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| # Configuration for Cog ⚙️ | ||
| # Reference: https://github.com/replicate/cog/blob/main/docs/yaml.md | ||
|
|
||
| build: | ||
| gpu: true | ||
| cuda: "11.7" | ||
| system_packages: | ||
| - "libgl1-mesa-glx" | ||
| - "libglib2.0-0" | ||
| python_version: "3.10" | ||
| python_packages: | ||
| - "timm==0.9.2" | ||
| - "transformers==4.30.2" | ||
| - "fairscale==0.4.13" | ||
| - "pycocoevalcap==1.2" | ||
| - "torch==1.13.0" | ||
| - "torchvision==0.14.0" | ||
| - "Pillow==9.5.0" | ||
| - "scipy==1.10.1" | ||
| - "opencv-python==4.7.0.72" | ||
| - "addict==2.4.0" | ||
| - "yapf==0.40.0" | ||
| - "supervision==0.10.0" | ||
| - git+https://github.com/openai/CLIP.git | ||
| - ipython | ||
|
|
||
| predict: "predict.py:Predictor" |
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| # Prediction interface for Cog ⚙️ | ||
| # https://github.com/replicate/cog/blob/main/docs/python.md | ||
|
|
||
| import os | ||
| import json | ||
| from typing import Any | ||
| import numpy as np | ||
| import random | ||
| import torch | ||
| import torchvision | ||
| import torchvision.transforms as transforms | ||
| from PIL import Image | ||
| import cv2 | ||
| import matplotlib.pyplot as plt | ||
| from cog import BasePredictor, Input, Path, BaseModel | ||
|
|
||
| from subprocess import call | ||
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||
| HOME = os.getcwd() | ||
| os.chdir("GroundingDINO") | ||
| call("pip install -q .", shell=True) | ||
| os.chdir(HOME) | ||
| os.chdir("segment_anything") | ||
| call("pip install -q .", shell=True) | ||
| os.chdir(HOME) | ||
|
|
||
| # Grounding DINO | ||
| import GroundingDINO.groundingdino.datasets.transforms as T | ||
| from GroundingDINO.groundingdino.models import build_model | ||
| from GroundingDINO.groundingdino.util.slconfig import SLConfig | ||
| from GroundingDINO.groundingdino.util.utils import ( | ||
| clean_state_dict, | ||
| get_phrases_from_posmap, | ||
| ) | ||
|
|
||
| # segment anything | ||
| from segment_anything import build_sam, build_sam_hq, SamPredictor | ||
|
|
||
| import sys | ||
|
|
||
| sys.path.append("Tag2Text") | ||
| from models.tag2text import ram | ||
|
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||
|
|
||
| class ModelOutput(BaseModel): | ||
| tags: str | ||
| rounding_box_img: Path | ||
| masked_img: Path | ||
| json_data: Any | ||
|
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||
|
|
||
| class Predictor(BasePredictor): | ||
| def setup(self): | ||
| """Load the model into memory to make running multiple predictions efficient""" | ||
| self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | ||
| normalize = transforms.Normalize( | ||
| mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] | ||
| ) | ||
| self.image_size = 384 | ||
| self.transform = transforms.Compose( | ||
| [ | ||
| transforms.Resize((self.image_size, self.image_size)), | ||
| transforms.ToTensor(), | ||
| normalize, | ||
| ] | ||
| ) | ||
|
|
||
| # load model | ||
| self.ram_model = ram( | ||
| pretrained="pretrained/ram_swin_large_14m.pth", | ||
| image_size=self.image_size, | ||
| vit="swin_l", | ||
| ) | ||
| self.ram_model.eval() | ||
| self.ram_model = self.ram_model.to(self.device) | ||
|
|
||
| self.model = load_model( | ||
| "GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py", | ||
| "pretrained/groundingdino_swint_ogc.pth", | ||
| device=self.device, | ||
| ) | ||
|
|
||
| self.sam = SamPredictor( | ||
| build_sam(checkpoint="pretrained/sam_vit_h_4b8939.pth").to(self.device) | ||
| ) | ||
| self.sam_hq = SamPredictor( | ||
| build_sam_hq(checkpoint="pretrained/sam_hq_vit_h.pth").to(self.device) | ||
| ) | ||
|
|
||
| def predict( | ||
| self, | ||
| input_image: Path = Input(description="Input image"), | ||
| use_sam_hq: bool = Input( | ||
| description="Use sam_hq instead of SAM for prediction", default=False | ||
| ), | ||
| ) -> ModelOutput: | ||
| """Run a single prediction on the model""" | ||
|
|
||
| # default settings | ||
| box_threshold = 0.25 | ||
| text_threshold = 0.2 | ||
| iou_threshold = 0.5 | ||
|
|
||
| image_pil, image = load_image(str(input_image)) | ||
|
|
||
| raw_image = image_pil.resize((self.image_size, self.image_size)) | ||
| raw_image = self.transform(raw_image).unsqueeze(0).to(self.device) | ||
|
|
||
| with torch.no_grad(): | ||
| tags, tags_chinese = self.ram_model.generate_tag(raw_image) | ||
|
|
||
| tags = tags[0].replace(" |", ",") | ||
|
|
||
| # run grounding dino model | ||
| boxes_filt, scores, pred_phrases = get_grounding_output( | ||
| self.model, image, tags, box_threshold, text_threshold, device=self.device | ||
| ) | ||
|
|
||
| predictor = self.sam_hq if use_sam_hq else self.sam | ||
|
|
||
| image = cv2.imread(str(input_image)) | ||
| image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | ||
| predictor.set_image(image) | ||
|
|
||
| size = image_pil.size | ||
| H, W = size[1], size[0] | ||
| for i in range(boxes_filt.size(0)): | ||
| boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H]) | ||
| boxes_filt[i][:2] -= boxes_filt[i][2:] / 2 | ||
| boxes_filt[i][2:] += boxes_filt[i][:2] | ||
|
|
||
| boxes_filt = boxes_filt.cpu() | ||
| # use NMS to handle overlapped boxes | ||
| print(f"Before NMS: {boxes_filt.shape[0]} boxes") | ||
| nms_idx = ( | ||
| torchvision.ops.nms(boxes_filt, scores, iou_threshold).numpy().tolist() | ||
| ) | ||
| boxes_filt = boxes_filt[nms_idx] | ||
| pred_phrases = [pred_phrases[idx] for idx in nms_idx] | ||
| print(f"After NMS: {boxes_filt.shape[0]} boxes") | ||
|
|
||
| transformed_boxes = predictor.transform.apply_boxes_torch( | ||
| boxes_filt, image.shape[:2] | ||
| ).to(self.device) | ||
|
|
||
| masks, _, _ = predictor.predict_torch( | ||
| point_coords=None, | ||
| point_labels=None, | ||
| boxes=transformed_boxes.to(self.device), | ||
| multimask_output=False, | ||
| ) | ||
|
|
||
| # draw output image | ||
| plt.figure(figsize=(10, 10)) | ||
| for mask in masks: | ||
| show_mask(mask.cpu().numpy(), plt.gca(), random_color=True) | ||
| for box, label in zip(boxes_filt, pred_phrases): | ||
| show_box(box.numpy(), plt.gca(), label) | ||
|
|
||
| rounding_box_path = "/tmp/automatic_label_output.png" | ||
| plt.axis("off") | ||
| plt.savefig( | ||
| Path(rounding_box_path), bbox_inches="tight", dpi=300, pad_inches=0.0 | ||
| ) | ||
| plt.close() | ||
|
|
||
| # save masks and json data | ||
| value = 0 # 0 for background | ||
| mask_img = torch.zeros(masks.shape[-2:]) | ||
| for idx, mask in enumerate(masks): | ||
| mask_img[mask.cpu().numpy()[0] == True] = value + idx + 1 | ||
| plt.figure(figsize=(10, 10)) | ||
| plt.imshow(mask_img.numpy()) | ||
| plt.axis("off") | ||
| masks_path = "/tmp/mask.png" | ||
| plt.savefig(masks_path, bbox_inches="tight", dpi=300, pad_inches=0.0) | ||
| plt.close() | ||
|
|
||
| json_data = { | ||
| "tags": tags, | ||
| "mask": [{"value": value, "label": "background"}], | ||
| } | ||
| for label, box in zip(pred_phrases, boxes_filt): | ||
| value += 1 | ||
| name, logit = label.split("(") | ||
| logit = logit[:-1] # the last is ')' | ||
| json_data["mask"].append( | ||
| { | ||
| "value": value, | ||
| "label": name, | ||
| "logit": float(logit), | ||
| "box": box.numpy().tolist(), | ||
| } | ||
| ) | ||
|
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||
| json_path = "/tmp/label.json" | ||
| with open(json_path, "w") as f: | ||
| json.dump(json_data, f) | ||
|
|
||
| return ModelOutput( | ||
| tags=tags, | ||
| masked_img=Path(masks_path), | ||
| rounding_box_img=Path(rounding_box_path), | ||
| json_data=Path(json_path), | ||
| ) | ||
|
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||
|
|
||
| def get_grounding_output( | ||
| model, image, caption, box_threshold, text_threshold, device="cpu" | ||
| ): | ||
| caption = caption.lower() | ||
| caption = caption.strip() | ||
| if not caption.endswith("."): | ||
| caption = caption + "." | ||
| model = model.to(device) | ||
| image = image.to(device) | ||
| with torch.no_grad(): | ||
| outputs = model(image[None], captions=[caption]) | ||
| logits = outputs["pred_logits"].cpu().sigmoid()[0] # (nq, 256) | ||
| boxes = outputs["pred_boxes"].cpu()[0] # (nq, 4) | ||
| logits.shape[0] | ||
|
|
||
| # filter output | ||
| logits_filt = logits.clone() | ||
| boxes_filt = boxes.clone() | ||
| filt_mask = logits_filt.max(dim=1)[0] > box_threshold | ||
| logits_filt = logits_filt[filt_mask] # num_filt, 256 | ||
| boxes_filt = boxes_filt[filt_mask] # num_filt, 4 | ||
| logits_filt.shape[0] | ||
|
|
||
| # get phrase | ||
| tokenlizer = model.tokenizer | ||
| tokenized = tokenlizer(caption) | ||
| # build pred | ||
| pred_phrases = [] | ||
| scores = [] | ||
| for logit, box in zip(logits_filt, boxes_filt): | ||
| pred_phrase = get_phrases_from_posmap( | ||
| logit > text_threshold, tokenized, tokenlizer | ||
| ) | ||
| pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})") | ||
| scores.append(logit.max().item()) | ||
|
|
||
| return boxes_filt, torch.Tensor(scores), pred_phrases | ||
|
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||
|
|
||
| def load_image(image_path): | ||
| # load image | ||
| image_pil = Image.open(image_path).convert("RGB") # load image | ||
|
|
||
| transform = T.Compose( | ||
| [ | ||
| T.RandomResize([800], max_size=1333), | ||
| T.ToTensor(), | ||
| T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), | ||
| ] | ||
| ) | ||
| image, _ = transform(image_pil, None) # 3, h, w | ||
| return image_pil, image | ||
|
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||
|
|
||
| def load_model(model_config_path, model_checkpoint_path, device): | ||
| args = SLConfig.fromfile(model_config_path) | ||
| args.device = device | ||
| model = build_model(args) | ||
| checkpoint = torch.load(model_checkpoint_path, map_location="cpu") | ||
| load_res = model.load_state_dict( | ||
| clean_state_dict(checkpoint["model"]), strict=False | ||
| ) | ||
| print(load_res) | ||
| _ = model.eval() | ||
| return model | ||
|
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|
|
||
| def show_mask(mask, ax, random_color=False): | ||
| 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_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1) | ||
| ax.imshow(mask_image) | ||
|
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||
|
|
||
| def show_box(box, ax, label): | ||
| x0, y0 = box[0], box[1] | ||
| w, h = box[2] - box[0], box[3] - box[1] | ||
| ax.add_patch( | ||
| plt.Rectangle((x0, y0), w, h, edgecolor="green", facecolor=(0, 0, 0, 0), lw=1.5) | ||
| ) | ||
| ax.text(x0, y0, label) |