├── .gitignore
├── LICENSE
├── README.md
├── examples
├── maskrcnn.py
├── maskrcnn_requirements.py
├── yolov5.py
└── yolov5_requirements.py
├── pytorch_clip_bbox
├── __init__.py
└── clip_bbox.py
├── requirements.txt
├── resources
└── preds.jpg
├── setup.cfg
└── setup.py
/.gitignore:
--------------------------------------------------------------------------------
1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
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48 | coverage.xml
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50 | *.py,cover
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78 | .ipynb_checkpoints
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80 | # IPython
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82 | ipython_config.py
83 |
84 | # pyenv
85 | .python-version
86 |
87 | # pipenv
88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies
90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not
91 | # install all needed dependencies.
92 | #Pipfile.lock
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94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow
95 | __pypackages__/
96 |
97 | # Celery stuff
98 | celerybeat-schedule
99 | celerybeat.pid
100 |
101 | # SageMath parsed files
102 | *.sage.py
103 |
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105 | .env
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129 | .pyre/
130 |
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/README.md:
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1 | # pytorch_clip_bbox: Implementation of the CLIP guided bbox ranking for Object Detection.
2 |
3 |
4 |
5 |
6 |
7 | Pytorch based library to rank predicted bounding boxes using text/image user's prompts.
8 |
9 | Usually, object detection models trains to detect common classes of objects such as "car", "person", "cup", "bottle".
10 | But sometimes we need to detect more complex classes such as "lady in the red dress", "bottle of whiskey", or "where is my red cup" instead of "person", "bottle", "cup" respectively.
11 | One way to solve this problem is to train more complex detectors that can detect more complex classes,
12 | but we propose to use text-driven object detection that allows detecting any complex classes that can be described by natural language.
13 | This library is written to rank predicted bounding boxes using text/image descriptions of complex classes.
14 |
15 | ## Install package
16 |
17 | ```bash
18 | pip install pytorch_clip_bbox
19 | ```
20 |
21 | ## Install the latest version
22 |
23 | ```bash
24 | pip install --upgrade git+https://github.com/bes-dev/pytorch_clip_bbox.git
25 | ```
26 |
27 | ## Features
28 | - The library supports multiple prompts (images or texts) as targets for filtering.
29 | - The library automatically detects the language of the input text, and multilingual translate it via google translate.
30 | - The library supports the original CLIP model by OpenAI and ruCLIP model by SberAI.
31 | - Simple integration with different object detection models.
32 |
33 | ## Usage
34 |
35 | We provide examples to integrate our library with different popular object detectors like: [YOLOv5](examples/yolov5.py), [MaskRCNN](examples/maskrcnn.py).
36 | Please, follow to [examples](examples/) to find more examples.
37 |
38 | ### Simple example to integrate pytorch_clip_bbox with MaskRCNN model
39 |
40 | ```bash
41 | $ pip install -r wheel cython opencv-python numpy torch torchvision pytorch_clip_bbox
42 | ```
43 |
44 | ```python
45 | import argparse
46 | import random
47 | import cv2
48 | import numpy as np
49 | import torch
50 | import torchvision.transforms as T
51 | import torchvision
52 | from pytorch_clip_bbox import ClipBBOX
53 |
54 | def get_coloured_mask(mask):
55 | colours = [[0, 255, 0],[0, 0, 255],[255, 0, 0],[0, 255, 255],[255, 255, 0],[255, 0, 255],[80, 70, 180],[250, 80, 190],[245, 145, 50],[70, 150, 250],[50, 190, 190]]
56 | r = np.zeros_like(mask).astype(np.uint8)
57 | g = np.zeros_like(mask).astype(np.uint8)
58 | b = np.zeros_like(mask).astype(np.uint8)
59 | c = colours[random.randrange(0,10)]
60 | r[mask == 1], g[mask == 1], b[mask == 1] = c
61 | coloured_mask = np.stack([r, g, b], axis=2)
62 | return coloured_mask, c
63 |
64 | def main(args):
65 | # build detector
66 | detector = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True).eval().to(args.device)
67 | clip_bbox = ClipBBOX(clip_type=args.clip_type).to(args.device)
68 | # add prompts
69 | if args.text_prompt is not None:
70 | for prompt in args.text_prompt.split(","):
71 | clip_bbox.add_prompt(text=prompt)
72 | if args.image_prompt is not None:
73 | image = cv2.cvtColor(cv2.imread(args.image_prompt), cv2.COLOR_BGR2RGB)
74 | image = torch.from_numpy(image).permute(2, 0, 1).unsqueeze(0)
75 | image = img / 255.0
76 | clip_bbox.add_prompt(image=image)
77 | image = cv2.imread(args.image)
78 | pred = detector([
79 | T.ToTensor()(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)).to(args.device)
80 | ])
81 | pred_score = list(pred[0]['scores'].detach().cpu().numpy())
82 | pred_threshold = [pred_score.index(x) for x in pred_score if x > args.confidence][-1]
83 | boxes = [[int(b) for b in box] for box in list(pred[0]['boxes'].detach().cpu().numpy())][:pred_threshold + 1]
84 | masks = (pred[0]['masks'] > 0.5).squeeze().detach().cpu().numpy()[:pred_threshold + 1]
85 | ranking = clip_bbox(image, boxes, top_k=args.top_k)
86 | for key in ranking.keys():
87 | if key == "loss":
88 | continue
89 | for box in ranking[key]["ranking"]:
90 | mask, color = get_coloured_mask(masks[box["idx"]])
91 | image = cv2.addWeighted(image, 1, mask, 0.5, 0)
92 | x1, y1, x2, y2 = box["rect"]
93 | cv2.rectangle(image, (x1, y1), (x2, y2), color, 6)
94 | cv2.rectangle(image, (x1, y1), (x2, y1-100), color, -1)
95 | cv2.putText(image, ranking[key]["src"], (x1 + 5, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 4, (0, 0, 0), thickness=5)
96 | if args.output_image is None:
97 | cv2.imshow("image", image)
98 | cv2.waitKey()
99 | else:
100 | cv2.imwrite(args.output_image, image)
101 |
102 |
103 | if __name__ == "__main__":
104 | parser = argparse.ArgumentParser()
105 | parser.add_argument("-i", "--image", type=str, help="Input image.")
106 | parser.add_argument("--device", type=str, default="cuda:0", help="inference device.")
107 | parser.add_argument("--confidence", type=float, default=0.7, help="confidence threshold [MaskRCNN].")
108 | parser.add_argument("--text-prompt", type=str, default=None, help="Text prompt.")
109 | parser.add_argument("--image-prompt", type=str, default=None, help="Image prompt.")
110 | parser.add_argument("--clip-type", type=str, default="clip_vit_b32", help="Type of CLIP model [ruclip, clip_vit_b32, clip_vit_b16].")
111 | parser.add_argument("--top-k", type=int, default=1, help="top_k predictions will be returned.")
112 | parser.add_argument("--output-image", type=str, default=None, help="Output image name.")
113 | args = parser.parse_args()
114 | main(args)
115 | ```
--------------------------------------------------------------------------------
/examples/maskrcnn.py:
--------------------------------------------------------------------------------
1 | """
2 | Copyright 2021 by Sergei Belousov
3 | Licensed under the Apache License, Version 2.0 (the "License");
4 | you may not use this file except in compliance with the License.
5 | You may obtain a copy of the License at
6 | http://www.apache.org/licenses/LICENSE-2.0
7 | Unless required by applicable law or agreed to in writing, software
8 | distributed under the License is distributed on an "AS IS" BASIS,
9 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
10 | See the License for the specific language governing permissions and
11 | limitations under the License.
12 | """
13 | import argparse
14 | import random
15 | import cv2
16 | import numpy as np
17 | import torch
18 | import torchvision.transforms as T
19 | import torchvision
20 | from pytorch_clip_bbox import ClipBBOX
21 |
22 | def get_coloured_mask(mask):
23 | colours = [[0, 255, 0],[0, 0, 255],[255, 0, 0],[0, 255, 255],[255, 255, 0],[255, 0, 255],[80, 70, 180],[250, 80, 190],[245, 145, 50],[70, 150, 250],[50, 190, 190]]
24 | r = np.zeros_like(mask).astype(np.uint8)
25 | g = np.zeros_like(mask).astype(np.uint8)
26 | b = np.zeros_like(mask).astype(np.uint8)
27 | c = colours[random.randrange(0,10)]
28 | r[mask == 1], g[mask == 1], b[mask == 1] = c
29 | coloured_mask = np.stack([r, g, b], axis=2)
30 | return coloured_mask, c
31 |
32 | def main(args):
33 | # build detector
34 | detector = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True).eval().to(args.device)
35 | clip_bbox = ClipBBOX(clip_type=args.clip_type).to(args.device)
36 | # add prompts
37 | if args.text_prompt is not None:
38 | for prompt in args.text_prompt.split(","):
39 | clip_bbox.add_prompt(text=prompt)
40 | if args.image_prompt is not None:
41 | image = cv2.cvtColor(cv2.imread(args.image_prompt), cv2.COLOR_BGR2RGB)
42 | image = torch.from_numpy(image).permute(2, 0, 1).unsqueeze(0)
43 | image = img / 255.0
44 | clip_bbox.add_prompt(image=image)
45 | image = cv2.imread(args.image)
46 | pred = detector([
47 | T.ToTensor()(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)).to(args.device)
48 | ])
49 | pred_score = list(pred[0]['scores'].detach().cpu().numpy())
50 | pred_threshold = [pred_score.index(x) for x in pred_score if x > args.confidence][-1]
51 | boxes = [[int(b) for b in box] for box in list(pred[0]['boxes'].detach().cpu().numpy())][:pred_threshold + 1]
52 | masks = (pred[0]['masks'] > 0.5).squeeze().detach().cpu().numpy()[:pred_threshold + 1]
53 | ranking = clip_bbox(image, boxes, top_k=args.top_k)
54 | for key in ranking.keys():
55 | if key == "loss":
56 | continue
57 | for box in ranking[key]["ranking"]:
58 | mask, color = get_coloured_mask(masks[box["idx"]])
59 | image = cv2.addWeighted(image, 1, mask, 0.5, 0)
60 | x1, y1, x2, y2 = box["rect"]
61 | cv2.rectangle(image, (x1, y1), (x2, y2), color, 6)
62 | cv2.rectangle(image, (x1, y1), (x2, y1-100), color, -1)
63 | cv2.putText(image, ranking[key]["src"], (x1 + 5, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 4, (0, 0, 0), thickness=5)
64 | if args.output_image is None:
65 | cv2.imshow("image", image)
66 | cv2.waitKey()
67 | else:
68 | cv2.imwrite(args.output_image, image)
69 |
70 |
71 | if __name__ == "__main__":
72 | parser = argparse.ArgumentParser()
73 | parser.add_argument("-i", "--image", type=str, help="Input image.")
74 | parser.add_argument("--device", type=str, default="cuda:0", help="inference device.")
75 | parser.add_argument("--confidence", type=float, default=0.7, help="confidence threshold [MaskRCNN].")
76 | parser.add_argument("--text-prompt", type=str, default=None, help="Text prompt.")
77 | parser.add_argument("--image-prompt", type=str, default=None, help="Image prompt.")
78 | parser.add_argument("--clip-type", type=str, default="clip_vit_b32", help="Type of CLIP model [ruclip, clip_vit_b32, clip_vit_b16].")
79 | parser.add_argument("--top-k", type=int, default=1, help="top_k predictions will be returned.")
80 | parser.add_argument("--output-image", type=str, default=None, help="Output image name.")
81 | args = parser.parse_args()
82 | main(args)
83 |
--------------------------------------------------------------------------------
/examples/maskrcnn_requirements.py:
--------------------------------------------------------------------------------
1 | wheel
2 | cython
3 | opencv-python
4 | numpy
5 | torch
6 | torchvision
7 | pytorch_clip_bbox
8 |
--------------------------------------------------------------------------------
/examples/yolov5.py:
--------------------------------------------------------------------------------
1 | """
2 | Copyright 2021 by Sergei Belousov
3 | Licensed under the Apache License, Version 2.0 (the "License");
4 | you may not use this file except in compliance with the License.
5 | You may obtain a copy of the License at
6 | http://www.apache.org/licenses/LICENSE-2.0
7 | Unless required by applicable law or agreed to in writing, software
8 | distributed under the License is distributed on an "AS IS" BASIS,
9 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
10 | See the License for the specific language governing permissions and
11 | limitations under the License.
12 | """
13 | import argparse
14 | import cv2
15 | import torch
16 | from pytorch_clip_bbox import ClipBBOX
17 |
18 | def extract_boxes(detections):
19 | boxes = []
20 | for i in range(detections.xyxy[0].size(0)):
21 | x1, y1, x2, y2, confidence, idx = detections.xyxy[0][i]
22 | boxes.append([int(x1), int(y1), int(x2), int(y2)])
23 | return boxes
24 |
25 | def main(args):
26 | # build detector
27 | detector = torch.hub.load("ultralytics/yolov5", "yolov5s").to(args.device)
28 | clip_bbox = ClipBBOX(clip_type=args.clip_type).to(args.device)
29 | # add prompts
30 | if args.text_prompt is not None:
31 | for prompt in args.text_prompt.split(","):
32 | clip_bbox.add_prompt(text=prompt)
33 | if args.image_prompt is not None:
34 | image = cv2.cvtColor(cv2.imread(args.image_prompt), cv2.COLOR_BGR2RGB)
35 | image = torch.from_numpy(image).permute(2, 0, 1).unsqueeze(0)
36 | image = img / 255.0
37 | clip_bbox.add_prompt(image=image)
38 | image = cv2.imread(args.image)
39 | detections = detector(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
40 | boxes = extract_boxes(detections)
41 | ranking = clip_bbox(image, boxes, top_k=args.top_k)
42 | for key in ranking.keys():
43 | if key == "loss":
44 | continue
45 | for box in ranking[key]["ranking"]:
46 | x1, y1, x2, y2 = box["rect"]
47 | cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 6)
48 | cv2.rectangle(image, (x1, y1), (x2, y1-100), (0, 255, 0), -1)
49 | cv2.putText(image, ranking[key]["src"], (x1 + 5, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 4, (0, 0, 0), thickness=5)
50 | if args.output_image is None:
51 | cv2.imshow("image", image)
52 | cv2.waitKey()
53 | else:
54 | cv2.imwrite(args.output_image, image)
55 |
56 |
57 | if __name__ == "__main__":
58 | parser = argparse.ArgumentParser()
59 | parser.add_argument("-i", "--image", type=str, help="Input image.")
60 | parser.add_argument("--device", type=str, default="cuda:0", help="inference device.")
61 | parser.add_argument("--text-prompt", type=str, default=None, help="Text prompt.")
62 | parser.add_argument("--image-prompt", type=str, default=None, help="Image prompt.")
63 | parser.add_argument("--clip-type", type=str, default="clip_vit_b32", help="Type of CLIP model [ruclip, clip_vit_b32, clip_vit_b16].")
64 | parser.add_argument("--top-k", type=int, default=1, help="top_k predictions will be returned.")
65 | parser.add_argument("--output-image", type=str, default=None, help="Output image name.")
66 | args = parser.parse_args()
67 | main(args)
68 |
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/examples/yolov5_requirements.py:
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1 | pytorch_clip_bbox
2 | matplotlib>=3.2.2
3 | numpy>=1.18.5
4 | opencv-python>=4.1.2
5 | Pillow>=7.1.2
6 | PyYAML>=5.3.1
7 | requests>=2.23.0
8 | scipy>=1.4.1
9 | torch>=1.7.0
10 | torchvision>=0.8.1
11 | tqdm>=4.41.0
12 | tensorboard>=2.4.1
13 | pandas>=1.1.4
14 | seaborn>=0.11.0
15 | thop
16 |
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/pytorch_clip_bbox/__init__.py:
--------------------------------------------------------------------------------
1 | """
2 | Copyright 2021 by Sergei Belousov
3 | Licensed under the Apache License, Version 2.0 (the "License");
4 | you may not use this file except in compliance with the License.
5 | You may obtain a copy of the License at
6 | http://www.apache.org/licenses/LICENSE-2.0
7 | Unless required by applicable law or agreed to in writing, software
8 | distributed under the License is distributed on an "AS IS" BASIS,
9 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
10 | See the License for the specific language governing permissions and
11 | limitations under the License.
12 | """
13 | from .clip_bbox import ClipBBOX
14 |
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/pytorch_clip_bbox/clip_bbox.py:
--------------------------------------------------------------------------------
1 | """
2 | Copyright 2021 by Sergei Belousov
3 | Licensed under the Apache License, Version 2.0 (the "License");
4 | you may not use this file except in compliance with the License.
5 | You may obtain a copy of the License at
6 | http://www.apache.org/licenses/LICENSE-2.0
7 | Unless required by applicable law or agreed to in writing, software
8 | distributed under the License is distributed on an "AS IS" BASIS,
9 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
10 | See the License for the specific language governing permissions and
11 | limitations under the License.
12 | """
13 | from typing import List, Dict, Tuple, Optional
14 | import cv2
15 | import numpy as np
16 | import torch
17 | import torch.nn as nn
18 | from pytorch_clip_guided_loss import get_clip_guided_loss
19 | from pytorch_clip_guided_loss.clip_guided_loss import CLIPPrompt
20 |
21 |
22 | class ClipBBOX(nn.Module):
23 | """ Implementation of the CLIP guided bbox refinement for Object Detection.
24 | Arguments:
25 | clip_type (str): type of the CLIP model.
26 | """
27 | def __init__(
28 | self,
29 | clip_type: str = "clip_vit_b32"
30 | ):
31 | super().__init__()
32 | # CLIP guided loss
33 | self.clip_loss = get_clip_guided_loss(clip_type, input_range=(0.0, 1.0))
34 | self.input_size = self.clip_loss.image_processor[0].size
35 | # utils
36 | self.register_buffer("device_info", torch.tensor(0))
37 |
38 | def add_prompt(
39 | self,
40 | image: Optional[torch.Tensor] = None,
41 | text: Optional[str] = None,
42 | weight: float = 1.0,
43 | label: Optional[str] = None,
44 | store_src: bool = True
45 | ) -> str:
46 | """Add prompt to loss function.
47 | Arguments:
48 | image (torch.Tensor): input image [Optional].
49 | text (str): input text [Optional].
50 | weight (float): importance of the prompt.
51 | label (str): label of the prompt [Optional].
52 | store_src (bool): store source data of the prompt.
53 | Returns:
54 | label (src): label of the prompt.
55 | """
56 | return self.clip_loss.add_prompt(image, text, weight, label, store_src)
57 |
58 | def get_prompt(self, label: str) -> Optional[CLIPPrompt]:
59 | """Get prompt if available.
60 | Arguments:
61 | label (str): label of the prompt [Optional].
62 | Returns:
63 | prompt (CLIPPrompt or None): prompt [Optional].
64 | """
65 | return self.clip_loss.get_prompt(label)
66 |
67 | def get_prompts_list(self) -> List[str]:
68 | """Get list of all available prompts.
69 | Returns:
70 | prompts (list): list of prompts labels.
71 | """
72 | return self.clip_loss.get_prompts_list()
73 |
74 | def delete_prompt(self, label: Optional[str] = None) -> None:
75 | """Add prompt to loss function.
76 | Arguments:
77 | label (str): label of the prompt to delete [Optional].
78 | """
79 | return self.clip_loss.delete_prompt(label)
80 |
81 | def clear_prompts(self) -> None:
82 | """Delete all available prompts."""
83 | return self.clip_loss.clear_prompts()
84 |
85 | @torch.no_grad()
86 | def forward(
87 | self,
88 | img: np.array,
89 | boxes: List[Tuple[int, int, int, int]],
90 | is_rgb: bool = False,
91 | top_k: int = 1,
92 | batch_size: int = 128
93 | ):
94 | """ CLIP guided filter for input bounding boxes.
95 | Argument:
96 | img (np.array): input image.
97 | boxes (List[Tuple[int, int, int, int]]): input bounding boxes in format [(xmin, ymin, xmax, ymax)]
98 | is_rgb (bool): is imput image in RGB/BGR format.
99 | top_k (int): top k best matches will be returned.
100 | Use top_k = -1 to return all boxes in ranked order.
101 | batch_size (int): batch size.
102 | Returns:
103 | outputs (List[Dict]): predicts in format:
104 | [{"rect": [x, y, w, h], "loss": loss_val}]
105 | """
106 | _img = img.copy() if is_rgb else cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
107 | batch = self._prepare_batch(_img, boxes).to(self.device_info.device)
108 | loss = self._predict_clip_loss(batch, batch_size)
109 | outputs = self._generate_output(boxes, loss, top_k)
110 | return outputs
111 |
112 | def _prepare_batch(self, img: np.array, boxes: List[Tuple[int, int, int, int]]) -> torch.Tensor:
113 | """ Crop region proposals and generate batch
114 | Argument:
115 | img (np.array): input image in opencv format (H, W, 3).
116 | boxes (List[Tuple[int, int, int, int]]): input boxes in format [(xmin, ymin, xmax, ymax)]
117 | Returns:
118 | batch (torch.Tensor): output batch (B, C, H, W).
119 | """
120 | batch = []
121 | for x1, y1, x2, y2 in boxes:
122 | crop = cv2.resize(img[y1:y2, x1:x2], self.input_size)
123 | batch.append(torch.from_numpy(crop).permute(2, 0, 1).unsqueeze(0))
124 | batch = torch.cat(batch, dim=0)
125 | # normalize batch
126 | batch = batch / (batch.max() + 1e-8)
127 | return batch
128 |
129 | def _predict_clip_loss(
130 | self,
131 | batch_full: torch.Tensor,
132 | batch_size: int = 128
133 | ) -> torch.Tensor:
134 | """ Predict CLIP loss for region proposals using user's prompts.
135 | Argument:
136 | batch_full (torch.Tensor): input batch (B, C, H, W).
137 | prompt_label (str): prompt label that uses for ranking.
138 | batch_size (int): batch size.
139 | Returns:
140 | loss (Dict[str, torch.Tensor]): output batch {"prompt_label": (B, )}.
141 | """
142 | loss = {}
143 | id_start = 0
144 | while id_start < batch_full.size(0):
145 | id_stop = min(id_start + batch_size, batch_full.size(0))
146 | batch = batch_full[id_start:id_stop]
147 | predicted_loss = self.clip_loss.image_loss(image=batch, reduce=None)
148 | for key, val in predicted_loss.items():
149 | if not key in loss:
150 | loss[key] = []
151 | loss[key].append(val.cpu())
152 | id_start = id_stop
153 | for key, val in loss.items():
154 | loss[key] = torch.cat(val, dim=0)
155 | return loss
156 |
157 | def _generate_output(
158 | self,
159 | boxes: List[Tuple[int, int, int, int]],
160 | loss: Dict[str, torch.Tensor],
161 | top_k: int = 1
162 | ) -> List[Dict]:
163 | """ Generate top_k predictions as an output of the model.
164 | Argument:
165 | boxes (List[Tuple[int, int, int, int]]): bounding boxes in format [(xmin, ymin, xmax, ymax)]
166 | loss (Dict[str, torch.Tensor]): predicted CLIP loss in format {"prompt_label": (B, )}.
167 | top_k (int): top k best matches will be returned.
168 | Use top_k = -1 to return all boxes in ranked order.
169 | Returns:
170 | outputs (List[Dict]): predicts in format:
171 | {
172 | : {
173 | "src": ,
174 | "ranking": [{"loss": loss, "idx": idx, "rect": [xmin, ymin, xmax, ymax]}]
175 | }
176 | }
177 | """
178 | output = {}
179 | top_k = min(top_k, len(boxes)) if top_k > 0 else len(boxes)
180 | for key, val in loss.items():
181 | output[key] = {}
182 | if key in self.get_prompts_list():
183 | output[key]["src"] = self.get_prompt(key).src
184 | if not isinstance(output[key]["src"], str):
185 | output[key]["src"] = key
186 | ranking = []
187 | vals, ids = val.sort()
188 | for i in range(top_k):
189 | ranking.append({
190 | "loss": vals[i],
191 | "idx": ids[i],
192 | "rect": boxes[ids[i]]
193 | })
194 | output[key]["ranking"] = ranking
195 | return output
196 |
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/requirements.txt:
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1 | torch
2 | opencv-python
3 | pytorch_clip_guided_loss
4 | numpy
5 |
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/resources/preds.jpg:
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https://raw.githubusercontent.com/bes-dev/pytorch_clip_bbox/1cc8ef2bdec8201153c4f5d3dc25b7f88b09bad4/resources/preds.jpg
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/setup.cfg:
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1 | [metadata]
2 | description-file = README.md
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/setup.py:
--------------------------------------------------------------------------------
1 | """
2 | Copyright 2021 by Sergei Belousov
3 |
4 | Licensed under the Apache License, Version 2.0 (the "License");
5 | you may not use this file except in compliance with the License.
6 | You may obtain a copy of the License at
7 |
8 | http://www.apache.org/licenses/LICENSE-2.0
9 |
10 | Unless required by applicable law or agreed to in writing, software
11 | distributed under the License is distributed on an "AS IS" BASIS,
12 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13 | See the License for the specific language governing permissions and
14 | limitations under the License.
15 | """
16 | from setuptools import setup, find_packages
17 |
18 | readme = open('README.md').read()
19 |
20 | VERSION = '2021.12.25.0'
21 |
22 | requirements = [
23 | 'wheel',
24 | 'Cython',
25 | 'cython',
26 | 'torch',
27 | 'opencv-python',
28 | 'numpy',
29 | 'pytorch_clip_guided_loss'
30 | ]
31 |
32 | setup(
33 | # Metadata
34 | name='pytorch_clip_bbox',
35 | version=VERSION,
36 | author='Sergei Belousov aka BeS',
37 | author_email='sergei.o.belousov@gmail.com',
38 | description='Pytorch implementation of the CLIP guided bbox refinement for Object Detection.',
39 | long_description=readme,
40 | long_description_content_type='text/markdown',
41 |
42 | # Package info
43 | packages=find_packages(exclude=('*test*',)),
44 |
45 | #
46 | zip_safe=True,
47 | install_requires=requirements,
48 |
49 | # Classifiers
50 | classifiers=[
51 | 'Programming Language :: Python :: 3',
52 | ],
53 |
54 |
55 | # install .json configs
56 | package_data={
57 | # "pytorch_clip_guided_loss": ["configs/*.yml"]
58 | },
59 | include_package_data=True
60 | )
61 |
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