.array(Image.open('images/painter_input_mask.jpg').convert('P'))\n\n\t# example of mask painter\n\tmask_color = 3\n\tmask_alpha = 0.7\n\tcontour_color = 1\n\tcontour_width = 5\n\n\t# save\n\tpainted_image = Image.fromarray(input_image)\n\tpainted_image.save('images/original.png')\n\n\tpainted_image = mask_painter(input_image, input_mask, mask_color, mask_alpha, contour_color, contour_width)\n\t# save\n\tpainted_image = Image.fromarray(input_image)\n\tpainted_image.save('images/original1.png')\n","source_hash":"c6b8ffea015704b7504f1c87e5e6d1f4d936e85eca1361f6beaa030296fd2aca","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tools.base_segmenter","uri":"program://Track-Anything/module/tools.base_segmenter#L1-L129","kind":"module","name":"tools.base_segmenter","path":"tools/base_segmenter.py","language":"python","start_line":1,"end_line":129,"context_start_line":1,"context_end_line":129,"code":"import time\nimport torch\nimport cv2\nfrom PIL import Image, ImageDraw, ImageOps\nimport numpy as np\nfrom typing import Union\nfrom segment_anything import sam_model_registry, SamPredictor, SamAutomaticMaskGenerator\nimport matplotlib.pyplot as plt\nimport PIL\nfrom .mask_painter import mask_painter\n\n\nclass BaseSegmenter:\n def __init__(self, SAM_checkpoint, model_type, device='cuda:0'):\n \"\"\"\n device: model device\n SAM_checkpoint: path of SAM checkpoint\n model_type: vit_b, vit_l, vit_h\n \"\"\"\n print(f\"Initializing BaseSegmenter to {device}\")\n assert model_type in ['vit_b', 'vit_l', 'vit_h'], 'model_type must be vit_b, vit_l, or vit_h'\n\n self.device = device\n self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32\n self.model = sam_model_registry[model_type](checkpoint=SAM_checkpoint)\n self.model.to(device=self.device)\n self.predictor = SamPredictor(self.model)\n self.embedded = False\n\n @torch.no_grad()\n def set_image(self, image: np.ndarray):\n # PIL.open(image_path) 3channel: RGB\n # image embedding: avoid encode the same image multiple times\n self.orignal_image = image\n if self.embedded:\n print('repeat embedding, please reset_image.')\n return\n self.predictor.set_image(image)\n self.embedded = True\n return\n \n @torch.no_grad()\n def reset_image(self):\n # reset image embeding\n self.predictor.reset_image()\n self.embedded = False\n\n def predict(self, prompts, mode, multimask=True):\n \"\"\"\n image: numpy array, h, w, 3\n prompts: dictionary, 3 keys: 'point_coords', 'point_labels', 'mask_input'\n prompts['point_coords']: numpy array [N,2]\n prompts['point_labels']: numpy array [1,N]\n prompts['mask_input']: numpy array [1,256,256]\n mode: 'point' (points only), 'mask' (mask only), 'both' (consider both)\n mask_outputs: True (return 3 masks), False (return 1 mask only)\n whem mask_outputs=True, mask_input=logits[np.argmax(scores), :, :][None, :, :]\n \"\"\"\n assert self.embedded, 'prediction is called before set_image (feature embedding).'\n assert mode in ['point', 'mask', 'both'], 'mode must be point, mask, or both'\n \n if mode == 'point':\n masks, scores, logits = self.predictor.predict(point_coords=prompts['point_coords'], \n point_labels=prompts['point_labels'], \n multimask_output=multimask)\n elif mode == 'mask':\n masks, scores, logits = self.predictor.predict(mask_input=prompts['mask_input'], \n multimask_output=multimask)\n elif mode == 'both': # both\n masks, scores, logits = self.predictor.predict(point_coords=prompts['point_coords'], \n point_labels=prompts['point_labels'], \n mask_input=prompts['mask_input'], \n multimask_output=multimask)\n else:\n raise(\"Not implement now!\")\n # masks (n, h, w), scores (n,), logits (n, 256, 256)\n return masks, scores, logits\n\n\nif __name__ == \"__main__\":\n # load and show an image\n image = cv2.imread('/hhd3/gaoshang/truck.jpg')\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # numpy array (h, w, 3)\n\n # initialise BaseSegmenter\n SAM_checkpoint= '/ssd1/gaomingqi/checkpoints/sam_vit_h_4b8939.pth'\n model_type = 'vit_h'\n device = \"cuda:4\"\n base_segmenter = BaseSegmenter(SAM_checkpoint=SAM_checkpoint, model_type=model_type, device=device)\n \n # image embedding (once embedded, multiple prompts can be applied)\n base_segmenter.set_image(image)\n \n # examples\n # point only ------------------------\n mode = 'point'\n prompts = {\n 'point_coords': np.array([[500, 375], [1125, 625]]),\n 'point_labels': np.array([1, 1]), \n }\n masks, scores, logits = base_segmenter.predict(prompts, mode, multimask=False) # masks (n, h, w), scores (n,), logits (n, 256, 256)\n painted_image = mask_painter(image, masks[np.argmax(scores)].astype('uint8'), background_alpha=0.8)\n painted_image = cv2.cvtColor(painted_image, cv2.COLOR_RGB2BGR) # numpy array (h, w, 3)\n cv2.imwrite('/hhd3/gaoshang/truck_point.jpg', painted_image)\n\n # both ------------------------\n mode = 'both'\n mask_input = logits[np.argmax(scores), :, :]\n prompts = {'mask_input': mask_input [None, :, :]}\n prompts = {\n 'point_coords': np.array([[500, 375], [1125, 625]]),\n 'point_labels': np.array([1, 0]), \n 'mask_input': mask_input[None, :, :]\n }\n masks, scores, logits = base_segmenter.predict(prompts, mode, multimask=True) # masks (n, h, w), scores (n,), logits (n, 256, 256)\n painted_image = mask_painter(image, masks[np.argmax(scores)].astype('uint8'), background_alpha=0.8)\n painted_image = cv2.cvtColor(painted_image, cv2.COLOR_RGB2BGR) # numpy array (h, w, 3)\n cv2.imwrite('/hhd3/gaoshang/truck_both.jpg', painted_image)\n\n # mask only ------------------------\n mode = 'mask'\n mask_input = logits[np.argmax(scores), :, :]\n \n prompts = {'mask_input': mask_input[None, :, :]}\n \n masks, scores, logits = base_segmenter.predict(prompts, mode, multimask=True) # masks (n, h, w), scores (n,), logits (n, 256, 256)\n painted_image = mask_painter(image, masks[np.argmax(scores)].astype('uint8'), background_alpha=0.8)\n painted_image = cv2.cvtColor(painted_image, cv2.COLOR_RGB2BGR) # numpy array (h, w, 3)\n cv2.imwrite('/hhd3/gaoshang/truck_mask.jpg', painted_image)","source_hash":"73f546d160f4a13e76370ad2b98c7d72277a90e0d51a9f97b27310c3223988f5","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tools.base_segmenter.BaseSegmenter","uri":"program://Track-Anything/class/tools.base_segmenter.BaseSegmenter#L13-L77","kind":"class","name":"BaseSegmenter","path":"tools/base_segmenter.py","language":"python","start_line":13,"end_line":77,"context_start_line":1,"context_end_line":97,"code":"import time\nimport torch\nimport cv2\nfrom PIL import Image, ImageDraw, ImageOps\nimport numpy as np\nfrom typing import Union\nfrom segment_anything import sam_model_registry, SamPredictor, SamAutomaticMaskGenerator\nimport matplotlib.pyplot as plt\nimport PIL\nfrom .mask_painter import mask_painter\n\n\nclass BaseSegmenter:\n def __init__(self, SAM_checkpoint, model_type, device='cuda:0'):\n \"\"\"\n device: model device\n SAM_checkpoint: path of SAM checkpoint\n model_type: vit_b, vit_l, vit_h\n \"\"\"\n print(f\"Initializing BaseSegmenter to {device}\")\n assert model_type in ['vit_b', 'vit_l', 'vit_h'], 'model_type must be vit_b, vit_l, or vit_h'\n\n self.device = device\n self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32\n self.model = sam_model_registry[model_type](checkpoint=SAM_checkpoint)\n self.model.to(device=self.device)\n self.predictor = SamPredictor(self.model)\n self.embedded = False\n\n @torch.no_grad()\n def set_image(self, image: np.ndarray):\n # PIL.open(image_path) 3channel: RGB\n # image embedding: avoid encode the same image multiple times\n self.orignal_image = image\n if self.embedded:\n print('repeat embedding, please reset_image.')\n return\n self.predictor.set_image(image)\n self.embedded = True\n return\n \n @torch.no_grad()\n def reset_image(self):\n # reset image embeding\n self.predictor.reset_image()\n self.embedded = False\n\n def predict(self, prompts, mode, multimask=True):\n \"\"\"\n image: numpy array, h, w, 3\n prompts: dictionary, 3 keys: 'point_coords', 'point_labels', 'mask_input'\n prompts['point_coords']: numpy array [N,2]\n prompts['point_labels']: numpy array [1,N]\n prompts['mask_input']: numpy array [1,256,256]\n mode: 'point' (points only), 'mask' (mask only), 'both' (consider both)\n mask_outputs: True (return 3 masks), False (return 1 mask only)\n whem mask_outputs=True, mask_input=logits[np.argmax(scores), :, :][None, :, :]\n \"\"\"\n assert self.embedded, 'prediction is called before set_image (feature embedding).'\n assert mode in ['point', 'mask', 'both'], 'mode must be point, mask, or both'\n \n if mode == 'point':\n masks, scores, logits = self.predictor.predict(point_coords=prompts['point_coords'], \n point_labels=prompts['point_labels'], \n multimask_output=multimask)\n elif mode == 'mask':\n masks, scores, logits = self.predictor.predict(mask_input=prompts['mask_input'], \n multimask_output=multimask)\n elif mode == 'both': # both\n masks, scores, logits = self.predictor.predict(point_coords=prompts['point_coords'], \n point_labels=prompts['point_labels'], \n mask_input=prompts['mask_input'], \n multimask_output=multimask)\n else:\n raise(\"Not implement now!\")\n # masks (n, h, w), scores (n,), logits (n, 256, 256)\n return masks, scores, logits\n\n\nif __name__ == \"__main__\":\n # load and show an image\n image = cv2.imread('/hhd3/gaoshang/truck.jpg')\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # numpy array (h, w, 3)\n\n # initialise BaseSegmenter\n SAM_checkpoint= '/ssd1/gaomingqi/checkpoints/sam_vit_h_4b8939.pth'\n model_type = 'vit_h'\n device = \"cuda:4\"\n base_segmenter = BaseSegmenter(SAM_checkpoint=SAM_checkpoint, model_type=model_type, device=device)\n \n # image embedding (once embedded, multiple prompts can be applied)\n base_segmenter.set_image(image)\n \n # examples\n # point only ------------------------\n mode = 'point'\n prompts = {","source_hash":"73f546d160f4a13e76370ad2b98c7d72277a90e0d51a9f97b27310c3223988f5","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tools.base_segmenter.__init__","uri":"program://Track-Anything/function/tools.base_segmenter.__init__#L14-L28","kind":"function","name":"__init__","path":"tools/base_segmenter.py","language":"python","start_line":14,"end_line":28,"context_start_line":1,"context_end_line":48,"code":"import time\nimport torch\nimport cv2\nfrom PIL import Image, ImageDraw, ImageOps\nimport numpy as np\nfrom typing import Union\nfrom segment_anything import sam_model_registry, SamPredictor, SamAutomaticMaskGenerator\nimport matplotlib.pyplot as plt\nimport PIL\nfrom .mask_painter import mask_painter\n\n\nclass BaseSegmenter:\n def __init__(self, SAM_checkpoint, model_type, device='cuda:0'):\n \"\"\"\n device: model device\n SAM_checkpoint: path of SAM checkpoint\n model_type: vit_b, vit_l, vit_h\n \"\"\"\n print(f\"Initializing BaseSegmenter to {device}\")\n assert model_type in ['vit_b', 'vit_l', 'vit_h'], 'model_type must be vit_b, vit_l, or vit_h'\n\n self.device = device\n self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32\n self.model = sam_model_registry[model_type](checkpoint=SAM_checkpoint)\n self.model.to(device=self.device)\n self.predictor = SamPredictor(self.model)\n self.embedded = False\n\n @torch.no_grad()\n def set_image(self, image: np.ndarray):\n # PIL.open(image_path) 3channel: RGB\n # image embedding: avoid encode the same image multiple times\n self.orignal_image = image\n if self.embedded:\n print('repeat embedding, please reset_image.')\n return\n self.predictor.set_image(image)\n self.embedded = True\n return\n \n @torch.no_grad()\n def reset_image(self):\n # reset image embeding\n self.predictor.reset_image()\n self.embedded = False\n\n def predict(self, prompts, mode, multimask=True):","source_hash":"73f546d160f4a13e76370ad2b98c7d72277a90e0d51a9f97b27310c3223988f5","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tools.base_segmenter.set_image","uri":"program://Track-Anything/function/tools.base_segmenter.set_image#L31-L40","kind":"function","name":"set_image","path":"tools/base_segmenter.py","language":"python","start_line":31,"end_line":40,"context_start_line":11,"context_end_line":60,"code":"\n\nclass BaseSegmenter:\n def __init__(self, SAM_checkpoint, model_type, device='cuda:0'):\n \"\"\"\n device: model device\n SAM_checkpoint: path of SAM checkpoint\n model_type: vit_b, vit_l, vit_h\n \"\"\"\n print(f\"Initializing BaseSegmenter to {device}\")\n assert model_type in ['vit_b', 'vit_l', 'vit_h'], 'model_type must be vit_b, vit_l, or vit_h'\n\n self.device = device\n self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32\n self.model = sam_model_registry[model_type](checkpoint=SAM_checkpoint)\n self.model.to(device=self.device)\n self.predictor = SamPredictor(self.model)\n self.embedded = False\n\n @torch.no_grad()\n def set_image(self, image: np.ndarray):\n # PIL.open(image_path) 3channel: RGB\n # image embedding: avoid encode the same image multiple times\n self.orignal_image = image\n if self.embedded:\n print('repeat embedding, please reset_image.')\n return\n self.predictor.set_image(image)\n self.embedded = True\n return\n \n @torch.no_grad()\n def reset_image(self):\n # reset image embeding\n self.predictor.reset_image()\n self.embedded = False\n\n def predict(self, prompts, mode, multimask=True):\n \"\"\"\n image: numpy array, h, w, 3\n prompts: dictionary, 3 keys: 'point_coords', 'point_labels', 'mask_input'\n prompts['point_coords']: numpy array [N,2]\n prompts['point_labels']: numpy array [1,N]\n prompts['mask_input']: numpy array [1,256,256]\n mode: 'point' (points only), 'mask' (mask only), 'both' (consider both)\n mask_outputs: True (return 3 masks), False (return 1 mask only)\n whem mask_outputs=True, mask_input=logits[np.argmax(scores), :, :][None, :, :]\n \"\"\"\n assert self.embedded, 'prediction is called before set_image (feature embedding).'\n assert mode in ['point', 'mask', 'both'], 'mode must be point, mask, or both'","source_hash":"73f546d160f4a13e76370ad2b98c7d72277a90e0d51a9f97b27310c3223988f5","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tools.base_segmenter.reset_image","uri":"program://Track-Anything/function/tools.base_segmenter.reset_image#L43-L46","kind":"function","name":"reset_image","path":"tools/base_segmenter.py","language":"python","start_line":43,"end_line":46,"context_start_line":23,"context_end_line":66,"code":" self.device = device\n self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32\n self.model = sam_model_registry[model_type](checkpoint=SAM_checkpoint)\n self.model.to(device=self.device)\n self.predictor = SamPredictor(self.model)\n self.embedded = False\n\n @torch.no_grad()\n def set_image(self, image: np.ndarray):\n # PIL.open(image_path) 3channel: RGB\n # image embedding: avoid encode the same image multiple times\n self.orignal_image = image\n if self.embedded:\n print('repeat embedding, please reset_image.')\n return\n self.predictor.set_image(image)\n self.embedded = True\n return\n \n @torch.no_grad()\n def reset_image(self):\n # reset image embeding\n self.predictor.reset_image()\n self.embedded = False\n\n def predict(self, prompts, mode, multimask=True):\n \"\"\"\n image: numpy array, h, w, 3\n prompts: dictionary, 3 keys: 'point_coords', 'point_labels', 'mask_input'\n prompts['point_coords']: numpy array [N,2]\n prompts['point_labels']: numpy array [1,N]\n prompts['mask_input']: numpy array [1,256,256]\n mode: 'point' (points only), 'mask' (mask only), 'both' (consider both)\n mask_outputs: True (return 3 masks), False (return 1 mask only)\n whem mask_outputs=True, mask_input=logits[np.argmax(scores), :, :][None, :, :]\n \"\"\"\n assert self.embedded, 'prediction is called before set_image (feature embedding).'\n assert mode in ['point', 'mask', 'both'], 'mode must be point, mask, or both'\n \n if mode == 'point':\n masks, scores, logits = self.predictor.predict(point_coords=prompts['point_coords'], \n point_labels=prompts['point_labels'], \n multimask_output=multimask)\n elif mode == 'mask':","source_hash":"73f546d160f4a13e76370ad2b98c7d72277a90e0d51a9f97b27310c3223988f5","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tools.base_segmenter.predict","uri":"program://Track-Anything/function/tools.base_segmenter.predict#L48-L77","kind":"function","name":"predict","path":"tools/base_segmenter.py","language":"python","start_line":48,"end_line":77,"context_start_line":28,"context_end_line":97,"code":" self.embedded = False\n\n @torch.no_grad()\n def set_image(self, image: np.ndarray):\n # PIL.open(image_path) 3channel: RGB\n # image embedding: avoid encode the same image multiple times\n self.orignal_image = image\n if self.embedded:\n print('repeat embedding, please reset_image.')\n return\n self.predictor.set_image(image)\n self.embedded = True\n return\n \n @torch.no_grad()\n def reset_image(self):\n # reset image embeding\n self.predictor.reset_image()\n self.embedded = False\n\n def predict(self, prompts, mode, multimask=True):\n \"\"\"\n image: numpy array, h, w, 3\n prompts: dictionary, 3 keys: 'point_coords', 'point_labels', 'mask_input'\n prompts['point_coords']: numpy array [N,2]\n prompts['point_labels']: numpy array [1,N]\n prompts['mask_input']: numpy array [1,256,256]\n mode: 'point' (points only), 'mask' (mask only), 'both' (consider both)\n mask_outputs: True (return 3 masks), False (return 1 mask only)\n whem mask_outputs=True, mask_input=logits[np.argmax(scores), :, :][None, :, :]\n \"\"\"\n assert self.embedded, 'prediction is called before set_image (feature embedding).'\n assert mode in ['point', 'mask', 'both'], 'mode must be point, mask, or both'\n \n if mode == 'point':\n masks, scores, logits = self.predictor.predict(point_coords=prompts['point_coords'], \n point_labels=prompts['point_labels'], \n multimask_output=multimask)\n elif mode == 'mask':\n masks, scores, logits = self.predictor.predict(mask_input=prompts['mask_input'], \n multimask_output=multimask)\n elif mode == 'both': # both\n masks, scores, logits = self.predictor.predict(point_coords=prompts['point_coords'], \n point_labels=prompts['point_labels'], \n mask_input=prompts['mask_input'], \n multimask_output=multimask)\n else:\n raise(\"Not implement now!\")\n # masks (n, h, w), scores (n,), logits (n, 256, 256)\n return masks, scores, logits\n\n\nif __name__ == \"__main__\":\n # load and show an image\n image = cv2.imread('/hhd3/gaoshang/truck.jpg')\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # numpy array (h, w, 3)\n\n # initialise BaseSegmenter\n SAM_checkpoint= '/ssd1/gaomingqi/checkpoints/sam_vit_h_4b8939.pth'\n model_type = 'vit_h'\n device = \"cuda:4\"\n base_segmenter = BaseSegmenter(SAM_checkpoint=SAM_checkpoint, model_type=model_type, device=device)\n \n # image embedding (once embedded, multiple prompts can be applied)\n base_segmenter.set_image(image)\n \n # examples\n # point only ------------------------\n mode = 'point'\n prompts = {","source_hash":"73f546d160f4a13e76370ad2b98c7d72277a90e0d51a9f97b27310c3223988f5","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tools.interact_tools","uri":"program://Track-Anything/module/tools.interact_tools#L1-L265","kind":"module","name":"tools.interact_tools","path":"tools/interact_tools.py","language":"python","start_line":1,"end_line":265,"context_start_line":1,"context_end_line":265,"code":"import time\nimport torch\nimport cv2\nfrom PIL import Image, ImageDraw, ImageOps\nimport numpy as np\nfrom typing import Union\nfrom segment_anything import sam_model_registry, SamPredictor, SamAutomaticMaskGenerator\nimport matplotlib.pyplot as plt\nimport PIL\nfrom .mask_painter import mask_painter as mask_painter2\nfrom .base_segmenter import BaseSegmenter\nfrom .painter import mask_painter, point_painter\nimport os\nimport requests\nimport sys \n\n\nmask_color = 3\nmask_alpha = 0.7\ncontour_color = 1\ncontour_width = 5\npoint_color_ne = 8\npoint_color_ps = 50\npoint_alpha = 0.9\npoint_radius = 15\ncontour_color = 2\ncontour_width = 5\n\n\nclass SamControler():\n def __init__(self, SAM_checkpoint, model_type, device):\n '''\n initialize sam controler\n '''\n\n \n self.sam_controler = BaseSegmenter(SAM_checkpoint, model_type, device)\n \n \n # def seg_again(self, image: np.ndarray):\n # '''\n # it is used when interact in video\n # '''\n # self.sam_controler.reset_image()\n # self.sam_controler.set_image(image)\n # return \n \n \n def first_frame_click(self, image: np.ndarray, points:np.ndarray, labels: np.ndarray, multimask=True,mask_color=3):\n '''\n it is used in first frame in video\n return: mask, logit, painted image(mask+point)\n '''\n # self.sam_controler.set_image(image)\n origal_image = self.sam_controler.orignal_image\n neg_flag = labels[-1]\n if neg_flag==1:\n #find neg\n prompts = {\n 'point_coords': points,\n 'point_labels': labels,\n }\n masks, scores, logits = self.sam_controler.predict(prompts, 'point', multimask)\n mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :]\n prompts = {\n 'point_coords': points,\n 'point_labels': labels,\n 'mask_input': logit[None, :, :]\n }\n masks, scores, logits = self.sam_controler.predict(prompts, 'both', multimask)\n mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :]\n else:\n #find positive\n prompts = {\n 'point_coords': points,\n 'point_labels': labels,\n }\n masks, scores, logits = self.sam_controler.predict(prompts, 'point', multimask)\n mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :]\n \n \n assert len(points)==len(labels)\n \n painted_image = mask_painter(image, mask.astype('uint8'), mask_color, mask_alpha, contour_color, contour_width)\n painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels>0)],axis = 1), point_color_ne, point_alpha, point_radius, contour_color, contour_width)\n painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels<1)],axis = 1), point_color_ps, point_alpha, point_radius, contour_color, contour_width)\n painted_image = Image.fromarray(painted_image)\n \n return mask, logit, painted_image\n \n # def interact_loop(self, image:np.ndarray, same: bool, points:np.ndarray, labels: np.ndarray, logits: np.ndarray=None, multimask=True):\n # origal_image = self.sam_controler.orignal_image\n # if same: \n # '''\n # true; loop in the same image\n # '''\n # prompts = {\n # 'point_coords': points,\n # 'point_labels': labels,\n # 'mask_input': logits[None, :, :]\n # }\n # masks, scores, logits = self.sam_controler.predict(prompts, 'both', multimask)\n # mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :]\n \n # painted_image = mask_painter(image, mask.astype('uint8'), mask_color, mask_alpha, contour_color, contour_width)\n # painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels>0)],axis = 1), point_color_ne, point_alpha, point_radius, contour_color, contour_width)\n # painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels<1)],axis = 1), point_color_ps, point_alpha, point_radius, contour_color, contour_width)\n # painted_image = Image.fromarray(painted_image)\n\n # return mask, logit, painted_image\n # else:\n # '''\n # loop in the different image, interact in the video \n # '''\n # if image is None:\n # raise('Image error')\n # else:\n # self.seg_again(image)\n # prompts = {\n # 'point_coords': points,\n # 'point_labels': labels,\n # }\n # masks, scores, logits = self.sam_controler.predict(prompts, 'point', multimask)\n # mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :]\n \n # painted_image = mask_painter(image, mask.astype('uint8'), mask_color, mask_alpha, contour_color, contour_width)\n # painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels>0)],axis = 1), point_color_ne, point_alpha, point_radius, contour_color, contour_width)\n # painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels<1)],axis = 1), point_color_ps, point_alpha, point_radius, contour_color, contour_width)\n # painted_image = Image.fromarray(painted_image)\n\n # return mask, logit, painted_image\n \n \n\n\n\n\n# def initialize():\n# '''\n# initialize sam controler\n# '''\n# checkpoint_url = \"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\"\n# folder = \"segmenter\"\n# SAM_checkpoint= './checkpoints/sam_vit_h_4b8939.pth'\n# download_checkpoint(checkpoint_url, folder, SAM_checkpoint)\n \n\n# model_type = 'vit_h'\n# device = \"cuda:0\"\n# sam_controler = BaseSegmenter(SAM_checkpoint, model_type, device)\n# return sam_controler\n\n\n# def seg_again(sam_controler, image: np.ndarray):\n# '''\n# it is used when interact in video\n# '''\n# sam_controler.reset_image()\n# sam_controler.set_image(image)\n# return\n \n\n# def first_frame_click(sam_controler, image: np.ndarray, points:np.ndarray, labels: np.ndarray, multimask=True):\n# '''\n# it is used in first frame in video\n# return: mask, logit, painted image(mask+point)\n# '''\n# sam_controler.set_image(image) \n# prompts = {\n# 'point_coords': points,\n# 'point_labels': labels,\n# }\n# masks, scores, logits = sam_controler.predict(prompts, 'point', multimask)\n# mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :]\n \n# assert len(points)==len(labels)\n \n# painted_image = mask_painter(image, mask.astype('uint8'), mask_color, mask_alpha, contour_color, contour_width)\n# painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels>0)],axis = 1), point_color_ne, point_alpha, point_radius, contour_color, contour_width)\n# painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels<1)],axis = 1), point_color_ps, point_alpha, point_radius, contour_color, contour_width)\n# painted_image = Image.fromarray(painted_image)\n \n# return mask, logit, painted_image\n\n# def interact_loop(sam_controler, image:np.ndarray, same: bool, points:np.ndarray, labels: np.ndarray, logits: np.ndarray=None, multimask=True):\n# if same: \n# '''\n# true; loop in the same image\n# '''\n# prompts = {\n# 'point_coords': points,\n# 'point_labels': labels,\n# 'mask_input': logits[None, :, :]\n# }\n# masks, scores, logits = sam_controler.predict(prompts, 'both', multimask)\n# mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :]\n \n# painted_image = mask_painter(image, mask.astype('uint8'), mask_color, mask_alpha, contour_color, contour_width)\n# painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels>0)],axis = 1), point_color_ne, point_alpha, point_radius, contour_color, contour_width)\n# painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels<1)],axis = 1), point_color_ps, point_alpha, point_radius, contour_color, contour_width)\n# painted_image = Image.fromarray(painted_image)\n\n# return mask, logit, painted_image\n# else:\n# '''\n# loop in the different image, interact in the video \n# '''\n# if image is None:\n# raise('Image error')\n# else:\n# seg_again(sam_controler, image)\n# prompts = {\n# 'point_coords': points,\n# 'point_labels': labels,\n# }\n# masks, scores, logits = sam_controler.predict(prompts, 'point', multimask)\n# mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :]\n \n# painted_image = mask_painter(image, mask.astype('uint8'), mask_color, mask_alpha, contour_color, contour_width)\n# painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels>0)],axis = 1), point_color_ne, point_alpha, point_radius, contour_color, contour_width)\n# painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels<1)],axis = 1), point_color_ps, point_alpha, point_radius, contour_color, contour_width)\n# painted_image = Image.fromarray(painted_image)\n\n# return mask, logit, painted_image\n \n \n\n\n# if __name__ == \"__main__\":\n# points = np.array([[500, 375], [1125, 625]])\n# labels = np.array([1, 1])\n# image = cv2.imread('/hhd3/gaoshang/truck.jpg')\n# image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n \n# sam_controler = initialize()\n# mask, logit, painted_image_full = first_frame_click(sam_controler,image, points, labels, multimask=True)\n# painted_image = mask_painter2(image, mask.astype('uint8'), background_alpha=0.8)\n# painted_image = cv2.cvtColor(painted_image, cv2.COLOR_RGB2BGR) # numpy array (h, w, 3)\n# cv2.imwrite('/hhd3/gaoshang/truck_point.jpg', painted_image)\n# cv2.imwrite('/hhd3/gaoshang/truck_change.jpg', image)\n# painted_image_full.save('/hhd3/gaoshang/truck_point_full.jpg')\n \n# mask, logit, painted_image_full = interact_loop(sam_controler,image,True, points, np.array([1, 0]), logit, multimask=True)\n# painted_image = mask_painter2(image, mask.astype('uint8'), background_alpha=0.8)\n# painted_image = cv2.cvtColor(painted_image, cv2.COLOR_RGB2BGR) # numpy array (h, w, 3)\n# cv2.imwrite('/hhd3/gaoshang/truck_same.jpg', painted_image)\n# painted_image_full.save('/hhd3/gaoshang/truck_same_full.jpg')\n \n# mask, logit, painted_image_full = interact_loop(sam_controler,image, False, points, labels, multimask=True)\n# painted_image = mask_painter2(image, mask.astype('uint8'), background_alpha=0.8)\n# painted_image = cv2.cvtColor(painted_image, cv2.COLOR_RGB2BGR) # numpy array (h, w, 3)\n# cv2.imwrite('/hhd3/gaoshang/truck_diff.jpg', painted_image)\n# painted_image_full.save('/hhd3/gaoshang/truck_diff_full.jpg')\n \n \n \n \n \n \n \n\n\n \n \n ","source_hash":"26b46f3b62a09f0ae5efdf5ff3310d397b91a25f6b56f356ad9f86cd09568818","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tools.interact_tools.SamControler","uri":"program://Track-Anything/class/tools.interact_tools.SamControler#L30-L89","kind":"class","name":"SamControler","path":"tools/interact_tools.py","language":"python","start_line":30,"end_line":89,"context_start_line":10,"context_end_line":109,"code":"from .mask_painter import mask_painter as mask_painter2\nfrom .base_segmenter import BaseSegmenter\nfrom .painter import mask_painter, point_painter\nimport os\nimport requests\nimport sys \n\n\nmask_color = 3\nmask_alpha = 0.7\ncontour_color = 1\ncontour_width = 5\npoint_color_ne = 8\npoint_color_ps = 50\npoint_alpha = 0.9\npoint_radius = 15\ncontour_color = 2\ncontour_width = 5\n\n\nclass SamControler():\n def __init__(self, SAM_checkpoint, model_type, device):\n '''\n initialize sam controler\n '''\n\n \n self.sam_controler = BaseSegmenter(SAM_checkpoint, model_type, device)\n \n \n # def seg_again(self, image: np.ndarray):\n # '''\n # it is used when interact in video\n # '''\n # self.sam_controler.reset_image()\n # self.sam_controler.set_image(image)\n # return \n \n \n def first_frame_click(self, image: np.ndarray, points:np.ndarray, labels: np.ndarray, multimask=True,mask_color=3):\n '''\n it is used in first frame in video\n return: mask, logit, painted image(mask+point)\n '''\n # self.sam_controler.set_image(image)\n origal_image = self.sam_controler.orignal_image\n neg_flag = labels[-1]\n if neg_flag==1:\n #find neg\n prompts = {\n 'point_coords': points,\n 'point_labels': labels,\n }\n masks, scores, logits = self.sam_controler.predict(prompts, 'point', multimask)\n mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :]\n prompts = {\n 'point_coords': points,\n 'point_labels': labels,\n 'mask_input': logit[None, :, :]\n }\n masks, scores, logits = self.sam_controler.predict(prompts, 'both', multimask)\n mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :]\n else:\n #find positive\n prompts = {\n 'point_coords': points,\n 'point_labels': labels,\n }\n masks, scores, logits = self.sam_controler.predict(prompts, 'point', multimask)\n mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :]\n \n \n assert len(points)==len(labels)\n \n painted_image = mask_painter(image, mask.astype('uint8'), mask_color, mask_alpha, contour_color, contour_width)\n painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels>0)],axis = 1), point_color_ne, point_alpha, point_radius, contour_color, contour_width)\n painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels<1)],axis = 1), point_color_ps, point_alpha, point_radius, contour_color, contour_width)\n painted_image = Image.fromarray(painted_image)\n \n return mask, logit, painted_image\n \n # def interact_loop(self, image:np.ndarray, same: bool, points:np.ndarray, labels: np.ndarray, logits: np.ndarray=None, multimask=True):\n # origal_image = self.sam_controler.orignal_image\n # if same: \n # '''\n # true; loop in the same image\n # '''\n # prompts = {\n # 'point_coords': points,\n # 'point_labels': labels,\n # 'mask_input': logits[None, :, :]\n # }\n # masks, scores, logits = self.sam_controler.predict(prompts, 'both', multimask)\n # mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :]\n \n # painted_image = mask_painter(image, mask.astype('uint8'), mask_color, mask_alpha, contour_color, contour_width)\n # painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels>0)],axis = 1), point_color_ne, point_alpha, point_radius, contour_color, contour_width)\n # painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels<1)],axis = 1), point_color_ps, point_alpha, point_radius, contour_color, contour_width)\n # painted_image = Image.fromarray(painted_image)\n","source_hash":"26b46f3b62a09f0ae5efdf5ff3310d397b91a25f6b56f356ad9f86cd09568818","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tools.interact_tools.__init__","uri":"program://Track-Anything/function/tools.interact_tools.__init__#L31-L37","kind":"function","name":"__init__","path":"tools/interact_tools.py","language":"python","start_line":31,"end_line":37,"context_start_line":11,"context_end_line":57,"code":"from .base_segmenter import BaseSegmenter\nfrom .painter import mask_painter, point_painter\nimport os\nimport requests\nimport sys \n\n\nmask_color = 3\nmask_alpha = 0.7\ncontour_color = 1\ncontour_width = 5\npoint_color_ne = 8\npoint_color_ps = 50\npoint_alpha = 0.9\npoint_radius = 15\ncontour_color = 2\ncontour_width = 5\n\n\nclass SamControler():\n def __init__(self, SAM_checkpoint, model_type, device):\n '''\n initialize sam controler\n '''\n\n \n self.sam_controler = BaseSegmenter(SAM_checkpoint, model_type, device)\n \n \n # def seg_again(self, image: np.ndarray):\n # '''\n # it is used when interact in video\n # '''\n # self.sam_controler.reset_image()\n # self.sam_controler.set_image(image)\n # return \n \n \n def first_frame_click(self, image: np.ndarray, points:np.ndarray, labels: np.ndarray, multimask=True,mask_color=3):\n '''\n it is used in first frame in video\n return: mask, logit, painted image(mask+point)\n '''\n # self.sam_controler.set_image(image)\n origal_image = self.sam_controler.orignal_image\n neg_flag = labels[-1]\n if neg_flag==1:","source_hash":"26b46f3b62a09f0ae5efdf5ff3310d397b91a25f6b56f356ad9f86cd09568818","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tools.interact_tools.first_frame_click","uri":"program://Track-Anything/function/tools.interact_tools.first_frame_click#L49-L89","kind":"function","name":"first_frame_click","path":"tools/interact_tools.py","language":"python","start_line":49,"end_line":89,"context_start_line":29,"context_end_line":109,"code":"\nclass SamControler():\n def __init__(self, SAM_checkpoint, model_type, device):\n '''\n initialize sam controler\n '''\n\n \n self.sam_controler = BaseSegmenter(SAM_checkpoint, model_type, device)\n \n \n # def seg_again(self, image: np.ndarray):\n # '''\n # it is used when interact in video\n # '''\n # self.sam_controler.reset_image()\n # self.sam_controler.set_image(image)\n # return \n \n \n def first_frame_click(self, image: np.ndarray, points:np.ndarray, labels: np.ndarray, multimask=True,mask_color=3):\n '''\n it is used in first frame in video\n return: mask, logit, painted image(mask+point)\n '''\n # self.sam_controler.set_image(image)\n origal_image = self.sam_controler.orignal_image\n neg_flag = labels[-1]\n if neg_flag==1:\n #find neg\n prompts = {\n 'point_coords': points,\n 'point_labels': labels,\n }\n masks, scores, logits = self.sam_controler.predict(prompts, 'point', multimask)\n mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :]\n prompts = {\n 'point_coords': points,\n 'point_labels': labels,\n 'mask_input': logit[None, :, :]\n }\n masks, scores, logits = self.sam_controler.predict(prompts, 'both', multimask)\n mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :]\n else:\n #find positive\n prompts = {\n 'point_coords': points,\n 'point_labels': labels,\n }\n masks, scores, logits = self.sam_controler.predict(prompts, 'point', multimask)\n mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :]\n \n \n assert len(points)==len(labels)\n \n painted_image = mask_painter(image, mask.astype('uint8'), mask_color, mask_alpha, contour_color, contour_width)\n painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels>0)],axis = 1), point_color_ne, point_alpha, point_radius, contour_color, contour_width)\n painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels<1)],axis = 1), point_color_ps, point_alpha, point_radius, contour_color, contour_width)\n painted_image = Image.fromarray(painted_image)\n \n return mask, logit, painted_image\n \n # def interact_loop(self, image:np.ndarray, same: bool, points:np.ndarray, labels: np.ndarray, logits: np.ndarray=None, multimask=True):\n # origal_image = self.sam_controler.orignal_image\n # if same: \n # '''\n # true; loop in the same image\n # '''\n # prompts = {\n # 'point_coords': points,\n # 'point_labels': labels,\n # 'mask_input': logits[None, :, :]\n # }\n # masks, scores, logits = self.sam_controler.predict(prompts, 'both', multimask)\n # mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :]\n \n # painted_image = mask_painter(image, mask.astype('uint8'), mask_color, mask_alpha, contour_color, contour_width)\n # painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels>0)],axis = 1), point_color_ne, point_alpha, point_radius, contour_color, contour_width)\n # painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels<1)],axis = 1), point_color_ps, point_alpha, point_radius, contour_color, contour_width)\n # painted_image = Image.fromarray(painted_image)\n","source_hash":"26b46f3b62a09f0ae5efdf5ff3310d397b91a25f6b56f356ad9f86cd09568818","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.base_tracker","uri":"program://Track-Anything/module/tracker.base_tracker#L1-L261","kind":"module","name":"tracker.base_tracker","path":"tracker/base_tracker.py","language":"python","start_line":1,"end_line":261,"context_start_line":1,"context_end_line":261,"code":"# import for debugging\nimport os\nimport glob\nimport numpy as np\nfrom PIL import Image\n# import for base_tracker\nimport torch\nimport yaml\nimport torch.nn.functional as F\nfrom tracker.model.network import XMem\nfrom inference.inference_core import InferenceCore\nfrom tracker.util.mask_mapper import MaskMapper\nfrom torchvision import transforms\nfrom tracker.util.range_transform import im_normalization\n\nfrom tools.painter import mask_painter\nfrom tools.base_segmenter import BaseSegmenter\nfrom torchvision.transforms import Resize\nimport progressbar\n\n\nclass BaseTracker:\n def __init__(self, xmem_checkpoint, device, sam_model=None, model_type=None) -> None:\n \"\"\"\n device: model device\n xmem_checkpoint: checkpoint of XMem model\n \"\"\"\n # load configurations\n with open(\"tracker/config/config.yaml\", 'r') as stream: \n config = yaml.safe_load(stream) \n # initialise XMem\n network = XMem(config, xmem_checkpoint).to(device).eval()\n # initialise IncerenceCore\n self.tracker = InferenceCore(network, config)\n # data transformation\n self.im_transform = transforms.Compose([\n transforms.ToTensor(),\n im_normalization,\n ])\n self.device = device\n \n # changable properties\n self.mapper = MaskMapper()\n self.initialised = False\n\n # # SAM-based refinement\n # self.sam_model = sam_model\n # self.resizer = Resize([256, 256])\n\n @torch.no_grad()\n def resize_mask(self, mask):\n # mask transform is applied AFTER mapper, so we need to post-process it in eval.py\n h, w = mask.shape[-2:]\n min_hw = min(h, w)\n return F.interpolate(mask, (int(h/min_hw*self.size), int(w/min_hw*self.size)), \n mode='nearest')\n\n @torch.no_grad()\n def track(self, frame, first_frame_annotation=None):\n \"\"\"\n Input: \n frames: numpy arrays (H, W, 3)\n logit: numpy array (H, W), logit\n\n Output:\n mask: numpy arrays (H, W)\n logit: numpy arrays, probability map (H, W)\n painted_image: numpy array (H, W, 3)\n \"\"\"\n\n if first_frame_annotation is not None: # first frame mask\n # initialisation\n mask, labels = self.mapper.convert_mask(first_frame_annotation)\n mask = torch.Tensor(mask).to(self.device)\n self.tracker.set_all_labels(list(self.mapper.remappings.values()))\n else:\n mask = None\n labels = None\n # prepare inputs\n frame_tensor = self.im_transform(frame).to(self.device)\n # track one frame\n probs, _ = self.tracker.step(frame_tensor, mask, labels) # logits 2 (bg fg) H W\n # # refine\n # if first_frame_annotation is None:\n # out_mask = self.sam_refinement(frame, logits[1], ti) \n\n # convert to mask\n out_mask = torch.argmax(probs, dim=0)\n out_mask = (out_mask.detach().cpu().numpy()).astype(np.uint8)\n\n final_mask = np.zeros_like(out_mask)\n \n # map back\n for k, v in self.mapper.remappings.items():\n final_mask[out_mask == v] = k\n\n num_objs = final_mask.max()\n painted_image = frame\n for obj in range(1, num_objs+1):\n if np.max(final_mask==obj) == 0:\n continue\n painted_image = mask_painter(painted_image, (final_mask==obj).astype('uint8'), mask_color=obj+1)\n\n # print(f'max memory allocated: {torch.cuda.max_memory_allocated()/(2**20)} MB')\n\n return final_mask, final_mask, painted_image\n\n @torch.no_grad()\n def sam_refinement(self, frame, logits, ti):\n \"\"\"\n refine segmentation results with mask prompt\n \"\"\"\n # convert to 1, 256, 256\n self.sam_model.set_image(frame)\n mode = 'mask'\n logits = logits.unsqueeze(0)\n logits = self.resizer(logits).cpu().numpy()\n prompts = {'mask_input': logits} # 1 256 256\n masks, scores, logits = self.sam_model.predict(prompts, mode, multimask=True) # masks (n, h, w), scores (n,), logits (n, 256, 256)\n painted_image = mask_painter(frame, masks[np.argmax(scores)].astype('uint8'), mask_alpha=0.8)\n painted_image = Image.fromarray(painted_image)\n painted_image.save(f'/ssd1/gaomingqi/refine/{ti:05d}.png')\n self.sam_model.reset_image()\n\n @torch.no_grad()\n def clear_memory(self):\n self.tracker.clear_memory()\n self.mapper.clear_labels()\n torch.cuda.empty_cache()\n\n\n## how to use:\n## 1/3) prepare device and xmem_checkpoint\n# device = 'cuda:2'\n# XMEM_checkpoint = '/ssd1/gaomingqi/checkpoints/XMem-s012.pth'\n## 2/3) initialise Base Tracker\n# tracker = BaseTracker(XMEM_checkpoint, device, None, device) # leave an interface for sam model (currently set None)\n## 3/3) \n\n\nif __name__ == '__main__':\n # video frames (take videos from DAVIS-2017 as examples)\n video_path_list = glob.glob(os.path.join('/ssd1/gaomingqi/datasets/davis/JPEGImages/480p/horsejump-high', '*.jpg'))\n video_path_list.sort()\n # load frames\n frames = []\n for video_path in video_path_list:\n frames.append(np.array(Image.open(video_path).convert('RGB')))\n frames = np.stack(frames, 0) # T, H, W, C\n # load first frame annotation\n first_frame_path = '/ssd1/gaomingqi/datasets/davis/Annotations/480p/horsejump-high/00000.png'\n first_frame_annotation = np.array(Image.open(first_frame_path).convert('P')) # H, W, C\n\n # ------------------------------------------------------------------------------------\n # how to use\n # ------------------------------------------------------------------------------------\n # 1/4: set checkpoint and device\n device = 'cuda:2'\n XMEM_checkpoint = '/ssd1/gaomingqi/checkpoints/XMem-s012.pth'\n # SAM_checkpoint= '/ssd1/gaomingqi/checkpoints/sam_vit_h_4b8939.pth'\n # model_type = 'vit_h'\n # ------------------------------------------------------------------------------------\n # 2/4: initialise inpainter\n tracker = BaseTracker(XMEM_checkpoint, device, None, device)\n # ------------------------------------------------------------------------------------\n # 3/4: for each frame, get tracking results by tracker.track(frame, first_frame_annotation)\n # frame: numpy array (H, W, C), first_frame_annotation: numpy array (H, W), leave it blank when tracking begins\n painted_frames = []\n for ti, frame in enumerate(frames):\n if ti == 0:\n mask, prob, painted_frame = tracker.track(frame, first_frame_annotation)\n # mask: \n else:\n mask, prob, painted_frame = tracker.track(frame)\n painted_frames.append(painted_frame)\n # ----------------------------------------------\n # 3/4: clear memory in XMEM for the next video\n tracker.clear_memory()\n # ----------------------------------------------\n # end\n # ----------------------------------------------\n print(f'max memory allocated: {torch.cuda.max_memory_allocated()/(2**20)} MB')\n # set saving path\n save_path = '/ssd1/gaomingqi/results/TAM/blackswan'\n if not os.path.exists(save_path):\n os.mkdir(save_path)\n # save\n for painted_frame in progressbar.progressbar(painted_frames):\n painted_frame = Image.fromarray(painted_frame)\n painted_frame.save(f'{save_path}/{ti:05d}.png')\n\n # tracker.clear_memory()\n # for ti, frame in enumerate(frames):\n # print(ti)\n # # if ti > 200:\n # # break\n # if ti == 0:\n # mask, prob, painted_image = tracker.track(frame, first_frame_annotation)\n # else:\n # mask, prob, painted_image = tracker.track(frame)\n # # save\n # painted_image = Image.fromarray(painted_image)\n # painted_image.save(f'/ssd1/gaomingqi/results/TrackA/gsw/{ti:05d}.png')\n\n # # track anything given in the first frame annotation\n # for ti, frame in enumerate(frames):\n # if ti == 0:\n # mask, prob, painted_image = tracker.track(frame, first_frame_annotation)\n # else:\n # mask, prob, painted_image = tracker.track(frame)\n # # save\n # painted_image = Image.fromarray(painted_image)\n # painted_image.save(f'/ssd1/gaomingqi/results/TrackA/horsejump-high/{ti:05d}.png')\n\n # # ----------------------------------------------------------\n # # another video\n # # ----------------------------------------------------------\n # # video frames\n # video_path_list = glob.glob(os.path.join('/ssd1/gaomingqi/datasets/davis/JPEGImages/480p/camel', '*.jpg'))\n # video_path_list.sort()\n # # first frame\n # first_frame_path = '/ssd1/gaomingqi/datasets/davis/Annotations/480p/camel/00000.png'\n # # load frames\n # frames = []\n # for video_path in video_path_list:\n # frames.append(np.array(Image.open(video_path).convert('RGB')))\n # frames = np.stack(frames, 0) # N, H, W, C\n # # load first frame annotation\n # first_frame_annotation = np.array(Image.open(first_frame_path).convert('P')) # H, W, C\n\n # print('first video done. clear.')\n\n # tracker.clear_memory()\n # # track anything given in the first frame annotation\n # for ti, frame in enumerate(frames):\n # if ti == 0:\n # mask, prob, painted_image = tracker.track(frame, first_frame_annotation)\n # else:\n # mask, prob, painted_image = tracker.track(frame)\n # # save\n # painted_image = Image.fromarray(painted_image)\n # painted_image.save(f'/ssd1/gaomingqi/results/TrackA/camel/{ti:05d}.png')\n\n # # failure case test\n # failure_path = '/ssd1/gaomingqi/failure'\n # frames = np.load(os.path.join(failure_path, 'video_frames.npy'))\n # # first_frame = np.array(Image.open(os.path.join(failure_path, 'template_frame.png')).convert('RGB'))\n # first_mask = np.array(Image.open(os.path.join(failure_path, 'template_mask.png')).convert('P'))\n # first_mask = np.clip(first_mask, 0, 1)\n\n # for ti, frame in enumerate(frames):\n # if ti == 0:\n # mask, probs, painted_image = tracker.track(frame, first_mask)\n # else:\n # mask, probs, painted_image = tracker.track(frame)\n # # save\n # painted_image = Image.fromarray(painted_image)\n # painted_image.save(f'/ssd1/gaomingqi/failure/LJ/{ti:05d}.png')\n # prob = Image.fromarray((probs[1].cpu().numpy()*255).astype('uint8'))\n\n # # prob.save(f'/ssd1/gaomingqi/failure/probs/{ti:05d}.png')","source_hash":"82ff5111f79077418e97354925c76b9560c299efe3e5ba863f65c337978778ef","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.base_tracker.BaseTracker","uri":"program://Track-Anything/class/tracker.base_tracker.BaseTracker#L22-L129","kind":"class","name":"BaseTracker","path":"tracker/base_tracker.py","language":"python","start_line":22,"end_line":129,"context_start_line":2,"context_end_line":149,"code":"import os\nimport glob\nimport numpy as np\nfrom PIL import Image\n# import for base_tracker\nimport torch\nimport yaml\nimport torch.nn.functional as F\nfrom tracker.model.network import XMem\nfrom inference.inference_core import InferenceCore\nfrom tracker.util.mask_mapper import MaskMapper\nfrom torchvision import transforms\nfrom tracker.util.range_transform import im_normalization\n\nfrom tools.painter import mask_painter\nfrom tools.base_segmenter import BaseSegmenter\nfrom torchvision.transforms import Resize\nimport progressbar\n\n\nclass BaseTracker:\n def __init__(self, xmem_checkpoint, device, sam_model=None, model_type=None) -> None:\n \"\"\"\n device: model device\n xmem_checkpoint: checkpoint of XMem model\n \"\"\"\n # load configurations\n with open(\"tracker/config/config.yaml\", 'r') as stream: \n config = yaml.safe_load(stream) \n # initialise XMem\n network = XMem(config, xmem_checkpoint).to(device).eval()\n # initialise IncerenceCore\n self.tracker = InferenceCore(network, config)\n # data transformation\n self.im_transform = transforms.Compose([\n transforms.ToTensor(),\n im_normalization,\n ])\n self.device = device\n \n # changable properties\n self.mapper = MaskMapper()\n self.initialised = False\n\n # # SAM-based refinement\n # self.sam_model = sam_model\n # self.resizer = Resize([256, 256])\n\n @torch.no_grad()\n def resize_mask(self, mask):\n # mask transform is applied AFTER mapper, so we need to post-process it in eval.py\n h, w = mask.shape[-2:]\n min_hw = min(h, w)\n return F.interpolate(mask, (int(h/min_hw*self.size), int(w/min_hw*self.size)), \n mode='nearest')\n\n @torch.no_grad()\n def track(self, frame, first_frame_annotation=None):\n \"\"\"\n Input: \n frames: numpy arrays (H, W, 3)\n logit: numpy array (H, W), logit\n\n Output:\n mask: numpy arrays (H, W)\n logit: numpy arrays, probability map (H, W)\n painted_image: numpy array (H, W, 3)\n \"\"\"\n\n if first_frame_annotation is not None: # first frame mask\n # initialisation\n mask, labels = self.mapper.convert_mask(first_frame_annotation)\n mask = torch.Tensor(mask).to(self.device)\n self.tracker.set_all_labels(list(self.mapper.remappings.values()))\n else:\n mask = None\n labels = None\n # prepare inputs\n frame_tensor = self.im_transform(frame).to(self.device)\n # track one frame\n probs, _ = self.tracker.step(frame_tensor, mask, labels) # logits 2 (bg fg) H W\n # # refine\n # if first_frame_annotation is None:\n # out_mask = self.sam_refinement(frame, logits[1], ti) \n\n # convert to mask\n out_mask = torch.argmax(probs, dim=0)\n out_mask = (out_mask.detach().cpu().numpy()).astype(np.uint8)\n\n final_mask = np.zeros_like(out_mask)\n \n # map back\n for k, v in self.mapper.remappings.items():\n final_mask[out_mask == v] = k\n\n num_objs = final_mask.max()\n painted_image = frame\n for obj in range(1, num_objs+1):\n if np.max(final_mask==obj) == 0:\n continue\n painted_image = mask_painter(painted_image, (final_mask==obj).astype('uint8'), mask_color=obj+1)\n\n # print(f'max memory allocated: {torch.cuda.max_memory_allocated()/(2**20)} MB')\n\n return final_mask, final_mask, painted_image\n\n @torch.no_grad()\n def sam_refinement(self, frame, logits, ti):\n \"\"\"\n refine segmentation results with mask prompt\n \"\"\"\n # convert to 1, 256, 256\n self.sam_model.set_image(frame)\n mode = 'mask'\n logits = logits.unsqueeze(0)\n logits = self.resizer(logits).cpu().numpy()\n prompts = {'mask_input': logits} # 1 256 256\n masks, scores, logits = self.sam_model.predict(prompts, mode, multimask=True) # masks (n, h, w), scores (n,), logits (n, 256, 256)\n painted_image = mask_painter(frame, masks[np.argmax(scores)].astype('uint8'), mask_alpha=0.8)\n painted_image = Image.fromarray(painted_image)\n painted_image.save(f'/ssd1/gaomingqi/refine/{ti:05d}.png')\n self.sam_model.reset_image()\n\n @torch.no_grad()\n def clear_memory(self):\n self.tracker.clear_memory()\n self.mapper.clear_labels()\n torch.cuda.empty_cache()\n\n\n## how to use:\n## 1/3) prepare device and xmem_checkpoint\n# device = 'cuda:2'\n# XMEM_checkpoint = '/ssd1/gaomingqi/checkpoints/XMem-s012.pth'\n## 2/3) initialise Base Tracker\n# tracker = BaseTracker(XMEM_checkpoint, device, None, device) # leave an interface for sam model (currently set None)\n## 3/3) \n\n\nif __name__ == '__main__':\n # video frames (take videos from DAVIS-2017 as examples)\n video_path_list = glob.glob(os.path.join('/ssd1/gaomingqi/datasets/davis/JPEGImages/480p/horsejump-high', '*.jpg'))\n video_path_list.sort()\n # load frames\n frames = []\n for video_path in video_path_list:\n frames.append(np.array(Image.open(video_path).convert('RGB')))\n frames = np.stack(frames, 0) # T, H, W, C","source_hash":"82ff5111f79077418e97354925c76b9560c299efe3e5ba863f65c337978778ef","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.base_tracker.__init__","uri":"program://Track-Anything/function/tracker.base_tracker.__init__#L23-L44","kind":"function","name":"__init__","path":"tracker/base_tracker.py","language":"python","start_line":23,"end_line":44,"context_start_line":3,"context_end_line":64,"code":"import glob\nimport numpy as np\nfrom PIL import Image\n# import for base_tracker\nimport torch\nimport yaml\nimport torch.nn.functional as F\nfrom tracker.model.network import XMem\nfrom inference.inference_core import InferenceCore\nfrom tracker.util.mask_mapper import MaskMapper\nfrom torchvision import transforms\nfrom tracker.util.range_transform import im_normalization\n\nfrom tools.painter import mask_painter\nfrom tools.base_segmenter import BaseSegmenter\nfrom torchvision.transforms import Resize\nimport progressbar\n\n\nclass BaseTracker:\n def __init__(self, xmem_checkpoint, device, sam_model=None, model_type=None) -> None:\n \"\"\"\n device: model device\n xmem_checkpoint: checkpoint of XMem model\n \"\"\"\n # load configurations\n with open(\"tracker/config/config.yaml\", 'r') as stream: \n config = yaml.safe_load(stream) \n # initialise XMem\n network = XMem(config, xmem_checkpoint).to(device).eval()\n # initialise IncerenceCore\n self.tracker = InferenceCore(network, config)\n # data transformation\n self.im_transform = transforms.Compose([\n transforms.ToTensor(),\n im_normalization,\n ])\n self.device = device\n \n # changable properties\n self.mapper = MaskMapper()\n self.initialised = False\n\n # # SAM-based refinement\n # self.sam_model = sam_model\n # self.resizer = Resize([256, 256])\n\n @torch.no_grad()\n def resize_mask(self, mask):\n # mask transform is applied AFTER mapper, so we need to post-process it in eval.py\n h, w = mask.shape[-2:]\n min_hw = min(h, w)\n return F.interpolate(mask, (int(h/min_hw*self.size), int(w/min_hw*self.size)), \n mode='nearest')\n\n @torch.no_grad()\n def track(self, frame, first_frame_annotation=None):\n \"\"\"\n Input: \n frames: numpy arrays (H, W, 3)\n logit: numpy array (H, W), logit\n","source_hash":"82ff5111f79077418e97354925c76b9560c299efe3e5ba863f65c337978778ef","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.base_tracker.resize_mask","uri":"program://Track-Anything/function/tracker.base_tracker.resize_mask#L51-L56","kind":"function","name":"resize_mask","path":"tracker/base_tracker.py","language":"python","start_line":51,"end_line":56,"context_start_line":31,"context_end_line":76,"code":" # initialise XMem\n network = XMem(config, xmem_checkpoint).to(device).eval()\n # initialise IncerenceCore\n self.tracker = InferenceCore(network, config)\n # data transformation\n self.im_transform = transforms.Compose([\n transforms.ToTensor(),\n im_normalization,\n ])\n self.device = device\n \n # changable properties\n self.mapper = MaskMapper()\n self.initialised = False\n\n # # SAM-based refinement\n # self.sam_model = sam_model\n # self.resizer = Resize([256, 256])\n\n @torch.no_grad()\n def resize_mask(self, mask):\n # mask transform is applied AFTER mapper, so we need to post-process it in eval.py\n h, w = mask.shape[-2:]\n min_hw = min(h, w)\n return F.interpolate(mask, (int(h/min_hw*self.size), int(w/min_hw*self.size)), \n mode='nearest')\n\n @torch.no_grad()\n def track(self, frame, first_frame_annotation=None):\n \"\"\"\n Input: \n frames: numpy arrays (H, W, 3)\n logit: numpy array (H, W), logit\n\n Output:\n mask: numpy arrays (H, W)\n logit: numpy arrays, probability map (H, W)\n painted_image: numpy array (H, W, 3)\n \"\"\"\n\n if first_frame_annotation is not None: # first frame mask\n # initialisation\n mask, labels = self.mapper.convert_mask(first_frame_annotation)\n mask = torch.Tensor(mask).to(self.device)\n self.tracker.set_all_labels(list(self.mapper.remappings.values()))\n else:","source_hash":"82ff5111f79077418e97354925c76b9560c299efe3e5ba863f65c337978778ef","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.base_tracker.track","uri":"program://Track-Anything/function/tracker.base_tracker.track#L59-L106","kind":"function","name":"track","path":"tracker/base_tracker.py","language":"python","start_line":59,"end_line":106,"context_start_line":39,"context_end_line":126,"code":" ])\n self.device = device\n \n # changable properties\n self.mapper = MaskMapper()\n self.initialised = False\n\n # # SAM-based refinement\n # self.sam_model = sam_model\n # self.resizer = Resize([256, 256])\n\n @torch.no_grad()\n def resize_mask(self, mask):\n # mask transform is applied AFTER mapper, so we need to post-process it in eval.py\n h, w = mask.shape[-2:]\n min_hw = min(h, w)\n return F.interpolate(mask, (int(h/min_hw*self.size), int(w/min_hw*self.size)), \n mode='nearest')\n\n @torch.no_grad()\n def track(self, frame, first_frame_annotation=None):\n \"\"\"\n Input: \n frames: numpy arrays (H, W, 3)\n logit: numpy array (H, W), logit\n\n Output:\n mask: numpy arrays (H, W)\n logit: numpy arrays, probability map (H, W)\n painted_image: numpy array (H, W, 3)\n \"\"\"\n\n if first_frame_annotation is not None: # first frame mask\n # initialisation\n mask, labels = self.mapper.convert_mask(first_frame_annotation)\n mask = torch.Tensor(mask).to(self.device)\n self.tracker.set_all_labels(list(self.mapper.remappings.values()))\n else:\n mask = None\n labels = None\n # prepare inputs\n frame_tensor = self.im_transform(frame).to(self.device)\n # track one frame\n probs, _ = self.tracker.step(frame_tensor, mask, labels) # logits 2 (bg fg) H W\n # # refine\n # if first_frame_annotation is None:\n # out_mask = self.sam_refinement(frame, logits[1], ti) \n\n # convert to mask\n out_mask = torch.argmax(probs, dim=0)\n out_mask = (out_mask.detach().cpu().numpy()).astype(np.uint8)\n\n final_mask = np.zeros_like(out_mask)\n \n # map back\n for k, v in self.mapper.remappings.items():\n final_mask[out_mask == v] = k\n\n num_objs = final_mask.max()\n painted_image = frame\n for obj in range(1, num_objs+1):\n if np.max(final_mask==obj) == 0:\n continue\n painted_image = mask_painter(painted_image, (final_mask==obj).astype('uint8'), mask_color=obj+1)\n\n # print(f'max memory allocated: {torch.cuda.max_memory_allocated()/(2**20)} MB')\n\n return final_mask, final_mask, painted_image\n\n @torch.no_grad()\n def sam_refinement(self, frame, logits, ti):\n \"\"\"\n refine segmentation results with mask prompt\n \"\"\"\n # convert to 1, 256, 256\n self.sam_model.set_image(frame)\n mode = 'mask'\n logits = logits.unsqueeze(0)\n logits = self.resizer(logits).cpu().numpy()\n prompts = {'mask_input': logits} # 1 256 256\n masks, scores, logits = self.sam_model.predict(prompts, mode, multimask=True) # masks (n, h, w), scores (n,), logits (n, 256, 256)\n painted_image = mask_painter(frame, masks[np.argmax(scores)].astype('uint8'), mask_alpha=0.8)\n painted_image = Image.fromarray(painted_image)\n painted_image.save(f'/ssd1/gaomingqi/refine/{ti:05d}.png')\n self.sam_model.reset_image()\n\n @torch.no_grad()\n def clear_memory(self):","source_hash":"82ff5111f79077418e97354925c76b9560c299efe3e5ba863f65c337978778ef","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.base_tracker.sam_refinement","uri":"program://Track-Anything/function/tracker.base_tracker.sam_refinement#L109-L123","kind":"function","name":"sam_refinement","path":"tracker/base_tracker.py","language":"python","start_line":109,"end_line":123,"context_start_line":89,"context_end_line":143,"code":" out_mask = (out_mask.detach().cpu().numpy()).astype(np.uint8)\n\n final_mask = np.zeros_like(out_mask)\n \n # map back\n for k, v in self.mapper.remappings.items():\n final_mask[out_mask == v] = k\n\n num_objs = final_mask.max()\n painted_image = frame\n for obj in range(1, num_objs+1):\n if np.max(final_mask==obj) == 0:\n continue\n painted_image = mask_painter(painted_image, (final_mask==obj).astype('uint8'), mask_color=obj+1)\n\n # print(f'max memory allocated: {torch.cuda.max_memory_allocated()/(2**20)} MB')\n\n return final_mask, final_mask, painted_image\n\n @torch.no_grad()\n def sam_refinement(self, frame, logits, ti):\n \"\"\"\n refine segmentation results with mask prompt\n \"\"\"\n # convert to 1, 256, 256\n self.sam_model.set_image(frame)\n mode = 'mask'\n logits = logits.unsqueeze(0)\n logits = self.resizer(logits).cpu().numpy()\n prompts = {'mask_input': logits} # 1 256 256\n masks, scores, logits = self.sam_model.predict(prompts, mode, multimask=True) # masks (n, h, w), scores (n,), logits (n, 256, 256)\n painted_image = mask_painter(frame, masks[np.argmax(scores)].astype('uint8'), mask_alpha=0.8)\n painted_image = Image.fromarray(painted_image)\n painted_image.save(f'/ssd1/gaomingqi/refine/{ti:05d}.png')\n self.sam_model.reset_image()\n\n @torch.no_grad()\n def clear_memory(self):\n self.tracker.clear_memory()\n self.mapper.clear_labels()\n torch.cuda.empty_cache()\n\n\n## how to use:\n## 1/3) prepare device and xmem_checkpoint\n# device = 'cuda:2'\n# XMEM_checkpoint = '/ssd1/gaomingqi/checkpoints/XMem-s012.pth'\n## 2/3) initialise Base Tracker\n# tracker = BaseTracker(XMEM_checkpoint, device, None, device) # leave an interface for sam model (currently set None)\n## 3/3) \n\n\nif __name__ == '__main__':\n # video frames (take videos from DAVIS-2017 as examples)\n video_path_list = glob.glob(os.path.join('/ssd1/gaomingqi/datasets/davis/JPEGImages/480p/horsejump-high', '*.jpg'))","source_hash":"82ff5111f79077418e97354925c76b9560c299efe3e5ba863f65c337978778ef","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.base_tracker.clear_memory","uri":"program://Track-Anything/function/tracker.base_tracker.clear_memory#L126-L129","kind":"function","name":"clear_memory","path":"tracker/base_tracker.py","language":"python","start_line":126,"end_line":129,"context_start_line":106,"context_end_line":149,"code":" return final_mask, final_mask, painted_image\n\n @torch.no_grad()\n def sam_refinement(self, frame, logits, ti):\n \"\"\"\n refine segmentation results with mask prompt\n \"\"\"\n # convert to 1, 256, 256\n self.sam_model.set_image(frame)\n mode = 'mask'\n logits = logits.unsqueeze(0)\n logits = self.resizer(logits).cpu().numpy()\n prompts = {'mask_input': logits} # 1 256 256\n masks, scores, logits = self.sam_model.predict(prompts, mode, multimask=True) # masks (n, h, w), scores (n,), logits (n, 256, 256)\n painted_image = mask_painter(frame, masks[np.argmax(scores)].astype('uint8'), mask_alpha=0.8)\n painted_image = Image.fromarray(painted_image)\n painted_image.save(f'/ssd1/gaomingqi/refine/{ti:05d}.png')\n self.sam_model.reset_image()\n\n @torch.no_grad()\n def clear_memory(self):\n self.tracker.clear_memory()\n self.mapper.clear_labels()\n torch.cuda.empty_cache()\n\n\n## how to use:\n## 1/3) prepare device and xmem_checkpoint\n# device = 'cuda:2'\n# XMEM_checkpoint = '/ssd1/gaomingqi/checkpoints/XMem-s012.pth'\n## 2/3) initialise Base Tracker\n# tracker = BaseTracker(XMEM_checkpoint, device, None, device) # leave an interface for sam model (currently set None)\n## 3/3) \n\n\nif __name__ == '__main__':\n # video frames (take videos from DAVIS-2017 as examples)\n video_path_list = glob.glob(os.path.join('/ssd1/gaomingqi/datasets/davis/JPEGImages/480p/horsejump-high', '*.jpg'))\n video_path_list.sort()\n # load frames\n frames = []\n for video_path in video_path_list:\n frames.append(np.array(Image.open(video_path).convert('RGB')))\n frames = np.stack(frames, 0) # T, H, W, C","source_hash":"82ff5111f79077418e97354925c76b9560c299efe3e5ba863f65c337978778ef","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.util.mask_mapper","uri":"program://Track-Anything/module/tracker.util.mask_mapper#L1-L78","kind":"module","name":"tracker.util.mask_mapper","path":"tracker/util/mask_mapper.py","language":"python","start_line":1,"end_line":78,"context_start_line":1,"context_end_line":78,"code":"import numpy as np\nimport torch\n\ndef all_to_onehot(masks, labels):\n if len(masks.shape) == 3:\n Ms = np.zeros((len(labels), masks.shape[0], masks.shape[1], masks.shape[2]), dtype=np.uint8)\n else:\n Ms = np.zeros((len(labels), masks.shape[0], masks.shape[1]), dtype=np.uint8)\n\n for ni, l in enumerate(labels):\n Ms[ni] = (masks == l).astype(np.uint8)\n \n return Ms\n\nclass MaskMapper:\n \"\"\"\n This class is used to convert a indexed-mask to a one-hot representation.\n It also takes care of remapping non-continuous indices\n It has two modes:\n 1. Default. Only masks with new indices are supposed to go into the remapper.\n This is also the case for YouTubeVOS.\n i.e., regions with index 0 are not \"background\", but \"don't care\".\n\n 2. Exhaustive. Regions with index 0 are considered \"background\".\n Every single pixel is considered to be \"labeled\".\n \"\"\"\n def __init__(self):\n self.labels = []\n self.remappings = {}\n\n # if coherent, no mapping is required\n self.coherent = True\n\n def clear_labels(self):\n self.labels = []\n self.remappings = {}\n # if coherent, no mapping is required\n self.coherent = True\n\n def convert_mask(self, mask, exhaustive=False):\n # mask is in index representation, H*W numpy array\n labels = np.unique(mask).astype(np.uint8)\n labels = labels[labels!=0].tolist()\n\n new_labels = list(set(labels) - set(self.labels))\n if not exhaustive:\n assert len(new_labels) == len(labels), 'Old labels found in non-exhaustive mode'\n\n # add new remappings\n for i, l in enumerate(new_labels):\n self.remappings[l] = i+len(self.labels)+1\n if self.coherent and i+len(self.labels)+1 != l:\n self.coherent = False\n\n if exhaustive:\n new_mapped_labels = range(1, len(self.labels)+len(new_labels)+1)\n else:\n if self.coherent:\n new_mapped_labels = new_labels\n else:\n new_mapped_labels = range(len(self.labels)+1, len(self.labels)+len(new_labels)+1)\n\n self.labels.extend(new_labels)\n mask = torch.from_numpy(all_to_onehot(mask, self.labels)).float()\n\n # mask num_objects*H*W\n return mask, new_mapped_labels\n\n\n def remap_index_mask(self, mask):\n # mask is in index representation, H*W numpy array\n if self.coherent:\n return mask\n\n new_mask = np.zeros_like(mask)\n for l, i in self.remappings.items():\n new_mask[mask==i] = l\n return new_mask","source_hash":"d1f4ee5b2ba9596a89c22d1cf19881a1cfd5637736938b23a841f636a6fbc7a9","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.util.mask_mapper.all_to_onehot","uri":"program://Track-Anything/function/tracker.util.mask_mapper.all_to_onehot#L4-L13","kind":"function","name":"all_to_onehot","path":"tracker/util/mask_mapper.py","language":"python","start_line":4,"end_line":13,"context_start_line":1,"context_end_line":33,"code":"import numpy as np\nimport torch\n\ndef all_to_onehot(masks, labels):\n if len(masks.shape) == 3:\n Ms = np.zeros((len(labels), masks.shape[0], masks.shape[1], masks.shape[2]), dtype=np.uint8)\n else:\n Ms = np.zeros((len(labels), masks.shape[0], masks.shape[1]), dtype=np.uint8)\n\n for ni, l in enumerate(labels):\n Ms[ni] = (masks == l).astype(np.uint8)\n \n return Ms\n\nclass MaskMapper:\n \"\"\"\n This class is used to convert a indexed-mask to a one-hot representation.\n It also takes care of remapping non-continuous indices\n It has two modes:\n 1. Default. Only masks with new indices are supposed to go into the remapper.\n This is also the case for YouTubeVOS.\n i.e., regions with index 0 are not \"background\", but \"don't care\".\n\n 2. Exhaustive. Regions with index 0 are considered \"background\".\n Every single pixel is considered to be \"labeled\".\n \"\"\"\n def __init__(self):\n self.labels = []\n self.remappings = {}\n\n # if coherent, no mapping is required\n self.coherent = True\n","source_hash":"d1f4ee5b2ba9596a89c22d1cf19881a1cfd5637736938b23a841f636a6fbc7a9","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.util.mask_mapper.MaskMapper","uri":"program://Track-Anything/class/tracker.util.mask_mapper.MaskMapper#L15-L78","kind":"class","name":"MaskMapper","path":"tracker/util/mask_mapper.py","language":"python","start_line":15,"end_line":78,"context_start_line":1,"context_end_line":78,"code":"import numpy as np\nimport torch\n\ndef all_to_onehot(masks, labels):\n if len(masks.shape) == 3:\n Ms = np.zeros((len(labels), masks.shape[0], masks.shape[1], masks.shape[2]), dtype=np.uint8)\n else:\n Ms = np.zeros((len(labels), masks.shape[0], masks.shape[1]), dtype=np.uint8)\n\n for ni, l in enumerate(labels):\n Ms[ni] = (masks == l).astype(np.uint8)\n \n return Ms\n\nclass MaskMapper:\n \"\"\"\n This class is used to convert a indexed-mask to a one-hot representation.\n It also takes care of remapping non-continuous indices\n It has two modes:\n 1. Default. Only masks with new indices are supposed to go into the remapper.\n This is also the case for YouTubeVOS.\n i.e., regions with index 0 are not \"background\", but \"don't care\".\n\n 2. Exhaustive. Regions with index 0 are considered \"background\".\n Every single pixel is considered to be \"labeled\".\n \"\"\"\n def __init__(self):\n self.labels = []\n self.remappings = {}\n\n # if coherent, no mapping is required\n self.coherent = True\n\n def clear_labels(self):\n self.labels = []\n self.remappings = {}\n # if coherent, no mapping is required\n self.coherent = True\n\n def convert_mask(self, mask, exhaustive=False):\n # mask is in index representation, H*W numpy array\n labels = np.unique(mask).astype(np.uint8)\n labels = labels[labels!=0].tolist()\n\n new_labels = list(set(labels) - set(self.labels))\n if not exhaustive:\n assert len(new_labels) == len(labels), 'Old labels found in non-exhaustive mode'\n\n # add new remappings\n for i, l in enumerate(new_labels):\n self.remappings[l] = i+len(self.labels)+1\n if self.coherent and i+len(self.labels)+1 != l:\n self.coherent = False\n\n if exhaustive:\n new_mapped_labels = range(1, len(self.labels)+len(new_labels)+1)\n else:\n if self.coherent:\n new_mapped_labels = new_labels\n else:\n new_mapped_labels = range(len(self.labels)+1, len(self.labels)+len(new_labels)+1)\n\n self.labels.extend(new_labels)\n mask = torch.from_numpy(all_to_onehot(mask, self.labels)).float()\n\n # mask num_objects*H*W\n return mask, new_mapped_labels\n\n\n def remap_index_mask(self, mask):\n # mask is in index representation, H*W numpy array\n if self.coherent:\n return mask\n\n new_mask = np.zeros_like(mask)\n for l, i in self.remappings.items():\n new_mask[mask==i] = l\n return new_mask","source_hash":"d1f4ee5b2ba9596a89c22d1cf19881a1cfd5637736938b23a841f636a6fbc7a9","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.util.mask_mapper.__init__","uri":"program://Track-Anything/function/tracker.util.mask_mapper.__init__#L27-L32","kind":"function","name":"__init__","path":"tracker/util/mask_mapper.py","language":"python","start_line":27,"end_line":32,"context_start_line":7,"context_end_line":52,"code":" else:\n Ms = np.zeros((len(labels), masks.shape[0], masks.shape[1]), dtype=np.uint8)\n\n for ni, l in enumerate(labels):\n Ms[ni] = (masks == l).astype(np.uint8)\n \n return Ms\n\nclass MaskMapper:\n \"\"\"\n This class is used to convert a indexed-mask to a one-hot representation.\n It also takes care of remapping non-continuous indices\n It has two modes:\n 1. Default. Only masks with new indices are supposed to go into the remapper.\n This is also the case for YouTubeVOS.\n i.e., regions with index 0 are not \"background\", but \"don't care\".\n\n 2. Exhaustive. Regions with index 0 are considered \"background\".\n Every single pixel is considered to be \"labeled\".\n \"\"\"\n def __init__(self):\n self.labels = []\n self.remappings = {}\n\n # if coherent, no mapping is required\n self.coherent = True\n\n def clear_labels(self):\n self.labels = []\n self.remappings = {}\n # if coherent, no mapping is required\n self.coherent = True\n\n def convert_mask(self, mask, exhaustive=False):\n # mask is in index representation, H*W numpy array\n labels = np.unique(mask).astype(np.uint8)\n labels = labels[labels!=0].tolist()\n\n new_labels = list(set(labels) - set(self.labels))\n if not exhaustive:\n assert len(new_labels) == len(labels), 'Old labels found in non-exhaustive mode'\n\n # add new remappings\n for i, l in enumerate(new_labels):\n self.remappings[l] = i+len(self.labels)+1\n if self.coherent and i+len(self.labels)+1 != l:","source_hash":"d1f4ee5b2ba9596a89c22d1cf19881a1cfd5637736938b23a841f636a6fbc7a9","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.util.mask_mapper.clear_labels","uri":"program://Track-Anything/function/tracker.util.mask_mapper.clear_labels#L34-L38","kind":"function","name":"clear_labels","path":"tracker/util/mask_mapper.py","language":"python","start_line":34,"end_line":38,"context_start_line":14,"context_end_line":58,"code":"\nclass MaskMapper:\n \"\"\"\n This class is used to convert a indexed-mask to a one-hot representation.\n It also takes care of remapping non-continuous indices\n It has two modes:\n 1. Default. Only masks with new indices are supposed to go into the remapper.\n This is also the case for YouTubeVOS.\n i.e., regions with index 0 are not \"background\", but \"don't care\".\n\n 2. Exhaustive. Regions with index 0 are considered \"background\".\n Every single pixel is considered to be \"labeled\".\n \"\"\"\n def __init__(self):\n self.labels = []\n self.remappings = {}\n\n # if coherent, no mapping is required\n self.coherent = True\n\n def clear_labels(self):\n self.labels = []\n self.remappings = {}\n # if coherent, no mapping is required\n self.coherent = True\n\n def convert_mask(self, mask, exhaustive=False):\n # mask is in index representation, H*W numpy array\n labels = np.unique(mask).astype(np.uint8)\n labels = labels[labels!=0].tolist()\n\n new_labels = list(set(labels) - set(self.labels))\n if not exhaustive:\n assert len(new_labels) == len(labels), 'Old labels found in non-exhaustive mode'\n\n # add new remappings\n for i, l in enumerate(new_labels):\n self.remappings[l] = i+len(self.labels)+1\n if self.coherent and i+len(self.labels)+1 != l:\n self.coherent = False\n\n if exhaustive:\n new_mapped_labels = range(1, len(self.labels)+len(new_labels)+1)\n else:\n if self.coherent:","source_hash":"d1f4ee5b2ba9596a89c22d1cf19881a1cfd5637736938b23a841f636a6fbc7a9","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.util.mask_mapper.convert_mask","uri":"program://Track-Anything/function/tracker.util.mask_mapper.convert_mask#L40-L67","kind":"function","name":"convert_mask","path":"tracker/util/mask_mapper.py","language":"python","start_line":40,"end_line":67,"context_start_line":20,"context_end_line":78,"code":" 1. Default. Only masks with new indices are supposed to go into the remapper.\n This is also the case for YouTubeVOS.\n i.e., regions with index 0 are not \"background\", but \"don't care\".\n\n 2. Exhaustive. Regions with index 0 are considered \"background\".\n Every single pixel is considered to be \"labeled\".\n \"\"\"\n def __init__(self):\n self.labels = []\n self.remappings = {}\n\n # if coherent, no mapping is required\n self.coherent = True\n\n def clear_labels(self):\n self.labels = []\n self.remappings = {}\n # if coherent, no mapping is required\n self.coherent = True\n\n def convert_mask(self, mask, exhaustive=False):\n # mask is in index representation, H*W numpy array\n labels = np.unique(mask).astype(np.uint8)\n labels = labels[labels!=0].tolist()\n\n new_labels = list(set(labels) - set(self.labels))\n if not exhaustive:\n assert len(new_labels) == len(labels), 'Old labels found in non-exhaustive mode'\n\n # add new remappings\n for i, l in enumerate(new_labels):\n self.remappings[l] = i+len(self.labels)+1\n if self.coherent and i+len(self.labels)+1 != l:\n self.coherent = False\n\n if exhaustive:\n new_mapped_labels = range(1, len(self.labels)+len(new_labels)+1)\n else:\n if self.coherent:\n new_mapped_labels = new_labels\n else:\n new_mapped_labels = range(len(self.labels)+1, len(self.labels)+len(new_labels)+1)\n\n self.labels.extend(new_labels)\n mask = torch.from_numpy(all_to_onehot(mask, self.labels)).float()\n\n # mask num_objects*H*W\n return mask, new_mapped_labels\n\n\n def remap_index_mask(self, mask):\n # mask is in index representation, H*W numpy array\n if self.coherent:\n return mask\n\n new_mask = np.zeros_like(mask)\n for l, i in self.remappings.items():\n new_mask[mask==i] = l\n return new_mask","source_hash":"d1f4ee5b2ba9596a89c22d1cf19881a1cfd5637736938b23a841f636a6fbc7a9","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.util.mask_mapper.remap_index_mask","uri":"program://Track-Anything/function/tracker.util.mask_mapper.remap_index_mask#L70-L78","kind":"function","name":"remap_index_mask","path":"tracker/util/mask_mapper.py","language":"python","start_line":70,"end_line":78,"context_start_line":50,"context_end_line":78,"code":" for i, l in enumerate(new_labels):\n self.remappings[l] = i+len(self.labels)+1\n if self.coherent and i+len(self.labels)+1 != l:\n self.coherent = False\n\n if exhaustive:\n new_mapped_labels = range(1, len(self.labels)+len(new_labels)+1)\n else:\n if self.coherent:\n new_mapped_labels = new_labels\n else:\n new_mapped_labels = range(len(self.labels)+1, len(self.labels)+len(new_labels)+1)\n\n self.labels.extend(new_labels)\n mask = torch.from_numpy(all_to_onehot(mask, self.labels)).float()\n\n # mask num_objects*H*W\n return mask, new_mapped_labels\n\n\n def remap_index_mask(self, mask):\n # mask is in index representation, H*W numpy array\n if self.coherent:\n return mask\n\n new_mask = np.zeros_like(mask)\n for l, i in self.remappings.items():\n new_mask[mask==i] = l\n return new_mask","source_hash":"d1f4ee5b2ba9596a89c22d1cf19881a1cfd5637736938b23a841f636a6fbc7a9","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.util.range_transform","uri":"program://Track-Anything/module/tracker.util.range_transform#L1-L12","kind":"module","name":"tracker.util.range_transform","path":"tracker/util/range_transform.py","language":"python","start_line":1,"end_line":12,"context_start_line":1,"context_end_line":12,"code":"import torchvision.transforms as transforms\n\nim_mean = (124, 116, 104)\n\nim_normalization = transforms.Normalize(\n mean=[0.485, 0.456, 0.406],\n std=[0.229, 0.224, 0.225]\n )\n\ninv_im_trans = transforms.Normalize(\n mean=[-0.485/0.229, -0.456/0.224, -0.406/0.225],\n std=[1/0.229, 1/0.224, 1/0.225])","source_hash":"f4bd56783eb59311c8b65885f0c35a6fb13c67a98e85c2f18901305027db92c3","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.util.tensor_util","uri":"program://Track-Anything/module/tracker.util.tensor_util#L1-L47","kind":"module","name":"tracker.util.tensor_util","path":"tracker/util/tensor_util.py","language":"python","start_line":1,"end_line":47,"context_start_line":1,"context_end_line":47,"code":"import torch.nn.functional as F\n\n\ndef compute_tensor_iu(seg, gt):\n intersection = (seg & gt).float().sum()\n union = (seg | gt).float().sum()\n\n return intersection, union\n\ndef compute_tensor_iou(seg, gt):\n intersection, union = compute_tensor_iu(seg, gt)\n iou = (intersection + 1e-6) / (union + 1e-6)\n \n return iou \n\n# STM\ndef pad_divide_by(in_img, d):\n h, w = in_img.shape[-2:]\n\n if h % d > 0:\n new_h = h + d - h % d\n else:\n new_h = h\n if w % d > 0:\n new_w = w + d - w % d\n else:\n new_w = w\n lh, uh = int((new_h-h) / 2), int(new_h-h) - int((new_h-h) / 2)\n lw, uw = int((new_w-w) / 2), int(new_w-w) - int((new_w-w) / 2)\n pad_array = (int(lw), int(uw), int(lh), int(uh))\n out = F.pad(in_img, pad_array)\n return out, pad_array\n\ndef unpad(img, pad):\n if len(img.shape) == 4:\n if pad[2]+pad[3] > 0:\n img = img[:,:,pad[2]:-pad[3],:]\n if pad[0]+pad[1] > 0:\n img = img[:,:,:,pad[0]:-pad[1]]\n elif len(img.shape) == 3:\n if pad[2]+pad[3] > 0:\n img = img[:,pad[2]:-pad[3],:]\n if pad[0]+pad[1] > 0:\n img = img[:,:,pad[0]:-pad[1]]\n else:\n raise NotImplementedError\n return img","source_hash":"225dd583786e6313ccb0f120695da6c9228998dc57bb7910b3c1e3fefc02d408","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.util.tensor_util.compute_tensor_iu","uri":"program://Track-Anything/function/tracker.util.tensor_util.compute_tensor_iu#L4-L8","kind":"function","name":"compute_tensor_iu","path":"tracker/util/tensor_util.py","language":"python","start_line":4,"end_line":8,"context_start_line":1,"context_end_line":28,"code":"import torch.nn.functional as F\n\n\ndef compute_tensor_iu(seg, gt):\n intersection = (seg & gt).float().sum()\n union = (seg | gt).float().sum()\n\n return intersection, union\n\ndef compute_tensor_iou(seg, gt):\n intersection, union = compute_tensor_iu(seg, gt)\n iou = (intersection + 1e-6) / (union + 1e-6)\n \n return iou \n\n# STM\ndef pad_divide_by(in_img, d):\n h, w = in_img.shape[-2:]\n\n if h % d > 0:\n new_h = h + d - h % d\n else:\n new_h = h\n if w % d > 0:\n new_w = w + d - w % d\n else:\n new_w = w\n lh, uh = int((new_h-h) / 2), int(new_h-h) - int((new_h-h) / 2)","source_hash":"225dd583786e6313ccb0f120695da6c9228998dc57bb7910b3c1e3fefc02d408","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.util.tensor_util.compute_tensor_iou","uri":"program://Track-Anything/function/tracker.util.tensor_util.compute_tensor_iou#L10-L14","kind":"function","name":"compute_tensor_iou","path":"tracker/util/tensor_util.py","language":"python","start_line":10,"end_line":14,"context_start_line":1,"context_end_line":34,"code":"import torch.nn.functional as F\n\n\ndef compute_tensor_iu(seg, gt):\n intersection = (seg & gt).float().sum()\n union = (seg | gt).float().sum()\n\n return intersection, union\n\ndef compute_tensor_iou(seg, gt):\n intersection, union = compute_tensor_iu(seg, gt)\n iou = (intersection + 1e-6) / (union + 1e-6)\n \n return iou \n\n# STM\ndef pad_divide_by(in_img, d):\n h, w = in_img.shape[-2:]\n\n if h % d > 0:\n new_h = h + d - h % d\n else:\n new_h = h\n if w % d > 0:\n new_w = w + d - w % d\n else:\n new_w = w\n lh, uh = int((new_h-h) / 2), int(new_h-h) - int((new_h-h) / 2)\n lw, uw = int((new_w-w) / 2), int(new_w-w) - int((new_w-w) / 2)\n pad_array = (int(lw), int(uw), int(lh), int(uh))\n out = F.pad(in_img, pad_array)\n return out, pad_array\n\ndef unpad(img, pad):","source_hash":"225dd583786e6313ccb0f120695da6c9228998dc57bb7910b3c1e3fefc02d408","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.util.tensor_util.pad_divide_by","uri":"program://Track-Anything/function/tracker.util.tensor_util.pad_divide_by#L17-L32","kind":"function","name":"pad_divide_by","path":"tracker/util/tensor_util.py","language":"python","start_line":17,"end_line":32,"context_start_line":1,"context_end_line":47,"code":"import torch.nn.functional as F\n\n\ndef compute_tensor_iu(seg, gt):\n intersection = (seg & gt).float().sum()\n union = (seg | gt).float().sum()\n\n return intersection, union\n\ndef compute_tensor_iou(seg, gt):\n intersection, union = compute_tensor_iu(seg, gt)\n iou = (intersection + 1e-6) / (union + 1e-6)\n \n return iou \n\n# STM\ndef pad_divide_by(in_img, d):\n h, w = in_img.shape[-2:]\n\n if h % d > 0:\n new_h = h + d - h % d\n else:\n new_h = h\n if w % d > 0:\n new_w = w + d - w % d\n else:\n new_w = w\n lh, uh = int((new_h-h) / 2), int(new_h-h) - int((new_h-h) / 2)\n lw, uw = int((new_w-w) / 2), int(new_w-w) - int((new_w-w) / 2)\n pad_array = (int(lw), int(uw), int(lh), int(uh))\n out = F.pad(in_img, pad_array)\n return out, pad_array\n\ndef unpad(img, pad):\n if len(img.shape) == 4:\n if pad[2]+pad[3] > 0:\n img = img[:,:,pad[2]:-pad[3],:]\n if pad[0]+pad[1] > 0:\n img = img[:,:,:,pad[0]:-pad[1]]\n elif len(img.shape) == 3:\n if pad[2]+pad[3] > 0:\n img = img[:,pad[2]:-pad[3],:]\n if pad[0]+pad[1] > 0:\n img = img[:,:,pad[0]:-pad[1]]\n else:\n raise NotImplementedError\n return img","source_hash":"225dd583786e6313ccb0f120695da6c9228998dc57bb7910b3c1e3fefc02d408","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.util.tensor_util.unpad","uri":"program://Track-Anything/function/tracker.util.tensor_util.unpad#L34-L47","kind":"function","name":"unpad","path":"tracker/util/tensor_util.py","language":"python","start_line":34,"end_line":47,"context_start_line":14,"context_end_line":47,"code":" return iou \n\n# STM\ndef pad_divide_by(in_img, d):\n h, w = in_img.shape[-2:]\n\n if h % d > 0:\n new_h = h + d - h % d\n else:\n new_h = h\n if w % d > 0:\n new_w = w + d - w % d\n else:\n new_w = w\n lh, uh = int((new_h-h) / 2), int(new_h-h) - int((new_h-h) / 2)\n lw, uw = int((new_w-w) / 2), int(new_w-w) - int((new_w-w) / 2)\n pad_array = (int(lw), int(uw), int(lh), int(uh))\n out = F.pad(in_img, pad_array)\n return out, pad_array\n\ndef unpad(img, pad):\n if len(img.shape) == 4:\n if pad[2]+pad[3] > 0:\n img = img[:,:,pad[2]:-pad[3],:]\n if pad[0]+pad[1] > 0:\n img = img[:,:,:,pad[0]:-pad[1]]\n elif len(img.shape) == 3:\n if pad[2]+pad[3] > 0:\n img = img[:,pad[2]:-pad[3],:]\n if pad[0]+pad[1] > 0:\n img = img[:,:,pad[0]:-pad[1]]\n else:\n raise NotImplementedError\n return img","source_hash":"225dd583786e6313ccb0f120695da6c9228998dc57bb7910b3c1e3fefc02d408","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.inference_core","uri":"program://Track-Anything/module/tracker.inference.inference_core#L1-L115","kind":"module","name":"tracker.inference.inference_core","path":"tracker/inference/inference_core.py","language":"python","start_line":1,"end_line":115,"context_start_line":1,"context_end_line":115,"code":"from inference.memory_manager import MemoryManager\nfrom model.network import XMem\nfrom model.aggregate import aggregate\n\nfrom tracker.util.tensor_util import pad_divide_by, unpad\n\n\nclass InferenceCore:\n def __init__(self, network:XMem, config):\n self.config = config\n self.network = network\n self.mem_every = config['mem_every']\n self.deep_update_every = config['deep_update_every']\n self.enable_long_term = config['enable_long_term']\n\n # if deep_update_every < 0, synchronize deep update with memory frame\n self.deep_update_sync = (self.deep_update_every < 0)\n\n self.clear_memory()\n self.all_labels = None\n\n def clear_memory(self):\n self.curr_ti = -1\n self.last_mem_ti = 0\n if not self.deep_update_sync:\n self.last_deep_update_ti = -self.deep_update_every\n self.memory = MemoryManager(config=self.config)\n\n def update_config(self, config):\n self.mem_every = config['mem_every']\n self.deep_update_every = config['deep_update_every']\n self.enable_long_term = config['enable_long_term']\n\n # if deep_update_every < 0, synchronize deep update with memory frame\n self.deep_update_sync = (self.deep_update_every < 0)\n self.memory.update_config(config)\n\n def set_all_labels(self, all_labels):\n # self.all_labels = [l.item() for l in all_labels]\n self.all_labels = all_labels\n\n def step(self, image, mask=None, valid_labels=None, end=False):\n # image: 3*H*W\n # mask: num_objects*H*W or None\n self.curr_ti += 1\n image, self.pad = pad_divide_by(image, 16)\n image = image.unsqueeze(0) # add the batch dimension\n\n is_mem_frame = ((self.curr_ti-self.last_mem_ti >= self.mem_every) or (mask is not None)) and (not end)\n need_segment = (self.curr_ti > 0) and ((valid_labels is None) or (len(self.all_labels) != len(valid_labels)))\n is_deep_update = (\n (self.deep_update_sync and is_mem_frame) or # synchronized\n (not self.deep_update_sync and self.curr_ti-self.last_deep_update_ti >= self.deep_update_every) # no-sync\n ) and (not end)\n is_normal_update = (not self.deep_update_sync or not is_deep_update) and (not end)\n\n key, shrinkage, selection, f16, f8, f4 = self.network.encode_key(image, \n need_ek=(self.enable_long_term or need_segment), \n need_sk=is_mem_frame)\n multi_scale_features = (f16, f8, f4)\n\n # segment the current frame is needed\n if need_segment:\n memory_readout = self.memory.match_memory(key, selection).unsqueeze(0)\n \n hidden, pred_logits_with_bg, pred_prob_with_bg = self.network.segment(multi_scale_features, memory_readout, \n self.memory.get_hidden(), h_out=is_normal_update, strip_bg=False)\n # remove batch dim\n pred_prob_with_bg = pred_prob_with_bg[0]\n pred_prob_no_bg = pred_prob_with_bg[1:]\n\n pred_logits_with_bg = pred_logits_with_bg[0]\n pred_logits_no_bg = pred_logits_with_bg[1:]\n\n if is_normal_update:\n self.memory.set_hidden(hidden)\n else:\n pred_prob_no_bg = pred_prob_with_bg = pred_logits_with_bg = pred_logits_no_bg = None\n\n # use the input mask if any\n if mask is not None:\n mask, _ = pad_divide_by(mask, 16)\n\n if pred_prob_no_bg is not None:\n # if we have a predicted mask, we work on it\n # make pred_prob_no_bg consistent with the input mask\n mask_regions = (mask.sum(0) > 0.5)\n pred_prob_no_bg[:, mask_regions] = 0\n # shift by 1 because mask/pred_prob_no_bg do not contain background\n mask = mask.type_as(pred_prob_no_bg)\n if valid_labels is not None:\n shift_by_one_non_labels = [i for i in range(pred_prob_no_bg.shape[0]) if (i+1) not in valid_labels]\n # non-labelled objects are copied from the predicted mask\n mask[shift_by_one_non_labels] = pred_prob_no_bg[shift_by_one_non_labels]\n pred_prob_with_bg = aggregate(mask, dim=0)\n\n # also create new hidden states\n self.memory.create_hidden_state(len(self.all_labels), key)\n\n # save as memory if needed\n if is_mem_frame:\n value, hidden = self.network.encode_value(image, f16, self.memory.get_hidden(), \n pred_prob_with_bg[1:].unsqueeze(0), is_deep_update=is_deep_update)\n self.memory.add_memory(key, shrinkage, value, self.all_labels, \n selection=selection if self.enable_long_term else None)\n self.last_mem_ti = self.curr_ti\n\n if is_deep_update:\n self.memory.set_hidden(hidden)\n self.last_deep_update_ti = self.curr_ti\n \n if pred_logits_with_bg is None:\n return unpad(pred_prob_with_bg, self.pad), None\n else:\n return unpad(pred_prob_with_bg, self.pad), unpad(pred_logits_with_bg, self.pad)","source_hash":"aef5d4637993b4580d0bff71327da1d487caf5e41849b40a6acf1bb00073f107","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.inference_core.InferenceCore","uri":"program://Track-Anything/class/tracker.inference.inference_core.InferenceCore#L8-L115","kind":"class","name":"InferenceCore","path":"tracker/inference/inference_core.py","language":"python","start_line":8,"end_line":115,"context_start_line":1,"context_end_line":115,"code":"from inference.memory_manager import MemoryManager\nfrom model.network import XMem\nfrom model.aggregate import aggregate\n\nfrom tracker.util.tensor_util import pad_divide_by, unpad\n\n\nclass InferenceCore:\n def __init__(self, network:XMem, config):\n self.config = config\n self.network = network\n self.mem_every = config['mem_every']\n self.deep_update_every = config['deep_update_every']\n self.enable_long_term = config['enable_long_term']\n\n # if deep_update_every < 0, synchronize deep update with memory frame\n self.deep_update_sync = (self.deep_update_every < 0)\n\n self.clear_memory()\n self.all_labels = None\n\n def clear_memory(self):\n self.curr_ti = -1\n self.last_mem_ti = 0\n if not self.deep_update_sync:\n self.last_deep_update_ti = -self.deep_update_every\n self.memory = MemoryManager(config=self.config)\n\n def update_config(self, config):\n self.mem_every = config['mem_every']\n self.deep_update_every = config['deep_update_every']\n self.enable_long_term = config['enable_long_term']\n\n # if deep_update_every < 0, synchronize deep update with memory frame\n self.deep_update_sync = (self.deep_update_every < 0)\n self.memory.update_config(config)\n\n def set_all_labels(self, all_labels):\n # self.all_labels = [l.item() for l in all_labels]\n self.all_labels = all_labels\n\n def step(self, image, mask=None, valid_labels=None, end=False):\n # image: 3*H*W\n # mask: num_objects*H*W or None\n self.curr_ti += 1\n image, self.pad = pad_divide_by(image, 16)\n image = image.unsqueeze(0) # add the batch dimension\n\n is_mem_frame = ((self.curr_ti-self.last_mem_ti >= self.mem_every) or (mask is not None)) and (not end)\n need_segment = (self.curr_ti > 0) and ((valid_labels is None) or (len(self.all_labels) != len(valid_labels)))\n is_deep_update = (\n (self.deep_update_sync and is_mem_frame) or # synchronized\n (not self.deep_update_sync and self.curr_ti-self.last_deep_update_ti >= self.deep_update_every) # no-sync\n ) and (not end)\n is_normal_update = (not self.deep_update_sync or not is_deep_update) and (not end)\n\n key, shrinkage, selection, f16, f8, f4 = self.network.encode_key(image, \n need_ek=(self.enable_long_term or need_segment), \n need_sk=is_mem_frame)\n multi_scale_features = (f16, f8, f4)\n\n # segment the current frame is needed\n if need_segment:\n memory_readout = self.memory.match_memory(key, selection).unsqueeze(0)\n \n hidden, pred_logits_with_bg, pred_prob_with_bg = self.network.segment(multi_scale_features, memory_readout, \n self.memory.get_hidden(), h_out=is_normal_update, strip_bg=False)\n # remove batch dim\n pred_prob_with_bg = pred_prob_with_bg[0]\n pred_prob_no_bg = pred_prob_with_bg[1:]\n\n pred_logits_with_bg = pred_logits_with_bg[0]\n pred_logits_no_bg = pred_logits_with_bg[1:]\n\n if is_normal_update:\n self.memory.set_hidden(hidden)\n else:\n pred_prob_no_bg = pred_prob_with_bg = pred_logits_with_bg = pred_logits_no_bg = None\n\n # use the input mask if any\n if mask is not None:\n mask, _ = pad_divide_by(mask, 16)\n\n if pred_prob_no_bg is not None:\n # if we have a predicted mask, we work on it\n # make pred_prob_no_bg consistent with the input mask\n mask_regions = (mask.sum(0) > 0.5)\n pred_prob_no_bg[:, mask_regions] = 0\n # shift by 1 because mask/pred_prob_no_bg do not contain background\n mask = mask.type_as(pred_prob_no_bg)\n if valid_labels is not None:\n shift_by_one_non_labels = [i for i in range(pred_prob_no_bg.shape[0]) if (i+1) not in valid_labels]\n # non-labelled objects are copied from the predicted mask\n mask[shift_by_one_non_labels] = pred_prob_no_bg[shift_by_one_non_labels]\n pred_prob_with_bg = aggregate(mask, dim=0)\n\n # also create new hidden states\n self.memory.create_hidden_state(len(self.all_labels), key)\n\n # save as memory if needed\n if is_mem_frame:\n value, hidden = self.network.encode_value(image, f16, self.memory.get_hidden(), \n pred_prob_with_bg[1:].unsqueeze(0), is_deep_update=is_deep_update)\n self.memory.add_memory(key, shrinkage, value, self.all_labels, \n selection=selection if self.enable_long_term else None)\n self.last_mem_ti = self.curr_ti\n\n if is_deep_update:\n self.memory.set_hidden(hidden)\n self.last_deep_update_ti = self.curr_ti\n \n if pred_logits_with_bg is None:\n return unpad(pred_prob_with_bg, self.pad), None\n else:\n return unpad(pred_prob_with_bg, self.pad), unpad(pred_logits_with_bg, self.pad)","source_hash":"aef5d4637993b4580d0bff71327da1d487caf5e41849b40a6acf1bb00073f107","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.inference_core.__init__","uri":"program://Track-Anything/function/tracker.inference.inference_core.__init__#L9-L20","kind":"function","name":"__init__","path":"tracker/inference/inference_core.py","language":"python","start_line":9,"end_line":20,"context_start_line":1,"context_end_line":40,"code":"from inference.memory_manager import MemoryManager\nfrom model.network import XMem\nfrom model.aggregate import aggregate\n\nfrom tracker.util.tensor_util import pad_divide_by, unpad\n\n\nclass InferenceCore:\n def __init__(self, network:XMem, config):\n self.config = config\n self.network = network\n self.mem_every = config['mem_every']\n self.deep_update_every = config['deep_update_every']\n self.enable_long_term = config['enable_long_term']\n\n # if deep_update_every < 0, synchronize deep update with memory frame\n self.deep_update_sync = (self.deep_update_every < 0)\n\n self.clear_memory()\n self.all_labels = None\n\n def clear_memory(self):\n self.curr_ti = -1\n self.last_mem_ti = 0\n if not self.deep_update_sync:\n self.last_deep_update_ti = -self.deep_update_every\n self.memory = MemoryManager(config=self.config)\n\n def update_config(self, config):\n self.mem_every = config['mem_every']\n self.deep_update_every = config['deep_update_every']\n self.enable_long_term = config['enable_long_term']\n\n # if deep_update_every < 0, synchronize deep update with memory frame\n self.deep_update_sync = (self.deep_update_every < 0)\n self.memory.update_config(config)\n\n def set_all_labels(self, all_labels):\n # self.all_labels = [l.item() for l in all_labels]\n self.all_labels = all_labels","source_hash":"aef5d4637993b4580d0bff71327da1d487caf5e41849b40a6acf1bb00073f107","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.inference_core.clear_memory","uri":"program://Track-Anything/function/tracker.inference.inference_core.clear_memory#L22-L27","kind":"function","name":"clear_memory","path":"tracker/inference/inference_core.py","language":"python","start_line":22,"end_line":27,"context_start_line":2,"context_end_line":47,"code":"from model.network import XMem\nfrom model.aggregate import aggregate\n\nfrom tracker.util.tensor_util import pad_divide_by, unpad\n\n\nclass InferenceCore:\n def __init__(self, network:XMem, config):\n self.config = config\n self.network = network\n self.mem_every = config['mem_every']\n self.deep_update_every = config['deep_update_every']\n self.enable_long_term = config['enable_long_term']\n\n # if deep_update_every < 0, synchronize deep update with memory frame\n self.deep_update_sync = (self.deep_update_every < 0)\n\n self.clear_memory()\n self.all_labels = None\n\n def clear_memory(self):\n self.curr_ti = -1\n self.last_mem_ti = 0\n if not self.deep_update_sync:\n self.last_deep_update_ti = -self.deep_update_every\n self.memory = MemoryManager(config=self.config)\n\n def update_config(self, config):\n self.mem_every = config['mem_every']\n self.deep_update_every = config['deep_update_every']\n self.enable_long_term = config['enable_long_term']\n\n # if deep_update_every < 0, synchronize deep update with memory frame\n self.deep_update_sync = (self.deep_update_every < 0)\n self.memory.update_config(config)\n\n def set_all_labels(self, all_labels):\n # self.all_labels = [l.item() for l in all_labels]\n self.all_labels = all_labels\n\n def step(self, image, mask=None, valid_labels=None, end=False):\n # image: 3*H*W\n # mask: num_objects*H*W or None\n self.curr_ti += 1\n image, self.pad = pad_divide_by(image, 16)\n image = image.unsqueeze(0) # add the batch dimension","source_hash":"aef5d4637993b4580d0bff71327da1d487caf5e41849b40a6acf1bb00073f107","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.inference_core.update_config","uri":"program://Track-Anything/function/tracker.inference.inference_core.update_config#L29-L36","kind":"function","name":"update_config","path":"tracker/inference/inference_core.py","language":"python","start_line":29,"end_line":36,"context_start_line":9,"context_end_line":56,"code":" def __init__(self, network:XMem, config):\n self.config = config\n self.network = network\n self.mem_every = config['mem_every']\n self.deep_update_every = config['deep_update_every']\n self.enable_long_term = config['enable_long_term']\n\n # if deep_update_every < 0, synchronize deep update with memory frame\n self.deep_update_sync = (self.deep_update_every < 0)\n\n self.clear_memory()\n self.all_labels = None\n\n def clear_memory(self):\n self.curr_ti = -1\n self.last_mem_ti = 0\n if not self.deep_update_sync:\n self.last_deep_update_ti = -self.deep_update_every\n self.memory = MemoryManager(config=self.config)\n\n def update_config(self, config):\n self.mem_every = config['mem_every']\n self.deep_update_every = config['deep_update_every']\n self.enable_long_term = config['enable_long_term']\n\n # if deep_update_every < 0, synchronize deep update with memory frame\n self.deep_update_sync = (self.deep_update_every < 0)\n self.memory.update_config(config)\n\n def set_all_labels(self, all_labels):\n # self.all_labels = [l.item() for l in all_labels]\n self.all_labels = all_labels\n\n def step(self, image, mask=None, valid_labels=None, end=False):\n # image: 3*H*W\n # mask: num_objects*H*W or None\n self.curr_ti += 1\n image, self.pad = pad_divide_by(image, 16)\n image = image.unsqueeze(0) # add the batch dimension\n\n is_mem_frame = ((self.curr_ti-self.last_mem_ti >= self.mem_every) or (mask is not None)) and (not end)\n need_segment = (self.curr_ti > 0) and ((valid_labels is None) or (len(self.all_labels) != len(valid_labels)))\n is_deep_update = (\n (self.deep_update_sync and is_mem_frame) or # synchronized\n (not self.deep_update_sync and self.curr_ti-self.last_deep_update_ti >= self.deep_update_every) # no-sync\n ) and (not end)\n is_normal_update = (not self.deep_update_sync or not is_deep_update) and (not end)\n","source_hash":"aef5d4637993b4580d0bff71327da1d487caf5e41849b40a6acf1bb00073f107","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.inference_core.set_all_labels","uri":"program://Track-Anything/function/tracker.inference.inference_core.set_all_labels#L38-L40","kind":"function","name":"set_all_labels","path":"tracker/inference/inference_core.py","language":"python","start_line":38,"end_line":40,"context_start_line":18,"context_end_line":60,"code":"\n self.clear_memory()\n self.all_labels = None\n\n def clear_memory(self):\n self.curr_ti = -1\n self.last_mem_ti = 0\n if not self.deep_update_sync:\n self.last_deep_update_ti = -self.deep_update_every\n self.memory = MemoryManager(config=self.config)\n\n def update_config(self, config):\n self.mem_every = config['mem_every']\n self.deep_update_every = config['deep_update_every']\n self.enable_long_term = config['enable_long_term']\n\n # if deep_update_every < 0, synchronize deep update with memory frame\n self.deep_update_sync = (self.deep_update_every < 0)\n self.memory.update_config(config)\n\n def set_all_labels(self, all_labels):\n # self.all_labels = [l.item() for l in all_labels]\n self.all_labels = all_labels\n\n def step(self, image, mask=None, valid_labels=None, end=False):\n # image: 3*H*W\n # mask: num_objects*H*W or None\n self.curr_ti += 1\n image, self.pad = pad_divide_by(image, 16)\n image = image.unsqueeze(0) # add the batch dimension\n\n is_mem_frame = ((self.curr_ti-self.last_mem_ti >= self.mem_every) or (mask is not None)) and (not end)\n need_segment = (self.curr_ti > 0) and ((valid_labels is None) or (len(self.all_labels) != len(valid_labels)))\n is_deep_update = (\n (self.deep_update_sync and is_mem_frame) or # synchronized\n (not self.deep_update_sync and self.curr_ti-self.last_deep_update_ti >= self.deep_update_every) # no-sync\n ) and (not end)\n is_normal_update = (not self.deep_update_sync or not is_deep_update) and (not end)\n\n key, shrinkage, selection, f16, f8, f4 = self.network.encode_key(image, \n need_ek=(self.enable_long_term or need_segment), \n need_sk=is_mem_frame)\n multi_scale_features = (f16, f8, f4)","source_hash":"aef5d4637993b4580d0bff71327da1d487caf5e41849b40a6acf1bb00073f107","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.inference_core.step","uri":"program://Track-Anything/function/tracker.inference.inference_core.step#L42-L115","kind":"function","name":"step","path":"tracker/inference/inference_core.py","language":"python","start_line":42,"end_line":115,"context_start_line":22,"context_end_line":115,"code":" def clear_memory(self):\n self.curr_ti = -1\n self.last_mem_ti = 0\n if not self.deep_update_sync:\n self.last_deep_update_ti = -self.deep_update_every\n self.memory = MemoryManager(config=self.config)\n\n def update_config(self, config):\n self.mem_every = config['mem_every']\n self.deep_update_every = config['deep_update_every']\n self.enable_long_term = config['enable_long_term']\n\n # if deep_update_every < 0, synchronize deep update with memory frame\n self.deep_update_sync = (self.deep_update_every < 0)\n self.memory.update_config(config)\n\n def set_all_labels(self, all_labels):\n # self.all_labels = [l.item() for l in all_labels]\n self.all_labels = all_labels\n\n def step(self, image, mask=None, valid_labels=None, end=False):\n # image: 3*H*W\n # mask: num_objects*H*W or None\n self.curr_ti += 1\n image, self.pad = pad_divide_by(image, 16)\n image = image.unsqueeze(0) # add the batch dimension\n\n is_mem_frame = ((self.curr_ti-self.last_mem_ti >= self.mem_every) or (mask is not None)) and (not end)\n need_segment = (self.curr_ti > 0) and ((valid_labels is None) or (len(self.all_labels) != len(valid_labels)))\n is_deep_update = (\n (self.deep_update_sync and is_mem_frame) or # synchronized\n (not self.deep_update_sync and self.curr_ti-self.last_deep_update_ti >= self.deep_update_every) # no-sync\n ) and (not end)\n is_normal_update = (not self.deep_update_sync or not is_deep_update) and (not end)\n\n key, shrinkage, selection, f16, f8, f4 = self.network.encode_key(image, \n need_ek=(self.enable_long_term or need_segment), \n need_sk=is_mem_frame)\n multi_scale_features = (f16, f8, f4)\n\n # segment the current frame is needed\n if need_segment:\n memory_readout = self.memory.match_memory(key, selection).unsqueeze(0)\n \n hidden, pred_logits_with_bg, pred_prob_with_bg = self.network.segment(multi_scale_features, memory_readout, \n self.memory.get_hidden(), h_out=is_normal_update, strip_bg=False)\n # remove batch dim\n pred_prob_with_bg = pred_prob_with_bg[0]\n pred_prob_no_bg = pred_prob_with_bg[1:]\n\n pred_logits_with_bg = pred_logits_with_bg[0]\n pred_logits_no_bg = pred_logits_with_bg[1:]\n\n if is_normal_update:\n self.memory.set_hidden(hidden)\n else:\n pred_prob_no_bg = pred_prob_with_bg = pred_logits_with_bg = pred_logits_no_bg = None\n\n # use the input mask if any\n if mask is not None:\n mask, _ = pad_divide_by(mask, 16)\n\n if pred_prob_no_bg is not None:\n # if we have a predicted mask, we work on it\n # make pred_prob_no_bg consistent with the input mask\n mask_regions = (mask.sum(0) > 0.5)\n pred_prob_no_bg[:, mask_regions] = 0\n # shift by 1 because mask/pred_prob_no_bg do not contain background\n mask = mask.type_as(pred_prob_no_bg)\n if valid_labels is not None:\n shift_by_one_non_labels = [i for i in range(pred_prob_no_bg.shape[0]) if (i+1) not in valid_labels]\n # non-labelled objects are copied from the predicted mask\n mask[shift_by_one_non_labels] = pred_prob_no_bg[shift_by_one_non_labels]\n pred_prob_with_bg = aggregate(mask, dim=0)\n\n # also create new hidden states\n self.memory.create_hidden_state(len(self.all_labels), key)\n\n # save as memory if needed\n if is_mem_frame:\n value, hidden = self.network.encode_value(image, f16, self.memory.get_hidden(), \n pred_prob_with_bg[1:].unsqueeze(0), is_deep_update=is_deep_update)\n self.memory.add_memory(key, shrinkage, value, self.all_labels, \n selection=selection if self.enable_long_term else None)\n self.last_mem_ti = self.curr_ti\n\n if is_deep_update:\n self.memory.set_hidden(hidden)\n self.last_deep_update_ti = self.curr_ti\n \n if pred_logits_with_bg is None:\n return unpad(pred_prob_with_bg, self.pad), None\n else:\n return unpad(pred_prob_with_bg, self.pad), unpad(pred_logits_with_bg, self.pad)","source_hash":"aef5d4637993b4580d0bff71327da1d487caf5e41849b40a6acf1bb00073f107","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.kv_memory_store","uri":"program://Track-Anything/module/tracker.inference.kv_memory_store#L1-L214","kind":"module","name":"tracker.inference.kv_memory_store","path":"tracker/inference/kv_memory_store.py","language":"python","start_line":1,"end_line":214,"context_start_line":1,"context_end_line":214,"code":"import torch\nfrom typing import List\n\nclass KeyValueMemoryStore:\n \"\"\"\n Works for key/value pairs type storage\n e.g., working and long-term memory\n \"\"\"\n\n \"\"\"\n An object group is created when new objects enter the video\n Objects in the same group share the same temporal extent\n i.e., objects initialized in the same frame are in the same group\n For DAVIS/interactive, there is only one object group\n For YouTubeVOS, there can be multiple object groups\n \"\"\"\n\n def __init__(self, count_usage: bool):\n self.count_usage = count_usage\n\n # keys are stored in a single tensor and are shared between groups/objects\n # values are stored as a list indexed by object groups\n self.k = None\n self.v = []\n self.obj_groups = []\n # for debugging only\n self.all_objects = []\n\n # shrinkage and selection are also single tensors\n self.s = self.e = None\n\n # usage\n if self.count_usage:\n self.use_count = self.life_count = None\n\n def add(self, key, value, shrinkage, selection, objects: List[int]):\n new_count = torch.zeros((key.shape[0], 1, key.shape[2]), device=key.device, dtype=torch.float32)\n new_life = torch.zeros((key.shape[0], 1, key.shape[2]), device=key.device, dtype=torch.float32) + 1e-7\n\n # add the key\n if self.k is None:\n self.k = key\n self.s = shrinkage\n self.e = selection\n if self.count_usage:\n self.use_count = new_count\n self.life_count = new_life\n else:\n self.k = torch.cat([self.k, key], -1)\n if shrinkage is not None:\n self.s = torch.cat([self.s, shrinkage], -1)\n if selection is not None:\n self.e = torch.cat([self.e, selection], -1)\n if self.count_usage:\n self.use_count = torch.cat([self.use_count, new_count], -1)\n self.life_count = torch.cat([self.life_count, new_life], -1)\n\n # add the value\n if objects is not None:\n # When objects is given, v is a tensor; used in working memory\n assert isinstance(value, torch.Tensor)\n # First consume objects that are already in the memory bank\n # cannot use set here because we need to preserve order\n # shift by one as background is not part of value\n remaining_objects = [obj-1 for obj in objects]\n for gi, group in enumerate(self.obj_groups):\n for obj in group:\n # should properly raise an error if there are overlaps in obj_groups\n remaining_objects.remove(obj)\n self.v[gi] = torch.cat([self.v[gi], value[group]], -1)\n\n # If there are remaining objects, add them as a new group\n if len(remaining_objects) > 0:\n new_group = list(remaining_objects)\n self.v.append(value[new_group])\n self.obj_groups.append(new_group)\n self.all_objects.extend(new_group)\n \n assert sorted(self.all_objects) == self.all_objects, 'Objects MUST be inserted in sorted order '\n else:\n # When objects is not given, v is a list that already has the object groups sorted\n # used in long-term memory\n assert isinstance(value, list)\n for gi, gv in enumerate(value):\n if gv is None:\n continue\n if gi < self.num_groups:\n self.v[gi] = torch.cat([self.v[gi], gv], -1)\n else:\n self.v.append(gv)\n\n def update_usage(self, usage):\n # increase all life count by 1\n # increase use of indexed elements\n if not self.count_usage:\n return\n \n self.use_count += usage.view_as(self.use_count)\n self.life_count += 1\n\n def sieve_by_range(self, start: int, end: int, min_size: int):\n # keep only the elements *outside* of this range (with some boundary conditions)\n # i.e., concat (a[:start], a[end:])\n # min_size is only used for values, we do not sieve values under this size\n # (because they are not consolidated)\n\n if end == 0:\n # negative 0 would not work as the end index!\n self.k = self.k[:,:,:start]\n if self.count_usage:\n self.use_count = self.use_count[:,:,:start]\n self.life_count = self.life_count[:,:,:start]\n if self.s is not None:\n self.s = self.s[:,:,:start]\n if self.e is not None:\n self.e = self.e[:,:,:start]\n \n for gi in range(self.num_groups):\n if self.v[gi].shape[-1] >= min_size:\n self.v[gi] = self.v[gi][:,:,:start]\n else:\n self.k = torch.cat([self.k[:,:,:start], self.k[:,:,end:]], -1)\n if self.count_usage:\n self.use_count = torch.cat([self.use_count[:,:,:start], self.use_count[:,:,end:]], -1)\n self.life_count = torch.cat([self.life_count[:,:,:start], self.life_count[:,:,end:]], -1)\n if self.s is not None:\n self.s = torch.cat([self.s[:,:,:start], self.s[:,:,end:]], -1)\n if self.e is not None:\n self.e = torch.cat([self.e[:,:,:start], self.e[:,:,end:]], -1)\n \n for gi in range(self.num_groups):\n if self.v[gi].shape[-1] >= min_size:\n self.v[gi] = torch.cat([self.v[gi][:,:,:start], self.v[gi][:,:,end:]], -1)\n\n def remove_obsolete_features(self, max_size: int):\n # normalize with life duration\n usage = self.get_usage().flatten()\n\n values, _ = torch.topk(usage, k=(self.size-max_size), largest=False, sorted=True)\n survived = (usage > values[-1])\n\n self.k = self.k[:, :, survived]\n self.s = self.s[:, :, survived] if self.s is not None else None\n # Long-term memory does not store ek so this should not be needed\n self.e = self.e[:, :, survived] if self.e is not None else None\n if self.num_groups > 1:\n raise NotImplementedError(\"\"\"The current data structure does not support feature removal with \n multiple object groups (e.g., some objects start to appear later in the video)\n The indices for \"survived\" is based on keys but not all values are present for every key\n Basically we need to remap the indices for keys to values\n \"\"\")\n for gi in range(self.num_groups):\n self.v[gi] = self.v[gi][:, :, survived]\n\n self.use_count = self.use_count[:, :, survived]\n self.life_count = self.life_count[:, :, survived]\n\n def get_usage(self):\n # return normalized usage\n if not self.count_usage:\n raise RuntimeError('I did not count usage!')\n else:\n usage = self.use_count / self.life_count\n return usage\n\n def get_all_sliced(self, start: int, end: int):\n # return k, sk, ek, usage in order, sliced by start and end\n\n if end == 0:\n # negative 0 would not work as the end index!\n k = self.k[:,:,start:]\n sk = self.s[:,:,start:] if self.s is not None else None\n ek = self.e[:,:,start:] if self.e is not None else None\n usage = self.get_usage()[:,:,start:]\n else:\n k = self.k[:,:,start:end]\n sk = self.s[:,:,start:end] if self.s is not None else None\n ek = self.e[:,:,start:end] if self.e is not None else None\n usage = self.get_usage()[:,:,start:end]\n\n return k, sk, ek, usage\n\n def get_v_size(self, ni: int):\n return self.v[ni].shape[2]\n\n def engaged(self):\n return self.k is not None\n\n @property\n def size(self):\n if self.k is None:\n return 0\n else:\n return self.k.shape[-1]\n\n @property\n def num_groups(self):\n return len(self.v)\n\n @property\n def key(self):\n return self.k\n\n @property\n def value(self):\n return self.v\n\n @property\n def shrinkage(self):\n return self.s\n\n @property\n def selection(self):\n return self.e","source_hash":"45cd3f299592f26ca7728345970261cfa39cdd750f37a2b11ff217e93aee7bb8","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.kv_memory_store.KeyValueMemoryStore","uri":"program://Track-Anything/class/tracker.inference.kv_memory_store.KeyValueMemoryStore#L4-L214","kind":"class","name":"KeyValueMemoryStore","path":"tracker/inference/kv_memory_store.py","language":"python","start_line":4,"end_line":214,"context_start_line":1,"context_end_line":214,"code":"import torch\nfrom typing import List\n\nclass KeyValueMemoryStore:\n \"\"\"\n Works for key/value pairs type storage\n e.g., working and long-term memory\n \"\"\"\n\n \"\"\"\n An object group is created when new objects enter the video\n Objects in the same group share the same temporal extent\n i.e., objects initialized in the same frame are in the same group\n For DAVIS/interactive, there is only one object group\n For YouTubeVOS, there can be multiple object groups\n \"\"\"\n\n def __init__(self, count_usage: bool):\n self.count_usage = count_usage\n\n # keys are stored in a single tensor and are shared between groups/objects\n # values are stored as a list indexed by object groups\n self.k = None\n self.v = []\n self.obj_groups = []\n # for debugging only\n self.all_objects = []\n\n # shrinkage and selection are also single tensors\n self.s = self.e = None\n\n # usage\n if self.count_usage:\n self.use_count = self.life_count = None\n\n def add(self, key, value, shrinkage, selection, objects: List[int]):\n new_count = torch.zeros((key.shape[0], 1, key.shape[2]), device=key.device, dtype=torch.float32)\n new_life = torch.zeros((key.shape[0], 1, key.shape[2]), device=key.device, dtype=torch.float32) + 1e-7\n\n # add the key\n if self.k is None:\n self.k = key\n self.s = shrinkage\n self.e = selection\n if self.count_usage:\n self.use_count = new_count\n self.life_count = new_life\n else:\n self.k = torch.cat([self.k, key], -1)\n if shrinkage is not None:\n self.s = torch.cat([self.s, shrinkage], -1)\n if selection is not None:\n self.e = torch.cat([self.e, selection], -1)\n if self.count_usage:\n self.use_count = torch.cat([self.use_count, new_count], -1)\n self.life_count = torch.cat([self.life_count, new_life], -1)\n\n # add the value\n if objects is not None:\n # When objects is given, v is a tensor; used in working memory\n assert isinstance(value, torch.Tensor)\n # First consume objects that are already in the memory bank\n # cannot use set here because we need to preserve order\n # shift by one as background is not part of value\n remaining_objects = [obj-1 for obj in objects]\n for gi, group in enumerate(self.obj_groups):\n for obj in group:\n # should properly raise an error if there are overlaps in obj_groups\n remaining_objects.remove(obj)\n self.v[gi] = torch.cat([self.v[gi], value[group]], -1)\n\n # If there are remaining objects, add them as a new group\n if len(remaining_objects) > 0:\n new_group = list(remaining_objects)\n self.v.append(value[new_group])\n self.obj_groups.append(new_group)\n self.all_objects.extend(new_group)\n \n assert sorted(self.all_objects) == self.all_objects, 'Objects MUST be inserted in sorted order '\n else:\n # When objects is not given, v is a list that already has the object groups sorted\n # used in long-term memory\n assert isinstance(value, list)\n for gi, gv in enumerate(value):\n if gv is None:\n continue\n if gi < self.num_groups:\n self.v[gi] = torch.cat([self.v[gi], gv], -1)\n else:\n self.v.append(gv)\n\n def update_usage(self, usage):\n # increase all life count by 1\n # increase use of indexed elements\n if not self.count_usage:\n return\n \n self.use_count += usage.view_as(self.use_count)\n self.life_count += 1\n\n def sieve_by_range(self, start: int, end: int, min_size: int):\n # keep only the elements *outside* of this range (with some boundary conditions)\n # i.e., concat (a[:start], a[end:])\n # min_size is only used for values, we do not sieve values under this size\n # (because they are not consolidated)\n\n if end == 0:\n # negative 0 would not work as the end index!\n self.k = self.k[:,:,:start]\n if self.count_usage:\n self.use_count = self.use_count[:,:,:start]\n self.life_count = self.life_count[:,:,:start]\n if self.s is not None:\n self.s = self.s[:,:,:start]\n if self.e is not None:\n self.e = self.e[:,:,:start]\n \n for gi in range(self.num_groups):\n if self.v[gi].shape[-1] >= min_size:\n self.v[gi] = self.v[gi][:,:,:start]\n else:\n self.k = torch.cat([self.k[:,:,:start], self.k[:,:,end:]], -1)\n if self.count_usage:\n self.use_count = torch.cat([self.use_count[:,:,:start], self.use_count[:,:,end:]], -1)\n self.life_count = torch.cat([self.life_count[:,:,:start], self.life_count[:,:,end:]], -1)\n if self.s is not None:\n self.s = torch.cat([self.s[:,:,:start], self.s[:,:,end:]], -1)\n if self.e is not None:\n self.e = torch.cat([self.e[:,:,:start], self.e[:,:,end:]], -1)\n \n for gi in range(self.num_groups):\n if self.v[gi].shape[-1] >= min_size:\n self.v[gi] = torch.cat([self.v[gi][:,:,:start], self.v[gi][:,:,end:]], -1)\n\n def remove_obsolete_features(self, max_size: int):\n # normalize with life duration\n usage = self.get_usage().flatten()\n\n values, _ = torch.topk(usage, k=(self.size-max_size), largest=False, sorted=True)\n survived = (usage > values[-1])\n\n self.k = self.k[:, :, survived]\n self.s = self.s[:, :, survived] if self.s is not None else None\n # Long-term memory does not store ek so this should not be needed\n self.e = self.e[:, :, survived] if self.e is not None else None\n if self.num_groups > 1:\n raise NotImplementedError(\"\"\"The current data structure does not support feature removal with \n multiple object groups (e.g., some objects start to appear later in the video)\n The indices for \"survived\" is based on keys but not all values are present for every key\n Basically we need to remap the indices for keys to values\n \"\"\")\n for gi in range(self.num_groups):\n self.v[gi] = self.v[gi][:, :, survived]\n\n self.use_count = self.use_count[:, :, survived]\n self.life_count = self.life_count[:, :, survived]\n\n def get_usage(self):\n # return normalized usage\n if not self.count_usage:\n raise RuntimeError('I did not count usage!')\n else:\n usage = self.use_count / self.life_count\n return usage\n\n def get_all_sliced(self, start: int, end: int):\n # return k, sk, ek, usage in order, sliced by start and end\n\n if end == 0:\n # negative 0 would not work as the end index!\n k = self.k[:,:,start:]\n sk = self.s[:,:,start:] if self.s is not None else None\n ek = self.e[:,:,start:] if self.e is not None else None\n usage = self.get_usage()[:,:,start:]\n else:\n k = self.k[:,:,start:end]\n sk = self.s[:,:,start:end] if self.s is not None else None\n ek = self.e[:,:,start:end] if self.e is not None else None\n usage = self.get_usage()[:,:,start:end]\n\n return k, sk, ek, usage\n\n def get_v_size(self, ni: int):\n return self.v[ni].shape[2]\n\n def engaged(self):\n return self.k is not None\n\n @property\n def size(self):\n if self.k is None:\n return 0\n else:\n return self.k.shape[-1]\n\n @property\n def num_groups(self):\n return len(self.v)\n\n @property\n def key(self):\n return self.k\n\n @property\n def value(self):\n return self.v\n\n @property\n def shrinkage(self):\n return self.s\n\n @property\n def selection(self):\n return self.e","source_hash":"45cd3f299592f26ca7728345970261cfa39cdd750f37a2b11ff217e93aee7bb8","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.kv_memory_store.__init__","uri":"program://Track-Anything/function/tracker.inference.kv_memory_store.__init__#L18-L34","kind":"function","name":"__init__","path":"tracker/inference/kv_memory_store.py","language":"python","start_line":18,"end_line":34,"context_start_line":1,"context_end_line":54,"code":"import torch\nfrom typing import List\n\nclass KeyValueMemoryStore:\n \"\"\"\n Works for key/value pairs type storage\n e.g., working and long-term memory\n \"\"\"\n\n \"\"\"\n An object group is created when new objects enter the video\n Objects in the same group share the same temporal extent\n i.e., objects initialized in the same frame are in the same group\n For DAVIS/interactive, there is only one object group\n For YouTubeVOS, there can be multiple object groups\n \"\"\"\n\n def __init__(self, count_usage: bool):\n self.count_usage = count_usage\n\n # keys are stored in a single tensor and are shared between groups/objects\n # values are stored as a list indexed by object groups\n self.k = None\n self.v = []\n self.obj_groups = []\n # for debugging only\n self.all_objects = []\n\n # shrinkage and selection are also single tensors\n self.s = self.e = None\n\n # usage\n if self.count_usage:\n self.use_count = self.life_count = None\n\n def add(self, key, value, shrinkage, selection, objects: List[int]):\n new_count = torch.zeros((key.shape[0], 1, key.shape[2]), device=key.device, dtype=torch.float32)\n new_life = torch.zeros((key.shape[0], 1, key.shape[2]), device=key.device, dtype=torch.float32) + 1e-7\n\n # add the key\n if self.k is None:\n self.k = key\n self.s = shrinkage\n self.e = selection\n if self.count_usage:\n self.use_count = new_count\n self.life_count = new_life\n else:\n self.k = torch.cat([self.k, key], -1)\n if shrinkage is not None:\n self.s = torch.cat([self.s, shrinkage], -1)\n if selection is not None:\n self.e = torch.cat([self.e, selection], -1)\n if self.count_usage:","source_hash":"45cd3f299592f26ca7728345970261cfa39cdd750f37a2b11ff217e93aee7bb8","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.kv_memory_store.add","uri":"program://Track-Anything/function/tracker.inference.kv_memory_store.add#L36-L90","kind":"function","name":"add","path":"tracker/inference/kv_memory_store.py","language":"python","start_line":36,"end_line":90,"context_start_line":16,"context_end_line":110,"code":" \"\"\"\n\n def __init__(self, count_usage: bool):\n self.count_usage = count_usage\n\n # keys are stored in a single tensor and are shared between groups/objects\n # values are stored as a list indexed by object groups\n self.k = None\n self.v = []\n self.obj_groups = []\n # for debugging only\n self.all_objects = []\n\n # shrinkage and selection are also single tensors\n self.s = self.e = None\n\n # usage\n if self.count_usage:\n self.use_count = self.life_count = None\n\n def add(self, key, value, shrinkage, selection, objects: List[int]):\n new_count = torch.zeros((key.shape[0], 1, key.shape[2]), device=key.device, dtype=torch.float32)\n new_life = torch.zeros((key.shape[0], 1, key.shape[2]), device=key.device, dtype=torch.float32) + 1e-7\n\n # add the key\n if self.k is None:\n self.k = key\n self.s = shrinkage\n self.e = selection\n if self.count_usage:\n self.use_count = new_count\n self.life_count = new_life\n else:\n self.k = torch.cat([self.k, key], -1)\n if shrinkage is not None:\n self.s = torch.cat([self.s, shrinkage], -1)\n if selection is not None:\n self.e = torch.cat([self.e, selection], -1)\n if self.count_usage:\n self.use_count = torch.cat([self.use_count, new_count], -1)\n self.life_count = torch.cat([self.life_count, new_life], -1)\n\n # add the value\n if objects is not None:\n # When objects is given, v is a tensor; used in working memory\n assert isinstance(value, torch.Tensor)\n # First consume objects that are already in the memory bank\n # cannot use set here because we need to preserve order\n # shift by one as background is not part of value\n remaining_objects = [obj-1 for obj in objects]\n for gi, group in enumerate(self.obj_groups):\n for obj in group:\n # should properly raise an error if there are overlaps in obj_groups\n remaining_objects.remove(obj)\n self.v[gi] = torch.cat([self.v[gi], value[group]], -1)\n\n # If there are remaining objects, add them as a new group\n if len(remaining_objects) > 0:\n new_group = list(remaining_objects)\n self.v.append(value[new_group])\n self.obj_groups.append(new_group)\n self.all_objects.extend(new_group)\n \n assert sorted(self.all_objects) == self.all_objects, 'Objects MUST be inserted in sorted order '\n else:\n # When objects is not given, v is a list that already has the object groups sorted\n # used in long-term memory\n assert isinstance(value, list)\n for gi, gv in enumerate(value):\n if gv is None:\n continue\n if gi < self.num_groups:\n self.v[gi] = torch.cat([self.v[gi], gv], -1)\n else:\n self.v.append(gv)\n\n def update_usage(self, usage):\n # increase all life count by 1\n # increase use of indexed elements\n if not self.count_usage:\n return\n \n self.use_count += usage.view_as(self.use_count)\n self.life_count += 1\n\n def sieve_by_range(self, start: int, end: int, min_size: int):\n # keep only the elements *outside* of this range (with some boundary conditions)\n # i.e., concat (a[:start], a[end:])\n # min_size is only used for values, we do not sieve values under this size\n # (because they are not consolidated)\n\n if end == 0:\n # negative 0 would not work as the end index!\n self.k = self.k[:,:,:start]\n if self.count_usage:","source_hash":"45cd3f299592f26ca7728345970261cfa39cdd750f37a2b11ff217e93aee7bb8","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.kv_memory_store.update_usage","uri":"program://Track-Anything/function/tracker.inference.kv_memory_store.update_usage#L92-L99","kind":"function","name":"update_usage","path":"tracker/inference/kv_memory_store.py","language":"python","start_line":92,"end_line":99,"context_start_line":72,"context_end_line":119,"code":" # If there are remaining objects, add them as a new group\n if len(remaining_objects) > 0:\n new_group = list(remaining_objects)\n self.v.append(value[new_group])\n self.obj_groups.append(new_group)\n self.all_objects.extend(new_group)\n \n assert sorted(self.all_objects) == self.all_objects, 'Objects MUST be inserted in sorted order '\n else:\n # When objects is not given, v is a list that already has the object groups sorted\n # used in long-term memory\n assert isinstance(value, list)\n for gi, gv in enumerate(value):\n if gv is None:\n continue\n if gi < self.num_groups:\n self.v[gi] = torch.cat([self.v[gi], gv], -1)\n else:\n self.v.append(gv)\n\n def update_usage(self, usage):\n # increase all life count by 1\n # increase use of indexed elements\n if not self.count_usage:\n return\n \n self.use_count += usage.view_as(self.use_count)\n self.life_count += 1\n\n def sieve_by_range(self, start: int, end: int, min_size: int):\n # keep only the elements *outside* of this range (with some boundary conditions)\n # i.e., concat (a[:start], a[end:])\n # min_size is only used for values, we do not sieve values under this size\n # (because they are not consolidated)\n\n if end == 0:\n # negative 0 would not work as the end index!\n self.k = self.k[:,:,:start]\n if self.count_usage:\n self.use_count = self.use_count[:,:,:start]\n self.life_count = self.life_count[:,:,:start]\n if self.s is not None:\n self.s = self.s[:,:,:start]\n if self.e is not None:\n self.e = self.e[:,:,:start]\n \n for gi in range(self.num_groups):\n if self.v[gi].shape[-1] >= min_size:","source_hash":"45cd3f299592f26ca7728345970261cfa39cdd750f37a2b11ff217e93aee7bb8","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.kv_memory_store.sieve_by_range","uri":"program://Track-Anything/function/tracker.inference.kv_memory_store.sieve_by_range#L101-L133","kind":"function","name":"sieve_by_range","path":"tracker/inference/kv_memory_store.py","language":"python","start_line":101,"end_line":133,"context_start_line":81,"context_end_line":153,"code":" # When objects is not given, v is a list that already has the object groups sorted\n # used in long-term memory\n assert isinstance(value, list)\n for gi, gv in enumerate(value):\n if gv is None:\n continue\n if gi < self.num_groups:\n self.v[gi] = torch.cat([self.v[gi], gv], -1)\n else:\n self.v.append(gv)\n\n def update_usage(self, usage):\n # increase all life count by 1\n # increase use of indexed elements\n if not self.count_usage:\n return\n \n self.use_count += usage.view_as(self.use_count)\n self.life_count += 1\n\n def sieve_by_range(self, start: int, end: int, min_size: int):\n # keep only the elements *outside* of this range (with some boundary conditions)\n # i.e., concat (a[:start], a[end:])\n # min_size is only used for values, we do not sieve values under this size\n # (because they are not consolidated)\n\n if end == 0:\n # negative 0 would not work as the end index!\n self.k = self.k[:,:,:start]\n if self.count_usage:\n self.use_count = self.use_count[:,:,:start]\n self.life_count = self.life_count[:,:,:start]\n if self.s is not None:\n self.s = self.s[:,:,:start]\n if self.e is not None:\n self.e = self.e[:,:,:start]\n \n for gi in range(self.num_groups):\n if self.v[gi].shape[-1] >= min_size:\n self.v[gi] = self.v[gi][:,:,:start]\n else:\n self.k = torch.cat([self.k[:,:,:start], self.k[:,:,end:]], -1)\n if self.count_usage:\n self.use_count = torch.cat([self.use_count[:,:,:start], self.use_count[:,:,end:]], -1)\n self.life_count = torch.cat([self.life_count[:,:,:start], self.life_count[:,:,end:]], -1)\n if self.s is not None:\n self.s = torch.cat([self.s[:,:,:start], self.s[:,:,end:]], -1)\n if self.e is not None:\n self.e = torch.cat([self.e[:,:,:start], self.e[:,:,end:]], -1)\n \n for gi in range(self.num_groups):\n if self.v[gi].shape[-1] >= min_size:\n self.v[gi] = torch.cat([self.v[gi][:,:,:start], self.v[gi][:,:,end:]], -1)\n\n def remove_obsolete_features(self, max_size: int):\n # normalize with life duration\n usage = self.get_usage().flatten()\n\n values, _ = torch.topk(usage, k=(self.size-max_size), largest=False, sorted=True)\n survived = (usage > values[-1])\n\n self.k = self.k[:, :, survived]\n self.s = self.s[:, :, survived] if self.s is not None else None\n # Long-term memory does not store ek so this should not be needed\n self.e = self.e[:, :, survived] if self.e is not None else None\n if self.num_groups > 1:\n raise NotImplementedError(\"\"\"The current data structure does not support feature removal with \n multiple object groups (e.g., some objects start to appear later in the video)\n The indices for \"survived\" is based on keys but not all values are present for every key\n Basically we need to remap the indices for keys to values\n \"\"\")\n for gi in range(self.num_groups):\n self.v[gi] = self.v[gi][:, :, survived]","source_hash":"45cd3f299592f26ca7728345970261cfa39cdd750f37a2b11ff217e93aee7bb8","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.kv_memory_store.remove_obsolete_features","uri":"program://Track-Anything/function/tracker.inference.kv_memory_store.remove_obsolete_features#L135-L156","kind":"function","name":"remove_obsolete_features","path":"tracker/inference/kv_memory_store.py","language":"python","start_line":135,"end_line":156,"context_start_line":115,"context_end_line":176,"code":" if self.e is not None:\n self.e = self.e[:,:,:start]\n \n for gi in range(self.num_groups):\n if self.v[gi].shape[-1] >= min_size:\n self.v[gi] = self.v[gi][:,:,:start]\n else:\n self.k = torch.cat([self.k[:,:,:start], self.k[:,:,end:]], -1)\n if self.count_usage:\n self.use_count = torch.cat([self.use_count[:,:,:start], self.use_count[:,:,end:]], -1)\n self.life_count = torch.cat([self.life_count[:,:,:start], self.life_count[:,:,end:]], -1)\n if self.s is not None:\n self.s = torch.cat([self.s[:,:,:start], self.s[:,:,end:]], -1)\n if self.e is not None:\n self.e = torch.cat([self.e[:,:,:start], self.e[:,:,end:]], -1)\n \n for gi in range(self.num_groups):\n if self.v[gi].shape[-1] >= min_size:\n self.v[gi] = torch.cat([self.v[gi][:,:,:start], self.v[gi][:,:,end:]], -1)\n\n def remove_obsolete_features(self, max_size: int):\n # normalize with life duration\n usage = self.get_usage().flatten()\n\n values, _ = torch.topk(usage, k=(self.size-max_size), largest=False, sorted=True)\n survived = (usage > values[-1])\n\n self.k = self.k[:, :, survived]\n self.s = self.s[:, :, survived] if self.s is not None else None\n # Long-term memory does not store ek so this should not be needed\n self.e = self.e[:, :, survived] if self.e is not None else None\n if self.num_groups > 1:\n raise NotImplementedError(\"\"\"The current data structure does not support feature removal with \n multiple object groups (e.g., some objects start to appear later in the video)\n The indices for \"survived\" is based on keys but not all values are present for every key\n Basically we need to remap the indices for keys to values\n \"\"\")\n for gi in range(self.num_groups):\n self.v[gi] = self.v[gi][:, :, survived]\n\n self.use_count = self.use_count[:, :, survived]\n self.life_count = self.life_count[:, :, survived]\n\n def get_usage(self):\n # return normalized usage\n if not self.count_usage:\n raise RuntimeError('I did not count usage!')\n else:\n usage = self.use_count / self.life_count\n return usage\n\n def get_all_sliced(self, start: int, end: int):\n # return k, sk, ek, usage in order, sliced by start and end\n\n if end == 0:\n # negative 0 would not work as the end index!\n k = self.k[:,:,start:]\n sk = self.s[:,:,start:] if self.s is not None else None\n ek = self.e[:,:,start:] if self.e is not None else None\n usage = self.get_usage()[:,:,start:]\n else:\n k = self.k[:,:,start:end]","source_hash":"45cd3f299592f26ca7728345970261cfa39cdd750f37a2b11ff217e93aee7bb8","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.kv_memory_store.get_usage","uri":"program://Track-Anything/function/tracker.inference.kv_memory_store.get_usage#L158-L164","kind":"function","name":"get_usage","path":"tracker/inference/kv_memory_store.py","language":"python","start_line":158,"end_line":164,"context_start_line":138,"context_end_line":184,"code":"\n values, _ = torch.topk(usage, k=(self.size-max_size), largest=False, sorted=True)\n survived = (usage > values[-1])\n\n self.k = self.k[:, :, survived]\n self.s = self.s[:, :, survived] if self.s is not None else None\n # Long-term memory does not store ek so this should not be needed\n self.e = self.e[:, :, survived] if self.e is not None else None\n if self.num_groups > 1:\n raise NotImplementedError(\"\"\"The current data structure does not support feature removal with \n multiple object groups (e.g., some objects start to appear later in the video)\n The indices for \"survived\" is based on keys but not all values are present for every key\n Basically we need to remap the indices for keys to values\n \"\"\")\n for gi in range(self.num_groups):\n self.v[gi] = self.v[gi][:, :, survived]\n\n self.use_count = self.use_count[:, :, survived]\n self.life_count = self.life_count[:, :, survived]\n\n def get_usage(self):\n # return normalized usage\n if not self.count_usage:\n raise RuntimeError('I did not count usage!')\n else:\n usage = self.use_count / self.life_count\n return usage\n\n def get_all_sliced(self, start: int, end: int):\n # return k, sk, ek, usage in order, sliced by start and end\n\n if end == 0:\n # negative 0 would not work as the end index!\n k = self.k[:,:,start:]\n sk = self.s[:,:,start:] if self.s is not None else None\n ek = self.e[:,:,start:] if self.e is not None else None\n usage = self.get_usage()[:,:,start:]\n else:\n k = self.k[:,:,start:end]\n sk = self.s[:,:,start:end] if self.s is not None else None\n ek = self.e[:,:,start:end] if self.e is not None else None\n usage = self.get_usage()[:,:,start:end]\n\n return k, sk, ek, usage\n\n def get_v_size(self, ni: int):\n return self.v[ni].shape[2]","source_hash":"45cd3f299592f26ca7728345970261cfa39cdd750f37a2b11ff217e93aee7bb8","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.kv_memory_store.get_all_sliced","uri":"program://Track-Anything/function/tracker.inference.kv_memory_store.get_all_sliced#L166-L181","kind":"function","name":"get_all_sliced","path":"tracker/inference/kv_memory_store.py","language":"python","start_line":166,"end_line":181,"context_start_line":146,"context_end_line":201,"code":" if self.num_groups > 1:\n raise NotImplementedError(\"\"\"The current data structure does not support feature removal with \n multiple object groups (e.g., some objects start to appear later in the video)\n The indices for \"survived\" is based on keys but not all values are present for every key\n Basically we need to remap the indices for keys to values\n \"\"\")\n for gi in range(self.num_groups):\n self.v[gi] = self.v[gi][:, :, survived]\n\n self.use_count = self.use_count[:, :, survived]\n self.life_count = self.life_count[:, :, survived]\n\n def get_usage(self):\n # return normalized usage\n if not self.count_usage:\n raise RuntimeError('I did not count usage!')\n else:\n usage = self.use_count / self.life_count\n return usage\n\n def get_all_sliced(self, start: int, end: int):\n # return k, sk, ek, usage in order, sliced by start and end\n\n if end == 0:\n # negative 0 would not work as the end index!\n k = self.k[:,:,start:]\n sk = self.s[:,:,start:] if self.s is not None else None\n ek = self.e[:,:,start:] if self.e is not None else None\n usage = self.get_usage()[:,:,start:]\n else:\n k = self.k[:,:,start:end]\n sk = self.s[:,:,start:end] if self.s is not None else None\n ek = self.e[:,:,start:end] if self.e is not None else None\n usage = self.get_usage()[:,:,start:end]\n\n return k, sk, ek, usage\n\n def get_v_size(self, ni: int):\n return self.v[ni].shape[2]\n\n def engaged(self):\n return self.k is not None\n\n @property\n def size(self):\n if self.k is None:\n return 0\n else:\n return self.k.shape[-1]\n\n @property\n def num_groups(self):\n return len(self.v)\n\n @property\n def key(self):","source_hash":"45cd3f299592f26ca7728345970261cfa39cdd750f37a2b11ff217e93aee7bb8","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.kv_memory_store.get_v_size","uri":"program://Track-Anything/function/tracker.inference.kv_memory_store.get_v_size#L183-L184","kind":"function","name":"get_v_size","path":"tracker/inference/kv_memory_store.py","language":"python","start_line":183,"end_line":184,"context_start_line":163,"context_end_line":204,"code":" usage = self.use_count / self.life_count\n return usage\n\n def get_all_sliced(self, start: int, end: int):\n # return k, sk, ek, usage in order, sliced by start and end\n\n if end == 0:\n # negative 0 would not work as the end index!\n k = self.k[:,:,start:]\n sk = self.s[:,:,start:] if self.s is not None else None\n ek = self.e[:,:,start:] if self.e is not None else None\n usage = self.get_usage()[:,:,start:]\n else:\n k = self.k[:,:,start:end]\n sk = self.s[:,:,start:end] if self.s is not None else None\n ek = self.e[:,:,start:end] if self.e is not None else None\n usage = self.get_usage()[:,:,start:end]\n\n return k, sk, ek, usage\n\n def get_v_size(self, ni: int):\n return self.v[ni].shape[2]\n\n def engaged(self):\n return self.k is not None\n\n @property\n def size(self):\n if self.k is None:\n return 0\n else:\n return self.k.shape[-1]\n\n @property\n def num_groups(self):\n return len(self.v)\n\n @property\n def key(self):\n return self.k\n\n @property","source_hash":"45cd3f299592f26ca7728345970261cfa39cdd750f37a2b11ff217e93aee7bb8","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.kv_memory_store.engaged","uri":"program://Track-Anything/function/tracker.inference.kv_memory_store.engaged#L186-L187","kind":"function","name":"engaged","path":"tracker/inference/kv_memory_store.py","language":"python","start_line":186,"end_line":187,"context_start_line":166,"context_end_line":207,"code":" def get_all_sliced(self, start: int, end: int):\n # return k, sk, ek, usage in order, sliced by start and end\n\n if end == 0:\n # negative 0 would not work as the end index!\n k = self.k[:,:,start:]\n sk = self.s[:,:,start:] if self.s is not None else None\n ek = self.e[:,:,start:] if self.e is not None else None\n usage = self.get_usage()[:,:,start:]\n else:\n k = self.k[:,:,start:end]\n sk = self.s[:,:,start:end] if self.s is not None else None\n ek = self.e[:,:,start:end] if self.e is not None else None\n usage = self.get_usage()[:,:,start:end]\n\n return k, sk, ek, usage\n\n def get_v_size(self, ni: int):\n return self.v[ni].shape[2]\n\n def engaged(self):\n return self.k is not None\n\n @property\n def size(self):\n if self.k is None:\n return 0\n else:\n return self.k.shape[-1]\n\n @property\n def num_groups(self):\n return len(self.v)\n\n @property\n def key(self):\n return self.k\n\n @property\n def value(self):\n return self.v\n","source_hash":"45cd3f299592f26ca7728345970261cfa39cdd750f37a2b11ff217e93aee7bb8","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.kv_memory_store.size","uri":"program://Track-Anything/function/tracker.inference.kv_memory_store.size#L190-L194","kind":"function","name":"size","path":"tracker/inference/kv_memory_store.py","language":"python","start_line":190,"end_line":194,"context_start_line":170,"context_end_line":214,"code":" # negative 0 would not work as the end index!\n k = self.k[:,:,start:]\n sk = self.s[:,:,start:] if self.s is not None else None\n ek = self.e[:,:,start:] if self.e is not None else None\n usage = self.get_usage()[:,:,start:]\n else:\n k = self.k[:,:,start:end]\n sk = self.s[:,:,start:end] if self.s is not None else None\n ek = self.e[:,:,start:end] if self.e is not None else None\n usage = self.get_usage()[:,:,start:end]\n\n return k, sk, ek, usage\n\n def get_v_size(self, ni: int):\n return self.v[ni].shape[2]\n\n def engaged(self):\n return self.k is not None\n\n @property\n def size(self):\n if self.k is None:\n return 0\n else:\n return self.k.shape[-1]\n\n @property\n def num_groups(self):\n return len(self.v)\n\n @property\n def key(self):\n return self.k\n\n @property\n def value(self):\n return self.v\n\n @property\n def shrinkage(self):\n return self.s\n\n @property\n def selection(self):\n return self.e","source_hash":"45cd3f299592f26ca7728345970261cfa39cdd750f37a2b11ff217e93aee7bb8","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.kv_memory_store.num_groups","uri":"program://Track-Anything/function/tracker.inference.kv_memory_store.num_groups#L197-L198","kind":"function","name":"num_groups","path":"tracker/inference/kv_memory_store.py","language":"python","start_line":197,"end_line":198,"context_start_line":177,"context_end_line":214,"code":" sk = self.s[:,:,start:end] if self.s is not None else None\n ek = self.e[:,:,start:end] if self.e is not None else None\n usage = self.get_usage()[:,:,start:end]\n\n return k, sk, ek, usage\n\n def get_v_size(self, ni: int):\n return self.v[ni].shape[2]\n\n def engaged(self):\n return self.k is not None\n\n @property\n def size(self):\n if self.k is None:\n return 0\n else:\n return self.k.shape[-1]\n\n @property\n def num_groups(self):\n return len(self.v)\n\n @property\n def key(self):\n return self.k\n\n @property\n def value(self):\n return self.v\n\n @property\n def shrinkage(self):\n return self.s\n\n @property\n def selection(self):\n return self.e","source_hash":"45cd3f299592f26ca7728345970261cfa39cdd750f37a2b11ff217e93aee7bb8","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.kv_memory_store.key","uri":"program://Track-Anything/function/tracker.inference.kv_memory_store.key#L201-L202","kind":"function","name":"key","path":"tracker/inference/kv_memory_store.py","language":"python","start_line":201,"end_line":202,"context_start_line":181,"context_end_line":214,"code":" return k, sk, ek, usage\n\n def get_v_size(self, ni: int):\n return self.v[ni].shape[2]\n\n def engaged(self):\n return self.k is not None\n\n @property\n def size(self):\n if self.k is None:\n return 0\n else:\n return self.k.shape[-1]\n\n @property\n def num_groups(self):\n return len(self.v)\n\n @property\n def key(self):\n return self.k\n\n @property\n def value(self):\n return self.v\n\n @property\n def shrinkage(self):\n return self.s\n\n @property\n def selection(self):\n return self.e","source_hash":"45cd3f299592f26ca7728345970261cfa39cdd750f37a2b11ff217e93aee7bb8","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.kv_memory_store.value","uri":"program://Track-Anything/function/tracker.inference.kv_memory_store.value#L205-L206","kind":"function","name":"value","path":"tracker/inference/kv_memory_store.py","language":"python","start_line":205,"end_line":206,"context_start_line":185,"context_end_line":214,"code":"\n def engaged(self):\n return self.k is not None\n\n @property\n def size(self):\n if self.k is None:\n return 0\n else:\n return self.k.shape[-1]\n\n @property\n def num_groups(self):\n return len(self.v)\n\n @property\n def key(self):\n return self.k\n\n @property\n def value(self):\n return self.v\n\n @property\n def shrinkage(self):\n return self.s\n\n @property\n def selection(self):\n return self.e","source_hash":"45cd3f299592f26ca7728345970261cfa39cdd750f37a2b11ff217e93aee7bb8","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.kv_memory_store.shrinkage","uri":"program://Track-Anything/function/tracker.inference.kv_memory_store.shrinkage#L209-L210","kind":"function","name":"shrinkage","path":"tracker/inference/kv_memory_store.py","language":"python","start_line":209,"end_line":210,"context_start_line":189,"context_end_line":214,"code":" @property\n def size(self):\n if self.k is None:\n return 0\n else:\n return self.k.shape[-1]\n\n @property\n def num_groups(self):\n return len(self.v)\n\n @property\n def key(self):\n return self.k\n\n @property\n def value(self):\n return self.v\n\n @property\n def shrinkage(self):\n return self.s\n\n @property\n def selection(self):\n return self.e","source_hash":"45cd3f299592f26ca7728345970261cfa39cdd750f37a2b11ff217e93aee7bb8","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.kv_memory_store.selection","uri":"program://Track-Anything/function/tracker.inference.kv_memory_store.selection#L213-L214","kind":"function","name":"selection","path":"tracker/inference/kv_memory_store.py","language":"python","start_line":213,"end_line":214,"context_start_line":193,"context_end_line":214,"code":" else:\n return self.k.shape[-1]\n\n @property\n def num_groups(self):\n return len(self.v)\n\n @property\n def key(self):\n return self.k\n\n @property\n def value(self):\n return self.v\n\n @property\n def shrinkage(self):\n return self.s\n\n @property\n def selection(self):\n return self.e","source_hash":"45cd3f299592f26ca7728345970261cfa39cdd750f37a2b11ff217e93aee7bb8","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.memory_manager","uri":"program://Track-Anything/module/tracker.inference.memory_manager#L1-L286","kind":"module","name":"tracker.inference.memory_manager","path":"tracker/inference/memory_manager.py","language":"python","start_line":1,"end_line":286,"context_start_line":1,"context_end_line":286,"code":"import torch\nimport warnings\n\nfrom inference.kv_memory_store import KeyValueMemoryStore\nfrom model.memory_util import *\n\n\nclass MemoryManager:\n \"\"\"\n Manages all three memory stores and the transition between working/long-term memory\n \"\"\"\n def __init__(self, config):\n self.hidden_dim = config['hidden_dim']\n self.top_k = config['top_k']\n\n self.enable_long_term = config['enable_long_term']\n self.enable_long_term_usage = config['enable_long_term_count_usage']\n if self.enable_long_term:\n self.max_mt_frames = config['max_mid_term_frames']\n self.min_mt_frames = config['min_mid_term_frames']\n self.num_prototypes = config['num_prototypes']\n self.max_long_elements = config['max_long_term_elements']\n\n # dimensions will be inferred from input later\n self.CK = self.CV = None\n self.H = self.W = None\n\n # The hidden state will be stored in a single tensor for all objects\n # B x num_objects x CH x H x W\n self.hidden = None\n\n self.work_mem = KeyValueMemoryStore(count_usage=self.enable_long_term)\n if self.enable_long_term:\n self.long_mem = KeyValueMemoryStore(count_usage=self.enable_long_term_usage)\n\n self.reset_config = True\n\n def update_config(self, config):\n self.reset_config = True\n self.hidden_dim = config['hidden_dim']\n self.top_k = config['top_k']\n\n assert self.enable_long_term == config['enable_long_term'], 'cannot update this'\n assert self.enable_long_term_usage == config['enable_long_term_count_usage'], 'cannot update this'\n\n self.enable_long_term_usage = config['enable_long_term_count_usage']\n if self.enable_long_term:\n self.max_mt_frames = config['max_mid_term_frames']\n self.min_mt_frames = config['min_mid_term_frames']\n self.num_prototypes = config['num_prototypes']\n self.max_long_elements = config['max_long_term_elements']\n\n def _readout(self, affinity, v):\n # this function is for a single object group\n return v @ affinity\n\n def match_memory(self, query_key, selection):\n # query_key: B x C^k x H x W\n # selection: B x C^k x H x W\n num_groups = self.work_mem.num_groups\n h, w = query_key.shape[-2:]\n\n query_key = query_key.flatten(start_dim=2)\n selection = selection.flatten(start_dim=2) if selection is not None else None\n\n \"\"\"\n Memory readout using keys\n \"\"\"\n\n if self.enable_long_term and self.long_mem.engaged():\n # Use long-term memory\n long_mem_size = self.long_mem.size\n memory_key = torch.cat([self.long_mem.key, self.work_mem.key], -1)\n shrinkage = torch.cat([self.long_mem.shrinkage, self.work_mem.shrinkage], -1) \n\n similarity = get_similarity(memory_key, shrinkage, query_key, selection)\n work_mem_similarity = similarity[:, long_mem_size:]\n long_mem_similarity = similarity[:, :long_mem_size]\n\n # get the usage with the first group\n # the first group always have all the keys valid\n affinity, usage = do_softmax(\n torch.cat([long_mem_similarity[:, -self.long_mem.get_v_size(0):], work_mem_similarity], 1), \n top_k=self.top_k, inplace=True, return_usage=True)\n affinity = [affinity]\n\n # compute affinity group by group as later groups only have a subset of keys\n for gi in range(1, num_groups):\n if gi < self.long_mem.num_groups:\n # merge working and lt similarities before softmax\n affinity_one_group = do_softmax(\n torch.cat([long_mem_similarity[:, -self.long_mem.get_v_size(gi):], \n work_mem_similarity[:, -self.work_mem.get_v_size(gi):]], 1), \n top_k=self.top_k, inplace=True)\n else:\n # no long-term memory for this group\n affinity_one_group = do_softmax(work_mem_similarity[:, -self.work_mem.get_v_size(gi):], \n top_k=self.top_k, inplace=(gi==num_groups-1))\n affinity.append(affinity_one_group)\n\n all_memory_value = []\n for gi, gv in enumerate(self.work_mem.value):\n # merge the working and lt values before readout\n if gi < self.long_mem.num_groups:\n all_memory_value.append(torch.cat([self.long_mem.value[gi], self.work_mem.value[gi]], -1))\n else:\n all_memory_value.append(gv)\n\n \"\"\"\n Record memory usage for working and long-term memory\n \"\"\"\n # ignore the index return for long-term memory\n work_usage = usage[:, long_mem_size:]\n self.work_mem.update_usage(work_usage.flatten())\n\n if self.enable_long_term_usage:\n # ignore the index return for working memory\n long_usage = usage[:, :long_mem_size]\n self.long_mem.update_usage(long_usage.flatten())\n else:\n # No long-term memory\n similarity = get_similarity(self.work_mem.key, self.work_mem.shrinkage, query_key, selection)\n\n if self.enable_long_term:\n affinity, usage = do_softmax(similarity, inplace=(num_groups==1), \n top_k=self.top_k, return_usage=True)\n\n # Record memory usage for working memory\n self.work_mem.update_usage(usage.flatten())\n else:\n affinity = do_softmax(similarity, inplace=(num_groups==1), \n top_k=self.top_k, return_usage=False)\n\n affinity = [affinity]\n\n # compute affinity group by group as later groups only have a subset of keys\n for gi in range(1, num_groups):\n affinity_one_group = do_softmax(similarity[:, -self.work_mem.get_v_size(gi):], \n top_k=self.top_k, inplace=(gi==num_groups-1))\n affinity.append(affinity_one_group)\n \n all_memory_value = self.work_mem.value\n\n # Shared affinity within each group\n all_readout_mem = torch.cat([\n self._readout(affinity[gi], gv)\n for gi, gv in enumerate(all_memory_value)\n ], 0)\n\n return all_readout_mem.view(all_readout_mem.shape[0], self.CV, h, w)\n\n def add_memory(self, key, shrinkage, value, objects, selection=None):\n # key: 1*C*H*W\n # value: 1*num_objects*C*H*W\n # objects contain a list of object indices\n if self.H is None or self.reset_config:\n self.reset_config = False\n self.H, self.W = key.shape[-2:]\n self.HW = self.H*self.W\n if self.enable_long_term:\n # convert from num. frames to num. nodes\n self.min_work_elements = self.min_mt_frames*self.HW\n self.max_work_elements = self.max_mt_frames*self.HW\n\n # key: 1*C*N\n # value: num_objects*C*N\n key = key.flatten(start_dim=2)\n shrinkage = shrinkage.flatten(start_dim=2) \n value = value[0].flatten(start_dim=2)\n\n self.CK = key.shape[1]\n self.CV = value.shape[1]\n\n if selection is not None:\n if not self.enable_long_term:\n warnings.warn('the selection factor is only needed in long-term mode', UserWarning)\n selection = selection.flatten(start_dim=2)\n\n self.work_mem.add(key, value, shrinkage, selection, objects)\n\n # long-term memory cleanup\n if self.enable_long_term:\n # Do memory compressed if needed\n if self.work_mem.size >= self.max_work_elements:\n # print('remove memory')\n # Remove obsolete features if needed\n if self.long_mem.size >= (self.max_long_elements-self.num_prototypes):\n self.long_mem.remove_obsolete_features(self.max_long_elements-self.num_prototypes)\n \n self.compress_features()\n\n def create_hidden_state(self, n, sample_key):\n # n is the TOTAL number of objects\n h, w = sample_key.shape[-2:]\n if self.hidden is None:\n self.hidden = torch.zeros((1, n, self.hidden_dim, h, w), device=sample_key.device)\n elif self.hidden.shape[1] != n:\n self.hidden = torch.cat([\n self.hidden, \n torch.zeros((1, n-self.hidden.shape[1], self.hidden_dim, h, w), device=sample_key.device)\n ], 1)\n\n assert(self.hidden.shape[1] == n)\n\n def set_hidden(self, hidden):\n self.hidden = hidden\n\n def get_hidden(self):\n return self.hidden\n\n def compress_features(self):\n HW = self.HW\n candidate_value = []\n total_work_mem_size = self.work_mem.size\n for gv in self.work_mem.value:\n # Some object groups might be added later in the video\n # So not all keys have values associated with all objects\n # We need to keep track of the key->value validity\n mem_size_in_this_group = gv.shape[-1]\n if mem_size_in_this_group == total_work_mem_size:\n # full LT\n candidate_value.append(gv[:,:,HW:-self.min_work_elements+HW])\n else:\n # mem_size is smaller than total_work_mem_size, but at least HW\n assert HW <= mem_size_in_this_group < total_work_mem_size\n if mem_size_in_this_group > self.min_work_elements+HW:\n # part of this object group still goes into LT\n candidate_value.append(gv[:,:,HW:-self.min_work_elements+HW])\n else:\n # this object group cannot go to the LT at all\n candidate_value.append(None)\n\n # perform memory consolidation\n prototype_key, prototype_value, prototype_shrinkage = self.consolidation(\n *self.work_mem.get_all_sliced(HW, -self.min_work_elements+HW), candidate_value)\n\n # remove consolidated working memory\n self.work_mem.sieve_by_range(HW, -self.min_work_elements+HW, min_size=self.min_work_elements+HW)\n\n # add to long-term memory\n self.long_mem.add(prototype_key, prototype_value, prototype_shrinkage, selection=None, objects=None)\n # print(f'long memory size: {self.long_mem.size}')\n # print(f'work memory size: {self.work_mem.size}')\n\n def consolidation(self, candidate_key, candidate_shrinkage, candidate_selection, usage, candidate_value):\n # keys: 1*C*N\n # values: num_objects*C*N\n N = candidate_key.shape[-1]\n\n # find the indices with max usage\n _, max_usage_indices = torch.topk(usage, k=self.num_prototypes, dim=-1, sorted=True)\n prototype_indices = max_usage_indices.flatten()\n\n # Prototypes are invalid for out-of-bound groups\n validity = [prototype_indices >= (N-gv.shape[2]) if gv is not None else None for gv in candidate_value]\n\n prototype_key = candidate_key[:, :, prototype_indices]\n prototype_selection = candidate_selection[:, :, prototype_indices] if candidate_selection is not None else None\n\n \"\"\"\n Potentiation step\n \"\"\"\n similarity = get_similarity(candidate_key, candidate_shrinkage, prototype_key, prototype_selection)\n\n # convert similarity to affinity\n # need to do it group by group since the softmax normalization would be different\n affinity = [\n do_softmax(similarity[:, -gv.shape[2]:, validity[gi]]) if gv is not None else None\n for gi, gv in enumerate(candidate_value)\n ]\n\n # some values can be have all False validity. Weed them out.\n affinity = [\n aff if aff is None or aff.shape[-1] > 0 else None for aff in affinity\n ]\n\n # readout the values\n prototype_value = [\n self._readout(affinity[gi], gv) if affinity[gi] is not None else None\n for gi, gv in enumerate(candidate_value)\n ]\n\n # readout the shrinkage term\n prototype_shrinkage = self._readout(affinity[0], candidate_shrinkage) if candidate_shrinkage is not None else None\n\n return prototype_key, prototype_value, prototype_shrinkage","source_hash":"5e543db3b63b8b2d99d4709c6794e437cbcef5bc0f52677355435b55d3734b11","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.memory_manager.MemoryManager","uri":"program://Track-Anything/class/tracker.inference.memory_manager.MemoryManager#L8-L286","kind":"class","name":"MemoryManager","path":"tracker/inference/memory_manager.py","language":"python","start_line":8,"end_line":286,"context_start_line":1,"context_end_line":286,"code":"import torch\nimport warnings\n\nfrom inference.kv_memory_store import KeyValueMemoryStore\nfrom model.memory_util import *\n\n\nclass MemoryManager:\n \"\"\"\n Manages all three memory stores and the transition between working/long-term memory\n \"\"\"\n def __init__(self, config):\n self.hidden_dim = config['hidden_dim']\n self.top_k = config['top_k']\n\n self.enable_long_term = config['enable_long_term']\n self.enable_long_term_usage = config['enable_long_term_count_usage']\n if self.enable_long_term:\n self.max_mt_frames = config['max_mid_term_frames']\n self.min_mt_frames = config['min_mid_term_frames']\n self.num_prototypes = config['num_prototypes']\n self.max_long_elements = config['max_long_term_elements']\n\n # dimensions will be inferred from input later\n self.CK = self.CV = None\n self.H = self.W = None\n\n # The hidden state will be stored in a single tensor for all objects\n # B x num_objects x CH x H x W\n self.hidden = None\n\n self.work_mem = KeyValueMemoryStore(count_usage=self.enable_long_term)\n if self.enable_long_term:\n self.long_mem = KeyValueMemoryStore(count_usage=self.enable_long_term_usage)\n\n self.reset_config = True\n\n def update_config(self, config):\n self.reset_config = True\n self.hidden_dim = config['hidden_dim']\n self.top_k = config['top_k']\n\n assert self.enable_long_term == config['enable_long_term'], 'cannot update this'\n assert self.enable_long_term_usage == config['enable_long_term_count_usage'], 'cannot update this'\n\n self.enable_long_term_usage = config['enable_long_term_count_usage']\n if self.enable_long_term:\n self.max_mt_frames = config['max_mid_term_frames']\n self.min_mt_frames = config['min_mid_term_frames']\n self.num_prototypes = config['num_prototypes']\n self.max_long_elements = config['max_long_term_elements']\n\n def _readout(self, affinity, v):\n # this function is for a single object group\n return v @ affinity\n\n def match_memory(self, query_key, selection):\n # query_key: B x C^k x H x W\n # selection: B x C^k x H x W\n num_groups = self.work_mem.num_groups\n h, w = query_key.shape[-2:]\n\n query_key = query_key.flatten(start_dim=2)\n selection = selection.flatten(start_dim=2) if selection is not None else None\n\n \"\"\"\n Memory readout using keys\n \"\"\"\n\n if self.enable_long_term and self.long_mem.engaged():\n # Use long-term memory\n long_mem_size = self.long_mem.size\n memory_key = torch.cat([self.long_mem.key, self.work_mem.key], -1)\n shrinkage = torch.cat([self.long_mem.shrinkage, self.work_mem.shrinkage], -1) \n\n similarity = get_similarity(memory_key, shrinkage, query_key, selection)\n work_mem_similarity = similarity[:, long_mem_size:]\n long_mem_similarity = similarity[:, :long_mem_size]\n\n # get the usage with the first group\n # the first group always have all the keys valid\n affinity, usage = do_softmax(\n torch.cat([long_mem_similarity[:, -self.long_mem.get_v_size(0):], work_mem_similarity], 1), \n top_k=self.top_k, inplace=True, return_usage=True)\n affinity = [affinity]\n\n # compute affinity group by group as later groups only have a subset of keys\n for gi in range(1, num_groups):\n if gi < self.long_mem.num_groups:\n # merge working and lt similarities before softmax\n affinity_one_group = do_softmax(\n torch.cat([long_mem_similarity[:, -self.long_mem.get_v_size(gi):], \n work_mem_similarity[:, -self.work_mem.get_v_size(gi):]], 1), \n top_k=self.top_k, inplace=True)\n else:\n # no long-term memory for this group\n affinity_one_group = do_softmax(work_mem_similarity[:, -self.work_mem.get_v_size(gi):], \n top_k=self.top_k, inplace=(gi==num_groups-1))\n affinity.append(affinity_one_group)\n\n all_memory_value = []\n for gi, gv in enumerate(self.work_mem.value):\n # merge the working and lt values before readout\n if gi < self.long_mem.num_groups:\n all_memory_value.append(torch.cat([self.long_mem.value[gi], self.work_mem.value[gi]], -1))\n else:\n all_memory_value.append(gv)\n\n \"\"\"\n Record memory usage for working and long-term memory\n \"\"\"\n # ignore the index return for long-term memory\n work_usage = usage[:, long_mem_size:]\n self.work_mem.update_usage(work_usage.flatten())\n\n if self.enable_long_term_usage:\n # ignore the index return for working memory\n long_usage = usage[:, :long_mem_size]\n self.long_mem.update_usage(long_usage.flatten())\n else:\n # No long-term memory\n similarity = get_similarity(self.work_mem.key, self.work_mem.shrinkage, query_key, selection)\n\n if self.enable_long_term:\n affinity, usage = do_softmax(similarity, inplace=(num_groups==1), \n top_k=self.top_k, return_usage=True)\n\n # Record memory usage for working memory\n self.work_mem.update_usage(usage.flatten())\n else:\n affinity = do_softmax(similarity, inplace=(num_groups==1), \n top_k=self.top_k, return_usage=False)\n\n affinity = [affinity]\n\n # compute affinity group by group as later groups only have a subset of keys\n for gi in range(1, num_groups):\n affinity_one_group = do_softmax(similarity[:, -self.work_mem.get_v_size(gi):], \n top_k=self.top_k, inplace=(gi==num_groups-1))\n affinity.append(affinity_one_group)\n \n all_memory_value = self.work_mem.value\n\n # Shared affinity within each group\n all_readout_mem = torch.cat([\n self._readout(affinity[gi], gv)\n for gi, gv in enumerate(all_memory_value)\n ], 0)\n\n return all_readout_mem.view(all_readout_mem.shape[0], self.CV, h, w)\n\n def add_memory(self, key, shrinkage, value, objects, selection=None):\n # key: 1*C*H*W\n # value: 1*num_objects*C*H*W\n # objects contain a list of object indices\n if self.H is None or self.reset_config:\n self.reset_config = False\n self.H, self.W = key.shape[-2:]\n self.HW = self.H*self.W\n if self.enable_long_term:\n # convert from num. frames to num. nodes\n self.min_work_elements = self.min_mt_frames*self.HW\n self.max_work_elements = self.max_mt_frames*self.HW\n\n # key: 1*C*N\n # value: num_objects*C*N\n key = key.flatten(start_dim=2)\n shrinkage = shrinkage.flatten(start_dim=2) \n value = value[0].flatten(start_dim=2)\n\n self.CK = key.shape[1]\n self.CV = value.shape[1]\n\n if selection is not None:\n if not self.enable_long_term:\n warnings.warn('the selection factor is only needed in long-term mode', UserWarning)\n selection = selection.flatten(start_dim=2)\n\n self.work_mem.add(key, value, shrinkage, selection, objects)\n\n # long-term memory cleanup\n if self.enable_long_term:\n # Do memory compressed if needed\n if self.work_mem.size >= self.max_work_elements:\n # print('remove memory')\n # Remove obsolete features if needed\n if self.long_mem.size >= (self.max_long_elements-self.num_prototypes):\n self.long_mem.remove_obsolete_features(self.max_long_elements-self.num_prototypes)\n \n self.compress_features()\n\n def create_hidden_state(self, n, sample_key):\n # n is the TOTAL number of objects\n h, w = sample_key.shape[-2:]\n if self.hidden is None:\n self.hidden = torch.zeros((1, n, self.hidden_dim, h, w), device=sample_key.device)\n elif self.hidden.shape[1] != n:\n self.hidden = torch.cat([\n self.hidden, \n torch.zeros((1, n-self.hidden.shape[1], self.hidden_dim, h, w), device=sample_key.device)\n ], 1)\n\n assert(self.hidden.shape[1] == n)\n\n def set_hidden(self, hidden):\n self.hidden = hidden\n\n def get_hidden(self):\n return self.hidden\n\n def compress_features(self):\n HW = self.HW\n candidate_value = []\n total_work_mem_size = self.work_mem.size\n for gv in self.work_mem.value:\n # Some object groups might be added later in the video\n # So not all keys have values associated with all objects\n # We need to keep track of the key->value validity\n mem_size_in_this_group = gv.shape[-1]\n if mem_size_in_this_group == total_work_mem_size:\n # full LT\n candidate_value.append(gv[:,:,HW:-self.min_work_elements+HW])\n else:\n # mem_size is smaller than total_work_mem_size, but at least HW\n assert HW <= mem_size_in_this_group < total_work_mem_size\n if mem_size_in_this_group > self.min_work_elements+HW:\n # part of this object group still goes into LT\n candidate_value.append(gv[:,:,HW:-self.min_work_elements+HW])\n else:\n # this object group cannot go to the LT at all\n candidate_value.append(None)\n\n # perform memory consolidation\n prototype_key, prototype_value, prototype_shrinkage = self.consolidation(\n *self.work_mem.get_all_sliced(HW, -self.min_work_elements+HW), candidate_value)\n\n # remove consolidated working memory\n self.work_mem.sieve_by_range(HW, -self.min_work_elements+HW, min_size=self.min_work_elements+HW)\n\n # add to long-term memory\n self.long_mem.add(prototype_key, prototype_value, prototype_shrinkage, selection=None, objects=None)\n # print(f'long memory size: {self.long_mem.size}')\n # print(f'work memory size: {self.work_mem.size}')\n\n def consolidation(self, candidate_key, candidate_shrinkage, candidate_selection, usage, candidate_value):\n # keys: 1*C*N\n # values: num_objects*C*N\n N = candidate_key.shape[-1]\n\n # find the indices with max usage\n _, max_usage_indices = torch.topk(usage, k=self.num_prototypes, dim=-1, sorted=True)\n prototype_indices = max_usage_indices.flatten()\n\n # Prototypes are invalid for out-of-bound groups\n validity = [prototype_indices >= (N-gv.shape[2]) if gv is not None else None for gv in candidate_value]\n\n prototype_key = candidate_key[:, :, prototype_indices]\n prototype_selection = candidate_selection[:, :, prototype_indices] if candidate_selection is not None else None\n\n \"\"\"\n Potentiation step\n \"\"\"\n similarity = get_similarity(candidate_key, candidate_shrinkage, prototype_key, prototype_selection)\n\n # convert similarity to affinity\n # need to do it group by group since the softmax normalization would be different\n affinity = [\n do_softmax(similarity[:, -gv.shape[2]:, validity[gi]]) if gv is not None else None\n for gi, gv in enumerate(candidate_value)\n ]\n\n # some values can be have all False validity. Weed them out.\n affinity = [\n aff if aff is None or aff.shape[-1] > 0 else None for aff in affinity\n ]\n\n # readout the values\n prototype_value = [\n self._readout(affinity[gi], gv) if affinity[gi] is not None else None\n for gi, gv in enumerate(candidate_value)\n ]\n\n # readout the shrinkage term\n prototype_shrinkage = self._readout(affinity[0], candidate_shrinkage) if candidate_shrinkage is not None else None\n\n return prototype_key, prototype_value, prototype_shrinkage","source_hash":"5e543db3b63b8b2d99d4709c6794e437cbcef5bc0f52677355435b55d3734b11","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.memory_manager.__init__","uri":"program://Track-Anything/function/tracker.inference.memory_manager.__init__#L12-L36","kind":"function","name":"__init__","path":"tracker/inference/memory_manager.py","language":"python","start_line":12,"end_line":36,"context_start_line":1,"context_end_line":56,"code":"import torch\nimport warnings\n\nfrom inference.kv_memory_store import KeyValueMemoryStore\nfrom model.memory_util import *\n\n\nclass MemoryManager:\n \"\"\"\n Manages all three memory stores and the transition between working/long-term memory\n \"\"\"\n def __init__(self, config):\n self.hidden_dim = config['hidden_dim']\n self.top_k = config['top_k']\n\n self.enable_long_term = config['enable_long_term']\n self.enable_long_term_usage = config['enable_long_term_count_usage']\n if self.enable_long_term:\n self.max_mt_frames = config['max_mid_term_frames']\n self.min_mt_frames = config['min_mid_term_frames']\n self.num_prototypes = config['num_prototypes']\n self.max_long_elements = config['max_long_term_elements']\n\n # dimensions will be inferred from input later\n self.CK = self.CV = None\n self.H = self.W = None\n\n # The hidden state will be stored in a single tensor for all objects\n # B x num_objects x CH x H x W\n self.hidden = None\n\n self.work_mem = KeyValueMemoryStore(count_usage=self.enable_long_term)\n if self.enable_long_term:\n self.long_mem = KeyValueMemoryStore(count_usage=self.enable_long_term_usage)\n\n self.reset_config = True\n\n def update_config(self, config):\n self.reset_config = True\n self.hidden_dim = config['hidden_dim']\n self.top_k = config['top_k']\n\n assert self.enable_long_term == config['enable_long_term'], 'cannot update this'\n assert self.enable_long_term_usage == config['enable_long_term_count_usage'], 'cannot update this'\n\n self.enable_long_term_usage = config['enable_long_term_count_usage']\n if self.enable_long_term:\n self.max_mt_frames = config['max_mid_term_frames']\n self.min_mt_frames = config['min_mid_term_frames']\n self.num_prototypes = config['num_prototypes']\n self.max_long_elements = config['max_long_term_elements']\n\n def _readout(self, affinity, v):\n # this function is for a single object group\n return v @ affinity\n","source_hash":"5e543db3b63b8b2d99d4709c6794e437cbcef5bc0f52677355435b55d3734b11","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.memory_manager.update_config","uri":"program://Track-Anything/function/tracker.inference.memory_manager.update_config#L38-L51","kind":"function","name":"update_config","path":"tracker/inference/memory_manager.py","language":"python","start_line":38,"end_line":51,"context_start_line":18,"context_end_line":71,"code":" if self.enable_long_term:\n self.max_mt_frames = config['max_mid_term_frames']\n self.min_mt_frames = config['min_mid_term_frames']\n self.num_prototypes = config['num_prototypes']\n self.max_long_elements = config['max_long_term_elements']\n\n # dimensions will be inferred from input later\n self.CK = self.CV = None\n self.H = self.W = None\n\n # The hidden state will be stored in a single tensor for all objects\n # B x num_objects x CH x H x W\n self.hidden = None\n\n self.work_mem = KeyValueMemoryStore(count_usage=self.enable_long_term)\n if self.enable_long_term:\n self.long_mem = KeyValueMemoryStore(count_usage=self.enable_long_term_usage)\n\n self.reset_config = True\n\n def update_config(self, config):\n self.reset_config = True\n self.hidden_dim = config['hidden_dim']\n self.top_k = config['top_k']\n\n assert self.enable_long_term == config['enable_long_term'], 'cannot update this'\n assert self.enable_long_term_usage == config['enable_long_term_count_usage'], 'cannot update this'\n\n self.enable_long_term_usage = config['enable_long_term_count_usage']\n if self.enable_long_term:\n self.max_mt_frames = config['max_mid_term_frames']\n self.min_mt_frames = config['min_mid_term_frames']\n self.num_prototypes = config['num_prototypes']\n self.max_long_elements = config['max_long_term_elements']\n\n def _readout(self, affinity, v):\n # this function is for a single object group\n return v @ affinity\n\n def match_memory(self, query_key, selection):\n # query_key: B x C^k x H x W\n # selection: B x C^k x H x W\n num_groups = self.work_mem.num_groups\n h, w = query_key.shape[-2:]\n\n query_key = query_key.flatten(start_dim=2)\n selection = selection.flatten(start_dim=2) if selection is not None else None\n\n \"\"\"\n Memory readout using keys\n \"\"\"\n\n if self.enable_long_term and self.long_mem.engaged():\n # Use long-term memory","source_hash":"5e543db3b63b8b2d99d4709c6794e437cbcef5bc0f52677355435b55d3734b11","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.memory_manager._readout","uri":"program://Track-Anything/function/tracker.inference.memory_manager._readout#L53-L55","kind":"function","name":"_readout","path":"tracker/inference/memory_manager.py","language":"python","start_line":53,"end_line":55,"context_start_line":33,"context_end_line":75,"code":" if self.enable_long_term:\n self.long_mem = KeyValueMemoryStore(count_usage=self.enable_long_term_usage)\n\n self.reset_config = True\n\n def update_config(self, config):\n self.reset_config = True\n self.hidden_dim = config['hidden_dim']\n self.top_k = config['top_k']\n\n assert self.enable_long_term == config['enable_long_term'], 'cannot update this'\n assert self.enable_long_term_usage == config['enable_long_term_count_usage'], 'cannot update this'\n\n self.enable_long_term_usage = config['enable_long_term_count_usage']\n if self.enable_long_term:\n self.max_mt_frames = config['max_mid_term_frames']\n self.min_mt_frames = config['min_mid_term_frames']\n self.num_prototypes = config['num_prototypes']\n self.max_long_elements = config['max_long_term_elements']\n\n def _readout(self, affinity, v):\n # this function is for a single object group\n return v @ affinity\n\n def match_memory(self, query_key, selection):\n # query_key: B x C^k x H x W\n # selection: B x C^k x H x W\n num_groups = self.work_mem.num_groups\n h, w = query_key.shape[-2:]\n\n query_key = query_key.flatten(start_dim=2)\n selection = selection.flatten(start_dim=2) if selection is not None else None\n\n \"\"\"\n Memory readout using keys\n \"\"\"\n\n if self.enable_long_term and self.long_mem.engaged():\n # Use long-term memory\n long_mem_size = self.long_mem.size\n memory_key = torch.cat([self.long_mem.key, self.work_mem.key], -1)\n shrinkage = torch.cat([self.long_mem.shrinkage, self.work_mem.shrinkage], -1) \n","source_hash":"5e543db3b63b8b2d99d4709c6794e437cbcef5bc0f52677355435b55d3734b11","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.memory_manager.match_memory","uri":"program://Track-Anything/function/tracker.inference.memory_manager.match_memory#L57-L150","kind":"function","name":"match_memory","path":"tracker/inference/memory_manager.py","language":"python","start_line":57,"end_line":150,"context_start_line":37,"context_end_line":170,"code":"\n def update_config(self, config):\n self.reset_config = True\n self.hidden_dim = config['hidden_dim']\n self.top_k = config['top_k']\n\n assert self.enable_long_term == config['enable_long_term'], 'cannot update this'\n assert self.enable_long_term_usage == config['enable_long_term_count_usage'], 'cannot update this'\n\n self.enable_long_term_usage = config['enable_long_term_count_usage']\n if self.enable_long_term:\n self.max_mt_frames = config['max_mid_term_frames']\n self.min_mt_frames = config['min_mid_term_frames']\n self.num_prototypes = config['num_prototypes']\n self.max_long_elements = config['max_long_term_elements']\n\n def _readout(self, affinity, v):\n # this function is for a single object group\n return v @ affinity\n\n def match_memory(self, query_key, selection):\n # query_key: B x C^k x H x W\n # selection: B x C^k x H x W\n num_groups = self.work_mem.num_groups\n h, w = query_key.shape[-2:]\n\n query_key = query_key.flatten(start_dim=2)\n selection = selection.flatten(start_dim=2) if selection is not None else None\n\n \"\"\"\n Memory readout using keys\n \"\"\"\n\n if self.enable_long_term and self.long_mem.engaged():\n # Use long-term memory\n long_mem_size = self.long_mem.size\n memory_key = torch.cat([self.long_mem.key, self.work_mem.key], -1)\n shrinkage = torch.cat([self.long_mem.shrinkage, self.work_mem.shrinkage], -1) \n\n similarity = get_similarity(memory_key, shrinkage, query_key, selection)\n work_mem_similarity = similarity[:, long_mem_size:]\n long_mem_similarity = similarity[:, :long_mem_size]\n\n # get the usage with the first group\n # the first group always have all the keys valid\n affinity, usage = do_softmax(\n torch.cat([long_mem_similarity[:, -self.long_mem.get_v_size(0):], work_mem_similarity], 1), \n top_k=self.top_k, inplace=True, return_usage=True)\n affinity = [affinity]\n\n # compute affinity group by group as later groups only have a subset of keys\n for gi in range(1, num_groups):\n if gi < self.long_mem.num_groups:\n # merge working and lt similarities before softmax\n affinity_one_group = do_softmax(\n torch.cat([long_mem_similarity[:, -self.long_mem.get_v_size(gi):], \n work_mem_similarity[:, -self.work_mem.get_v_size(gi):]], 1), \n top_k=self.top_k, inplace=True)\n else:\n # no long-term memory for this group\n affinity_one_group = do_softmax(work_mem_similarity[:, -self.work_mem.get_v_size(gi):], \n top_k=self.top_k, inplace=(gi==num_groups-1))\n affinity.append(affinity_one_group)\n\n all_memory_value = []\n for gi, gv in enumerate(self.work_mem.value):\n # merge the working and lt values before readout\n if gi < self.long_mem.num_groups:\n all_memory_value.append(torch.cat([self.long_mem.value[gi], self.work_mem.value[gi]], -1))\n else:\n all_memory_value.append(gv)\n\n \"\"\"\n Record memory usage for working and long-term memory\n \"\"\"\n # ignore the index return for long-term memory\n work_usage = usage[:, long_mem_size:]\n self.work_mem.update_usage(work_usage.flatten())\n\n if self.enable_long_term_usage:\n # ignore the index return for working memory\n long_usage = usage[:, :long_mem_size]\n self.long_mem.update_usage(long_usage.flatten())\n else:\n # No long-term memory\n similarity = get_similarity(self.work_mem.key, self.work_mem.shrinkage, query_key, selection)\n\n if self.enable_long_term:\n affinity, usage = do_softmax(similarity, inplace=(num_groups==1), \n top_k=self.top_k, return_usage=True)\n\n # Record memory usage for working memory\n self.work_mem.update_usage(usage.flatten())\n else:\n affinity = do_softmax(similarity, inplace=(num_groups==1), \n top_k=self.top_k, return_usage=False)\n\n affinity = [affinity]\n\n # compute affinity group by group as later groups only have a subset of keys\n for gi in range(1, num_groups):\n affinity_one_group = do_softmax(similarity[:, -self.work_mem.get_v_size(gi):], \n top_k=self.top_k, inplace=(gi==num_groups-1))\n affinity.append(affinity_one_group)\n \n all_memory_value = self.work_mem.value\n\n # Shared affinity within each group\n all_readout_mem = torch.cat([\n self._readout(affinity[gi], gv)\n for gi, gv in enumerate(all_memory_value)\n ], 0)\n\n return all_readout_mem.view(all_readout_mem.shape[0], self.CV, h, w)\n\n def add_memory(self, key, shrinkage, value, objects, selection=None):\n # key: 1*C*H*W\n # value: 1*num_objects*C*H*W\n # objects contain a list of object indices\n if self.H is None or self.reset_config:\n self.reset_config = False\n self.H, self.W = key.shape[-2:]\n self.HW = self.H*self.W\n if self.enable_long_term:\n # convert from num. frames to num. nodes\n self.min_work_elements = self.min_mt_frames*self.HW\n self.max_work_elements = self.max_mt_frames*self.HW\n\n # key: 1*C*N\n # value: num_objects*C*N\n key = key.flatten(start_dim=2)\n shrinkage = shrinkage.flatten(start_dim=2) \n value = value[0].flatten(start_dim=2)\n","source_hash":"5e543db3b63b8b2d99d4709c6794e437cbcef5bc0f52677355435b55d3734b11","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.memory_manager.add_memory","uri":"program://Track-Anything/function/tracker.inference.memory_manager.add_memory#L152-L190","kind":"function","name":"add_memory","path":"tracker/inference/memory_manager.py","language":"python","start_line":152,"end_line":190,"context_start_line":132,"context_end_line":210,"code":" top_k=self.top_k, return_usage=False)\n\n affinity = [affinity]\n\n # compute affinity group by group as later groups only have a subset of keys\n for gi in range(1, num_groups):\n affinity_one_group = do_softmax(similarity[:, -self.work_mem.get_v_size(gi):], \n top_k=self.top_k, inplace=(gi==num_groups-1))\n affinity.append(affinity_one_group)\n \n all_memory_value = self.work_mem.value\n\n # Shared affinity within each group\n all_readout_mem = torch.cat([\n self._readout(affinity[gi], gv)\n for gi, gv in enumerate(all_memory_value)\n ], 0)\n\n return all_readout_mem.view(all_readout_mem.shape[0], self.CV, h, w)\n\n def add_memory(self, key, shrinkage, value, objects, selection=None):\n # key: 1*C*H*W\n # value: 1*num_objects*C*H*W\n # objects contain a list of object indices\n if self.H is None or self.reset_config:\n self.reset_config = False\n self.H, self.W = key.shape[-2:]\n self.HW = self.H*self.W\n if self.enable_long_term:\n # convert from num. frames to num. nodes\n self.min_work_elements = self.min_mt_frames*self.HW\n self.max_work_elements = self.max_mt_frames*self.HW\n\n # key: 1*C*N\n # value: num_objects*C*N\n key = key.flatten(start_dim=2)\n shrinkage = shrinkage.flatten(start_dim=2) \n value = value[0].flatten(start_dim=2)\n\n self.CK = key.shape[1]\n self.CV = value.shape[1]\n\n if selection is not None:\n if not self.enable_long_term:\n warnings.warn('the selection factor is only needed in long-term mode', UserWarning)\n selection = selection.flatten(start_dim=2)\n\n self.work_mem.add(key, value, shrinkage, selection, objects)\n\n # long-term memory cleanup\n if self.enable_long_term:\n # Do memory compressed if needed\n if self.work_mem.size >= self.max_work_elements:\n # print('remove memory')\n # Remove obsolete features if needed\n if self.long_mem.size >= (self.max_long_elements-self.num_prototypes):\n self.long_mem.remove_obsolete_features(self.max_long_elements-self.num_prototypes)\n \n self.compress_features()\n\n def create_hidden_state(self, n, sample_key):\n # n is the TOTAL number of objects\n h, w = sample_key.shape[-2:]\n if self.hidden is None:\n self.hidden = torch.zeros((1, n, self.hidden_dim, h, w), device=sample_key.device)\n elif self.hidden.shape[1] != n:\n self.hidden = torch.cat([\n self.hidden, \n torch.zeros((1, n-self.hidden.shape[1], self.hidden_dim, h, w), device=sample_key.device)\n ], 1)\n\n assert(self.hidden.shape[1] == n)\n\n def set_hidden(self, hidden):\n self.hidden = hidden\n\n def get_hidden(self):\n return self.hidden\n","source_hash":"5e543db3b63b8b2d99d4709c6794e437cbcef5bc0f52677355435b55d3734b11","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.memory_manager.create_hidden_state","uri":"program://Track-Anything/function/tracker.inference.memory_manager.create_hidden_state#L192-L203","kind":"function","name":"create_hidden_state","path":"tracker/inference/memory_manager.py","language":"python","start_line":192,"end_line":203,"context_start_line":172,"context_end_line":223,"code":" self.CV = value.shape[1]\n\n if selection is not None:\n if not self.enable_long_term:\n warnings.warn('the selection factor is only needed in long-term mode', UserWarning)\n selection = selection.flatten(start_dim=2)\n\n self.work_mem.add(key, value, shrinkage, selection, objects)\n\n # long-term memory cleanup\n if self.enable_long_term:\n # Do memory compressed if needed\n if self.work_mem.size >= self.max_work_elements:\n # print('remove memory')\n # Remove obsolete features if needed\n if self.long_mem.size >= (self.max_long_elements-self.num_prototypes):\n self.long_mem.remove_obsolete_features(self.max_long_elements-self.num_prototypes)\n \n self.compress_features()\n\n def create_hidden_state(self, n, sample_key):\n # n is the TOTAL number of objects\n h, w = sample_key.shape[-2:]\n if self.hidden is None:\n self.hidden = torch.zeros((1, n, self.hidden_dim, h, w), device=sample_key.device)\n elif self.hidden.shape[1] != n:\n self.hidden = torch.cat([\n self.hidden, \n torch.zeros((1, n-self.hidden.shape[1], self.hidden_dim, h, w), device=sample_key.device)\n ], 1)\n\n assert(self.hidden.shape[1] == n)\n\n def set_hidden(self, hidden):\n self.hidden = hidden\n\n def get_hidden(self):\n return self.hidden\n\n def compress_features(self):\n HW = self.HW\n candidate_value = []\n total_work_mem_size = self.work_mem.size\n for gv in self.work_mem.value:\n # Some object groups might be added later in the video\n # So not all keys have values associated with all objects\n # We need to keep track of the key->value validity\n mem_size_in_this_group = gv.shape[-1]\n if mem_size_in_this_group == total_work_mem_size:\n # full LT\n candidate_value.append(gv[:,:,HW:-self.min_work_elements+HW])\n else:","source_hash":"5e543db3b63b8b2d99d4709c6794e437cbcef5bc0f52677355435b55d3734b11","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.memory_manager.set_hidden","uri":"program://Track-Anything/function/tracker.inference.memory_manager.set_hidden#L205-L206","kind":"function","name":"set_hidden","path":"tracker/inference/memory_manager.py","language":"python","start_line":205,"end_line":206,"context_start_line":185,"context_end_line":226,"code":" # print('remove memory')\n # Remove obsolete features if needed\n if self.long_mem.size >= (self.max_long_elements-self.num_prototypes):\n self.long_mem.remove_obsolete_features(self.max_long_elements-self.num_prototypes)\n \n self.compress_features()\n\n def create_hidden_state(self, n, sample_key):\n # n is the TOTAL number of objects\n h, w = sample_key.shape[-2:]\n if self.hidden is None:\n self.hidden = torch.zeros((1, n, self.hidden_dim, h, w), device=sample_key.device)\n elif self.hidden.shape[1] != n:\n self.hidden = torch.cat([\n self.hidden, \n torch.zeros((1, n-self.hidden.shape[1], self.hidden_dim, h, w), device=sample_key.device)\n ], 1)\n\n assert(self.hidden.shape[1] == n)\n\n def set_hidden(self, hidden):\n self.hidden = hidden\n\n def get_hidden(self):\n return self.hidden\n\n def compress_features(self):\n HW = self.HW\n candidate_value = []\n total_work_mem_size = self.work_mem.size\n for gv in self.work_mem.value:\n # Some object groups might be added later in the video\n # So not all keys have values associated with all objects\n # We need to keep track of the key->value validity\n mem_size_in_this_group = gv.shape[-1]\n if mem_size_in_this_group == total_work_mem_size:\n # full LT\n candidate_value.append(gv[:,:,HW:-self.min_work_elements+HW])\n else:\n # mem_size is smaller than total_work_mem_size, but at least HW\n assert HW <= mem_size_in_this_group < total_work_mem_size\n if mem_size_in_this_group > self.min_work_elements+HW:","source_hash":"5e543db3b63b8b2d99d4709c6794e437cbcef5bc0f52677355435b55d3734b11","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.memory_manager.get_hidden","uri":"program://Track-Anything/function/tracker.inference.memory_manager.get_hidden#L208-L209","kind":"function","name":"get_hidden","path":"tracker/inference/memory_manager.py","language":"python","start_line":208,"end_line":209,"context_start_line":188,"context_end_line":229,"code":" self.long_mem.remove_obsolete_features(self.max_long_elements-self.num_prototypes)\n \n self.compress_features()\n\n def create_hidden_state(self, n, sample_key):\n # n is the TOTAL number of objects\n h, w = sample_key.shape[-2:]\n if self.hidden is None:\n self.hidden = torch.zeros((1, n, self.hidden_dim, h, w), device=sample_key.device)\n elif self.hidden.shape[1] != n:\n self.hidden = torch.cat([\n self.hidden, \n torch.zeros((1, n-self.hidden.shape[1], self.hidden_dim, h, w), device=sample_key.device)\n ], 1)\n\n assert(self.hidden.shape[1] == n)\n\n def set_hidden(self, hidden):\n self.hidden = hidden\n\n def get_hidden(self):\n return self.hidden\n\n def compress_features(self):\n HW = self.HW\n candidate_value = []\n total_work_mem_size = self.work_mem.size\n for gv in self.work_mem.value:\n # Some object groups might be added later in the video\n # So not all keys have values associated with all objects\n # We need to keep track of the key->value validity\n mem_size_in_this_group = gv.shape[-1]\n if mem_size_in_this_group == total_work_mem_size:\n # full LT\n candidate_value.append(gv[:,:,HW:-self.min_work_elements+HW])\n else:\n # mem_size is smaller than total_work_mem_size, but at least HW\n assert HW <= mem_size_in_this_group < total_work_mem_size\n if mem_size_in_this_group > self.min_work_elements+HW:\n # part of this object group still goes into LT\n candidate_value.append(gv[:,:,HW:-self.min_work_elements+HW])\n else:","source_hash":"5e543db3b63b8b2d99d4709c6794e437cbcef5bc0f52677355435b55d3734b11","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.memory_manager.compress_features","uri":"program://Track-Anything/function/tracker.inference.memory_manager.compress_features#L211-L241","kind":"function","name":"compress_features","path":"tracker/inference/memory_manager.py","language":"python","start_line":211,"end_line":241,"context_start_line":191,"context_end_line":261,"code":"\n def create_hidden_state(self, n, sample_key):\n # n is the TOTAL number of objects\n h, w = sample_key.shape[-2:]\n if self.hidden is None:\n self.hidden = torch.zeros((1, n, self.hidden_dim, h, w), device=sample_key.device)\n elif self.hidden.shape[1] != n:\n self.hidden = torch.cat([\n self.hidden, \n torch.zeros((1, n-self.hidden.shape[1], self.hidden_dim, h, w), device=sample_key.device)\n ], 1)\n\n assert(self.hidden.shape[1] == n)\n\n def set_hidden(self, hidden):\n self.hidden = hidden\n\n def get_hidden(self):\n return self.hidden\n\n def compress_features(self):\n HW = self.HW\n candidate_value = []\n total_work_mem_size = self.work_mem.size\n for gv in self.work_mem.value:\n # Some object groups might be added later in the video\n # So not all keys have values associated with all objects\n # We need to keep track of the key->value validity\n mem_size_in_this_group = gv.shape[-1]\n if mem_size_in_this_group == total_work_mem_size:\n # full LT\n candidate_value.append(gv[:,:,HW:-self.min_work_elements+HW])\n else:\n # mem_size is smaller than total_work_mem_size, but at least HW\n assert HW <= mem_size_in_this_group < total_work_mem_size\n if mem_size_in_this_group > self.min_work_elements+HW:\n # part of this object group still goes into LT\n candidate_value.append(gv[:,:,HW:-self.min_work_elements+HW])\n else:\n # this object group cannot go to the LT at all\n candidate_value.append(None)\n\n # perform memory consolidation\n prototype_key, prototype_value, prototype_shrinkage = self.consolidation(\n *self.work_mem.get_all_sliced(HW, -self.min_work_elements+HW), candidate_value)\n\n # remove consolidated working memory\n self.work_mem.sieve_by_range(HW, -self.min_work_elements+HW, min_size=self.min_work_elements+HW)\n\n # add to long-term memory\n self.long_mem.add(prototype_key, prototype_value, prototype_shrinkage, selection=None, objects=None)\n # print(f'long memory size: {self.long_mem.size}')\n # print(f'work memory size: {self.work_mem.size}')\n\n def consolidation(self, candidate_key, candidate_shrinkage, candidate_selection, usage, candidate_value):\n # keys: 1*C*N\n # values: num_objects*C*N\n N = candidate_key.shape[-1]\n\n # find the indices with max usage\n _, max_usage_indices = torch.topk(usage, k=self.num_prototypes, dim=-1, sorted=True)\n prototype_indices = max_usage_indices.flatten()\n\n # Prototypes are invalid for out-of-bound groups\n validity = [prototype_indices >= (N-gv.shape[2]) if gv is not None else None for gv in candidate_value]\n\n prototype_key = candidate_key[:, :, prototype_indices]\n prototype_selection = candidate_selection[:, :, prototype_indices] if candidate_selection is not None else None\n\n \"\"\"\n Potentiation step","source_hash":"5e543db3b63b8b2d99d4709c6794e437cbcef5bc0f52677355435b55d3734b11","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.inference.memory_manager.consolidation","uri":"program://Track-Anything/function/tracker.inference.memory_manager.consolidation#L245-L286","kind":"function","name":"consolidation","path":"tracker/inference/memory_manager.py","language":"python","start_line":245,"end_line":286,"context_start_line":225,"context_end_line":286,"code":" assert HW <= mem_size_in_this_group < total_work_mem_size\n if mem_size_in_this_group > self.min_work_elements+HW:\n # part of this object group still goes into LT\n candidate_value.append(gv[:,:,HW:-self.min_work_elements+HW])\n else:\n # this object group cannot go to the LT at all\n candidate_value.append(None)\n\n # perform memory consolidation\n prototype_key, prototype_value, prototype_shrinkage = self.consolidation(\n *self.work_mem.get_all_sliced(HW, -self.min_work_elements+HW), candidate_value)\n\n # remove consolidated working memory\n self.work_mem.sieve_by_range(HW, -self.min_work_elements+HW, min_size=self.min_work_elements+HW)\n\n # add to long-term memory\n self.long_mem.add(prototype_key, prototype_value, prototype_shrinkage, selection=None, objects=None)\n # print(f'long memory size: {self.long_mem.size}')\n # print(f'work memory size: {self.work_mem.size}')\n\n def consolidation(self, candidate_key, candidate_shrinkage, candidate_selection, usage, candidate_value):\n # keys: 1*C*N\n # values: num_objects*C*N\n N = candidate_key.shape[-1]\n\n # find the indices with max usage\n _, max_usage_indices = torch.topk(usage, k=self.num_prototypes, dim=-1, sorted=True)\n prototype_indices = max_usage_indices.flatten()\n\n # Prototypes are invalid for out-of-bound groups\n validity = [prototype_indices >= (N-gv.shape[2]) if gv is not None else None for gv in candidate_value]\n\n prototype_key = candidate_key[:, :, prototype_indices]\n prototype_selection = candidate_selection[:, :, prototype_indices] if candidate_selection is not None else None\n\n \"\"\"\n Potentiation step\n \"\"\"\n similarity = get_similarity(candidate_key, candidate_shrinkage, prototype_key, prototype_selection)\n\n # convert similarity to affinity\n # need to do it group by group since the softmax normalization would be different\n affinity = [\n do_softmax(similarity[:, -gv.shape[2]:, validity[gi]]) if gv is not None else None\n for gi, gv in enumerate(candidate_value)\n ]\n\n # some values can be have all False validity. Weed them out.\n affinity = [\n aff if aff is None or aff.shape[-1] > 0 else None for aff in affinity\n ]\n\n # readout the values\n prototype_value = [\n self._readout(affinity[gi], gv) if affinity[gi] is not None else None\n for gi, gv in enumerate(candidate_value)\n ]\n\n # readout the shrinkage term\n prototype_shrinkage = self._readout(affinity[0], candidate_shrinkage) if candidate_shrinkage is not None else None\n\n return prototype_key, prototype_value, prototype_shrinkage","source_hash":"5e543db3b63b8b2d99d4709c6794e437cbcef5bc0f52677355435b55d3734b11","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.losses","uri":"program://Track-Anything/module/tracker.model.losses#L1-L68","kind":"module","name":"tracker.model.losses","path":"tracker/model/losses.py","language":"python","start_line":1,"end_line":68,"context_start_line":1,"context_end_line":68,"code":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom collections import defaultdict\n\n\ndef dice_loss(input_mask, cls_gt):\n num_objects = input_mask.shape[1]\n losses = []\n for i in range(num_objects):\n mask = input_mask[:,i].flatten(start_dim=1)\n # background not in mask, so we add one to cls_gt\n gt = (cls_gt==(i+1)).float().flatten(start_dim=1)\n numerator = 2 * (mask * gt).sum(-1)\n denominator = mask.sum(-1) + gt.sum(-1)\n loss = 1 - (numerator + 1) / (denominator + 1)\n losses.append(loss)\n return torch.cat(losses).mean()\n\n\n# https://stackoverflow.com/questions/63735255/how-do-i-compute-bootstrapped-cross-entropy-loss-in-pytorch\nclass BootstrappedCE(nn.Module):\n def __init__(self, start_warm, end_warm, top_p=0.15):\n super().__init__()\n\n self.start_warm = start_warm\n self.end_warm = end_warm\n self.top_p = top_p\n\n def forward(self, input, target, it):\n if it < self.start_warm:\n return F.cross_entropy(input, target), 1.0\n\n raw_loss = F.cross_entropy(input, target, reduction='none').view(-1)\n num_pixels = raw_loss.numel()\n\n if it > self.end_warm:\n this_p = self.top_p\n else:\n this_p = self.top_p + (1-self.top_p)*((self.end_warm-it)/(self.end_warm-self.start_warm))\n loss, _ = torch.topk(raw_loss, int(num_pixels * this_p), sorted=False)\n return loss.mean(), this_p\n\n\nclass LossComputer:\n def __init__(self, config):\n super().__init__()\n self.config = config\n self.bce = BootstrappedCE(config['start_warm'], config['end_warm'])\n\n def compute(self, data, num_objects, it):\n losses = defaultdict(int)\n\n b, t = data['rgb'].shape[:2]\n\n losses['total_loss'] = 0\n for ti in range(1, t):\n for bi in range(b):\n loss, p = self.bce(data[f'logits_{ti}'][bi:bi+1, :num_objects[bi]+1], data['cls_gt'][bi:bi+1,ti,0], it)\n losses['p'] += p / b / (t-1)\n losses[f'ce_loss_{ti}'] += loss / b\n\n losses['total_loss'] += losses['ce_loss_%d'%ti]\n losses[f'dice_loss_{ti}'] = dice_loss(data[f'masks_{ti}'], data['cls_gt'][:,ti,0])\n losses['total_loss'] += losses[f'dice_loss_{ti}']\n\n return losses","source_hash":"a466de37bc958c693a51284783e3b32adead7566fd8f355250a2480d0e0669ff","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.losses.dice_loss","uri":"program://Track-Anything/function/tracker.model.losses.dice_loss#L8-L19","kind":"function","name":"dice_loss","path":"tracker/model/losses.py","language":"python","start_line":8,"end_line":19,"context_start_line":1,"context_end_line":39,"code":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom collections import defaultdict\n\n\ndef dice_loss(input_mask, cls_gt):\n num_objects = input_mask.shape[1]\n losses = []\n for i in range(num_objects):\n mask = input_mask[:,i].flatten(start_dim=1)\n # background not in mask, so we add one to cls_gt\n gt = (cls_gt==(i+1)).float().flatten(start_dim=1)\n numerator = 2 * (mask * gt).sum(-1)\n denominator = mask.sum(-1) + gt.sum(-1)\n loss = 1 - (numerator + 1) / (denominator + 1)\n losses.append(loss)\n return torch.cat(losses).mean()\n\n\n# https://stackoverflow.com/questions/63735255/how-do-i-compute-bootstrapped-cross-entropy-loss-in-pytorch\nclass BootstrappedCE(nn.Module):\n def __init__(self, start_warm, end_warm, top_p=0.15):\n super().__init__()\n\n self.start_warm = start_warm\n self.end_warm = end_warm\n self.top_p = top_p\n\n def forward(self, input, target, it):\n if it < self.start_warm:\n return F.cross_entropy(input, target), 1.0\n\n raw_loss = F.cross_entropy(input, target, reduction='none').view(-1)\n num_pixels = raw_loss.numel()\n\n if it > self.end_warm:\n this_p = self.top_p","source_hash":"a466de37bc958c693a51284783e3b32adead7566fd8f355250a2480d0e0669ff","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.losses.BootstrappedCE","uri":"program://Track-Anything/class/tracker.model.losses.BootstrappedCE#L23-L43","kind":"class","name":"BootstrappedCE","path":"tracker/model/losses.py","language":"python","start_line":23,"end_line":43,"context_start_line":3,"context_end_line":63,"code":"import torch.nn.functional as F\n\nfrom collections import defaultdict\n\n\ndef dice_loss(input_mask, cls_gt):\n num_objects = input_mask.shape[1]\n losses = []\n for i in range(num_objects):\n mask = input_mask[:,i].flatten(start_dim=1)\n # background not in mask, so we add one to cls_gt\n gt = (cls_gt==(i+1)).float().flatten(start_dim=1)\n numerator = 2 * (mask * gt).sum(-1)\n denominator = mask.sum(-1) + gt.sum(-1)\n loss = 1 - (numerator + 1) / (denominator + 1)\n losses.append(loss)\n return torch.cat(losses).mean()\n\n\n# https://stackoverflow.com/questions/63735255/how-do-i-compute-bootstrapped-cross-entropy-loss-in-pytorch\nclass BootstrappedCE(nn.Module):\n def __init__(self, start_warm, end_warm, top_p=0.15):\n super().__init__()\n\n self.start_warm = start_warm\n self.end_warm = end_warm\n self.top_p = top_p\n\n def forward(self, input, target, it):\n if it < self.start_warm:\n return F.cross_entropy(input, target), 1.0\n\n raw_loss = F.cross_entropy(input, target, reduction='none').view(-1)\n num_pixels = raw_loss.numel()\n\n if it > self.end_warm:\n this_p = self.top_p\n else:\n this_p = self.top_p + (1-self.top_p)*((self.end_warm-it)/(self.end_warm-self.start_warm))\n loss, _ = torch.topk(raw_loss, int(num_pixels * this_p), sorted=False)\n return loss.mean(), this_p\n\n\nclass LossComputer:\n def __init__(self, config):\n super().__init__()\n self.config = config\n self.bce = BootstrappedCE(config['start_warm'], config['end_warm'])\n\n def compute(self, data, num_objects, it):\n losses = defaultdict(int)\n\n b, t = data['rgb'].shape[:2]\n\n losses['total_loss'] = 0\n for ti in range(1, t):\n for bi in range(b):\n loss, p = self.bce(data[f'logits_{ti}'][bi:bi+1, :num_objects[bi]+1], data['cls_gt'][bi:bi+1,ti,0], it)\n losses['p'] += p / b / (t-1)\n losses[f'ce_loss_{ti}'] += loss / b\n","source_hash":"a466de37bc958c693a51284783e3b32adead7566fd8f355250a2480d0e0669ff","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.losses.LossComputer","uri":"program://Track-Anything/class/tracker.model.losses.LossComputer#L46-L68","kind":"class","name":"LossComputer","path":"tracker/model/losses.py","language":"python","start_line":46,"end_line":68,"context_start_line":26,"context_end_line":68,"code":"\n self.start_warm = start_warm\n self.end_warm = end_warm\n self.top_p = top_p\n\n def forward(self, input, target, it):\n if it < self.start_warm:\n return F.cross_entropy(input, target), 1.0\n\n raw_loss = F.cross_entropy(input, target, reduction='none').view(-1)\n num_pixels = raw_loss.numel()\n\n if it > self.end_warm:\n this_p = self.top_p\n else:\n this_p = self.top_p + (1-self.top_p)*((self.end_warm-it)/(self.end_warm-self.start_warm))\n loss, _ = torch.topk(raw_loss, int(num_pixels * this_p), sorted=False)\n return loss.mean(), this_p\n\n\nclass LossComputer:\n def __init__(self, config):\n super().__init__()\n self.config = config\n self.bce = BootstrappedCE(config['start_warm'], config['end_warm'])\n\n def compute(self, data, num_objects, it):\n losses = defaultdict(int)\n\n b, t = data['rgb'].shape[:2]\n\n losses['total_loss'] = 0\n for ti in range(1, t):\n for bi in range(b):\n loss, p = self.bce(data[f'logits_{ti}'][bi:bi+1, :num_objects[bi]+1], data['cls_gt'][bi:bi+1,ti,0], it)\n losses['p'] += p / b / (t-1)\n losses[f'ce_loss_{ti}'] += loss / b\n\n losses['total_loss'] += losses['ce_loss_%d'%ti]\n losses[f'dice_loss_{ti}'] = dice_loss(data[f'masks_{ti}'], data['cls_gt'][:,ti,0])\n losses['total_loss'] += losses[f'dice_loss_{ti}']\n\n return losses","source_hash":"a466de37bc958c693a51284783e3b32adead7566fd8f355250a2480d0e0669ff","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.losses.__init__","uri":"program://Track-Anything/function/tracker.model.losses.__init__#L47-L50","kind":"function","name":"__init__","path":"tracker/model/losses.py","language":"python","start_line":47,"end_line":50,"context_start_line":27,"context_end_line":68,"code":" self.start_warm = start_warm\n self.end_warm = end_warm\n self.top_p = top_p\n\n def forward(self, input, target, it):\n if it < self.start_warm:\n return F.cross_entropy(input, target), 1.0\n\n raw_loss = F.cross_entropy(input, target, reduction='none').view(-1)\n num_pixels = raw_loss.numel()\n\n if it > self.end_warm:\n this_p = self.top_p\n else:\n this_p = self.top_p + (1-self.top_p)*((self.end_warm-it)/(self.end_warm-self.start_warm))\n loss, _ = torch.topk(raw_loss, int(num_pixels * this_p), sorted=False)\n return loss.mean(), this_p\n\n\nclass LossComputer:\n def __init__(self, config):\n super().__init__()\n self.config = config\n self.bce = BootstrappedCE(config['start_warm'], config['end_warm'])\n\n def compute(self, data, num_objects, it):\n losses = defaultdict(int)\n\n b, t = data['rgb'].shape[:2]\n\n losses['total_loss'] = 0\n for ti in range(1, t):\n for bi in range(b):\n loss, p = self.bce(data[f'logits_{ti}'][bi:bi+1, :num_objects[bi]+1], data['cls_gt'][bi:bi+1,ti,0], it)\n losses['p'] += p / b / (t-1)\n losses[f'ce_loss_{ti}'] += loss / b\n\n losses['total_loss'] += losses['ce_loss_%d'%ti]\n losses[f'dice_loss_{ti}'] = dice_loss(data[f'masks_{ti}'], data['cls_gt'][:,ti,0])\n losses['total_loss'] += losses[f'dice_loss_{ti}']\n\n return losses","source_hash":"a466de37bc958c693a51284783e3b32adead7566fd8f355250a2480d0e0669ff","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.losses.forward","uri":"program://Track-Anything/function/tracker.model.losses.forward#L31-L43","kind":"function","name":"forward","path":"tracker/model/losses.py","language":"python","start_line":31,"end_line":43,"context_start_line":11,"context_end_line":63,"code":" for i in range(num_objects):\n mask = input_mask[:,i].flatten(start_dim=1)\n # background not in mask, so we add one to cls_gt\n gt = (cls_gt==(i+1)).float().flatten(start_dim=1)\n numerator = 2 * (mask * gt).sum(-1)\n denominator = mask.sum(-1) + gt.sum(-1)\n loss = 1 - (numerator + 1) / (denominator + 1)\n losses.append(loss)\n return torch.cat(losses).mean()\n\n\n# https://stackoverflow.com/questions/63735255/how-do-i-compute-bootstrapped-cross-entropy-loss-in-pytorch\nclass BootstrappedCE(nn.Module):\n def __init__(self, start_warm, end_warm, top_p=0.15):\n super().__init__()\n\n self.start_warm = start_warm\n self.end_warm = end_warm\n self.top_p = top_p\n\n def forward(self, input, target, it):\n if it < self.start_warm:\n return F.cross_entropy(input, target), 1.0\n\n raw_loss = F.cross_entropy(input, target, reduction='none').view(-1)\n num_pixels = raw_loss.numel()\n\n if it > self.end_warm:\n this_p = self.top_p\n else:\n this_p = self.top_p + (1-self.top_p)*((self.end_warm-it)/(self.end_warm-self.start_warm))\n loss, _ = torch.topk(raw_loss, int(num_pixels * this_p), sorted=False)\n return loss.mean(), this_p\n\n\nclass LossComputer:\n def __init__(self, config):\n super().__init__()\n self.config = config\n self.bce = BootstrappedCE(config['start_warm'], config['end_warm'])\n\n def compute(self, data, num_objects, it):\n losses = defaultdict(int)\n\n b, t = data['rgb'].shape[:2]\n\n losses['total_loss'] = 0\n for ti in range(1, t):\n for bi in range(b):\n loss, p = self.bce(data[f'logits_{ti}'][bi:bi+1, :num_objects[bi]+1], data['cls_gt'][bi:bi+1,ti,0], it)\n losses['p'] += p / b / (t-1)\n losses[f'ce_loss_{ti}'] += loss / b\n","source_hash":"a466de37bc958c693a51284783e3b32adead7566fd8f355250a2480d0e0669ff","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.losses.compute","uri":"program://Track-Anything/function/tracker.model.losses.compute#L52-L68","kind":"function","name":"compute","path":"tracker/model/losses.py","language":"python","start_line":52,"end_line":68,"context_start_line":32,"context_end_line":68,"code":" if it < self.start_warm:\n return F.cross_entropy(input, target), 1.0\n\n raw_loss = F.cross_entropy(input, target, reduction='none').view(-1)\n num_pixels = raw_loss.numel()\n\n if it > self.end_warm:\n this_p = self.top_p\n else:\n this_p = self.top_p + (1-self.top_p)*((self.end_warm-it)/(self.end_warm-self.start_warm))\n loss, _ = torch.topk(raw_loss, int(num_pixels * this_p), sorted=False)\n return loss.mean(), this_p\n\n\nclass LossComputer:\n def __init__(self, config):\n super().__init__()\n self.config = config\n self.bce = BootstrappedCE(config['start_warm'], config['end_warm'])\n\n def compute(self, data, num_objects, it):\n losses = defaultdict(int)\n\n b, t = data['rgb'].shape[:2]\n\n losses['total_loss'] = 0\n for ti in range(1, t):\n for bi in range(b):\n loss, p = self.bce(data[f'logits_{ti}'][bi:bi+1, :num_objects[bi]+1], data['cls_gt'][bi:bi+1,ti,0], it)\n losses['p'] += p / b / (t-1)\n losses[f'ce_loss_{ti}'] += loss / b\n\n losses['total_loss'] += losses['ce_loss_%d'%ti]\n losses[f'dice_loss_{ti}'] = dice_loss(data[f'masks_{ti}'], data['cls_gt'][:,ti,0])\n losses['total_loss'] += losses[f'dice_loss_{ti}']\n\n return losses","source_hash":"a466de37bc958c693a51284783e3b32adead7566fd8f355250a2480d0e0669ff","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.network","uri":"program://Track-Anything/module/tracker.model.network#L1-L198","kind":"module","name":"tracker.model.network","path":"tracker/model/network.py","language":"python","start_line":1,"end_line":198,"context_start_line":1,"context_end_line":198,"code":"\"\"\"\nThis file defines XMem, the highest level nn.Module interface\nDuring training, it is used by trainer.py\nDuring evaluation, it is used by inference_core.py\n\nIt further depends on modules.py which gives more detailed implementations of sub-modules\n\"\"\"\n\nimport torch\nimport torch.nn as nn\n\nfrom model.aggregate import aggregate\nfrom model.modules import *\nfrom model.memory_util import *\n\n\nclass XMem(nn.Module):\n def __init__(self, config, model_path=None, map_location=None):\n \"\"\"\n model_path/map_location are used in evaluation only\n map_location is for converting models saved in cuda to cpu\n \"\"\"\n super().__init__()\n model_weights = self.init_hyperparameters(config, model_path, map_location)\n\n self.single_object = config.get('single_object', False)\n print(f'Single object mode: {self.single_object}')\n\n self.key_encoder = KeyEncoder()\n self.value_encoder = ValueEncoder(self.value_dim, self.hidden_dim, self.single_object)\n\n # Projection from f16 feature space to key/value space\n self.key_proj = KeyProjection(1024, self.key_dim)\n\n self.decoder = Decoder(self.value_dim, self.hidden_dim)\n\n if model_weights is not None:\n self.load_weights(model_weights, init_as_zero_if_needed=True)\n\n def encode_key(self, frame, need_sk=True, need_ek=True): \n # Determine input shape\n if len(frame.shape) == 5:\n # shape is b*t*c*h*w\n need_reshape = True\n b, t = frame.shape[:2]\n # flatten so that we can feed them into a 2D CNN\n frame = frame.flatten(start_dim=0, end_dim=1)\n elif len(frame.shape) == 4:\n # shape is b*c*h*w\n need_reshape = False\n else:\n raise NotImplementedError\n \n f16, f8, f4 = self.key_encoder(frame)\n key, shrinkage, selection = self.key_proj(f16, need_sk, need_ek)\n\n if need_reshape:\n # B*C*T*H*W\n key = key.view(b, t, *key.shape[-3:]).transpose(1, 2).contiguous()\n if shrinkage is not None:\n shrinkage = shrinkage.view(b, t, *shrinkage.shape[-3:]).transpose(1, 2).contiguous()\n if selection is not None:\n selection = selection.view(b, t, *selection.shape[-3:]).transpose(1, 2).contiguous()\n\n # B*T*C*H*W\n f16 = f16.view(b, t, *f16.shape[-3:])\n f8 = f8.view(b, t, *f8.shape[-3:])\n f4 = f4.view(b, t, *f4.shape[-3:])\n\n return key, shrinkage, selection, f16, f8, f4\n\n def encode_value(self, frame, image_feat_f16, h16, masks, is_deep_update=True): \n num_objects = masks.shape[1]\n if num_objects != 1:\n others = torch.cat([\n torch.sum(\n masks[:, [j for j in range(num_objects) if i!=j]]\n , dim=1, keepdim=True)\n for i in range(num_objects)], 1)\n else:\n others = torch.zeros_like(masks)\n\n g16, h16 = self.value_encoder(frame, image_feat_f16, h16, masks, others, is_deep_update)\n\n return g16, h16\n\n # Used in training only. \n # This step is replaced by MemoryManager in test time\n def read_memory(self, query_key, query_selection, memory_key, \n memory_shrinkage, memory_value):\n \"\"\"\n query_key : B * CK * H * W\n query_selection : B * CK * H * W\n memory_key : B * CK * T * H * W\n memory_shrinkage: B * 1 * T * H * W\n memory_value : B * num_objects * CV * T * H * W\n \"\"\"\n batch_size, num_objects = memory_value.shape[:2]\n memory_value = memory_value.flatten(start_dim=1, end_dim=2)\n\n affinity = get_affinity(memory_key, memory_shrinkage, query_key, query_selection)\n memory = readout(affinity, memory_value)\n memory = memory.view(batch_size, num_objects, self.value_dim, *memory.shape[-2:])\n\n return memory\n\n def segment(self, multi_scale_features, memory_readout,\n hidden_state, selector=None, h_out=True, strip_bg=True): \n\n hidden_state, logits = self.decoder(*multi_scale_features, hidden_state, memory_readout, h_out=h_out)\n prob = torch.sigmoid(logits)\n if selector is not None:\n prob = prob * selector\n \n logits, prob = aggregate(prob, dim=1, return_logits=True)\n if strip_bg:\n # Strip away the background\n prob = prob[:, 1:]\n\n return hidden_state, logits, prob\n\n def forward(self, mode, *args, **kwargs):\n if mode == 'encode_key':\n return self.encode_key(*args, **kwargs)\n elif mode == 'encode_value':\n return self.encode_value(*args, **kwargs)\n elif mode == 'read_memory':\n return self.read_memory(*args, **kwargs)\n elif mode == 'segment':\n return self.segment(*args, **kwargs)\n else:\n raise NotImplementedError\n\n def init_hyperparameters(self, config, model_path=None, map_location=None):\n \"\"\"\n Init three hyperparameters: key_dim, value_dim, and hidden_dim\n If model_path is provided, we load these from the model weights\n The actual parameters are then updated to the config in-place\n\n Otherwise we load it either from the config or default\n \"\"\"\n if model_path is not None:\n # load the model and key/value/hidden dimensions with some hacks\n # config is updated with the loaded parameters\n model_weights = torch.load(model_path, map_location=\"cpu\")\n self.key_dim = model_weights['key_proj.key_proj.weight'].shape[0]\n self.value_dim = model_weights['value_encoder.fuser.block2.conv2.weight'].shape[0]\n self.disable_hidden = 'decoder.hidden_update.transform.weight' not in model_weights\n if self.disable_hidden:\n self.hidden_dim = 0\n else:\n self.hidden_dim = model_weights['decoder.hidden_update.transform.weight'].shape[0]//3\n print(f'Hyperparameters read from the model weights: '\n f'C^k={self.key_dim}, C^v={self.value_dim}, C^h={self.hidden_dim}')\n else:\n model_weights = None\n # load dimensions from config or default\n if 'key_dim' not in config:\n self.key_dim = 64\n print(f'key_dim not found in config. Set to default {self.key_dim}')\n else:\n self.key_dim = config['key_dim']\n\n if 'value_dim' not in config:\n self.value_dim = 512\n print(f'value_dim not found in config. Set to default {self.value_dim}')\n else:\n self.value_dim = config['value_dim']\n\n if 'hidden_dim' not in config:\n self.hidden_dim = 64\n print(f'hidden_dim not found in config. Set to default {self.hidden_dim}')\n else:\n self.hidden_dim = config['hidden_dim']\n\n self.disable_hidden = (self.hidden_dim <= 0)\n\n config['key_dim'] = self.key_dim\n config['value_dim'] = self.value_dim\n config['hidden_dim'] = self.hidden_dim\n\n return model_weights\n\n def load_weights(self, src_dict, init_as_zero_if_needed=False):\n # Maps SO weight (without other_mask) to MO weight (with other_mask)\n for k in list(src_dict.keys()):\n if k == 'value_encoder.conv1.weight':\n if src_dict[k].shape[1] == 4:\n print('Converting weights from single object to multiple objects.')\n pads = torch.zeros((64,1,7,7), device=src_dict[k].device)\n if not init_as_zero_if_needed:\n print('Randomly initialized padding.')\n nn.init.orthogonal_(pads)\n else:\n print('Zero-initialized padding.')\n src_dict[k] = torch.cat([src_dict[k], pads], 1)\n\n self.load_state_dict(src_dict)","source_hash":"6b17e0a133ef22b55fbc8e7cc363f9c5c2677121aaabcb03cabad4833f2ad445","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.network.XMem","uri":"program://Track-Anything/class/tracker.model.network.XMem#L17-L198","kind":"class","name":"XMem","path":"tracker/model/network.py","language":"python","start_line":17,"end_line":198,"context_start_line":1,"context_end_line":198,"code":"\"\"\"\nThis file defines XMem, the highest level nn.Module interface\nDuring training, it is used by trainer.py\nDuring evaluation, it is used by inference_core.py\n\nIt further depends on modules.py which gives more detailed implementations of sub-modules\n\"\"\"\n\nimport torch\nimport torch.nn as nn\n\nfrom model.aggregate import aggregate\nfrom model.modules import *\nfrom model.memory_util import *\n\n\nclass XMem(nn.Module):\n def __init__(self, config, model_path=None, map_location=None):\n \"\"\"\n model_path/map_location are used in evaluation only\n map_location is for converting models saved in cuda to cpu\n \"\"\"\n super().__init__()\n model_weights = self.init_hyperparameters(config, model_path, map_location)\n\n self.single_object = config.get('single_object', False)\n print(f'Single object mode: {self.single_object}')\n\n self.key_encoder = KeyEncoder()\n self.value_encoder = ValueEncoder(self.value_dim, self.hidden_dim, self.single_object)\n\n # Projection from f16 feature space to key/value space\n self.key_proj = KeyProjection(1024, self.key_dim)\n\n self.decoder = Decoder(self.value_dim, self.hidden_dim)\n\n if model_weights is not None:\n self.load_weights(model_weights, init_as_zero_if_needed=True)\n\n def encode_key(self, frame, need_sk=True, need_ek=True): \n # Determine input shape\n if len(frame.shape) == 5:\n # shape is b*t*c*h*w\n need_reshape = True\n b, t = frame.shape[:2]\n # flatten so that we can feed them into a 2D CNN\n frame = frame.flatten(start_dim=0, end_dim=1)\n elif len(frame.shape) == 4:\n # shape is b*c*h*w\n need_reshape = False\n else:\n raise NotImplementedError\n \n f16, f8, f4 = self.key_encoder(frame)\n key, shrinkage, selection = self.key_proj(f16, need_sk, need_ek)\n\n if need_reshape:\n # B*C*T*H*W\n key = key.view(b, t, *key.shape[-3:]).transpose(1, 2).contiguous()\n if shrinkage is not None:\n shrinkage = shrinkage.view(b, t, *shrinkage.shape[-3:]).transpose(1, 2).contiguous()\n if selection is not None:\n selection = selection.view(b, t, *selection.shape[-3:]).transpose(1, 2).contiguous()\n\n # B*T*C*H*W\n f16 = f16.view(b, t, *f16.shape[-3:])\n f8 = f8.view(b, t, *f8.shape[-3:])\n f4 = f4.view(b, t, *f4.shape[-3:])\n\n return key, shrinkage, selection, f16, f8, f4\n\n def encode_value(self, frame, image_feat_f16, h16, masks, is_deep_update=True): \n num_objects = masks.shape[1]\n if num_objects != 1:\n others = torch.cat([\n torch.sum(\n masks[:, [j for j in range(num_objects) if i!=j]]\n , dim=1, keepdim=True)\n for i in range(num_objects)], 1)\n else:\n others = torch.zeros_like(masks)\n\n g16, h16 = self.value_encoder(frame, image_feat_f16, h16, masks, others, is_deep_update)\n\n return g16, h16\n\n # Used in training only. \n # This step is replaced by MemoryManager in test time\n def read_memory(self, query_key, query_selection, memory_key, \n memory_shrinkage, memory_value):\n \"\"\"\n query_key : B * CK * H * W\n query_selection : B * CK * H * W\n memory_key : B * CK * T * H * W\n memory_shrinkage: B * 1 * T * H * W\n memory_value : B * num_objects * CV * T * H * W\n \"\"\"\n batch_size, num_objects = memory_value.shape[:2]\n memory_value = memory_value.flatten(start_dim=1, end_dim=2)\n\n affinity = get_affinity(memory_key, memory_shrinkage, query_key, query_selection)\n memory = readout(affinity, memory_value)\n memory = memory.view(batch_size, num_objects, self.value_dim, *memory.shape[-2:])\n\n return memory\n\n def segment(self, multi_scale_features, memory_readout,\n hidden_state, selector=None, h_out=True, strip_bg=True): \n\n hidden_state, logits = self.decoder(*multi_scale_features, hidden_state, memory_readout, h_out=h_out)\n prob = torch.sigmoid(logits)\n if selector is not None:\n prob = prob * selector\n \n logits, prob = aggregate(prob, dim=1, return_logits=True)\n if strip_bg:\n # Strip away the background\n prob = prob[:, 1:]\n\n return hidden_state, logits, prob\n\n def forward(self, mode, *args, **kwargs):\n if mode == 'encode_key':\n return self.encode_key(*args, **kwargs)\n elif mode == 'encode_value':\n return self.encode_value(*args, **kwargs)\n elif mode == 'read_memory':\n return self.read_memory(*args, **kwargs)\n elif mode == 'segment':\n return self.segment(*args, **kwargs)\n else:\n raise NotImplementedError\n\n def init_hyperparameters(self, config, model_path=None, map_location=None):\n \"\"\"\n Init three hyperparameters: key_dim, value_dim, and hidden_dim\n If model_path is provided, we load these from the model weights\n The actual parameters are then updated to the config in-place\n\n Otherwise we load it either from the config or default\n \"\"\"\n if model_path is not None:\n # load the model and key/value/hidden dimensions with some hacks\n # config is updated with the loaded parameters\n model_weights = torch.load(model_path, map_location=\"cpu\")\n self.key_dim = model_weights['key_proj.key_proj.weight'].shape[0]\n self.value_dim = model_weights['value_encoder.fuser.block2.conv2.weight'].shape[0]\n self.disable_hidden = 'decoder.hidden_update.transform.weight' not in model_weights\n if self.disable_hidden:\n self.hidden_dim = 0\n else:\n self.hidden_dim = model_weights['decoder.hidden_update.transform.weight'].shape[0]//3\n print(f'Hyperparameters read from the model weights: '\n f'C^k={self.key_dim}, C^v={self.value_dim}, C^h={self.hidden_dim}')\n else:\n model_weights = None\n # load dimensions from config or default\n if 'key_dim' not in config:\n self.key_dim = 64\n print(f'key_dim not found in config. Set to default {self.key_dim}')\n else:\n self.key_dim = config['key_dim']\n\n if 'value_dim' not in config:\n self.value_dim = 512\n print(f'value_dim not found in config. Set to default {self.value_dim}')\n else:\n self.value_dim = config['value_dim']\n\n if 'hidden_dim' not in config:\n self.hidden_dim = 64\n print(f'hidden_dim not found in config. Set to default {self.hidden_dim}')\n else:\n self.hidden_dim = config['hidden_dim']\n\n self.disable_hidden = (self.hidden_dim <= 0)\n\n config['key_dim'] = self.key_dim\n config['value_dim'] = self.value_dim\n config['hidden_dim'] = self.hidden_dim\n\n return model_weights\n\n def load_weights(self, src_dict, init_as_zero_if_needed=False):\n # Maps SO weight (without other_mask) to MO weight (with other_mask)\n for k in list(src_dict.keys()):\n if k == 'value_encoder.conv1.weight':\n if src_dict[k].shape[1] == 4:\n print('Converting weights from single object to multiple objects.')\n pads = torch.zeros((64,1,7,7), device=src_dict[k].device)\n if not init_as_zero_if_needed:\n print('Randomly initialized padding.')\n nn.init.orthogonal_(pads)\n else:\n print('Zero-initialized padding.')\n src_dict[k] = torch.cat([src_dict[k], pads], 1)\n\n self.load_state_dict(src_dict)","source_hash":"6b17e0a133ef22b55fbc8e7cc363f9c5c2677121aaabcb03cabad4833f2ad445","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.network.__init__","uri":"program://Track-Anything/function/tracker.model.network.__init__#L18-L38","kind":"function","name":"__init__","path":"tracker/model/network.py","language":"python","start_line":18,"end_line":38,"context_start_line":1,"context_end_line":58,"code":"\"\"\"\nThis file defines XMem, the highest level nn.Module interface\nDuring training, it is used by trainer.py\nDuring evaluation, it is used by inference_core.py\n\nIt further depends on modules.py which gives more detailed implementations of sub-modules\n\"\"\"\n\nimport torch\nimport torch.nn as nn\n\nfrom model.aggregate import aggregate\nfrom model.modules import *\nfrom model.memory_util import *\n\n\nclass XMem(nn.Module):\n def __init__(self, config, model_path=None, map_location=None):\n \"\"\"\n model_path/map_location are used in evaluation only\n map_location is for converting models saved in cuda to cpu\n \"\"\"\n super().__init__()\n model_weights = self.init_hyperparameters(config, model_path, map_location)\n\n self.single_object = config.get('single_object', False)\n print(f'Single object mode: {self.single_object}')\n\n self.key_encoder = KeyEncoder()\n self.value_encoder = ValueEncoder(self.value_dim, self.hidden_dim, self.single_object)\n\n # Projection from f16 feature space to key/value space\n self.key_proj = KeyProjection(1024, self.key_dim)\n\n self.decoder = Decoder(self.value_dim, self.hidden_dim)\n\n if model_weights is not None:\n self.load_weights(model_weights, init_as_zero_if_needed=True)\n\n def encode_key(self, frame, need_sk=True, need_ek=True): \n # Determine input shape\n if len(frame.shape) == 5:\n # shape is b*t*c*h*w\n need_reshape = True\n b, t = frame.shape[:2]\n # flatten so that we can feed them into a 2D CNN\n frame = frame.flatten(start_dim=0, end_dim=1)\n elif len(frame.shape) == 4:\n # shape is b*c*h*w\n need_reshape = False\n else:\n raise NotImplementedError\n \n f16, f8, f4 = self.key_encoder(frame)\n key, shrinkage, selection = self.key_proj(f16, need_sk, need_ek)\n\n if need_reshape:\n # B*C*T*H*W","source_hash":"6b17e0a133ef22b55fbc8e7cc363f9c5c2677121aaabcb03cabad4833f2ad445","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.network.encode_key","uri":"program://Track-Anything/function/tracker.model.network.encode_key#L40-L70","kind":"function","name":"encode_key","path":"tracker/model/network.py","language":"python","start_line":40,"end_line":70,"context_start_line":20,"context_end_line":90,"code":" model_path/map_location are used in evaluation only\n map_location is for converting models saved in cuda to cpu\n \"\"\"\n super().__init__()\n model_weights = self.init_hyperparameters(config, model_path, map_location)\n\n self.single_object = config.get('single_object', False)\n print(f'Single object mode: {self.single_object}')\n\n self.key_encoder = KeyEncoder()\n self.value_encoder = ValueEncoder(self.value_dim, self.hidden_dim, self.single_object)\n\n # Projection from f16 feature space to key/value space\n self.key_proj = KeyProjection(1024, self.key_dim)\n\n self.decoder = Decoder(self.value_dim, self.hidden_dim)\n\n if model_weights is not None:\n self.load_weights(model_weights, init_as_zero_if_needed=True)\n\n def encode_key(self, frame, need_sk=True, need_ek=True): \n # Determine input shape\n if len(frame.shape) == 5:\n # shape is b*t*c*h*w\n need_reshape = True\n b, t = frame.shape[:2]\n # flatten so that we can feed them into a 2D CNN\n frame = frame.flatten(start_dim=0, end_dim=1)\n elif len(frame.shape) == 4:\n # shape is b*c*h*w\n need_reshape = False\n else:\n raise NotImplementedError\n \n f16, f8, f4 = self.key_encoder(frame)\n key, shrinkage, selection = self.key_proj(f16, need_sk, need_ek)\n\n if need_reshape:\n # B*C*T*H*W\n key = key.view(b, t, *key.shape[-3:]).transpose(1, 2).contiguous()\n if shrinkage is not None:\n shrinkage = shrinkage.view(b, t, *shrinkage.shape[-3:]).transpose(1, 2).contiguous()\n if selection is not None:\n selection = selection.view(b, t, *selection.shape[-3:]).transpose(1, 2).contiguous()\n\n # B*T*C*H*W\n f16 = f16.view(b, t, *f16.shape[-3:])\n f8 = f8.view(b, t, *f8.shape[-3:])\n f4 = f4.view(b, t, *f4.shape[-3:])\n\n return key, shrinkage, selection, f16, f8, f4\n\n def encode_value(self, frame, image_feat_f16, h16, masks, is_deep_update=True): \n num_objects = masks.shape[1]\n if num_objects != 1:\n others = torch.cat([\n torch.sum(\n masks[:, [j for j in range(num_objects) if i!=j]]\n , dim=1, keepdim=True)\n for i in range(num_objects)], 1)\n else:\n others = torch.zeros_like(masks)\n\n g16, h16 = self.value_encoder(frame, image_feat_f16, h16, masks, others, is_deep_update)\n\n return g16, h16\n\n # Used in training only. \n # This step is replaced by MemoryManager in test time\n def read_memory(self, query_key, query_selection, memory_key, \n memory_shrinkage, memory_value):","source_hash":"6b17e0a133ef22b55fbc8e7cc363f9c5c2677121aaabcb03cabad4833f2ad445","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.network.encode_value","uri":"program://Track-Anything/function/tracker.model.network.encode_value#L72-L85","kind":"function","name":"encode_value","path":"tracker/model/network.py","language":"python","start_line":72,"end_line":85,"context_start_line":52,"context_end_line":105,"code":" raise NotImplementedError\n \n f16, f8, f4 = self.key_encoder(frame)\n key, shrinkage, selection = self.key_proj(f16, need_sk, need_ek)\n\n if need_reshape:\n # B*C*T*H*W\n key = key.view(b, t, *key.shape[-3:]).transpose(1, 2).contiguous()\n if shrinkage is not None:\n shrinkage = shrinkage.view(b, t, *shrinkage.shape[-3:]).transpose(1, 2).contiguous()\n if selection is not None:\n selection = selection.view(b, t, *selection.shape[-3:]).transpose(1, 2).contiguous()\n\n # B*T*C*H*W\n f16 = f16.view(b, t, *f16.shape[-3:])\n f8 = f8.view(b, t, *f8.shape[-3:])\n f4 = f4.view(b, t, *f4.shape[-3:])\n\n return key, shrinkage, selection, f16, f8, f4\n\n def encode_value(self, frame, image_feat_f16, h16, masks, is_deep_update=True): \n num_objects = masks.shape[1]\n if num_objects != 1:\n others = torch.cat([\n torch.sum(\n masks[:, [j for j in range(num_objects) if i!=j]]\n , dim=1, keepdim=True)\n for i in range(num_objects)], 1)\n else:\n others = torch.zeros_like(masks)\n\n g16, h16 = self.value_encoder(frame, image_feat_f16, h16, masks, others, is_deep_update)\n\n return g16, h16\n\n # Used in training only. \n # This step is replaced by MemoryManager in test time\n def read_memory(self, query_key, query_selection, memory_key, \n memory_shrinkage, memory_value):\n \"\"\"\n query_key : B * CK * H * W\n query_selection : B * CK * H * W\n memory_key : B * CK * T * H * W\n memory_shrinkage: B * 1 * T * H * W\n memory_value : B * num_objects * CV * T * H * W\n \"\"\"\n batch_size, num_objects = memory_value.shape[:2]\n memory_value = memory_value.flatten(start_dim=1, end_dim=2)\n\n affinity = get_affinity(memory_key, memory_shrinkage, query_key, query_selection)\n memory = readout(affinity, memory_value)\n memory = memory.view(batch_size, num_objects, self.value_dim, *memory.shape[-2:])\n\n return memory","source_hash":"6b17e0a133ef22b55fbc8e7cc363f9c5c2677121aaabcb03cabad4833f2ad445","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.network.read_memory","uri":"program://Track-Anything/function/tracker.model.network.read_memory#L89-L105","kind":"function","name":"read_memory","path":"tracker/model/network.py","language":"python","start_line":89,"end_line":105,"context_start_line":69,"context_end_line":125,"code":"\n return key, shrinkage, selection, f16, f8, f4\n\n def encode_value(self, frame, image_feat_f16, h16, masks, is_deep_update=True): \n num_objects = masks.shape[1]\n if num_objects != 1:\n others = torch.cat([\n torch.sum(\n masks[:, [j for j in range(num_objects) if i!=j]]\n , dim=1, keepdim=True)\n for i in range(num_objects)], 1)\n else:\n others = torch.zeros_like(masks)\n\n g16, h16 = self.value_encoder(frame, image_feat_f16, h16, masks, others, is_deep_update)\n\n return g16, h16\n\n # Used in training only. \n # This step is replaced by MemoryManager in test time\n def read_memory(self, query_key, query_selection, memory_key, \n memory_shrinkage, memory_value):\n \"\"\"\n query_key : B * CK * H * W\n query_selection : B * CK * H * W\n memory_key : B * CK * T * H * W\n memory_shrinkage: B * 1 * T * H * W\n memory_value : B * num_objects * CV * T * H * W\n \"\"\"\n batch_size, num_objects = memory_value.shape[:2]\n memory_value = memory_value.flatten(start_dim=1, end_dim=2)\n\n affinity = get_affinity(memory_key, memory_shrinkage, query_key, query_selection)\n memory = readout(affinity, memory_value)\n memory = memory.view(batch_size, num_objects, self.value_dim, *memory.shape[-2:])\n\n return memory\n\n def segment(self, multi_scale_features, memory_readout,\n hidden_state, selector=None, h_out=True, strip_bg=True): \n\n hidden_state, logits = self.decoder(*multi_scale_features, hidden_state, memory_readout, h_out=h_out)\n prob = torch.sigmoid(logits)\n if selector is not None:\n prob = prob * selector\n \n logits, prob = aggregate(prob, dim=1, return_logits=True)\n if strip_bg:\n # Strip away the background\n prob = prob[:, 1:]\n\n return hidden_state, logits, prob\n\n def forward(self, mode, *args, **kwargs):\n if mode == 'encode_key':\n return self.encode_key(*args, **kwargs)\n elif mode == 'encode_value':","source_hash":"6b17e0a133ef22b55fbc8e7cc363f9c5c2677121aaabcb03cabad4833f2ad445","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.network.segment","uri":"program://Track-Anything/function/tracker.model.network.segment#L107-L120","kind":"function","name":"segment","path":"tracker/model/network.py","language":"python","start_line":107,"end_line":120,"context_start_line":87,"context_end_line":140,"code":" # Used in training only. \n # This step is replaced by MemoryManager in test time\n def read_memory(self, query_key, query_selection, memory_key, \n memory_shrinkage, memory_value):\n \"\"\"\n query_key : B * CK * H * W\n query_selection : B * CK * H * W\n memory_key : B * CK * T * H * W\n memory_shrinkage: B * 1 * T * H * W\n memory_value : B * num_objects * CV * T * H * W\n \"\"\"\n batch_size, num_objects = memory_value.shape[:2]\n memory_value = memory_value.flatten(start_dim=1, end_dim=2)\n\n affinity = get_affinity(memory_key, memory_shrinkage, query_key, query_selection)\n memory = readout(affinity, memory_value)\n memory = memory.view(batch_size, num_objects, self.value_dim, *memory.shape[-2:])\n\n return memory\n\n def segment(self, multi_scale_features, memory_readout,\n hidden_state, selector=None, h_out=True, strip_bg=True): \n\n hidden_state, logits = self.decoder(*multi_scale_features, hidden_state, memory_readout, h_out=h_out)\n prob = torch.sigmoid(logits)\n if selector is not None:\n prob = prob * selector\n \n logits, prob = aggregate(prob, dim=1, return_logits=True)\n if strip_bg:\n # Strip away the background\n prob = prob[:, 1:]\n\n return hidden_state, logits, prob\n\n def forward(self, mode, *args, **kwargs):\n if mode == 'encode_key':\n return self.encode_key(*args, **kwargs)\n elif mode == 'encode_value':\n return self.encode_value(*args, **kwargs)\n elif mode == 'read_memory':\n return self.read_memory(*args, **kwargs)\n elif mode == 'segment':\n return self.segment(*args, **kwargs)\n else:\n raise NotImplementedError\n\n def init_hyperparameters(self, config, model_path=None, map_location=None):\n \"\"\"\n Init three hyperparameters: key_dim, value_dim, and hidden_dim\n If model_path is provided, we load these from the model weights\n The actual parameters are then updated to the config in-place\n\n Otherwise we load it either from the config or default","source_hash":"6b17e0a133ef22b55fbc8e7cc363f9c5c2677121aaabcb03cabad4833f2ad445","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.network.forward","uri":"program://Track-Anything/function/tracker.model.network.forward#L122-L132","kind":"function","name":"forward","path":"tracker/model/network.py","language":"python","start_line":122,"end_line":132,"context_start_line":102,"context_end_line":152,"code":" memory = readout(affinity, memory_value)\n memory = memory.view(batch_size, num_objects, self.value_dim, *memory.shape[-2:])\n\n return memory\n\n def segment(self, multi_scale_features, memory_readout,\n hidden_state, selector=None, h_out=True, strip_bg=True): \n\n hidden_state, logits = self.decoder(*multi_scale_features, hidden_state, memory_readout, h_out=h_out)\n prob = torch.sigmoid(logits)\n if selector is not None:\n prob = prob * selector\n \n logits, prob = aggregate(prob, dim=1, return_logits=True)\n if strip_bg:\n # Strip away the background\n prob = prob[:, 1:]\n\n return hidden_state, logits, prob\n\n def forward(self, mode, *args, **kwargs):\n if mode == 'encode_key':\n return self.encode_key(*args, **kwargs)\n elif mode == 'encode_value':\n return self.encode_value(*args, **kwargs)\n elif mode == 'read_memory':\n return self.read_memory(*args, **kwargs)\n elif mode == 'segment':\n return self.segment(*args, **kwargs)\n else:\n raise NotImplementedError\n\n def init_hyperparameters(self, config, model_path=None, map_location=None):\n \"\"\"\n Init three hyperparameters: key_dim, value_dim, and hidden_dim\n If model_path is provided, we load these from the model weights\n The actual parameters are then updated to the config in-place\n\n Otherwise we load it either from the config or default\n \"\"\"\n if model_path is not None:\n # load the model and key/value/hidden dimensions with some hacks\n # config is updated with the loaded parameters\n model_weights = torch.load(model_path, map_location=\"cpu\")\n self.key_dim = model_weights['key_proj.key_proj.weight'].shape[0]\n self.value_dim = model_weights['value_encoder.fuser.block2.conv2.weight'].shape[0]\n self.disable_hidden = 'decoder.hidden_update.transform.weight' not in model_weights\n if self.disable_hidden:\n self.hidden_dim = 0\n else:\n self.hidden_dim = model_weights['decoder.hidden_update.transform.weight'].shape[0]//3","source_hash":"6b17e0a133ef22b55fbc8e7cc363f9c5c2677121aaabcb03cabad4833f2ad445","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.network.init_hyperparameters","uri":"program://Track-Anything/function/tracker.model.network.init_hyperparameters#L134-L182","kind":"function","name":"init_hyperparameters","path":"tracker/model/network.py","language":"python","start_line":134,"end_line":182,"context_start_line":114,"context_end_line":198,"code":" \n logits, prob = aggregate(prob, dim=1, return_logits=True)\n if strip_bg:\n # Strip away the background\n prob = prob[:, 1:]\n\n return hidden_state, logits, prob\n\n def forward(self, mode, *args, **kwargs):\n if mode == 'encode_key':\n return self.encode_key(*args, **kwargs)\n elif mode == 'encode_value':\n return self.encode_value(*args, **kwargs)\n elif mode == 'read_memory':\n return self.read_memory(*args, **kwargs)\n elif mode == 'segment':\n return self.segment(*args, **kwargs)\n else:\n raise NotImplementedError\n\n def init_hyperparameters(self, config, model_path=None, map_location=None):\n \"\"\"\n Init three hyperparameters: key_dim, value_dim, and hidden_dim\n If model_path is provided, we load these from the model weights\n The actual parameters are then updated to the config in-place\n\n Otherwise we load it either from the config or default\n \"\"\"\n if model_path is not None:\n # load the model and key/value/hidden dimensions with some hacks\n # config is updated with the loaded parameters\n model_weights = torch.load(model_path, map_location=\"cpu\")\n self.key_dim = model_weights['key_proj.key_proj.weight'].shape[0]\n self.value_dim = model_weights['value_encoder.fuser.block2.conv2.weight'].shape[0]\n self.disable_hidden = 'decoder.hidden_update.transform.weight' not in model_weights\n if self.disable_hidden:\n self.hidden_dim = 0\n else:\n self.hidden_dim = model_weights['decoder.hidden_update.transform.weight'].shape[0]//3\n print(f'Hyperparameters read from the model weights: '\n f'C^k={self.key_dim}, C^v={self.value_dim}, C^h={self.hidden_dim}')\n else:\n model_weights = None\n # load dimensions from config or default\n if 'key_dim' not in config:\n self.key_dim = 64\n print(f'key_dim not found in config. Set to default {self.key_dim}')\n else:\n self.key_dim = config['key_dim']\n\n if 'value_dim' not in config:\n self.value_dim = 512\n print(f'value_dim not found in config. Set to default {self.value_dim}')\n else:\n self.value_dim = config['value_dim']\n\n if 'hidden_dim' not in config:\n self.hidden_dim = 64\n print(f'hidden_dim not found in config. Set to default {self.hidden_dim}')\n else:\n self.hidden_dim = config['hidden_dim']\n\n self.disable_hidden = (self.hidden_dim <= 0)\n\n config['key_dim'] = self.key_dim\n config['value_dim'] = self.value_dim\n config['hidden_dim'] = self.hidden_dim\n\n return model_weights\n\n def load_weights(self, src_dict, init_as_zero_if_needed=False):\n # Maps SO weight (without other_mask) to MO weight (with other_mask)\n for k in list(src_dict.keys()):\n if k == 'value_encoder.conv1.weight':\n if src_dict[k].shape[1] == 4:\n print('Converting weights from single object to multiple objects.')\n pads = torch.zeros((64,1,7,7), device=src_dict[k].device)\n if not init_as_zero_if_needed:\n print('Randomly initialized padding.')\n nn.init.orthogonal_(pads)\n else:\n print('Zero-initialized padding.')\n src_dict[k] = torch.cat([src_dict[k], pads], 1)\n\n self.load_state_dict(src_dict)","source_hash":"6b17e0a133ef22b55fbc8e7cc363f9c5c2677121aaabcb03cabad4833f2ad445","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.network.load_weights","uri":"program://Track-Anything/function/tracker.model.network.load_weights#L184-L198","kind":"function","name":"load_weights","path":"tracker/model/network.py","language":"python","start_line":184,"end_line":198,"context_start_line":164,"context_end_line":198,"code":" if 'value_dim' not in config:\n self.value_dim = 512\n print(f'value_dim not found in config. Set to default {self.value_dim}')\n else:\n self.value_dim = config['value_dim']\n\n if 'hidden_dim' not in config:\n self.hidden_dim = 64\n print(f'hidden_dim not found in config. Set to default {self.hidden_dim}')\n else:\n self.hidden_dim = config['hidden_dim']\n\n self.disable_hidden = (self.hidden_dim <= 0)\n\n config['key_dim'] = self.key_dim\n config['value_dim'] = self.value_dim\n config['hidden_dim'] = self.hidden_dim\n\n return model_weights\n\n def load_weights(self, src_dict, init_as_zero_if_needed=False):\n # Maps SO weight (without other_mask) to MO weight (with other_mask)\n for k in list(src_dict.keys()):\n if k == 'value_encoder.conv1.weight':\n if src_dict[k].shape[1] == 4:\n print('Converting weights from single object to multiple objects.')\n pads = torch.zeros((64,1,7,7), device=src_dict[k].device)\n if not init_as_zero_if_needed:\n print('Randomly initialized padding.')\n nn.init.orthogonal_(pads)\n else:\n print('Zero-initialized padding.')\n src_dict[k] = torch.cat([src_dict[k], pads], 1)\n\n self.load_state_dict(src_dict)","source_hash":"6b17e0a133ef22b55fbc8e7cc363f9c5c2677121aaabcb03cabad4833f2ad445","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.trainer","uri":"program://Track-Anything/module/tracker.model.trainer#L1-L244","kind":"module","name":"tracker.model.trainer","path":"tracker/model/trainer.py","language":"python","start_line":1,"end_line":244,"context_start_line":1,"context_end_line":244,"code":"\"\"\"\ntrainer.py - warpper and utility functions for network training\nCompute loss, back-prop, update parameters, logging, etc.\n\"\"\"\nimport datetime\nimport os\nimport time\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\n\nfrom model.network import XMem\nfrom model.losses import LossComputer\nfrom util.log_integrator import Integrator\nfrom util.image_saver import pool_pairs\n\n\nclass XMemTrainer:\n def __init__(self, config, logger=None, save_path=None, local_rank=0, world_size=1):\n self.config = config\n self.num_frames = config['num_frames']\n self.num_ref_frames = config['num_ref_frames']\n self.deep_update_prob = config['deep_update_prob']\n self.local_rank = local_rank\n\n self.XMem = nn.parallel.DistributedDataParallel(\n XMem(config).cuda(), \n device_ids=[local_rank], output_device=local_rank, broadcast_buffers=False)\n\n # Set up logger when local_rank=0\n self.logger = logger\n self.save_path = save_path\n if logger is not None:\n self.last_time = time.time()\n self.logger.log_string('model_size', str(sum([param.nelement() for param in self.XMem.parameters()])))\n self.train_integrator = Integrator(self.logger, distributed=True, local_rank=local_rank, world_size=world_size)\n self.loss_computer = LossComputer(config)\n\n self.train()\n self.optimizer = optim.AdamW(filter(\n lambda p: p.requires_grad, self.XMem.parameters()), lr=config['lr'], weight_decay=config['weight_decay'])\n self.scheduler = optim.lr_scheduler.MultiStepLR(self.optimizer, config['steps'], config['gamma'])\n if config['amp']:\n self.scaler = torch.cuda.amp.GradScaler()\n\n # Logging info\n self.log_text_interval = config['log_text_interval']\n self.log_image_interval = config['log_image_interval']\n self.save_network_interval = config['save_network_interval']\n self.save_checkpoint_interval = config['save_checkpoint_interval']\n if config['debug']:\n self.log_text_interval = self.log_image_interval = 1\n\n def do_pass(self, data, max_it, it=0):\n # No need to store the gradient outside training\n torch.set_grad_enabled(self._is_train)\n\n for k, v in data.items():\n if type(v) != list and type(v) != dict and type(v) != int:\n data[k] = v.cuda(non_blocking=True)\n\n out = {}\n frames = data['rgb']\n first_frame_gt = data['first_frame_gt'].float()\n b = frames.shape[0]\n num_filled_objects = [o.item() for o in data['info']['num_objects']]\n num_objects = first_frame_gt.shape[2]\n selector = data['selector'].unsqueeze(2).unsqueeze(2)\n\n global_avg = 0\n\n with torch.cuda.amp.autocast(enabled=self.config['amp']):\n # image features never change, compute once\n key, shrinkage, selection, f16, f8, f4 = self.XMem('encode_key', frames)\n\n filler_one = torch.zeros(1, dtype=torch.int64)\n hidden = torch.zeros((b, num_objects, self.config['hidden_dim'], *key.shape[-2:]))\n v16, hidden = self.XMem('encode_value', frames[:,0], f16[:,0], hidden, first_frame_gt[:,0])\n values = v16.unsqueeze(3) # add the time dimension\n\n for ti in range(1, self.num_frames):\n if ti <= self.num_ref_frames:\n ref_values = values\n ref_keys = key[:,:,:ti]\n ref_shrinkage = shrinkage[:,:,:ti] if shrinkage is not None else None\n else:\n # pick num_ref_frames random frames\n # this is not very efficient but I think we would \n # need broadcasting in gather which we don't have\n indices = [\n torch.cat([filler_one, torch.randperm(ti-1)[:self.num_ref_frames-1]+1])\n for _ in range(b)]\n ref_values = torch.stack([\n values[bi, :, :, indices[bi]] for bi in range(b)\n ], 0)\n ref_keys = torch.stack([\n key[bi, :, indices[bi]] for bi in range(b)\n ], 0)\n ref_shrinkage = torch.stack([\n shrinkage[bi, :, indices[bi]] for bi in range(b)\n ], 0) if shrinkage is not None else None\n\n # Segment frame ti\n memory_readout = self.XMem('read_memory', key[:,:,ti], selection[:,:,ti] if selection is not None else None, \n ref_keys, ref_shrinkage, ref_values)\n hidden, logits, masks = self.XMem('segment', (f16[:,ti], f8[:,ti], f4[:,ti]), memory_readout, \n hidden, selector, h_out=(ti < (self.num_frames-1)))\n\n # No need to encode the last frame\n if ti < (self.num_frames-1):\n is_deep_update = np.random.rand() < self.deep_update_prob\n v16, hidden = self.XMem('encode_value', frames[:,ti], f16[:,ti], hidden, masks, is_deep_update=is_deep_update)\n values = torch.cat([values, v16.unsqueeze(3)], 3)\n\n out[f'masks_{ti}'] = masks\n out[f'logits_{ti}'] = logits\n\n if self._do_log or self._is_train:\n losses = self.loss_computer.compute({**data, **out}, num_filled_objects, it)\n\n # Logging\n if self._do_log:\n self.integrator.add_dict(losses)\n if self._is_train:\n if it % self.log_image_interval == 0 and it != 0:\n if self.logger is not None:\n images = {**data, **out}\n size = (384, 384)\n self.logger.log_cv2('train/pairs', pool_pairs(images, size, num_filled_objects), it)\n\n if self._is_train:\n\n if (it) % self.log_text_interval == 0 and it != 0:\n time_spent = time.time()-self.last_time\n\n if self.logger is not None:\n self.logger.log_scalar('train/lr', self.scheduler.get_last_lr()[0], it)\n self.logger.log_metrics('train', 'time', (time_spent)/self.log_text_interval, it)\n \n global_avg = 0.5*(global_avg) + 0.5*(time_spent)\n eta_seconds = global_avg * (max_it - it) / 100\n eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))\n print(f'ETA: {eta_string}')\n \n self.last_time = time.time()\n self.train_integrator.finalize('train', it)\n self.train_integrator.reset_except_hooks()\n\n if it % self.save_network_interval == 0 and it != 0:\n if self.logger is not None:\n self.save_network(it)\n\n if it % self.save_checkpoint_interval == 0 and it != 0:\n if self.logger is not None:\n self.save_checkpoint(it)\n\n # Backward pass\n self.optimizer.zero_grad(set_to_none=True)\n if self.config['amp']:\n self.scaler.scale(losses['total_loss']).backward()\n self.scaler.step(self.optimizer)\n self.scaler.update()\n else:\n losses['total_loss'].backward() \n self.optimizer.step()\n\n self.scheduler.step()\n\n def save_network(self, it):\n if self.save_path is None:\n print('Saving has been disabled.')\n return\n \n os.makedirs(os.path.dirname(self.save_path), exist_ok=True)\n model_path = f'{self.save_path}_{it}.pth'\n torch.save(self.XMem.module.state_dict(), model_path)\n print(f'Network saved to {model_path}.')\n\n def save_checkpoint(self, it):\n if self.save_path is None:\n print('Saving has been disabled.')\n return\n\n os.makedirs(os.path.dirname(self.save_path), exist_ok=True)\n checkpoint_path = f'{self.save_path}_checkpoint_{it}.pth'\n checkpoint = { \n 'it': it,\n 'network': self.XMem.module.state_dict(),\n 'optimizer': self.optimizer.state_dict(),\n 'scheduler': self.scheduler.state_dict()}\n torch.save(checkpoint, checkpoint_path)\n print(f'Checkpoint saved to {checkpoint_path}.')\n\n def load_checkpoint(self, path):\n # This method loads everything and should be used to resume training\n map_location = 'cuda:%d' % self.local_rank\n checkpoint = torch.load(path, map_location={'cpu': map_location})\n\n it = checkpoint['it']\n network = checkpoint['network']\n optimizer = checkpoint['optimizer']\n scheduler = checkpoint['scheduler']\n\n map_location = 'cuda:%d' % self.local_rank\n self.XMem.module.load_state_dict(network)\n self.optimizer.load_state_dict(optimizer)\n self.scheduler.load_state_dict(scheduler)\n\n print('Network weights, optimizer states, and scheduler states loaded.')\n\n return it\n\n def load_network_in_memory(self, src_dict):\n self.XMem.module.load_weights(src_dict)\n print('Network weight loaded from memory.')\n\n def load_network(self, path):\n # This method loads only the network weight and should be used to load a pretrained model\n map_location = 'cuda:%d' % self.local_rank\n src_dict = torch.load(path, map_location={'cpu': map_location})\n\n self.load_network_in_memory(src_dict)\n print(f'Network weight loaded from {path}')\n\n def train(self):\n self._is_train = True\n self._do_log = True\n self.integrator = self.train_integrator\n self.XMem.eval()\n return self\n\n def val(self):\n self._is_train = False\n self._do_log = True\n self.XMem.eval()\n return self\n\n def test(self):\n self._is_train = False\n self._do_log = False\n self.XMem.eval()\n return self\n","source_hash":"33f38cfc80a2e3d37cdf5c3910c41cd3b2b15ca407d8b53920396e4c2dbaab18","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.trainer.XMemTrainer","uri":"program://Track-Anything/class/tracker.model.trainer.XMemTrainer#L19-L243","kind":"class","name":"XMemTrainer","path":"tracker/model/trainer.py","language":"python","start_line":19,"end_line":243,"context_start_line":1,"context_end_line":244,"code":"\"\"\"\ntrainer.py - warpper and utility functions for network training\nCompute loss, back-prop, update parameters, logging, etc.\n\"\"\"\nimport datetime\nimport os\nimport time\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\n\nfrom model.network import XMem\nfrom model.losses import LossComputer\nfrom util.log_integrator import Integrator\nfrom util.image_saver import pool_pairs\n\n\nclass XMemTrainer:\n def __init__(self, config, logger=None, save_path=None, local_rank=0, world_size=1):\n self.config = config\n self.num_frames = config['num_frames']\n self.num_ref_frames = config['num_ref_frames']\n self.deep_update_prob = config['deep_update_prob']\n self.local_rank = local_rank\n\n self.XMem = nn.parallel.DistributedDataParallel(\n XMem(config).cuda(), \n device_ids=[local_rank], output_device=local_rank, broadcast_buffers=False)\n\n # Set up logger when local_rank=0\n self.logger = logger\n self.save_path = save_path\n if logger is not None:\n self.last_time = time.time()\n self.logger.log_string('model_size', str(sum([param.nelement() for param in self.XMem.parameters()])))\n self.train_integrator = Integrator(self.logger, distributed=True, local_rank=local_rank, world_size=world_size)\n self.loss_computer = LossComputer(config)\n\n self.train()\n self.optimizer = optim.AdamW(filter(\n lambda p: p.requires_grad, self.XMem.parameters()), lr=config['lr'], weight_decay=config['weight_decay'])\n self.scheduler = optim.lr_scheduler.MultiStepLR(self.optimizer, config['steps'], config['gamma'])\n if config['amp']:\n self.scaler = torch.cuda.amp.GradScaler()\n\n # Logging info\n self.log_text_interval = config['log_text_interval']\n self.log_image_interval = config['log_image_interval']\n self.save_network_interval = config['save_network_interval']\n self.save_checkpoint_interval = config['save_checkpoint_interval']\n if config['debug']:\n self.log_text_interval = self.log_image_interval = 1\n\n def do_pass(self, data, max_it, it=0):\n # No need to store the gradient outside training\n torch.set_grad_enabled(self._is_train)\n\n for k, v in data.items():\n if type(v) != list and type(v) != dict and type(v) != int:\n data[k] = v.cuda(non_blocking=True)\n\n out = {}\n frames = data['rgb']\n first_frame_gt = data['first_frame_gt'].float()\n b = frames.shape[0]\n num_filled_objects = [o.item() for o in data['info']['num_objects']]\n num_objects = first_frame_gt.shape[2]\n selector = data['selector'].unsqueeze(2).unsqueeze(2)\n\n global_avg = 0\n\n with torch.cuda.amp.autocast(enabled=self.config['amp']):\n # image features never change, compute once\n key, shrinkage, selection, f16, f8, f4 = self.XMem('encode_key', frames)\n\n filler_one = torch.zeros(1, dtype=torch.int64)\n hidden = torch.zeros((b, num_objects, self.config['hidden_dim'], *key.shape[-2:]))\n v16, hidden = self.XMem('encode_value', frames[:,0], f16[:,0], hidden, first_frame_gt[:,0])\n values = v16.unsqueeze(3) # add the time dimension\n\n for ti in range(1, self.num_frames):\n if ti <= self.num_ref_frames:\n ref_values = values\n ref_keys = key[:,:,:ti]\n ref_shrinkage = shrinkage[:,:,:ti] if shrinkage is not None else None\n else:\n # pick num_ref_frames random frames\n # this is not very efficient but I think we would \n # need broadcasting in gather which we don't have\n indices = [\n torch.cat([filler_one, torch.randperm(ti-1)[:self.num_ref_frames-1]+1])\n for _ in range(b)]\n ref_values = torch.stack([\n values[bi, :, :, indices[bi]] for bi in range(b)\n ], 0)\n ref_keys = torch.stack([\n key[bi, :, indices[bi]] for bi in range(b)\n ], 0)\n ref_shrinkage = torch.stack([\n shrinkage[bi, :, indices[bi]] for bi in range(b)\n ], 0) if shrinkage is not None else None\n\n # Segment frame ti\n memory_readout = self.XMem('read_memory', key[:,:,ti], selection[:,:,ti] if selection is not None else None, \n ref_keys, ref_shrinkage, ref_values)\n hidden, logits, masks = self.XMem('segment', (f16[:,ti], f8[:,ti], f4[:,ti]), memory_readout, \n hidden, selector, h_out=(ti < (self.num_frames-1)))\n\n # No need to encode the last frame\n if ti < (self.num_frames-1):\n is_deep_update = np.random.rand() < self.deep_update_prob\n v16, hidden = self.XMem('encode_value', frames[:,ti], f16[:,ti], hidden, masks, is_deep_update=is_deep_update)\n values = torch.cat([values, v16.unsqueeze(3)], 3)\n\n out[f'masks_{ti}'] = masks\n out[f'logits_{ti}'] = logits\n\n if self._do_log or self._is_train:\n losses = self.loss_computer.compute({**data, **out}, num_filled_objects, it)\n\n # Logging\n if self._do_log:\n self.integrator.add_dict(losses)\n if self._is_train:\n if it % self.log_image_interval == 0 and it != 0:\n if self.logger is not None:\n images = {**data, **out}\n size = (384, 384)\n self.logger.log_cv2('train/pairs', pool_pairs(images, size, num_filled_objects), it)\n\n if self._is_train:\n\n if (it) % self.log_text_interval == 0 and it != 0:\n time_spent = time.time()-self.last_time\n\n if self.logger is not None:\n self.logger.log_scalar('train/lr', self.scheduler.get_last_lr()[0], it)\n self.logger.log_metrics('train', 'time', (time_spent)/self.log_text_interval, it)\n \n global_avg = 0.5*(global_avg) + 0.5*(time_spent)\n eta_seconds = global_avg * (max_it - it) / 100\n eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))\n print(f'ETA: {eta_string}')\n \n self.last_time = time.time()\n self.train_integrator.finalize('train', it)\n self.train_integrator.reset_except_hooks()\n\n if it % self.save_network_interval == 0 and it != 0:\n if self.logger is not None:\n self.save_network(it)\n\n if it % self.save_checkpoint_interval == 0 and it != 0:\n if self.logger is not None:\n self.save_checkpoint(it)\n\n # Backward pass\n self.optimizer.zero_grad(set_to_none=True)\n if self.config['amp']:\n self.scaler.scale(losses['total_loss']).backward()\n self.scaler.step(self.optimizer)\n self.scaler.update()\n else:\n losses['total_loss'].backward() \n self.optimizer.step()\n\n self.scheduler.step()\n\n def save_network(self, it):\n if self.save_path is None:\n print('Saving has been disabled.')\n return\n \n os.makedirs(os.path.dirname(self.save_path), exist_ok=True)\n model_path = f'{self.save_path}_{it}.pth'\n torch.save(self.XMem.module.state_dict(), model_path)\n print(f'Network saved to {model_path}.')\n\n def save_checkpoint(self, it):\n if self.save_path is None:\n print('Saving has been disabled.')\n return\n\n os.makedirs(os.path.dirname(self.save_path), exist_ok=True)\n checkpoint_path = f'{self.save_path}_checkpoint_{it}.pth'\n checkpoint = { \n 'it': it,\n 'network': self.XMem.module.state_dict(),\n 'optimizer': self.optimizer.state_dict(),\n 'scheduler': self.scheduler.state_dict()}\n torch.save(checkpoint, checkpoint_path)\n print(f'Checkpoint saved to {checkpoint_path}.')\n\n def load_checkpoint(self, path):\n # This method loads everything and should be used to resume training\n map_location = 'cuda:%d' % self.local_rank\n checkpoint = torch.load(path, map_location={'cpu': map_location})\n\n it = checkpoint['it']\n network = checkpoint['network']\n optimizer = checkpoint['optimizer']\n scheduler = checkpoint['scheduler']\n\n map_location = 'cuda:%d' % self.local_rank\n self.XMem.module.load_state_dict(network)\n self.optimizer.load_state_dict(optimizer)\n self.scheduler.load_state_dict(scheduler)\n\n print('Network weights, optimizer states, and scheduler states loaded.')\n\n return it\n\n def load_network_in_memory(self, src_dict):\n self.XMem.module.load_weights(src_dict)\n print('Network weight loaded from memory.')\n\n def load_network(self, path):\n # This method loads only the network weight and should be used to load a pretrained model\n map_location = 'cuda:%d' % self.local_rank\n src_dict = torch.load(path, map_location={'cpu': map_location})\n\n self.load_network_in_memory(src_dict)\n print(f'Network weight loaded from {path}')\n\n def train(self):\n self._is_train = True\n self._do_log = True\n self.integrator = self.train_integrator\n self.XMem.eval()\n return self\n\n def val(self):\n self._is_train = False\n self._do_log = True\n self.XMem.eval()\n return self\n\n def test(self):\n self._is_train = False\n self._do_log = False\n self.XMem.eval()\n return self\n","source_hash":"33f38cfc80a2e3d37cdf5c3910c41cd3b2b15ca407d8b53920396e4c2dbaab18","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.trainer.__init__","uri":"program://Track-Anything/function/tracker.model.trainer.__init__#L20-L53","kind":"function","name":"__init__","path":"tracker/model/trainer.py","language":"python","start_line":20,"end_line":53,"context_start_line":1,"context_end_line":73,"code":"\"\"\"\ntrainer.py - warpper and utility functions for network training\nCompute loss, back-prop, update parameters, logging, etc.\n\"\"\"\nimport datetime\nimport os\nimport time\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\n\nfrom model.network import XMem\nfrom model.losses import LossComputer\nfrom util.log_integrator import Integrator\nfrom util.image_saver import pool_pairs\n\n\nclass XMemTrainer:\n def __init__(self, config, logger=None, save_path=None, local_rank=0, world_size=1):\n self.config = config\n self.num_frames = config['num_frames']\n self.num_ref_frames = config['num_ref_frames']\n self.deep_update_prob = config['deep_update_prob']\n self.local_rank = local_rank\n\n self.XMem = nn.parallel.DistributedDataParallel(\n XMem(config).cuda(), \n device_ids=[local_rank], output_device=local_rank, broadcast_buffers=False)\n\n # Set up logger when local_rank=0\n self.logger = logger\n self.save_path = save_path\n if logger is not None:\n self.last_time = time.time()\n self.logger.log_string('model_size', str(sum([param.nelement() for param in self.XMem.parameters()])))\n self.train_integrator = Integrator(self.logger, distributed=True, local_rank=local_rank, world_size=world_size)\n self.loss_computer = LossComputer(config)\n\n self.train()\n self.optimizer = optim.AdamW(filter(\n lambda p: p.requires_grad, self.XMem.parameters()), lr=config['lr'], weight_decay=config['weight_decay'])\n self.scheduler = optim.lr_scheduler.MultiStepLR(self.optimizer, config['steps'], config['gamma'])\n if config['amp']:\n self.scaler = torch.cuda.amp.GradScaler()\n\n # Logging info\n self.log_text_interval = config['log_text_interval']\n self.log_image_interval = config['log_image_interval']\n self.save_network_interval = config['save_network_interval']\n self.save_checkpoint_interval = config['save_checkpoint_interval']\n if config['debug']:\n self.log_text_interval = self.log_image_interval = 1\n\n def do_pass(self, data, max_it, it=0):\n # No need to store the gradient outside training\n torch.set_grad_enabled(self._is_train)\n\n for k, v in data.items():\n if type(v) != list and type(v) != dict and type(v) != int:\n data[k] = v.cuda(non_blocking=True)\n\n out = {}\n frames = data['rgb']\n first_frame_gt = data['first_frame_gt'].float()\n b = frames.shape[0]\n num_filled_objects = [o.item() for o in data['info']['num_objects']]\n num_objects = first_frame_gt.shape[2]\n selector = data['selector'].unsqueeze(2).unsqueeze(2)\n\n global_avg = 0\n\n with torch.cuda.amp.autocast(enabled=self.config['amp']):","source_hash":"33f38cfc80a2e3d37cdf5c3910c41cd3b2b15ca407d8b53920396e4c2dbaab18","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.trainer.do_pass","uri":"program://Track-Anything/function/tracker.model.trainer.do_pass#L55-L168","kind":"function","name":"do_pass","path":"tracker/model/trainer.py","language":"python","start_line":55,"end_line":168,"context_start_line":35,"context_end_line":188,"code":" self.last_time = time.time()\n self.logger.log_string('model_size', str(sum([param.nelement() for param in self.XMem.parameters()])))\n self.train_integrator = Integrator(self.logger, distributed=True, local_rank=local_rank, world_size=world_size)\n self.loss_computer = LossComputer(config)\n\n self.train()\n self.optimizer = optim.AdamW(filter(\n lambda p: p.requires_grad, self.XMem.parameters()), lr=config['lr'], weight_decay=config['weight_decay'])\n self.scheduler = optim.lr_scheduler.MultiStepLR(self.optimizer, config['steps'], config['gamma'])\n if config['amp']:\n self.scaler = torch.cuda.amp.GradScaler()\n\n # Logging info\n self.log_text_interval = config['log_text_interval']\n self.log_image_interval = config['log_image_interval']\n self.save_network_interval = config['save_network_interval']\n self.save_checkpoint_interval = config['save_checkpoint_interval']\n if config['debug']:\n self.log_text_interval = self.log_image_interval = 1\n\n def do_pass(self, data, max_it, it=0):\n # No need to store the gradient outside training\n torch.set_grad_enabled(self._is_train)\n\n for k, v in data.items():\n if type(v) != list and type(v) != dict and type(v) != int:\n data[k] = v.cuda(non_blocking=True)\n\n out = {}\n frames = data['rgb']\n first_frame_gt = data['first_frame_gt'].float()\n b = frames.shape[0]\n num_filled_objects = [o.item() for o in data['info']['num_objects']]\n num_objects = first_frame_gt.shape[2]\n selector = data['selector'].unsqueeze(2).unsqueeze(2)\n\n global_avg = 0\n\n with torch.cuda.amp.autocast(enabled=self.config['amp']):\n # image features never change, compute once\n key, shrinkage, selection, f16, f8, f4 = self.XMem('encode_key', frames)\n\n filler_one = torch.zeros(1, dtype=torch.int64)\n hidden = torch.zeros((b, num_objects, self.config['hidden_dim'], *key.shape[-2:]))\n v16, hidden = self.XMem('encode_value', frames[:,0], f16[:,0], hidden, first_frame_gt[:,0])\n values = v16.unsqueeze(3) # add the time dimension\n\n for ti in range(1, self.num_frames):\n if ti <= self.num_ref_frames:\n ref_values = values\n ref_keys = key[:,:,:ti]\n ref_shrinkage = shrinkage[:,:,:ti] if shrinkage is not None else None\n else:\n # pick num_ref_frames random frames\n # this is not very efficient but I think we would \n # need broadcasting in gather which we don't have\n indices = [\n torch.cat([filler_one, torch.randperm(ti-1)[:self.num_ref_frames-1]+1])\n for _ in range(b)]\n ref_values = torch.stack([\n values[bi, :, :, indices[bi]] for bi in range(b)\n ], 0)\n ref_keys = torch.stack([\n key[bi, :, indices[bi]] for bi in range(b)\n ], 0)\n ref_shrinkage = torch.stack([\n shrinkage[bi, :, indices[bi]] for bi in range(b)\n ], 0) if shrinkage is not None else None\n\n # Segment frame ti\n memory_readout = self.XMem('read_memory', key[:,:,ti], selection[:,:,ti] if selection is not None else None, \n ref_keys, ref_shrinkage, ref_values)\n hidden, logits, masks = self.XMem('segment', (f16[:,ti], f8[:,ti], f4[:,ti]), memory_readout, \n hidden, selector, h_out=(ti < (self.num_frames-1)))\n\n # No need to encode the last frame\n if ti < (self.num_frames-1):\n is_deep_update = np.random.rand() < self.deep_update_prob\n v16, hidden = self.XMem('encode_value', frames[:,ti], f16[:,ti], hidden, masks, is_deep_update=is_deep_update)\n values = torch.cat([values, v16.unsqueeze(3)], 3)\n\n out[f'masks_{ti}'] = masks\n out[f'logits_{ti}'] = logits\n\n if self._do_log or self._is_train:\n losses = self.loss_computer.compute({**data, **out}, num_filled_objects, it)\n\n # Logging\n if self._do_log:\n self.integrator.add_dict(losses)\n if self._is_train:\n if it % self.log_image_interval == 0 and it != 0:\n if self.logger is not None:\n images = {**data, **out}\n size = (384, 384)\n self.logger.log_cv2('train/pairs', pool_pairs(images, size, num_filled_objects), it)\n\n if self._is_train:\n\n if (it) % self.log_text_interval == 0 and it != 0:\n time_spent = time.time()-self.last_time\n\n if self.logger is not None:\n self.logger.log_scalar('train/lr', self.scheduler.get_last_lr()[0], it)\n self.logger.log_metrics('train', 'time', (time_spent)/self.log_text_interval, it)\n \n global_avg = 0.5*(global_avg) + 0.5*(time_spent)\n eta_seconds = global_avg * (max_it - it) / 100\n eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))\n print(f'ETA: {eta_string}')\n \n self.last_time = time.time()\n self.train_integrator.finalize('train', it)\n self.train_integrator.reset_except_hooks()\n\n if it % self.save_network_interval == 0 and it != 0:\n if self.logger is not None:\n self.save_network(it)\n\n if it % self.save_checkpoint_interval == 0 and it != 0:\n if self.logger is not None:\n self.save_checkpoint(it)\n\n # Backward pass\n self.optimizer.zero_grad(set_to_none=True)\n if self.config['amp']:\n self.scaler.scale(losses['total_loss']).backward()\n self.scaler.step(self.optimizer)\n self.scaler.update()\n else:\n losses['total_loss'].backward() \n self.optimizer.step()\n\n self.scheduler.step()\n\n def save_network(self, it):\n if self.save_path is None:\n print('Saving has been disabled.')\n return\n \n os.makedirs(os.path.dirname(self.save_path), exist_ok=True)\n model_path = f'{self.save_path}_{it}.pth'\n torch.save(self.XMem.module.state_dict(), model_path)\n print(f'Network saved to {model_path}.')\n\n def save_checkpoint(self, it):\n if self.save_path is None:\n print('Saving has been disabled.')\n return\n\n os.makedirs(os.path.dirname(self.save_path), exist_ok=True)\n checkpoint_path = f'{self.save_path}_checkpoint_{it}.pth'\n checkpoint = { \n 'it': it,","source_hash":"33f38cfc80a2e3d37cdf5c3910c41cd3b2b15ca407d8b53920396e4c2dbaab18","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.trainer.save_network","uri":"program://Track-Anything/function/tracker.model.trainer.save_network#L170-L178","kind":"function","name":"save_network","path":"tracker/model/trainer.py","language":"python","start_line":170,"end_line":178,"context_start_line":150,"context_end_line":198,"code":" if it % self.save_network_interval == 0 and it != 0:\n if self.logger is not None:\n self.save_network(it)\n\n if it % self.save_checkpoint_interval == 0 and it != 0:\n if self.logger is not None:\n self.save_checkpoint(it)\n\n # Backward pass\n self.optimizer.zero_grad(set_to_none=True)\n if self.config['amp']:\n self.scaler.scale(losses['total_loss']).backward()\n self.scaler.step(self.optimizer)\n self.scaler.update()\n else:\n losses['total_loss'].backward() \n self.optimizer.step()\n\n self.scheduler.step()\n\n def save_network(self, it):\n if self.save_path is None:\n print('Saving has been disabled.')\n return\n \n os.makedirs(os.path.dirname(self.save_path), exist_ok=True)\n model_path = f'{self.save_path}_{it}.pth'\n torch.save(self.XMem.module.state_dict(), model_path)\n print(f'Network saved to {model_path}.')\n\n def save_checkpoint(self, it):\n if self.save_path is None:\n print('Saving has been disabled.')\n return\n\n os.makedirs(os.path.dirname(self.save_path), exist_ok=True)\n checkpoint_path = f'{self.save_path}_checkpoint_{it}.pth'\n checkpoint = { \n 'it': it,\n 'network': self.XMem.module.state_dict(),\n 'optimizer': self.optimizer.state_dict(),\n 'scheduler': self.scheduler.state_dict()}\n torch.save(checkpoint, checkpoint_path)\n print(f'Checkpoint saved to {checkpoint_path}.')\n\n def load_checkpoint(self, path):\n # This method loads everything and should be used to resume training\n map_location = 'cuda:%d' % self.local_rank\n checkpoint = torch.load(path, map_location={'cpu': map_location})","source_hash":"33f38cfc80a2e3d37cdf5c3910c41cd3b2b15ca407d8b53920396e4c2dbaab18","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.trainer.save_checkpoint","uri":"program://Track-Anything/function/tracker.model.trainer.save_checkpoint#L180-L193","kind":"function","name":"save_checkpoint","path":"tracker/model/trainer.py","language":"python","start_line":180,"end_line":193,"context_start_line":160,"context_end_line":213,"code":" if self.config['amp']:\n self.scaler.scale(losses['total_loss']).backward()\n self.scaler.step(self.optimizer)\n self.scaler.update()\n else:\n losses['total_loss'].backward() \n self.optimizer.step()\n\n self.scheduler.step()\n\n def save_network(self, it):\n if self.save_path is None:\n print('Saving has been disabled.')\n return\n \n os.makedirs(os.path.dirname(self.save_path), exist_ok=True)\n model_path = f'{self.save_path}_{it}.pth'\n torch.save(self.XMem.module.state_dict(), model_path)\n print(f'Network saved to {model_path}.')\n\n def save_checkpoint(self, it):\n if self.save_path is None:\n print('Saving has been disabled.')\n return\n\n os.makedirs(os.path.dirname(self.save_path), exist_ok=True)\n checkpoint_path = f'{self.save_path}_checkpoint_{it}.pth'\n checkpoint = { \n 'it': it,\n 'network': self.XMem.module.state_dict(),\n 'optimizer': self.optimizer.state_dict(),\n 'scheduler': self.scheduler.state_dict()}\n torch.save(checkpoint, checkpoint_path)\n print(f'Checkpoint saved to {checkpoint_path}.')\n\n def load_checkpoint(self, path):\n # This method loads everything and should be used to resume training\n map_location = 'cuda:%d' % self.local_rank\n checkpoint = torch.load(path, map_location={'cpu': map_location})\n\n it = checkpoint['it']\n network = checkpoint['network']\n optimizer = checkpoint['optimizer']\n scheduler = checkpoint['scheduler']\n\n map_location = 'cuda:%d' % self.local_rank\n self.XMem.module.load_state_dict(network)\n self.optimizer.load_state_dict(optimizer)\n self.scheduler.load_state_dict(scheduler)\n\n print('Network weights, optimizer states, and scheduler states loaded.')\n\n return it\n","source_hash":"33f38cfc80a2e3d37cdf5c3910c41cd3b2b15ca407d8b53920396e4c2dbaab18","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.trainer.load_checkpoint","uri":"program://Track-Anything/function/tracker.model.trainer.load_checkpoint#L195-L212","kind":"function","name":"load_checkpoint","path":"tracker/model/trainer.py","language":"python","start_line":195,"end_line":212,"context_start_line":175,"context_end_line":232,"code":" os.makedirs(os.path.dirname(self.save_path), exist_ok=True)\n model_path = f'{self.save_path}_{it}.pth'\n torch.save(self.XMem.module.state_dict(), model_path)\n print(f'Network saved to {model_path}.')\n\n def save_checkpoint(self, it):\n if self.save_path is None:\n print('Saving has been disabled.')\n return\n\n os.makedirs(os.path.dirname(self.save_path), exist_ok=True)\n checkpoint_path = f'{self.save_path}_checkpoint_{it}.pth'\n checkpoint = { \n 'it': it,\n 'network': self.XMem.module.state_dict(),\n 'optimizer': self.optimizer.state_dict(),\n 'scheduler': self.scheduler.state_dict()}\n torch.save(checkpoint, checkpoint_path)\n print(f'Checkpoint saved to {checkpoint_path}.')\n\n def load_checkpoint(self, path):\n # This method loads everything and should be used to resume training\n map_location = 'cuda:%d' % self.local_rank\n checkpoint = torch.load(path, map_location={'cpu': map_location})\n\n it = checkpoint['it']\n network = checkpoint['network']\n optimizer = checkpoint['optimizer']\n scheduler = checkpoint['scheduler']\n\n map_location = 'cuda:%d' % self.local_rank\n self.XMem.module.load_state_dict(network)\n self.optimizer.load_state_dict(optimizer)\n self.scheduler.load_state_dict(scheduler)\n\n print('Network weights, optimizer states, and scheduler states loaded.')\n\n return it\n\n def load_network_in_memory(self, src_dict):\n self.XMem.module.load_weights(src_dict)\n print('Network weight loaded from memory.')\n\n def load_network(self, path):\n # This method loads only the network weight and should be used to load a pretrained model\n map_location = 'cuda:%d' % self.local_rank\n src_dict = torch.load(path, map_location={'cpu': map_location})\n\n self.load_network_in_memory(src_dict)\n print(f'Network weight loaded from {path}')\n\n def train(self):\n self._is_train = True\n self._do_log = True\n self.integrator = self.train_integrator\n self.XMem.eval()\n return self\n","source_hash":"33f38cfc80a2e3d37cdf5c3910c41cd3b2b15ca407d8b53920396e4c2dbaab18","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.trainer.load_network_in_memory","uri":"program://Track-Anything/function/tracker.model.trainer.load_network_in_memory#L214-L216","kind":"function","name":"load_network_in_memory","path":"tracker/model/trainer.py","language":"python","start_line":214,"end_line":216,"context_start_line":194,"context_end_line":236,"code":"\n def load_checkpoint(self, path):\n # This method loads everything and should be used to resume training\n map_location = 'cuda:%d' % self.local_rank\n checkpoint = torch.load(path, map_location={'cpu': map_location})\n\n it = checkpoint['it']\n network = checkpoint['network']\n optimizer = checkpoint['optimizer']\n scheduler = checkpoint['scheduler']\n\n map_location = 'cuda:%d' % self.local_rank\n self.XMem.module.load_state_dict(network)\n self.optimizer.load_state_dict(optimizer)\n self.scheduler.load_state_dict(scheduler)\n\n print('Network weights, optimizer states, and scheduler states loaded.')\n\n return it\n\n def load_network_in_memory(self, src_dict):\n self.XMem.module.load_weights(src_dict)\n print('Network weight loaded from memory.')\n\n def load_network(self, path):\n # This method loads only the network weight and should be used to load a pretrained model\n map_location = 'cuda:%d' % self.local_rank\n src_dict = torch.load(path, map_location={'cpu': map_location})\n\n self.load_network_in_memory(src_dict)\n print(f'Network weight loaded from {path}')\n\n def train(self):\n self._is_train = True\n self._do_log = True\n self.integrator = self.train_integrator\n self.XMem.eval()\n return self\n\n def val(self):\n self._is_train = False\n self._do_log = True\n self.XMem.eval()","source_hash":"33f38cfc80a2e3d37cdf5c3910c41cd3b2b15ca407d8b53920396e4c2dbaab18","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.trainer.load_network","uri":"program://Track-Anything/function/tracker.model.trainer.load_network#L218-L224","kind":"function","name":"load_network","path":"tracker/model/trainer.py","language":"python","start_line":218,"end_line":224,"context_start_line":198,"context_end_line":244,"code":" checkpoint = torch.load(path, map_location={'cpu': map_location})\n\n it = checkpoint['it']\n network = checkpoint['network']\n optimizer = checkpoint['optimizer']\n scheduler = checkpoint['scheduler']\n\n map_location = 'cuda:%d' % self.local_rank\n self.XMem.module.load_state_dict(network)\n self.optimizer.load_state_dict(optimizer)\n self.scheduler.load_state_dict(scheduler)\n\n print('Network weights, optimizer states, and scheduler states loaded.')\n\n return it\n\n def load_network_in_memory(self, src_dict):\n self.XMem.module.load_weights(src_dict)\n print('Network weight loaded from memory.')\n\n def load_network(self, path):\n # This method loads only the network weight and should be used to load a pretrained model\n map_location = 'cuda:%d' % self.local_rank\n src_dict = torch.load(path, map_location={'cpu': map_location})\n\n self.load_network_in_memory(src_dict)\n print(f'Network weight loaded from {path}')\n\n def train(self):\n self._is_train = True\n self._do_log = True\n self.integrator = self.train_integrator\n self.XMem.eval()\n return self\n\n def val(self):\n self._is_train = False\n self._do_log = True\n self.XMem.eval()\n return self\n\n def test(self):\n self._is_train = False\n self._do_log = False\n self.XMem.eval()\n return self\n","source_hash":"33f38cfc80a2e3d37cdf5c3910c41cd3b2b15ca407d8b53920396e4c2dbaab18","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.trainer.train","uri":"program://Track-Anything/function/tracker.model.trainer.train#L226-L231","kind":"function","name":"train","path":"tracker/model/trainer.py","language":"python","start_line":226,"end_line":231,"context_start_line":206,"context_end_line":244,"code":" self.XMem.module.load_state_dict(network)\n self.optimizer.load_state_dict(optimizer)\n self.scheduler.load_state_dict(scheduler)\n\n print('Network weights, optimizer states, and scheduler states loaded.')\n\n return it\n\n def load_network_in_memory(self, src_dict):\n self.XMem.module.load_weights(src_dict)\n print('Network weight loaded from memory.')\n\n def load_network(self, path):\n # This method loads only the network weight and should be used to load a pretrained model\n map_location = 'cuda:%d' % self.local_rank\n src_dict = torch.load(path, map_location={'cpu': map_location})\n\n self.load_network_in_memory(src_dict)\n print(f'Network weight loaded from {path}')\n\n def train(self):\n self._is_train = True\n self._do_log = True\n self.integrator = self.train_integrator\n self.XMem.eval()\n return self\n\n def val(self):\n self._is_train = False\n self._do_log = True\n self.XMem.eval()\n return self\n\n def test(self):\n self._is_train = False\n self._do_log = False\n self.XMem.eval()\n return self\n","source_hash":"33f38cfc80a2e3d37cdf5c3910c41cd3b2b15ca407d8b53920396e4c2dbaab18","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.trainer.val","uri":"program://Track-Anything/function/tracker.model.trainer.val#L233-L237","kind":"function","name":"val","path":"tracker/model/trainer.py","language":"python","start_line":233,"end_line":237,"context_start_line":213,"context_end_line":244,"code":"\n def load_network_in_memory(self, src_dict):\n self.XMem.module.load_weights(src_dict)\n print('Network weight loaded from memory.')\n\n def load_network(self, path):\n # This method loads only the network weight and should be used to load a pretrained model\n map_location = 'cuda:%d' % self.local_rank\n src_dict = torch.load(path, map_location={'cpu': map_location})\n\n self.load_network_in_memory(src_dict)\n print(f'Network weight loaded from {path}')\n\n def train(self):\n self._is_train = True\n self._do_log = True\n self.integrator = self.train_integrator\n self.XMem.eval()\n return self\n\n def val(self):\n self._is_train = False\n self._do_log = True\n self.XMem.eval()\n return self\n\n def test(self):\n self._is_train = False\n self._do_log = False\n self.XMem.eval()\n return self\n","source_hash":"33f38cfc80a2e3d37cdf5c3910c41cd3b2b15ca407d8b53920396e4c2dbaab18","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.trainer.test","uri":"program://Track-Anything/function/tracker.model.trainer.test#L239-L243","kind":"function","name":"test","path":"tracker/model/trainer.py","language":"python","start_line":239,"end_line":243,"context_start_line":219,"context_end_line":244,"code":" # This method loads only the network weight and should be used to load a pretrained model\n map_location = 'cuda:%d' % self.local_rank\n src_dict = torch.load(path, map_location={'cpu': map_location})\n\n self.load_network_in_memory(src_dict)\n print(f'Network weight loaded from {path}')\n\n def train(self):\n self._is_train = True\n self._do_log = True\n self.integrator = self.train_integrator\n self.XMem.eval()\n return self\n\n def val(self):\n self._is_train = False\n self._do_log = True\n self.XMem.eval()\n return self\n\n def test(self):\n self._is_train = False\n self._do_log = False\n self.XMem.eval()\n return self\n","source_hash":"33f38cfc80a2e3d37cdf5c3910c41cd3b2b15ca407d8b53920396e4c2dbaab18","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.resnet","uri":"program://Track-Anything/module/tracker.model.resnet#L1-L165","kind":"module","name":"tracker.model.resnet","path":"tracker/model/resnet.py","language":"python","start_line":1,"end_line":165,"context_start_line":1,"context_end_line":165,"code":"\"\"\"\nresnet.py - A modified ResNet structure\nWe append extra channels to the first conv by some network surgery\n\"\"\"\n\nfrom collections import OrderedDict\nimport math\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils import model_zoo\n\n\ndef load_weights_add_extra_dim(target, source_state, extra_dim=1):\n\tnew_dict = OrderedDict()\n\n\tfor k1, v1 in target.state_dict().items():\n\t\tif not 'num_batches_tracked' in k1:\n\t\t\tif k1 in source_state:\n\t\t\t\ttar_v = source_state[k1]\n\n\t\t\t\tif v1.shape != tar_v.shape:\n\t\t\t\t\t# Init the new segmentation channel with zeros\n\t\t\t\t\t# print(v1.shape, tar_v.shape)\n\t\t\t\t\tc, _, w, h = v1.shape\n\t\t\t\t\tpads = torch.zeros((c,extra_dim,w,h), device=tar_v.device)\n\t\t\t\t\tnn.init.orthogonal_(pads)\n\t\t\t\t\ttar_v = torch.cat([tar_v, pads], 1)\n\n\t\t\t\tnew_dict[k1] = tar_v\n\n\ttarget.load_state_dict(new_dict)\n\n\nmodel_urls = {\n\t'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',\n\t'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',\n}\n\n\ndef conv3x3(in_planes, out_planes, stride=1, dilation=1):\n\treturn nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,\n\t\t\t\t\t padding=dilation, dilation=dilation, bias=False)\n\n\nclass BasicBlock(nn.Module):\n\texpansion = 1\n\n\tdef __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):\n\t\tsuper(BasicBlock, self).__init__()\n\t\tself.conv1 = conv3x3(inplanes, planes, stride=stride, dilation=dilation)\n\t\tself.bn1 = nn.BatchNorm2d(planes)\n\t\tself.relu = nn.ReLU(inplace=True)\n\t\tself.conv2 = conv3x3(planes, planes, stride=1, dilation=dilation)\n\t\tself.bn2 = nn.BatchNorm2d(planes)\n\t\tself.downsample = downsample\n\t\tself.stride = stride\n\n\tdef forward(self, x):\n\t\tresidual = x\n\n\t\tout = self.conv1(x)\n\t\tout = self.bn1(out)\n\t\tout = self.relu(out)\n\n\t\tout = self.conv2(out)\n\t\tout = self.bn2(out)\n\n\t\tif self.downsample is not None:\n\t\t\tresidual = self.downsample(x)\n\n\t\tout += residual\n\t\tout = self.relu(out)\n\n\t\treturn out\n\n\nclass Bottleneck(nn.Module):\n\texpansion = 4\n\n\tdef __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):\n\t\tsuper(Bottleneck, self).__init__()\n\t\tself.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n\t\tself.bn1 = nn.BatchNorm2d(planes)\n\t\tself.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, dilation=dilation,\n\t\t\t\t\t\t\t padding=dilation, bias=False)\n\t\tself.bn2 = nn.BatchNorm2d(planes)\n\t\tself.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n\t\tself.bn3 = nn.BatchNorm2d(planes * 4)\n\t\tself.relu = nn.ReLU(inplace=True)\n\t\tself.downsample = downsample\n\t\tself.stride = stride\n\n\tdef forward(self, x):\n\t\tresidual = x\n\n\t\tout = self.conv1(x)\n\t\tout = self.bn1(out)\n\t\tout = self.relu(out)\n\n\t\tout = self.conv2(out)\n\t\tout = self.bn2(out)\n\t\tout = self.relu(out)\n\n\t\tout = self.conv3(out)\n\t\tout = self.bn3(out)\n\n\t\tif self.downsample is not None:\n\t\t\tresidual = self.downsample(x)\n\n\t\tout += residual\n\t\tout = self.relu(out)\n\n\t\treturn out\n\n\nclass ResNet(nn.Module):\n\tdef __init__(self, block, layers=(3, 4, 23, 3), extra_dim=0):\n\t\tself.inplanes = 64\n\t\tsuper(ResNet, self).__init__()\n\t\tself.conv1 = nn.Conv2d(3+extra_dim, 64, kernel_size=7, stride=2, padding=3, bias=False)\n\t\tself.bn1 = nn.BatchNorm2d(64)\n\t\tself.relu = nn.ReLU(inplace=True)\n\t\tself.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n\t\tself.layer1 = self._make_layer(block, 64, layers[0])\n\t\tself.layer2 = self._make_layer(block, 128, layers[1], stride=2)\n\t\tself.layer3 = self._make_layer(block, 256, layers[2], stride=2)\n\t\tself.layer4 = self._make_layer(block, 512, layers[3], stride=2)\n\n\t\tfor m in self.modules():\n\t\t\tif isinstance(m, nn.Conv2d):\n\t\t\t\tn = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n\t\t\t\tm.weight.data.normal_(0, math.sqrt(2. / n))\n\t\t\telif isinstance(m, nn.BatchNorm2d):\n\t\t\t\tm.weight.data.fill_(1)\n\t\t\t\tm.bias.data.zero_()\n\n\tdef _make_layer(self, block, planes, blocks, stride=1, dilation=1):\n\t\tdownsample = None\n\t\tif stride != 1 or self.inplanes != planes * block.expansion:\n\t\t\tdownsample = nn.Sequential(\n\t\t\t\tnn.Conv2d(self.inplanes, planes * block.expansion,\n\t\t\t\t\t\t kernel_size=1, stride=stride, bias=False),\n\t\t\t\tnn.BatchNorm2d(planes * block.expansion),\n\t\t\t)\n\n\t\tlayers = [block(self.inplanes, planes, stride, downsample)]\n\t\tself.inplanes = planes * block.expansion\n\t\tfor i in range(1, blocks):\n\t\t\tlayers.append(block(self.inplanes, planes, dilation=dilation))\n\n\t\treturn nn.Sequential(*layers)\n\ndef resnet18(pretrained=True, extra_dim=0):\n\tmodel = ResNet(BasicBlock, [2, 2, 2, 2], extra_dim)\n\tif pretrained:\n\t\tload_weights_add_extra_dim(model, model_zoo.load_url(model_urls['resnet18']), extra_dim)\n\treturn model\n\ndef resnet50(pretrained=True, extra_dim=0):\n\tmodel = ResNet(Bottleneck, [3, 4, 6, 3], extra_dim)\n\tif pretrained:\n\t\tload_weights_add_extra_dim(model, model_zoo.load_url(model_urls['resnet50']), extra_dim)\n\treturn model\n","source_hash":"d783845e6b1b75bda8fb323048c1f6e0974e663dc0c1773194906e7e084439aa","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.resnet.load_weights_add_extra_dim","uri":"program://Track-Anything/function/tracker.model.resnet.load_weights_add_extra_dim#L14-L32","kind":"function","name":"load_weights_add_extra_dim","path":"tracker/model/resnet.py","language":"python","start_line":14,"end_line":32,"context_start_line":1,"context_end_line":52,"code":"\"\"\"\nresnet.py - A modified ResNet structure\nWe append extra channels to the first conv by some network surgery\n\"\"\"\n\nfrom collections import OrderedDict\nimport math\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils import model_zoo\n\n\ndef load_weights_add_extra_dim(target, source_state, extra_dim=1):\n\tnew_dict = OrderedDict()\n\n\tfor k1, v1 in target.state_dict().items():\n\t\tif not 'num_batches_tracked' in k1:\n\t\t\tif k1 in source_state:\n\t\t\t\ttar_v = source_state[k1]\n\n\t\t\t\tif v1.shape != tar_v.shape:\n\t\t\t\t\t# Init the new segmentation channel with zeros\n\t\t\t\t\t# print(v1.shape, tar_v.shape)\n\t\t\t\t\tc, _, w, h = v1.shape\n\t\t\t\t\tpads = torch.zeros((c,extra_dim,w,h), device=tar_v.device)\n\t\t\t\t\tnn.init.orthogonal_(pads)\n\t\t\t\t\ttar_v = torch.cat([tar_v, pads], 1)\n\n\t\t\t\tnew_dict[k1] = tar_v\n\n\ttarget.load_state_dict(new_dict)\n\n\nmodel_urls = {\n\t'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',\n\t'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',\n}\n\n\ndef conv3x3(in_planes, out_planes, stride=1, dilation=1):\n\treturn nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,\n\t\t\t\t\t padding=dilation, dilation=dilation, bias=False)\n\n\nclass BasicBlock(nn.Module):\n\texpansion = 1\n\n\tdef __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):\n\t\tsuper(BasicBlock, self).__init__()\n\t\tself.conv1 = conv3x3(inplanes, planes, stride=stride, dilation=dilation)\n\t\tself.bn1 = nn.BatchNorm2d(planes)","source_hash":"d783845e6b1b75bda8fb323048c1f6e0974e663dc0c1773194906e7e084439aa","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.resnet.conv3x3","uri":"program://Track-Anything/function/tracker.model.resnet.conv3x3#L41-L43","kind":"function","name":"conv3x3","path":"tracker/model/resnet.py","language":"python","start_line":41,"end_line":43,"context_start_line":21,"context_end_line":63,"code":"\n\t\t\t\tif v1.shape != tar_v.shape:\n\t\t\t\t\t# Init the new segmentation channel with zeros\n\t\t\t\t\t# print(v1.shape, tar_v.shape)\n\t\t\t\t\tc, _, w, h = v1.shape\n\t\t\t\t\tpads = torch.zeros((c,extra_dim,w,h), device=tar_v.device)\n\t\t\t\t\tnn.init.orthogonal_(pads)\n\t\t\t\t\ttar_v = torch.cat([tar_v, pads], 1)\n\n\t\t\t\tnew_dict[k1] = tar_v\n\n\ttarget.load_state_dict(new_dict)\n\n\nmodel_urls = {\n\t'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',\n\t'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',\n}\n\n\ndef conv3x3(in_planes, out_planes, stride=1, dilation=1):\n\treturn nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,\n\t\t\t\t\t padding=dilation, dilation=dilation, bias=False)\n\n\nclass BasicBlock(nn.Module):\n\texpansion = 1\n\n\tdef __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):\n\t\tsuper(BasicBlock, self).__init__()\n\t\tself.conv1 = conv3x3(inplanes, planes, stride=stride, dilation=dilation)\n\t\tself.bn1 = nn.BatchNorm2d(planes)\n\t\tself.relu = nn.ReLU(inplace=True)\n\t\tself.conv2 = conv3x3(planes, planes, stride=1, dilation=dilation)\n\t\tself.bn2 = nn.BatchNorm2d(planes)\n\t\tself.downsample = downsample\n\t\tself.stride = stride\n\n\tdef forward(self, x):\n\t\tresidual = x\n\n\t\tout = self.conv1(x)\n\t\tout = self.bn1(out)","source_hash":"d783845e6b1b75bda8fb323048c1f6e0974e663dc0c1773194906e7e084439aa","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.resnet.BasicBlock","uri":"program://Track-Anything/class/tracker.model.resnet.BasicBlock#L46-L75","kind":"class","name":"BasicBlock","path":"tracker/model/resnet.py","language":"python","start_line":46,"end_line":75,"context_start_line":26,"context_end_line":95,"code":"\t\t\t\t\tpads = torch.zeros((c,extra_dim,w,h), device=tar_v.device)\n\t\t\t\t\tnn.init.orthogonal_(pads)\n\t\t\t\t\ttar_v = torch.cat([tar_v, pads], 1)\n\n\t\t\t\tnew_dict[k1] = tar_v\n\n\ttarget.load_state_dict(new_dict)\n\n\nmodel_urls = {\n\t'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',\n\t'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',\n}\n\n\ndef conv3x3(in_planes, out_planes, stride=1, dilation=1):\n\treturn nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,\n\t\t\t\t\t padding=dilation, dilation=dilation, bias=False)\n\n\nclass BasicBlock(nn.Module):\n\texpansion = 1\n\n\tdef __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):\n\t\tsuper(BasicBlock, self).__init__()\n\t\tself.conv1 = conv3x3(inplanes, planes, stride=stride, dilation=dilation)\n\t\tself.bn1 = nn.BatchNorm2d(planes)\n\t\tself.relu = nn.ReLU(inplace=True)\n\t\tself.conv2 = conv3x3(planes, planes, stride=1, dilation=dilation)\n\t\tself.bn2 = nn.BatchNorm2d(planes)\n\t\tself.downsample = downsample\n\t\tself.stride = stride\n\n\tdef forward(self, x):\n\t\tresidual = x\n\n\t\tout = self.conv1(x)\n\t\tout = self.bn1(out)\n\t\tout = self.relu(out)\n\n\t\tout = self.conv2(out)\n\t\tout = self.bn2(out)\n\n\t\tif self.downsample is not None:\n\t\t\tresidual = self.downsample(x)\n\n\t\tout += residual\n\t\tout = self.relu(out)\n\n\t\treturn out\n\n\nclass Bottleneck(nn.Module):\n\texpansion = 4\n\n\tdef __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):\n\t\tsuper(Bottleneck, self).__init__()\n\t\tself.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n\t\tself.bn1 = nn.BatchNorm2d(planes)\n\t\tself.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, dilation=dilation,\n\t\t\t\t\t\t\t padding=dilation, bias=False)\n\t\tself.bn2 = nn.BatchNorm2d(planes)\n\t\tself.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n\t\tself.bn3 = nn.BatchNorm2d(planes * 4)\n\t\tself.relu = nn.ReLU(inplace=True)\n\t\tself.downsample = downsample\n\t\tself.stride = stride\n\n\tdef forward(self, x):\n\t\tresidual = x","source_hash":"d783845e6b1b75bda8fb323048c1f6e0974e663dc0c1773194906e7e084439aa","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.resnet.Bottleneck","uri":"program://Track-Anything/class/tracker.model.resnet.Bottleneck#L78-L114","kind":"class","name":"Bottleneck","path":"tracker/model/resnet.py","language":"python","start_line":78,"end_line":114,"context_start_line":58,"context_end_line":134,"code":"\n\tdef forward(self, x):\n\t\tresidual = x\n\n\t\tout = self.conv1(x)\n\t\tout = self.bn1(out)\n\t\tout = self.relu(out)\n\n\t\tout = self.conv2(out)\n\t\tout = self.bn2(out)\n\n\t\tif self.downsample is not None:\n\t\t\tresidual = self.downsample(x)\n\n\t\tout += residual\n\t\tout = self.relu(out)\n\n\t\treturn out\n\n\nclass Bottleneck(nn.Module):\n\texpansion = 4\n\n\tdef __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):\n\t\tsuper(Bottleneck, self).__init__()\n\t\tself.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n\t\tself.bn1 = nn.BatchNorm2d(planes)\n\t\tself.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, dilation=dilation,\n\t\t\t\t\t\t\t padding=dilation, bias=False)\n\t\tself.bn2 = nn.BatchNorm2d(planes)\n\t\tself.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n\t\tself.bn3 = nn.BatchNorm2d(planes * 4)\n\t\tself.relu = nn.ReLU(inplace=True)\n\t\tself.downsample = downsample\n\t\tself.stride = stride\n\n\tdef forward(self, x):\n\t\tresidual = x\n\n\t\tout = self.conv1(x)\n\t\tout = self.bn1(out)\n\t\tout = self.relu(out)\n\n\t\tout = self.conv2(out)\n\t\tout = self.bn2(out)\n\t\tout = self.relu(out)\n\n\t\tout = self.conv3(out)\n\t\tout = self.bn3(out)\n\n\t\tif self.downsample is not None:\n\t\t\tresidual = self.downsample(x)\n\n\t\tout += residual\n\t\tout = self.relu(out)\n\n\t\treturn out\n\n\nclass ResNet(nn.Module):\n\tdef __init__(self, block, layers=(3, 4, 23, 3), extra_dim=0):\n\t\tself.inplanes = 64\n\t\tsuper(ResNet, self).__init__()\n\t\tself.conv1 = nn.Conv2d(3+extra_dim, 64, kernel_size=7, stride=2, padding=3, bias=False)\n\t\tself.bn1 = nn.BatchNorm2d(64)\n\t\tself.relu = nn.ReLU(inplace=True)\n\t\tself.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n\t\tself.layer1 = self._make_layer(block, 64, layers[0])\n\t\tself.layer2 = self._make_layer(block, 128, layers[1], stride=2)\n\t\tself.layer3 = self._make_layer(block, 256, layers[2], stride=2)\n\t\tself.layer4 = self._make_layer(block, 512, layers[3], stride=2)\n\n\t\tfor m in self.modules():\n\t\t\tif isinstance(m, nn.Conv2d):\n\t\t\t\tn = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n\t\t\t\tm.weight.data.normal_(0, math.sqrt(2. / n))\n\t\t\telif isinstance(m, nn.BatchNorm2d):","source_hash":"d783845e6b1b75bda8fb323048c1f6e0974e663dc0c1773194906e7e084439aa","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.resnet.ResNet","uri":"program://Track-Anything/class/tracker.model.resnet.ResNet#L117-L152","kind":"class","name":"ResNet","path":"tracker/model/resnet.py","language":"python","start_line":117,"end_line":152,"context_start_line":97,"context_end_line":165,"code":"\t\tout = self.conv1(x)\n\t\tout = self.bn1(out)\n\t\tout = self.relu(out)\n\n\t\tout = self.conv2(out)\n\t\tout = self.bn2(out)\n\t\tout = self.relu(out)\n\n\t\tout = self.conv3(out)\n\t\tout = self.bn3(out)\n\n\t\tif self.downsample is not None:\n\t\t\tresidual = self.downsample(x)\n\n\t\tout += residual\n\t\tout = self.relu(out)\n\n\t\treturn out\n\n\nclass ResNet(nn.Module):\n\tdef __init__(self, block, layers=(3, 4, 23, 3), extra_dim=0):\n\t\tself.inplanes = 64\n\t\tsuper(ResNet, self).__init__()\n\t\tself.conv1 = nn.Conv2d(3+extra_dim, 64, kernel_size=7, stride=2, padding=3, bias=False)\n\t\tself.bn1 = nn.BatchNorm2d(64)\n\t\tself.relu = nn.ReLU(inplace=True)\n\t\tself.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n\t\tself.layer1 = self._make_layer(block, 64, layers[0])\n\t\tself.layer2 = self._make_layer(block, 128, layers[1], stride=2)\n\t\tself.layer3 = self._make_layer(block, 256, layers[2], stride=2)\n\t\tself.layer4 = self._make_layer(block, 512, layers[3], stride=2)\n\n\t\tfor m in self.modules():\n\t\t\tif isinstance(m, nn.Conv2d):\n\t\t\t\tn = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n\t\t\t\tm.weight.data.normal_(0, math.sqrt(2. / n))\n\t\t\telif isinstance(m, nn.BatchNorm2d):\n\t\t\t\tm.weight.data.fill_(1)\n\t\t\t\tm.bias.data.zero_()\n\n\tdef _make_layer(self, block, planes, blocks, stride=1, dilation=1):\n\t\tdownsample = None\n\t\tif stride != 1 or self.inplanes != planes * block.expansion:\n\t\t\tdownsample = nn.Sequential(\n\t\t\t\tnn.Conv2d(self.inplanes, planes * block.expansion,\n\t\t\t\t\t\t kernel_size=1, stride=stride, bias=False),\n\t\t\t\tnn.BatchNorm2d(planes * block.expansion),\n\t\t\t)\n\n\t\tlayers = [block(self.inplanes, planes, stride, downsample)]\n\t\tself.inplanes = planes * block.expansion\n\t\tfor i in range(1, blocks):\n\t\t\tlayers.append(block(self.inplanes, planes, dilation=dilation))\n\n\t\treturn nn.Sequential(*layers)\n\ndef resnet18(pretrained=True, extra_dim=0):\n\tmodel = ResNet(BasicBlock, [2, 2, 2, 2], extra_dim)\n\tif pretrained:\n\t\tload_weights_add_extra_dim(model, model_zoo.load_url(model_urls['resnet18']), extra_dim)\n\treturn model\n\ndef resnet50(pretrained=True, extra_dim=0):\n\tmodel = ResNet(Bottleneck, [3, 4, 6, 3], extra_dim)\n\tif pretrained:\n\t\tload_weights_add_extra_dim(model, model_zoo.load_url(model_urls['resnet50']), extra_dim)\n\treturn model\n","source_hash":"d783845e6b1b75bda8fb323048c1f6e0974e663dc0c1773194906e7e084439aa","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.resnet.resnet18","uri":"program://Track-Anything/function/tracker.model.resnet.resnet18#L154-L158","kind":"function","name":"resnet18","path":"tracker/model/resnet.py","language":"python","start_line":154,"end_line":158,"context_start_line":134,"context_end_line":165,"code":"\t\t\telif isinstance(m, nn.BatchNorm2d):\n\t\t\t\tm.weight.data.fill_(1)\n\t\t\t\tm.bias.data.zero_()\n\n\tdef _make_layer(self, block, planes, blocks, stride=1, dilation=1):\n\t\tdownsample = None\n\t\tif stride != 1 or self.inplanes != planes * block.expansion:\n\t\t\tdownsample = nn.Sequential(\n\t\t\t\tnn.Conv2d(self.inplanes, planes * block.expansion,\n\t\t\t\t\t\t kernel_size=1, stride=stride, bias=False),\n\t\t\t\tnn.BatchNorm2d(planes * block.expansion),\n\t\t\t)\n\n\t\tlayers = [block(self.inplanes, planes, stride, downsample)]\n\t\tself.inplanes = planes * block.expansion\n\t\tfor i in range(1, blocks):\n\t\t\tlayers.append(block(self.inplanes, planes, dilation=dilation))\n\n\t\treturn nn.Sequential(*layers)\n\ndef resnet18(pretrained=True, extra_dim=0):\n\tmodel = ResNet(BasicBlock, [2, 2, 2, 2], extra_dim)\n\tif pretrained:\n\t\tload_weights_add_extra_dim(model, model_zoo.load_url(model_urls['resnet18']), extra_dim)\n\treturn model\n\ndef resnet50(pretrained=True, extra_dim=0):\n\tmodel = ResNet(Bottleneck, [3, 4, 6, 3], extra_dim)\n\tif pretrained:\n\t\tload_weights_add_extra_dim(model, model_zoo.load_url(model_urls['resnet50']), extra_dim)\n\treturn model\n","source_hash":"d783845e6b1b75bda8fb323048c1f6e0974e663dc0c1773194906e7e084439aa","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.resnet.resnet50","uri":"program://Track-Anything/function/tracker.model.resnet.resnet50#L160-L164","kind":"function","name":"resnet50","path":"tracker/model/resnet.py","language":"python","start_line":160,"end_line":164,"context_start_line":140,"context_end_line":165,"code":"\t\tif stride != 1 or self.inplanes != planes * block.expansion:\n\t\t\tdownsample = nn.Sequential(\n\t\t\t\tnn.Conv2d(self.inplanes, planes * block.expansion,\n\t\t\t\t\t\t kernel_size=1, stride=stride, bias=False),\n\t\t\t\tnn.BatchNorm2d(planes * block.expansion),\n\t\t\t)\n\n\t\tlayers = [block(self.inplanes, planes, stride, downsample)]\n\t\tself.inplanes = planes * block.expansion\n\t\tfor i in range(1, blocks):\n\t\t\tlayers.append(block(self.inplanes, planes, dilation=dilation))\n\n\t\treturn nn.Sequential(*layers)\n\ndef resnet18(pretrained=True, extra_dim=0):\n\tmodel = ResNet(BasicBlock, [2, 2, 2, 2], extra_dim)\n\tif pretrained:\n\t\tload_weights_add_extra_dim(model, model_zoo.load_url(model_urls['resnet18']), extra_dim)\n\treturn model\n\ndef resnet50(pretrained=True, extra_dim=0):\n\tmodel = ResNet(Bottleneck, [3, 4, 6, 3], extra_dim)\n\tif pretrained:\n\t\tload_weights_add_extra_dim(model, model_zoo.load_url(model_urls['resnet50']), extra_dim)\n\treturn model\n","source_hash":"d783845e6b1b75bda8fb323048c1f6e0974e663dc0c1773194906e7e084439aa","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.resnet.__init__","uri":"program://Track-Anything/function/tracker.model.resnet.__init__#L118-L136","kind":"function","name":"__init__","path":"tracker/model/resnet.py","language":"python","start_line":118,"end_line":136,"context_start_line":98,"context_end_line":156,"code":"\t\tout = self.bn1(out)\n\t\tout = self.relu(out)\n\n\t\tout = self.conv2(out)\n\t\tout = self.bn2(out)\n\t\tout = self.relu(out)\n\n\t\tout = self.conv3(out)\n\t\tout = self.bn3(out)\n\n\t\tif self.downsample is not None:\n\t\t\tresidual = self.downsample(x)\n\n\t\tout += residual\n\t\tout = self.relu(out)\n\n\t\treturn out\n\n\nclass ResNet(nn.Module):\n\tdef __init__(self, block, layers=(3, 4, 23, 3), extra_dim=0):\n\t\tself.inplanes = 64\n\t\tsuper(ResNet, self).__init__()\n\t\tself.conv1 = nn.Conv2d(3+extra_dim, 64, kernel_size=7, stride=2, padding=3, bias=False)\n\t\tself.bn1 = nn.BatchNorm2d(64)\n\t\tself.relu = nn.ReLU(inplace=True)\n\t\tself.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n\t\tself.layer1 = self._make_layer(block, 64, layers[0])\n\t\tself.layer2 = self._make_layer(block, 128, layers[1], stride=2)\n\t\tself.layer3 = self._make_layer(block, 256, layers[2], stride=2)\n\t\tself.layer4 = self._make_layer(block, 512, layers[3], stride=2)\n\n\t\tfor m in self.modules():\n\t\t\tif isinstance(m, nn.Conv2d):\n\t\t\t\tn = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n\t\t\t\tm.weight.data.normal_(0, math.sqrt(2. / n))\n\t\t\telif isinstance(m, nn.BatchNorm2d):\n\t\t\t\tm.weight.data.fill_(1)\n\t\t\t\tm.bias.data.zero_()\n\n\tdef _make_layer(self, block, planes, blocks, stride=1, dilation=1):\n\t\tdownsample = None\n\t\tif stride != 1 or self.inplanes != planes * block.expansion:\n\t\t\tdownsample = nn.Sequential(\n\t\t\t\tnn.Conv2d(self.inplanes, planes * block.expansion,\n\t\t\t\t\t\t kernel_size=1, stride=stride, bias=False),\n\t\t\t\tnn.BatchNorm2d(planes * block.expansion),\n\t\t\t)\n\n\t\tlayers = [block(self.inplanes, planes, stride, downsample)]\n\t\tself.inplanes = planes * block.expansion\n\t\tfor i in range(1, blocks):\n\t\t\tlayers.append(block(self.inplanes, planes, dilation=dilation))\n\n\t\treturn nn.Sequential(*layers)\n\ndef resnet18(pretrained=True, extra_dim=0):\n\tmodel = ResNet(BasicBlock, [2, 2, 2, 2], extra_dim)\n\tif pretrained:","source_hash":"d783845e6b1b75bda8fb323048c1f6e0974e663dc0c1773194906e7e084439aa","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.resnet.forward","uri":"program://Track-Anything/function/tracker.model.resnet.forward#L94-L114","kind":"function","name":"forward","path":"tracker/model/resnet.py","language":"python","start_line":94,"end_line":114,"context_start_line":74,"context_end_line":134,"code":"\n\t\treturn out\n\n\nclass Bottleneck(nn.Module):\n\texpansion = 4\n\n\tdef __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):\n\t\tsuper(Bottleneck, self).__init__()\n\t\tself.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n\t\tself.bn1 = nn.BatchNorm2d(planes)\n\t\tself.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, dilation=dilation,\n\t\t\t\t\t\t\t padding=dilation, bias=False)\n\t\tself.bn2 = nn.BatchNorm2d(planes)\n\t\tself.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n\t\tself.bn3 = nn.BatchNorm2d(planes * 4)\n\t\tself.relu = nn.ReLU(inplace=True)\n\t\tself.downsample = downsample\n\t\tself.stride = stride\n\n\tdef forward(self, x):\n\t\tresidual = x\n\n\t\tout = self.conv1(x)\n\t\tout = self.bn1(out)\n\t\tout = self.relu(out)\n\n\t\tout = self.conv2(out)\n\t\tout = self.bn2(out)\n\t\tout = self.relu(out)\n\n\t\tout = self.conv3(out)\n\t\tout = self.bn3(out)\n\n\t\tif self.downsample is not None:\n\t\t\tresidual = self.downsample(x)\n\n\t\tout += residual\n\t\tout = self.relu(out)\n\n\t\treturn out\n\n\nclass ResNet(nn.Module):\n\tdef __init__(self, block, layers=(3, 4, 23, 3), extra_dim=0):\n\t\tself.inplanes = 64\n\t\tsuper(ResNet, self).__init__()\n\t\tself.conv1 = nn.Conv2d(3+extra_dim, 64, kernel_size=7, stride=2, padding=3, bias=False)\n\t\tself.bn1 = nn.BatchNorm2d(64)\n\t\tself.relu = nn.ReLU(inplace=True)\n\t\tself.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n\t\tself.layer1 = self._make_layer(block, 64, layers[0])\n\t\tself.layer2 = self._make_layer(block, 128, layers[1], stride=2)\n\t\tself.layer3 = self._make_layer(block, 256, layers[2], stride=2)\n\t\tself.layer4 = self._make_layer(block, 512, layers[3], stride=2)\n\n\t\tfor m in self.modules():\n\t\t\tif isinstance(m, nn.Conv2d):\n\t\t\t\tn = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n\t\t\t\tm.weight.data.normal_(0, math.sqrt(2. / n))\n\t\t\telif isinstance(m, nn.BatchNorm2d):","source_hash":"d783845e6b1b75bda8fb323048c1f6e0974e663dc0c1773194906e7e084439aa","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.resnet._make_layer","uri":"program://Track-Anything/function/tracker.model.resnet._make_layer#L138-L152","kind":"function","name":"_make_layer","path":"tracker/model/resnet.py","language":"python","start_line":138,"end_line":152,"context_start_line":118,"context_end_line":165,"code":"\tdef __init__(self, block, layers=(3, 4, 23, 3), extra_dim=0):\n\t\tself.inplanes = 64\n\t\tsuper(ResNet, self).__init__()\n\t\tself.conv1 = nn.Conv2d(3+extra_dim, 64, kernel_size=7, stride=2, padding=3, bias=False)\n\t\tself.bn1 = nn.BatchNorm2d(64)\n\t\tself.relu = nn.ReLU(inplace=True)\n\t\tself.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n\t\tself.layer1 = self._make_layer(block, 64, layers[0])\n\t\tself.layer2 = self._make_layer(block, 128, layers[1], stride=2)\n\t\tself.layer3 = self._make_layer(block, 256, layers[2], stride=2)\n\t\tself.layer4 = self._make_layer(block, 512, layers[3], stride=2)\n\n\t\tfor m in self.modules():\n\t\t\tif isinstance(m, nn.Conv2d):\n\t\t\t\tn = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n\t\t\t\tm.weight.data.normal_(0, math.sqrt(2. / n))\n\t\t\telif isinstance(m, nn.BatchNorm2d):\n\t\t\t\tm.weight.data.fill_(1)\n\t\t\t\tm.bias.data.zero_()\n\n\tdef _make_layer(self, block, planes, blocks, stride=1, dilation=1):\n\t\tdownsample = None\n\t\tif stride != 1 or self.inplanes != planes * block.expansion:\n\t\t\tdownsample = nn.Sequential(\n\t\t\t\tnn.Conv2d(self.inplanes, planes * block.expansion,\n\t\t\t\t\t\t kernel_size=1, stride=stride, bias=False),\n\t\t\t\tnn.BatchNorm2d(planes * block.expansion),\n\t\t\t)\n\n\t\tlayers = [block(self.inplanes, planes, stride, downsample)]\n\t\tself.inplanes = planes * block.expansion\n\t\tfor i in range(1, blocks):\n\t\t\tlayers.append(block(self.inplanes, planes, dilation=dilation))\n\n\t\treturn nn.Sequential(*layers)\n\ndef resnet18(pretrained=True, extra_dim=0):\n\tmodel = ResNet(BasicBlock, [2, 2, 2, 2], extra_dim)\n\tif pretrained:\n\t\tload_weights_add_extra_dim(model, model_zoo.load_url(model_urls['resnet18']), extra_dim)\n\treturn model\n\ndef resnet50(pretrained=True, extra_dim=0):\n\tmodel = ResNet(Bottleneck, [3, 4, 6, 3], extra_dim)\n\tif pretrained:\n\t\tload_weights_add_extra_dim(model, model_zoo.load_url(model_urls['resnet50']), extra_dim)\n\treturn model\n","source_hash":"d783845e6b1b75bda8fb323048c1f6e0974e663dc0c1773194906e7e084439aa","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.memory_util","uri":"program://Track-Anything/module/tracker.model.memory_util#L1-L80","kind":"module","name":"tracker.model.memory_util","path":"tracker/model/memory_util.py","language":"python","start_line":1,"end_line":80,"context_start_line":1,"context_end_line":80,"code":"import math\nimport numpy as np\nimport torch\nfrom typing import Optional\n\n\ndef get_similarity(mk, ms, qk, qe):\n # used for training/inference and memory reading/memory potentiation\n # mk: B x CK x [N] - Memory keys\n # ms: B x 1 x [N] - Memory shrinkage\n # qk: B x CK x [HW/P] - Query keys\n # qe: B x CK x [HW/P] - Query selection\n # Dimensions in [] are flattened\n CK = mk.shape[1]\n mk = mk.flatten(start_dim=2)\n ms = ms.flatten(start_dim=1).unsqueeze(2) if ms is not None else None\n qk = qk.flatten(start_dim=2)\n qe = qe.flatten(start_dim=2) if qe is not None else None\n\n if qe is not None:\n # See appendix for derivation\n # or you can just trust me ヽ(ー_ー )ノ\n mk = mk.transpose(1, 2)\n a_sq = (mk.pow(2) @ qe)\n two_ab = 2 * (mk @ (qk * qe))\n b_sq = (qe * qk.pow(2)).sum(1, keepdim=True)\n similarity = (-a_sq+two_ab-b_sq)\n else:\n # similar to STCN if we don't have the selection term\n a_sq = mk.pow(2).sum(1).unsqueeze(2)\n two_ab = 2 * (mk.transpose(1, 2) @ qk)\n similarity = (-a_sq+two_ab)\n\n if ms is not None:\n similarity = similarity * ms / math.sqrt(CK) # B*N*HW\n else:\n similarity = similarity / math.sqrt(CK) # B*N*HW\n\n return similarity\n\ndef do_softmax(similarity, top_k: Optional[int]=None, inplace=False, return_usage=False):\n # normalize similarity with top-k softmax\n # similarity: B x N x [HW/P]\n # use inplace with care\n if top_k is not None:\n values, indices = torch.topk(similarity, k=top_k, dim=1)\n\n x_exp = values.exp_()\n x_exp /= torch.sum(x_exp, dim=1, keepdim=True)\n if inplace:\n similarity.zero_().scatter_(1, indices, x_exp) # B*N*HW\n affinity = similarity\n else:\n affinity = torch.zeros_like(similarity).scatter_(1, indices, x_exp) # B*N*HW\n else:\n maxes = torch.max(similarity, dim=1, keepdim=True)[0]\n x_exp = torch.exp(similarity - maxes)\n x_exp_sum = torch.sum(x_exp, dim=1, keepdim=True)\n affinity = x_exp / x_exp_sum \n indices = None\n\n if return_usage:\n return affinity, affinity.sum(dim=2)\n\n return affinity\n\ndef get_affinity(mk, ms, qk, qe):\n # shorthand used in training with no top-k\n similarity = get_similarity(mk, ms, qk, qe)\n affinity = do_softmax(similarity)\n return affinity\n\ndef readout(affinity, mv):\n B, CV, T, H, W = mv.shape\n\n mo = mv.view(B, CV, T*H*W) \n mem = torch.bmm(mo, affinity)\n mem = mem.view(B, CV, H, W)\n\n return mem","source_hash":"cdf06ea5e60b67737660d3a1eadd85f2431b4ad4ff0f2f166bda18660440751f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.memory_util.get_similarity","uri":"program://Track-Anything/function/tracker.model.memory_util.get_similarity#L7-L39","kind":"function","name":"get_similarity","path":"tracker/model/memory_util.py","language":"python","start_line":7,"end_line":39,"context_start_line":1,"context_end_line":59,"code":"import math\nimport numpy as np\nimport torch\nfrom typing import Optional\n\n\ndef get_similarity(mk, ms, qk, qe):\n # used for training/inference and memory reading/memory potentiation\n # mk: B x CK x [N] - Memory keys\n # ms: B x 1 x [N] - Memory shrinkage\n # qk: B x CK x [HW/P] - Query keys\n # qe: B x CK x [HW/P] - Query selection\n # Dimensions in [] are flattened\n CK = mk.shape[1]\n mk = mk.flatten(start_dim=2)\n ms = ms.flatten(start_dim=1).unsqueeze(2) if ms is not None else None\n qk = qk.flatten(start_dim=2)\n qe = qe.flatten(start_dim=2) if qe is not None else None\n\n if qe is not None:\n # See appendix for derivation\n # or you can just trust me ヽ(ー_ー )ノ\n mk = mk.transpose(1, 2)\n a_sq = (mk.pow(2) @ qe)\n two_ab = 2 * (mk @ (qk * qe))\n b_sq = (qe * qk.pow(2)).sum(1, keepdim=True)\n similarity = (-a_sq+two_ab-b_sq)\n else:\n # similar to STCN if we don't have the selection term\n a_sq = mk.pow(2).sum(1).unsqueeze(2)\n two_ab = 2 * (mk.transpose(1, 2) @ qk)\n similarity = (-a_sq+two_ab)\n\n if ms is not None:\n similarity = similarity * ms / math.sqrt(CK) # B*N*HW\n else:\n similarity = similarity / math.sqrt(CK) # B*N*HW\n\n return similarity\n\ndef do_softmax(similarity, top_k: Optional[int]=None, inplace=False, return_usage=False):\n # normalize similarity with top-k softmax\n # similarity: B x N x [HW/P]\n # use inplace with care\n if top_k is not None:\n values, indices = torch.topk(similarity, k=top_k, dim=1)\n\n x_exp = values.exp_()\n x_exp /= torch.sum(x_exp, dim=1, keepdim=True)\n if inplace:\n similarity.zero_().scatter_(1, indices, x_exp) # B*N*HW\n affinity = similarity\n else:\n affinity = torch.zeros_like(similarity).scatter_(1, indices, x_exp) # B*N*HW\n else:\n maxes = torch.max(similarity, dim=1, keepdim=True)[0]\n x_exp = torch.exp(similarity - maxes)\n x_exp_sum = torch.sum(x_exp, dim=1, keepdim=True)\n affinity = x_exp / x_exp_sum ","source_hash":"cdf06ea5e60b67737660d3a1eadd85f2431b4ad4ff0f2f166bda18660440751f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.memory_util.do_softmax","uri":"program://Track-Anything/function/tracker.model.memory_util.do_softmax#L41-L65","kind":"function","name":"do_softmax","path":"tracker/model/memory_util.py","language":"python","start_line":41,"end_line":65,"context_start_line":21,"context_end_line":80,"code":" # See appendix for derivation\n # or you can just trust me ヽ(ー_ー )ノ\n mk = mk.transpose(1, 2)\n a_sq = (mk.pow(2) @ qe)\n two_ab = 2 * (mk @ (qk * qe))\n b_sq = (qe * qk.pow(2)).sum(1, keepdim=True)\n similarity = (-a_sq+two_ab-b_sq)\n else:\n # similar to STCN if we don't have the selection term\n a_sq = mk.pow(2).sum(1).unsqueeze(2)\n two_ab = 2 * (mk.transpose(1, 2) @ qk)\n similarity = (-a_sq+two_ab)\n\n if ms is not None:\n similarity = similarity * ms / math.sqrt(CK) # B*N*HW\n else:\n similarity = similarity / math.sqrt(CK) # B*N*HW\n\n return similarity\n\ndef do_softmax(similarity, top_k: Optional[int]=None, inplace=False, return_usage=False):\n # normalize similarity with top-k softmax\n # similarity: B x N x [HW/P]\n # use inplace with care\n if top_k is not None:\n values, indices = torch.topk(similarity, k=top_k, dim=1)\n\n x_exp = values.exp_()\n x_exp /= torch.sum(x_exp, dim=1, keepdim=True)\n if inplace:\n similarity.zero_().scatter_(1, indices, x_exp) # B*N*HW\n affinity = similarity\n else:\n affinity = torch.zeros_like(similarity).scatter_(1, indices, x_exp) # B*N*HW\n else:\n maxes = torch.max(similarity, dim=1, keepdim=True)[0]\n x_exp = torch.exp(similarity - maxes)\n x_exp_sum = torch.sum(x_exp, dim=1, keepdim=True)\n affinity = x_exp / x_exp_sum \n indices = None\n\n if return_usage:\n return affinity, affinity.sum(dim=2)\n\n return affinity\n\ndef get_affinity(mk, ms, qk, qe):\n # shorthand used in training with no top-k\n similarity = get_similarity(mk, ms, qk, qe)\n affinity = do_softmax(similarity)\n return affinity\n\ndef readout(affinity, mv):\n B, CV, T, H, W = mv.shape\n\n mo = mv.view(B, CV, T*H*W) \n mem = torch.bmm(mo, affinity)\n mem = mem.view(B, CV, H, W)\n\n return mem","source_hash":"cdf06ea5e60b67737660d3a1eadd85f2431b4ad4ff0f2f166bda18660440751f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.memory_util.get_affinity","uri":"program://Track-Anything/function/tracker.model.memory_util.get_affinity#L67-L71","kind":"function","name":"get_affinity","path":"tracker/model/memory_util.py","language":"python","start_line":67,"end_line":71,"context_start_line":47,"context_end_line":80,"code":"\n x_exp = values.exp_()\n x_exp /= torch.sum(x_exp, dim=1, keepdim=True)\n if inplace:\n similarity.zero_().scatter_(1, indices, x_exp) # B*N*HW\n affinity = similarity\n else:\n affinity = torch.zeros_like(similarity).scatter_(1, indices, x_exp) # B*N*HW\n else:\n maxes = torch.max(similarity, dim=1, keepdim=True)[0]\n x_exp = torch.exp(similarity - maxes)\n x_exp_sum = torch.sum(x_exp, dim=1, keepdim=True)\n affinity = x_exp / x_exp_sum \n indices = None\n\n if return_usage:\n return affinity, affinity.sum(dim=2)\n\n return affinity\n\ndef get_affinity(mk, ms, qk, qe):\n # shorthand used in training with no top-k\n similarity = get_similarity(mk, ms, qk, qe)\n affinity = do_softmax(similarity)\n return affinity\n\ndef readout(affinity, mv):\n B, CV, T, H, W = mv.shape\n\n mo = mv.view(B, CV, T*H*W) \n mem = torch.bmm(mo, affinity)\n mem = mem.view(B, CV, H, W)\n\n return mem","source_hash":"cdf06ea5e60b67737660d3a1eadd85f2431b4ad4ff0f2f166bda18660440751f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.memory_util.readout","uri":"program://Track-Anything/function/tracker.model.memory_util.readout#L73-L80","kind":"function","name":"readout","path":"tracker/model/memory_util.py","language":"python","start_line":73,"end_line":80,"context_start_line":53,"context_end_line":80,"code":" else:\n affinity = torch.zeros_like(similarity).scatter_(1, indices, x_exp) # B*N*HW\n else:\n maxes = torch.max(similarity, dim=1, keepdim=True)[0]\n x_exp = torch.exp(similarity - maxes)\n x_exp_sum = torch.sum(x_exp, dim=1, keepdim=True)\n affinity = x_exp / x_exp_sum \n indices = None\n\n if return_usage:\n return affinity, affinity.sum(dim=2)\n\n return affinity\n\ndef get_affinity(mk, ms, qk, qe):\n # shorthand used in training with no top-k\n similarity = get_similarity(mk, ms, qk, qe)\n affinity = do_softmax(similarity)\n return affinity\n\ndef readout(affinity, mv):\n B, CV, T, H, W = mv.shape\n\n mo = mv.view(B, CV, T*H*W) \n mem = torch.bmm(mo, affinity)\n mem = mem.view(B, CV, H, W)\n\n return mem","source_hash":"cdf06ea5e60b67737660d3a1eadd85f2431b4ad4ff0f2f166bda18660440751f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.group_modules","uri":"program://Track-Anything/module/tracker.model.group_modules#L1-L82","kind":"module","name":"tracker.model.group_modules","path":"tracker/model/group_modules.py","language":"python","start_line":1,"end_line":82,"context_start_line":1,"context_end_line":82,"code":"\"\"\"\nGroup-specific modules\nThey handle features that also depends on the mask. \nFeatures are typically of shape\n batch_size * num_objects * num_channels * H * W\n\nAll of them are permutation equivariant w.r.t. to the num_objects dimension\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\ndef interpolate_groups(g, ratio, mode, align_corners):\n batch_size, num_objects = g.shape[:2]\n g = F.interpolate(g.flatten(start_dim=0, end_dim=1), \n scale_factor=ratio, mode=mode, align_corners=align_corners)\n g = g.view(batch_size, num_objects, *g.shape[1:])\n return g\n\ndef upsample_groups(g, ratio=2, mode='bilinear', align_corners=False):\n return interpolate_groups(g, ratio, mode, align_corners)\n\ndef downsample_groups(g, ratio=1/2, mode='area', align_corners=None):\n return interpolate_groups(g, ratio, mode, align_corners)\n\n\nclass GConv2D(nn.Conv2d):\n def forward(self, g):\n batch_size, num_objects = g.shape[:2]\n g = super().forward(g.flatten(start_dim=0, end_dim=1))\n return g.view(batch_size, num_objects, *g.shape[1:])\n\n\nclass GroupResBlock(nn.Module):\n def __init__(self, in_dim, out_dim):\n super().__init__()\n\n if in_dim == out_dim:\n self.downsample = None\n else:\n self.downsample = GConv2D(in_dim, out_dim, kernel_size=3, padding=1)\n\n self.conv1 = GConv2D(in_dim, out_dim, kernel_size=3, padding=1)\n self.conv2 = GConv2D(out_dim, out_dim, kernel_size=3, padding=1)\n \n def forward(self, g):\n out_g = self.conv1(F.relu(g))\n out_g = self.conv2(F.relu(out_g))\n \n if self.downsample is not None:\n g = self.downsample(g)\n\n return out_g + g\n\n\nclass MainToGroupDistributor(nn.Module):\n def __init__(self, x_transform=None, method='cat', reverse_order=False):\n super().__init__()\n\n self.x_transform = x_transform\n self.method = method\n self.reverse_order = reverse_order\n\n def forward(self, x, g):\n num_objects = g.shape[1]\n\n if self.x_transform is not None:\n x = self.x_transform(x)\n\n if self.method == 'cat':\n if self.reverse_order:\n g = torch.cat([g, x.unsqueeze(1).expand(-1,num_objects,-1,-1,-1)], 2)\n else:\n g = torch.cat([x.unsqueeze(1).expand(-1,num_objects,-1,-1,-1), g], 2)\n elif self.method == 'add':\n g = x.unsqueeze(1).expand(-1,num_objects,-1,-1,-1) + g\n else:\n raise NotImplementedError\n\n return g","source_hash":"19fb2896838e0ac52e2376e20f868110763207370829f3f06ae8f81d58d11d6b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.group_modules.interpolate_groups","uri":"program://Track-Anything/function/tracker.model.group_modules.interpolate_groups#L15-L20","kind":"function","name":"interpolate_groups","path":"tracker/model/group_modules.py","language":"python","start_line":15,"end_line":20,"context_start_line":1,"context_end_line":40,"code":"\"\"\"\nGroup-specific modules\nThey handle features that also depends on the mask. \nFeatures are typically of shape\n batch_size * num_objects * num_channels * H * W\n\nAll of them are permutation equivariant w.r.t. to the num_objects dimension\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\ndef interpolate_groups(g, ratio, mode, align_corners):\n batch_size, num_objects = g.shape[:2]\n g = F.interpolate(g.flatten(start_dim=0, end_dim=1), \n scale_factor=ratio, mode=mode, align_corners=align_corners)\n g = g.view(batch_size, num_objects, *g.shape[1:])\n return g\n\ndef upsample_groups(g, ratio=2, mode='bilinear', align_corners=False):\n return interpolate_groups(g, ratio, mode, align_corners)\n\ndef downsample_groups(g, ratio=1/2, mode='area', align_corners=None):\n return interpolate_groups(g, ratio, mode, align_corners)\n\n\nclass GConv2D(nn.Conv2d):\n def forward(self, g):\n batch_size, num_objects = g.shape[:2]\n g = super().forward(g.flatten(start_dim=0, end_dim=1))\n return g.view(batch_size, num_objects, *g.shape[1:])\n\n\nclass GroupResBlock(nn.Module):\n def __init__(self, in_dim, out_dim):\n super().__init__()\n\n if in_dim == out_dim:","source_hash":"19fb2896838e0ac52e2376e20f868110763207370829f3f06ae8f81d58d11d6b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.group_modules.upsample_groups","uri":"program://Track-Anything/function/tracker.model.group_modules.upsample_groups#L22-L23","kind":"function","name":"upsample_groups","path":"tracker/model/group_modules.py","language":"python","start_line":22,"end_line":23,"context_start_line":2,"context_end_line":43,"code":"Group-specific modules\nThey handle features that also depends on the mask. \nFeatures are typically of shape\n batch_size * num_objects * num_channels * H * W\n\nAll of them are permutation equivariant w.r.t. to the num_objects dimension\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\ndef interpolate_groups(g, ratio, mode, align_corners):\n batch_size, num_objects = g.shape[:2]\n g = F.interpolate(g.flatten(start_dim=0, end_dim=1), \n scale_factor=ratio, mode=mode, align_corners=align_corners)\n g = g.view(batch_size, num_objects, *g.shape[1:])\n return g\n\ndef upsample_groups(g, ratio=2, mode='bilinear', align_corners=False):\n return interpolate_groups(g, ratio, mode, align_corners)\n\ndef downsample_groups(g, ratio=1/2, mode='area', align_corners=None):\n return interpolate_groups(g, ratio, mode, align_corners)\n\n\nclass GConv2D(nn.Conv2d):\n def forward(self, g):\n batch_size, num_objects = g.shape[:2]\n g = super().forward(g.flatten(start_dim=0, end_dim=1))\n return g.view(batch_size, num_objects, *g.shape[1:])\n\n\nclass GroupResBlock(nn.Module):\n def __init__(self, in_dim, out_dim):\n super().__init__()\n\n if in_dim == out_dim:\n self.downsample = None\n else:\n self.downsample = GConv2D(in_dim, out_dim, kernel_size=3, padding=1)","source_hash":"19fb2896838e0ac52e2376e20f868110763207370829f3f06ae8f81d58d11d6b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.group_modules.downsample_groups","uri":"program://Track-Anything/function/tracker.model.group_modules.downsample_groups#L25-L26","kind":"function","name":"downsample_groups","path":"tracker/model/group_modules.py","language":"python","start_line":25,"end_line":26,"context_start_line":5,"context_end_line":46,"code":" batch_size * num_objects * num_channels * H * W\n\nAll of them are permutation equivariant w.r.t. to the num_objects dimension\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\ndef interpolate_groups(g, ratio, mode, align_corners):\n batch_size, num_objects = g.shape[:2]\n g = F.interpolate(g.flatten(start_dim=0, end_dim=1), \n scale_factor=ratio, mode=mode, align_corners=align_corners)\n g = g.view(batch_size, num_objects, *g.shape[1:])\n return g\n\ndef upsample_groups(g, ratio=2, mode='bilinear', align_corners=False):\n return interpolate_groups(g, ratio, mode, align_corners)\n\ndef downsample_groups(g, ratio=1/2, mode='area', align_corners=None):\n return interpolate_groups(g, ratio, mode, align_corners)\n\n\nclass GConv2D(nn.Conv2d):\n def forward(self, g):\n batch_size, num_objects = g.shape[:2]\n g = super().forward(g.flatten(start_dim=0, end_dim=1))\n return g.view(batch_size, num_objects, *g.shape[1:])\n\n\nclass GroupResBlock(nn.Module):\n def __init__(self, in_dim, out_dim):\n super().__init__()\n\n if in_dim == out_dim:\n self.downsample = None\n else:\n self.downsample = GConv2D(in_dim, out_dim, kernel_size=3, padding=1)\n\n self.conv1 = GConv2D(in_dim, out_dim, kernel_size=3, padding=1)\n self.conv2 = GConv2D(out_dim, out_dim, kernel_size=3, padding=1)","source_hash":"19fb2896838e0ac52e2376e20f868110763207370829f3f06ae8f81d58d11d6b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.group_modules.GConv2D","uri":"program://Track-Anything/class/tracker.model.group_modules.GConv2D#L29-L33","kind":"class","name":"GConv2D","path":"tracker/model/group_modules.py","language":"python","start_line":29,"end_line":33,"context_start_line":9,"context_end_line":53,"code":"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\ndef interpolate_groups(g, ratio, mode, align_corners):\n batch_size, num_objects = g.shape[:2]\n g = F.interpolate(g.flatten(start_dim=0, end_dim=1), \n scale_factor=ratio, mode=mode, align_corners=align_corners)\n g = g.view(batch_size, num_objects, *g.shape[1:])\n return g\n\ndef upsample_groups(g, ratio=2, mode='bilinear', align_corners=False):\n return interpolate_groups(g, ratio, mode, align_corners)\n\ndef downsample_groups(g, ratio=1/2, mode='area', align_corners=None):\n return interpolate_groups(g, ratio, mode, align_corners)\n\n\nclass GConv2D(nn.Conv2d):\n def forward(self, g):\n batch_size, num_objects = g.shape[:2]\n g = super().forward(g.flatten(start_dim=0, end_dim=1))\n return g.view(batch_size, num_objects, *g.shape[1:])\n\n\nclass GroupResBlock(nn.Module):\n def __init__(self, in_dim, out_dim):\n super().__init__()\n\n if in_dim == out_dim:\n self.downsample = None\n else:\n self.downsample = GConv2D(in_dim, out_dim, kernel_size=3, padding=1)\n\n self.conv1 = GConv2D(in_dim, out_dim, kernel_size=3, padding=1)\n self.conv2 = GConv2D(out_dim, out_dim, kernel_size=3, padding=1)\n \n def forward(self, g):\n out_g = self.conv1(F.relu(g))\n out_g = self.conv2(F.relu(out_g))\n \n if self.downsample is not None:\n g = self.downsample(g)","source_hash":"19fb2896838e0ac52e2376e20f868110763207370829f3f06ae8f81d58d11d6b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.group_modules.GroupResBlock","uri":"program://Track-Anything/class/tracker.model.group_modules.GroupResBlock#L36-L55","kind":"class","name":"GroupResBlock","path":"tracker/model/group_modules.py","language":"python","start_line":36,"end_line":55,"context_start_line":16,"context_end_line":75,"code":" batch_size, num_objects = g.shape[:2]\n g = F.interpolate(g.flatten(start_dim=0, end_dim=1), \n scale_factor=ratio, mode=mode, align_corners=align_corners)\n g = g.view(batch_size, num_objects, *g.shape[1:])\n return g\n\ndef upsample_groups(g, ratio=2, mode='bilinear', align_corners=False):\n return interpolate_groups(g, ratio, mode, align_corners)\n\ndef downsample_groups(g, ratio=1/2, mode='area', align_corners=None):\n return interpolate_groups(g, ratio, mode, align_corners)\n\n\nclass GConv2D(nn.Conv2d):\n def forward(self, g):\n batch_size, num_objects = g.shape[:2]\n g = super().forward(g.flatten(start_dim=0, end_dim=1))\n return g.view(batch_size, num_objects, *g.shape[1:])\n\n\nclass GroupResBlock(nn.Module):\n def __init__(self, in_dim, out_dim):\n super().__init__()\n\n if in_dim == out_dim:\n self.downsample = None\n else:\n self.downsample = GConv2D(in_dim, out_dim, kernel_size=3, padding=1)\n\n self.conv1 = GConv2D(in_dim, out_dim, kernel_size=3, padding=1)\n self.conv2 = GConv2D(out_dim, out_dim, kernel_size=3, padding=1)\n \n def forward(self, g):\n out_g = self.conv1(F.relu(g))\n out_g = self.conv2(F.relu(out_g))\n \n if self.downsample is not None:\n g = self.downsample(g)\n\n return out_g + g\n\n\nclass MainToGroupDistributor(nn.Module):\n def __init__(self, x_transform=None, method='cat', reverse_order=False):\n super().__init__()\n\n self.x_transform = x_transform\n self.method = method\n self.reverse_order = reverse_order\n\n def forward(self, x, g):\n num_objects = g.shape[1]\n\n if self.x_transform is not None:\n x = self.x_transform(x)\n\n if self.method == 'cat':\n if self.reverse_order:\n g = torch.cat([g, x.unsqueeze(1).expand(-1,num_objects,-1,-1,-1)], 2)\n else:","source_hash":"19fb2896838e0ac52e2376e20f868110763207370829f3f06ae8f81d58d11d6b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.group_modules.MainToGroupDistributor","uri":"program://Track-Anything/class/tracker.model.group_modules.MainToGroupDistributor#L58-L82","kind":"class","name":"MainToGroupDistributor","path":"tracker/model/group_modules.py","language":"python","start_line":58,"end_line":82,"context_start_line":38,"context_end_line":82,"code":" super().__init__()\n\n if in_dim == out_dim:\n self.downsample = None\n else:\n self.downsample = GConv2D(in_dim, out_dim, kernel_size=3, padding=1)\n\n self.conv1 = GConv2D(in_dim, out_dim, kernel_size=3, padding=1)\n self.conv2 = GConv2D(out_dim, out_dim, kernel_size=3, padding=1)\n \n def forward(self, g):\n out_g = self.conv1(F.relu(g))\n out_g = self.conv2(F.relu(out_g))\n \n if self.downsample is not None:\n g = self.downsample(g)\n\n return out_g + g\n\n\nclass MainToGroupDistributor(nn.Module):\n def __init__(self, x_transform=None, method='cat', reverse_order=False):\n super().__init__()\n\n self.x_transform = x_transform\n self.method = method\n self.reverse_order = reverse_order\n\n def forward(self, x, g):\n num_objects = g.shape[1]\n\n if self.x_transform is not None:\n x = self.x_transform(x)\n\n if self.method == 'cat':\n if self.reverse_order:\n g = torch.cat([g, x.unsqueeze(1).expand(-1,num_objects,-1,-1,-1)], 2)\n else:\n g = torch.cat([x.unsqueeze(1).expand(-1,num_objects,-1,-1,-1), g], 2)\n elif self.method == 'add':\n g = x.unsqueeze(1).expand(-1,num_objects,-1,-1,-1) + g\n else:\n raise NotImplementedError\n\n return g","source_hash":"19fb2896838e0ac52e2376e20f868110763207370829f3f06ae8f81d58d11d6b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.group_modules.forward","uri":"program://Track-Anything/function/tracker.model.group_modules.forward#L66-L82","kind":"function","name":"forward","path":"tracker/model/group_modules.py","language":"python","start_line":66,"end_line":82,"context_start_line":46,"context_end_line":82,"code":" self.conv2 = GConv2D(out_dim, out_dim, kernel_size=3, padding=1)\n \n def forward(self, g):\n out_g = self.conv1(F.relu(g))\n out_g = self.conv2(F.relu(out_g))\n \n if self.downsample is not None:\n g = self.downsample(g)\n\n return out_g + g\n\n\nclass MainToGroupDistributor(nn.Module):\n def __init__(self, x_transform=None, method='cat', reverse_order=False):\n super().__init__()\n\n self.x_transform = x_transform\n self.method = method\n self.reverse_order = reverse_order\n\n def forward(self, x, g):\n num_objects = g.shape[1]\n\n if self.x_transform is not None:\n x = self.x_transform(x)\n\n if self.method == 'cat':\n if self.reverse_order:\n g = torch.cat([g, x.unsqueeze(1).expand(-1,num_objects,-1,-1,-1)], 2)\n else:\n g = torch.cat([x.unsqueeze(1).expand(-1,num_objects,-1,-1,-1), g], 2)\n elif self.method == 'add':\n g = x.unsqueeze(1).expand(-1,num_objects,-1,-1,-1) + g\n else:\n raise NotImplementedError\n\n return g","source_hash":"19fb2896838e0ac52e2376e20f868110763207370829f3f06ae8f81d58d11d6b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.group_modules.__init__","uri":"program://Track-Anything/function/tracker.model.group_modules.__init__#L59-L64","kind":"function","name":"__init__","path":"tracker/model/group_modules.py","language":"python","start_line":59,"end_line":64,"context_start_line":39,"context_end_line":82,"code":"\n if in_dim == out_dim:\n self.downsample = None\n else:\n self.downsample = GConv2D(in_dim, out_dim, kernel_size=3, padding=1)\n\n self.conv1 = GConv2D(in_dim, out_dim, kernel_size=3, padding=1)\n self.conv2 = GConv2D(out_dim, out_dim, kernel_size=3, padding=1)\n \n def forward(self, g):\n out_g = self.conv1(F.relu(g))\n out_g = self.conv2(F.relu(out_g))\n \n if self.downsample is not None:\n g = self.downsample(g)\n\n return out_g + g\n\n\nclass MainToGroupDistributor(nn.Module):\n def __init__(self, x_transform=None, method='cat', reverse_order=False):\n super().__init__()\n\n self.x_transform = x_transform\n self.method = method\n self.reverse_order = reverse_order\n\n def forward(self, x, g):\n num_objects = g.shape[1]\n\n if self.x_transform is not None:\n x = self.x_transform(x)\n\n if self.method == 'cat':\n if self.reverse_order:\n g = torch.cat([g, x.unsqueeze(1).expand(-1,num_objects,-1,-1,-1)], 2)\n else:\n g = torch.cat([x.unsqueeze(1).expand(-1,num_objects,-1,-1,-1), g], 2)\n elif self.method == 'add':\n g = x.unsqueeze(1).expand(-1,num_objects,-1,-1,-1) + g\n else:\n raise NotImplementedError\n\n return g","source_hash":"19fb2896838e0ac52e2376e20f868110763207370829f3f06ae8f81d58d11d6b","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.cbam","uri":"program://Track-Anything/module/tracker.model.cbam#L1-L77","kind":"module","name":"tracker.model.cbam","path":"tracker/model/cbam.py","language":"python","start_line":1,"end_line":77,"context_start_line":1,"context_end_line":77,"code":"# Modified from https://github.com/Jongchan/attention-module/blob/master/MODELS/cbam.py\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass BasicConv(nn.Module):\n def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True):\n super(BasicConv, self).__init__()\n self.out_channels = out_planes\n self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)\n\n def forward(self, x):\n x = self.conv(x)\n return x\n\nclass Flatten(nn.Module):\n def forward(self, x):\n return x.view(x.size(0), -1)\n\nclass ChannelGate(nn.Module):\n def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max']):\n super(ChannelGate, self).__init__()\n self.gate_channels = gate_channels\n self.mlp = nn.Sequential(\n Flatten(),\n nn.Linear(gate_channels, gate_channels // reduction_ratio),\n nn.ReLU(),\n nn.Linear(gate_channels // reduction_ratio, gate_channels)\n )\n self.pool_types = pool_types\n def forward(self, x):\n channel_att_sum = None\n for pool_type in self.pool_types:\n if pool_type=='avg':\n avg_pool = F.avg_pool2d( x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3)))\n channel_att_raw = self.mlp( avg_pool )\n elif pool_type=='max':\n max_pool = F.max_pool2d( x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3)))\n channel_att_raw = self.mlp( max_pool )\n\n if channel_att_sum is None:\n channel_att_sum = channel_att_raw\n else:\n channel_att_sum = channel_att_sum + channel_att_raw\n\n scale = torch.sigmoid( channel_att_sum ).unsqueeze(2).unsqueeze(3).expand_as(x)\n return x * scale\n\nclass ChannelPool(nn.Module):\n def forward(self, x):\n return torch.cat( (torch.max(x,1)[0].unsqueeze(1), torch.mean(x,1).unsqueeze(1)), dim=1 )\n\nclass SpatialGate(nn.Module):\n def __init__(self):\n super(SpatialGate, self).__init__()\n kernel_size = 7\n self.compress = ChannelPool()\n self.spatial = BasicConv(2, 1, kernel_size, stride=1, padding=(kernel_size-1) // 2)\n def forward(self, x):\n x_compress = self.compress(x)\n x_out = self.spatial(x_compress)\n scale = torch.sigmoid(x_out) # broadcasting\n return x * scale\n\nclass CBAM(nn.Module):\n def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max'], no_spatial=False):\n super(CBAM, self).__init__()\n self.ChannelGate = ChannelGate(gate_channels, reduction_ratio, pool_types)\n self.no_spatial=no_spatial\n if not no_spatial:\n self.SpatialGate = SpatialGate()\n def forward(self, x):\n x_out = self.ChannelGate(x)\n if not self.no_spatial:\n x_out = self.SpatialGate(x_out)\n return x_out","source_hash":"be1b3090d8bdec613bad2120e24ffdfd92e3bc751ba9e663bd797ddf9e667f7f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.cbam.BasicConv","uri":"program://Track-Anything/class/tracker.model.cbam.BasicConv#L7-L15","kind":"class","name":"BasicConv","path":"tracker/model/cbam.py","language":"python","start_line":7,"end_line":15,"context_start_line":1,"context_end_line":35,"code":"# Modified from https://github.com/Jongchan/attention-module/blob/master/MODELS/cbam.py\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass BasicConv(nn.Module):\n def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True):\n super(BasicConv, self).__init__()\n self.out_channels = out_planes\n self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)\n\n def forward(self, x):\n x = self.conv(x)\n return x\n\nclass Flatten(nn.Module):\n def forward(self, x):\n return x.view(x.size(0), -1)\n\nclass ChannelGate(nn.Module):\n def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max']):\n super(ChannelGate, self).__init__()\n self.gate_channels = gate_channels\n self.mlp = nn.Sequential(\n Flatten(),\n nn.Linear(gate_channels, gate_channels // reduction_ratio),\n nn.ReLU(),\n nn.Linear(gate_channels // reduction_ratio, gate_channels)\n )\n self.pool_types = pool_types\n def forward(self, x):\n channel_att_sum = None\n for pool_type in self.pool_types:\n if pool_type=='avg':","source_hash":"be1b3090d8bdec613bad2120e24ffdfd92e3bc751ba9e663bd797ddf9e667f7f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.cbam.Flatten","uri":"program://Track-Anything/class/tracker.model.cbam.Flatten#L17-L19","kind":"class","name":"Flatten","path":"tracker/model/cbam.py","language":"python","start_line":17,"end_line":19,"context_start_line":1,"context_end_line":39,"code":"# Modified from https://github.com/Jongchan/attention-module/blob/master/MODELS/cbam.py\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass BasicConv(nn.Module):\n def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True):\n super(BasicConv, self).__init__()\n self.out_channels = out_planes\n self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)\n\n def forward(self, x):\n x = self.conv(x)\n return x\n\nclass Flatten(nn.Module):\n def forward(self, x):\n return x.view(x.size(0), -1)\n\nclass ChannelGate(nn.Module):\n def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max']):\n super(ChannelGate, self).__init__()\n self.gate_channels = gate_channels\n self.mlp = nn.Sequential(\n Flatten(),\n nn.Linear(gate_channels, gate_channels // reduction_ratio),\n nn.ReLU(),\n nn.Linear(gate_channels // reduction_ratio, gate_channels)\n )\n self.pool_types = pool_types\n def forward(self, x):\n channel_att_sum = None\n for pool_type in self.pool_types:\n if pool_type=='avg':\n avg_pool = F.avg_pool2d( x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3)))\n channel_att_raw = self.mlp( avg_pool )\n elif pool_type=='max':\n max_pool = F.max_pool2d( x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3)))","source_hash":"be1b3090d8bdec613bad2120e24ffdfd92e3bc751ba9e663bd797ddf9e667f7f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.cbam.ChannelGate","uri":"program://Track-Anything/class/tracker.model.cbam.ChannelGate#L21-L48","kind":"class","name":"ChannelGate","path":"tracker/model/cbam.py","language":"python","start_line":21,"end_line":48,"context_start_line":1,"context_end_line":68,"code":"# Modified from https://github.com/Jongchan/attention-module/blob/master/MODELS/cbam.py\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass BasicConv(nn.Module):\n def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True):\n super(BasicConv, self).__init__()\n self.out_channels = out_planes\n self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)\n\n def forward(self, x):\n x = self.conv(x)\n return x\n\nclass Flatten(nn.Module):\n def forward(self, x):\n return x.view(x.size(0), -1)\n\nclass ChannelGate(nn.Module):\n def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max']):\n super(ChannelGate, self).__init__()\n self.gate_channels = gate_channels\n self.mlp = nn.Sequential(\n Flatten(),\n nn.Linear(gate_channels, gate_channels // reduction_ratio),\n nn.ReLU(),\n nn.Linear(gate_channels // reduction_ratio, gate_channels)\n )\n self.pool_types = pool_types\n def forward(self, x):\n channel_att_sum = None\n for pool_type in self.pool_types:\n if pool_type=='avg':\n avg_pool = F.avg_pool2d( x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3)))\n channel_att_raw = self.mlp( avg_pool )\n elif pool_type=='max':\n max_pool = F.max_pool2d( x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3)))\n channel_att_raw = self.mlp( max_pool )\n\n if channel_att_sum is None:\n channel_att_sum = channel_att_raw\n else:\n channel_att_sum = channel_att_sum + channel_att_raw\n\n scale = torch.sigmoid( channel_att_sum ).unsqueeze(2).unsqueeze(3).expand_as(x)\n return x * scale\n\nclass ChannelPool(nn.Module):\n def forward(self, x):\n return torch.cat( (torch.max(x,1)[0].unsqueeze(1), torch.mean(x,1).unsqueeze(1)), dim=1 )\n\nclass SpatialGate(nn.Module):\n def __init__(self):\n super(SpatialGate, self).__init__()\n kernel_size = 7\n self.compress = ChannelPool()\n self.spatial = BasicConv(2, 1, kernel_size, stride=1, padding=(kernel_size-1) // 2)\n def forward(self, x):\n x_compress = self.compress(x)\n x_out = self.spatial(x_compress)\n scale = torch.sigmoid(x_out) # broadcasting\n return x * scale\n\nclass CBAM(nn.Module):\n def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max'], no_spatial=False):\n super(CBAM, self).__init__()","source_hash":"be1b3090d8bdec613bad2120e24ffdfd92e3bc751ba9e663bd797ddf9e667f7f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.cbam.ChannelPool","uri":"program://Track-Anything/class/tracker.model.cbam.ChannelPool#L50-L52","kind":"class","name":"ChannelPool","path":"tracker/model/cbam.py","language":"python","start_line":50,"end_line":52,"context_start_line":30,"context_end_line":72,"code":" )\n self.pool_types = pool_types\n def forward(self, x):\n channel_att_sum = None\n for pool_type in self.pool_types:\n if pool_type=='avg':\n avg_pool = F.avg_pool2d( x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3)))\n channel_att_raw = self.mlp( avg_pool )\n elif pool_type=='max':\n max_pool = F.max_pool2d( x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3)))\n channel_att_raw = self.mlp( max_pool )\n\n if channel_att_sum is None:\n channel_att_sum = channel_att_raw\n else:\n channel_att_sum = channel_att_sum + channel_att_raw\n\n scale = torch.sigmoid( channel_att_sum ).unsqueeze(2).unsqueeze(3).expand_as(x)\n return x * scale\n\nclass ChannelPool(nn.Module):\n def forward(self, x):\n return torch.cat( (torch.max(x,1)[0].unsqueeze(1), torch.mean(x,1).unsqueeze(1)), dim=1 )\n\nclass SpatialGate(nn.Module):\n def __init__(self):\n super(SpatialGate, self).__init__()\n kernel_size = 7\n self.compress = ChannelPool()\n self.spatial = BasicConv(2, 1, kernel_size, stride=1, padding=(kernel_size-1) // 2)\n def forward(self, x):\n x_compress = self.compress(x)\n x_out = self.spatial(x_compress)\n scale = torch.sigmoid(x_out) # broadcasting\n return x * scale\n\nclass CBAM(nn.Module):\n def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max'], no_spatial=False):\n super(CBAM, self).__init__()\n self.ChannelGate = ChannelGate(gate_channels, reduction_ratio, pool_types)\n self.no_spatial=no_spatial\n if not no_spatial:\n self.SpatialGate = SpatialGate()","source_hash":"be1b3090d8bdec613bad2120e24ffdfd92e3bc751ba9e663bd797ddf9e667f7f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.cbam.SpatialGate","uri":"program://Track-Anything/class/tracker.model.cbam.SpatialGate#L54-L64","kind":"class","name":"SpatialGate","path":"tracker/model/cbam.py","language":"python","start_line":54,"end_line":64,"context_start_line":34,"context_end_line":77,"code":" for pool_type in self.pool_types:\n if pool_type=='avg':\n avg_pool = F.avg_pool2d( x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3)))\n channel_att_raw = self.mlp( avg_pool )\n elif pool_type=='max':\n max_pool = F.max_pool2d( x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3)))\n channel_att_raw = self.mlp( max_pool )\n\n if channel_att_sum is None:\n channel_att_sum = channel_att_raw\n else:\n channel_att_sum = channel_att_sum + channel_att_raw\n\n scale = torch.sigmoid( channel_att_sum ).unsqueeze(2).unsqueeze(3).expand_as(x)\n return x * scale\n\nclass ChannelPool(nn.Module):\n def forward(self, x):\n return torch.cat( (torch.max(x,1)[0].unsqueeze(1), torch.mean(x,1).unsqueeze(1)), dim=1 )\n\nclass SpatialGate(nn.Module):\n def __init__(self):\n super(SpatialGate, self).__init__()\n kernel_size = 7\n self.compress = ChannelPool()\n self.spatial = BasicConv(2, 1, kernel_size, stride=1, padding=(kernel_size-1) // 2)\n def forward(self, x):\n x_compress = self.compress(x)\n x_out = self.spatial(x_compress)\n scale = torch.sigmoid(x_out) # broadcasting\n return x * scale\n\nclass CBAM(nn.Module):\n def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max'], no_spatial=False):\n super(CBAM, self).__init__()\n self.ChannelGate = ChannelGate(gate_channels, reduction_ratio, pool_types)\n self.no_spatial=no_spatial\n if not no_spatial:\n self.SpatialGate = SpatialGate()\n def forward(self, x):\n x_out = self.ChannelGate(x)\n if not self.no_spatial:\n x_out = self.SpatialGate(x_out)\n return x_out","source_hash":"be1b3090d8bdec613bad2120e24ffdfd92e3bc751ba9e663bd797ddf9e667f7f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.cbam.CBAM","uri":"program://Track-Anything/class/tracker.model.cbam.CBAM#L66-L77","kind":"class","name":"CBAM","path":"tracker/model/cbam.py","language":"python","start_line":66,"end_line":77,"context_start_line":46,"context_end_line":77,"code":"\n scale = torch.sigmoid( channel_att_sum ).unsqueeze(2).unsqueeze(3).expand_as(x)\n return x * scale\n\nclass ChannelPool(nn.Module):\n def forward(self, x):\n return torch.cat( (torch.max(x,1)[0].unsqueeze(1), torch.mean(x,1).unsqueeze(1)), dim=1 )\n\nclass SpatialGate(nn.Module):\n def __init__(self):\n super(SpatialGate, self).__init__()\n kernel_size = 7\n self.compress = ChannelPool()\n self.spatial = BasicConv(2, 1, kernel_size, stride=1, padding=(kernel_size-1) // 2)\n def forward(self, x):\n x_compress = self.compress(x)\n x_out = self.spatial(x_compress)\n scale = torch.sigmoid(x_out) # broadcasting\n return x * scale\n\nclass CBAM(nn.Module):\n def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max'], no_spatial=False):\n super(CBAM, self).__init__()\n self.ChannelGate = ChannelGate(gate_channels, reduction_ratio, pool_types)\n self.no_spatial=no_spatial\n if not no_spatial:\n self.SpatialGate = SpatialGate()\n def forward(self, x):\n x_out = self.ChannelGate(x)\n if not self.no_spatial:\n x_out = self.SpatialGate(x_out)\n return x_out","source_hash":"be1b3090d8bdec613bad2120e24ffdfd92e3bc751ba9e663bd797ddf9e667f7f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.cbam.__init__","uri":"program://Track-Anything/function/tracker.model.cbam.__init__#L67-L72","kind":"function","name":"__init__","path":"tracker/model/cbam.py","language":"python","start_line":67,"end_line":72,"context_start_line":47,"context_end_line":77,"code":" scale = torch.sigmoid( channel_att_sum ).unsqueeze(2).unsqueeze(3).expand_as(x)\n return x * scale\n\nclass ChannelPool(nn.Module):\n def forward(self, x):\n return torch.cat( (torch.max(x,1)[0].unsqueeze(1), torch.mean(x,1).unsqueeze(1)), dim=1 )\n\nclass SpatialGate(nn.Module):\n def __init__(self):\n super(SpatialGate, self).__init__()\n kernel_size = 7\n self.compress = ChannelPool()\n self.spatial = BasicConv(2, 1, kernel_size, stride=1, padding=(kernel_size-1) // 2)\n def forward(self, x):\n x_compress = self.compress(x)\n x_out = self.spatial(x_compress)\n scale = torch.sigmoid(x_out) # broadcasting\n return x * scale\n\nclass CBAM(nn.Module):\n def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max'], no_spatial=False):\n super(CBAM, self).__init__()\n self.ChannelGate = ChannelGate(gate_channels, reduction_ratio, pool_types)\n self.no_spatial=no_spatial\n if not no_spatial:\n self.SpatialGate = SpatialGate()\n def forward(self, x):\n x_out = self.ChannelGate(x)\n if not self.no_spatial:\n x_out = self.SpatialGate(x_out)\n return x_out","source_hash":"be1b3090d8bdec613bad2120e24ffdfd92e3bc751ba9e663bd797ddf9e667f7f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.cbam.forward","uri":"program://Track-Anything/function/tracker.model.cbam.forward#L73-L77","kind":"function","name":"forward","path":"tracker/model/cbam.py","language":"python","start_line":73,"end_line":77,"context_start_line":53,"context_end_line":77,"code":"\nclass SpatialGate(nn.Module):\n def __init__(self):\n super(SpatialGate, self).__init__()\n kernel_size = 7\n self.compress = ChannelPool()\n self.spatial = BasicConv(2, 1, kernel_size, stride=1, padding=(kernel_size-1) // 2)\n def forward(self, x):\n x_compress = self.compress(x)\n x_out = self.spatial(x_compress)\n scale = torch.sigmoid(x_out) # broadcasting\n return x * scale\n\nclass CBAM(nn.Module):\n def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max'], no_spatial=False):\n super(CBAM, self).__init__()\n self.ChannelGate = ChannelGate(gate_channels, reduction_ratio, pool_types)\n self.no_spatial=no_spatial\n if not no_spatial:\n self.SpatialGate = SpatialGate()\n def forward(self, x):\n x_out = self.ChannelGate(x)\n if not self.no_spatial:\n x_out = self.SpatialGate(x_out)\n return x_out","source_hash":"be1b3090d8bdec613bad2120e24ffdfd92e3bc751ba9e663bd797ddf9e667f7f","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.modules","uri":"program://Track-Anything/module/tracker.model.modules#L1-L250","kind":"module","name":"tracker.model.modules","path":"tracker/model/modules.py","language":"python","start_line":1,"end_line":250,"context_start_line":1,"context_end_line":250,"code":"\"\"\"\nmodules.py - This file stores the rather boring network blocks.\n\nx - usually means features that only depends on the image\ng - usually means features that also depends on the mask. \n They might have an extra \"group\" or \"num_objects\" dimension, hence\n batch_size * num_objects * num_channels * H * W\n\nThe trailing number of a variable usually denote the stride\n\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom model.group_modules import *\nfrom model import resnet\nfrom model.cbam import CBAM\n\n\nclass FeatureFusionBlock(nn.Module):\n def __init__(self, x_in_dim, g_in_dim, g_mid_dim, g_out_dim):\n super().__init__()\n\n self.distributor = MainToGroupDistributor()\n self.block1 = GroupResBlock(x_in_dim+g_in_dim, g_mid_dim)\n self.attention = CBAM(g_mid_dim)\n self.block2 = GroupResBlock(g_mid_dim, g_out_dim)\n\n def forward(self, x, g):\n batch_size, num_objects = g.shape[:2]\n\n g = self.distributor(x, g)\n g = self.block1(g)\n r = self.attention(g.flatten(start_dim=0, end_dim=1))\n r = r.view(batch_size, num_objects, *r.shape[1:])\n\n g = self.block2(g+r)\n\n return g\n\n\nclass HiddenUpdater(nn.Module):\n # Used in the decoder, multi-scale feature + GRU\n def __init__(self, g_dims, mid_dim, hidden_dim):\n super().__init__()\n self.hidden_dim = hidden_dim\n\n self.g16_conv = GConv2D(g_dims[0], mid_dim, kernel_size=1)\n self.g8_conv = GConv2D(g_dims[1], mid_dim, kernel_size=1)\n self.g4_conv = GConv2D(g_dims[2], mid_dim, kernel_size=1)\n\n self.transform = GConv2D(mid_dim+hidden_dim, hidden_dim*3, kernel_size=3, padding=1)\n\n nn.init.xavier_normal_(self.transform.weight)\n\n def forward(self, g, h):\n g = self.g16_conv(g[0]) + self.g8_conv(downsample_groups(g[1], ratio=1/2)) + \\\n self.g4_conv(downsample_groups(g[2], ratio=1/4))\n\n g = torch.cat([g, h], 2)\n\n # defined slightly differently than standard GRU, \n # namely the new value is generated before the forget gate.\n # might provide better gradient but frankly it was initially just an \n # implementation error that I never bothered fixing\n values = self.transform(g)\n forget_gate = torch.sigmoid(values[:,:,:self.hidden_dim])\n update_gate = torch.sigmoid(values[:,:,self.hidden_dim:self.hidden_dim*2])\n new_value = torch.tanh(values[:,:,self.hidden_dim*2:])\n new_h = forget_gate*h*(1-update_gate) + update_gate*new_value\n\n return new_h\n\n\nclass HiddenReinforcer(nn.Module):\n # Used in the value encoder, a single GRU\n def __init__(self, g_dim, hidden_dim):\n super().__init__()\n self.hidden_dim = hidden_dim\n self.transform = GConv2D(g_dim+hidden_dim, hidden_dim*3, kernel_size=3, padding=1)\n\n nn.init.xavier_normal_(self.transform.weight)\n\n def forward(self, g, h):\n g = torch.cat([g, h], 2)\n\n # defined slightly differently than standard GRU, \n # namely the new value is generated before the forget gate.\n # might provide better gradient but frankly it was initially just an \n # implementation error that I never bothered fixing\n values = self.transform(g)\n forget_gate = torch.sigmoid(values[:,:,:self.hidden_dim])\n update_gate = torch.sigmoid(values[:,:,self.hidden_dim:self.hidden_dim*2])\n new_value = torch.tanh(values[:,:,self.hidden_dim*2:])\n new_h = forget_gate*h*(1-update_gate) + update_gate*new_value\n\n return new_h\n\n\nclass ValueEncoder(nn.Module):\n def __init__(self, value_dim, hidden_dim, single_object=False):\n super().__init__()\n \n self.single_object = single_object\n network = resnet.resnet18(pretrained=True, extra_dim=1 if single_object else 2)\n self.conv1 = network.conv1\n self.bn1 = network.bn1\n self.relu = network.relu # 1/2, 64\n self.maxpool = network.maxpool\n\n self.layer1 = network.layer1 # 1/4, 64\n self.layer2 = network.layer2 # 1/8, 128\n self.layer3 = network.layer3 # 1/16, 256\n\n self.distributor = MainToGroupDistributor()\n self.fuser = FeatureFusionBlock(1024, 256, value_dim, value_dim)\n if hidden_dim > 0:\n self.hidden_reinforce = HiddenReinforcer(value_dim, hidden_dim)\n else:\n self.hidden_reinforce = None\n\n def forward(self, image, image_feat_f16, h, masks, others, is_deep_update=True):\n # image_feat_f16 is the feature from the key encoder\n if not self.single_object:\n g = torch.stack([masks, others], 2)\n else:\n g = masks.unsqueeze(2)\n g = self.distributor(image, g)\n\n batch_size, num_objects = g.shape[:2]\n g = g.flatten(start_dim=0, end_dim=1)\n\n g = self.conv1(g)\n g = self.bn1(g) # 1/2, 64\n g = self.maxpool(g) # 1/4, 64\n g = self.relu(g) \n\n g = self.layer1(g) # 1/4\n g = self.layer2(g) # 1/8\n g = self.layer3(g) # 1/16\n\n g = g.view(batch_size, num_objects, *g.shape[1:])\n g = self.fuser(image_feat_f16, g)\n\n if is_deep_update and self.hidden_reinforce is not None:\n h = self.hidden_reinforce(g, h)\n\n return g, h\n \n\nclass KeyEncoder(nn.Module):\n def __init__(self):\n super().__init__()\n network = resnet.resnet50(pretrained=True)\n self.conv1 = network.conv1\n self.bn1 = network.bn1\n self.relu = network.relu # 1/2, 64\n self.maxpool = network.maxpool\n\n self.res2 = network.layer1 # 1/4, 256\n self.layer2 = network.layer2 # 1/8, 512\n self.layer3 = network.layer3 # 1/16, 1024\n\n def forward(self, f):\n x = self.conv1(f) \n x = self.bn1(x)\n x = self.relu(x) # 1/2, 64\n x = self.maxpool(x) # 1/4, 64\n f4 = self.res2(x) # 1/4, 256\n f8 = self.layer2(f4) # 1/8, 512\n f16 = self.layer3(f8) # 1/16, 1024\n\n return f16, f8, f4\n\n\nclass UpsampleBlock(nn.Module):\n def __init__(self, skip_dim, g_up_dim, g_out_dim, scale_factor=2):\n super().__init__()\n self.skip_conv = nn.Conv2d(skip_dim, g_up_dim, kernel_size=3, padding=1)\n self.distributor = MainToGroupDistributor(method='add')\n self.out_conv = GroupResBlock(g_up_dim, g_out_dim)\n self.scale_factor = scale_factor\n\n def forward(self, skip_f, up_g):\n skip_f = self.skip_conv(skip_f)\n g = upsample_groups(up_g, ratio=self.scale_factor)\n g = self.distributor(skip_f, g)\n g = self.out_conv(g)\n return g\n\n\nclass KeyProjection(nn.Module):\n def __init__(self, in_dim, keydim):\n super().__init__()\n\n self.key_proj = nn.Conv2d(in_dim, keydim, kernel_size=3, padding=1)\n # shrinkage\n self.d_proj = nn.Conv2d(in_dim, 1, kernel_size=3, padding=1)\n # selection\n self.e_proj = nn.Conv2d(in_dim, keydim, kernel_size=3, padding=1)\n\n nn.init.orthogonal_(self.key_proj.weight.data)\n nn.init.zeros_(self.key_proj.bias.data)\n \n def forward(self, x, need_s, need_e):\n shrinkage = self.d_proj(x)**2 + 1 if (need_s) else None\n selection = torch.sigmoid(self.e_proj(x)) if (need_e) else None\n\n return self.key_proj(x), shrinkage, selection\n\n\nclass Decoder(nn.Module):\n def __init__(self, val_dim, hidden_dim):\n super().__init__()\n\n self.fuser = FeatureFusionBlock(1024, val_dim+hidden_dim, 512, 512)\n if hidden_dim > 0:\n self.hidden_update = HiddenUpdater([512, 256, 256+1], 256, hidden_dim)\n else:\n self.hidden_update = None\n \n self.up_16_8 = UpsampleBlock(512, 512, 256) # 1/16 -> 1/8\n self.up_8_4 = UpsampleBlock(256, 256, 256) # 1/8 -> 1/4\n\n self.pred = nn.Conv2d(256, 1, kernel_size=3, padding=1, stride=1)\n\n def forward(self, f16, f8, f4, hidden_state, memory_readout, h_out=True):\n batch_size, num_objects = memory_readout.shape[:2]\n\n if self.hidden_update is not None:\n g16 = self.fuser(f16, torch.cat([memory_readout, hidden_state], 2))\n else:\n g16 = self.fuser(f16, memory_readout)\n\n g8 = self.up_16_8(f8, g16)\n g4 = self.up_8_4(f4, g8)\n logits = self.pred(F.relu(g4.flatten(start_dim=0, end_dim=1)))\n\n if h_out and self.hidden_update is not None:\n g4 = torch.cat([g4, logits.view(batch_size, num_objects, 1, *logits.shape[-2:])], 2)\n hidden_state = self.hidden_update([g16, g8, g4], hidden_state)\n else:\n hidden_state = None\n \n logits = F.interpolate(logits, scale_factor=4, mode='bilinear', align_corners=False)\n logits = logits.view(batch_size, num_objects, *logits.shape[-2:])\n\n return hidden_state, logits","source_hash":"5018ae51e4140c1da9bd5b09fc6d44c3ebf50c98950eca19fbc1050f1ad22827","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.modules.FeatureFusionBlock","uri":"program://Track-Anything/class/tracker.model.modules.FeatureFusionBlock#L22-L41","kind":"class","name":"FeatureFusionBlock","path":"tracker/model/modules.py","language":"python","start_line":22,"end_line":41,"context_start_line":2,"context_end_line":61,"code":"modules.py - This file stores the rather boring network blocks.\n\nx - usually means features that only depends on the image\ng - usually means features that also depends on the mask. \n They might have an extra \"group\" or \"num_objects\" dimension, hence\n batch_size * num_objects * num_channels * H * W\n\nThe trailing number of a variable usually denote the stride\n\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom model.group_modules import *\nfrom model import resnet\nfrom model.cbam import CBAM\n\n\nclass FeatureFusionBlock(nn.Module):\n def __init__(self, x_in_dim, g_in_dim, g_mid_dim, g_out_dim):\n super().__init__()\n\n self.distributor = MainToGroupDistributor()\n self.block1 = GroupResBlock(x_in_dim+g_in_dim, g_mid_dim)\n self.attention = CBAM(g_mid_dim)\n self.block2 = GroupResBlock(g_mid_dim, g_out_dim)\n\n def forward(self, x, g):\n batch_size, num_objects = g.shape[:2]\n\n g = self.distributor(x, g)\n g = self.block1(g)\n r = self.attention(g.flatten(start_dim=0, end_dim=1))\n r = r.view(batch_size, num_objects, *r.shape[1:])\n\n g = self.block2(g+r)\n\n return g\n\n\nclass HiddenUpdater(nn.Module):\n # Used in the decoder, multi-scale feature + GRU\n def __init__(self, g_dims, mid_dim, hidden_dim):\n super().__init__()\n self.hidden_dim = hidden_dim\n\n self.g16_conv = GConv2D(g_dims[0], mid_dim, kernel_size=1)\n self.g8_conv = GConv2D(g_dims[1], mid_dim, kernel_size=1)\n self.g4_conv = GConv2D(g_dims[2], mid_dim, kernel_size=1)\n\n self.transform = GConv2D(mid_dim+hidden_dim, hidden_dim*3, kernel_size=3, padding=1)\n\n nn.init.xavier_normal_(self.transform.weight)\n\n def forward(self, g, h):\n g = self.g16_conv(g[0]) + self.g8_conv(downsample_groups(g[1], ratio=1/2)) + \\\n self.g4_conv(downsample_groups(g[2], ratio=1/4))\n","source_hash":"5018ae51e4140c1da9bd5b09fc6d44c3ebf50c98950eca19fbc1050f1ad22827","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.modules.HiddenUpdater","uri":"program://Track-Anything/class/tracker.model.modules.HiddenUpdater#L44-L74","kind":"class","name":"HiddenUpdater","path":"tracker/model/modules.py","language":"python","start_line":44,"end_line":74,"context_start_line":24,"context_end_line":94,"code":" super().__init__()\n\n self.distributor = MainToGroupDistributor()\n self.block1 = GroupResBlock(x_in_dim+g_in_dim, g_mid_dim)\n self.attention = CBAM(g_mid_dim)\n self.block2 = GroupResBlock(g_mid_dim, g_out_dim)\n\n def forward(self, x, g):\n batch_size, num_objects = g.shape[:2]\n\n g = self.distributor(x, g)\n g = self.block1(g)\n r = self.attention(g.flatten(start_dim=0, end_dim=1))\n r = r.view(batch_size, num_objects, *r.shape[1:])\n\n g = self.block2(g+r)\n\n return g\n\n\nclass HiddenUpdater(nn.Module):\n # Used in the decoder, multi-scale feature + GRU\n def __init__(self, g_dims, mid_dim, hidden_dim):\n super().__init__()\n self.hidden_dim = hidden_dim\n\n self.g16_conv = GConv2D(g_dims[0], mid_dim, kernel_size=1)\n self.g8_conv = GConv2D(g_dims[1], mid_dim, kernel_size=1)\n self.g4_conv = GConv2D(g_dims[2], mid_dim, kernel_size=1)\n\n self.transform = GConv2D(mid_dim+hidden_dim, hidden_dim*3, kernel_size=3, padding=1)\n\n nn.init.xavier_normal_(self.transform.weight)\n\n def forward(self, g, h):\n g = self.g16_conv(g[0]) + self.g8_conv(downsample_groups(g[1], ratio=1/2)) + \\\n self.g4_conv(downsample_groups(g[2], ratio=1/4))\n\n g = torch.cat([g, h], 2)\n\n # defined slightly differently than standard GRU, \n # namely the new value is generated before the forget gate.\n # might provide better gradient but frankly it was initially just an \n # implementation error that I never bothered fixing\n values = self.transform(g)\n forget_gate = torch.sigmoid(values[:,:,:self.hidden_dim])\n update_gate = torch.sigmoid(values[:,:,self.hidden_dim:self.hidden_dim*2])\n new_value = torch.tanh(values[:,:,self.hidden_dim*2:])\n new_h = forget_gate*h*(1-update_gate) + update_gate*new_value\n\n return new_h\n\n\nclass HiddenReinforcer(nn.Module):\n # Used in the value encoder, a single GRU\n def __init__(self, g_dim, hidden_dim):\n super().__init__()\n self.hidden_dim = hidden_dim\n self.transform = GConv2D(g_dim+hidden_dim, hidden_dim*3, kernel_size=3, padding=1)\n\n nn.init.xavier_normal_(self.transform.weight)\n\n def forward(self, g, h):\n g = torch.cat([g, h], 2)\n\n # defined slightly differently than standard GRU, \n # namely the new value is generated before the forget gate.\n # might provide better gradient but frankly it was initially just an \n # implementation error that I never bothered fixing\n values = self.transform(g)\n forget_gate = torch.sigmoid(values[:,:,:self.hidden_dim])","source_hash":"5018ae51e4140c1da9bd5b09fc6d44c3ebf50c98950eca19fbc1050f1ad22827","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.modules.HiddenReinforcer","uri":"program://Track-Anything/class/tracker.model.modules.HiddenReinforcer#L77-L99","kind":"class","name":"HiddenReinforcer","path":"tracker/model/modules.py","language":"python","start_line":77,"end_line":99,"context_start_line":57,"context_end_line":119,"code":"\n def forward(self, g, h):\n g = self.g16_conv(g[0]) + self.g8_conv(downsample_groups(g[1], ratio=1/2)) + \\\n self.g4_conv(downsample_groups(g[2], ratio=1/4))\n\n g = torch.cat([g, h], 2)\n\n # defined slightly differently than standard GRU, \n # namely the new value is generated before the forget gate.\n # might provide better gradient but frankly it was initially just an \n # implementation error that I never bothered fixing\n values = self.transform(g)\n forget_gate = torch.sigmoid(values[:,:,:self.hidden_dim])\n update_gate = torch.sigmoid(values[:,:,self.hidden_dim:self.hidden_dim*2])\n new_value = torch.tanh(values[:,:,self.hidden_dim*2:])\n new_h = forget_gate*h*(1-update_gate) + update_gate*new_value\n\n return new_h\n\n\nclass HiddenReinforcer(nn.Module):\n # Used in the value encoder, a single GRU\n def __init__(self, g_dim, hidden_dim):\n super().__init__()\n self.hidden_dim = hidden_dim\n self.transform = GConv2D(g_dim+hidden_dim, hidden_dim*3, kernel_size=3, padding=1)\n\n nn.init.xavier_normal_(self.transform.weight)\n\n def forward(self, g, h):\n g = torch.cat([g, h], 2)\n\n # defined slightly differently than standard GRU, \n # namely the new value is generated before the forget gate.\n # might provide better gradient but frankly it was initially just an \n # implementation error that I never bothered fixing\n values = self.transform(g)\n forget_gate = torch.sigmoid(values[:,:,:self.hidden_dim])\n update_gate = torch.sigmoid(values[:,:,self.hidden_dim:self.hidden_dim*2])\n new_value = torch.tanh(values[:,:,self.hidden_dim*2:])\n new_h = forget_gate*h*(1-update_gate) + update_gate*new_value\n\n return new_h\n\n\nclass ValueEncoder(nn.Module):\n def __init__(self, value_dim, hidden_dim, single_object=False):\n super().__init__()\n \n self.single_object = single_object\n network = resnet.resnet18(pretrained=True, extra_dim=1 if single_object else 2)\n self.conv1 = network.conv1\n self.bn1 = network.bn1\n self.relu = network.relu # 1/2, 64\n self.maxpool = network.maxpool\n\n self.layer1 = network.layer1 # 1/4, 64\n self.layer2 = network.layer2 # 1/8, 128\n self.layer3 = network.layer3 # 1/16, 256\n\n self.distributor = MainToGroupDistributor()\n self.fuser = FeatureFusionBlock(1024, 256, value_dim, value_dim)\n if hidden_dim > 0:","source_hash":"5018ae51e4140c1da9bd5b09fc6d44c3ebf50c98950eca19fbc1050f1ad22827","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.modules.ValueEncoder","uri":"program://Track-Anything/class/tracker.model.modules.ValueEncoder#L102-L150","kind":"class","name":"ValueEncoder","path":"tracker/model/modules.py","language":"python","start_line":102,"end_line":150,"context_start_line":82,"context_end_line":170,"code":" self.transform = GConv2D(g_dim+hidden_dim, hidden_dim*3, kernel_size=3, padding=1)\n\n nn.init.xavier_normal_(self.transform.weight)\n\n def forward(self, g, h):\n g = torch.cat([g, h], 2)\n\n # defined slightly differently than standard GRU, \n # namely the new value is generated before the forget gate.\n # might provide better gradient but frankly it was initially just an \n # implementation error that I never bothered fixing\n values = self.transform(g)\n forget_gate = torch.sigmoid(values[:,:,:self.hidden_dim])\n update_gate = torch.sigmoid(values[:,:,self.hidden_dim:self.hidden_dim*2])\n new_value = torch.tanh(values[:,:,self.hidden_dim*2:])\n new_h = forget_gate*h*(1-update_gate) + update_gate*new_value\n\n return new_h\n\n\nclass ValueEncoder(nn.Module):\n def __init__(self, value_dim, hidden_dim, single_object=False):\n super().__init__()\n \n self.single_object = single_object\n network = resnet.resnet18(pretrained=True, extra_dim=1 if single_object else 2)\n self.conv1 = network.conv1\n self.bn1 = network.bn1\n self.relu = network.relu # 1/2, 64\n self.maxpool = network.maxpool\n\n self.layer1 = network.layer1 # 1/4, 64\n self.layer2 = network.layer2 # 1/8, 128\n self.layer3 = network.layer3 # 1/16, 256\n\n self.distributor = MainToGroupDistributor()\n self.fuser = FeatureFusionBlock(1024, 256, value_dim, value_dim)\n if hidden_dim > 0:\n self.hidden_reinforce = HiddenReinforcer(value_dim, hidden_dim)\n else:\n self.hidden_reinforce = None\n\n def forward(self, image, image_feat_f16, h, masks, others, is_deep_update=True):\n # image_feat_f16 is the feature from the key encoder\n if not self.single_object:\n g = torch.stack([masks, others], 2)\n else:\n g = masks.unsqueeze(2)\n g = self.distributor(image, g)\n\n batch_size, num_objects = g.shape[:2]\n g = g.flatten(start_dim=0, end_dim=1)\n\n g = self.conv1(g)\n g = self.bn1(g) # 1/2, 64\n g = self.maxpool(g) # 1/4, 64\n g = self.relu(g) \n\n g = self.layer1(g) # 1/4\n g = self.layer2(g) # 1/8\n g = self.layer3(g) # 1/16\n\n g = g.view(batch_size, num_objects, *g.shape[1:])\n g = self.fuser(image_feat_f16, g)\n\n if is_deep_update and self.hidden_reinforce is not None:\n h = self.hidden_reinforce(g, h)\n\n return g, h\n \n\nclass KeyEncoder(nn.Module):\n def __init__(self):\n super().__init__()\n network = resnet.resnet50(pretrained=True)\n self.conv1 = network.conv1\n self.bn1 = network.bn1\n self.relu = network.relu # 1/2, 64\n self.maxpool = network.maxpool\n\n self.res2 = network.layer1 # 1/4, 256\n self.layer2 = network.layer2 # 1/8, 512\n self.layer3 = network.layer3 # 1/16, 1024\n\n def forward(self, f):\n x = self.conv1(f) \n x = self.bn1(x)\n x = self.relu(x) # 1/2, 64\n x = self.maxpool(x) # 1/4, 64","source_hash":"5018ae51e4140c1da9bd5b09fc6d44c3ebf50c98950eca19fbc1050f1ad22827","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.modules.KeyEncoder","uri":"program://Track-Anything/class/tracker.model.modules.KeyEncoder#L153-L175","kind":"class","name":"KeyEncoder","path":"tracker/model/modules.py","language":"python","start_line":153,"end_line":175,"context_start_line":133,"context_end_line":195,"code":" g = g.flatten(start_dim=0, end_dim=1)\n\n g = self.conv1(g)\n g = self.bn1(g) # 1/2, 64\n g = self.maxpool(g) # 1/4, 64\n g = self.relu(g) \n\n g = self.layer1(g) # 1/4\n g = self.layer2(g) # 1/8\n g = self.layer3(g) # 1/16\n\n g = g.view(batch_size, num_objects, *g.shape[1:])\n g = self.fuser(image_feat_f16, g)\n\n if is_deep_update and self.hidden_reinforce is not None:\n h = self.hidden_reinforce(g, h)\n\n return g, h\n \n\nclass KeyEncoder(nn.Module):\n def __init__(self):\n super().__init__()\n network = resnet.resnet50(pretrained=True)\n self.conv1 = network.conv1\n self.bn1 = network.bn1\n self.relu = network.relu # 1/2, 64\n self.maxpool = network.maxpool\n\n self.res2 = network.layer1 # 1/4, 256\n self.layer2 = network.layer2 # 1/8, 512\n self.layer3 = network.layer3 # 1/16, 1024\n\n def forward(self, f):\n x = self.conv1(f) \n x = self.bn1(x)\n x = self.relu(x) # 1/2, 64\n x = self.maxpool(x) # 1/4, 64\n f4 = self.res2(x) # 1/4, 256\n f8 = self.layer2(f4) # 1/8, 512\n f16 = self.layer3(f8) # 1/16, 1024\n\n return f16, f8, f4\n\n\nclass UpsampleBlock(nn.Module):\n def __init__(self, skip_dim, g_up_dim, g_out_dim, scale_factor=2):\n super().__init__()\n self.skip_conv = nn.Conv2d(skip_dim, g_up_dim, kernel_size=3, padding=1)\n self.distributor = MainToGroupDistributor(method='add')\n self.out_conv = GroupResBlock(g_up_dim, g_out_dim)\n self.scale_factor = scale_factor\n\n def forward(self, skip_f, up_g):\n skip_f = self.skip_conv(skip_f)\n g = upsample_groups(up_g, ratio=self.scale_factor)\n g = self.distributor(skip_f, g)\n g = self.out_conv(g)\n return g\n\n\nclass KeyProjection(nn.Module):\n def __init__(self, in_dim, keydim):","source_hash":"5018ae51e4140c1da9bd5b09fc6d44c3ebf50c98950eca19fbc1050f1ad22827","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.modules.UpsampleBlock","uri":"program://Track-Anything/class/tracker.model.modules.UpsampleBlock#L178-L191","kind":"class","name":"UpsampleBlock","path":"tracker/model/modules.py","language":"python","start_line":178,"end_line":191,"context_start_line":158,"context_end_line":211,"code":" self.bn1 = network.bn1\n self.relu = network.relu # 1/2, 64\n self.maxpool = network.maxpool\n\n self.res2 = network.layer1 # 1/4, 256\n self.layer2 = network.layer2 # 1/8, 512\n self.layer3 = network.layer3 # 1/16, 1024\n\n def forward(self, f):\n x = self.conv1(f) \n x = self.bn1(x)\n x = self.relu(x) # 1/2, 64\n x = self.maxpool(x) # 1/4, 64\n f4 = self.res2(x) # 1/4, 256\n f8 = self.layer2(f4) # 1/8, 512\n f16 = self.layer3(f8) # 1/16, 1024\n\n return f16, f8, f4\n\n\nclass UpsampleBlock(nn.Module):\n def __init__(self, skip_dim, g_up_dim, g_out_dim, scale_factor=2):\n super().__init__()\n self.skip_conv = nn.Conv2d(skip_dim, g_up_dim, kernel_size=3, padding=1)\n self.distributor = MainToGroupDistributor(method='add')\n self.out_conv = GroupResBlock(g_up_dim, g_out_dim)\n self.scale_factor = scale_factor\n\n def forward(self, skip_f, up_g):\n skip_f = self.skip_conv(skip_f)\n g = upsample_groups(up_g, ratio=self.scale_factor)\n g = self.distributor(skip_f, g)\n g = self.out_conv(g)\n return g\n\n\nclass KeyProjection(nn.Module):\n def __init__(self, in_dim, keydim):\n super().__init__()\n\n self.key_proj = nn.Conv2d(in_dim, keydim, kernel_size=3, padding=1)\n # shrinkage\n self.d_proj = nn.Conv2d(in_dim, 1, kernel_size=3, padding=1)\n # selection\n self.e_proj = nn.Conv2d(in_dim, keydim, kernel_size=3, padding=1)\n\n nn.init.orthogonal_(self.key_proj.weight.data)\n nn.init.zeros_(self.key_proj.bias.data)\n \n def forward(self, x, need_s, need_e):\n shrinkage = self.d_proj(x)**2 + 1 if (need_s) else None\n selection = torch.sigmoid(self.e_proj(x)) if (need_e) else None\n\n return self.key_proj(x), shrinkage, selection","source_hash":"5018ae51e4140c1da9bd5b09fc6d44c3ebf50c98950eca19fbc1050f1ad22827","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.modules.KeyProjection","uri":"program://Track-Anything/class/tracker.model.modules.KeyProjection#L194-L211","kind":"class","name":"KeyProjection","path":"tracker/model/modules.py","language":"python","start_line":194,"end_line":211,"context_start_line":174,"context_end_line":231,"code":"\n return f16, f8, f4\n\n\nclass UpsampleBlock(nn.Module):\n def __init__(self, skip_dim, g_up_dim, g_out_dim, scale_factor=2):\n super().__init__()\n self.skip_conv = nn.Conv2d(skip_dim, g_up_dim, kernel_size=3, padding=1)\n self.distributor = MainToGroupDistributor(method='add')\n self.out_conv = GroupResBlock(g_up_dim, g_out_dim)\n self.scale_factor = scale_factor\n\n def forward(self, skip_f, up_g):\n skip_f = self.skip_conv(skip_f)\n g = upsample_groups(up_g, ratio=self.scale_factor)\n g = self.distributor(skip_f, g)\n g = self.out_conv(g)\n return g\n\n\nclass KeyProjection(nn.Module):\n def __init__(self, in_dim, keydim):\n super().__init__()\n\n self.key_proj = nn.Conv2d(in_dim, keydim, kernel_size=3, padding=1)\n # shrinkage\n self.d_proj = nn.Conv2d(in_dim, 1, kernel_size=3, padding=1)\n # selection\n self.e_proj = nn.Conv2d(in_dim, keydim, kernel_size=3, padding=1)\n\n nn.init.orthogonal_(self.key_proj.weight.data)\n nn.init.zeros_(self.key_proj.bias.data)\n \n def forward(self, x, need_s, need_e):\n shrinkage = self.d_proj(x)**2 + 1 if (need_s) else None\n selection = torch.sigmoid(self.e_proj(x)) if (need_e) else None\n\n return self.key_proj(x), shrinkage, selection\n\n\nclass Decoder(nn.Module):\n def __init__(self, val_dim, hidden_dim):\n super().__init__()\n\n self.fuser = FeatureFusionBlock(1024, val_dim+hidden_dim, 512, 512)\n if hidden_dim > 0:\n self.hidden_update = HiddenUpdater([512, 256, 256+1], 256, hidden_dim)\n else:\n self.hidden_update = None\n \n self.up_16_8 = UpsampleBlock(512, 512, 256) # 1/16 -> 1/8\n self.up_8_4 = UpsampleBlock(256, 256, 256) # 1/8 -> 1/4\n\n self.pred = nn.Conv2d(256, 1, kernel_size=3, padding=1, stride=1)\n\n def forward(self, f16, f8, f4, hidden_state, memory_readout, h_out=True):\n batch_size, num_objects = memory_readout.shape[:2]\n","source_hash":"5018ae51e4140c1da9bd5b09fc6d44c3ebf50c98950eca19fbc1050f1ad22827","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.modules.Decoder","uri":"program://Track-Anything/class/tracker.model.modules.Decoder#L214-L250","kind":"class","name":"Decoder","path":"tracker/model/modules.py","language":"python","start_line":214,"end_line":250,"context_start_line":194,"context_end_line":250,"code":"class KeyProjection(nn.Module):\n def __init__(self, in_dim, keydim):\n super().__init__()\n\n self.key_proj = nn.Conv2d(in_dim, keydim, kernel_size=3, padding=1)\n # shrinkage\n self.d_proj = nn.Conv2d(in_dim, 1, kernel_size=3, padding=1)\n # selection\n self.e_proj = nn.Conv2d(in_dim, keydim, kernel_size=3, padding=1)\n\n nn.init.orthogonal_(self.key_proj.weight.data)\n nn.init.zeros_(self.key_proj.bias.data)\n \n def forward(self, x, need_s, need_e):\n shrinkage = self.d_proj(x)**2 + 1 if (need_s) else None\n selection = torch.sigmoid(self.e_proj(x)) if (need_e) else None\n\n return self.key_proj(x), shrinkage, selection\n\n\nclass Decoder(nn.Module):\n def __init__(self, val_dim, hidden_dim):\n super().__init__()\n\n self.fuser = FeatureFusionBlock(1024, val_dim+hidden_dim, 512, 512)\n if hidden_dim > 0:\n self.hidden_update = HiddenUpdater([512, 256, 256+1], 256, hidden_dim)\n else:\n self.hidden_update = None\n \n self.up_16_8 = UpsampleBlock(512, 512, 256) # 1/16 -> 1/8\n self.up_8_4 = UpsampleBlock(256, 256, 256) # 1/8 -> 1/4\n\n self.pred = nn.Conv2d(256, 1, kernel_size=3, padding=1, stride=1)\n\n def forward(self, f16, f8, f4, hidden_state, memory_readout, h_out=True):\n batch_size, num_objects = memory_readout.shape[:2]\n\n if self.hidden_update is not None:\n g16 = self.fuser(f16, torch.cat([memory_readout, hidden_state], 2))\n else:\n g16 = self.fuser(f16, memory_readout)\n\n g8 = self.up_16_8(f8, g16)\n g4 = self.up_8_4(f4, g8)\n logits = self.pred(F.relu(g4.flatten(start_dim=0, end_dim=1)))\n\n if h_out and self.hidden_update is not None:\n g4 = torch.cat([g4, logits.view(batch_size, num_objects, 1, *logits.shape[-2:])], 2)\n hidden_state = self.hidden_update([g16, g8, g4], hidden_state)\n else:\n hidden_state = None\n \n logits = F.interpolate(logits, scale_factor=4, mode='bilinear', align_corners=False)\n logits = logits.view(batch_size, num_objects, *logits.shape[-2:])\n\n return hidden_state, logits","source_hash":"5018ae51e4140c1da9bd5b09fc6d44c3ebf50c98950eca19fbc1050f1ad22827","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.modules.__init__","uri":"program://Track-Anything/function/tracker.model.modules.__init__#L215-L227","kind":"function","name":"__init__","path":"tracker/model/modules.py","language":"python","start_line":215,"end_line":227,"context_start_line":195,"context_end_line":247,"code":" def __init__(self, in_dim, keydim):\n super().__init__()\n\n self.key_proj = nn.Conv2d(in_dim, keydim, kernel_size=3, padding=1)\n # shrinkage\n self.d_proj = nn.Conv2d(in_dim, 1, kernel_size=3, padding=1)\n # selection\n self.e_proj = nn.Conv2d(in_dim, keydim, kernel_size=3, padding=1)\n\n nn.init.orthogonal_(self.key_proj.weight.data)\n nn.init.zeros_(self.key_proj.bias.data)\n \n def forward(self, x, need_s, need_e):\n shrinkage = self.d_proj(x)**2 + 1 if (need_s) else None\n selection = torch.sigmoid(self.e_proj(x)) if (need_e) else None\n\n return self.key_proj(x), shrinkage, selection\n\n\nclass Decoder(nn.Module):\n def __init__(self, val_dim, hidden_dim):\n super().__init__()\n\n self.fuser = FeatureFusionBlock(1024, val_dim+hidden_dim, 512, 512)\n if hidden_dim > 0:\n self.hidden_update = HiddenUpdater([512, 256, 256+1], 256, hidden_dim)\n else:\n self.hidden_update = None\n \n self.up_16_8 = UpsampleBlock(512, 512, 256) # 1/16 -> 1/8\n self.up_8_4 = UpsampleBlock(256, 256, 256) # 1/8 -> 1/4\n\n self.pred = nn.Conv2d(256, 1, kernel_size=3, padding=1, stride=1)\n\n def forward(self, f16, f8, f4, hidden_state, memory_readout, h_out=True):\n batch_size, num_objects = memory_readout.shape[:2]\n\n if self.hidden_update is not None:\n g16 = self.fuser(f16, torch.cat([memory_readout, hidden_state], 2))\n else:\n g16 = self.fuser(f16, memory_readout)\n\n g8 = self.up_16_8(f8, g16)\n g4 = self.up_8_4(f4, g8)\n logits = self.pred(F.relu(g4.flatten(start_dim=0, end_dim=1)))\n\n if h_out and self.hidden_update is not None:\n g4 = torch.cat([g4, logits.view(batch_size, num_objects, 1, *logits.shape[-2:])], 2)\n hidden_state = self.hidden_update([g16, g8, g4], hidden_state)\n else:\n hidden_state = None\n \n logits = F.interpolate(logits, scale_factor=4, mode='bilinear', align_corners=False)","source_hash":"5018ae51e4140c1da9bd5b09fc6d44c3ebf50c98950eca19fbc1050f1ad22827","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.modules.forward","uri":"program://Track-Anything/function/tracker.model.modules.forward#L229-L250","kind":"function","name":"forward","path":"tracker/model/modules.py","language":"python","start_line":229,"end_line":250,"context_start_line":209,"context_end_line":250,"code":" selection = torch.sigmoid(self.e_proj(x)) if (need_e) else None\n\n return self.key_proj(x), shrinkage, selection\n\n\nclass Decoder(nn.Module):\n def __init__(self, val_dim, hidden_dim):\n super().__init__()\n\n self.fuser = FeatureFusionBlock(1024, val_dim+hidden_dim, 512, 512)\n if hidden_dim > 0:\n self.hidden_update = HiddenUpdater([512, 256, 256+1], 256, hidden_dim)\n else:\n self.hidden_update = None\n \n self.up_16_8 = UpsampleBlock(512, 512, 256) # 1/16 -> 1/8\n self.up_8_4 = UpsampleBlock(256, 256, 256) # 1/8 -> 1/4\n\n self.pred = nn.Conv2d(256, 1, kernel_size=3, padding=1, stride=1)\n\n def forward(self, f16, f8, f4, hidden_state, memory_readout, h_out=True):\n batch_size, num_objects = memory_readout.shape[:2]\n\n if self.hidden_update is not None:\n g16 = self.fuser(f16, torch.cat([memory_readout, hidden_state], 2))\n else:\n g16 = self.fuser(f16, memory_readout)\n\n g8 = self.up_16_8(f8, g16)\n g4 = self.up_8_4(f4, g8)\n logits = self.pred(F.relu(g4.flatten(start_dim=0, end_dim=1)))\n\n if h_out and self.hidden_update is not None:\n g4 = torch.cat([g4, logits.view(batch_size, num_objects, 1, *logits.shape[-2:])], 2)\n hidden_state = self.hidden_update([g16, g8, g4], hidden_state)\n else:\n hidden_state = None\n \n logits = F.interpolate(logits, scale_factor=4, mode='bilinear', align_corners=False)\n logits = logits.view(batch_size, num_objects, *logits.shape[-2:])\n\n return hidden_state, logits","source_hash":"5018ae51e4140c1da9bd5b09fc6d44c3ebf50c98950eca19fbc1050f1ad22827","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.aggregate","uri":"program://Track-Anything/module/tracker.model.aggregate#L1-L17","kind":"module","name":"tracker.model.aggregate","path":"tracker/model/aggregate.py","language":"python","start_line":1,"end_line":17,"context_start_line":1,"context_end_line":17,"code":"import torch\nimport torch.nn.functional as F\n\n\n# Soft aggregation from STM\ndef aggregate(prob, dim, return_logits=False):\n new_prob = torch.cat([\n torch.prod(1-prob, dim=dim, keepdim=True),\n prob\n ], dim).clamp(1e-7, 1-1e-7)\n logits = torch.log((new_prob /(1-new_prob)))\n prob = F.softmax(logits, dim=dim)\n\n if return_logits:\n return logits, prob\n else:\n return prob","source_hash":"936f72781a57730e972fbfd3f9398eec1475c1429ec516be1eae65ff3946d9e7","truncated":false} {"repo_id":"Track-Anything","entity_id":"py:tracker.model.aggregate.aggregate","uri":"program://Track-Anything/function/tracker.model.aggregate.aggregate#L6-L17","kind":"function","name":"aggregate","path":"tracker/model/aggregate.py","language":"python","start_line":6,"end_line":17,"context_start_line":1,"context_end_line":17,"code":"import torch\nimport torch.nn.functional as F\n\n\n# Soft aggregation from STM\ndef aggregate(prob, dim, return_logits=False):\n new_prob = torch.cat([\n torch.prod(1-prob, dim=dim, keepdim=True),\n prob\n ], dim).clamp(1e-7, 1-1e-7)\n logits = torch.log((new_prob /(1-new_prob)))\n prob = F.softmax(logits, dim=dim)\n\n if return_logits:\n return logits, prob\n else:\n return prob","source_hash":"936f72781a57730e972fbfd3f9398eec1475c1429ec516be1eae65ff3946d9e7","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:demo.py","uri":"program://Track-Anything/file/demo.py","kind":"file","name":"demo.py","path":"demo.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from metaseg import SegAutoMaskPredictor, SegManualMaskPredictor, SahiAutoSegmentation, sahi_sliced_predict\n\n# For image\n\ndef automask_image_app(image_path, model_type, points_per_side, points_per_batch, min_area):\n SegAutoMaskPredictor().image_predict(\n source=image_path,\n model_type=model_type, # vit_l, vit_h, vit_b\n points_per_side=points_per_side,\n points_per_batch=points_per_batch,\n min_area=min_area,\n output_path=\"output.png\",\n show=False,\n save=True,\n )\n return \"output.png\"\n\n\n# For video\n\ndef automask_video_app(video_path, model_type, points_per_side, points_per_batch, min_area):","source_hash":"e112615210a4a3c358dc95f368064333cbd942b56960ed8a14a913eb455b8f05","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:app.py","uri":"program://Track-Anything/file/app.py","kind":"file","name":"app.py","path":"app.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import gradio as gr\nimport argparse\nimport gdown\nimport cv2\nimport numpy as np\nimport os\nimport sys\nsys.path.append(sys.path[0]+\"/tracker\")\nsys.path.append(sys.path[0]+\"/tracker/model\")\nfrom track_anything import TrackingAnything\nfrom track_anything import parse_augment\nimport requests\nimport json\nimport torchvision\nimport torch \nfrom tools.painter import mask_painter\nimport psutil\nimport time\ntry: \n from mmcv.cnn import ConvModule\nexcept:","source_hash":"21d2c55f3fcfa74fc1eff2135fa6381b8f8470dd6c58c999e5adef16e4064eea","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:text_server.py","uri":"program://Track-Anything/file/text_server.py","kind":"file","name":"text_server.py","path":"text_server.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import os\nimport sys\nimport cv2\nimport time\nimport json\nimport queue\nimport numpy as np\nimport requests\nimport concurrent.futures\nfrom PIL import Image\nfrom flask import Flask, render_template, request, jsonify, send_file\nimport torchvision\nimport torch\n\nfrom demo import automask_image_app, automask_video_app, sahi_autoseg_app\nsys.path.append(sys.path[0] + \"/tracker\")\nsys.path.append(sys.path[0] + \"/tracker/model\")\nfrom track_anything import TrackingAnything\nfrom track_anything import parse_augment\n\n# ... (all the functions defined in the original code except the Gradio part)","source_hash":"50b2875c0c4d91501b5ecebc15a34ca02e93fc817a951600f3c8ba95d2d36d24","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:track_anything.py","uri":"program://Track-Anything/file/track_anything.py","kind":"file","name":"track_anything.py","path":"track_anything.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import PIL\nfrom tqdm import tqdm\n\nfrom tools.interact_tools import SamControler\nfrom tracker.base_tracker import BaseTracker\nfrom inpainter.base_inpainter import BaseInpainter\nimport numpy as np\nimport argparse\n\n\n\nclass TrackingAnything():\n def __init__(self, sam_checkpoint, xmem_checkpoint, e2fgvi_checkpoint, args):\n self.args = args\n self.sam_checkpoint = sam_checkpoint\n self.xmem_checkpoint = xmem_checkpoint\n self.e2fgvi_checkpoint = e2fgvi_checkpoint\n self.samcontroler = SamControler(self.sam_checkpoint, args.sam_model_type, args.device)\n self.xmem = BaseTracker(self.xmem_checkpoint, device=args.device)\n self.baseinpainter = BaseInpainter(self.e2fgvi_checkpoint, args.device) \n # def inference_step(self, first_flag: bool, interact_flag: bool, image: np.ndarray, ","source_hash":"4a5b0b9e1d9dccdc700b21a496a6f29574916434416975019848dc19540bce6a","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:inpainter/base_inpainter.py","uri":"program://Track-Anything/file/inpainter/base_inpainter.py","kind":"file","name":"inpainter/base_inpainter.py","path":"inpainter/base_inpainter.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import os\nimport glob\nfrom PIL import Image\n\nimport torch\nimport yaml\nimport cv2\nimport importlib\nimport numpy as np\nfrom tqdm import tqdm\n\nfrom inpainter.util.tensor_util import resize_frames, resize_masks\n\n\nclass BaseInpainter:\n\tdef __init__(self, E2FGVI_checkpoint, device) -> None:\n\t\t\"\"\"\n\t\tE2FGVI_checkpoint: checkpoint of inpainter (version hq, with multi-resolution support)\n\t\t\"\"\"\n\t\tnet = importlib.import_module('inpainter.model.e2fgvi_hq')\n\t\tself.model = net.InpaintGenerator().to(device)","source_hash":"f7bbf56a57382aa2181be0c78b8aaff516bf820569cf9724ee7a01a3ed7b916c","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:inpainter/util/tensor_util.py","uri":"program://Track-Anything/file/inpainter/util/tensor_util.py","kind":"file","name":"inpainter/util/tensor_util.py","path":"inpainter/util/tensor_util.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import cv2\nimport numpy as np\n\n# resize frames\ndef resize_frames(frames, size=None):\n \"\"\"\n size: (w, h)\n \"\"\"\n if size is not None:\n frames = [cv2.resize(f, size) for f in frames]\n frames = np.stack(frames, 0)\n\n return frames\n\n# resize frames\ndef resize_masks(masks, size=None):\n \"\"\"\n size: (w, h)\n \"\"\"\n if size is not None:\n masks = [np.expand_dims(cv2.resize(m, size), 2) for m in masks]","source_hash":"226d60eef4a77f4ff9ee821b50bed556839d762adb0320c7c2c3af252e3ec9be","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:inpainter/model/e2fgvi_hq.py","uri":"program://Track-Anything/file/inpainter/model/e2fgvi_hq.py","kind":"file","name":"inpainter/model/e2fgvi_hq.py","path":"inpainter/model/e2fgvi_hq.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"''' Towards An End-to-End Framework for Video Inpainting\n'''\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom inpainter.model.modules.flow_comp import SPyNet\nfrom inpainter.model.modules.feat_prop import BidirectionalPropagation, SecondOrderDeformableAlignment\nfrom inpainter.model.modules.tfocal_transformer_hq import TemporalFocalTransformerBlock, SoftSplit, SoftComp\nfrom inpainter.model.modules.spectral_norm import spectral_norm as _spectral_norm\n\n\nclass BaseNetwork(nn.Module):\n def __init__(self):\n super(BaseNetwork, self).__init__()\n\n def print_network(self):\n if isinstance(self, list):\n self = self[0]\n num_params = 0","source_hash":"f81e75e68e498f851b09f5019288e3cb6bf57f664c0f205e6ef5a9f2259e18d4","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:inpainter/model/e2fgvi.py","uri":"program://Track-Anything/file/inpainter/model/e2fgvi.py","kind":"file","name":"inpainter/model/e2fgvi.py","path":"inpainter/model/e2fgvi.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"''' Towards An End-to-End Framework for Video Inpainting\n'''\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom model.modules.flow_comp import SPyNet\nfrom model.modules.feat_prop import BidirectionalPropagation, SecondOrderDeformableAlignment\nfrom model.modules.tfocal_transformer import TemporalFocalTransformerBlock, SoftSplit, SoftComp\nfrom model.modules.spectral_norm import spectral_norm as _spectral_norm\n\n\nclass BaseNetwork(nn.Module):\n def __init__(self):\n super(BaseNetwork, self).__init__()\n\n def print_network(self):\n if isinstance(self, list):\n self = self[0]\n num_params = 0","source_hash":"e9eb1165951e88d199c5f0879247c7b928e6e3272407a49b548f8acc981278c6","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:inpainter/model/modules/tfocal_transformer_hq.py","uri":"program://Track-Anything/file/inpainter/model/modules/tfocal_transformer_hq.py","kind":"file","name":"inpainter/model/modules/tfocal_transformer_hq.py","path":"inpainter/model/modules/tfocal_transformer_hq.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"\n This code is based on:\n [1] FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting, ICCV 2021\n https://github.com/ruiliu-ai/FuseFormer\n [2] Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet, ICCV 2021\n https://github.com/yitu-opensource/T2T-ViT\n [3] Focal Self-attention for Local-Global Interactions in Vision Transformers, NeurIPS 2021\n https://github.com/microsoft/Focal-Transformer \n\"\"\"\n\nimport math\nfrom functools import reduce\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass SoftSplit(nn.Module):\n def __init__(self, channel, hidden, kernel_size, stride, padding,\n t2t_param):","source_hash":"ac19b36ce0b778ac79c37bf9cbce9dd45b0a5c7cb224f047c9c4e7c29725e882","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:inpainter/model/modules/flow_comp.py","uri":"program://Track-Anything/file/inpainter/model/modules/flow_comp.py","kind":"file","name":"inpainter/model/modules/flow_comp.py","path":"inpainter/model/modules/flow_comp.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import numpy as np\n\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch\n\nfrom mmcv.cnn import ConvModule\nfrom mmengine.runner import load_checkpoint\n\n\nclass FlowCompletionLoss(nn.Module):\n \"\"\"Flow completion loss\"\"\"\n def __init__(self):\n super().__init__()\n self.fix_spynet = SPyNet()\n for p in self.fix_spynet.parameters():\n p.requires_grad = False\n\n self.l1_criterion = nn.L1Loss()\n\n def forward(self, pred_flows, gt_local_frames):","source_hash":"b7598bbabf8f6af1a5f6ab2d6af3783f28fdbfe9d8bb6698620a6f77223b382b","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:inpainter/model/modules/feat_prop.py","uri":"program://Track-Anything/file/inpainter/model/modules/feat_prop.py","kind":"file","name":"inpainter/model/modules/feat_prop.py","path":"inpainter/model/modules/feat_prop.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"\n BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment, CVPR 2022\n\"\"\"\nimport torch\nimport torch.nn as nn\n\nfrom mmcv.ops import ModulatedDeformConv2d, modulated_deform_conv2d\nfrom mmengine.model import constant_init\n\nfrom inpainter.model.modules.flow_comp import flow_warp\n\n\nclass SecondOrderDeformableAlignment(ModulatedDeformConv2d):\n \"\"\"Second-order deformable alignment module.\"\"\"\n def __init__(self, *args, **kwargs):\n self.max_residue_magnitude = kwargs.pop('max_residue_magnitude', 10)\n\n super(SecondOrderDeformableAlignment, self).__init__(*args, **kwargs)\n\n self.conv_offset = nn.Sequential(\n nn.Conv2d(3 * self.out_channels + 4, self.out_channels, 3, 1, 1),","source_hash":"7f226418d038f218389a9267c4a4d6489a625289367728b09d2c7443ce19391b","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:inpainter/model/modules/tfocal_transformer.py","uri":"program://Track-Anything/file/inpainter/model/modules/tfocal_transformer.py","kind":"file","name":"inpainter/model/modules/tfocal_transformer.py","path":"inpainter/model/modules/tfocal_transformer.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"\n This code is based on:\n [1] FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting, ICCV 2021\n https://github.com/ruiliu-ai/FuseFormer\n [2] Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet, ICCV 2021\n https://github.com/yitu-opensource/T2T-ViT\n [3] Focal Self-attention for Local-Global Interactions in Vision Transformers, NeurIPS 2021\n https://github.com/microsoft/Focal-Transformer \n\"\"\"\n\nimport math\nfrom functools import reduce\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass SoftSplit(nn.Module):\n def __init__(self, channel, hidden, kernel_size, stride, padding,\n t2t_param):","source_hash":"55c9167ddc8b6b05c864d7dd8d1ae1ba4a24fc60e905e5be4912ac93397d4e89","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:inpainter/model/modules/spectral_norm.py","uri":"program://Track-Anything/file/inpainter/model/modules/spectral_norm.py","kind":"file","name":"inpainter/model/modules/spectral_norm.py","path":"inpainter/model/modules/spectral_norm.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"\nSpectral Normalization from https://arxiv.org/abs/1802.05957\n\"\"\"\nimport torch\nfrom torch.nn.functional import normalize\n\n\nclass SpectralNorm(object):\n # Invariant before and after each forward call:\n # u = normalize(W @ v)\n # NB: At initialization, this invariant is not enforced\n\n _version = 1\n\n # At version 1:\n # made `W` not a buffer,\n # added `v` as a buffer, and\n # made eval mode use `W = u @ W_orig @ v` rather than the stored `W`.\n\n def __init__(self, name='weight', n_power_iterations=1, dim=0, eps=1e-12):\n self.name = name","source_hash":"1ce8754d2e8fe34b3898a1f9e40c8b2e196da1596e468f0cd8167cace625028f","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:tools/mask_painter.py","uri":"program://Track-Anything/file/tools/mask_painter.py","kind":"file","name":"tools/mask_painter.py","path":"tools/mask_painter.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import cv2\nimport torch\nimport numpy as np\nfrom PIL import Image\nimport copy\nimport time\n\n\ndef colormap(rgb=True):\n\tcolor_list = np.array(\n\t\t[\n\t\t\t0.000, 0.000, 0.000,\n\t\t\t1.000, 1.000, 1.000,\n\t\t\t1.000, 0.498, 0.313,\n\t\t\t0.392, 0.581, 0.929,\n\t\t\t0.000, 0.447, 0.741,\n\t\t\t0.850, 0.325, 0.098,\n\t\t\t0.929, 0.694, 0.125,\n\t\t\t0.494, 0.184, 0.556,\n\t\t\t0.466, 0.674, 0.188,\n\t\t\t0.301, 0.745, 0.933,","source_hash":"23f33d25d706a5a2bfabe9bd2a393d01df2b4ebd1232498659dc9a9e18a1d848","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:tools/painter.py","uri":"program://Track-Anything/file/tools/painter.py","kind":"file","name":"tools/painter.py","path":"tools/painter.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# paint masks, contours, or points on images, with specified colors\nimport cv2\nimport torch\nimport numpy as np\nfrom PIL import Image\nimport copy\nimport time\n\n\ndef colormap(rgb=True):\n\tcolor_list = np.array(\n\t\t[\n\t\t\t0.000, 0.000, 0.000,\n\t\t\t1.000, 1.000, 1.000,\n\t\t\t1.000, 0.498, 0.313,\n\t\t\t0.392, 0.581, 0.929,\n\t\t\t0.000, 0.447, 0.741,\n\t\t\t0.850, 0.325, 0.098,\n\t\t\t0.929, 0.694, 0.125,\n\t\t\t0.494, 0.184, 0.556,\n\t\t\t0.466, 0.674, 0.188,","source_hash":"c6b8ffea015704b7504f1c87e5e6d1f4d936e85eca1361f6beaa030296fd2aca","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:tools/base_segmenter.py","uri":"program://Track-Anything/file/tools/base_segmenter.py","kind":"file","name":"tools/base_segmenter.py","path":"tools/base_segmenter.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import time\nimport torch\nimport cv2\nfrom PIL import Image, ImageDraw, ImageOps\nimport numpy as np\nfrom typing import Union\nfrom segment_anything import sam_model_registry, SamPredictor, SamAutomaticMaskGenerator\nimport matplotlib.pyplot as plt\nimport PIL\nfrom .mask_painter import mask_painter\n\n\nclass BaseSegmenter:\n def __init__(self, SAM_checkpoint, model_type, device='cuda:0'):\n \"\"\"\n device: model device\n SAM_checkpoint: path of SAM checkpoint\n model_type: vit_b, vit_l, vit_h\n \"\"\"\n print(f\"Initializing BaseSegmenter to {device}\")\n assert model_type in ['vit_b', 'vit_l', 'vit_h'], 'model_type must be vit_b, vit_l, or vit_h'","source_hash":"73f546d160f4a13e76370ad2b98c7d72277a90e0d51a9f97b27310c3223988f5","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:tools/interact_tools.py","uri":"program://Track-Anything/file/tools/interact_tools.py","kind":"file","name":"tools/interact_tools.py","path":"tools/interact_tools.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import time\nimport torch\nimport cv2\nfrom PIL import Image, ImageDraw, ImageOps\nimport numpy as np\nfrom typing import Union\nfrom segment_anything import sam_model_registry, SamPredictor, SamAutomaticMaskGenerator\nimport matplotlib.pyplot as plt\nimport PIL\nfrom .mask_painter import mask_painter as mask_painter2\nfrom .base_segmenter import BaseSegmenter\nfrom .painter import mask_painter, point_painter\nimport os\nimport requests\nimport sys \n\n\nmask_color = 3\nmask_alpha = 0.7\ncontour_color = 1\ncontour_width = 5","source_hash":"26b46f3b62a09f0ae5efdf5ff3310d397b91a25f6b56f356ad9f86cd09568818","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:tracker/base_tracker.py","uri":"program://Track-Anything/file/tracker/base_tracker.py","kind":"file","name":"tracker/base_tracker.py","path":"tracker/base_tracker.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# import for debugging\nimport os\nimport glob\nimport numpy as np\nfrom PIL import Image\n# import for base_tracker\nimport torch\nimport yaml\nimport torch.nn.functional as F\nfrom tracker.model.network import XMem\nfrom inference.inference_core import InferenceCore\nfrom tracker.util.mask_mapper import MaskMapper\nfrom torchvision import transforms\nfrom tracker.util.range_transform import im_normalization\n\nfrom tools.painter import mask_painter\nfrom tools.base_segmenter import BaseSegmenter\nfrom torchvision.transforms import Resize\nimport progressbar\n\n","source_hash":"82ff5111f79077418e97354925c76b9560c299efe3e5ba863f65c337978778ef","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:tracker/util/mask_mapper.py","uri":"program://Track-Anything/file/tracker/util/mask_mapper.py","kind":"file","name":"tracker/util/mask_mapper.py","path":"tracker/util/mask_mapper.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import numpy as np\nimport torch\n\ndef all_to_onehot(masks, labels):\n if len(masks.shape) == 3:\n Ms = np.zeros((len(labels), masks.shape[0], masks.shape[1], masks.shape[2]), dtype=np.uint8)\n else:\n Ms = np.zeros((len(labels), masks.shape[0], masks.shape[1]), dtype=np.uint8)\n\n for ni, l in enumerate(labels):\n Ms[ni] = (masks == l).astype(np.uint8)\n \n return Ms\n\nclass MaskMapper:\n \"\"\"\n This class is used to convert a indexed-mask to a one-hot representation.\n It also takes care of remapping non-continuous indices\n It has two modes:\n 1. Default. Only masks with new indices are supposed to go into the remapper.\n This is also the case for YouTubeVOS.","source_hash":"d1f4ee5b2ba9596a89c22d1cf19881a1cfd5637736938b23a841f636a6fbc7a9","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:tracker/util/range_transform.py","uri":"program://Track-Anything/file/tracker/util/range_transform.py","kind":"file","name":"tracker/util/range_transform.py","path":"tracker/util/range_transform.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":12,"code":"import torchvision.transforms as transforms\n\nim_mean = (124, 116, 104)\n\nim_normalization = transforms.Normalize(\n mean=[0.485, 0.456, 0.406],\n std=[0.229, 0.224, 0.225]\n )\n\ninv_im_trans = transforms.Normalize(\n mean=[-0.485/0.229, -0.456/0.224, -0.406/0.225],\n std=[1/0.229, 1/0.224, 1/0.225])","source_hash":"f4bd56783eb59311c8b65885f0c35a6fb13c67a98e85c2f18901305027db92c3","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:tracker/util/tensor_util.py","uri":"program://Track-Anything/file/tracker/util/tensor_util.py","kind":"file","name":"tracker/util/tensor_util.py","path":"tracker/util/tensor_util.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import torch.nn.functional as F\n\n\ndef compute_tensor_iu(seg, gt):\n intersection = (seg & gt).float().sum()\n union = (seg | gt).float().sum()\n\n return intersection, union\n\ndef compute_tensor_iou(seg, gt):\n intersection, union = compute_tensor_iu(seg, gt)\n iou = (intersection + 1e-6) / (union + 1e-6)\n \n return iou \n\n# STM\ndef pad_divide_by(in_img, d):\n h, w = in_img.shape[-2:]\n\n if h % d > 0:\n new_h = h + d - h % d","source_hash":"225dd583786e6313ccb0f120695da6c9228998dc57bb7910b3c1e3fefc02d408","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:tracker/inference/inference_core.py","uri":"program://Track-Anything/file/tracker/inference/inference_core.py","kind":"file","name":"tracker/inference/inference_core.py","path":"tracker/inference/inference_core.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"from inference.memory_manager import MemoryManager\nfrom model.network import XMem\nfrom model.aggregate import aggregate\n\nfrom tracker.util.tensor_util import pad_divide_by, unpad\n\n\nclass InferenceCore:\n def __init__(self, network:XMem, config):\n self.config = config\n self.network = network\n self.mem_every = config['mem_every']\n self.deep_update_every = config['deep_update_every']\n self.enable_long_term = config['enable_long_term']\n\n # if deep_update_every < 0, synchronize deep update with memory frame\n self.deep_update_sync = (self.deep_update_every < 0)\n\n self.clear_memory()\n self.all_labels = None\n","source_hash":"aef5d4637993b4580d0bff71327da1d487caf5e41849b40a6acf1bb00073f107","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:tracker/inference/kv_memory_store.py","uri":"program://Track-Anything/file/tracker/inference/kv_memory_store.py","kind":"file","name":"tracker/inference/kv_memory_store.py","path":"tracker/inference/kv_memory_store.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import torch\nfrom typing import List\n\nclass KeyValueMemoryStore:\n \"\"\"\n Works for key/value pairs type storage\n e.g., working and long-term memory\n \"\"\"\n\n \"\"\"\n An object group is created when new objects enter the video\n Objects in the same group share the same temporal extent\n i.e., objects initialized in the same frame are in the same group\n For DAVIS/interactive, there is only one object group\n For YouTubeVOS, there can be multiple object groups\n \"\"\"\n\n def __init__(self, count_usage: bool):\n self.count_usage = count_usage\n\n # keys are stored in a single tensor and are shared between groups/objects","source_hash":"45cd3f299592f26ca7728345970261cfa39cdd750f37a2b11ff217e93aee7bb8","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:tracker/inference/memory_manager.py","uri":"program://Track-Anything/file/tracker/inference/memory_manager.py","kind":"file","name":"tracker/inference/memory_manager.py","path":"tracker/inference/memory_manager.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import torch\nimport warnings\n\nfrom inference.kv_memory_store import KeyValueMemoryStore\nfrom model.memory_util import *\n\n\nclass MemoryManager:\n \"\"\"\n Manages all three memory stores and the transition between working/long-term memory\n \"\"\"\n def __init__(self, config):\n self.hidden_dim = config['hidden_dim']\n self.top_k = config['top_k']\n\n self.enable_long_term = config['enable_long_term']\n self.enable_long_term_usage = config['enable_long_term_count_usage']\n if self.enable_long_term:\n self.max_mt_frames = config['max_mid_term_frames']\n self.min_mt_frames = config['min_mid_term_frames']\n self.num_prototypes = config['num_prototypes']","source_hash":"5e543db3b63b8b2d99d4709c6794e437cbcef5bc0f52677355435b55d3734b11","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:tracker/model/losses.py","uri":"program://Track-Anything/file/tracker/model/losses.py","kind":"file","name":"tracker/model/losses.py","path":"tracker/model/losses.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom collections import defaultdict\n\n\ndef dice_loss(input_mask, cls_gt):\n num_objects = input_mask.shape[1]\n losses = []\n for i in range(num_objects):\n mask = input_mask[:,i].flatten(start_dim=1)\n # background not in mask, so we add one to cls_gt\n gt = (cls_gt==(i+1)).float().flatten(start_dim=1)\n numerator = 2 * (mask * gt).sum(-1)\n denominator = mask.sum(-1) + gt.sum(-1)\n loss = 1 - (numerator + 1) / (denominator + 1)\n losses.append(loss)\n return torch.cat(losses).mean()\n\n","source_hash":"a466de37bc958c693a51284783e3b32adead7566fd8f355250a2480d0e0669ff","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:tracker/model/network.py","uri":"program://Track-Anything/file/tracker/model/network.py","kind":"file","name":"tracker/model/network.py","path":"tracker/model/network.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"\nThis file defines XMem, the highest level nn.Module interface\nDuring training, it is used by trainer.py\nDuring evaluation, it is used by inference_core.py\n\nIt further depends on modules.py which gives more detailed implementations of sub-modules\n\"\"\"\n\nimport torch\nimport torch.nn as nn\n\nfrom model.aggregate import aggregate\nfrom model.modules import *\nfrom model.memory_util import *\n\n\nclass XMem(nn.Module):\n def __init__(self, config, model_path=None, map_location=None):\n \"\"\"\n model_path/map_location are used in evaluation only\n map_location is for converting models saved in cuda to cpu","source_hash":"6b17e0a133ef22b55fbc8e7cc363f9c5c2677121aaabcb03cabad4833f2ad445","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:tracker/model/trainer.py","uri":"program://Track-Anything/file/tracker/model/trainer.py","kind":"file","name":"tracker/model/trainer.py","path":"tracker/model/trainer.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"\ntrainer.py - warpper and utility functions for network training\nCompute loss, back-prop, update parameters, logging, etc.\n\"\"\"\nimport datetime\nimport os\nimport time\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\n\nfrom model.network import XMem\nfrom model.losses import LossComputer\nfrom util.log_integrator import Integrator\nfrom util.image_saver import pool_pairs\n\n\nclass XMemTrainer:\n def __init__(self, config, logger=None, save_path=None, local_rank=0, world_size=1):\n self.config = config","source_hash":"33f38cfc80a2e3d37cdf5c3910c41cd3b2b15ca407d8b53920396e4c2dbaab18","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:tracker/model/resnet.py","uri":"program://Track-Anything/file/tracker/model/resnet.py","kind":"file","name":"tracker/model/resnet.py","path":"tracker/model/resnet.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"\nresnet.py - A modified ResNet structure\nWe append extra channels to the first conv by some network surgery\n\"\"\"\n\nfrom collections import OrderedDict\nimport math\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils import model_zoo\n\n\ndef load_weights_add_extra_dim(target, source_state, extra_dim=1):\n\tnew_dict = OrderedDict()\n\n\tfor k1, v1 in target.state_dict().items():\n\t\tif not 'num_batches_tracked' in k1:\n\t\t\tif k1 in source_state:\n\t\t\t\ttar_v = source_state[k1]\n","source_hash":"d783845e6b1b75bda8fb323048c1f6e0974e663dc0c1773194906e7e084439aa","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:tracker/model/memory_util.py","uri":"program://Track-Anything/file/tracker/model/memory_util.py","kind":"file","name":"tracker/model/memory_util.py","path":"tracker/model/memory_util.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import math\nimport numpy as np\nimport torch\nfrom typing import Optional\n\n\ndef get_similarity(mk, ms, qk, qe):\n # used for training/inference and memory reading/memory potentiation\n # mk: B x CK x [N] - Memory keys\n # ms: B x 1 x [N] - Memory shrinkage\n # qk: B x CK x [HW/P] - Query keys\n # qe: B x CK x [HW/P] - Query selection\n # Dimensions in [] are flattened\n CK = mk.shape[1]\n mk = mk.flatten(start_dim=2)\n ms = ms.flatten(start_dim=1).unsqueeze(2) if ms is not None else None\n qk = qk.flatten(start_dim=2)\n qe = qe.flatten(start_dim=2) if qe is not None else None\n\n if qe is not None:\n # See appendix for derivation","source_hash":"cdf06ea5e60b67737660d3a1eadd85f2431b4ad4ff0f2f166bda18660440751f","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:tracker/model/group_modules.py","uri":"program://Track-Anything/file/tracker/model/group_modules.py","kind":"file","name":"tracker/model/group_modules.py","path":"tracker/model/group_modules.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"\nGroup-specific modules\nThey handle features that also depends on the mask. \nFeatures are typically of shape\n batch_size * num_objects * num_channels * H * W\n\nAll of them are permutation equivariant w.r.t. to the num_objects dimension\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\ndef interpolate_groups(g, ratio, mode, align_corners):\n batch_size, num_objects = g.shape[:2]\n g = F.interpolate(g.flatten(start_dim=0, end_dim=1), \n scale_factor=ratio, mode=mode, align_corners=align_corners)\n g = g.view(batch_size, num_objects, *g.shape[1:])\n return g\n","source_hash":"19fb2896838e0ac52e2376e20f868110763207370829f3f06ae8f81d58d11d6b","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:tracker/model/cbam.py","uri":"program://Track-Anything/file/tracker/model/cbam.py","kind":"file","name":"tracker/model/cbam.py","path":"tracker/model/cbam.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# Modified from https://github.com/Jongchan/attention-module/blob/master/MODELS/cbam.py\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass BasicConv(nn.Module):\n def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True):\n super(BasicConv, self).__init__()\n self.out_channels = out_planes\n self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)\n\n def forward(self, x):\n x = self.conv(x)\n return x\n\nclass Flatten(nn.Module):\n def forward(self, x):\n return x.view(x.size(0), -1)\n\nclass ChannelGate(nn.Module):","source_hash":"be1b3090d8bdec613bad2120e24ffdfd92e3bc751ba9e663bd797ddf9e667f7f","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:tracker/model/modules.py","uri":"program://Track-Anything/file/tracker/model/modules.py","kind":"file","name":"tracker/model/modules.py","path":"tracker/model/modules.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"\"\"\"\nmodules.py - This file stores the rather boring network blocks.\n\nx - usually means features that only depends on the image\ng - usually means features that also depends on the mask. \n They might have an extra \"group\" or \"num_objects\" dimension, hence\n batch_size * num_objects * num_channels * H * W\n\nThe trailing number of a variable usually denote the stride\n\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom model.group_modules import *\nfrom model import resnet\nfrom model.cbam import CBAM\n\n","source_hash":"5018ae51e4140c1da9bd5b09fc6d44c3ebf50c98950eca19fbc1050f1ad22827","truncated":false} {"repo_id":"Track-Anything","entity_id":"file:tracker/model/aggregate.py","uri":"program://Track-Anything/file/tracker/model/aggregate.py","kind":"file","name":"tracker/model/aggregate.py","path":"tracker/model/aggregate.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":17,"code":"import torch\nimport torch.nn.functional as F\n\n\n# Soft aggregation from STM\ndef aggregate(prob, dim, return_logits=False):\n new_prob = torch.cat([\n torch.prod(1-prob, dim=dim, keepdim=True),\n prob\n ], dim).clamp(1e-7, 1-1e-7)\n logits = torch.log((new_prob /(1-new_prob)))\n prob = F.softmax(logits, dim=dim)\n\n if return_logits:\n return logits, prob\n else:\n return prob","source_hash":"936f72781a57730e972fbfd3f9398eec1475c1429ec516be1eae65ff3946d9e7","truncated":false}