initial commit

.gitattributes

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+*.png filter=lfs diff=lfs merge=lfs -text
+*.ttf filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text

app.py

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+import os
+import cv2
+import einops 
+import gradio as gr
+import numpy as np
+import torch
+import random
+from huggingface_hub import hf_hub_download
+
+from pytorch_lightning import seed_everything
+from utils.resize import resize_image, HWC3
+from cldm.model import create_model, load_state_dict
+from cldm.ddim_lle import DDIMSampler as DDIMSampler_LLE
+from cldm.ddim_hlg import DDIMSampler as DDIMSampler_HLG
+from automation_pose_mask.openpose import OpenposeDetector
+from automation_pose_mask.auto_mask import MaskDetector
+from PIL import Image
+from rembg import remove
+
+from utils.config import (
+    model_yaml,
+    category_dict,
+    attribute_dict
+)
+
+##########################################
+# ✅ Download model files from HF Hub
+##########################################
+
+MODEL_REPO = "NguyenDinhHieu/EquiFashionModel"
+
+openpose_body_model_path = hf_hub_download(MODEL_REPO, filename="body_pose_model.pth")
+openpose_hand_model_path = hf_hub_download(MODEL_REPO, filename="hand_pose_model.pth")
+sam_model_path = hf_hub_download(MODEL_REPO, filename="open_clip_pytorch_model.bin")
+my_model_path = hf_hub_download(MODEL_REPO, filename="hfd_100epochs.ckpt")
+
+##########################################
+# ✅ Initialize model on GPU once
+##########################################
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+apply_openpose = OpenposeDetector(
+    body_model_path=openpose_body_model_path,
+    hand_model_path=openpose_hand_model_path
+)
+
+apply_mask = MaskDetector(sam_model_path=sam_model_path)
+
+model = create_model(model_yaml).to(device)
+model.load_state_dict(load_state_dict(my_model_path, location=device))
+model.eval()
+
+hlg_sampler = DDIMSampler_HLG(model)
+lle_sampler = DDIMSampler_LLE(model)
+
+##########################################
+# ✅ Example images
+##########################################
+
+example_path = os.path.join(os.path.dirname(__file__), "preselected_images")
+example_image_list = [os.path.join(example_path, x) for x in os.listdir(example_path)]
+
+##########################################
+# ✅ Utility functions (unchanged)
+##########################################
+
+def pil_to_binary_mask(pil_image, threshold=0):
+    np_image = np.array(pil_image)
+    grayscale_image = Image.fromarray(np_image).convert("L")
+    binary_mask = (np.array(grayscale_image) > threshold).astype(np.uint8) * 255
+    return Image.fromarray(binary_mask)
+
+def add_white_background(image):
+    image = image.convert("RGBA")
+    white_bg = Image.new("RGBA", image.size, "WHITE")
+    white_bg.paste(image, (0, 0), image)
+    return white_bg.convert("RGB")
+
+##########################################
+# ✅ HLG PROCESS (unchanged except device fix)
+##########################################
+
+def hlg_process(hlg_prompt, input_image, category, a_prompt, n_prompt,
+                num_samples, image_resolution, detect_resolution, ddim_steps,
+                guess_mode, strength, scale, seed, eta):
+
+    with torch.no_grad():
+        input_image = HWC3(input_image)
+        detected_map, _ = apply_openpose(resize_image(input_image, detect_resolution))
+        detected_map = HWC3(detected_map)
+        img = resize_image(input_image, image_resolution)
+        H, W, C = img.shape
+
+        detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_NEAREST)
+
+        control = torch.from_numpy(detected_map).float().to(device) / 255.0
+        control = torch.stack([control for _ in range(num_samples)], dim=0)
+        control = einops.rearrange(control, 'b h w c -> b c h w')
+
+        if seed == -1:
+            seed = random.randint(0, 4294967294)
+        seed_everything(seed)
+
+        cond = {
+            "c_concat": [control],
+            "c_crossattn": [model.get_learned_conditioning([hlg_prompt + ', ' + a_prompt] * num_samples)]
+        }
+        un_cond = {
+            "c_concat": None if guess_mode else [control],
+            "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]
+        }
+        shape = (4, H // 8, W // 8)
+
+        model.control_scales = ([strength] * 13)
+
+        samples, _ = hlg_sampler.sample(ddim_steps, num_samples, shape, cond,
+                                        verbose=False, eta=eta,
+                                        unconditional_guidance_scale=scale,
+                                        unconditional_conditioning=un_cond)
+
+        x_samples = model.decode_first_stage(samples)
+        x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c')
+                     * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)
+
+        results = [Image.fromarray(x_samples[i]) for i in range(num_samples)]
+        return [add_white_background(remove(img)) for img in results]
+
+##########################################
+# ✅ LLE PROCESS (unchanged except device fix)
+##########################################
+
+def lle_process(lle_prompt, dict_img_mask, category, a_prompt, n_prompt,
+                num_samples, image_resolution, detect_resolution, ddim_steps,
+                guess_mode, strength, scale, seed, eta, attribute, selection_mode):
+
+    input_image = dict_img_mask["background"].convert("RGB")
+    input_image = HWC3(np.array(input_image))
+
+    detected_map, keypoints = apply_openpose(resize_image(input_image, detect_resolution))
+    detected_map = HWC3(detected_map)
+
+    if selection_mode == "Automatically recognize":
+        mask = apply_mask(resize_image(input_image, detect_resolution), keypoints,
+                          category=category, attribute=attribute, sam_mode=True)
+    else:
+        mask = pil_to_binary_mask(dict_img_mask['layers'][0].convert("RGB"))
+
+    if mask is not None:
+        mask = torch.from_numpy(np.array(mask.convert("L"))).float().to(device) / 255.0
+        mask = mask.unsqueeze(0).unsqueeze(0)
+
+    img = resize_image(input_image, image_resolution)
+    H, W, C = img.shape
+
+    init_img = torch.from_numpy(img).float().to(device) / 127.0 - 1.0
+    init_img = einops.rearrange(init_img, 'h w c -> 1 c h w')
+    init_img = torch.stack([init_img] * num_samples)
+
+    detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_NEAREST)
+    control = torch.from_numpy(detected_map).float().to(device) / 255.0
+    control = torch.stack([control]*num_samples)
+    control = einops.rearrange(control, 'b h w c -> b c h w')
+
+    if seed == -1:
+        seed = random.randint(0, 4294967294)
+    seed_everything(seed)
+
+    cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([lle_prompt + ', ' + a_prompt] * num_samples)]}
+    un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]}
+    shape = (4, H // 8, W // 8)
+
+    samples, _ = lle_sampler.sample(ddim_steps, num_samples, shape, cond,
+                                     verbose=False, eta=eta,
+                                     unconditional_guidance_scale=scale,
+                                     unconditional_conditioning=un_cond,
+                                     init_img=init_img, mask=mask,
+                                     english_attribute=attribute)
+
+    x_samples = model.decode_first_stage(samples)
+    x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c')
+                 * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)
+
+    return [Image.fromarray(x_samples[i]) for i in range(num_samples)]
+
+##########################################
+# ✅ Send result to attribute editor
+##########################################
+
+def result2input(images):
+    return {"background": images[-1], "layers": None, "composite": None}
+
+##########################################
+# ✅ FULL UI (unchanged)
+##########################################
+
+def create_hfddm():
+    with gr.Blocks().queue() as app:
+
+        category = gr.Radio(list(category_dict.values()), value=list(category_dict.values())[0], label="Clothing Category")
+
+        with gr.Row():
+            with gr.Column():
+                with gr.Tab("Draft Design"):
+                    hlg_prompt = gr.Textbox(label="High-level design prompt")
+                    hlg_input_image = gr.Image(sources=("upload", "webcam"), type="numpy", value=example_image_list[0], label="Reference pose")
+                    gr.Examples(inputs=hlg_input_image, examples=example_image_list)
+                    hlg_run = gr.Button("Generate")
+
+                with gr.Tab("Attribute Editing"):
+                    lle_prompt = gr.Textbox(label="Attribute prompt")
+                    lle_input_image = gr.ImageEditor(sources='upload', type="pil", label="Edit regions", value=example_image_list[0])
+                    gr.Examples(inputs=lle_input_image, examples=example_image_list)
+                    selection_mode = gr.Radio(["Automatically recognize", "User interface"], label="Mask Selection", value="Automatically recognize")
+
+                    current_tab = {}
+                    lle_run = {}
+                    for tab_elem in attribute_dict.values():
+                        with gr.Tab(tab_elem):
+                            current_tab[tab_elem] = gr.Label(value=tab_elem, visible=False)
+                            lle_run[tab_elem] = gr.Button("Generate")
+
+            with gr.Column():
+                result_gallery = gr.Gallery(label="Result", show_label=False, elem_id="gallery", selected_index=0, interactive=False)
+                send2llg = gr.Button("Send to Attribute Editing")
+
+        with gr.Accordion("Advanced Options", open=False):
+            num_samples = gr.Slider(label="Images", minimum=1, maximum=1, value=1, step=1)
+            image_resolution = gr.Slider(label="Resolution", minimum=256, maximum=768, value=512, step=64)
+            strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01)
+            guess_mode = gr.Checkbox(label='Guess Mode', value=False)
+            detect_resolution = gr.Slider(label="Pose Detection Resolution", minimum=128, maximum=1024, value=512, step=1)
+            ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=100, step=1, visible=False)
+            scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
+            seed = gr.Slider(label="Seed", minimum=-1, maximum=4294967294, value=11, step=1)
+            eta = gr.Number(label="ETA (DDIM)", value=0.0)
+            a_prompt = gr.Textbox(label="Added Prompt", value='best quality, extremely detailed, masterpiece, 8k, white background')
+            n_prompt = gr.Textbox(label="Negative Prompt", value='worst quality, low quality, bad anatomy, watermark, signature, blurry')
+
+        hlg_run.click(fn=hlg_process, inputs=[hlg_prompt, hlg_input_image, category, a_prompt, n_prompt,
+                                              num_samples, image_resolution, detect_resolution, ddim_steps,
+                                              guess_mode, strength, scale, seed, eta], outputs=[result_gallery])
+
+        for tab_elem in attribute_dict.values():
+            lle_run[tab_elem].click(fn=lle_process, inputs=[lle_prompt, lle_input_image, category, a_prompt, n_prompt,
+                                                            num_samples, image_resolution, detect_resolution,
+                                                            ddim_steps, guess_mode, strength, scale, seed, eta,
+                                                            current_tab[tab_elem], selection_mode], outputs=[result_gallery])
+
+        send2llg.click(fn=result2input, inputs=result_gallery, outputs=lle_input_image)
+
+    return app
+
+hfddm_block = create_hfddm()
+
+demo = gr.Blocks(title="AI Fashion Design", theme=gr.themes.Monochrome(secondary_hue="orange", neutral_hue="gray")).queue()
+
+with demo:
+    gr.Markdown("# **AI Fashion Design** 👗")
+    with gr.Tab("Fashion Design"):
+        hfddm_block.render()
+
+demo.launch()

automation_pose_mask/auto_mask/__init__.py

@@ -0,0 +1,187 @@
+import torch
+import sys
+import argparse
+import numpy as np
+from pathlib import Path
+
+
+from .segment_anything import sam_model_registry, SamAutomaticMaskGenerator, SamPredictor
+from .utils import load_img_to_array, save_array_to_img, dilate_mask, \
+    show_mask, show_points, get_clicked_point
+import torch.nn.functional as F
+import torch.nn as nn
+
+from matplotlib import pyplot as plt
+import cv2
+import json
+from PIL import Image
+
+
+class MaskDetector:
+    def __init__(self,
+                 sam_model_path="/model/sam_vit_h_4b8939.pth",
+                 sam_model_type="vit_h",
+                 device="cuda"):
+
+        self.sam_model_path = sam_model_path
+        self.sam_model_type = sam_model_type
+        self.device = device
+        self.dilate_kernel_size = 15
+        self.predictor = None
+            
+    def onload(self):
+        
+        self.predictor = SamPredictor(sam_model_registry[self.sam_model_type](checkpoint=self.sam_model_path).to(self.device))
+
+    def offload(self):
+        del self.predictor
+        torch.cuda.empty_cache()
+        
+        self.predictor = None
+
+    def __call__(self, input_img, keypoints, category, attribute, draw_masks=False,sam_mode=True):
+        if self.predictor is None:
+            self.onload()
+        # img = load_img_to_array(input_img)
+        img = input_img # np.array
+        self.predictor.set_image(img)
+        candidate = keypoints['candidate']
+        subset = keypoints['subset']
+
+        # save mask
+        out_dir = "./mask_result/"
+        path = Path(out_dir)
+        path.mkdir(parents=True, exist_ok=True)
+
+        if attribute == "A1" or attribute == "A5":
+            if category in ["Dress", "Jumpsuit", "Coat", "Pant", "Skirt", "Shirt"]:
+                x1, y3, x2, y4 = 128, 285, 378, 638
+                # 应该以左右肘为x
+                for i in range(18):
+                    for n in range(len(subset)):
+                        if i == 3:
+                            index = int(subset[n][i]) # 根据i的值确定坐标
+                            if index == -1:
+                                continue
+                            x1, y1 = candidate[index][0:2] # #左肘
+                        if i == 6:
+                            index = int(subset[n][i]) # 根据i的值确定坐标
+                            if index == -1:
+                                continue
+                            x2, y2 = candidate[index][0:2] # # 右肘
+                        if i == 8:
+                            index = int(subset[n][i]) # 根据i的值确定坐标
+                            if index == -1:
+                                continue
+                            x3, y3 = candidate[index][0:2] # 左臀
+                        if i == 13:
+                            index = int(subset[n][i]) # 根据i的值确定坐标
+                            if index == -1:
+                                continue
+                            x4, y4 = candidate[index][0:2] #右踝   
+
+                boxes =[[int(x1)-100,int(y3),int(x2)+100,int(y4)+60]]  # 提供的边界框# boxes = [[128, 285, 378, 638]] # 根据数据集特点确定
+                # boxes =[[int(x1)-100,int(y3)+120,int(x2)+100,int(y4)+40]] 
+            elif category in ["Blouse", "Sweater", "Shirt"]:
+                x1, y1,x2, y2=188, 205, 317, 322
+                for i in range(18):
+                    for n in range(len(subset)):
+                        if i == 8:
+                            index = int(subset[n][i]) # 根据i的值确定坐标
+                            if index == -1:
+                                continue
+                            x1, y1 = candidate[index][0:2] # #左肘
+                        if i == 11:
+                            index = int(subset[n][i]) # 根据i的值确定坐标
+                            if index == -1:
+                                continue
+                            x2, y2 = candidate[index][0:2] # # 右肘
+                boxes =[[int(x1)-20,int(y1)-60,int(x2)+20,int(y2)+20]]  # 提供的边界框# boxes = [[188, 205, 317, 322]]
+                # boxes =[[int(x1)-30,int(y1)-60,int(x2)+40,int(y2)+20]]
+            
+
+        elif attribute in ["A2", "A3"]:
+            x1, y1,x2, y2 = 110, 42, 187, 338
+            x3, y3,x4, y4=307, 44, 387, 325
+            for i in range(18):
+                for n in range(len(subset)):
+                    if i == 2:
+                        index = int(subset[n][i]) # 根据i的值确定坐标
+                        if index == -1:
+                            continue
+                        x1, y1 = candidate[index][0:2] # #左肘
+                    if i == 4:
+                        index = int(subset[n][i]) # 根据i的值确定坐标
+                        if index == -1:
+                            continue
+                        x2, y2 = candidate[index][0:2] # # 右肘
+                    if i == 5:
+                        index = int(subset[n][i]) # 根据i的值确定��标
+                        if index == -1:
+                            continue
+                        x3, y3 = candidate[index][0:2] # 左臀
+                    if i == 7:
+                        index = int(subset[n][i]) # 根据i的值确定坐标
+                        if index == -1:
+                            continue
+                        x4, y4 = candidate[index][0:2] #  
+            boxes =[[int(x1)-80,int(y1)-30,int(x2)+20,int(y2)+40],[int(x3)-15,int(y3)-30,int(x4)+40,int(y4)+40]] # boxes = [[110, 42, 187, 338], [307, 44, 387, 325]]
+            # boxes =[[int(x1)-80,int(y1)+70,int(x2),int(y2)-50],[int(x3)+20,int(y3)+60,int(x4)+40,int(y4)-50]]
+            
+        elif attribute == "A4":
+            x1, y1 = 190, 35
+            for i in range(18):
+                for n in range(len(subset)):
+                    if i == 1:
+                        index = int(subset[n][i]) # 根据i的值确定坐标
+                        if index == -1:
+                            continue
+                        x1, y1 = candidate[index][0:2] # #左肘
+            boxes =[[int(x1)-60,int(y1)-40,int(x1)+60,int(y1)+40]]  # 根据脖子位置向四周扩散# boxes = [[190, 35, 310, 115]]
+            # boxes =[[int(x1)-80,int(y1)-40,int(x1)+80,int(y1)+40]]
+       
+        if sam_mode: 
+            first_mask = True # 循环外部先创建一个标志，用于指示是否是第一个掩码
+            for idx in range(len(boxes)):
+                masks, _, _ = self.predictor.predict(box=np.array([boxes[idx]]))  # 预测代码
+                masks = masks.astype(np.uint8) * 255
+                if self.dilate_kernel_size is not None:
+                    masks = [dilate_mask(mask, self.dilate_kernel_size) for mask in masks]
+                    if draw_masks:
+                        for ii, m in enumerate(masks):
+                            mask_p = out_dir / f"maskss_{idx}_{ii}.jpg"
+                            save_array_to_img(m, mask_p)
+
+                    mask = masks[-1]  # 取最后一个
+                    # 如果是第一个掩码，则直接赋值给 merged_mask
+                    if first_mask:
+                        merged_mask = mask.copy()
+                        first_mask = False
+                    else:
+                        # 否则，将当前掩码与之前的 merged_mask 合并
+                        merged_mask = np.maximum(merged_mask, mask)
+        else:
+            # 全部直接使用坐标矩形区域;
+            mask = np.zeros_like(img[:,:,0])
+            for box in boxes:
+                x1, y1, x2, y2 = box
+                mask[y1:y2, x1:x2] = 255
+            merged_mask = mask
+       
+        # 在原始图像上绘制所有边界框
+        for box in boxes:
+            x1, y1, x2, y2 = box
+            cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)  # 绿色矩形表示边界框
+
+        # merged_mask_path = out_dir + "merged_mask.jpg"
+        # save_array_to_img(merged_mask, merged_mask_path)
+
+        # 保存带有边界框的图像
+        # bbox_image_path = out_dir + "bbox_image.jpg"
+        # save_array_to_img(img, bbox_image_path)
+
+        alpha_mask_img = Image.fromarray(merged_mask, mode='L')
+        
+        self.offload()
+
+        return alpha_mask_img

automation_pose_mask/auto_mask/segment_anything/__init__.py

@@ -0,0 +1,15 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .build_sam import (
+    build_sam,
+    build_sam_vit_h,
+    build_sam_vit_l,
+    build_sam_vit_b,
+    sam_model_registry,
+)
+from .predictor import SamPredictor
+from .automatic_mask_generator import SamAutomaticMaskGenerator

automation_pose_mask/auto_mask/segment_anything/automatic_mask_generator.py

@@ -0,0 +1,372 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import torch
+from torchvision.ops.boxes import batched_nms, box_area  # type: ignore
+
+from typing import Any, Dict, List, Optional, Tuple
+
+from .modeling import Sam
+from .predictor import SamPredictor
+from .utils.amg import (
+    MaskData,
+    area_from_rle,
+    batch_iterator,
+    batched_mask_to_box,
+    box_xyxy_to_xywh,
+    build_all_layer_point_grids,
+    calculate_stability_score,
+    coco_encode_rle,
+    generate_crop_boxes,
+    is_box_near_crop_edge,
+    mask_to_rle_pytorch,
+    remove_small_regions,
+    rle_to_mask,
+    uncrop_boxes_xyxy,
+    uncrop_masks,
+    uncrop_points,
+)
+
+
+class SamAutomaticMaskGenerator:
+    def __init__(
+        self,
+        model: Sam,
+        points_per_side: Optional[int] = 32,
+        points_per_batch: int = 64,
+        pred_iou_thresh: float = 0.88,
+        stability_score_thresh: float = 0.95,
+        stability_score_offset: float = 1.0,
+        box_nms_thresh: float = 0.7,
+        crop_n_layers: int = 0,
+        crop_nms_thresh: float = 0.7,
+        crop_overlap_ratio: float = 512 / 1500,
+        crop_n_points_downscale_factor: int = 1,
+        point_grids: Optional[List[np.ndarray]] = None,
+        min_mask_region_area: int = 0,
+        output_mode: str = "binary_mask",
+    ) -> None:
+        """
+        Using a SAM model, generates masks for the entire image.
+        Generates a grid of point prompts over the image, then filters
+        low quality and duplicate masks. The default settings are chosen
+        for SAM with a ViT-H backbone.
+
+        Arguments:
+          model (Sam): The SAM model to use for mask prediction.
+          points_per_side (int or None): The number of points to be sampled
+            along one side of the image. The total number of points is
+            points_per_side**2. If None, 'point_grids' must provide explicit
+            point sampling.
+          points_per_batch (int): Sets the number of points run simultaneously
+            by the model. Higher numbers may be faster but use more GPU memory.
+          pred_iou_thresh (float): A filtering threshold in [0,1], using the
+            model's predicted mask quality.
+          stability_score_thresh (float): A filtering threshold in [0,1], using
+            the stability of the mask under changes to the cutoff used to binarize
+            the model's mask predictions.
+          stability_score_offset (float): The amount to shift the cutoff when
+            calculated the stability score.
+          box_nms_thresh (float): The box IoU cutoff used by non-maximal
+            suppression to filter duplicate masks.
+          crops_n_layers (int): If >0, mask prediction will be run again on
+            crops of the image. Sets the number of layers to run, where each
+            layer has 2**i_layer number of image crops.
+          crops_nms_thresh (float): The box IoU cutoff used by non-maximal
+            suppression to filter duplicate masks between different crops.
+          crop_overlap_ratio (float): Sets the degree to which crops overlap.
+            In the first crop layer, crops will overlap by this fraction of
+            the image length. Later layers with more crops scale down this overlap.
+          crop_n_points_downscale_factor (int): The number of points-per-side
+            sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
+          point_grids (list(np.ndarray) or None): A list over explicit grids
+            of points used for sampling, normalized to [0,1]. The nth grid in the
+            list is used in the nth crop layer. Exclusive with points_per_side.
+          min_mask_region_area (int): If >0, postprocessing will be applied
+            to remove disconnected regions and holes in masks with area smaller
+            than min_mask_region_area. Requires opencv.
+          output_mode (str): The form masks are returned in. Can be 'binary_mask',
+            'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.
+            For large resolutions, 'binary_mask' may consume large amounts of
+            memory.
+        """
+
+        assert (points_per_side is None) != (
+            point_grids is None
+        ), "Exactly one of points_per_side or point_grid must be provided."
+        if points_per_side is not None:
+            self.point_grids = build_all_layer_point_grids(
+                points_per_side,
+                crop_n_layers,
+                crop_n_points_downscale_factor,
+            )
+        elif point_grids is not None:
+            self.point_grids = point_grids
+        else:
+            raise ValueError("Can't have both points_per_side and point_grid be None.")
+
+        assert output_mode in [
+            "binary_mask",
+            "uncompressed_rle",
+            "coco_rle",
+        ], f"Unknown output_mode {output_mode}."
+        if output_mode == "coco_rle":
+            from pycocotools import mask as mask_utils  # type: ignore # noqa: F401
+
+        if min_mask_region_area > 0:
+            import cv2  # type: ignore # noqa: F401
+
+        self.predictor = SamPredictor(model)
+        self.points_per_batch = points_per_batch
+        self.pred_iou_thresh = pred_iou_thresh
+        self.stability_score_thresh = stability_score_thresh
+        self.stability_score_offset = stability_score_offset
+        self.box_nms_thresh = box_nms_thresh
+        self.crop_n_layers = crop_n_layers
+        self.crop_nms_thresh = crop_nms_thresh
+        self.crop_overlap_ratio = crop_overlap_ratio
+        self.crop_n_points_downscale_factor = crop_n_points_downscale_factor
+        self.min_mask_region_area = min_mask_region_area
+        self.output_mode = output_mode
+
+    @torch.no_grad()
+    def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:
+        """
+        Generates masks for the given image.
+
+        Arguments:
+          image (np.ndarray): The image to generate masks for, in HWC uint8 format.
+
+        Returns:
+           list(dict(str, any)): A list over records for masks. Each record is
+             a dict containing the following keys:
+               segmentation (dict(str, any) or np.ndarray): The mask. If
+                 output_mode='binary_mask', is an array of shape HW. Otherwise,
+                 is a dictionary containing the RLE.
+               bbox (list(float)): The box around the mask, in XYWH format.
+               area (int): The area in pixels of the mask.
+               predicted_iou (float): The model's own prediction of the mask's
+                 quality. This is filtered by the pred_iou_thresh parameter.
+               point_coords (list(list(float))): The point coordinates input
+                 to the model to generate this mask.
+               stability_score (float): A measure of the mask's quality. This
+                 is filtered on using the stability_score_thresh parameter.
+               crop_box (list(float)): The crop of the image used to generate
+                 the mask, given in XYWH format.
+        """
+
+        # Generate masks
+        mask_data = self._generate_masks(image)
+
+        # Filter small disconnected regions and holes in masks
+        if self.min_mask_region_area > 0:
+            mask_data = self.postprocess_small_regions(
+                mask_data,
+                self.min_mask_region_area,
+                max(self.box_nms_thresh, self.crop_nms_thresh),
+            )
+
+        # Encode masks
+        if self.output_mode == "coco_rle":
+            mask_data["segmentations"] = [coco_encode_rle(rle) for rle in mask_data["rles"]]
+        elif self.output_mode == "binary_mask":
+            mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]]
+        else:
+            mask_data["segmentations"] = mask_data["rles"]
+
+        # Write mask records
+        curr_anns = []
+        for idx in range(len(mask_data["segmentations"])):
+            ann = {
+                "segmentation": mask_data["segmentations"][idx],
+                "area": area_from_rle(mask_data["rles"][idx]),
+                "bbox": box_xyxy_to_xywh(mask_data["boxes"][idx]).tolist(),
+                "predicted_iou": mask_data["iou_preds"][idx].item(),
+                "point_coords": [mask_data["points"][idx].tolist()],
+                "stability_score": mask_data["stability_score"][idx].item(),
+                "crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(),
+            }
+            curr_anns.append(ann)
+
+        return curr_anns
+
+    def _generate_masks(self, image: np.ndarray) -> MaskData:
+        orig_size = image.shape[:2]
+        crop_boxes, layer_idxs = generate_crop_boxes(
+            orig_size, self.crop_n_layers, self.crop_overlap_ratio
+        )
+
+        # Iterate over image crops
+        data = MaskData()
+        for crop_box, layer_idx in zip(crop_boxes, layer_idxs):
+            crop_data = self._process_crop(image, crop_box, layer_idx, orig_size)
+            data.cat(crop_data)
+
+        # Remove duplicate masks between crops
+        if len(crop_boxes) > 1:
+            # Prefer masks from smaller crops
+            scores = 1 / box_area(data["crop_boxes"])
+            scores = scores.to(data["boxes"].device)
+            keep_by_nms = batched_nms(
+                data["boxes"].float(),
+                scores,
+                torch.zeros(len(data["boxes"])),  # categories
+                iou_threshold=self.crop_nms_thresh,
+            )
+            data.filter(keep_by_nms)
+
+        data.to_numpy()
+        return data
+
+    def _process_crop(
+        self,
+        image: np.ndarray,
+        crop_box: List[int],
+        crop_layer_idx: int,
+        orig_size: Tuple[int, ...],
+    ) -> MaskData:
+        # Crop the image and calculate embeddings
+        x0, y0, x1, y1 = crop_box
+        cropped_im = image[y0:y1, x0:x1, :]
+        cropped_im_size = cropped_im.shape[:2]
+        self.predictor.set_image(cropped_im)
+
+        # Get points for this crop
+        points_scale = np.array(cropped_im_size)[None, ::-1]
+        points_for_image = self.point_grids[crop_layer_idx] * points_scale
+
+        # Generate masks for this crop in batches
+        data = MaskData()
+        for (points,) in batch_iterator(self.points_per_batch, points_for_image):
+            batch_data = self._process_batch(points, cropped_im_size, crop_box, orig_size)
+            data.cat(batch_data)
+            del batch_data
+        self.predictor.reset_image()
+
+        # Remove duplicates within this crop.
+        keep_by_nms = batched_nms(
+            data["boxes"].float(),
+            data["iou_preds"],
+            torch.zeros(len(data["boxes"])),  # categories
+            iou_threshold=self.box_nms_thresh,
+        )
+        data.filter(keep_by_nms)
+
+        # Return to the original image frame
+        data["boxes"] = uncrop_boxes_xyxy(data["boxes"], crop_box)
+        data["points"] = uncrop_points(data["points"], crop_box)
+        data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(data["rles"]))])
+
+        return data
+
+    def _process_batch(
+        self,
+        points: np.ndarray,
+        im_size: Tuple[int, ...],
+        crop_box: List[int],
+        orig_size: Tuple[int, ...],
+    ) -> MaskData:
+        orig_h, orig_w = orig_size
+
+        # Run model on this batch
+        transformed_points = self.predictor.transform.apply_coords(points, im_size)
+        in_points = torch.as_tensor(transformed_points, device=self.predictor.device)
+        in_labels = torch.ones(in_points.shape[0], dtype=torch.int, device=in_points.device)
+        masks, iou_preds, _ = self.predictor.predict_torch(
+            in_points[:, None, :],
+            in_labels[:, None],
+            multimask_output=True,
+            return_logits=True,
+        )
+
+        # Serialize predictions and store in MaskData
+        data = MaskData(
+            masks=masks.flatten(0, 1),
+            iou_preds=iou_preds.flatten(0, 1),
+            points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)),
+        )
+        del masks
+
+        # Filter by predicted IoU
+        if self.pred_iou_thresh > 0.0:
+            keep_mask = data["iou_preds"] > self.pred_iou_thresh
+            data.filter(keep_mask)
+
+        # Calculate stability score
+        data["stability_score"] = calculate_stability_score(
+            data["masks"], self.predictor.model.mask_threshold, self.stability_score_offset
+        )
+        if self.stability_score_thresh > 0.0:
+            keep_mask = data["stability_score"] >= self.stability_score_thresh
+            data.filter(keep_mask)
+
+        # Threshold masks and calculate boxes
+        data["masks"] = data["masks"] > self.predictor.model.mask_threshold
+        data["boxes"] = batched_mask_to_box(data["masks"])
+
+        # Filter boxes that touch crop boundaries
+        keep_mask = ~is_box_near_crop_edge(data["boxes"], crop_box, [0, 0, orig_w, orig_h])
+        if not torch.all(keep_mask):
+            data.filter(keep_mask)
+
+        # Compress to RLE
+        data["masks"] = uncrop_masks(data["masks"], crop_box, orig_h, orig_w)
+        data["rles"] = mask_to_rle_pytorch(data["masks"])
+        del data["masks"]
+
+        return data
+
+    @staticmethod
+    def postprocess_small_regions(
+        mask_data: MaskData, min_area: int, nms_thresh: float
+    ) -> MaskData:
+        """
+        Removes small disconnected regions and holes in masks, then reruns
+        box NMS to remove any new duplicates.
+
+        Edits mask_data in place.
+
+        Requires open-cv as a dependency.
+        """
+        if len(mask_data["rles"]) == 0:
+            return mask_data
+
+        # Filter small disconnected regions and holes
+        new_masks = []
+        scores = []
+        for rle in mask_data["rles"]:
+            mask = rle_to_mask(rle)
+
+            mask, changed = remove_small_regions(mask, min_area, mode="holes")
+            unchanged = not changed
+            mask, changed = remove_small_regions(mask, min_area, mode="islands")
+            unchanged = unchanged and not changed
+
+            new_masks.append(torch.as_tensor(mask).unsqueeze(0))
+            # Give score=0 to changed masks and score=1 to unchanged masks
+            # so NMS will prefer ones that didn't need postprocessing
+            scores.append(float(unchanged))
+
+        # Recalculate boxes and remove any new duplicates
+        masks = torch.cat(new_masks, dim=0)
+        boxes = batched_mask_to_box(masks)
+        keep_by_nms = batched_nms(
+            boxes.float(),
+            torch.as_tensor(scores),
+            torch.zeros(len(boxes)),  # categories
+            iou_threshold=nms_thresh,
+        )
+
+        # Only recalculate RLEs for masks that have changed
+        for i_mask in keep_by_nms:
+            if scores[i_mask] == 0.0:
+                mask_torch = masks[i_mask].unsqueeze(0)
+                mask_data["rles"][i_mask] = mask_to_rle_pytorch(mask_torch)[0]
+                mask_data["boxes"][i_mask] = boxes[i_mask]  # update res directly
+        mask_data.filter(keep_by_nms)
+
+        return mask_data

automation_pose_mask/auto_mask/segment_anything/build_sam.py

@@ -0,0 +1,155 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+
+from functools import partial
+
+from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWayTransformer, TinyViT
+
+
+def build_sam_vit_h(checkpoint=None):
+    return _build_sam(
+        encoder_embed_dim=1280,
+        encoder_depth=32,
+        encoder_num_heads=16,
+        encoder_global_attn_indexes=[7, 15, 23, 31],
+        checkpoint=checkpoint,
+    )
+
+
+build_sam = build_sam_vit_h
+
+
+def build_sam_vit_l(checkpoint=None):
+    return _build_sam(
+        encoder_embed_dim=1024,
+        encoder_depth=24,
+        encoder_num_heads=16,
+        encoder_global_attn_indexes=[5, 11, 17, 23],
+        checkpoint=checkpoint,
+    )
+
+
+def build_sam_vit_b(checkpoint=None):
+    return _build_sam(
+        encoder_embed_dim=768,
+        encoder_depth=12,
+        encoder_num_heads=12,
+        encoder_global_attn_indexes=[2, 5, 8, 11],
+        checkpoint=checkpoint,
+    )
+
+def build_sam_vit_t(checkpoint=None):
+    prompt_embed_dim = 256
+    image_size = 1024
+    vit_patch_size = 16
+    image_embedding_size = image_size // vit_patch_size
+    mobile_sam = Sam(
+            image_encoder=TinyViT(img_size=1024, in_chans=3, num_classes=1000,
+                embed_dims=[64, 128, 160, 320],
+                depths=[2, 2, 6, 2],
+                num_heads=[2, 4, 5, 10],
+                window_sizes=[7, 7, 14, 7],
+                mlp_ratio=4.,
+                drop_rate=0.,
+                drop_path_rate=0.0,
+                use_checkpoint=False,
+                mbconv_expand_ratio=4.0,
+                local_conv_size=3,
+                layer_lr_decay=0.8
+            ),
+            prompt_encoder=PromptEncoder(
+            embed_dim=prompt_embed_dim,
+            image_embedding_size=(image_embedding_size, image_embedding_size),
+            input_image_size=(image_size, image_size),
+            mask_in_chans=16,
+            ),
+            mask_decoder=MaskDecoder(
+                    num_multimask_outputs=3,
+                    transformer=TwoWayTransformer(
+                    depth=2,
+                    embedding_dim=prompt_embed_dim,
+                    mlp_dim=2048,
+                    num_heads=8,
+                ),
+                transformer_dim=prompt_embed_dim,
+                iou_head_depth=3,
+                iou_head_hidden_dim=256,
+            ),
+            pixel_mean=[123.675, 116.28, 103.53],
+            pixel_std=[58.395, 57.12, 57.375],
+        )
+
+    mobile_sam.eval()
+    if checkpoint is not None:
+        with open(checkpoint, "rb") as f:
+            state_dict = torch.load(f)
+        mobile_sam.load_state_dict(state_dict)
+    return mobile_sam
+
+sam_model_registry = {
+    "default": build_sam_vit_h,
+    "vit_h": build_sam_vit_h,
+    "vit_l": build_sam_vit_l,
+    "vit_b": build_sam_vit_b,
+    "vit_t": build_sam_vit_t,
+}
+
+
+def _build_sam(
+    encoder_embed_dim,
+    encoder_depth,
+    encoder_num_heads,
+    encoder_global_attn_indexes,
+    checkpoint=None,
+):
+    prompt_embed_dim = 256
+    image_size = 1024
+    vit_patch_size = 16
+    image_embedding_size = image_size // vit_patch_size
+    sam = Sam(
+        image_encoder=ImageEncoderViT(
+            depth=encoder_depth,
+            embed_dim=encoder_embed_dim,
+            img_size=image_size,
+            mlp_ratio=4,
+            norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
+            num_heads=encoder_num_heads,
+            patch_size=vit_patch_size,
+            qkv_bias=True,
+            use_rel_pos=True,
+            global_attn_indexes=encoder_global_attn_indexes,
+            window_size=14,
+            out_chans=prompt_embed_dim,
+        ),
+        prompt_encoder=PromptEncoder(
+            embed_dim=prompt_embed_dim,
+            image_embedding_size=(image_embedding_size, image_embedding_size),
+            input_image_size=(image_size, image_size),
+            mask_in_chans=16,
+        ),
+        mask_decoder=MaskDecoder(
+            num_multimask_outputs=3,
+            transformer=TwoWayTransformer(
+                depth=2,
+                embedding_dim=prompt_embed_dim,
+                mlp_dim=2048,
+                num_heads=8,
+            ),
+            transformer_dim=prompt_embed_dim,
+            iou_head_depth=3,
+            iou_head_hidden_dim=256,
+        ),
+        pixel_mean=[123.675, 116.28, 103.53],
+        pixel_std=[58.395, 57.12, 57.375],
+    )
+    sam.eval()
+    if checkpoint is not None:
+        with open(checkpoint, "rb") as f:
+            state_dict = torch.load(f)
+        sam.load_state_dict(state_dict)
+    return sam

automation_pose_mask/auto_mask/segment_anything/modeling/__init__.py

@@ -0,0 +1,12 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .sam import Sam
+from .image_encoder import ImageEncoderViT
+from .mask_decoder import MaskDecoder
+from .prompt_encoder import PromptEncoder
+from .transformer import TwoWayTransformer
+from .tiny_vit_sam import TinyViT

automation_pose_mask/auto_mask/segment_anything/modeling/common.py

@@ -0,0 +1,43 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+
+from typing import Type
+
+
+class MLPBlock(nn.Module):
+    def __init__(
+        self,
+        embedding_dim: int,
+        mlp_dim: int,
+        act: Type[nn.Module] = nn.GELU,
+    ) -> None:
+        super().__init__()
+        self.lin1 = nn.Linear(embedding_dim, mlp_dim)
+        self.lin2 = nn.Linear(mlp_dim, embedding_dim)
+        self.act = act()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.lin2(self.act(self.lin1(x)))
+
+
+# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
+# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119  # noqa
+class LayerNorm2d(nn.Module):
+    def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(num_channels))
+        self.bias = nn.Parameter(torch.zeros(num_channels))
+        self.eps = eps
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        u = x.mean(1, keepdim=True)
+        s = (x - u).pow(2).mean(1, keepdim=True)
+        x = (x - u) / torch.sqrt(s + self.eps)
+        x = self.weight[:, None, None] * x + self.bias[:, None, None]
+        return x

automation_pose_mask/auto_mask/segment_anything/modeling/image_encoder.py

@@ -0,0 +1,395 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from typing import Optional, Tuple, Type
+
+from .common import LayerNorm2d, MLPBlock
+
+
+# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
+class ImageEncoderViT(nn.Module):
+    def __init__(
+        self,
+        img_size: int = 1024,
+        patch_size: int = 16,
+        in_chans: int = 3,
+        embed_dim: int = 768,
+        depth: int = 12,
+        num_heads: int = 12,
+        mlp_ratio: float = 4.0,
+        out_chans: int = 256,
+        qkv_bias: bool = True,
+        norm_layer: Type[nn.Module] = nn.LayerNorm,
+        act_layer: Type[nn.Module] = nn.GELU,
+        use_abs_pos: bool = True,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        window_size: int = 0,
+        global_attn_indexes: Tuple[int, ...] = (),
+    ) -> None:
+        """
+        Args:
+            img_size (int): Input image size.
+            patch_size (int): Patch size.
+            in_chans (int): Number of input image channels.
+            embed_dim (int): Patch embedding dimension.
+            depth (int): Depth of ViT.
+            num_heads (int): Number of attention heads in each ViT block.
+            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+            qkv_bias (bool): If True, add a learnable bias to query, key, value.
+            norm_layer (nn.Module): Normalization layer.
+            act_layer (nn.Module): Activation layer.
+            use_abs_pos (bool): If True, use absolute positional embeddings.
+            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            window_size (int): Window size for window attention blocks.
+            global_attn_indexes (list): Indexes for blocks using global attention.
+        """
+        super().__init__()
+        self.img_size = img_size
+
+        self.patch_embed = PatchEmbed(
+            kernel_size=(patch_size, patch_size),
+            stride=(patch_size, patch_size),
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+        )
+
+        self.pos_embed: Optional[nn.Parameter] = None
+        if use_abs_pos:
+            # Initialize absolute positional embedding with pretrain image size.
+            self.pos_embed = nn.Parameter(
+                torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim)
+            )
+
+        self.blocks = nn.ModuleList()
+        for i in range(depth):
+            block = Block(
+                dim=embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+                use_rel_pos=use_rel_pos,
+                rel_pos_zero_init=rel_pos_zero_init,
+                window_size=window_size if i not in global_attn_indexes else 0,
+                input_size=(img_size // patch_size, img_size // patch_size),
+            )
+            self.blocks.append(block)
+
+        self.neck = nn.Sequential(
+            nn.Conv2d(
+                embed_dim,
+                out_chans,
+                kernel_size=1,
+                bias=False,
+            ),
+            LayerNorm2d(out_chans),
+            nn.Conv2d(
+                out_chans,
+                out_chans,
+                kernel_size=3,
+                padding=1,
+                bias=False,
+            ),
+            LayerNorm2d(out_chans),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.patch_embed(x)
+        if self.pos_embed is not None:
+            x = x + self.pos_embed
+
+        for blk in self.blocks:
+            x = blk(x)
+
+        x = self.neck(x.permute(0, 3, 1, 2))
+
+        return x
+
+
+class Block(nn.Module):
+    """Transformer blocks with support of window attention and residual propagation blocks"""
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = True,
+        norm_layer: Type[nn.Module] = nn.LayerNorm,
+        act_layer: Type[nn.Module] = nn.GELU,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        window_size: int = 0,
+        input_size: Optional[Tuple[int, int]] = None,
+    ) -> None:
+        """
+        Args:
+            dim (int): Number of input channels.
+            num_heads (int): Number of attention heads in each ViT block.
+            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+            qkv_bias (bool): If True, add a learnable bias to query, key, value.
+            norm_layer (nn.Module): Normalization layer.
+            act_layer (nn.Module): Activation layer.
+            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            window_size (int): Window size for window attention blocks. If it equals 0, then
+                use global attention.
+            input_size (int or None): Input resolution for calculating the relative positional
+                parameter size.
+        """
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            use_rel_pos=use_rel_pos,
+            rel_pos_zero_init=rel_pos_zero_init,
+            input_size=input_size if window_size == 0 else (window_size, window_size),
+        )
+
+        self.norm2 = norm_layer(dim)
+        self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)
+
+        self.window_size = window_size
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        shortcut = x
+        x = self.norm1(x)
+        # Window partition
+        if self.window_size > 0:
+            H, W = x.shape[1], x.shape[2]
+            x, pad_hw = window_partition(x, self.window_size)
+
+        x = self.attn(x)
+        # Reverse window partition
+        if self.window_size > 0:
+            x = window_unpartition(x, self.window_size, pad_hw, (H, W))
+
+        x = shortcut + x
+        x = x + self.mlp(self.norm2(x))
+
+        return x
+
+
+class Attention(nn.Module):
+    """Multi-head Attention block with relative position embeddings."""
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = True,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        input_size: Optional[Tuple[int, int]] = None,
+    ) -> None:
+        """
+        Args:
+            dim (int): Number of input channels.
+            num_heads (int): Number of attention heads.
+            qkv_bias (bool:  If True, add a learnable bias to query, key, value.
+            rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            input_size (int or None): Input resolution for calculating the relative positional
+                parameter size.
+        """
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.proj = nn.Linear(dim, dim)
+
+        self.use_rel_pos = use_rel_pos
+        if self.use_rel_pos:
+            assert (
+                input_size is not None
+            ), "Input size must be provided if using relative positional encoding."
+            # initialize relative positional embeddings
+            self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
+            self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        B, H, W, _ = x.shape
+        # qkv with shape (3, B, nHead, H * W, C)
+        qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        # q, k, v with shape (B * nHead, H * W, C)
+        q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
+
+        attn = (q * self.scale) @ k.transpose(-2, -1)
+
+        if self.use_rel_pos:
+            attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))
+
+        attn = attn.softmax(dim=-1)
+        x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
+        x = self.proj(x)
+
+        return x
+
+
+def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
+    """
+    Partition into non-overlapping windows with padding if needed.
+    Args:
+        x (tensor): input tokens with [B, H, W, C].
+        window_size (int): window size.
+
+    Returns:
+        windows: windows after partition with [B * num_windows, window_size, window_size, C].
+        (Hp, Wp): padded height and width before partition
+    """
+    B, H, W, C = x.shape
+
+    pad_h = (window_size - H % window_size) % window_size
+    pad_w = (window_size - W % window_size) % window_size
+    if pad_h > 0 or pad_w > 0:
+        x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
+    Hp, Wp = H + pad_h, W + pad_w
+
+    x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows, (Hp, Wp)
+
+
+def window_unpartition(
+    windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]
+) -> torch.Tensor:
+    """
+    Window unpartition into original sequences and removing padding.
+    Args:
+        x (tensor): input tokens with [B * num_windows, window_size, window_size, C].
+        window_size (int): window size.
+        pad_hw (Tuple): padded height and width (Hp, Wp).
+        hw (Tuple): original height and width (H, W) before padding.
+
+    Returns:
+        x: unpartitioned sequences with [B, H, W, C].
+    """
+    Hp, Wp = pad_hw
+    H, W = hw
+    B = windows.shape[0] // (Hp * Wp // window_size // window_size)
+    x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
+
+    if Hp > H or Wp > W:
+        x = x[:, :H, :W, :].contiguous()
+    return x
+
+
+def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
+    """
+    Get relative positional embeddings according to the relative positions of
+        query and key sizes.
+    Args:
+        q_size (int): size of query q.
+        k_size (int): size of key k.
+        rel_pos (Tensor): relative position embeddings (L, C).
+
+    Returns:
+        Extracted positional embeddings according to relative positions.
+    """
+    max_rel_dist = int(2 * max(q_size, k_size) - 1)
+    # Interpolate rel pos if needed.
+    if rel_pos.shape[0] != max_rel_dist:
+        # Interpolate rel pos.
+        rel_pos_resized = F.interpolate(
+            rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
+            size=max_rel_dist,
+            mode="linear",
+        )
+        rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
+    else:
+        rel_pos_resized = rel_pos
+
+    # Scale the coords with short length if shapes for q and k are different.
+    q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
+    k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
+    relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
+
+    return rel_pos_resized[relative_coords.long()]
+
+
+def add_decomposed_rel_pos(
+    attn: torch.Tensor,
+    q: torch.Tensor,
+    rel_pos_h: torch.Tensor,
+    rel_pos_w: torch.Tensor,
+    q_size: Tuple[int, int],
+    k_size: Tuple[int, int],
+) -> torch.Tensor:
+    """
+    Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
+    https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py   # noqa B950
+    Args:
+        attn (Tensor): attention map.
+        q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
+        rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
+        rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
+        q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
+        k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
+
+    Returns:
+        attn (Tensor): attention map with added relative positional embeddings.
+    """
+    q_h, q_w = q_size
+    k_h, k_w = k_size
+    Rh = get_rel_pos(q_h, k_h, rel_pos_h)
+    Rw = get_rel_pos(q_w, k_w, rel_pos_w)
+
+    B, _, dim = q.shape
+    r_q = q.reshape(B, q_h, q_w, dim)
+    rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
+    rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
+
+    attn = (
+        attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
+    ).view(B, q_h * q_w, k_h * k_w)
+
+    return attn
+
+
+class PatchEmbed(nn.Module):
+    """
+    Image to Patch Embedding.
+    """
+
+    def __init__(
+        self,
+        kernel_size: Tuple[int, int] = (16, 16),
+        stride: Tuple[int, int] = (16, 16),
+        padding: Tuple[int, int] = (0, 0),
+        in_chans: int = 3,
+        embed_dim: int = 768,
+    ) -> None:
+        """
+        Args:
+            kernel_size (Tuple): kernel size of the projection layer.
+            stride (Tuple): stride of the projection layer.
+            padding (Tuple): padding size of the projection layer.
+            in_chans (int): Number of input image channels.
+            embed_dim (int):  embed_dim (int): Patch embedding dimension.
+        """
+        super().__init__()
+
+        self.proj = nn.Conv2d(
+            in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.proj(x)
+        # B C H W -> B H W C
+        x = x.permute(0, 2, 3, 1)
+        return x

automation_pose_mask/auto_mask/segment_anything/modeling/mask_decoder.py

@@ -0,0 +1,176 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from typing import List, Tuple, Type
+
+from .common import LayerNorm2d
+
+
+class MaskDecoder(nn.Module):
+    def __init__(
+        self,
+        *,
+        transformer_dim: int,
+        transformer: nn.Module,
+        num_multimask_outputs: int = 3,
+        activation: Type[nn.Module] = nn.GELU,
+        iou_head_depth: int = 3,
+        iou_head_hidden_dim: int = 256,
+    ) -> None:
+        """
+        Predicts masks given an image and prompt embeddings, using a
+        tranformer architecture.
+
+        Arguments:
+          transformer_dim (int): the channel dimension of the transformer
+          transformer (nn.Module): the transformer used to predict masks
+          num_multimask_outputs (int): the number of masks to predict
+            when disambiguating masks
+          activation (nn.Module): the type of activation to use when
+            upscaling masks
+          iou_head_depth (int): the depth of the MLP used to predict
+            mask quality
+          iou_head_hidden_dim (int): the hidden dimension of the MLP
+            used to predict mask quality
+        """
+        super().__init__()
+        self.transformer_dim = transformer_dim
+        self.transformer = transformer
+
+        self.num_multimask_outputs = num_multimask_outputs
+
+        self.iou_token = nn.Embedding(1, transformer_dim)
+        self.num_mask_tokens = num_multimask_outputs + 1
+        self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)
+
+        self.output_upscaling = nn.Sequential(
+            nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
+            LayerNorm2d(transformer_dim // 4),
+            activation(),
+            nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),
+            activation(),
+        )
+        self.output_hypernetworks_mlps = nn.ModuleList(
+            [
+                MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)
+                for i in range(self.num_mask_tokens)
+            ]
+        )
+
+        self.iou_prediction_head = MLP(
+            transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth
+        )
+
+    def forward(
+        self,
+        image_embeddings: torch.Tensor,
+        image_pe: torch.Tensor,
+        sparse_prompt_embeddings: torch.Tensor,
+        dense_prompt_embeddings: torch.Tensor,
+        multimask_output: bool,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Predict masks given image and prompt embeddings.
+
+        Arguments:
+          image_embeddings (torch.Tensor): the embeddings from the image encoder
+          image_pe (torch.Tensor): positional encoding with the shape of image_embeddings
+          sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes
+          dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs
+          multimask_output (bool): Whether to return multiple masks or a single
+            mask.
+
+        Returns:
+          torch.Tensor: batched predicted masks
+          torch.Tensor: batched predictions of mask quality
+        """
+        masks, iou_pred = self.predict_masks(
+            image_embeddings=image_embeddings,
+            image_pe=image_pe,
+            sparse_prompt_embeddings=sparse_prompt_embeddings,
+            dense_prompt_embeddings=dense_prompt_embeddings,
+        )
+
+        # Select the correct mask or masks for outptu
+        if multimask_output:
+            mask_slice = slice(1, None)
+        else:
+            mask_slice = slice(0, 1)
+        masks = masks[:, mask_slice, :, :]
+        iou_pred = iou_pred[:, mask_slice]
+
+        # Prepare output
+        return masks, iou_pred
+
+    def predict_masks(
+        self,
+        image_embeddings: torch.Tensor,
+        image_pe: torch.Tensor,
+        sparse_prompt_embeddings: torch.Tensor,
+        dense_prompt_embeddings: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Predicts masks. See 'forward' for more details."""
+        # Concatenate output tokens
+        output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
+        output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)
+        tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
+
+        # Expand per-image data in batch direction to be per-mask
+        src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
+        src = src + dense_prompt_embeddings
+        pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
+        b, c, h, w = src.shape
+
+        # Run the transformer
+        hs, src = self.transformer(src, pos_src, tokens)
+        iou_token_out = hs[:, 0, :]
+        mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :]
+
+        # Upscale mask embeddings and predict masks using the mask tokens
+        src = src.transpose(1, 2).view(b, c, h, w)
+        upscaled_embedding = self.output_upscaling(src)
+        hyper_in_list: List[torch.Tensor] = []
+        for i in range(self.num_mask_tokens):
+            hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))
+        hyper_in = torch.stack(hyper_in_list, dim=1)
+        b, c, h, w = upscaled_embedding.shape
+        masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)
+
+        # Generate mask quality predictions
+        iou_pred = self.iou_prediction_head(iou_token_out)
+
+        return masks, iou_pred
+
+
+# Lightly adapted from
+# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa
+class MLP(nn.Module):
+    def __init__(
+        self,
+        input_dim: int,
+        hidden_dim: int,
+        output_dim: int,
+        num_layers: int,
+        sigmoid_output: bool = False,
+    ) -> None:
+        super().__init__()
+        self.num_layers = num_layers
+        h = [hidden_dim] * (num_layers - 1)
+        self.layers = nn.ModuleList(
+            nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
+        )
+        self.sigmoid_output = sigmoid_output
+
+    def forward(self, x):
+        for i, layer in enumerate(self.layers):
+            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
+        if self.sigmoid_output:
+            x = F.sigmoid(x)
+        return x

automation_pose_mask/auto_mask/segment_anything/modeling/prompt_encoder.py

@@ -0,0 +1,214 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import torch
+from torch import nn
+
+from typing import Any, Optional, Tuple, Type
+
+from .common import LayerNorm2d
+
+
+class PromptEncoder(nn.Module):
+    def __init__(
+        self,
+        embed_dim: int,
+        image_embedding_size: Tuple[int, int],
+        input_image_size: Tuple[int, int],
+        mask_in_chans: int,
+        activation: Type[nn.Module] = nn.GELU,
+    ) -> None:
+        """
+        Encodes prompts for input to SAM's mask decoder.
+
+        Arguments:
+          embed_dim (int): The prompts' embedding dimension
+          image_embedding_size (tuple(int, int)): The spatial size of the
+            image embedding, as (H, W).
+          input_image_size (int): The padded size of the image as input
+            to the image encoder, as (H, W).
+          mask_in_chans (int): The number of hidden channels used for
+            encoding input masks.
+          activation (nn.Module): The activation to use when encoding
+            input masks.
+        """
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.input_image_size = input_image_size
+        self.image_embedding_size = image_embedding_size
+        self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)
+
+        self.num_point_embeddings: int = 4  # pos/neg point + 2 box corners
+        point_embeddings = [nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)]
+        self.point_embeddings = nn.ModuleList(point_embeddings)
+        self.not_a_point_embed = nn.Embedding(1, embed_dim)
+
+        self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1])
+        self.mask_downscaling = nn.Sequential(
+            nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),
+            LayerNorm2d(mask_in_chans // 4),
+            activation(),
+            nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),
+            LayerNorm2d(mask_in_chans),
+            activation(),
+            nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),
+        )
+        self.no_mask_embed = nn.Embedding(1, embed_dim)
+
+    def get_dense_pe(self) -> torch.Tensor:
+        """
+        Returns the positional encoding used to encode point prompts,
+        applied to a dense set of points the shape of the image encoding.
+
+        Returns:
+          torch.Tensor: Positional encoding with shape
+            1x(embed_dim)x(embedding_h)x(embedding_w)
+        """
+        return self.pe_layer(self.image_embedding_size).unsqueeze(0)
+
+    def _embed_points(
+        self,
+        points: torch.Tensor,
+        labels: torch.Tensor,
+        pad: bool,
+    ) -> torch.Tensor:
+        """Embeds point prompts."""
+        points = points + 0.5  # Shift to center of pixel
+        if pad:
+            padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device)
+            padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)
+            points = torch.cat([points, padding_point], dim=1)
+            labels = torch.cat([labels, padding_label], dim=1)
+        point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size)
+        point_embedding[labels == -1] = 0.0
+        point_embedding[labels == -1] += self.not_a_point_embed.weight
+        point_embedding[labels == 0] += self.point_embeddings[0].weight
+        point_embedding[labels == 1] += self.point_embeddings[1].weight
+        return point_embedding
+
+    def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
+        """Embeds box prompts."""
+        boxes = boxes + 0.5  # Shift to center of pixel
+        coords = boxes.reshape(-1, 2, 2)
+        corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size)
+        corner_embedding[:, 0, :] += self.point_embeddings[2].weight
+        corner_embedding[:, 1, :] += self.point_embeddings[3].weight
+        return corner_embedding
+
+    def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
+        """Embeds mask inputs."""
+        mask_embedding = self.mask_downscaling(masks)
+        return mask_embedding
+
+    def _get_batch_size(
+        self,
+        points: Optional[Tuple[torch.Tensor, torch.Tensor]],
+        boxes: Optional[torch.Tensor],
+        masks: Optional[torch.Tensor],
+    ) -> int:
+        """
+        Gets the batch size of the output given the batch size of the input prompts.
+        """
+        if points is not None:
+            return points[0].shape[0]
+        elif boxes is not None:
+            return boxes.shape[0]
+        elif masks is not None:
+            return masks.shape[0]
+        else:
+            return 1
+
+    def _get_device(self) -> torch.device:
+        return self.point_embeddings[0].weight.device
+
+    def forward(
+        self,
+        points: Optional[Tuple[torch.Tensor, torch.Tensor]],
+        boxes: Optional[torch.Tensor],
+        masks: Optional[torch.Tensor],
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Embeds different types of prompts, returning both sparse and dense
+        embeddings.
+
+        Arguments:
+          points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates
+            and labels to embed.
+          boxes (torch.Tensor or none): boxes to embed
+          masks (torch.Tensor or none): masks to embed
+
+        Returns:
+          torch.Tensor: sparse embeddings for the points and boxes, with shape
+            BxNx(embed_dim), where N is determined by the number of input points
+            and boxes.
+          torch.Tensor: dense embeddings for the masks, in the shape
+            Bx(embed_dim)x(embed_H)x(embed_W)
+        """
+        bs = self._get_batch_size(points, boxes, masks)
+        sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device())
+        if points is not None:
+            coords, labels = points
+            point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))
+            sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)
+        if boxes is not None:
+            box_embeddings = self._embed_boxes(boxes)
+            sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)
+
+        if masks is not None:
+            dense_embeddings = self._embed_masks(masks)
+        else:
+            dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
+                bs, -1, self.image_embedding_size[0], self.image_embedding_size[1]
+            )
+
+        return sparse_embeddings, dense_embeddings
+
+
+class PositionEmbeddingRandom(nn.Module):
+    """
+    Positional encoding using random spatial frequencies.
+    """
+
+    def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
+        super().__init__()
+        if scale is None or scale <= 0.0:
+            scale = 1.0
+        self.register_buffer(
+            "positional_encoding_gaussian_matrix",
+            scale * torch.randn((2, num_pos_feats)),
+        )
+
+    def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
+        """Positionally encode points that are normalized to [0,1]."""
+        # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
+        coords = 2 * coords - 1
+        coords = coords @ self.positional_encoding_gaussian_matrix
+        coords = 2 * np.pi * coords
+        # outputs d_1 x ... x d_n x C shape
+        return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)
+
+    def forward(self, size: Tuple[int, int]) -> torch.Tensor:
+        """Generate positional encoding for a grid of the specified size."""
+        h, w = size
+        device: Any = self.positional_encoding_gaussian_matrix.device
+        grid = torch.ones((h, w), device=device, dtype=torch.float32)
+        y_embed = grid.cumsum(dim=0) - 0.5
+        x_embed = grid.cumsum(dim=1) - 0.5
+        y_embed = y_embed / h
+        x_embed = x_embed / w
+
+        pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))
+        return pe.permute(2, 0, 1)  # C x H x W
+
+    def forward_with_coords(
+        self, coords_input: torch.Tensor, image_size: Tuple[int, int]
+    ) -> torch.Tensor:
+        """Positionally encode points that are not normalized to [0,1]."""
+        coords = coords_input.clone()
+        coords[:, :, 0] = coords[:, :, 0] / image_size[1]
+        coords[:, :, 1] = coords[:, :, 1] / image_size[0]
+        return self._pe_encoding(coords.to(torch.float))  # B x N x C

automation_pose_mask/auto_mask/segment_anything/modeling/sam.py

@@ -0,0 +1,174 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from typing import Any, Dict, List, Tuple, Union
+from .tiny_vit_sam import TinyViT
+from .image_encoder import ImageEncoderViT
+from .mask_decoder import MaskDecoder
+from .prompt_encoder import PromptEncoder
+
+
+class Sam(nn.Module):
+    mask_threshold: float = 0.0
+    image_format: str = "RGB"
+
+    def __init__(
+        self,
+        image_encoder: Union[ImageEncoderViT, TinyViT],
+        prompt_encoder: PromptEncoder,
+        mask_decoder: MaskDecoder,
+        pixel_mean: List[float] = [123.675, 116.28, 103.53],
+        pixel_std: List[float] = [58.395, 57.12, 57.375],
+    ) -> None:
+        """
+        SAM predicts object masks from an image and input prompts.
+
+        Arguments:
+          image_encoder (ImageEncoderViT): The backbone used to encode the
+            image into image embeddings that allow for efficient mask prediction.
+          prompt_encoder (PromptEncoder): Encodes various types of input prompts.
+          mask_decoder (MaskDecoder): Predicts masks from the image embeddings
+            and encoded prompts.
+          pixel_mean (list(float)): Mean values for normalizing pixels in the input image.
+          pixel_std (list(float)): Std values for normalizing pixels in the input image.
+        """
+        super().__init__()
+        self.image_encoder = image_encoder
+        self.prompt_encoder = prompt_encoder
+        self.mask_decoder = mask_decoder
+        self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False)
+        self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False)
+
+    @property
+    def device(self) -> Any:
+        return self.pixel_mean.device
+
+    @torch.no_grad()
+    def forward(
+        self,
+        batched_input: List[Dict[str, Any]],
+        multimask_output: bool,
+    ) -> List[Dict[str, torch.Tensor]]:
+        """
+        Predicts masks end-to-end from provided images and prompts.
+        If prompts are not known in advance, using SamPredictor is
+        recommended over calling the model directly.
+
+        Arguments:
+          batched_input (list(dict)): A list over input images, each a
+            dictionary with the following keys. A prompt key can be
+            excluded if it is not present.
+              'image': The image as a torch tensor in 3xHxW format,
+                already transformed for input to the model.
+              'original_size': (tuple(int, int)) The original size of
+                the image before transformation, as (H, W).
+              'point_coords': (torch.Tensor) Batched point prompts for
+                this image, with shape BxNx2. Already transformed to the
+                input frame of the model.
+              'point_labels': (torch.Tensor) Batched labels for point prompts,
+                with shape BxN.
+              'boxes': (torch.Tensor) Batched box inputs, with shape Bx4.
+                Already transformed to the input frame of the model.
+              'mask_inputs': (torch.Tensor) Batched mask inputs to the model,
+                in the form Bx1xHxW.
+          multimask_output (bool): Whether the model should predict multiple
+            disambiguating masks, or return a single mask.
+
+        Returns:
+          (list(dict)): A list over input images, where each element is
+            as dictionary with the following keys.
+              'masks': (torch.Tensor) Batched binary mask predictions,
+                with shape BxCxHxW, where B is the number of input promts,
+                C is determiend by multimask_output, and (H, W) is the
+                original size of the image.
+              'iou_predictions': (torch.Tensor) The model's predictions
+                of mask quality, in shape BxC.
+              'low_res_logits': (torch.Tensor) Low resolution logits with
+                shape BxCxHxW, where H=W=256. Can be passed as mask input
+                to subsequent iterations of prediction.
+        """
+        input_images = torch.stack([self.preprocess(x["image"]) for x in batched_input], dim=0)
+        image_embeddings = self.image_encoder(input_images)
+
+        outputs = []
+        for image_record, curr_embedding in zip(batched_input, image_embeddings):
+            if "point_coords" in image_record:
+                points = (image_record["point_coords"], image_record["point_labels"])
+            else:
+                points = None
+            sparse_embeddings, dense_embeddings = self.prompt_encoder(
+                points=points,
+                boxes=image_record.get("boxes", None),
+                masks=image_record.get("mask_inputs", None),
+            )
+            low_res_masks, iou_predictions = self.mask_decoder(
+                image_embeddings=curr_embedding.unsqueeze(0),
+                image_pe=self.prompt_encoder.get_dense_pe(),
+                sparse_prompt_embeddings=sparse_embeddings,
+                dense_prompt_embeddings=dense_embeddings,
+                multimask_output=multimask_output,
+            )
+            masks = self.postprocess_masks(
+                low_res_masks,
+                input_size=image_record["image"].shape[-2:],
+                original_size=image_record["original_size"],
+            )
+            masks = masks > self.mask_threshold
+            outputs.append(
+                {
+                    "masks": masks,
+                    "iou_predictions": iou_predictions,
+                    "low_res_logits": low_res_masks,
+                }
+            )
+        return outputs
+
+    def postprocess_masks(
+        self,
+        masks: torch.Tensor,
+        input_size: Tuple[int, ...],
+        original_size: Tuple[int, ...],
+    ) -> torch.Tensor:
+        """
+        Remove padding and upscale masks to the original image size.
+
+        Arguments:
+          masks (torch.Tensor): Batched masks from the mask_decoder,
+            in BxCxHxW format.
+          input_size (tuple(int, int)): The size of the image input to the
+            model, in (H, W) format. Used to remove padding.
+          original_size (tuple(int, int)): The original size of the image
+            before resizing for input to the model, in (H, W) format.
+
+        Returns:
+          (torch.Tensor): Batched masks in BxCxHxW format, where (H, W)
+            is given by original_size.
+        """
+        masks = F.interpolate(
+            masks,
+            (self.image_encoder.img_size, self.image_encoder.img_size),
+            mode="bilinear",
+            align_corners=False,
+        )
+        masks = masks[..., : input_size[0], : input_size[1]]
+        masks = F.interpolate(masks, original_size, mode="bilinear", align_corners=False)
+        return masks
+
+    def preprocess(self, x: torch.Tensor) -> torch.Tensor:
+        """Normalize pixel values and pad to a square input."""
+        # Normalize colors
+        x = (x - self.pixel_mean) / self.pixel_std
+
+        # Pad
+        h, w = x.shape[-2:]
+        padh = self.image_encoder.img_size - h
+        padw = self.image_encoder.img_size - w
+        x = F.pad(x, (0, padw, 0, padh))
+        return x

automation_pose_mask/auto_mask/segment_anything/modeling/tiny_vit_sam.py

@@ -0,0 +1,719 @@
+# --------------------------------------------------------
+# TinyViT Model Architecture
+# Copyright (c) 2022 Microsoft
+# Adapted from LeViT and Swin Transformer
+#   LeViT: (https://github.com/facebookresearch/levit)
+#   Swin: (https://github.com/microsoft/swin-transformer)
+# Build the TinyViT Model
+# --------------------------------------------------------
+
+import itertools
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath as TimmDropPath,\
+    to_2tuple, trunc_normal_
+from timm.models.registry import register_model
+from typing import Tuple
+
+
+class Conv2d_BN(torch.nn.Sequential):
+    def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1,
+                 groups=1, bn_weight_init=1):
+        super().__init__()
+        self.add_module('c', torch.nn.Conv2d(
+            a, b, ks, stride, pad, dilation, groups, bias=False))
+        bn = torch.nn.BatchNorm2d(b)
+        torch.nn.init.constant_(bn.weight, bn_weight_init)
+        torch.nn.init.constant_(bn.bias, 0)
+        self.add_module('bn', bn)
+
+    @torch.no_grad()
+    def fuse(self):
+        c, bn = self._modules.values()
+        w = bn.weight / (bn.running_var + bn.eps)**0.5
+        w = c.weight * w[:, None, None, None]
+        b = bn.bias - bn.running_mean * bn.weight / \
+            (bn.running_var + bn.eps)**0.5
+        m = torch.nn.Conv2d(w.size(1) * self.c.groups, w.size(
+            0), w.shape[2:], stride=self.c.stride, padding=self.c.padding, dilation=self.c.dilation, groups=self.c.groups)
+        m.weight.data.copy_(w)
+        m.bias.data.copy_(b)
+        return m
+
+
+class DropPath(TimmDropPath):
+    def __init__(self, drop_prob=None):
+        super().__init__(drop_prob=drop_prob)
+        self.drop_prob = drop_prob
+
+    def __repr__(self):
+        msg = super().__repr__()
+        msg += f'(drop_prob={self.drop_prob})'
+        return msg
+
+
+class PatchEmbed(nn.Module):
+    def __init__(self, in_chans, embed_dim, resolution, activation):
+        super().__init__()
+        img_size: Tuple[int, int] = to_2tuple(resolution)
+        self.patches_resolution = (img_size[0] // 4, img_size[1] // 4)
+        self.num_patches = self.patches_resolution[0] * \
+            self.patches_resolution[1]
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+        n = embed_dim
+        self.seq = nn.Sequential(
+            Conv2d_BN(in_chans, n // 2, 3, 2, 1),
+            activation(),
+            Conv2d_BN(n // 2, n, 3, 2, 1),
+        )
+
+    def forward(self, x):
+        return self.seq(x)
+
+
+class MBConv(nn.Module):
+    def __init__(self, in_chans, out_chans, expand_ratio,
+                 activation, drop_path):
+        super().__init__()
+        self.in_chans = in_chans
+        self.hidden_chans = int(in_chans * expand_ratio)
+        self.out_chans = out_chans
+
+        self.conv1 = Conv2d_BN(in_chans, self.hidden_chans, ks=1)
+        self.act1 = activation()
+
+        self.conv2 = Conv2d_BN(self.hidden_chans, self.hidden_chans,
+                               ks=3, stride=1, pad=1, groups=self.hidden_chans)
+        self.act2 = activation()
+
+        self.conv3 = Conv2d_BN(
+            self.hidden_chans, out_chans, ks=1, bn_weight_init=0.0)
+        self.act3 = activation()
+
+        self.drop_path = DropPath(
+            drop_path) if drop_path > 0. else nn.Identity()
+
+    def forward(self, x):
+        shortcut = x
+
+        x = self.conv1(x)
+        x = self.act1(x)
+
+        x = self.conv2(x)
+        x = self.act2(x)
+
+        x = self.conv3(x)
+
+        x = self.drop_path(x)
+
+        x += shortcut
+        x = self.act3(x)
+
+        return x
+
+
+class PatchMerging(nn.Module):
+    def __init__(self, input_resolution, dim, out_dim, activation):
+        super().__init__()
+
+        self.input_resolution = input_resolution
+        self.dim = dim
+        self.out_dim = out_dim
+        self.act = activation()
+        self.conv1 = Conv2d_BN(dim, out_dim, 1, 1, 0)
+        stride_c=2
+        if(out_dim==320 or out_dim==448 or out_dim==576):
+            stride_c=1
+        self.conv2 = Conv2d_BN(out_dim, out_dim, 3, stride_c, 1, groups=out_dim)
+        self.conv3 = Conv2d_BN(out_dim, out_dim, 1, 1, 0)
+
+    def forward(self, x):
+        if x.ndim == 3:
+            H, W = self.input_resolution
+            B = len(x)
+            # (B, C, H, W)
+            x = x.view(B, H, W, -1).permute(0, 3, 1, 2)
+
+        x = self.conv1(x)
+        x = self.act(x)
+
+        x = self.conv2(x)
+        x = self.act(x)
+        x = self.conv3(x)
+        x = x.flatten(2).transpose(1, 2)
+        return x
+
+
+class ConvLayer(nn.Module):
+    def __init__(self, dim, input_resolution, depth,
+                 activation,
+                 drop_path=0., downsample=None, use_checkpoint=False,
+                 out_dim=None,
+                 conv_expand_ratio=4.,
+                 ):
+
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            MBConv(dim, dim, conv_expand_ratio, activation,
+                   drop_path[i] if isinstance(drop_path, list) else drop_path,
+                   )
+            for i in range(depth)])
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(
+                input_resolution, dim=dim, out_dim=out_dim, activation=activation)
+        else:
+            self.downsample = None
+
+    def forward(self, x):
+        for blk in self.blocks:
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x)
+            else:
+                x = blk(x)
+        if self.downsample is not None:
+            x = self.downsample(x)
+        return x
+
+
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None,
+                 out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.norm = nn.LayerNorm(in_features)
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.act = act_layer()
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.norm(x)
+
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class Attention(torch.nn.Module):
+    def __init__(self, dim, key_dim, num_heads=8,
+                 attn_ratio=4,
+                 resolution=(14, 14),
+                 ):
+        super().__init__()
+        # (h, w)
+        assert isinstance(resolution, tuple) and len(resolution) == 2
+        self.num_heads = num_heads
+        self.scale = key_dim ** -0.5
+        self.key_dim = key_dim
+        self.nh_kd = nh_kd = key_dim * num_heads
+        self.d = int(attn_ratio * key_dim)
+        self.dh = int(attn_ratio * key_dim) * num_heads
+        self.attn_ratio = attn_ratio
+        h = self.dh + nh_kd * 2
+
+        self.norm = nn.LayerNorm(dim)
+        self.qkv = nn.Linear(dim, h)
+        self.proj = nn.Linear(self.dh, dim)
+
+        points = list(itertools.product(
+            range(resolution[0]), range(resolution[1])))
+        N = len(points)
+        attention_offsets = {}
+        idxs = []
+        for p1 in points:
+            for p2 in points:
+                offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1]))
+                if offset not in attention_offsets:
+                    attention_offsets[offset] = len(attention_offsets)
+                idxs.append(attention_offsets[offset])
+        self.attention_biases = torch.nn.Parameter(
+            torch.zeros(num_heads, len(attention_offsets)))
+        self.register_buffer('attention_bias_idxs',
+                             torch.LongTensor(idxs).view(N, N),
+                             persistent=False)
+
+    @torch.no_grad()
+    def train(self, mode=True):
+        super().train(mode)
+        if mode and hasattr(self, 'ab'):
+            del self.ab
+        else:
+            #self.ab = self.attention_biases[:, self.attention_bias_idxs]
+            self.register_buffer('ab',
+                        self.attention_biases[:, self.attention_bias_idxs],
+                        persistent=False)
+
+    def forward(self, x):  # x (B,N,C)
+        B, N, _ = x.shape
+
+        # Normalization
+        x = self.norm(x)
+
+        qkv = self.qkv(x)
+        # (B, N, num_heads, d)
+        q, k, v = qkv.view(B, N, self.num_heads, -
+                           1).split([self.key_dim, self.key_dim, self.d], dim=3)
+        # (B, num_heads, N, d)
+        q = q.permute(0, 2, 1, 3)
+        k = k.permute(0, 2, 1, 3)
+        v = v.permute(0, 2, 1, 3)
+
+        attn = (
+            (q @ k.transpose(-2, -1)) * self.scale
+            +
+            (self.attention_biases[:, self.attention_bias_idxs]
+             if self.training else self.ab)
+        )
+        attn = attn.softmax(dim=-1)
+        x = (attn @ v).transpose(1, 2).reshape(B, N, self.dh)
+        x = self.proj(x)
+        return x
+
+
+class TinyViTBlock(nn.Module):
+    r""" TinyViT Block.
+
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int, int]): Input resulotion.
+        num_heads (int): Number of attention heads.
+        window_size (int): Window size.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        drop (float, optional): Dropout rate. Default: 0.0
+        drop_path (float, optional): Stochastic depth rate. Default: 0.0
+        local_conv_size (int): the kernel size of the convolution between
+                               Attention and MLP. Default: 3
+        activation: the activation function. Default: nn.GELU
+    """
+
+    def __init__(self, dim, input_resolution, num_heads, window_size=7,
+                 mlp_ratio=4., drop=0., drop_path=0.,
+                 local_conv_size=3,
+                 activation=nn.GELU,
+                 ):
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.num_heads = num_heads
+        assert window_size > 0, 'window_size must be greater than 0'
+        self.window_size = window_size
+        self.mlp_ratio = mlp_ratio
+
+        self.drop_path = DropPath(
+            drop_path) if drop_path > 0. else nn.Identity()
+
+        assert dim % num_heads == 0, 'dim must be divisible by num_heads'
+        head_dim = dim // num_heads
+
+        window_resolution = (window_size, window_size)
+        self.attn = Attention(dim, head_dim, num_heads,
+                              attn_ratio=1, resolution=window_resolution)
+
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        mlp_activation = activation
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim,
+                       act_layer=mlp_activation, drop=drop)
+
+        pad = local_conv_size // 2
+        self.local_conv = Conv2d_BN(
+            dim, dim, ks=local_conv_size, stride=1, pad=pad, groups=dim)
+
+    def forward(self, x):
+        H, W = self.input_resolution
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+        res_x = x
+        if H == self.window_size and W == self.window_size:
+            x = self.attn(x)
+        else:
+            x = x.view(B, H, W, C)
+            pad_b = (self.window_size - H %
+                     self.window_size) % self.window_size
+            pad_r = (self.window_size - W %
+                     self.window_size) % self.window_size
+            padding = pad_b > 0 or pad_r > 0
+
+            if padding:
+                x = F.pad(x, (0, 0, 0, pad_r, 0, pad_b))
+
+            pH, pW = H + pad_b, W + pad_r
+            nH = pH // self.window_size
+            nW = pW // self.window_size
+            # window partition
+            x = x.view(B, nH, self.window_size, nW, self.window_size, C).transpose(2, 3).reshape(
+                B * nH * nW, self.window_size * self.window_size, C)
+            x = self.attn(x)
+            # window reverse
+            x = x.view(B, nH, nW, self.window_size, self.window_size,
+                       C).transpose(2, 3).reshape(B, pH, pW, C)
+
+            if padding:
+                x = x[:, :H, :W].contiguous()
+
+            x = x.view(B, L, C)
+
+        x = res_x + self.drop_path(x)
+
+        x = x.transpose(1, 2).reshape(B, C, H, W)
+        x = self.local_conv(x)
+        x = x.view(B, C, L).transpose(1, 2)
+
+        x = x + self.drop_path(self.mlp(x))
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
+               f"window_size={self.window_size}, mlp_ratio={self.mlp_ratio}"
+
+
+class BasicLayer(nn.Module):
+    """ A basic TinyViT layer for one stage.
+
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        depth (int): Number of blocks.
+        num_heads (int): Number of attention heads.
+        window_size (int): Local window size.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        drop (float, optional): Dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+        local_conv_size: the kernel size of the depthwise convolution between attention and MLP. Default: 3
+        activation: the activation function. Default: nn.GELU
+        out_dim: the output dimension of the layer. Default: dim
+    """
+
+    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+                 mlp_ratio=4., drop=0.,
+                 drop_path=0., downsample=None, use_checkpoint=False,
+                 local_conv_size=3,
+                 activation=nn.GELU,
+                 out_dim=None,
+                 ):
+
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            TinyViTBlock(dim=dim, input_resolution=input_resolution,
+                         num_heads=num_heads, window_size=window_size,
+                         mlp_ratio=mlp_ratio,
+                         drop=drop,
+                         drop_path=drop_path[i] if isinstance(
+                             drop_path, list) else drop_path,
+                         local_conv_size=local_conv_size,
+                         activation=activation,
+                         )
+            for i in range(depth)])
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(
+                input_resolution, dim=dim, out_dim=out_dim, activation=activation)
+        else:
+            self.downsample = None
+
+    def forward(self, x):
+        for blk in self.blocks:
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x)
+            else:
+                x = blk(x)
+        if self.downsample is not None:
+            x = self.downsample(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
+
+class LayerNorm2d(nn.Module):
+    def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(num_channels))
+        self.bias = nn.Parameter(torch.zeros(num_channels))
+        self.eps = eps
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        u = x.mean(1, keepdim=True)
+        s = (x - u).pow(2).mean(1, keepdim=True)
+        x = (x - u) / torch.sqrt(s + self.eps)
+        x = self.weight[:, None, None] * x + self.bias[:, None, None]
+        return x
+class TinyViT(nn.Module):
+    def __init__(self, img_size=224, in_chans=3, num_classes=1000,
+                 embed_dims=[96, 192, 384, 768], depths=[2, 2, 6, 2],
+                 num_heads=[3, 6, 12, 24],
+                 window_sizes=[7, 7, 14, 7],
+                 mlp_ratio=4.,
+                 drop_rate=0.,
+                 drop_path_rate=0.1,
+                 use_checkpoint=False,
+                 mbconv_expand_ratio=4.0,
+                 local_conv_size=3,
+                 layer_lr_decay=1.0,
+                 ):
+        super().__init__()
+        self.img_size=img_size
+        self.num_classes = num_classes
+        self.depths = depths
+        self.num_layers = len(depths)
+        self.mlp_ratio = mlp_ratio
+
+        activation = nn.GELU
+
+        self.patch_embed = PatchEmbed(in_chans=in_chans,
+                                      embed_dim=embed_dims[0],
+                                      resolution=img_size,
+                                      activation=activation)
+
+        patches_resolution = self.patch_embed.patches_resolution
+        self.patches_resolution = patches_resolution
+
+        # stochastic depth
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate,
+                                                sum(depths))]  # stochastic depth decay rule
+
+        # build layers
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            kwargs = dict(dim=embed_dims[i_layer],
+                        input_resolution=(patches_resolution[0] // (2 ** (i_layer-1 if i_layer == 3 else i_layer)),
+                                patches_resolution[1] // (2 ** (i_layer-1 if i_layer == 3 else i_layer))),
+                        #   input_resolution=(patches_resolution[0] // (2 ** i_layer),
+                        #                     patches_resolution[1] // (2 ** i_layer)),
+                          depth=depths[i_layer],
+                          drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
+                          downsample=PatchMerging if (
+                              i_layer < self.num_layers - 1) else None,
+                          use_checkpoint=use_checkpoint,
+                          out_dim=embed_dims[min(
+                              i_layer + 1, len(embed_dims) - 1)],
+                          activation=activation,
+                          )
+            if i_layer == 0:
+                layer = ConvLayer(
+                    conv_expand_ratio=mbconv_expand_ratio,
+                    **kwargs,
+                )
+            else:
+                layer = BasicLayer(
+                    num_heads=num_heads[i_layer],
+                    window_size=window_sizes[i_layer],
+                    mlp_ratio=self.mlp_ratio,
+                    drop=drop_rate,
+                    local_conv_size=local_conv_size,
+                    **kwargs)
+            self.layers.append(layer)
+
+        # Classifier head
+        self.norm_head = nn.LayerNorm(embed_dims[-1])
+        self.head = nn.Linear(
+            embed_dims[-1], num_classes) if num_classes > 0 else torch.nn.Identity()
+
+        # init weights
+        self.apply(self._init_weights)
+        self.set_layer_lr_decay(layer_lr_decay)
+        self.neck = nn.Sequential(
+            nn.Conv2d(
+                embed_dims[-1],
+                256,
+                kernel_size=1,
+                bias=False,
+            ),
+            LayerNorm2d(256),
+            nn.Conv2d(
+                256,
+                256,
+                kernel_size=3,
+                padding=1,
+                bias=False,
+            ),
+            LayerNorm2d(256),
+        )
+    def set_layer_lr_decay(self, layer_lr_decay):
+        decay_rate = layer_lr_decay
+
+        # layers -> blocks (depth)
+        depth = sum(self.depths)
+        lr_scales = [decay_rate ** (depth - i - 1) for i in range(depth)]
+        #print("LR SCALES:", lr_scales)
+
+        def _set_lr_scale(m, scale):
+            for p in m.parameters():
+                p.lr_scale = scale
+
+        self.patch_embed.apply(lambda x: _set_lr_scale(x, lr_scales[0]))
+        i = 0
+        for layer in self.layers:
+            for block in layer.blocks:
+                block.apply(lambda x: _set_lr_scale(x, lr_scales[i]))
+                i += 1
+            if layer.downsample is not None:
+                layer.downsample.apply(
+                    lambda x: _set_lr_scale(x, lr_scales[i - 1]))
+        assert i == depth
+        for m in [self.norm_head, self.head]:
+            m.apply(lambda x: _set_lr_scale(x, lr_scales[-1]))
+
+        for k, p in self.named_parameters():
+            p.param_name = k
+
+        def _check_lr_scale(m):
+            for p in m.parameters():
+                assert hasattr(p, 'lr_scale'), p.param_name
+
+        self.apply(_check_lr_scale)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    @torch.jit.ignore
+    def no_weight_decay_keywords(self):
+        return {'attention_biases'}
+
+    def forward_features(self, x):
+        # x: (N, C, H, W)
+        x = self.patch_embed(x)
+
+        x = self.layers[0](x)
+        start_i = 1
+
+        for i in range(start_i, len(self.layers)):
+            layer = self.layers[i]
+            x = layer(x)
+        B,_,C=x.size()
+        x = x.view(B, 64, 64, C)
+        x=x.permute(0, 3, 1, 2)
+        x=self.neck(x)
+        return x
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        #x = self.norm_head(x)
+        #x = self.head(x)
+        return x
+
+
+_checkpoint_url_format = \
+    'https://github.com/wkcn/TinyViT-model-zoo/releases/download/checkpoints/{}.pth'
+_provided_checkpoints = {
+    'tiny_vit_5m_224': 'tiny_vit_5m_22kto1k_distill',
+    'tiny_vit_11m_224': 'tiny_vit_11m_22kto1k_distill',
+    'tiny_vit_21m_224': 'tiny_vit_21m_22kto1k_distill',
+    'tiny_vit_21m_384': 'tiny_vit_21m_22kto1k_384_distill',
+    'tiny_vit_21m_512': 'tiny_vit_21m_22kto1k_512_distill',
+}
+
+
+def register_tiny_vit_model(fn):
+    '''Register a TinyViT model
+    It is a wrapper of `register_model` with loading the pretrained checkpoint.
+    '''
+    def fn_wrapper(pretrained=False, **kwargs):
+        model = fn()
+        if pretrained:
+            model_name = fn.__name__
+            assert model_name in _provided_checkpoints, \
+                f'Sorry that the checkpoint `{model_name}` is not provided yet.'
+            url = _checkpoint_url_format.format(
+                _provided_checkpoints[model_name])
+            checkpoint = torch.hub.load_state_dict_from_url(
+                url=url,
+                map_location='cpu', check_hash=False,
+            )
+            model.load_state_dict(checkpoint['model'])
+
+        return model
+
+    # rename the name of fn_wrapper
+    fn_wrapper.__name__ = fn.__name__
+    return register_model(fn_wrapper)
+
+
+@register_tiny_vit_model
+def tiny_vit_5m_224(pretrained=False, num_classes=1000, drop_path_rate=0.0):
+    return TinyViT(
+        num_classes=num_classes,
+        embed_dims=[64, 128, 160, 320],
+        depths=[2, 2, 6, 2],
+        num_heads=[2, 4, 5, 10],
+        window_sizes=[7, 7, 14, 7],
+        drop_path_rate=drop_path_rate,
+    )
+
+
+@register_tiny_vit_model
+def tiny_vit_11m_224(pretrained=False, num_classes=1000, drop_path_rate=0.1):
+    return TinyViT(
+        num_classes=num_classes,
+        embed_dims=[64, 128, 256, 448],
+        depths=[2, 2, 6, 2],
+        num_heads=[2, 4, 8, 14],
+        window_sizes=[7, 7, 14, 7],
+        drop_path_rate=drop_path_rate,
+    )
+
+
+@register_tiny_vit_model
+def tiny_vit_21m_224(pretrained=False, num_classes=1000, drop_path_rate=0.2):
+    return TinyViT(
+        num_classes=num_classes,
+        embed_dims=[96, 192, 384, 576],
+        depths=[2, 2, 6, 2],
+        num_heads=[3, 6, 12, 18],
+        window_sizes=[7, 7, 14, 7],
+        drop_path_rate=drop_path_rate,
+    )
+
+
+@register_tiny_vit_model
+def tiny_vit_21m_384(pretrained=False, num_classes=1000, drop_path_rate=0.1):
+    return TinyViT(
+        img_size=384,
+        num_classes=num_classes,
+        embed_dims=[96, 192, 384, 576],
+        depths=[2, 2, 6, 2],
+        num_heads=[3, 6, 12, 18],
+        window_sizes=[12, 12, 24, 12],
+        drop_path_rate=drop_path_rate,
+    )
+
+
+@register_tiny_vit_model
+def tiny_vit_21m_512(pretrained=False, num_classes=1000, drop_path_rate=0.1):
+    return TinyViT(
+        img_size=512,
+        num_classes=num_classes,
+        embed_dims=[96, 192, 384, 576],
+        depths=[2, 2, 6, 2],
+        num_heads=[3, 6, 12, 18],
+        window_sizes=[16, 16, 32, 16],
+        drop_path_rate=drop_path_rate,
+    )

automation_pose_mask/auto_mask/segment_anything/modeling/transformer.py

@@ -0,0 +1,240 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+from torch import Tensor, nn
+
+import math
+from typing import Tuple, Type
+
+from .common import MLPBlock
+
+
+class TwoWayTransformer(nn.Module):
+    def __init__(
+        self,
+        depth: int,
+        embedding_dim: int,
+        num_heads: int,
+        mlp_dim: int,
+        activation: Type[nn.Module] = nn.ReLU,
+        attention_downsample_rate: int = 2,
+    ) -> None:
+        """
+        A transformer decoder that attends to an input image using
+        queries whose positional embedding is supplied.
+
+        Args:
+          depth (int): number of layers in the transformer
+          embedding_dim (int): the channel dimension for the input embeddings
+          num_heads (int): the number of heads for multihead attention. Must
+            divide embedding_dim
+          mlp_dim (int): the channel dimension internal to the MLP block
+          activation (nn.Module): the activation to use in the MLP block
+        """
+        super().__init__()
+        self.depth = depth
+        self.embedding_dim = embedding_dim
+        self.num_heads = num_heads
+        self.mlp_dim = mlp_dim
+        self.layers = nn.ModuleList()
+
+        for i in range(depth):
+            self.layers.append(
+                TwoWayAttentionBlock(
+                    embedding_dim=embedding_dim,
+                    num_heads=num_heads,
+                    mlp_dim=mlp_dim,
+                    activation=activation,
+                    attention_downsample_rate=attention_downsample_rate,
+                    skip_first_layer_pe=(i == 0),
+                )
+            )
+
+        self.final_attn_token_to_image = Attention(
+            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
+        )
+        self.norm_final_attn = nn.LayerNorm(embedding_dim)
+
+    def forward(
+        self,
+        image_embedding: Tensor,
+        image_pe: Tensor,
+        point_embedding: Tensor,
+    ) -> Tuple[Tensor, Tensor]:
+        """
+        Args:
+          image_embedding (torch.Tensor): image to attend to. Should be shape
+            B x embedding_dim x h x w for any h and w.
+          image_pe (torch.Tensor): the positional encoding to add to the image. Must
+            have the same shape as image_embedding.
+          point_embedding (torch.Tensor): the embedding to add to the query points.
+            Must have shape B x N_points x embedding_dim for any N_points.
+
+        Returns:
+          torch.Tensor: the processed point_embedding
+          torch.Tensor: the processed image_embedding
+        """
+        # BxCxHxW -> BxHWxC == B x N_image_tokens x C
+        bs, c, h, w = image_embedding.shape
+        image_embedding = image_embedding.flatten(2).permute(0, 2, 1)
+        image_pe = image_pe.flatten(2).permute(0, 2, 1)
+
+        # Prepare queries
+        queries = point_embedding
+        keys = image_embedding
+
+        # Apply transformer blocks and final layernorm
+        for layer in self.layers:
+            queries, keys = layer(
+                queries=queries,
+                keys=keys,
+                query_pe=point_embedding,
+                key_pe=image_pe,
+            )
+
+        # Apply the final attenion layer from the points to the image
+        q = queries + point_embedding
+        k = keys + image_pe
+        attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)
+        queries = queries + attn_out
+        queries = self.norm_final_attn(queries)
+
+        return queries, keys
+
+
+class TwoWayAttentionBlock(nn.Module):
+    def __init__(
+        self,
+        embedding_dim: int,
+        num_heads: int,
+        mlp_dim: int = 2048,
+        activation: Type[nn.Module] = nn.ReLU,
+        attention_downsample_rate: int = 2,
+        skip_first_layer_pe: bool = False,
+    ) -> None:
+        """
+        A transformer block with four layers: (1) self-attention of sparse
+        inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp
+        block on sparse inputs, and (4) cross attention of dense inputs to sparse
+        inputs.
+
+        Arguments:
+          embedding_dim (int): the channel dimension of the embeddings
+          num_heads (int): the number of heads in the attention layers
+          mlp_dim (int): the hidden dimension of the mlp block
+          activation (nn.Module): the activation of the mlp block
+          skip_first_layer_pe (bool): skip the PE on the first layer
+        """
+        super().__init__()
+        self.self_attn = Attention(embedding_dim, num_heads)
+        self.norm1 = nn.LayerNorm(embedding_dim)
+
+        self.cross_attn_token_to_image = Attention(
+            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
+        )
+        self.norm2 = nn.LayerNorm(embedding_dim)
+
+        self.mlp = MLPBlock(embedding_dim, mlp_dim, activation)
+        self.norm3 = nn.LayerNorm(embedding_dim)
+
+        self.norm4 = nn.LayerNorm(embedding_dim)
+        self.cross_attn_image_to_token = Attention(
+            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
+        )
+
+        self.skip_first_layer_pe = skip_first_layer_pe
+
+    def forward(
+        self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor
+    ) -> Tuple[Tensor, Tensor]:
+        # Self attention block
+        if self.skip_first_layer_pe:
+            queries = self.self_attn(q=queries, k=queries, v=queries)
+        else:
+            q = queries + query_pe
+            attn_out = self.self_attn(q=q, k=q, v=queries)
+            queries = queries + attn_out
+        queries = self.norm1(queries)
+
+        # Cross attention block, tokens attending to image embedding
+        q = queries + query_pe
+        k = keys + key_pe
+        attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)
+        queries = queries + attn_out
+        queries = self.norm2(queries)
+
+        # MLP block
+        mlp_out = self.mlp(queries)
+        queries = queries + mlp_out
+        queries = self.norm3(queries)
+
+        # Cross attention block, image embedding attending to tokens
+        q = queries + query_pe
+        k = keys + key_pe
+        attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)
+        keys = keys + attn_out
+        keys = self.norm4(keys)
+
+        return queries, keys
+
+
+class Attention(nn.Module):
+    """
+    An attention layer that allows for downscaling the size of the embedding
+    after projection to queries, keys, and values.
+    """
+
+    def __init__(
+        self,
+        embedding_dim: int,
+        num_heads: int,
+        downsample_rate: int = 1,
+    ) -> None:
+        super().__init__()
+        self.embedding_dim = embedding_dim
+        self.internal_dim = embedding_dim // downsample_rate
+        self.num_heads = num_heads
+        assert self.internal_dim % num_heads == 0, "num_heads must divide embedding_dim."
+
+        self.q_proj = nn.Linear(embedding_dim, self.internal_dim)
+        self.k_proj = nn.Linear(embedding_dim, self.internal_dim)
+        self.v_proj = nn.Linear(embedding_dim, self.internal_dim)
+        self.out_proj = nn.Linear(self.internal_dim, embedding_dim)
+
+    def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:
+        b, n, c = x.shape
+        x = x.reshape(b, n, num_heads, c // num_heads)
+        return x.transpose(1, 2)  # B x N_heads x N_tokens x C_per_head
+
+    def _recombine_heads(self, x: Tensor) -> Tensor:
+        b, n_heads, n_tokens, c_per_head = x.shape
+        x = x.transpose(1, 2)
+        return x.reshape(b, n_tokens, n_heads * c_per_head)  # B x N_tokens x C
+
+    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
+        # Input projections
+        q = self.q_proj(q)
+        k = self.k_proj(k)
+        v = self.v_proj(v)
+
+        # Separate into heads
+        q = self._separate_heads(q, self.num_heads)
+        k = self._separate_heads(k, self.num_heads)
+        v = self._separate_heads(v, self.num_heads)
+
+        # Attention
+        _, _, _, c_per_head = q.shape
+        attn = q @ k.permute(0, 1, 3, 2)  # B x N_heads x N_tokens x N_tokens
+        attn = attn / math.sqrt(c_per_head)
+        attn = torch.softmax(attn, dim=-1)
+
+        # Get output
+        out = attn @ v
+        out = self._recombine_heads(out)
+        out = self.out_proj(out)
+
+        return out

automation_pose_mask/auto_mask/segment_anything/predictor.py

@@ -0,0 +1,269 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import torch
+
+from .modeling import Sam
+
+from typing import Optional, Tuple
+
+from .utils.transforms import ResizeLongestSide
+
+
+class SamPredictor:
+    def __init__(
+        self,
+        sam_model: Sam,
+    ) -> None:
+        """
+        Uses SAM to calculate the image embedding for an image, and then
+        allow repeated, efficient mask prediction given prompts.
+
+        Arguments:
+          sam_model (Sam): The model to use for mask prediction.
+        """
+        super().__init__()
+        self.model = sam_model
+        self.transform = ResizeLongestSide(sam_model.image_encoder.img_size)
+        self.reset_image()
+
+    def set_image(
+        self,
+        image: np.ndarray,
+        image_format: str = "RGB",
+    ) -> None:
+        """
+        Calculates the image embeddings for the provided image, allowing
+        masks to be predicted with the 'predict' method.
+
+        Arguments:
+          image (np.ndarray): The image for calculating masks. Expects an
+            image in HWC uint8 format, with pixel values in [0, 255].
+          image_format (str): The color format of the image, in ['RGB', 'BGR'].
+        """
+        assert image_format in [
+            "RGB",
+            "BGR",
+        ], f"image_format must be in ['RGB', 'BGR'], is {image_format}."
+        if image_format != self.model.image_format:
+            image = image[..., ::-1]
+
+        # Transform the image to the form expected by the model
+        input_image = self.transform.apply_image(image)
+        input_image_torch = torch.as_tensor(input_image, device=self.device)
+        input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :]
+
+        self.set_torch_image(input_image_torch, image.shape[:2])
+
+    @torch.no_grad()
+    def set_torch_image(
+        self,
+        transformed_image: torch.Tensor,
+        original_image_size: Tuple[int, ...],
+    ) -> None:
+        """
+        Calculates the image embeddings for the provided image, allowing
+        masks to be predicted with the 'predict' method. Expects the input
+        image to be already transformed to the format expected by the model.
+
+        Arguments:
+          transformed_image (torch.Tensor): The input image, with shape
+            1x3xHxW, which has been transformed with ResizeLongestSide.
+          original_image_size (tuple(int, int)): The size of the image
+            before transformation, in (H, W) format.
+        """
+        assert (
+            len(transformed_image.shape) == 4
+            and transformed_image.shape[1] == 3
+            and max(*transformed_image.shape[2:]) == self.model.image_encoder.img_size
+        ), f"set_torch_image input must be BCHW with long side {self.model.image_encoder.img_size}."
+        self.reset_image()
+
+        self.original_size = original_image_size
+        self.input_size = tuple(transformed_image.shape[-2:])
+        input_image = self.model.preprocess(transformed_image)
+        self.features = self.model.image_encoder(input_image)
+        self.is_image_set = True
+
+    def predict(
+        self,
+        point_coords: Optional[np.ndarray] = None,
+        point_labels: Optional[np.ndarray] = None,
+        box: Optional[np.ndarray] = None,
+        mask_input: Optional[np.ndarray] = None,
+        multimask_output: bool = True,
+        return_logits: bool = False,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Predict masks for the given input prompts, using the currently set image.
+
+        Arguments:
+          point_coords (np.ndarray or None): A Nx2 array of point prompts to the
+            model. Each point is in (X,Y) in pixels.
+          point_labels (np.ndarray or None): A length N array of labels for the
+            point prompts. 1 indicates a foreground point and 0 indicates a
+            background point.
+          box (np.ndarray or None): A length 4 array given a box prompt to the
+            model, in XYXY format.
+          mask_input (np.ndarray): A low resolution mask input to the model, typically
+            coming from a previous prediction iteration. Has form 1xHxW, where
+            for SAM, H=W=256.
+          multimask_output (bool): If true, the model will return three masks.
+            For ambiguous input prompts (such as a single click), this will often
+            produce better masks than a single prediction. If only a single
+            mask is needed, the model's predicted quality score can be used
+            to select the best mask. For non-ambiguous prompts, such as multiple
+            input prompts, multimask_output=False can give better results.
+          return_logits (bool): If true, returns un-thresholded masks logits
+            instead of a binary mask.
+
+        Returns:
+          (np.ndarray): The output masks in CxHxW format, where C is the
+            number of masks, and (H, W) is the original image size.
+          (np.ndarray): An array of length C containing the model's
+            predictions for the quality of each mask.
+          (np.ndarray): An array of shape CxHxW, where C is the number
+            of masks and H=W=256. These low resolution logits can be passed to
+            a subsequent iteration as mask input.
+        """
+        if not self.is_image_set:
+            raise RuntimeError("An image must be set with .set_image(...) before mask prediction.")
+
+        # Transform input prompts
+        coords_torch, labels_torch, box_torch, mask_input_torch = None, None, None, None
+        if point_coords is not None:
+            assert (
+                point_labels is not None
+            ), "point_labels must be supplied if point_coords is supplied."
+            point_coords = self.transform.apply_coords(point_coords, self.original_size)
+            coords_torch = torch.as_tensor(point_coords, dtype=torch.float, device=self.device)
+            labels_torch = torch.as_tensor(point_labels, dtype=torch.int, device=self.device)
+            coords_torch, labels_torch = coords_torch[None, :, :], labels_torch[None, :]
+        if box is not None:
+            box = self.transform.apply_boxes(box, self.original_size)
+            box_torch = torch.as_tensor(box, dtype=torch.float, device=self.device)
+            box_torch = box_torch[None, :]
+        if mask_input is not None:
+            mask_input_torch = torch.as_tensor(mask_input, dtype=torch.float, device=self.device)
+            mask_input_torch = mask_input_torch[None, :, :, :]
+
+        masks, iou_predictions, low_res_masks = self.predict_torch(
+            coords_torch,
+            labels_torch,
+            box_torch,
+            mask_input_torch,
+            multimask_output,
+            return_logits=return_logits,
+        )
+
+        masks = masks[0].detach().cpu().numpy()
+        iou_predictions = iou_predictions[0].detach().cpu().numpy()
+        low_res_masks = low_res_masks[0].detach().cpu().numpy()
+        return masks, iou_predictions, low_res_masks
+
+    @torch.no_grad()
+    def predict_torch(
+        self,
+        point_coords: Optional[torch.Tensor],
+        point_labels: Optional[torch.Tensor],
+        boxes: Optional[torch.Tensor] = None,
+        mask_input: Optional[torch.Tensor] = None,
+        multimask_output: bool = True,
+        return_logits: bool = False,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Predict masks for the given input prompts, using the currently set image.
+        Input prompts are batched torch tensors and are expected to already be
+        transformed to the input frame using ResizeLongestSide.
+
+        Arguments:
+          point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the
+            model. Each point is in (X,Y) in pixels.
+          point_labels (torch.Tensor or None): A BxN array of labels for the
+            point prompts. 1 indicates a foreground point and 0 indicates a
+            background point.
+          box (np.ndarray or None): A Bx4 array given a box prompt to the
+            model, in XYXY format.
+          mask_input (np.ndarray): A low resolution mask input to the model, typically
+            coming from a previous prediction iteration. Has form Bx1xHxW, where
+            for SAM, H=W=256. Masks returned by a previous iteration of the
+            predict method do not need further transformation.
+          multimask_output (bool): If true, the model will return three masks.
+            For ambiguous input prompts (such as a single click), this will often
+            produce better masks than a single prediction. If only a single
+            mask is needed, the model's predicted quality score can be used
+            to select the best mask. For non-ambiguous prompts, such as multiple
+            input prompts, multimask_output=False can give better results.
+          return_logits (bool): If true, returns un-thresholded masks logits
+            instead of a binary mask.
+
+        Returns:
+          (torch.Tensor): The output masks in BxCxHxW format, where C is the
+            number of masks, and (H, W) is the original image size.
+          (torch.Tensor): An array of shape BxC containing the model's
+            predictions for the quality of each mask.
+          (torch.Tensor): An array of shape BxCxHxW, where C is the number
+            of masks and H=W=256. These low res logits can be passed to
+            a subsequent iteration as mask input.
+        """
+        if not self.is_image_set:
+            raise RuntimeError("An image must be set with .set_image(...) before mask prediction.")
+
+        if point_coords is not None:
+            points = (point_coords, point_labels)
+        else:
+            points = None
+
+        # Embed prompts
+        sparse_embeddings, dense_embeddings = self.model.prompt_encoder(
+            points=points,
+            boxes=boxes,
+            masks=mask_input,
+        )
+
+        # Predict masks
+        low_res_masks, iou_predictions = self.model.mask_decoder(
+            image_embeddings=self.features,
+            image_pe=self.model.prompt_encoder.get_dense_pe(),
+            sparse_prompt_embeddings=sparse_embeddings,
+            dense_prompt_embeddings=dense_embeddings,
+            multimask_output=multimask_output,
+        )
+
+        # Upscale the masks to the original image resolution
+        masks = self.model.postprocess_masks(low_res_masks, self.input_size, self.original_size)
+
+        if not return_logits:
+            masks = masks > self.model.mask_threshold
+
+        return masks, iou_predictions, low_res_masks
+
+    def get_image_embedding(self) -> torch.Tensor:
+        """
+        Returns the image embeddings for the currently set image, with
+        shape 1xCxHxW, where C is the embedding dimension and (H,W) are
+        the embedding spatial dimension of SAM (typically C=256, H=W=64).
+        """
+        if not self.is_image_set:
+            raise RuntimeError(
+                "An image must be set with .set_image(...) to generate an embedding."
+            )
+        assert self.features is not None, "Features must exist if an image has been set."
+        return self.features
+
+    @property
+    def device(self) -> torch.device:
+        return self.model.device
+
+    def reset_image(self) -> None:
+        """Resets the currently set image."""
+        self.is_image_set = False
+        self.features = None
+        self.orig_h = None
+        self.orig_w = None
+        self.input_h = None
+        self.input_w = None

automation_pose_mask/auto_mask/segment_anything/utils/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.

automation_pose_mask/auto_mask/segment_anything/utils/amg.py

@@ -0,0 +1,346 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import torch
+
+import math
+from copy import deepcopy
+from itertools import product
+from typing import Any, Dict, Generator, ItemsView, List, Tuple
+
+
+class MaskData:
+    """
+    A structure for storing masks and their related data in batched format.
+    Implements basic filtering and concatenation.
+    """
+
+    def __init__(self, **kwargs) -> None:
+        for v in kwargs.values():
+            assert isinstance(
+                v, (list, np.ndarray, torch.Tensor)
+            ), "MaskData only supports list, numpy arrays, and torch tensors."
+        self._stats = dict(**kwargs)
+
+    def __setitem__(self, key: str, item: Any) -> None:
+        assert isinstance(
+            item, (list, np.ndarray, torch.Tensor)
+        ), "MaskData only supports list, numpy arrays, and torch tensors."
+        self._stats[key] = item
+
+    def __delitem__(self, key: str) -> None:
+        del self._stats[key]
+
+    def __getitem__(self, key: str) -> Any:
+        return self._stats[key]
+
+    def items(self) -> ItemsView[str, Any]:
+        return self._stats.items()
+
+    def filter(self, keep: torch.Tensor) -> None:
+        for k, v in self._stats.items():
+            if v is None:
+                self._stats[k] = None
+            elif isinstance(v, torch.Tensor):
+                self._stats[k] = v[torch.as_tensor(keep, device=v.device)]
+            elif isinstance(v, np.ndarray):
+                self._stats[k] = v[keep.detach().cpu().numpy()]
+            elif isinstance(v, list) and keep.dtype == torch.bool:
+                self._stats[k] = [a for i, a in enumerate(v) if keep[i]]
+            elif isinstance(v, list):
+                self._stats[k] = [v[i] for i in keep]
+            else:
+                raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.")
+
+    def cat(self, new_stats: "MaskData") -> None:
+        for k, v in new_stats.items():
+            if k not in self._stats or self._stats[k] is None:
+                self._stats[k] = deepcopy(v)
+            elif isinstance(v, torch.Tensor):
+                self._stats[k] = torch.cat([self._stats[k], v], dim=0)
+            elif isinstance(v, np.ndarray):
+                self._stats[k] = np.concatenate([self._stats[k], v], axis=0)
+            elif isinstance(v, list):
+                self._stats[k] = self._stats[k] + deepcopy(v)
+            else:
+                raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.")
+
+    def to_numpy(self) -> None:
+        for k, v in self._stats.items():
+            if isinstance(v, torch.Tensor):
+                self._stats[k] = v.detach().cpu().numpy()
+
+
+def is_box_near_crop_edge(
+    boxes: torch.Tensor, crop_box: List[int], orig_box: List[int], atol: float = 20.0
+) -> torch.Tensor:
+    """Filter masks at the edge of a crop, but not at the edge of the original image."""
+    crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device)
+    orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device)
+    boxes = uncrop_boxes_xyxy(boxes, crop_box).float()
+    near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0)
+    near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0)
+    near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge)
+    return torch.any(near_crop_edge, dim=1)
+
+
+def box_xyxy_to_xywh(box_xyxy: torch.Tensor) -> torch.Tensor:
+    box_xywh = deepcopy(box_xyxy)
+    box_xywh[2] = box_xywh[2] - box_xywh[0]
+    box_xywh[3] = box_xywh[3] - box_xywh[1]
+    return box_xywh
+
+
+def batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]:
+    assert len(args) > 0 and all(
+        len(a) == len(args[0]) for a in args
+    ), "Batched iteration must have inputs of all the same size."
+    n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0)
+    for b in range(n_batches):
+        yield [arg[b * batch_size : (b + 1) * batch_size] for arg in args]
+
+
+def mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]:
+    """
+    Encodes masks to an uncompressed RLE, in the format expected by
+    pycoco tools.
+    """
+    # Put in fortran order and flatten h,w
+    b, h, w = tensor.shape
+    tensor = tensor.permute(0, 2, 1).flatten(1)
+
+    # Compute change indices
+    diff = tensor[:, 1:] ^ tensor[:, :-1]
+    change_indices = diff.nonzero()
+
+    # Encode run length
+    out = []
+    for i in range(b):
+        cur_idxs = change_indices[change_indices[:, 0] == i, 1]
+        cur_idxs = torch.cat(
+            [
+                torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device),
+                cur_idxs + 1,
+                torch.tensor([h * w], dtype=cur_idxs.dtype, device=cur_idxs.device),
+            ]
+        )
+        btw_idxs = cur_idxs[1:] - cur_idxs[:-1]
+        counts = [] if tensor[i, 0] == 0 else [0]
+        counts.extend(btw_idxs.detach().cpu().tolist())
+        out.append({"size": [h, w], "counts": counts})
+    return out
+
+
+def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray:
+    """Compute a binary mask from an uncompressed RLE."""
+    h, w = rle["size"]
+    mask = np.empty(h * w, dtype=bool)
+    idx = 0
+    parity = False
+    for count in rle["counts"]:
+        mask[idx : idx + count] = parity
+        idx += count
+        parity ^= True
+    mask = mask.reshape(w, h)
+    return mask.transpose()  # Put in C order
+
+
+def area_from_rle(rle: Dict[str, Any]) -> int:
+    return sum(rle["counts"][1::2])
+
+
+def calculate_stability_score(
+    masks: torch.Tensor, mask_threshold: float, threshold_offset: float
+) -> torch.Tensor:
+    """
+    Computes the stability score for a batch of masks. The stability
+    score is the IoU between the binary masks obtained by thresholding
+    the predicted mask logits at high and low values.
+    """
+    # One mask is always contained inside the other.
+    # Save memory by preventing unnecesary cast to torch.int64
+    intersections = (
+        (masks > (mask_threshold + threshold_offset))
+        .sum(-1, dtype=torch.int16)
+        .sum(-1, dtype=torch.int32)
+    )
+    unions = (
+        (masks > (mask_threshold - threshold_offset))
+        .sum(-1, dtype=torch.int16)
+        .sum(-1, dtype=torch.int32)
+    )
+    return intersections / unions
+
+
+def build_point_grid(n_per_side: int) -> np.ndarray:
+    """Generates a 2D grid of points evenly spaced in [0,1]x[0,1]."""
+    offset = 1 / (2 * n_per_side)
+    points_one_side = np.linspace(offset, 1 - offset, n_per_side)
+    points_x = np.tile(points_one_side[None, :], (n_per_side, 1))
+    points_y = np.tile(points_one_side[:, None], (1, n_per_side))
+    points = np.stack([points_x, points_y], axis=-1).reshape(-1, 2)
+    return points
+
+
+def build_all_layer_point_grids(
+    n_per_side: int, n_layers: int, scale_per_layer: int
+) -> List[np.ndarray]:
+    """Generates point grids for all crop layers."""
+    points_by_layer = []
+    for i in range(n_layers + 1):
+        n_points = int(n_per_side / (scale_per_layer**i))
+        points_by_layer.append(build_point_grid(n_points))
+    return points_by_layer
+
+
+def generate_crop_boxes(
+    im_size: Tuple[int, ...], n_layers: int, overlap_ratio: float
+) -> Tuple[List[List[int]], List[int]]:
+    """
+    Generates a list of crop boxes of different sizes. Each layer
+    has (2**i)**2 boxes for the ith layer.
+    """
+    crop_boxes, layer_idxs = [], []
+    im_h, im_w = im_size
+    short_side = min(im_h, im_w)
+
+    # Original image
+    crop_boxes.append([0, 0, im_w, im_h])
+    layer_idxs.append(0)
+
+    def crop_len(orig_len, n_crops, overlap):
+        return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops))
+
+    for i_layer in range(n_layers):
+        n_crops_per_side = 2 ** (i_layer + 1)
+        overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side))
+
+        crop_w = crop_len(im_w, n_crops_per_side, overlap)
+        crop_h = crop_len(im_h, n_crops_per_side, overlap)
+
+        crop_box_x0 = [int((crop_w - overlap) * i) for i in range(n_crops_per_side)]
+        crop_box_y0 = [int((crop_h - overlap) * i) for i in range(n_crops_per_side)]
+
+        # Crops in XYWH format
+        for x0, y0 in product(crop_box_x0, crop_box_y0):
+            box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)]
+            crop_boxes.append(box)
+            layer_idxs.append(i_layer + 1)
+
+    return crop_boxes, layer_idxs
+
+
+def uncrop_boxes_xyxy(boxes: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
+    x0, y0, _, _ = crop_box
+    offset = torch.tensor([[x0, y0, x0, y0]], device=boxes.device)
+    # Check if boxes has a channel dimension
+    if len(boxes.shape) == 3:
+        offset = offset.unsqueeze(1)
+    return boxes + offset
+
+
+def uncrop_points(points: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
+    x0, y0, _, _ = crop_box
+    offset = torch.tensor([[x0, y0]], device=points.device)
+    # Check if points has a channel dimension
+    if len(points.shape) == 3:
+        offset = offset.unsqueeze(1)
+    return points + offset
+
+
+def uncrop_masks(
+    masks: torch.Tensor, crop_box: List[int], orig_h: int, orig_w: int
+) -> torch.Tensor:
+    x0, y0, x1, y1 = crop_box
+    if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h:
+        return masks
+    # Coordinate transform masks
+    pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0)
+    pad = (x0, pad_x - x0, y0, pad_y - y0)
+    return torch.nn.functional.pad(masks, pad, value=0)
+
+
+def remove_small_regions(
+    mask: np.ndarray, area_thresh: float, mode: str
+) -> Tuple[np.ndarray, bool]:
+    """
+    Removes small disconnected regions and holes in a mask. Returns the
+    mask and an indicator of if the mask has been modified.
+    """
+    import cv2  # type: ignore
+
+    assert mode in ["holes", "islands"]
+    correct_holes = mode == "holes"
+    working_mask = (correct_holes ^ mask).astype(np.uint8)
+    n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)
+    sizes = stats[:, -1][1:]  # Row 0 is background label
+    small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]
+    if len(small_regions) == 0:
+        return mask, False
+    fill_labels = [0] + small_regions
+    if not correct_holes:
+        fill_labels = [i for i in range(n_labels) if i not in fill_labels]
+        # If every region is below threshold, keep largest
+        if len(fill_labels) == 0:
+            fill_labels = [int(np.argmax(sizes)) + 1]
+    mask = np.isin(regions, fill_labels)
+    return mask, True
+
+
+def coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]:
+    from pycocotools import mask as mask_utils  # type: ignore
+
+    h, w = uncompressed_rle["size"]
+    rle = mask_utils.frPyObjects(uncompressed_rle, h, w)
+    rle["counts"] = rle["counts"].decode("utf-8")  # Necessary to serialize with json
+    return rle
+
+
+def batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor:
+    """
+    Calculates boxes in XYXY format around masks. Return [0,0,0,0] for
+    an empty mask. For input shape C1xC2x...xHxW, the output shape is C1xC2x...x4.
+    """
+    # torch.max below raises an error on empty inputs, just skip in this case
+    if torch.numel(masks) == 0:
+        return torch.zeros(*masks.shape[:-2], 4, device=masks.device)
+
+    # Normalize shape to CxHxW
+    shape = masks.shape
+    h, w = shape[-2:]
+    if len(shape) > 2:
+        masks = masks.flatten(0, -3)
+    else:
+        masks = masks.unsqueeze(0)
+
+    # Get top and bottom edges
+    in_height, _ = torch.max(masks, dim=-1)
+    in_height_coords = in_height * torch.arange(h, device=in_height.device)[None, :]
+    bottom_edges, _ = torch.max(in_height_coords, dim=-1)
+    in_height_coords = in_height_coords + h * (~in_height)
+    top_edges, _ = torch.min(in_height_coords, dim=-1)
+
+    # Get left and right edges
+    in_width, _ = torch.max(masks, dim=-2)
+    in_width_coords = in_width * torch.arange(w, device=in_width.device)[None, :]
+    right_edges, _ = torch.max(in_width_coords, dim=-1)
+    in_width_coords = in_width_coords + w * (~in_width)
+    left_edges, _ = torch.min(in_width_coords, dim=-1)
+
+    # If the mask is empty the right edge will be to the left of the left edge.
+    # Replace these boxes with [0, 0, 0, 0]
+    empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges)
+    out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1)
+    out = out * (~empty_filter).unsqueeze(-1)
+
+    # Return to original shape
+    if len(shape) > 2:
+        out = out.reshape(*shape[:-2], 4)
+    else:
+        out = out[0]
+
+    return out

automation_pose_mask/auto_mask/segment_anything/utils/onnx.py

@@ -0,0 +1,144 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+from typing import Tuple
+
+from ..modeling import Sam
+from .amg import calculate_stability_score
+
+
+class SamOnnxModel(nn.Module):
+    """
+    This model should not be called directly, but is used in ONNX export.
+    It combines the prompt encoder, mask decoder, and mask postprocessing of Sam,
+    with some functions modified to enable model tracing. Also supports extra
+    options controlling what information. See the ONNX export script for details.
+    """
+
+    def __init__(
+        self,
+        model: Sam,
+        return_single_mask: bool,
+        use_stability_score: bool = False,
+        return_extra_metrics: bool = False,
+    ) -> None:
+        super().__init__()
+        self.mask_decoder = model.mask_decoder
+        self.model = model
+        self.img_size = model.image_encoder.img_size
+        self.return_single_mask = return_single_mask
+        self.use_stability_score = use_stability_score
+        self.stability_score_offset = 1.0
+        self.return_extra_metrics = return_extra_metrics
+
+    @staticmethod
+    def resize_longest_image_size(
+        input_image_size: torch.Tensor, longest_side: int
+    ) -> torch.Tensor:
+        input_image_size = input_image_size.to(torch.float32)
+        scale = longest_side / torch.max(input_image_size)
+        transformed_size = scale * input_image_size
+        transformed_size = torch.floor(transformed_size + 0.5).to(torch.int64)
+        return transformed_size
+
+    def _embed_points(self, point_coords: torch.Tensor, point_labels: torch.Tensor) -> torch.Tensor:
+        point_coords = point_coords + 0.5
+        point_coords = point_coords / self.img_size
+        point_embedding = self.model.prompt_encoder.pe_layer._pe_encoding(point_coords)
+        point_labels = point_labels.unsqueeze(-1).expand_as(point_embedding)
+
+        point_embedding = point_embedding * (point_labels != -1)
+        point_embedding = point_embedding + self.model.prompt_encoder.not_a_point_embed.weight * (
+            point_labels == -1
+        )
+
+        for i in range(self.model.prompt_encoder.num_point_embeddings):
+            point_embedding = point_embedding + self.model.prompt_encoder.point_embeddings[
+                i
+            ].weight * (point_labels == i)
+
+        return point_embedding
+
+    def _embed_masks(self, input_mask: torch.Tensor, has_mask_input: torch.Tensor) -> torch.Tensor:
+        mask_embedding = has_mask_input * self.model.prompt_encoder.mask_downscaling(input_mask)
+        mask_embedding = mask_embedding + (
+            1 - has_mask_input
+        ) * self.model.prompt_encoder.no_mask_embed.weight.reshape(1, -1, 1, 1)
+        return mask_embedding
+
+    def mask_postprocessing(self, masks: torch.Tensor, orig_im_size: torch.Tensor) -> torch.Tensor:
+        masks = F.interpolate(
+            masks,
+            size=(self.img_size, self.img_size),
+            mode="bilinear",
+            align_corners=False,
+        )
+
+        prepadded_size = self.resize_longest_image_size(orig_im_size, self.img_size).to(torch.int64)
+        masks = masks[..., : prepadded_size[0], : prepadded_size[1]]
+
+        orig_im_size = orig_im_size.to(torch.int64)
+        h, w = orig_im_size[0], orig_im_size[1]
+        masks = F.interpolate(masks, size=(h, w), mode="bilinear", align_corners=False)
+        return masks
+
+    def select_masks(
+        self, masks: torch.Tensor, iou_preds: torch.Tensor, num_points: int
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Determine if we should return the multiclick mask or not from the number of points.
+        # The reweighting is used to avoid control flow.
+        score_reweight = torch.tensor(
+            [[1000] + [0] * (self.model.mask_decoder.num_mask_tokens - 1)]
+        ).to(iou_preds.device)
+        score = iou_preds + (num_points - 2.5) * score_reweight
+        best_idx = torch.argmax(score, dim=1)
+        masks = masks[torch.arange(masks.shape[0]), best_idx, :, :].unsqueeze(1)
+        iou_preds = iou_preds[torch.arange(masks.shape[0]), best_idx].unsqueeze(1)
+
+        return masks, iou_preds
+
+    @torch.no_grad()
+    def forward(
+        self,
+        image_embeddings: torch.Tensor,
+        point_coords: torch.Tensor,
+        point_labels: torch.Tensor,
+        mask_input: torch.Tensor,
+        has_mask_input: torch.Tensor,
+        orig_im_size: torch.Tensor,
+    ):
+        sparse_embedding = self._embed_points(point_coords, point_labels)
+        dense_embedding = self._embed_masks(mask_input, has_mask_input)
+
+        masks, scores = self.model.mask_decoder.predict_masks(
+            image_embeddings=image_embeddings,
+            image_pe=self.model.prompt_encoder.get_dense_pe(),
+            sparse_prompt_embeddings=sparse_embedding,
+            dense_prompt_embeddings=dense_embedding,
+        )
+
+        if self.use_stability_score:
+            scores = calculate_stability_score(
+                masks, self.model.mask_threshold, self.stability_score_offset
+            )
+
+        if self.return_single_mask:
+            masks, scores = self.select_masks(masks, scores, point_coords.shape[1])
+
+        upscaled_masks = self.mask_postprocessing(masks, orig_im_size)
+
+        if self.return_extra_metrics:
+            stability_scores = calculate_stability_score(
+                upscaled_masks, self.model.mask_threshold, self.stability_score_offset
+            )
+            areas = (upscaled_masks > self.model.mask_threshold).sum(-1).sum(-1)
+            return upscaled_masks, scores, stability_scores, areas, masks
+
+        return upscaled_masks, scores, masks

automation_pose_mask/auto_mask/segment_anything/utils/transforms.py

@@ -0,0 +1,102 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import torch
+from torch.nn import functional as F
+from torchvision.transforms.functional import resize, to_pil_image  # type: ignore
+
+from copy import deepcopy
+from typing import Tuple
+
+
+class ResizeLongestSide:
+    """
+    Resizes images to longest side 'target_length', as well as provides
+    methods for resizing coordinates and boxes. Provides methods for
+    transforming both numpy array and batched torch tensors.
+    """
+
+    def __init__(self, target_length: int) -> None:
+        self.target_length = target_length
+
+    def apply_image(self, image: np.ndarray) -> np.ndarray:
+        """
+        Expects a numpy array with shape HxWxC in uint8 format.
+        """
+        target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)
+        return np.array(resize(to_pil_image(image), target_size))
+
+    def apply_coords(self, coords: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
+        """
+        Expects a numpy array of length 2 in the final dimension. Requires the
+        original image size in (H, W) format.
+        """
+        old_h, old_w = original_size
+        new_h, new_w = self.get_preprocess_shape(
+            original_size[0], original_size[1], self.target_length
+        )
+        coords = deepcopy(coords).astype(float)
+        coords[..., 0] = coords[..., 0] * (new_w / old_w)
+        coords[..., 1] = coords[..., 1] * (new_h / old_h)
+        return coords
+
+    def apply_boxes(self, boxes: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
+        """
+        Expects a numpy array shape Bx4. Requires the original image size
+        in (H, W) format.
+        """
+        boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size)
+        return boxes.reshape(-1, 4)
+
+    def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor:
+        """
+        Expects batched images with shape BxCxHxW and float format. This
+        transformation may not exactly match apply_image. apply_image is
+        the transformation expected by the model.
+        """
+        # Expects an image in BCHW format. May not exactly match apply_image.
+        target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)
+        return F.interpolate(
+            image, target_size, mode="bilinear", align_corners=False, antialias=True
+        )
+
+    def apply_coords_torch(
+        self, coords: torch.Tensor, original_size: Tuple[int, ...]
+    ) -> torch.Tensor:
+        """
+        Expects a torch tensor with length 2 in the last dimension. Requires the
+        original image size in (H, W) format.
+        """
+        old_h, old_w = original_size
+        new_h, new_w = self.get_preprocess_shape(
+            original_size[0], original_size[1], self.target_length
+        )
+        coords = deepcopy(coords).to(torch.float)
+        coords[..., 0] = coords[..., 0] * (new_w / old_w)
+        coords[..., 1] = coords[..., 1] * (new_h / old_h)
+        return coords
+
+    def apply_boxes_torch(
+        self, boxes: torch.Tensor, original_size: Tuple[int, ...]
+    ) -> torch.Tensor:
+        """
+        Expects a torch tensor with shape Bx4. Requires the original image
+        size in (H, W) format.
+        """
+        boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size)
+        return boxes.reshape(-1, 4)
+
+    @staticmethod
+    def get_preprocess_shape(oldh: int, oldw: int, long_side_length: int) -> Tuple[int, int]:
+        """
+        Compute the output size given input size and target long side length.
+        """
+        scale = long_side_length * 1.0 / max(oldh, oldw)
+        newh, neww = oldh * scale, oldw * scale
+        neww = int(neww + 0.5)
+        newh = int(newh + 0.5)
+        return (newh, neww)

automation_pose_mask/auto_mask/utils/__init__.py

@@ -0,0 +1 @@
+from .utils import *

automation_pose_mask/auto_mask/utils/crop_for_replacing.py

@@ -0,0 +1,101 @@
+import cv2
+import numpy as np
+from typing import Tuple
+
+def resize_and_pad(image: np.ndarray, mask: np.ndarray, target_size: int = 512) -> Tuple[np.ndarray, np.ndarray]:
+    """
+    Resizes an image and its corresponding mask to have the longer side equal to `target_size` and pads them to make them
+    both have the same size. The resulting image and mask have dimensions (target_size, target_size).
+
+    Args:
+        image: A numpy array representing the image to resize and pad.
+        mask: A numpy array representing the mask to resize and pad.
+        target_size: An integer specifying the desired size of the longer side after resizing.
+
+    Returns:
+        A tuple containing two numpy arrays - the resized and padded image and the resized and padded mask.
+    """
+    height, width, _ = image.shape
+    max_dim = max(height, width)
+    scale = target_size / max_dim
+    new_height = int(height * scale)
+    new_width = int(width * scale)
+    image_resized = cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_LINEAR)
+    mask_resized = cv2.resize(mask, (new_width, new_height), interpolation=cv2.INTER_LINEAR)
+    pad_height = target_size - new_height
+    pad_width = target_size - new_width
+    top_pad = pad_height // 2
+    bottom_pad = pad_height - top_pad
+    left_pad = pad_width // 2
+    right_pad = pad_width - left_pad
+    image_padded = np.pad(image_resized, ((top_pad, bottom_pad), (left_pad, right_pad), (0, 0)), mode='constant')
+    mask_padded = np.pad(mask_resized, ((top_pad, bottom_pad), (left_pad, right_pad)), mode='constant')
+    return image_padded, mask_padded, (top_pad, bottom_pad, left_pad, right_pad)
+
+def recover_size(image_padded: np.ndarray, mask_padded: np.ndarray, orig_size: Tuple[int, int], 
+                 padding_factors: Tuple[int, int, int, int]) -> Tuple[np.ndarray, np.ndarray]:
+    """
+    Resizes a padded and resized image and mask to the original size.
+
+    Args:
+        image_padded: A numpy array representing the padded and resized image.
+        mask_padded: A numpy array representing the padded and resized mask.
+        orig_size: A tuple containing two integers - the original height and width of the image before resizing and padding.
+
+    Returns:
+        A tuple containing two numpy arrays - the recovered image and the recovered mask with dimensions `orig_size`.
+    """
+    h,w,c = image_padded.shape
+    top_pad, bottom_pad, left_pad, right_pad = padding_factors
+    image = image_padded[top_pad:h-bottom_pad, left_pad:w-right_pad, :]
+    mask = mask_padded[top_pad:h-bottom_pad, left_pad:w-right_pad]
+    image_resized = cv2.resize(image, orig_size[::-1], interpolation=cv2.INTER_LINEAR)
+    mask_resized = cv2.resize(mask, orig_size[::-1], interpolation=cv2.INTER_LINEAR)
+    return image_resized, mask_resized
+
+
+
+
+if __name__ == '__main__':
+
+    # image = cv2.imread('example/boat.jpg')
+    # mask = cv2.imread('example/boat_mask_2.png', cv2.IMREAD_GRAYSCALE)
+    # image = cv2.imread('example/groceries.jpg')
+    # mask = cv2.imread('example/groceries_mask_2.png', cv2.IMREAD_GRAYSCALE)
+    # image = cv2.imread('example/bridge.jpg')
+    # mask = cv2.imread('example/bridge_mask_2.png', cv2.IMREAD_GRAYSCALE)
+    # image = cv2.imread('example/person_umbrella.jpg')
+    # mask = cv2.imread('example/person_umbrella_mask_2.png', cv2.IMREAD_GRAYSCALE)
+    # image = cv2.imread('example/hippopotamus.jpg')
+    # mask = cv2.imread('example/hippopotamus_mask_1.png', cv2.IMREAD_GRAYSCALE)
+    image = cv2.imread('/data1/yutao/projects/IAM/Inpaint-Anything/example/fill-anything/sample5.jpeg')
+    mask = cv2.imread('/data1/yutao/projects/IAM/Inpaint-Anything/example/fill-anything/sample5/mask.png', cv2.IMREAD_GRAYSCALE)
+    print(image.shape)
+    print(mask.shape)
+    cv2.imwrite('original_image.jpg', image)
+    cv2.imwrite('original_mask.jpg', mask)
+    image_padded, mask_padded, padding_factors = resize_and_pad(image, mask)
+    cv2.imwrite('padded_image.png', image_padded)
+    cv2.imwrite('padded_mask.png', mask_padded)
+    print(image_padded.shape, mask_padded.shape, padding_factors)
+
+    # ^ ------------------------------------------------------------------------------------
+    # ^ Please conduct inpainting or filling here on the cropped image with the cropped mask
+    # ^ ------------------------------------------------------------------------------------
+
+    # resize and pad the image and mask
+
+    # perform some operation on the 512x512 image and mask
+    # ...
+
+    # recover the image and mask to the original size
+    height, width, _ = image.shape
+    image_resized, mask_resized = recover_size(image_padded, mask_padded, (height, width), padding_factors)
+
+    # save the resized and recovered image and mask
+    cv2.imwrite('resized_and_padded_image.png', image_padded)
+    cv2.imwrite('resized_and_padded_mask.png', mask_padded)
+    cv2.imwrite('recovered_image.png', image_resized)
+    cv2.imwrite('recovered_mask.png', mask_resized)
+
+        

automation_pose_mask/auto_mask/utils/frames2video copy.py

@@ -0,0 +1,35 @@
+import cv2
+import imageio 
+import os 
+import shutil
+from tqdm import tqdm
+from glob import glob 
+from os import path as osp
+
+def frames2video(frames_list, video_path, fps=30, remove_tmp=False):
+    # frames_list: frames dir or list of images.
+    if isinstance(frames_list, str):
+        frames_list = glob(f'{frames_list}/*.jpg')
+    video_dir = os.path.dirname(video_path)
+    if not os.path.exists(video_dir):
+        os.makedirs(video_dir)
+    # writer = imageio.get_writer(video_path, fps=fps)
+    writer = imageio.get_writer(video_dir, fps=fps, plugin='ffmpeg')
+    for frame in tqdm(frames_list, 'Export video'):
+        if isinstance(frame, str):
+            frame = imageio.imread(frame)
+        else:
+            # convert cv2 (rgb) to PIL
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            frame = imageio.core.util.Array(frame)
+        writer.append_data(frame)
+    writer.close()
+    print(f'find video at {video_path}.')
+    if remove_tmp and isinstance(frames_list, str):
+        shutil.rmtree(frames_list)
+
+if __name__ == '__main__':
+    video_path = './demo/soccerball/original_video.mp4'
+    frame_path = '/data0/datasets/davis/JPEGImages/480p/soccerball'
+    fps = 30
+    frames2video(frame_path, video_path, fps, True)

automation_pose_mask/auto_mask/utils/frames2video.py

@@ -0,0 +1,31 @@
+import os
+import imageio
+from imageio_ffmpeg import write_frames
+
+def write_frames(frame_path, fps, size, codec='libx264', quality=8):
+    for filename in sorted(os.listdir(frame_path)):
+        if not filename.endswith('.jpg'):
+            continue
+        yield imageio.imread(os.path.join(frame_path, filename))
+
+def frames2video(frame_path, video_path, fps, show_progress=False, codec='libx264', quality=8):
+    sample_frame = imageio.imread(os.path.join(frame_path, os.listdir(frame_path)[0]))
+    height, width, _ = sample_frame.shape
+    size = (width, height)
+    video_dir = os.path.dirname(video_path)
+    if not os.path.exists(video_dir):
+        os.makedirs(video_dir)
+    writer = imageio.get_writer(video_path, fps=fps, codec=codec, quality=quality)
+
+    for frame in write_frames(frame_path, fps=fps, size=size, codec=codec, quality=quality):
+        writer.append_data(frame)
+        if show_progress:
+            print(f'Frame {writer.get_length()} written')
+
+    writer.close()
+
+if __name__ == '__main__':
+    frame_path = "/data0/datasets/davis/JPEGImages/480p/dog-gooses/"
+    video_path = "./demo/dog-gooses/original_video.mp4"
+    fps = 30
+    frames2video(frame_path, video_path, fps, False)

automation_pose_mask/auto_mask/utils/get_point_coor.py

@@ -0,0 +1,12 @@
+import cv2
+
+def click_event(event, x, y, flags, param):
+    if event == cv2.EVENT_LBUTTONDOWN:
+        print("Point coordinates ({}, {})".format(x, y))
+img = cv2.imread("./example/remove-anything/dog.jpg")
+
+cv2.imshow("Image", img)
+cv2.setMouseCallback("Image", click_event)
+cv2.waitKey(0)
+
+cv2.destroyAllWindows()

automation_pose_mask/auto_mask/utils/mask_processing.py

@@ -0,0 +1,160 @@
+import cv2
+from matplotlib import pyplot as plt
+import PIL.Image as Image
+import numpy as np
+
+
+def crop_for_filling_pre(image: np.array, mask: np.array, crop_size: int = 512):
+    # Calculate the aspect ratio of the image
+    height, width = image.shape[:2]
+    aspect_ratio = float(width) / float(height)
+
+    # If the shorter side is less than 512, resize the image proportionally
+    if min(height, width) < crop_size:
+        if height < width:
+            new_height = crop_size
+            new_width = int(new_height * aspect_ratio)
+        else:
+            new_width = crop_size
+            new_height = int(new_width / aspect_ratio)
+
+        image = cv2.resize(image, (new_width, new_height))
+        mask = cv2.resize(mask, (new_width, new_height))
+
+    # Find the bounding box of the mask
+    x, y, w, h = cv2.boundingRect(mask)
+
+    # Update the height and width of the resized image
+    height, width = image.shape[:2]
+
+    # # If the 512x512 square cannot cover the entire mask, resize the image accordingly
+    if w > crop_size or h > crop_size:
+        # padding to square at first
+        if height < width:
+            padding = width - height
+            image = np.pad(image, ((padding // 2, padding - padding // 2), (0, 0), (0, 0)), 'constant')
+            mask = np.pad(mask, ((padding // 2, padding - padding // 2), (0, 0)), 'constant')
+        else:
+            padding = height - width
+            image = np.pad(image, ((0, 0), (padding // 2, padding - padding // 2), (0, 0)), 'constant')
+            mask = np.pad(mask, ((0, 0), (padding // 2, padding - padding // 2)), 'constant')
+
+        resize_factor = crop_size / max(w, h)
+        image = cv2.resize(image, (0, 0), fx=resize_factor, fy=resize_factor)
+        mask = cv2.resize(mask, (0, 0), fx=resize_factor, fy=resize_factor)
+        x, y, w, h = cv2.boundingRect(mask)
+
+    # Calculate the crop coordinates
+    crop_x = min(max(x + w // 2 - crop_size // 2, 0), width - crop_size)
+    crop_y = min(max(y + h // 2 - crop_size // 2, 0), height - crop_size)
+
+    # Crop the image
+    cropped_image = image[crop_y:crop_y + crop_size, crop_x:crop_x + crop_size]
+    cropped_mask = mask[crop_y:crop_y + crop_size, crop_x:crop_x + crop_size]
+
+    return cropped_image, cropped_mask
+    
+    
+def crop_for_filling_post(
+        image: np.array,
+        mask: np.array,
+        filled_image: np.array, 
+        crop_size: int = 512,
+        ):
+    image_copy = image.copy()
+    mask_copy = mask.copy()
+    # Calculate the aspect ratio of the image
+    height, width = image.shape[:2]
+    height_ori, width_ori = height, width
+    aspect_ratio = float(width) / float(height)
+
+    # If the shorter side is less than 512, resize the image proportionally
+    if min(height, width) < crop_size:
+        if height < width:
+            new_height = crop_size
+            new_width = int(new_height * aspect_ratio)
+        else:
+            new_width = crop_size
+            new_height = int(new_width / aspect_ratio)
+
+        image = cv2.resize(image, (new_width, new_height))
+        mask = cv2.resize(mask, (new_width, new_height))
+
+    # Find the bounding box of the mask
+    x, y, w, h = cv2.boundingRect(mask)
+
+    # Update the height and width of the resized image
+    height, width = image.shape[:2]
+
+    # # If the 512x512 square cannot cover the entire mask, resize the image accordingly
+    if w > crop_size or h > crop_size:
+        flag_padding = True
+        # padding to square at first
+        if height < width:
+            padding = width - height
+            image = np.pad(image, ((padding // 2, padding - padding // 2), (0, 0), (0, 0)), 'constant')
+            mask = np.pad(mask, ((padding // 2, padding - padding // 2), (0, 0)), 'constant')
+            padding_side = 'h'
+        else:
+            padding = height - width
+            image = np.pad(image, ((0, 0), (padding // 2, padding - padding // 2), (0, 0)), 'constant')
+            mask = np.pad(mask, ((0, 0), (padding // 2, padding - padding // 2)), 'constant')
+            padding_side = 'w'
+
+        resize_factor = crop_size / max(w, h)
+        image = cv2.resize(image, (0, 0), fx=resize_factor, fy=resize_factor)
+        mask = cv2.resize(mask, (0, 0), fx=resize_factor, fy=resize_factor)
+        x, y, w, h = cv2.boundingRect(mask)
+    else:
+        flag_padding = False
+
+    # Calculate the crop coordinates
+    crop_x = min(max(x + w // 2 - crop_size // 2, 0), width - crop_size)
+    crop_y = min(max(y + h // 2 - crop_size // 2, 0), height - crop_size)
+
+    # Fill the image
+    image[crop_y:crop_y + crop_size, crop_x:crop_x + crop_size] = filled_image
+    if flag_padding:
+        image = cv2.resize(image, (0, 0), fx=1/resize_factor, fy=1/resize_factor)
+        if padding_side == 'h':
+            image = image[padding // 2:padding // 2 + height_ori, :]
+        else:
+            image = image[:, padding // 2:padding // 2 + width_ori]
+
+    image = cv2.resize(image, (width_ori, height_ori))
+
+    image_copy[mask_copy==255] = image[mask_copy==255]
+    return image_copy
+
+
+if __name__ == '__main__':
+
+    # image = cv2.imread('example/boat.jpg')
+    # mask = cv2.imread('example/boat_mask_2.png', cv2.IMREAD_GRAYSCALE)
+    image = cv2.imread('./example/groceries.jpg')
+    mask = cv2.imread('example/groceries_mask_2.png', cv2.IMREAD_GRAYSCALE)
+    # image = cv2.imread('example/bridge.jpg')
+    # mask = cv2.imread('example/bridge_mask_2.png', cv2.IMREAD_GRAYSCALE)
+    # image = cv2.imread('example/person_umbrella.jpg')
+    # mask = cv2.imread('example/person_umbrella_mask_2.png', cv2.IMREAD_GRAYSCALE)
+    # image = cv2.imread('example/hippopotamus.jpg')
+    # mask = cv2.imread('example/hippopotamus_mask_1.png', cv2.IMREAD_GRAYSCALE)
+
+    cropped_image, cropped_mask = crop_for_filling_pre(image, mask)
+    # ^ ------------------------------------------------------------------------------------
+    # ^ Please conduct inpainting or filling here on the cropped image with the cropped mask
+    # ^ ------------------------------------------------------------------------------------
+
+    # e.g.
+    # cropped_image[cropped_mask==255] = 0
+    cv2.imwrite('cropped_image.jpg', cropped_image)
+    cv2.imwrite('cropped_mask.jpg', cropped_mask)
+    print(cropped_image.shape)
+    print(cropped_mask.shape)
+
+    image = crop_for_filling_post(image, mask, cropped_image)
+    cv2.imwrite('filled_image.jpg', image)
+    print(image.shape)
+
+
+    

automation_pose_mask/auto_mask/utils/paste_object.py

@@ -0,0 +1,50 @@
+import cv2
+import numpy as np
+
+def paste_object(source, source_mask, target, target_coords, resize_scale=1):
+    assert target_coords[0] < target.shape[1] and target_coords[1] < target.shape[0]
+    # Find the bounding box of the source_mask
+    x, y, w, h = cv2.boundingRect(source_mask)
+    assert h < source.shape[0] and w < source.shape[1]
+    obj = source[y:y+h, x:x+w]
+    obj_msk = source_mask[y:y+h, x:x+w]
+    if resize_scale != 1:
+        obj = cv2.resize(obj, (0,0), fx=resize_scale, fy=resize_scale)
+        obj_msk = cv2.resize(obj_msk, (0,0), fx=resize_scale, fy=resize_scale)
+        _, _, w, h = cv2.boundingRect(obj_msk)
+
+    xt = max(0, target_coords[0]-w//2)
+    yt = max(0, target_coords[1]-h//2)
+    if target_coords[0]-w//2 < 0:
+        obj = obj[:, w//2-target_coords[0]:]
+        obj_msk = obj_msk[:, w//2-target_coords[0]:]
+    if target_coords[0]+w//2 > target.shape[1]:
+        obj = obj[:, :target.shape[1]-target_coords[0]+w//2]
+        obj_msk = obj_msk[:, :target.shape[1]-target_coords[0]+w//2]
+    if target_coords[1]-h//2 < 0:
+        obj = obj[h//2-target_coords[1]:, :]
+        obj_msk = obj_msk[h//2-target_coords[1]:, :]
+    if target_coords[1]+h//2 > target.shape[0]:
+        obj = obj[:target.shape[0]-target_coords[1]+h//2, :]
+        obj_msk = obj_msk[:target.shape[0]-target_coords[1]+h//2, :]
+    _, _, w, h = cv2.boundingRect(obj_msk)
+
+    target[yt:yt+h, xt:xt+w][obj_msk==255] = obj[obj_msk==255]
+    target_mask = np.zeros_like(target)
+    target_mask = cv2.cvtColor(target_mask, cv2.COLOR_BGR2GRAY)
+    target_mask[yt:yt+h, xt:xt+w][obj_msk==255] = 255
+    
+    return target, target_mask
+
+if __name__ == '__main__':
+    source = cv2.imread('example/boat.jpg')
+    source_mask = cv2.imread('example/boat_mask_1.png', 0)
+    target = cv2.imread('example/hippopotamus.jpg')
+    print(source.shape, source_mask.shape, target.shape)
+    
+    target_coords = (700, 400)  # (x, y)
+    resize_scale = 1
+    target, target_mask = paste_object(source, source_mask, target, target_coords, resize_scale)
+    cv2.imwrite('target_pasted.png', target)
+    cv2.imwrite('target_mask.png', target_mask)
+    print(target.shape, target_mask.shape)

automation_pose_mask/auto_mask/utils/utils.py

@@ -0,0 +1,85 @@
+import cv2
+import numpy as np
+from PIL import Image
+from typing import Any, Dict, List
+
+
+def load_img_to_array(img_p):
+    img = Image.open(img_p)
+    if img.mode == "RGBA":
+        img = img.convert("RGB")
+    return np.array(img)
+
+
+def save_array_to_img(img_arr, img_p):
+    Image.fromarray(img_arr.astype(np.uint8)).save(img_p)
+
+
+def dilate_mask(mask, dilate_factor=15):
+    mask = mask.astype(np.uint8)
+    mask = cv2.dilate(
+        mask,
+        np.ones((dilate_factor, dilate_factor), np.uint8),
+        iterations=1
+    )
+    return mask
+
+def erode_mask(mask, dilate_factor=15):
+    mask = mask.astype(np.uint8)
+    mask = cv2.erode(
+        mask,
+        np.ones((dilate_factor, dilate_factor), np.uint8),
+        iterations=1
+    )
+    return mask
+
+def show_mask(ax, mask: np.ndarray, random_color=False):
+    mask = mask.astype(np.uint8)
+    if np.max(mask) == 255:
+        mask = mask / 255
+    if random_color:
+        color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
+    else:
+        color = np.array([30 / 255, 144 / 255, 255 / 255, 0.6])
+    h, w = mask.shape[-2:]
+    mask_img = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
+    ax.imshow(mask_img)
+
+
+def show_points(ax, coords: List[List[float]], labels: List[int], size=375):
+    coords = np.array(coords)
+    labels = np.array(labels)
+    color_table = {0: 'red', 1: 'green'}
+    for label_value, color in color_table.items():
+        points = coords[labels == label_value]
+        ax.scatter(points[:, 0], points[:, 1], color=color, marker='*',
+                   s=size, edgecolor='white', linewidth=1.25)
+
+def get_clicked_point(img_path):
+    img = cv2.imread(img_path)
+    cv2.namedWindow("image")
+    cv2.imshow("image", img)
+
+    last_point = []
+    keep_looping = True
+
+    def mouse_callback(event, x, y, flags, param):
+        nonlocal last_point, keep_looping, img
+
+        if event == cv2.EVENT_LBUTTONDOWN:
+            if last_point:
+                cv2.circle(img, tuple(last_point), 5, (0, 0, 0), -1)
+            last_point = [x, y]
+            cv2.circle(img, tuple(last_point), 5, (0, 0, 255), -1)
+            cv2.imshow("image", img)
+        elif event == cv2.EVENT_RBUTTONDOWN:
+            keep_looping = False
+
+    cv2.setMouseCallback("image", mouse_callback)
+
+    while keep_looping:
+        cv2.waitKey(1)
+
+    cv2.destroyAllWindows()
+
+    return last_point

automation_pose_mask/auto_mask/utils/video2frames.py

@@ -0,0 +1,25 @@
+import cv2
+import imageio 
+import os 
+import shutil
+from tqdm import tqdm
+from glob import glob 
+from os import path as osp
+
+def video2frames(video_path, frame_path):
+    video = cv2.VideoCapture(video_path)
+    os.makedirs(frame_path, exist_ok=True)
+    frame_num = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+    fps = video.get(cv2.CAP_PROP_FPS)
+    initial_img = None
+    for idx in tqdm(range(frame_num), 'Extract frames'):
+        success, image = video.read()
+        if idx == 0: initial_img = image.copy()
+        assert success, 'extract the {}th frame in video {} failed!'.format(idx, video_path)
+        cv2.imwrite("{}/{:05d}.jpg".format(frame_path, idx), image)
+    return fps, initial_img
+
+if __name__ == '__main__':
+    video_path = './example/remove-anything-video/breakdance-flare/original_video.mp4'
+    frame_path = './example/remove-anything-video/breakdance-flare/frames/'
+    fps, initial_img = video2frames(video_path, frame_path)

automation_pose_mask/auto_mask/utils/visualize_bbox.py

@@ -0,0 +1,14 @@
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+import numpy as np
+import cv2
+
+
+img = plt.imread('../example/fill-anything/sample1.png')
+fig, ax = plt.subplots(1)
+ax.imshow(img)
+
+x1, y1, x2, y2 = 230, 283, 352, 407
+rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=2, edgecolor='r', facecolor='none')
+ax.add_patch(rect)
+plt.savefig('bbox.png')