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adding checkpoints

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  1. checkpoints/depth_anything_vitb14.pth +3 -0
  2. checkpoints/depth_anything_vitl14.pth +3 -0
  3. checkpoints/depth_anything_vits14.pth +3 -0
  4. torchhub/facebookresearch_dinov2_main/CODE_OF_CONDUCT.md +80 -0
  5. torchhub/facebookresearch_dinov2_main/CONTRIBUTING.md +31 -0
  6. torchhub/facebookresearch_dinov2_main/LICENSE +400 -0
  7. torchhub/facebookresearch_dinov2_main/MODEL_CARD.md +201 -0
  8. torchhub/facebookresearch_dinov2_main/README.md +277 -0
  9. torchhub/facebookresearch_dinov2_main/conda.yaml +22 -0
  10. torchhub/facebookresearch_dinov2_main/dinov2/.DS_Store +0 -0
  11. torchhub/facebookresearch_dinov2_main/dinov2/__init__.py +7 -0
  12. torchhub/facebookresearch_dinov2_main/dinov2/configs/.DS_Store +0 -0
  13. torchhub/facebookresearch_dinov2_main/dinov2/configs/__init__.py +23 -0
  14. torchhub/facebookresearch_dinov2_main/dinov2/configs/eval/vitb14_pretrain.yaml +6 -0
  15. torchhub/facebookresearch_dinov2_main/dinov2/configs/eval/vitg14_pretrain.yaml +7 -0
  16. torchhub/facebookresearch_dinov2_main/dinov2/configs/eval/vitl14_pretrain.yaml +6 -0
  17. torchhub/facebookresearch_dinov2_main/dinov2/configs/eval/vits14_pretrain.yaml +6 -0
  18. torchhub/facebookresearch_dinov2_main/dinov2/configs/ssl_default_config.yaml +115 -0
  19. torchhub/facebookresearch_dinov2_main/dinov2/configs/train/vitg14.yaml +26 -0
  20. torchhub/facebookresearch_dinov2_main/dinov2/configs/train/vitl14.yaml +26 -0
  21. torchhub/facebookresearch_dinov2_main/dinov2/configs/train/vitl16_short.yaml +6 -0
  22. torchhub/facebookresearch_dinov2_main/dinov2/data/.DS_Store +0 -0
  23. torchhub/facebookresearch_dinov2_main/dinov2/data/__init__.py +11 -0
  24. torchhub/facebookresearch_dinov2_main/dinov2/data/adapters.py +29 -0
  25. torchhub/facebookresearch_dinov2_main/dinov2/data/augmentations.py +119 -0
  26. torchhub/facebookresearch_dinov2_main/dinov2/data/collate.py +50 -0
  27. torchhub/facebookresearch_dinov2_main/dinov2/data/datasets/__init__.py +8 -0
  28. torchhub/facebookresearch_dinov2_main/dinov2/data/datasets/decoders.py +32 -0
  29. torchhub/facebookresearch_dinov2_main/dinov2/data/datasets/extended.py +39 -0
  30. torchhub/facebookresearch_dinov2_main/dinov2/data/datasets/image_net.py +291 -0
  31. torchhub/facebookresearch_dinov2_main/dinov2/data/datasets/image_net_22k.py +303 -0
  32. torchhub/facebookresearch_dinov2_main/dinov2/data/loaders.py +223 -0
  33. torchhub/facebookresearch_dinov2_main/dinov2/data/masking.py +87 -0
  34. torchhub/facebookresearch_dinov2_main/dinov2/data/samplers.py +230 -0
  35. torchhub/facebookresearch_dinov2_main/dinov2/data/transforms.py +92 -0
  36. torchhub/facebookresearch_dinov2_main/dinov2/distributed/__init__.py +271 -0
  37. torchhub/facebookresearch_dinov2_main/dinov2/eval/__init__.py +5 -0
  38. torchhub/facebookresearch_dinov2_main/dinov2/eval/knn.py +405 -0
  39. torchhub/facebookresearch_dinov2_main/dinov2/eval/linear.py +626 -0
  40. torchhub/facebookresearch_dinov2_main/dinov2/eval/log_regression.py +445 -0
  41. torchhub/facebookresearch_dinov2_main/dinov2/eval/metrics.py +114 -0
  42. torchhub/facebookresearch_dinov2_main/dinov2/eval/setup.py +76 -0
  43. torchhub/facebookresearch_dinov2_main/dinov2/eval/utils.py +147 -0
  44. torchhub/facebookresearch_dinov2_main/dinov2/fsdp/__init__.py +158 -0
  45. torchhub/facebookresearch_dinov2_main/dinov2/layers/.DS_Store +0 -0
  46. torchhub/facebookresearch_dinov2_main/dinov2/layers/__init__.py +12 -0
  47. torchhub/facebookresearch_dinov2_main/dinov2/layers/attention.py +81 -0
  48. torchhub/facebookresearch_dinov2_main/dinov2/layers/block.py +252 -0
  49. torchhub/facebookresearch_dinov2_main/dinov2/layers/dino_head.py +59 -0
  50. torchhub/facebookresearch_dinov2_main/dinov2/layers/drop_path.py +35 -0
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torchhub/facebookresearch_dinov2_main/MODEL_CARD.md ADDED
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1
+ # Model Card for DINOv2-S/B/L/g
2
+
3
+ These are Vision Transformer models trained following the method described in the paper:
4
+ "DINOv2: Learning Robust Visual Features without Supervision"
5
+
6
+ We provide 4 models: 1 ViT-g trained from scratch, and 3 ViT-S/B/L models distilled from the ViT-g.
7
+
8
+ ## Model Details
9
+ The model takes an image as input and returns a class token and patch tokens.
10
+
11
+ The embedding dimension is:
12
+ - 384 for ViT-S.
13
+ - 768 for ViT-B.
14
+ - 1024 for ViT-L.
15
+ - 1536 for ViT-g.
16
+
17
+ The models follow a Transformer architecture, with a patch size of 14.
18
+
19
+ For a 224x224 image, this results in 1 class token + 256 patch tokens.
20
+
21
+ The models can accept larger images provided the image shapes are multiples of the patch size (14).
22
+ If this condition is not verified, the model will crop to the closest smaller multiple of the patch size.
23
+
24
+ ### Model Description
25
+
26
+ - **Developed by:** Meta AI
27
+ - **Model type:** Vision Transformer
28
+ - **License:** CC-BY-NC
29
+
30
+ - **Repository:** https://github.com/facebookresearch/dinov2
31
+ - **Paper:** https://arxiv.org/abs/2304.07193
32
+ - **Demo:** https://dinov2.metademolab.com/
33
+
34
+ ## Uses
35
+
36
+ The models are vision backbones providing multi-purpose features for downstream tasks.
37
+
38
+ ### Direct Use
39
+
40
+ The models can be used without fine-tuning, with downstream classifiers as simple as linear layers, to obtain competitive results:
41
+ - on depth estimation, semantic segmentation, using linear layers.
42
+ - on image classification, using k-NN classifiers on the class token.
43
+ - on image classification, with logistic regression classifiers applied on the class token.
44
+ - on image classification, with a linear layer applied on the class token and the average of the patch tokens.
45
+ - on image retrieval using nearest neighbors.
46
+
47
+ ### Downstream Use
48
+
49
+ It is technically possible to perform fine-tuning on the models, for small gains (we measured +2% on ImageNet-1k classification).
50
+ We recommend keeping this as a very last step and only when necessary, as the features already provide good performance out-of-the-box.
51
+
52
+ ## Bias, Risks, and Limitations
53
+
54
+ Despite improvements thanks to the training method not using annotations, we still observe significant biases in our models toward rich households from Western countries.
55
+
56
+ ### Recommendations
57
+
58
+ We expect fine-tuning will increase the biases in the features produced by the model as they will be tuned to the fine-tuning labels.
59
+
60
+ ## How to Get Started with the Model
61
+
62
+ Use the code below to get started with the model.
63
+
64
+ ```python
65
+ import torch
66
+ dinov2_vits14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vits14')
67
+ dinov2_vitb14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitb14')
68
+ dinov2_vitl14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitl14')
69
+ dinov2_vitg14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitg14')
70
+ ```
71
+
72
+ ## Training Details
73
+
74
+ ### Training Data
75
+
76
+ - **Training data:** LVD-142M (see paper)
77
+ - **Training regime:** fp16 using PyTorch-FSDP mixed-precision.
78
+
79
+ ### Training Procedure
80
+
81
+ - **Training objective:**
82
+ - DINO self-distillation loss with multi-crop
83
+ - iBOT masked-image modeling loss
84
+ - KoLeo regularization on [CLS] tokens
85
+ - **Architectures:**
86
+ - ViT-S (21M params): Patch size 14, embedding dimension 384, 6 heads, MLP FFN
87
+ - ViT-B (86M params): Patch size 14, embedding dimension 768, 12 heads, MLP FFN
88
+ - ViT-L (0.3B params): Patch size 14, embedding dimension 1024, 16 heads, MLP FFN
89
+ - ViT-g (1.1B params): Patch size 14, embedding dimension 1536, 24 heads, SwiGLU FFN
90
+ - **Distillation:**
91
+ - Distillation follows the standard DINOv2 pretraining procedure, except the teacher is a pretrained ViT-g, frozen.
92
+
93
+ ## Evaluation
94
+
95
+ We refer users to the associated paper for the evaluation protocols.
96
+
97
+ <table>
98
+ <tr>
99
+ <th>model</th>
100
+ <th colspan="3">ImageNet-1k</th>
101
+ <th>NYU-Depth v2</th>
102
+ <th>SUN-RGBD</th>
103
+ <th>ADE20k</th>
104
+ <th>iNaturalist 2018</th>
105
+ <th>Oxford-H</th>
106
+ </tr>
107
+ <tr>
108
+ <th rowspan="2">task</th>
109
+ <th>classif. (acc)</th>
110
+ <th>classif. (acc)</th>
111
+ <th>classif. V2 (acc)</th>
112
+ <th>depth (RMSE)</th>
113
+ <th>depth (RMSE)</th>
114
+ <th>segm. (mAP)</th>
115
+ <th>classif. (acc)</th>
116
+ <th>retrieval (mAP)</th>
117
+ </tr>
118
+ <tr>
119
+ <!-- <th>^</th> -->
120
+ <th>k-NN</th>
121
+ <th>linear</th>
122
+ <th>linear</th>
123
+ <th>linear<br />4 layers</th>
124
+ <th>NYU-D transfer</th>
125
+ <th>multiscale</th>
126
+ <th>linear</th>
127
+ <th>nearest neighbor</th>
128
+ </tr>
129
+ <tr>
130
+ <td>ViT-S/14</td>
131
+ <td align="right">79.0%</td>
132
+ <td align="right">81.1%</td>
133
+ <td align="right">70.8%</td>
134
+ <td align="right">0.417</td>
135
+ <td align="right">0.431</td>
136
+ <td align="right">47.2</td>
137
+ <td align="right">69.5%</td>
138
+ <td align="right">43.2</td>
139
+ </tr>
140
+ <tr>
141
+ <td>ViT-B/14</td>
142
+ <td align="right">82.1%</td>
143
+ <td align="right">84.5%</td>
144
+ <td align="right">74.9%</td>
145
+ <td align="right">0.362</td>
146
+ <td align="right">0.400</td>
147
+ <td align="right">51.3</td>
148
+ <td align="right">76.3%</td>
149
+ <td align="right">49.5</td>
150
+ </tr>
151
+ <tr>
152
+ <td>ViT-L/14</td>
153
+ <td align="right">83.5%</td>
154
+ <td align="right">86.3%</td>
155
+ <td align="right">77.6%</td>
156
+ <td align="right">0.333</td>
157
+ <td align="right">0.396</td>
158
+ <td align="right">53.1</td>
159
+ <td align="right">79.8%</td>
160
+ <td align="right">54.0</td>
161
+ </tr>
162
+ <tr>
163
+ <td>ViT-g/14</td>
164
+ <td align="right">83.5%</td>
165
+ <td align="right">86.5%</td>
166
+ <td align="right">78.4%</td>
167
+ <td align="right">0.298</td>
168
+ <td align="right">0.362</td>
169
+ <td align="right">53.0</td>
170
+ <td align="right">81.6%</td>
171
+ <td align="right">52.3</td>
172
+ </tr>
173
+ </table>
174
+
175
+ ## Environmental Impact
176
+
177
+ - **Hardware Type:** Nvidia A100
178
+ - **Hours used:** 22,000 for ViT-g, 4,500 for ViT-S distillation, 5,300 for ViT-B distillation, 8,000 for ViT-L distillation
179
+ - **Cloud Provider:** Private infra
180
+ - **Compute Region:** USA
181
+ - **Carbon Emitted:** 7t CO2eq
182
+
183
+ #### Hardware
184
+
185
+ Nvidia A100 GPUs
186
+
187
+ #### Software
188
+
189
+ PyTorch 2.0,
190
+ xFormers 0.0.18
191
+
192
+ **BibTeX**
193
+
194
+ ```
195
+ @misc{oquab2023dinov2,
196
+ title={DINOv2: Learning Robust Visual Features without Supervision},
197
+ author={Oquab, Maxime and Darcet, Timothée and Moutakanni, Theo and Vo, Huy and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and Howes, Russell and Huang, Po-Yao and Xu, Hu and Sharma, Vasu and Li, Shang-Wen and Galuba, Wojciech and Rabbat, Mike and Assran, Mido and Ballas, Nicolas and Synnaeve, Gabriel and Misra, Ishan and Jegou, Herve and Mairal, Julien and Labatut, Patrick and Joulin, Armand and Bojanowski, Piotr},
198
+ journal={arXiv:2304.07193},
199
+ year={2023}
200
+ }
201
+ ```
torchhub/facebookresearch_dinov2_main/README.md ADDED
@@ -0,0 +1,277 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # DINOv2: Learning Robust Visual Features without Supervision
2
+
3
+ **[Meta AI Research, FAIR](https://ai.facebook.com/research/)**
4
+
5
+ Maxime Oquab,
6
+ Timothée Darcet,
7
+ Théo Moutakanni,
8
+ Huy V. Vo,
9
+ Marc Szafraniec,
10
+ Vasil Khalidov,
11
+ Patrick Labatut,
12
+ Armand Joulin,
13
+ Piotr Bojanowski
14
+
15
+ [[`Paper`](https://arxiv.org/abs/2304.07193)] [[`Blog`](https://ai.facebook.com/blog/dino-v2-computer-vision-self-supervised-learning/)] [[`Demo`](https://dinov2.metademolab.com)] [[`BibTeX`](#citing-dinov2)]
16
+
17
+ PyTorch implementation and pretrained models for DINOv2. For details, see the paper: **[DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193)**.
18
+
19
+ DINOv2 models produce high-performance visual features that can be directly employed with classifiers as simple as linear layers on a variety of computer vision tasks; these visual features are robust and perform well across domains without any requirement for fine-tuning. The models were pretrained on a dataset of 142 M images without using any labels or annotations.
20
+
21
+ https://github.com/facebookresearch/dinov2/assets/60359573/f168823e-7922-415a-b429-578badf5c356
22
+
23
+ <div align="center">
24
+ Visualization of the three first principal components of the patch features of all frames, mapped to RGB values.
25
+ </div>
26
+
27
+ ## Pretrained models
28
+
29
+ <table style="margin: auto">
30
+ <tr>
31
+ <th>model</th>
32
+ <th># of<br />params</th>
33
+ <th>ImageNet<br />k-NN</th>
34
+ <th>ImageNet<br />linear</th>
35
+ <th>download</th>
36
+ </tr>
37
+ <tr>
38
+ <td>ViT-S/14 distilled</td>
39
+ <td align="right">21 M</td>
40
+ <td align="right">79.0%</td>
41
+ <td align="right">81.1%</td>
42
+ <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vits14/dinov2_vits14_pretrain.pth">backbone only</a></td>
43
+ </tr>
44
+ <tr>
45
+ <td>ViT-B/14 distilled</td>
46
+ <td align="right">86 M</td>
47
+ <td align="right">82.1%</td>
48
+ <td align="right">84.5%</td>
49
+ <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vitb14/dinov2_vitb14_pretrain.pth">backbone only</a></td>
50
+ </tr>
51
+ <tr>
52
+ <td>ViT-L/14 distilled</td>
53
+ <td align="right">300 M</td>
54
+ <td align="right">83.5%</td>
55
+ <td align="right">86.3%</td>
56
+ <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vitl14/dinov2_vitl14_pretrain.pth">backbone only</a></td>
57
+ </tr>
58
+ <tr>
59
+ <td>ViT-g/14</td>
60
+ <td align="right">1,100 M</td>
61
+ <td align="right">83.5%</td>
62
+ <td align="right">86.5%</td>
63
+ <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vitg14/dinov2_vitg14_pretrain.pth">backbone only</a></td>
64
+ </tr>
65
+ </table>
66
+
67
+ ### Pretrained models via PyTorch Hub
68
+
69
+ Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install PyTorch (the only required dependency for loading the model). Installing PyTorch with CUDA support is strongly recommended.
70
+
71
+ A corresponding [model card](MODEL_CARD.md) is included in the repository.
72
+
73
+ ```python
74
+ import torch
75
+
76
+ dinov2_vits14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vits14')
77
+ dinov2_vitb14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitb14')
78
+ dinov2_vitl14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitl14')
79
+ dinov2_vitg14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitg14')
80
+ ```
81
+
82
+ ## Installation
83
+
84
+ The training and evaluation code requires PyTorch 2.0 and [xFormers](https://github.com/facebookresearch/xformers) 0.0.18 as well as a number of other 3rd party packages. Note that the code has only been tested with the specified versions and also expects a Linux environment. To setup all the required dependencies for training and evaluation, please follow the instructions below:
85
+
86
+ *[conda](https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html)* **(Recommended)** - Clone the repository and then create and activate a `dinov2` conda environment using the provided environment definition:
87
+
88
+ ```shell
89
+ conda env create -f conda.yaml
90
+ conda activate dinov2
91
+ ```
92
+
93
+ *[pip](https://pip.pypa.io/en/stable/getting-started/)* - Clone the repository and then use the provided `requirements.txt` to install the dependencies:
94
+
95
+ ```shell
96
+ pip install -r requirements.txt
97
+ ```
98
+
99
+ ## Data preparation
100
+
101
+ ### ImageNet-1k
102
+
103
+ The root directory of the dataset should hold the following contents:
104
+
105
+ - `<ROOT>/test/ILSVRC2012_test_00000001.JPEG`
106
+ - `<ROOT>/test/[..]`
107
+ - `<ROOT>/test/ILSVRC2012_test_00100000.JPEG`
108
+ - `<ROOT>/train/n01440764/n01440764_10026.JPEG`
109
+ - `<ROOT>/train/[...]`
110
+ - `<ROOT>/train/n15075141/n15075141_9993.JPEG`
111
+ - `<ROOT>/val/n01440764/ILSVRC2012_val_00000293.JPEG`
112
+ - `<ROOT>/val/[...]`
113
+ - `<ROOT>/val/n15075141/ILSVRC2012_val_00049174.JPEG`
114
+ - `<ROOT>/labels.txt`
115
+
116
+ The provided dataset implementation expects a few additional metadata files to be present under the extra directory:
117
+
118
+ - `<EXTRA>/class-ids-TRAIN.npy`
119
+ - `<EXTRA>/class-ids-VAL.npy`
120
+ - `<EXTRA>/class-names-TRAIN.npy`
121
+ - `<EXTRA>/class-names-VAL.npy`
122
+ - `<EXTRA>/entries-TEST.npy`
123
+ - `<EXTRA>/entries-TRAIN.npy`
124
+ - `<EXTRA>/entries-VAL.npy`
125
+
126
+ These metadata files can be generated (once) with the following lines of Python code:
127
+
128
+ ```python
129
+ from dinov2.data.datasets import ImageNet
130
+
131
+ for split in ImageNet.Split:
132
+ dataset = ImageNet(split=split, root="<ROOT>", extra="<EXTRA>")
133
+ dataset.dump_extra()
134
+ ```
135
+
136
+ Note that the root and extra directories do not have to be distinct directories.
137
+
138
+ ### ImageNet-22k
139
+
140
+ Please adapt the [dataset class](dinov2/data/datasets/image_net_22k.py) to match your local setup.
141
+
142
+ <br />
143
+
144
+ :warning: To execute the commands provided in the next sections for training and evaluation, the `dinov2` package should be included in the Python module search path, i.e. simply prefix the command to run with `PYTHONPATH=.`.
145
+
146
+ ## Training
147
+
148
+ ### Fast setup: training DINOv2 ViT-L/16 on ImageNet-1k
149
+
150
+ Run DINOv2 training on 4 A100-80GB nodes (32 GPUs) in a SLURM cluster environment with submitit:
151
+
152
+ ```shell
153
+ python dinov2/run/train/train.py \
154
+ --nodes 4 \
155
+ --config-file dinov2/configs/train/vitl16_short.yaml \
156
+ --output-dir <PATH/TO/OUTPUT/DIR> \
157
+ train.dataset_path=ImageNet:split=TRAIN:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET>
158
+ ```
159
+
160
+ Training time is approximately 1 day and the resulting checkpoint should reach 81.6% on k-NN eval and 82.9% on linear eval.
161
+
162
+ The training code saves the weights of the teacher in the `eval` folder every 12500 iterations for evaluation.
163
+
164
+ ### Long setup: training DINOv2 ViT-L/14 on ImageNet-22k
165
+
166
+ Run DINOv2 training on 12 A100-80GB nodes (96 GPUs) in a SLURM cluster environment with submitit:
167
+
168
+ ```shell
169
+ python dinov2/run/train/train.py \
170
+ --nodes 12 \
171
+ --config-file dinov2/configs/train/vitl14.yaml \
172
+ --output-dir <PATH/TO/OUTPUT/DIR> \
173
+ train.dataset_path=ImageNet22k:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET>
174
+ ```
175
+
176
+ Training time is approximately 3.3 days and the resulting checkpoint should reach 82.0% on k-NN eval and 84.5% on linear eval.
177
+
178
+ The training code saves the weights of the teacher in the `eval` folder every 12500 iterations for evaluation.
179
+
180
+
181
+ ## Evaluation
182
+
183
+ The training code regularly saves the teacher weights. In order to evaluate the model, run the following evaluation on a single node:
184
+
185
+ ### k-NN classification on ImageNet-1k
186
+
187
+ ```shell
188
+ python dinov2/run/eval/knn.py \
189
+ --config-file <PATH/TO/OUTPUT/DIR>/config.yaml \
190
+ --pretrained-weights <PATH/TO/OUTPUT/DIR>/eval/training_24999/teacher_checkpoint.pth \
191
+ --output-dir <PATH/TO/OUTPUT/DIR>/eval/training_24999/knn \
192
+ --train-dataset ImageNet:split=TRAIN:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET> \
193
+ --val-dataset ImageNet:split=VAL:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET>
194
+ ```
195
+
196
+ ### Logistic regression classification on ImageNet-1k
197
+
198
+ ```shell
199
+ python dinov2/run/eval/log_regression.py \
200
+ --config-file <PATH/TO/OUTPUT/DIR>/config.yaml \
201
+ --pretrained-weights <PATH/TO/OUTPUT/DIR>/eval/training_24999/teacher_checkpoint.pth \
202
+ --output-dir <PATH/TO/OUTPUT/DIR>/eval/training_24999/logreg \
203
+ --train-dataset ImageNet:split=TRAIN:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET> \
204
+ --val-dataset ImageNet:split=VAL:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET>
205
+ ```
206
+
207
+ ### Linear classification with data augmentation on ImageNet-1k
208
+
209
+ ```shell
210
+ python dinov2/run/eval/linear.py \
211
+ --config-file <PATH/TO/OUTPUT/DIR>/config.yaml \
212
+ --pretrained-weights <PATH/TO/OUTPUT/DIR>/eval/training_24999/teacher_checkpoint.pth \
213
+ --output-dir <PATH/TO/OUTPUT/DIR>/eval/training_24999/linear \
214
+ --train-dataset ImageNet:split=TRAIN:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET> \
215
+ --val-dataset ImageNet:split=VAL:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET>
216
+ ```
217
+
218
+ We release the weights from evaluating the different models:
219
+
220
+ <table style="margin: auto">
221
+ <tr>
222
+ <th>model</th>
223
+ <th>ImageNet<br />top-1</th>
224
+ <th>linear evaluation</th>
225
+ </tr>
226
+ <tr>
227
+ <td>ViT-S/14 distilled</td>
228
+ <td align="right">81.1%</td>
229
+ <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vits14/dinov2_vits14_linear_head.pth">linear head weights</a></td>
230
+ </tr>
231
+ <tr>
232
+ <td>ViT-B/14 distilled</td>
233
+ <td align="right">84.5%</td>
234
+ <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vitb14/dinov2_vitb14_linear_head.pth">linear head weights</a></td>
235
+ </tr>
236
+ <tr>
237
+ <td>ViT-L/14 distilled</td>
238
+ <td align="right">86.3%</td>
239
+ <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vitl14/dinov2_vitl14_linear_head.pth">linear head weights</a></td>
240
+ </tr>
241
+ <tr>
242
+ <td>ViT-g/14</td>
243
+ <td align="right">86.5%</td>
244
+ <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vitg14/dinov2_vitg14_linear_head.pth">linear head weights</a></td>
245
+ </tr>
246
+ </table>
247
+
248
+ The performance of the provided pretrained model weights can be evaluated as follows on ImageNet-1k:
249
+
250
+ ```shell
251
+ python dinov2/run/eval/linear.py \
252
+ --config-file dinov2/configs/eval/vitg14_pretrain.yaml \
253
+ --pretrained-weights https://dl.fbaipublicfiles.com/dinov2/dinov2_vitg14/dinov2_vitg14_pretrain.pth \
254
+ --train-dataset ImageNet:split=TRAIN:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET> \
255
+ --val-dataset ImageNet:split=VAL:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET>
256
+ ```
257
+
258
+ ## License
259
+
260
+ DINOv2 code and model weights are released under the CC-BY-NC 4.0 license. See [LICENSE](LICENSE) for additional details.
261
+
262
+ ## Contributing
263
+
264
+ See [contributing](CONTRIBUTING.md) and the [code of conduct](CODE_OF_CONDUCT.md).
265
+
266
+ ## Citing DINOv2
267
+
268
+ If you find this repository useful, please consider giving a star :star: and citation :t-rex::
269
+
270
+ ```
271
+ @misc{oquab2023dinov2,
272
+ title={DINOv2: Learning Robust Visual Features without Supervision},
273
+ author={Oquab, Maxime and Darcet, Timothée and Moutakanni, Theo and Vo, Huy V. and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and Howes, Russell and Huang, Po-Yao and Xu, Hu and Sharma, Vasu and Li, Shang-Wen and Galuba, Wojciech and Rabbat, Mike and Assran, Mido and Ballas, Nicolas and Synnaeve, Gabriel and Misra, Ishan and Jegou, Herve and Mairal, Julien and Labatut, Patrick and Joulin, Armand and Bojanowski, Piotr},
274
+ journal={arXiv:2304.07193},
275
+ year={2023}
276
+ }
277
+ ```
torchhub/facebookresearch_dinov2_main/conda.yaml ADDED
@@ -0,0 +1,22 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ name: dinov2
2
+ channels:
3
+ - defaults
4
+ - pytorch
5
+ - nvidia
6
+ - xformers
7
+ - conda-forge
8
+ dependencies:
9
+ - python=3.9
10
+ - pytorch::pytorch=2.0.0
11
+ - pytorch::pytorch-cuda=11.7.0
12
+ - pytorch::torchvision=0.15.0
13
+ - omegaconf
14
+ - torchmetrics=0.10.3
15
+ - fvcore
16
+ - iopath
17
+ - xformers::xformers=0.0.18
18
+ - pip
19
+ - pip:
20
+ - git+https://github.com/facebookincubator/submitit
21
+ - --extra-index-url https://pypi.nvidia.com
22
+ - cuml-cu11
torchhub/facebookresearch_dinov2_main/dinov2/.DS_Store ADDED
Binary file (6.15 kB). View file
 
torchhub/facebookresearch_dinov2_main/dinov2/__init__.py ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ __version__ = "0.0.1"
torchhub/facebookresearch_dinov2_main/dinov2/configs/.DS_Store ADDED
Binary file (6.15 kB). View file
 
torchhub/facebookresearch_dinov2_main/dinov2/configs/__init__.py ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import pathlib
8
+
9
+ from omegaconf import OmegaConf
10
+
11
+
12
+ def load_config(config_name: str):
13
+ config_filename = config_name + ".yaml"
14
+ return OmegaConf.load(pathlib.Path(__file__).parent.resolve() / config_filename)
15
+
16
+
17
+ dinov2_default_config = load_config("ssl_default_config")
18
+
19
+
20
+ def load_and_merge_config(config_name: str):
21
+ default_config = OmegaConf.create(dinov2_default_config)
22
+ loaded_config = load_config(config_name)
23
+ return OmegaConf.merge(default_config, loaded_config)
torchhub/facebookresearch_dinov2_main/dinov2/configs/eval/vitb14_pretrain.yaml ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ student:
2
+ arch: vit_base
3
+ patch_size: 14
4
+ crops:
5
+ global_crops_size: 518 # this is to set up the position embeddings properly
6
+ local_crops_size: 98
torchhub/facebookresearch_dinov2_main/dinov2/configs/eval/vitg14_pretrain.yaml ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ student:
2
+ arch: vit_giant2
3
+ patch_size: 14
4
+ ffn_layer: swiglufused
5
+ crops:
6
+ global_crops_size: 518 # this is to set up the position embeddings properly
7
+ local_crops_size: 98
torchhub/facebookresearch_dinov2_main/dinov2/configs/eval/vitl14_pretrain.yaml ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ student:
2
+ arch: vit_large
3
+ patch_size: 14
4
+ crops:
5
+ global_crops_size: 518 # this is to set up the position embeddings properly
6
+ local_crops_size: 98
torchhub/facebookresearch_dinov2_main/dinov2/configs/eval/vits14_pretrain.yaml ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ student:
2
+ arch: vit_small
3
+ patch_size: 14
4
+ crops:
5
+ global_crops_size: 518 # this is to set up the position embeddings properly
6
+ local_crops_size: 98
torchhub/facebookresearch_dinov2_main/dinov2/configs/ssl_default_config.yaml ADDED
@@ -0,0 +1,115 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ MODEL:
2
+ WEIGHTS: ''
3
+ compute_precision:
4
+ grad_scaler: true
5
+ teacher:
6
+ backbone:
7
+ sharding_strategy: SHARD_GRAD_OP
8
+ mixed_precision:
9
+ param_dtype: fp16
10
+ reduce_dtype: fp16
11
+ buffer_dtype: fp32
12
+ dino_head:
13
+ sharding_strategy: SHARD_GRAD_OP
14
+ mixed_precision:
15
+ param_dtype: fp16
16
+ reduce_dtype: fp16
17
+ buffer_dtype: fp32
18
+ ibot_head:
19
+ sharding_strategy: SHARD_GRAD_OP
20
+ mixed_precision:
21
+ param_dtype: fp16
22
+ reduce_dtype: fp16
23
+ buffer_dtype: fp32
24
+ student:
25
+ backbone:
26
+ sharding_strategy: SHARD_GRAD_OP
27
+ mixed_precision:
28
+ param_dtype: fp16
29
+ reduce_dtype: fp16
30
+ buffer_dtype: fp32
31
+ dino_head:
32
+ sharding_strategy: SHARD_GRAD_OP
33
+ mixed_precision:
34
+ param_dtype: fp16
35
+ reduce_dtype: fp32
36
+ buffer_dtype: fp32
37
+ ibot_head:
38
+ sharding_strategy: SHARD_GRAD_OP
39
+ mixed_precision:
40
+ param_dtype: fp16
41
+ reduce_dtype: fp32
42
+ buffer_dtype: fp32
43
+ dino:
44
+ loss_weight: 1.0
45
+ head_n_prototypes: 65536
46
+ head_bottleneck_dim: 256
47
+ head_nlayers: 3
48
+ head_hidden_dim: 2048
49
+ koleo_loss_weight: 0.1
50
+ ibot:
51
+ loss_weight: 1.0
52
+ mask_sample_probability: 0.5
53
+ mask_ratio_min_max:
54
+ - 0.1
55
+ - 0.5
56
+ separate_head: false
57
+ head_n_prototypes: 65536
58
+ head_bottleneck_dim: 256
59
+ head_nlayers: 3
60
+ head_hidden_dim: 2048
61
+ train:
62
+ batch_size_per_gpu: 64
63
+ dataset_path: ImageNet:split=TRAIN
64
+ output_dir: .
65
+ saveckp_freq: 20
66
+ seed: 0
67
+ num_workers: 10
68
+ OFFICIAL_EPOCH_LENGTH: 1250
69
+ cache_dataset: true
70
+ centering: "centering" # or "sinkhorn_knopp"
71
+ student:
72
+ arch: vit_large
73
+ patch_size: 16
74
+ drop_path_rate: 0.3
75
+ layerscale: 1.0e-05
76
+ drop_path_uniform: true
77
+ pretrained_weights: ''
78
+ ffn_layer: "mlp"
79
+ block_chunks: 0
80
+ qkv_bias: true
81
+ proj_bias: true
82
+ ffn_bias: true
83
+ teacher:
84
+ momentum_teacher: 0.992
85
+ final_momentum_teacher: 1
86
+ warmup_teacher_temp: 0.04
87
+ teacher_temp: 0.07
88
+ warmup_teacher_temp_epochs: 30
89
+ optim:
90
+ epochs: 100
91
+ weight_decay: 0.04
92
+ weight_decay_end: 0.4
93
+ base_lr: 0.004 # learning rate for a batch size of 1024
94
+ lr: 0. # will be set after applying scaling rule
95
+ warmup_epochs: 10
96
+ min_lr: 1.0e-06
97
+ clip_grad: 3.0
98
+ freeze_last_layer_epochs: 1
99
+ scaling_rule: sqrt_wrt_1024
100
+ patch_embed_lr_mult: 0.2
101
+ layerwise_decay: 0.9
102
+ adamw_beta1: 0.9
103
+ adamw_beta2: 0.999
104
+ crops:
105
+ global_crops_scale:
106
+ - 0.32
107
+ - 1.0
108
+ local_crops_number: 8
109
+ local_crops_scale:
110
+ - 0.05
111
+ - 0.32
112
+ global_crops_size: 224
113
+ local_crops_size: 96
114
+ evaluation:
115
+ eval_period_iterations: 12500
torchhub/facebookresearch_dinov2_main/dinov2/configs/train/vitg14.yaml ADDED
@@ -0,0 +1,26 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ dino:
2
+ head_n_prototypes: 131072
3
+ head_bottleneck_dim: 384
4
+ ibot:
5
+ separate_head: true
6
+ head_n_prototypes: 131072
7
+ train:
8
+ batch_size_per_gpu: 12
9
+ dataset_path: ImageNet22k
10
+ centering: sinkhorn_knopp
11
+ student:
12
+ arch: vit_giant2
13
+ patch_size: 14
14
+ drop_path_rate: 0.4
15
+ ffn_layer: swiglufused
16
+ block_chunks: 4
17
+ teacher:
18
+ momentum_teacher: 0.994
19
+ optim:
20
+ epochs: 500
21
+ weight_decay_end: 0.2
22
+ base_lr: 2.0e-04 # learning rate for a batch size of 1024
23
+ warmup_epochs: 80
24
+ layerwise_decay: 1.0
25
+ crops:
26
+ local_crops_size: 98
torchhub/facebookresearch_dinov2_main/dinov2/configs/train/vitl14.yaml ADDED
@@ -0,0 +1,26 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ dino:
2
+ head_n_prototypes: 131072
3
+ head_bottleneck_dim: 384
4
+ ibot:
5
+ separate_head: true
6
+ head_n_prototypes: 131072
7
+ train:
8
+ batch_size_per_gpu: 32
9
+ dataset_path: ImageNet22k
10
+ centering: sinkhorn_knopp
11
+ student:
12
+ arch: vit_large
13
+ patch_size: 14
14
+ drop_path_rate: 0.4
15
+ ffn_layer: swiglufused
16
+ block_chunks: 4
17
+ teacher:
18
+ momentum_teacher: 0.994
19
+ optim:
20
+ epochs: 500
21
+ weight_decay_end: 0.2
22
+ base_lr: 2.0e-04 # learning rate for a batch size of 1024
23
+ warmup_epochs: 80
24
+ layerwise_decay: 1.0
25
+ crops:
26
+ local_crops_size: 98
torchhub/facebookresearch_dinov2_main/dinov2/configs/train/vitl16_short.yaml ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ # this corresponds to the default config
2
+ train:
3
+ dataset_path: ImageNet:split=TRAIN
4
+ batch_size_per_gpu: 64
5
+ student:
6
+ block_chunks: 4
torchhub/facebookresearch_dinov2_main/dinov2/data/.DS_Store ADDED
Binary file (6.15 kB). View file
 
torchhub/facebookresearch_dinov2_main/dinov2/data/__init__.py ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ from .adapters import DatasetWithEnumeratedTargets
8
+ from .loaders import make_data_loader, make_dataset, SamplerType
9
+ from .collate import collate_data_and_cast
10
+ from .masking import MaskingGenerator
11
+ from .augmentations import DataAugmentationDINO
torchhub/facebookresearch_dinov2_main/dinov2/data/adapters.py ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ from typing import Any, Tuple
8
+
9
+ from torch.utils.data import Dataset
10
+
11
+
12
+ class DatasetWithEnumeratedTargets(Dataset):
13
+ def __init__(self, dataset):
14
+ self._dataset = dataset
15
+
16
+ def get_image_data(self, index: int) -> bytes:
17
+ return self._dataset.get_image_data(index)
18
+
19
+ def get_target(self, index: int) -> Tuple[Any, int]:
20
+ target = self._dataset.get_target(index)
21
+ return (index, target)
22
+
23
+ def __getitem__(self, index: int) -> Tuple[Any, Tuple[Any, int]]:
24
+ image, target = self._dataset[index]
25
+ target = index if target is None else target
26
+ return image, (index, target)
27
+
28
+ def __len__(self) -> int:
29
+ return len(self._dataset)
torchhub/facebookresearch_dinov2_main/dinov2/data/augmentations.py ADDED
@@ -0,0 +1,119 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import logging
8
+
9
+ from torchvision import transforms
10
+
11
+ from .transforms import (
12
+ GaussianBlur,
13
+ make_normalize_transform,
14
+ )
15
+
16
+
17
+ logger = logging.getLogger("dinov2")
18
+
19
+
20
+ class DataAugmentationDINO(object):
21
+ def __init__(
22
+ self,
23
+ global_crops_scale,
24
+ local_crops_scale,
25
+ local_crops_number,
26
+ global_crops_size=224,
27
+ local_crops_size=96,
28
+ ):
29
+ self.global_crops_scale = global_crops_scale
30
+ self.local_crops_scale = local_crops_scale
31
+ self.local_crops_number = local_crops_number
32
+ self.global_crops_size = global_crops_size
33
+ self.local_crops_size = local_crops_size
34
+
35
+ logger.info("###################################")
36
+ logger.info("Using data augmentation parameters:")
37
+ logger.info(f"global_crops_scale: {global_crops_scale}")
38
+ logger.info(f"local_crops_scale: {local_crops_scale}")
39
+ logger.info(f"local_crops_number: {local_crops_number}")
40
+ logger.info(f"global_crops_size: {global_crops_size}")
41
+ logger.info(f"local_crops_size: {local_crops_size}")
42
+ logger.info("###################################")
43
+
44
+ # random resized crop and flip
45
+ self.geometric_augmentation_global = transforms.Compose(
46
+ [
47
+ transforms.RandomResizedCrop(
48
+ global_crops_size, scale=global_crops_scale, interpolation=transforms.InterpolationMode.BICUBIC
49
+ ),
50
+ transforms.RandomHorizontalFlip(p=0.5),
51
+ ]
52
+ )
53
+
54
+ self.geometric_augmentation_local = transforms.Compose(
55
+ [
56
+ transforms.RandomResizedCrop(
57
+ local_crops_size, scale=local_crops_scale, interpolation=transforms.InterpolationMode.BICUBIC
58
+ ),
59
+ transforms.RandomHorizontalFlip(p=0.5),
60
+ ]
61
+ )
62
+
63
+ # color distorsions / blurring
64
+ color_jittering = transforms.Compose(
65
+ [
66
+ transforms.RandomApply(
67
+ [transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.2, hue=0.1)],
68
+ p=0.8,
69
+ ),
70
+ transforms.RandomGrayscale(p=0.2),
71
+ ]
72
+ )
73
+
74
+ global_transfo1_extra = GaussianBlur(p=1.0)
75
+
76
+ global_transfo2_extra = transforms.Compose(
77
+ [
78
+ GaussianBlur(p=0.1),
79
+ transforms.RandomSolarize(threshold=128, p=0.2),
80
+ ]
81
+ )
82
+
83
+ local_transfo_extra = GaussianBlur(p=0.5)
84
+
85
+ # normalization
86
+ self.normalize = transforms.Compose(
87
+ [
88
+ transforms.ToTensor(),
89
+ make_normalize_transform(),
90
+ ]
91
+ )
92
+
93
+ self.global_transfo1 = transforms.Compose([color_jittering, global_transfo1_extra, self.normalize])
94
+ self.global_transfo2 = transforms.Compose([color_jittering, global_transfo2_extra, self.normalize])
95
+ self.local_transfo = transforms.Compose([color_jittering, local_transfo_extra, self.normalize])
96
+
97
+ def __call__(self, image):
98
+ output = {}
99
+
100
+ # global crops:
101
+ im1_base = self.geometric_augmentation_global(image)
102
+ global_crop_1 = self.global_transfo1(im1_base)
103
+
104
+ im2_base = self.geometric_augmentation_global(image)
105
+ global_crop_2 = self.global_transfo2(im2_base)
106
+
107
+ output["global_crops"] = [global_crop_1, global_crop_2]
108
+
109
+ # global crops for teacher:
110
+ output["global_crops_teacher"] = [global_crop_1, global_crop_2]
111
+
112
+ # local crops:
113
+ local_crops = [
114
+ self.local_transfo(self.geometric_augmentation_local(image)) for _ in range(self.local_crops_number)
115
+ ]
116
+ output["local_crops"] = local_crops
117
+ output["offsets"] = ()
118
+
119
+ return output
torchhub/facebookresearch_dinov2_main/dinov2/data/collate.py ADDED
@@ -0,0 +1,50 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import torch
8
+ import random
9
+
10
+
11
+ def collate_data_and_cast(samples_list, mask_ratio_tuple, mask_probability, dtype, n_tokens=None, mask_generator=None):
12
+ # dtype = torch.half # TODO: Remove
13
+
14
+ n_global_crops = len(samples_list[0][0]["global_crops"])
15
+ n_local_crops = len(samples_list[0][0]["local_crops"])
16
+
17
+ collated_global_crops = torch.stack([s[0]["global_crops"][i] for i in range(n_global_crops) for s in samples_list])
18
+
19
+ collated_local_crops = torch.stack([s[0]["local_crops"][i] for i in range(n_local_crops) for s in samples_list])
20
+
21
+ B = len(collated_global_crops)
22
+ N = n_tokens
23
+ n_samples_masked = int(B * mask_probability)
24
+ probs = torch.linspace(*mask_ratio_tuple, n_samples_masked + 1)
25
+ upperbound = 0
26
+ masks_list = []
27
+ for i in range(0, n_samples_masked):
28
+ prob_min = probs[i]
29
+ prob_max = probs[i + 1]
30
+ masks_list.append(torch.BoolTensor(mask_generator(int(N * random.uniform(prob_min, prob_max)))))
31
+ upperbound += int(N * prob_max)
32
+ for i in range(n_samples_masked, B):
33
+ masks_list.append(torch.BoolTensor(mask_generator(0)))
34
+
35
+ random.shuffle(masks_list)
36
+
37
+ collated_masks = torch.stack(masks_list).flatten(1)
38
+ mask_indices_list = collated_masks.flatten().nonzero().flatten()
39
+
40
+ masks_weight = (1 / collated_masks.sum(-1).clamp(min=1.0)).unsqueeze(-1).expand_as(collated_masks)[collated_masks]
41
+
42
+ return {
43
+ "collated_global_crops": collated_global_crops.to(dtype),
44
+ "collated_local_crops": collated_local_crops.to(dtype),
45
+ "collated_masks": collated_masks,
46
+ "mask_indices_list": mask_indices_list,
47
+ "masks_weight": masks_weight,
48
+ "upperbound": upperbound,
49
+ "n_masked_patches": torch.full((1,), fill_value=mask_indices_list.shape[0], dtype=torch.long),
50
+ }
torchhub/facebookresearch_dinov2_main/dinov2/data/datasets/__init__.py ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ from .image_net import ImageNet
8
+ from .image_net_22k import ImageNet22k
torchhub/facebookresearch_dinov2_main/dinov2/data/datasets/decoders.py ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ from io import BytesIO
8
+ from typing import Any
9
+
10
+ from PIL import Image
11
+
12
+
13
+ class Decoder:
14
+ def decode(self) -> Any:
15
+ raise NotImplementedError
16
+
17
+
18
+ class ImageDataDecoder(Decoder):
19
+ def __init__(self, image_data: bytes) -> None:
20
+ self._image_data = image_data
21
+
22
+ def decode(self) -> Image:
23
+ f = BytesIO(self._image_data)
24
+ return Image.open(f).convert(mode="RGB")
25
+
26
+
27
+ class TargetDecoder(Decoder):
28
+ def __init__(self, target: Any):
29
+ self._target = target
30
+
31
+ def decode(self) -> Any:
32
+ return self._target
torchhub/facebookresearch_dinov2_main/dinov2/data/datasets/extended.py ADDED
@@ -0,0 +1,39 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ from typing import Any, Tuple
8
+
9
+ from torchvision.datasets import VisionDataset
10
+
11
+ from .decoders import TargetDecoder, ImageDataDecoder
12
+
13
+
14
+ class ExtendedVisionDataset(VisionDataset):
15
+ def __init__(self, *args, **kwargs) -> None:
16
+ super().__init__(*args, **kwargs) # type: ignore
17
+
18
+ def get_image_data(self, index: int) -> bytes:
19
+ raise NotImplementedError
20
+
21
+ def get_target(self, index: int) -> Any:
22
+ raise NotImplementedError
23
+
24
+ def __getitem__(self, index: int) -> Tuple[Any, Any]:
25
+ try:
26
+ image_data = self.get_image_data(index)
27
+ image = ImageDataDecoder(image_data).decode()
28
+ except Exception as e:
29
+ raise RuntimeError(f"can not read image for sample {index}") from e
30
+ target = self.get_target(index)
31
+ target = TargetDecoder(target).decode()
32
+
33
+ if self.transforms is not None:
34
+ image, target = self.transforms(image, target)
35
+
36
+ return image, target
37
+
38
+ def __len__(self) -> int:
39
+ raise NotImplementedError
torchhub/facebookresearch_dinov2_main/dinov2/data/datasets/image_net.py ADDED
@@ -0,0 +1,291 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import csv
8
+ from enum import Enum
9
+ import logging
10
+ import os
11
+ from typing import Callable, List, Optional, Tuple, Union
12
+
13
+ import numpy as np
14
+
15
+ from .extended import ExtendedVisionDataset
16
+
17
+
18
+ logger = logging.getLogger("dinov2")
19
+ _Target = int
20
+
21
+
22
+ class _Split(Enum):
23
+ TRAIN = "train"
24
+ VAL = "val"
25
+ TEST = "test" # NOTE: torchvision does not support the test split
26
+
27
+ @property
28
+ def length(self) -> int:
29
+ split_lengths = {
30
+ _Split.TRAIN: 1_281_167,
31
+ _Split.VAL: 50_000,
32
+ _Split.TEST: 100_000,
33
+ }
34
+ return split_lengths[self]
35
+
36
+ def get_dirname(self, class_id: Optional[str] = None) -> str:
37
+ return self.value if class_id is None else os.path.join(self.value, class_id)
38
+
39
+ def get_image_relpath(self, actual_index: int, class_id: Optional[str] = None) -> str:
40
+ dirname = self.get_dirname(class_id)
41
+ if self == _Split.TRAIN:
42
+ basename = f"{class_id}_{actual_index}"
43
+ else: # self in (_Split.VAL, _Split.TEST):
44
+ basename = f"ILSVRC2012_{self.value}_{actual_index:08d}"
45
+ return os.path.join(dirname, basename + ".JPEG")
46
+
47
+ def parse_image_relpath(self, image_relpath: str) -> Tuple[str, int]:
48
+ assert self != _Split.TEST
49
+ dirname, filename = os.path.split(image_relpath)
50
+ class_id = os.path.split(dirname)[-1]
51
+ basename, _ = os.path.splitext(filename)
52
+ actual_index = int(basename.split("_")[-1])
53
+ return class_id, actual_index
54
+
55
+
56
+ class ImageNet(ExtendedVisionDataset):
57
+ Target = Union[_Target]
58
+ Split = Union[_Split]
59
+
60
+ def __init__(
61
+ self,
62
+ *,
63
+ split: "ImageNet.Split",
64
+ root: str,
65
+ extra: str,
66
+ transforms: Optional[Callable] = None,
67
+ transform: Optional[Callable] = None,
68
+ target_transform: Optional[Callable] = None,
69
+ ) -> None:
70
+ super().__init__(root, transforms, transform, target_transform)
71
+ self._extra_root = extra
72
+ self._split = split
73
+
74
+ self._entries = None
75
+ self._class_ids = None
76
+ self._class_names = None
77
+
78
+ @property
79
+ def split(self) -> "ImageNet.Split":
80
+ return self._split
81
+
82
+ def _get_extra_full_path(self, extra_path: str) -> str:
83
+ return os.path.join(self._extra_root, extra_path)
84
+
85
+ def _load_extra(self, extra_path: str) -> np.ndarray:
86
+ extra_full_path = self._get_extra_full_path(extra_path)
87
+ return np.load(extra_full_path, mmap_mode="r")
88
+
89
+ def _save_extra(self, extra_array: np.ndarray, extra_path: str) -> None:
90
+ extra_full_path = self._get_extra_full_path(extra_path)
91
+ os.makedirs(self._extra_root, exist_ok=True)
92
+ np.save(extra_full_path, extra_array)
93
+
94
+ @property
95
+ def _entries_path(self) -> str:
96
+ return f"entries-{self._split.value.upper()}.npy"
97
+
98
+ @property
99
+ def _class_ids_path(self) -> str:
100
+ return f"class-ids-{self._split.value.upper()}.npy"
101
+
102
+ @property
103
+ def _class_names_path(self) -> str:
104
+ return f"class-names-{self._split.value.upper()}.npy"
105
+
106
+ def _get_entries(self) -> np.ndarray:
107
+ if self._entries is None:
108
+ self._entries = self._load_extra(self._entries_path)
109
+ assert self._entries is not None
110
+ return self._entries
111
+
112
+ def _get_class_ids(self) -> np.ndarray:
113
+ if self._split == _Split.TEST:
114
+ assert False, "Class IDs are not available in TEST split"
115
+ if self._class_ids is None:
116
+ self._class_ids = self._load_extra(self._class_ids_path)
117
+ assert self._class_ids is not None
118
+ return self._class_ids
119
+
120
+ def _get_class_names(self) -> np.ndarray:
121
+ if self._split == _Split.TEST:
122
+ assert False, "Class names are not available in TEST split"
123
+ if self._class_names is None:
124
+ self._class_names = self._load_extra(self._class_names_path)
125
+ assert self._class_names is not None
126
+ return self._class_names
127
+
128
+ def find_class_id(self, class_index: int) -> str:
129
+ class_ids = self._get_class_ids()
130
+ return str(class_ids[class_index])
131
+
132
+ def find_class_name(self, class_index: int) -> str:
133
+ class_names = self._get_class_names()
134
+ return str(class_names[class_index])
135
+
136
+ def get_image_data(self, index: int) -> bytes:
137
+ entries = self._get_entries()
138
+ actual_index = entries[index]["actual_index"]
139
+
140
+ class_id = self.get_class_id(index)
141
+
142
+ image_relpath = self.split.get_image_relpath(actual_index, class_id)
143
+ image_full_path = os.path.join(self.root, image_relpath)
144
+ with open(image_full_path, mode="rb") as f:
145
+ image_data = f.read()
146
+ return image_data
147
+
148
+ def get_target(self, index: int) -> Optional[Target]:
149
+ entries = self._get_entries()
150
+ class_index = entries[index]["class_index"]
151
+ return None if self.split == _Split.TEST else int(class_index)
152
+
153
+ def get_targets(self) -> Optional[np.ndarray]:
154
+ entries = self._get_entries()
155
+ return None if self.split == _Split.TEST else entries["class_index"]
156
+
157
+ def get_class_id(self, index: int) -> Optional[str]:
158
+ entries = self._get_entries()
159
+ class_id = entries[index]["class_id"]
160
+ return None if self.split == _Split.TEST else str(class_id)
161
+
162
+ def get_class_name(self, index: int) -> Optional[str]:
163
+ entries = self._get_entries()
164
+ class_name = entries[index]["class_name"]
165
+ return None if self.split == _Split.TEST else str(class_name)
166
+
167
+ def __len__(self) -> int:
168
+ entries = self._get_entries()
169
+ assert len(entries) == self.split.length
170
+ return len(entries)
171
+
172
+ def _load_labels(self, labels_path: str) -> List[Tuple[str, str]]:
173
+ labels_full_path = os.path.join(self.root, labels_path)
174
+ labels = []
175
+
176
+ try:
177
+ with open(labels_full_path, "r") as f:
178
+ reader = csv.reader(f)
179
+ for row in reader:
180
+ class_id, class_name = row
181
+ labels.append((class_id, class_name))
182
+ except OSError as e:
183
+ raise RuntimeError(f'can not read labels file "{labels_full_path}"') from e
184
+
185
+ return labels
186
+
187
+ def _dump_entries(self) -> None:
188
+ split = self.split
189
+ if split == ImageNet.Split.TEST:
190
+ dataset = None
191
+ sample_count = split.length
192
+ max_class_id_length, max_class_name_length = 0, 0
193
+ else:
194
+ labels_path = "labels.txt"
195
+ logger.info(f'loading labels from "{labels_path}"')
196
+ labels = self._load_labels(labels_path)
197
+
198
+ # NOTE: Using torchvision ImageFolder for consistency
199
+ from torchvision.datasets import ImageFolder
200
+
201
+ dataset_root = os.path.join(self.root, split.get_dirname())
202
+ dataset = ImageFolder(dataset_root)
203
+ sample_count = len(dataset)
204
+ max_class_id_length, max_class_name_length = -1, -1
205
+ for sample in dataset.samples:
206
+ _, class_index = sample
207
+ class_id, class_name = labels[class_index]
208
+ max_class_id_length = max(len(class_id), max_class_id_length)
209
+ max_class_name_length = max(len(class_name), max_class_name_length)
210
+
211
+ dtype = np.dtype(
212
+ [
213
+ ("actual_index", "<u4"),
214
+ ("class_index", "<u4"),
215
+ ("class_id", f"U{max_class_id_length}"),
216
+ ("class_name", f"U{max_class_name_length}"),
217
+ ]
218
+ )
219
+ entries_array = np.empty(sample_count, dtype=dtype)
220
+
221
+ if split == ImageNet.Split.TEST:
222
+ old_percent = -1
223
+ for index in range(sample_count):
224
+ percent = 100 * (index + 1) // sample_count
225
+ if percent > old_percent:
226
+ logger.info(f"creating entries: {percent}%")
227
+ old_percent = percent
228
+
229
+ actual_index = index + 1
230
+ class_index = np.uint32(-1)
231
+ class_id, class_name = "", ""
232
+ entries_array[index] = (actual_index, class_index, class_id, class_name)
233
+ else:
234
+ class_names = {class_id: class_name for class_id, class_name in labels}
235
+
236
+ assert dataset
237
+ old_percent = -1
238
+ for index in range(sample_count):
239
+ percent = 100 * (index + 1) // sample_count
240
+ if percent > old_percent:
241
+ logger.info(f"creating entries: {percent}%")
242
+ old_percent = percent
243
+
244
+ image_full_path, class_index = dataset.samples[index]
245
+ image_relpath = os.path.relpath(image_full_path, self.root)
246
+ class_id, actual_index = split.parse_image_relpath(image_relpath)
247
+ class_name = class_names[class_id]
248
+ entries_array[index] = (actual_index, class_index, class_id, class_name)
249
+
250
+ logger.info(f'saving entries to "{self._entries_path}"')
251
+ self._save_extra(entries_array, self._entries_path)
252
+
253
+ def _dump_class_ids_and_names(self) -> None:
254
+ split = self.split
255
+ if split == ImageNet.Split.TEST:
256
+ return
257
+
258
+ entries_array = self._load_extra(self._entries_path)
259
+
260
+ max_class_id_length, max_class_name_length, max_class_index = -1, -1, -1
261
+ for entry in entries_array:
262
+ class_index, class_id, class_name = (
263
+ entry["class_index"],
264
+ entry["class_id"],
265
+ entry["class_name"],
266
+ )
267
+ max_class_index = max(int(class_index), max_class_index)
268
+ max_class_id_length = max(len(str(class_id)), max_class_id_length)
269
+ max_class_name_length = max(len(str(class_name)), max_class_name_length)
270
+
271
+ class_count = max_class_index + 1
272
+ class_ids_array = np.empty(class_count, dtype=f"U{max_class_id_length}")
273
+ class_names_array = np.empty(class_count, dtype=f"U{max_class_name_length}")
274
+ for entry in entries_array:
275
+ class_index, class_id, class_name = (
276
+ entry["class_index"],
277
+ entry["class_id"],
278
+ entry["class_name"],
279
+ )
280
+ class_ids_array[class_index] = class_id
281
+ class_names_array[class_index] = class_name
282
+
283
+ logger.info(f'saving class IDs to "{self._class_ids_path}"')
284
+ self._save_extra(class_ids_array, self._class_ids_path)
285
+
286
+ logger.info(f'saving class names to "{self._class_names_path}"')
287
+ self._save_extra(class_names_array, self._class_names_path)
288
+
289
+ def dump_extra(self) -> None:
290
+ self._dump_entries()
291
+ self._dump_class_ids_and_names()
torchhub/facebookresearch_dinov2_main/dinov2/data/datasets/image_net_22k.py ADDED
@@ -0,0 +1,303 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ from dataclasses import dataclass
8
+ from enum import Enum
9
+ from functools import lru_cache
10
+ from gzip import GzipFile
11
+ from io import BytesIO
12
+ from mmap import ACCESS_READ, mmap
13
+ import os
14
+ from typing import Any, Callable, List, Optional, Set, Tuple
15
+ import warnings
16
+
17
+ import numpy as np
18
+
19
+ from .extended import ExtendedVisionDataset
20
+
21
+
22
+ _Labels = int
23
+
24
+ _DEFAULT_MMAP_CACHE_SIZE = 16 # Warning: This can exhaust file descriptors
25
+
26
+
27
+ @dataclass
28
+ class _ClassEntry:
29
+ block_offset: int
30
+ maybe_filename: Optional[str] = None
31
+
32
+
33
+ @dataclass
34
+ class _Entry:
35
+ class_index: int # noqa: E701
36
+ start_offset: int
37
+ end_offset: int
38
+ filename: str
39
+
40
+
41
+ class _Split(Enum):
42
+ TRAIN = "train"
43
+ VAL = "val"
44
+
45
+ @property
46
+ def length(self) -> int:
47
+ return {
48
+ _Split.TRAIN: 11_797_647,
49
+ _Split.VAL: 561_050,
50
+ }[self]
51
+
52
+ def entries_path(self):
53
+ return f"imagenet21kp_{self.value}.txt"
54
+
55
+
56
+ def _get_tarball_path(class_id: str) -> str:
57
+ return f"{class_id}.tar"
58
+
59
+
60
+ def _make_mmap_tarball(tarballs_root: str, mmap_cache_size: int):
61
+ @lru_cache(maxsize=mmap_cache_size)
62
+ def _mmap_tarball(class_id: str) -> mmap:
63
+ tarball_path = _get_tarball_path(class_id)
64
+ tarball_full_path = os.path.join(tarballs_root, tarball_path)
65
+ with open(tarball_full_path) as f:
66
+ return mmap(fileno=f.fileno(), length=0, access=ACCESS_READ)
67
+
68
+ return _mmap_tarball
69
+
70
+
71
+ class ImageNet22k(ExtendedVisionDataset):
72
+ _GZIPPED_INDICES: Set[int] = {
73
+ 841_545,
74
+ 1_304_131,
75
+ 2_437_921,
76
+ 2_672_079,
77
+ 2_795_676,
78
+ 2_969_786,
79
+ 6_902_965,
80
+ 6_903_550,
81
+ 6_903_628,
82
+ 7_432_557,
83
+ 7_432_589,
84
+ 7_813_809,
85
+ 8_329_633,
86
+ 10_296_990,
87
+ 10_417_652,
88
+ 10_492_265,
89
+ 10_598_078,
90
+ 10_782_398,
91
+ 10_902_612,
92
+ 11_203_736,
93
+ 11_342_890,
94
+ 11_397_596,
95
+ 11_589_762,
96
+ 11_705_103,
97
+ 12_936_875,
98
+ 13_289_782,
99
+ }
100
+ Labels = _Labels
101
+
102
+ def __init__(
103
+ self,
104
+ *,
105
+ root: str,
106
+ extra: str,
107
+ transforms: Optional[Callable] = None,
108
+ transform: Optional[Callable] = None,
109
+ target_transform: Optional[Callable] = None,
110
+ mmap_cache_size: int = _DEFAULT_MMAP_CACHE_SIZE,
111
+ ) -> None:
112
+ super().__init__(root, transforms, transform, target_transform)
113
+ self._extra_root = extra
114
+
115
+ entries_path = self._get_entries_path(root)
116
+ self._entries = self._load_extra(entries_path)
117
+
118
+ class_ids_path = self._get_class_ids_path(root)
119
+ self._class_ids = self._load_extra(class_ids_path)
120
+
121
+ self._gzipped_indices = ImageNet22k._GZIPPED_INDICES
122
+ self._mmap_tarball = _make_mmap_tarball(self._tarballs_root, mmap_cache_size)
123
+
124
+ def _get_entries_path(self, root: Optional[str] = None) -> str:
125
+ return "entries.npy"
126
+
127
+ def _get_class_ids_path(self, root: Optional[str] = None) -> str:
128
+ return "class-ids.npy"
129
+
130
+ def _find_class_ids(self, path: str) -> List[str]:
131
+ class_ids = []
132
+
133
+ with os.scandir(path) as entries:
134
+ for entry in entries:
135
+ root, ext = os.path.splitext(entry.name)
136
+ if ext != ".tar":
137
+ continue
138
+ class_ids.append(root)
139
+
140
+ return sorted(class_ids)
141
+
142
+ def _load_entries_class_ids(self, root: Optional[str] = None) -> Tuple[List[_Entry], List[str]]:
143
+ root = self.get_root(root)
144
+ entries: List[_Entry] = []
145
+ class_ids = self._find_class_ids(root)
146
+
147
+ for class_index, class_id in enumerate(class_ids):
148
+ path = os.path.join(root, "blocks", f"{class_id}.log")
149
+ class_entries = []
150
+
151
+ try:
152
+ with open(path) as f:
153
+ for line in f:
154
+ line = line.rstrip()
155
+ block, filename = line.split(":")
156
+ block_offset = int(block[6:])
157
+ filename = filename[1:]
158
+
159
+ maybe_filename = None
160
+ if filename != "** Block of NULs **":
161
+ maybe_filename = filename
162
+ _, ext = os.path.splitext(filename)
163
+ # assert ext == ".JPEG"
164
+
165
+ class_entry = _ClassEntry(block_offset, maybe_filename)
166
+ class_entries.append(class_entry)
167
+ except OSError as e:
168
+ raise RuntimeError(f'can not read blocks file "{path}"') from e
169
+
170
+ assert class_entries[-1].maybe_filename is None
171
+
172
+ for class_entry1, class_entry2 in zip(class_entries, class_entries[1:]):
173
+ assert class_entry1.block_offset <= class_entry2.block_offset
174
+ start_offset = 512 * class_entry1.block_offset
175
+ end_offset = 512 * class_entry2.block_offset
176
+ assert class_entry1.maybe_filename is not None
177
+ filename = class_entry1.maybe_filename
178
+ entry = _Entry(class_index, start_offset, end_offset, filename)
179
+ # Skip invalid image files (PIL throws UnidentifiedImageError)
180
+ if filename == "n06470073_47249.JPEG":
181
+ continue
182
+ entries.append(entry)
183
+
184
+ return entries, class_ids
185
+
186
+ def _load_extra(self, extra_path: str) -> np.ndarray:
187
+ extra_root = self._extra_root
188
+ extra_full_path = os.path.join(extra_root, extra_path)
189
+ return np.load(extra_full_path, mmap_mode="r")
190
+
191
+ def _save_extra(self, extra_array: np.ndarray, extra_path: str) -> None:
192
+ extra_root = self._extra_root
193
+ extra_full_path = os.path.join(extra_root, extra_path)
194
+ os.makedirs(extra_root, exist_ok=True)
195
+ np.save(extra_full_path, extra_array)
196
+
197
+ @property
198
+ def _tarballs_root(self) -> str:
199
+ return self.root
200
+
201
+ def find_class_id(self, class_index: int) -> str:
202
+ return str(self._class_ids[class_index])
203
+
204
+ def get_image_data(self, index: int) -> bytes:
205
+ entry = self._entries[index]
206
+ class_id = entry["class_id"]
207
+ class_mmap = self._mmap_tarball(class_id)
208
+
209
+ start_offset, end_offset = entry["start_offset"], entry["end_offset"]
210
+ try:
211
+ mapped_data = class_mmap[start_offset:end_offset]
212
+ data = mapped_data[512:] # Skip entry header block
213
+
214
+ if len(data) >= 2 and tuple(data[:2]) == (0x1F, 0x8B):
215
+ assert index in self._gzipped_indices, f"unexpected gzip header for sample {index}"
216
+ with GzipFile(fileobj=BytesIO(data)) as g:
217
+ data = g.read()
218
+ except Exception as e:
219
+ raise RuntimeError(f"can not retrieve image data for sample {index} " f'from "{class_id}" tarball') from e
220
+
221
+ return data
222
+
223
+ def get_target(self, index: int) -> Any:
224
+ return int(self._entries[index]["class_index"])
225
+
226
+ def get_targets(self) -> np.ndarray:
227
+ return self._entries["class_index"]
228
+
229
+ def get_class_id(self, index: int) -> str:
230
+ return str(self._entries[index]["class_id"])
231
+
232
+ def get_class_ids(self) -> np.ndarray:
233
+ return self._entries["class_id"]
234
+
235
+ def __getitem__(self, index: int) -> Tuple[Any, Any]:
236
+ with warnings.catch_warnings():
237
+ warnings.simplefilter("ignore")
238
+ return super().__getitem__(index)
239
+
240
+ def __len__(self) -> int:
241
+ return len(self._entries)
242
+
243
+ def _dump_entries(self, *args, **kwargs) -> None:
244
+ entries, class_ids = self._load_entries_class_ids(*args, **kwargs)
245
+
246
+ max_class_id_length, max_filename_length, max_class_index = -1, -1, -1
247
+ for entry in entries:
248
+ class_id = class_ids[entry.class_index]
249
+ max_class_index = max(entry.class_index, max_class_index)
250
+ max_class_id_length = max(len(class_id), max_class_id_length)
251
+ max_filename_length = max(len(entry.filename), max_filename_length)
252
+
253
+ dtype = np.dtype(
254
+ [
255
+ ("class_index", "<u4"),
256
+ ("class_id", f"U{max_class_id_length}"),
257
+ ("start_offset", "<u4"),
258
+ ("end_offset", "<u4"),
259
+ ("filename", f"U{max_filename_length}"),
260
+ ]
261
+ )
262
+ sample_count = len(entries)
263
+ entries_array = np.empty(sample_count, dtype=dtype)
264
+ for i, entry in enumerate(entries):
265
+ class_index = entry.class_index
266
+ class_id = class_ids[class_index]
267
+ start_offset = entry.start_offset
268
+ end_offset = entry.end_offset
269
+ filename = entry.filename
270
+ entries_array[i] = (
271
+ class_index,
272
+ class_id,
273
+ start_offset,
274
+ end_offset,
275
+ filename,
276
+ )
277
+
278
+ entries_path = self._get_entries_path(*args, **kwargs)
279
+ self._save_extra(entries_array, entries_path)
280
+
281
+ def _dump_class_ids(self, *args, **kwargs) -> None:
282
+ entries_path = self._get_entries_path(*args, **kwargs)
283
+ entries_array = self._load_extra(entries_path)
284
+
285
+ max_class_id_length, max_class_index = -1, -1
286
+ for entry in entries_array:
287
+ class_index, class_id = entry["class_index"], entry["class_id"]
288
+ max_class_index = max(int(class_index), max_class_index)
289
+ max_class_id_length = max(len(str(class_id)), max_class_id_length)
290
+
291
+ class_ids_array = np.empty(max_class_index + 1, dtype=f"U{max_class_id_length}")
292
+ for entry in entries_array:
293
+ class_index, class_id = entry["class_index"], entry["class_id"]
294
+ class_ids_array[class_index] = class_id
295
+ class_ids_path = self._get_class_ids_path(*args, **kwargs)
296
+ self._save_extra(class_ids_array, class_ids_path)
297
+
298
+ def _dump_extra(self, *args, **kwargs) -> None:
299
+ self._dump_entries(*args, *kwargs)
300
+ self._dump_class_ids(*args, *kwargs)
301
+
302
+ def dump_extra(self, root: Optional[str] = None) -> None:
303
+ return self._dump_extra(root)
torchhub/facebookresearch_dinov2_main/dinov2/data/loaders.py ADDED
@@ -0,0 +1,223 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import logging
8
+ from enum import Enum
9
+ from typing import Any, Callable, List, Optional, TypeVar
10
+
11
+ import torch
12
+ from torch.utils.data import Sampler
13
+
14
+ from .datasets import ImageNet, ImageNet22k
15
+ from .samplers import EpochSampler, InfiniteSampler, ShardedInfiniteSampler
16
+
17
+
18
+ logger = logging.getLogger("dinov2")
19
+
20
+
21
+ class SamplerType(Enum):
22
+ DISTRIBUTED = 0
23
+ EPOCH = 1
24
+ INFINITE = 2
25
+ SHARDED_INFINITE = 3
26
+ SHARDED_INFINITE_NEW = 4
27
+
28
+
29
+ def _make_bool_str(b: bool) -> str:
30
+ return "yes" if b else "no"
31
+
32
+
33
+ def _make_sample_transform(image_transform: Optional[Callable] = None, target_transform: Optional[Callable] = None):
34
+ def transform(sample):
35
+ image, target = sample
36
+ if image_transform is not None:
37
+ image = image_transform(image)
38
+ if target_transform is not None:
39
+ target = target_transform(target)
40
+ return image, target
41
+
42
+ return transform
43
+
44
+
45
+ def _parse_dataset_str(dataset_str: str):
46
+ tokens = dataset_str.split(":")
47
+
48
+ name = tokens[0]
49
+ kwargs = {}
50
+
51
+ for token in tokens[1:]:
52
+ key, value = token.split("=")
53
+ assert key in ("root", "extra", "split")
54
+ kwargs[key] = value
55
+
56
+ if name == "ImageNet":
57
+ class_ = ImageNet
58
+ if "split" in kwargs:
59
+ kwargs["split"] = ImageNet.Split[kwargs["split"]]
60
+ elif name == "ImageNet22k":
61
+ class_ = ImageNet22k
62
+ else:
63
+ raise ValueError(f'Unsupported dataset "{name}"')
64
+
65
+ return class_, kwargs
66
+
67
+
68
+ def make_dataset(
69
+ *,
70
+ dataset_str: str,
71
+ transform: Optional[Callable] = None,
72
+ target_transform: Optional[Callable] = None,
73
+ ):
74
+ """
75
+ Creates a dataset with the specified parameters.
76
+
77
+ Args:
78
+ dataset_str: A dataset string description (e.g. ImageNet:split=TRAIN).
79
+ transform: A transform to apply to images.
80
+ target_transform: A transform to apply to targets.
81
+
82
+ Returns:
83
+ The created dataset.
84
+ """
85
+ logger.info(f'using dataset: "{dataset_str}"')
86
+
87
+ class_, kwargs = _parse_dataset_str(dataset_str)
88
+ dataset = class_(transform=transform, target_transform=target_transform, **kwargs)
89
+
90
+ logger.info(f"# of dataset samples: {len(dataset):,d}")
91
+
92
+ # Aggregated datasets do not expose (yet) these attributes, so add them.
93
+ if not hasattr(dataset, "transform"):
94
+ setattr(dataset, "transform", transform)
95
+ if not hasattr(dataset, "target_transform"):
96
+ setattr(dataset, "target_transform", target_transform)
97
+
98
+ return dataset
99
+
100
+
101
+ def _make_sampler(
102
+ *,
103
+ dataset,
104
+ type: Optional[SamplerType] = None,
105
+ shuffle: bool = False,
106
+ seed: int = 0,
107
+ size: int = -1,
108
+ advance: int = 0,
109
+ ) -> Optional[Sampler]:
110
+ sample_count = len(dataset)
111
+
112
+ if type == SamplerType.INFINITE:
113
+ logger.info("sampler: infinite")
114
+ if size > 0:
115
+ raise ValueError("sampler size > 0 is invalid")
116
+ return InfiniteSampler(
117
+ sample_count=sample_count,
118
+ shuffle=shuffle,
119
+ seed=seed,
120
+ advance=advance,
121
+ )
122
+ elif type in (SamplerType.SHARDED_INFINITE, SamplerType.SHARDED_INFINITE_NEW):
123
+ logger.info("sampler: sharded infinite")
124
+ if size > 0:
125
+ raise ValueError("sampler size > 0 is invalid")
126
+ # TODO: Remove support for old shuffling
127
+ use_new_shuffle_tensor_slice = type == SamplerType.SHARDED_INFINITE_NEW
128
+ return ShardedInfiniteSampler(
129
+ sample_count=sample_count,
130
+ shuffle=shuffle,
131
+ seed=seed,
132
+ advance=advance,
133
+ use_new_shuffle_tensor_slice=use_new_shuffle_tensor_slice,
134
+ )
135
+ elif type == SamplerType.EPOCH:
136
+ logger.info("sampler: epoch")
137
+ if advance > 0:
138
+ raise NotImplementedError("sampler advance > 0 is not supported")
139
+ size = size if size > 0 else sample_count
140
+ logger.info(f"# of samples / epoch: {size:,d}")
141
+ return EpochSampler(
142
+ size=size,
143
+ sample_count=sample_count,
144
+ shuffle=shuffle,
145
+ seed=seed,
146
+ )
147
+ elif type == SamplerType.DISTRIBUTED:
148
+ logger.info("sampler: distributed")
149
+ if size > 0:
150
+ raise ValueError("sampler size > 0 is invalid")
151
+ if advance > 0:
152
+ raise ValueError("sampler advance > 0 is invalid")
153
+ return torch.utils.data.DistributedSampler(
154
+ dataset=dataset,
155
+ shuffle=shuffle,
156
+ seed=seed,
157
+ drop_last=False,
158
+ )
159
+
160
+ logger.info("sampler: none")
161
+ return None
162
+
163
+
164
+ T = TypeVar("T")
165
+
166
+
167
+ def make_data_loader(
168
+ *,
169
+ dataset,
170
+ batch_size: int,
171
+ num_workers: int,
172
+ shuffle: bool = True,
173
+ seed: int = 0,
174
+ sampler_type: Optional[SamplerType] = SamplerType.INFINITE,
175
+ sampler_size: int = -1,
176
+ sampler_advance: int = 0,
177
+ drop_last: bool = True,
178
+ persistent_workers: bool = False,
179
+ collate_fn: Optional[Callable[[List[T]], Any]] = None,
180
+ ):
181
+ """
182
+ Creates a data loader with the specified parameters.
183
+
184
+ Args:
185
+ dataset: A dataset (third party, LaViDa or WebDataset).
186
+ batch_size: The size of batches to generate.
187
+ num_workers: The number of workers to use.
188
+ shuffle: Whether to shuffle samples.
189
+ seed: The random seed to use.
190
+ sampler_type: Which sampler to use: EPOCH, INFINITE, SHARDED_INFINITE, SHARDED_INFINITE_NEW, DISTRIBUTED or None.
191
+ sampler_size: The number of images per epoch (when applicable) or -1 for the entire dataset.
192
+ sampler_advance: How many samples to skip (when applicable).
193
+ drop_last: Whether the last non-full batch of data should be dropped.
194
+ persistent_workers: maintain the workers Dataset instances alive after a dataset has been consumed once.
195
+ collate_fn: Function that performs batch collation
196
+ """
197
+
198
+ sampler = _make_sampler(
199
+ dataset=dataset,
200
+ type=sampler_type,
201
+ shuffle=shuffle,
202
+ seed=seed,
203
+ size=sampler_size,
204
+ advance=sampler_advance,
205
+ )
206
+
207
+ logger.info("using PyTorch data loader")
208
+ data_loader = torch.utils.data.DataLoader(
209
+ dataset,
210
+ sampler=sampler,
211
+ batch_size=batch_size,
212
+ num_workers=num_workers,
213
+ pin_memory=True,
214
+ drop_last=drop_last,
215
+ persistent_workers=persistent_workers,
216
+ collate_fn=collate_fn,
217
+ )
218
+
219
+ try:
220
+ logger.info(f"# of batches: {len(data_loader):,d}")
221
+ except TypeError: # data loader has no length
222
+ logger.info("infinite data loader")
223
+ return data_loader
torchhub/facebookresearch_dinov2_main/dinov2/data/masking.py ADDED
@@ -0,0 +1,87 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import random
8
+ import math
9
+ import numpy as np
10
+
11
+
12
+ class MaskingGenerator:
13
+ def __init__(
14
+ self,
15
+ input_size,
16
+ num_masking_patches=None,
17
+ min_num_patches=4,
18
+ max_num_patches=None,
19
+ min_aspect=0.3,
20
+ max_aspect=None,
21
+ ):
22
+ if not isinstance(input_size, tuple):
23
+ input_size = (input_size,) * 2
24
+ self.height, self.width = input_size
25
+
26
+ self.num_patches = self.height * self.width
27
+ self.num_masking_patches = num_masking_patches
28
+
29
+ self.min_num_patches = min_num_patches
30
+ self.max_num_patches = num_masking_patches if max_num_patches is None else max_num_patches
31
+
32
+ max_aspect = max_aspect or 1 / min_aspect
33
+ self.log_aspect_ratio = (math.log(min_aspect), math.log(max_aspect))
34
+
35
+ def __repr__(self):
36
+ repr_str = "Generator(%d, %d -> [%d ~ %d], max = %d, %.3f ~ %.3f)" % (
37
+ self.height,
38
+ self.width,
39
+ self.min_num_patches,
40
+ self.max_num_patches,
41
+ self.num_masking_patches,
42
+ self.log_aspect_ratio[0],
43
+ self.log_aspect_ratio[1],
44
+ )
45
+ return repr_str
46
+
47
+ def get_shape(self):
48
+ return self.height, self.width
49
+
50
+ def _mask(self, mask, max_mask_patches):
51
+ delta = 0
52
+ for _ in range(10):
53
+ target_area = random.uniform(self.min_num_patches, max_mask_patches)
54
+ aspect_ratio = math.exp(random.uniform(*self.log_aspect_ratio))
55
+ h = int(round(math.sqrt(target_area * aspect_ratio)))
56
+ w = int(round(math.sqrt(target_area / aspect_ratio)))
57
+ if w < self.width and h < self.height:
58
+ top = random.randint(0, self.height - h)
59
+ left = random.randint(0, self.width - w)
60
+
61
+ num_masked = mask[top : top + h, left : left + w].sum()
62
+ # Overlap
63
+ if 0 < h * w - num_masked <= max_mask_patches:
64
+ for i in range(top, top + h):
65
+ for j in range(left, left + w):
66
+ if mask[i, j] == 0:
67
+ mask[i, j] = 1
68
+ delta += 1
69
+
70
+ if delta > 0:
71
+ break
72
+ return delta
73
+
74
+ def __call__(self, num_masking_patches=0):
75
+ mask = np.zeros(shape=self.get_shape(), dtype=bool)
76
+ mask_count = 0
77
+ while mask_count < num_masking_patches:
78
+ max_mask_patches = num_masking_patches - mask_count
79
+ max_mask_patches = min(max_mask_patches, self.max_num_patches)
80
+
81
+ delta = self._mask(mask, max_mask_patches)
82
+ if delta == 0:
83
+ break
84
+ else:
85
+ mask_count += delta
86
+
87
+ return mask
torchhub/facebookresearch_dinov2_main/dinov2/data/samplers.py ADDED
@@ -0,0 +1,230 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import itertools
8
+ from typing import Any, Optional
9
+ import warnings
10
+
11
+ import numpy as np
12
+ import torch
13
+ from torch.utils.data.sampler import Sampler
14
+
15
+ import dinov2.distributed as distributed
16
+
17
+
18
+ class EpochSampler(Sampler):
19
+ def __init__(
20
+ self,
21
+ *,
22
+ size: int,
23
+ sample_count: int,
24
+ shuffle: bool = False,
25
+ seed: int = 0,
26
+ start: Optional[int] = None,
27
+ step: Optional[int] = None,
28
+ ):
29
+ self._size = size
30
+ self._sample_count = sample_count
31
+ self._shuffle = shuffle
32
+ self._seed = seed
33
+ self._start = distributed.get_global_rank() if start is None else start
34
+ self._step = distributed.get_global_size() if step is None else step
35
+ self._epoch = 0
36
+
37
+ def __iter__(self):
38
+ count = (self._size + self._sample_count - 1) // self._sample_count
39
+ tiled_indices = np.tile(np.arange(self._sample_count), count)
40
+ if self._shuffle:
41
+ seed = self._seed * self._epoch if self._seed != 0 else self._epoch
42
+ rng = np.random.default_rng(seed)
43
+ iterable = rng.choice(tiled_indices, self._size, replace=False)
44
+ else:
45
+ iterable = tiled_indices[: self._size]
46
+
47
+ yield from itertools.islice(iterable, self._start, None, self._step)
48
+
49
+ def __len__(self):
50
+ return (self._size - self._start + self._step - 1) // self._step
51
+
52
+ def set_epoch(self, epoch):
53
+ self._epoch = epoch
54
+
55
+
56
+ def _get_numpy_dtype(size: int) -> Any:
57
+ return np.int32 if size <= 2**31 else np.int64
58
+
59
+
60
+ def _get_torch_dtype(size: int) -> Any:
61
+ return torch.int32 if size <= 2**31 else torch.int64
62
+
63
+
64
+ def _generate_randperm_indices(*, size: int, generator: torch.Generator):
65
+ """Generate the indices of a random permutation."""
66
+ dtype = _get_torch_dtype(size)
67
+ # This is actually matching PyTorch's CPU implementation, see: https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/TensorFactories.cpp#L900-L921
68
+ perm = torch.arange(size, dtype=dtype)
69
+ for i in range(size):
70
+ j = torch.randint(i, size, size=(1,), generator=generator).item()
71
+
72
+ # Always swap even if no-op
73
+ value = perm[j].item()
74
+ perm[j] = perm[i].item()
75
+ perm[i] = value
76
+ yield value
77
+
78
+
79
+ class InfiniteSampler(Sampler):
80
+ def __init__(
81
+ self,
82
+ *,
83
+ sample_count: int,
84
+ shuffle: bool = False,
85
+ seed: int = 0,
86
+ start: Optional[int] = None,
87
+ step: Optional[int] = None,
88
+ advance: int = 0,
89
+ ):
90
+ self._sample_count = sample_count
91
+ self._seed = seed
92
+ self._shuffle = shuffle
93
+ self._start = distributed.get_global_rank() if start is None else start
94
+ self._step = distributed.get_global_size() if step is None else step
95
+ self._advance = advance
96
+
97
+ def __iter__(self):
98
+ if self._shuffle:
99
+ iterator = self._shuffled_iterator()
100
+ else:
101
+ iterator = self._iterator()
102
+
103
+ yield from itertools.islice(iterator, self._advance, None)
104
+
105
+ def _iterator(self):
106
+ assert not self._shuffle
107
+
108
+ while True:
109
+ iterable = range(self._sample_count)
110
+ yield from itertools.islice(iterable, self._start, None, self._step)
111
+
112
+ def _shuffled_iterator(self):
113
+ assert self._shuffle
114
+
115
+ # Instantiate a generator here (rather than in the ctor) to keep the class
116
+ # picklable (requirement of mp.spawn)
117
+ generator = torch.Generator().manual_seed(self._seed)
118
+
119
+ while True:
120
+ iterable = _generate_randperm_indices(size=self._sample_count, generator=generator)
121
+ yield from itertools.islice(iterable, self._start, None, self._step)
122
+
123
+
124
+ # The following function is somewhat equivalent to _new_shuffle_tensor_slice below,
125
+ # but avoids a full in-place random permutation generation.
126
+ def _shuffle_tensor_slice(
127
+ *, tensor: torch.Tensor, start: int = 0, step: int = 1, generator: torch.Generator
128
+ ) -> np.ndarray:
129
+ stop = len(tensor)
130
+ count = stop // step
131
+ drop_count = stop - step * count
132
+ if drop_count:
133
+ warnings.warn(f"# of dropped samples: {drop_count}")
134
+
135
+ dtype = _get_numpy_dtype(stop)
136
+ result = np.empty(count, dtype=dtype)
137
+
138
+ for i in range(count):
139
+ j = torch.randint(0, i + 1, size=(1,), generator=generator).item() if i > 0 else 0
140
+
141
+ result[i] = result[j]
142
+ result[j] = tensor[start + i * step].item()
143
+
144
+ return result
145
+
146
+
147
+ def _new_shuffle_tensor_slice(
148
+ *, tensor: torch.Tensor, start: int = 0, step: int = 1, generator: torch.Generator
149
+ ) -> np.ndarray:
150
+ stop = len(tensor)
151
+ count = stop // step
152
+ dtype = torch.int64 # Needed for using randperm result as indices
153
+ count = stop // step
154
+ drop_count = stop - step * count
155
+ if drop_count:
156
+ warnings.warn(f"# of dropped samples: {drop_count}")
157
+ indices = torch.randperm(count, dtype=dtype, generator=generator)
158
+ return tensor[start::step][indices].numpy()
159
+
160
+
161
+ def _make_seed(seed: int, start: int, iter_count: int) -> int:
162
+ # NOTE: Tried a few variants (including iter_count << 32), this one worked best.
163
+ return seed + start + (iter_count << 24)
164
+
165
+
166
+ class ShardedInfiniteSampler(Sampler):
167
+ def __init__(
168
+ self,
169
+ *,
170
+ sample_count: int,
171
+ shuffle: bool = False,
172
+ seed: int = 0,
173
+ start: Optional[int] = None,
174
+ step: Optional[int] = None,
175
+ advance: int = 0,
176
+ use_new_shuffle_tensor_slice: bool = False,
177
+ ):
178
+ self._sample_count = sample_count
179
+ self._seed = seed
180
+ self._shuffle = shuffle
181
+ self._start = distributed.get_global_rank() if start is None else start
182
+ self._step = distributed.get_global_size() if step is None else step
183
+ self._advance = advance
184
+ self._iter_count = 0
185
+ self._shuffle_tensor_slice_fn = (
186
+ _new_shuffle_tensor_slice if use_new_shuffle_tensor_slice else _shuffle_tensor_slice
187
+ )
188
+
189
+ def __iter__(self):
190
+ iter_count = self._advance // self._sample_count
191
+ if iter_count > 0:
192
+ self._advance -= iter_count * self._sample_count
193
+ self._iter_count += iter_count
194
+
195
+ if self._shuffle:
196
+ iterator = self._shuffled_iterator()
197
+ else:
198
+ iterator = self._iterator()
199
+
200
+ yield from itertools.islice(iterator, self._advance, None)
201
+
202
+ def _iterator(self):
203
+ assert not self._shuffle
204
+
205
+ while True:
206
+ iterable = range(self._sample_count)
207
+ yield from itertools.islice(iterable, self._start, None, self._step)
208
+
209
+ def _shuffled_iterator(self):
210
+ assert self._shuffle
211
+
212
+ # Instantiate a generator here (rather than in the ctor) to be keep the class
213
+ # picklable (requirement of mp.spawn)
214
+ generator = torch.Generator()
215
+
216
+ # Always shuffle everything first
217
+ generator.manual_seed(self._seed)
218
+ dtype = _get_torch_dtype(self._sample_count)
219
+ perm = torch.randperm(self._sample_count, dtype=dtype, generator=generator)
220
+
221
+ while True:
222
+ # Re-seed on each iteration to allow skipping whole permutations
223
+ seed = _make_seed(self._seed, self._start, self._iter_count)
224
+ generator.manual_seed(seed)
225
+
226
+ iterable = self._shuffle_tensor_slice_fn(
227
+ tensor=perm, start=self._start, step=self._step, generator=generator
228
+ )
229
+ yield from iterable
230
+ self._iter_count += 1
torchhub/facebookresearch_dinov2_main/dinov2/data/transforms.py ADDED
@@ -0,0 +1,92 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ from typing import Sequence
8
+
9
+ import torch
10
+ from torchvision import transforms
11
+
12
+
13
+ class GaussianBlur(transforms.RandomApply):
14
+ """
15
+ Apply Gaussian Blur to the PIL image.
16
+ """
17
+
18
+ def __init__(self, *, p: float = 0.5, radius_min: float = 0.1, radius_max: float = 2.0):
19
+ # NOTE: torchvision is applying 1 - probability to return the original image
20
+ keep_p = 1 - p
21
+ transform = transforms.GaussianBlur(kernel_size=9, sigma=(radius_min, radius_max))
22
+ super().__init__(transforms=[transform], p=keep_p)
23
+
24
+
25
+ class MaybeToTensor(transforms.ToTensor):
26
+ """
27
+ Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor, or keep as is if already a tensor.
28
+ """
29
+
30
+ def __call__(self, pic):
31
+ """
32
+ Args:
33
+ pic (PIL Image, numpy.ndarray or torch.tensor): Image to be converted to tensor.
34
+ Returns:
35
+ Tensor: Converted image.
36
+ """
37
+ if isinstance(pic, torch.Tensor):
38
+ return pic
39
+ return super().__call__(pic)
40
+
41
+
42
+ # Use timm's names
43
+ IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
44
+ IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
45
+
46
+
47
+ def make_normalize_transform(
48
+ mean: Sequence[float] = IMAGENET_DEFAULT_MEAN,
49
+ std: Sequence[float] = IMAGENET_DEFAULT_STD,
50
+ ) -> transforms.Normalize:
51
+ return transforms.Normalize(mean=mean, std=std)
52
+
53
+
54
+ # This roughly matches torchvision's preset for classification training:
55
+ # https://github.com/pytorch/vision/blob/main/references/classification/presets.py#L6-L44
56
+ def make_classification_train_transform(
57
+ *,
58
+ crop_size: int = 224,
59
+ interpolation=transforms.InterpolationMode.BICUBIC,
60
+ hflip_prob: float = 0.5,
61
+ mean: Sequence[float] = IMAGENET_DEFAULT_MEAN,
62
+ std: Sequence[float] = IMAGENET_DEFAULT_STD,
63
+ ):
64
+ transforms_list = [transforms.RandomResizedCrop(crop_size, interpolation=interpolation)]
65
+ if hflip_prob > 0.0:
66
+ transforms_list.append(transforms.RandomHorizontalFlip(hflip_prob))
67
+ transforms_list.extend(
68
+ [
69
+ MaybeToTensor(),
70
+ make_normalize_transform(mean=mean, std=std),
71
+ ]
72
+ )
73
+ return transforms.Compose(transforms_list)
74
+
75
+
76
+ # This matches (roughly) torchvision's preset for classification evaluation:
77
+ # https://github.com/pytorch/vision/blob/main/references/classification/presets.py#L47-L69
78
+ def make_classification_eval_transform(
79
+ *,
80
+ resize_size: int = 256,
81
+ interpolation=transforms.InterpolationMode.BICUBIC,
82
+ crop_size: int = 224,
83
+ mean: Sequence[float] = IMAGENET_DEFAULT_MEAN,
84
+ std: Sequence[float] = IMAGENET_DEFAULT_STD,
85
+ ) -> transforms.Compose:
86
+ transforms_list = [
87
+ transforms.Resize(resize_size, interpolation=interpolation),
88
+ transforms.CenterCrop(crop_size),
89
+ MaybeToTensor(),
90
+ make_normalize_transform(mean=mean, std=std),
91
+ ]
92
+ return transforms.Compose(transforms_list)
torchhub/facebookresearch_dinov2_main/dinov2/distributed/__init__.py ADDED
@@ -0,0 +1,271 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import os
8
+ import random
9
+ import re
10
+ import socket
11
+ from typing import Dict, List
12
+
13
+ import torch
14
+ import torch.distributed as dist
15
+
16
+ _LOCAL_RANK = -1
17
+ _LOCAL_WORLD_SIZE = -1
18
+
19
+
20
+ def is_enabled() -> bool:
21
+ """
22
+ Returns:
23
+ True if distributed training is enabled
24
+ """
25
+ return dist.is_available() and dist.is_initialized()
26
+
27
+
28
+ def get_global_size() -> int:
29
+ """
30
+ Returns:
31
+ The number of processes in the process group
32
+ """
33
+ return dist.get_world_size() if is_enabled() else 1
34
+
35
+
36
+ def get_global_rank() -> int:
37
+ """
38
+ Returns:
39
+ The rank of the current process within the global process group.
40
+ """
41
+ return dist.get_rank() if is_enabled() else 0
42
+
43
+
44
+ def get_local_rank() -> int:
45
+ """
46
+ Returns:
47
+ The rank of the current process within the local (per-machine) process group.
48
+ """
49
+ if not is_enabled():
50
+ return 0
51
+ assert 0 <= _LOCAL_RANK < _LOCAL_WORLD_SIZE
52
+ return _LOCAL_RANK
53
+
54
+
55
+ def get_local_size() -> int:
56
+ """
57
+ Returns:
58
+ The size of the per-machine process group,
59
+ i.e. the number of processes per machine.
60
+ """
61
+ if not is_enabled():
62
+ return 1
63
+ assert 0 <= _LOCAL_RANK < _LOCAL_WORLD_SIZE
64
+ return _LOCAL_WORLD_SIZE
65
+
66
+
67
+ def is_main_process() -> bool:
68
+ """
69
+ Returns:
70
+ True if the current process is the main one.
71
+ """
72
+ return get_global_rank() == 0
73
+
74
+
75
+ def _restrict_print_to_main_process() -> None:
76
+ """
77
+ This function disables printing when not in the main process
78
+ """
79
+ import builtins as __builtin__
80
+
81
+ builtin_print = __builtin__.print
82
+
83
+ def print(*args, **kwargs):
84
+ force = kwargs.pop("force", False)
85
+ if is_main_process() or force:
86
+ builtin_print(*args, **kwargs)
87
+
88
+ __builtin__.print = print
89
+
90
+
91
+ def _get_master_port(seed: int = 0) -> int:
92
+ MIN_MASTER_PORT, MAX_MASTER_PORT = (20_000, 60_000)
93
+
94
+ master_port_str = os.environ.get("MASTER_PORT")
95
+ if master_port_str is None:
96
+ rng = random.Random(seed)
97
+ return rng.randint(MIN_MASTER_PORT, MAX_MASTER_PORT)
98
+
99
+ return int(master_port_str)
100
+
101
+
102
+ def _get_available_port() -> int:
103
+ with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
104
+ # A "" host address means INADDR_ANY i.e. binding to all interfaces.
105
+ # Note this is not compatible with IPv6.
106
+ s.bind(("", 0))
107
+ port = s.getsockname()[1]
108
+ return port
109
+
110
+
111
+ _TORCH_DISTRIBUTED_ENV_VARS = (
112
+ "MASTER_ADDR",
113
+ "MASTER_PORT",
114
+ "RANK",
115
+ "WORLD_SIZE",
116
+ "LOCAL_RANK",
117
+ "LOCAL_WORLD_SIZE",
118
+ )
119
+
120
+
121
+ def _collect_env_vars() -> Dict[str, str]:
122
+ return {env_var: os.environ[env_var] for env_var in _TORCH_DISTRIBUTED_ENV_VARS if env_var in os.environ}
123
+
124
+
125
+ def _is_slurm_job_process() -> bool:
126
+ return "SLURM_JOB_ID" in os.environ
127
+
128
+
129
+ def _parse_slurm_node_list(s: str) -> List[str]:
130
+ nodes = []
131
+ # Extract "hostname", "hostname[1-2,3,4-5]," substrings
132
+ p = re.compile(r"(([^\[]+)(?:\[([^\]]+)\])?),?")
133
+ for m in p.finditer(s):
134
+ prefix, suffixes = s[m.start(2) : m.end(2)], s[m.start(3) : m.end(3)]
135
+ for suffix in suffixes.split(","):
136
+ span = suffix.split("-")
137
+ if len(span) == 1:
138
+ nodes.append(prefix + suffix)
139
+ else:
140
+ width = len(span[0])
141
+ start, end = int(span[0]), int(span[1]) + 1
142
+ nodes.extend([prefix + f"{i:0{width}}" for i in range(start, end)])
143
+ return nodes
144
+
145
+
146
+ def _check_env_variable(key: str, new_value: str):
147
+ # Only check for difference with preset environment variables
148
+ if key in os.environ and os.environ[key] != new_value:
149
+ raise RuntimeError(f"Cannot export environment variables as {key} is already set")
150
+
151
+
152
+ class _TorchDistributedEnvironment:
153
+ def __init__(self):
154
+ self.master_addr = "127.0.0.1"
155
+ self.master_port = 0
156
+ self.rank = -1
157
+ self.world_size = -1
158
+ self.local_rank = -1
159
+ self.local_world_size = -1
160
+
161
+ if _is_slurm_job_process():
162
+ return self._set_from_slurm_env()
163
+
164
+ env_vars = _collect_env_vars()
165
+ if not env_vars:
166
+ # Environment is not set
167
+ pass
168
+ elif len(env_vars) == len(_TORCH_DISTRIBUTED_ENV_VARS):
169
+ # Environment is fully set
170
+ return self._set_from_preset_env()
171
+ else:
172
+ # Environment is partially set
173
+ collected_env_vars = ", ".join(env_vars.keys())
174
+ raise RuntimeError(f"Partially set environment: {collected_env_vars}")
175
+
176
+ if torch.cuda.device_count() > 0:
177
+ return self._set_from_local()
178
+
179
+ raise RuntimeError("Can't initialize PyTorch distributed environment")
180
+
181
+ # Slurm job created with sbatch, submitit, etc...
182
+ def _set_from_slurm_env(self):
183
+ # logger.info("Initialization from Slurm environment")
184
+ job_id = int(os.environ["SLURM_JOB_ID"])
185
+ node_count = int(os.environ["SLURM_JOB_NUM_NODES"])
186
+ nodes = _parse_slurm_node_list(os.environ["SLURM_JOB_NODELIST"])
187
+ assert len(nodes) == node_count
188
+
189
+ self.master_addr = nodes[0]
190
+ self.master_port = _get_master_port(seed=job_id)
191
+ self.rank = int(os.environ["SLURM_PROCID"])
192
+ self.world_size = int(os.environ["SLURM_NTASKS"])
193
+ assert self.rank < self.world_size
194
+ self.local_rank = int(os.environ["SLURM_LOCALID"])
195
+ self.local_world_size = self.world_size // node_count
196
+ assert self.local_rank < self.local_world_size
197
+
198
+ # Single node job with preset environment (i.e. torchrun)
199
+ def _set_from_preset_env(self):
200
+ # logger.info("Initialization from preset environment")
201
+ self.master_addr = os.environ["MASTER_ADDR"]
202
+ self.master_port = os.environ["MASTER_PORT"]
203
+ self.rank = int(os.environ["RANK"])
204
+ self.world_size = int(os.environ["WORLD_SIZE"])
205
+ assert self.rank < self.world_size
206
+ self.local_rank = int(os.environ["LOCAL_RANK"])
207
+ self.local_world_size = int(os.environ["LOCAL_WORLD_SIZE"])
208
+ assert self.local_rank < self.local_world_size
209
+
210
+ # Single node and GPU job (i.e. local script run)
211
+ def _set_from_local(self):
212
+ # logger.info("Initialization from local")
213
+ self.master_addr = "127.0.0.1"
214
+ self.master_port = _get_available_port()
215
+ self.rank = 0
216
+ self.world_size = 1
217
+ self.local_rank = 0
218
+ self.local_world_size = 1
219
+
220
+ def export(self, *, overwrite: bool) -> "_TorchDistributedEnvironment":
221
+ # See the "Environment variable initialization" section from
222
+ # https://pytorch.org/docs/stable/distributed.html for the complete list of
223
+ # environment variables required for the env:// initialization method.
224
+ env_vars = {
225
+ "MASTER_ADDR": self.master_addr,
226
+ "MASTER_PORT": str(self.master_port),
227
+ "RANK": str(self.rank),
228
+ "WORLD_SIZE": str(self.world_size),
229
+ "LOCAL_RANK": str(self.local_rank),
230
+ "LOCAL_WORLD_SIZE": str(self.local_world_size),
231
+ }
232
+ if not overwrite:
233
+ for k, v in env_vars.items():
234
+ _check_env_variable(k, v)
235
+
236
+ os.environ.update(env_vars)
237
+ return self
238
+
239
+
240
+ def enable(*, set_cuda_current_device: bool = True, overwrite: bool = False, allow_nccl_timeout: bool = False):
241
+ """Enable distributed mode
242
+
243
+ Args:
244
+ set_cuda_current_device: If True, call torch.cuda.set_device() to set the
245
+ current PyTorch CUDA device to the one matching the local rank.
246
+ overwrite: If True, overwrites already set variables. Else fails.
247
+ """
248
+
249
+ global _LOCAL_RANK, _LOCAL_WORLD_SIZE
250
+ if _LOCAL_RANK >= 0 or _LOCAL_WORLD_SIZE >= 0:
251
+ raise RuntimeError("Distributed mode has already been enabled")
252
+ torch_env = _TorchDistributedEnvironment()
253
+ torch_env.export(overwrite=overwrite)
254
+
255
+ if set_cuda_current_device:
256
+ torch.cuda.set_device(torch_env.local_rank)
257
+
258
+ if allow_nccl_timeout:
259
+ # This allows to use torch distributed timeout in a NCCL backend
260
+ key, value = "NCCL_ASYNC_ERROR_HANDLING", "1"
261
+ if not overwrite:
262
+ _check_env_variable(key, value)
263
+ os.environ[key] = value
264
+
265
+ dist.init_process_group(backend="nccl")
266
+ dist.barrier()
267
+
268
+ # Finalize setup
269
+ _LOCAL_RANK = torch_env.local_rank
270
+ _LOCAL_WORLD_SIZE = torch_env.local_world_size
271
+ _restrict_print_to_main_process()
torchhub/facebookresearch_dinov2_main/dinov2/eval/__init__.py ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
torchhub/facebookresearch_dinov2_main/dinov2/eval/knn.py ADDED
@@ -0,0 +1,405 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import argparse
8
+ from functools import partial
9
+ import json
10
+ import logging
11
+ import os
12
+ import sys
13
+ from typing import List, Optional
14
+
15
+ import torch
16
+ from torch.nn.functional import one_hot, softmax
17
+
18
+ import dinov2.distributed as distributed
19
+ from dinov2.data import SamplerType, make_data_loader, make_dataset
20
+ from dinov2.data.transforms import make_classification_eval_transform
21
+ from dinov2.eval.metrics import AccuracyAveraging, build_topk_accuracy_metric
22
+ from dinov2.eval.setup import get_args_parser as get_setup_args_parser
23
+ from dinov2.eval.setup import setup_and_build_model
24
+ from dinov2.eval.utils import ModelWithNormalize, evaluate, extract_features
25
+
26
+
27
+ logger = logging.getLogger("dinov2")
28
+
29
+
30
+ def get_args_parser(
31
+ description: Optional[str] = None,
32
+ parents: Optional[List[argparse.ArgumentParser]] = None,
33
+ add_help: bool = True,
34
+ ):
35
+ parents = parents or []
36
+ setup_args_parser = get_setup_args_parser(parents=parents, add_help=False)
37
+ parents = [setup_args_parser]
38
+ parser = argparse.ArgumentParser(
39
+ description=description,
40
+ parents=parents,
41
+ add_help=add_help,
42
+ )
43
+ parser.add_argument(
44
+ "--train-dataset",
45
+ dest="train_dataset_str",
46
+ type=str,
47
+ help="Training dataset",
48
+ )
49
+ parser.add_argument(
50
+ "--val-dataset",
51
+ dest="val_dataset_str",
52
+ type=str,
53
+ help="Validation dataset",
54
+ )
55
+ parser.add_argument(
56
+ "--nb_knn",
57
+ nargs="+",
58
+ type=int,
59
+ help="Number of NN to use. 20 is usually working the best.",
60
+ )
61
+ parser.add_argument(
62
+ "--temperature",
63
+ type=float,
64
+ help="Temperature used in the voting coefficient",
65
+ )
66
+ parser.add_argument(
67
+ "--gather-on-cpu",
68
+ action="store_true",
69
+ help="Whether to gather the train features on cpu, slower"
70
+ "but useful to avoid OOM for large datasets (e.g. ImageNet22k).",
71
+ )
72
+ parser.add_argument(
73
+ "--batch-size",
74
+ type=int,
75
+ help="Batch size.",
76
+ )
77
+ parser.add_argument(
78
+ "--n-per-class-list",
79
+ nargs="+",
80
+ type=int,
81
+ help="Number to take per class",
82
+ )
83
+ parser.add_argument(
84
+ "--n-tries",
85
+ type=int,
86
+ help="Number of tries",
87
+ )
88
+ parser.set_defaults(
89
+ train_dataset_str="ImageNet:split=TRAIN",
90
+ val_dataset_str="ImageNet:split=VAL",
91
+ nb_knn=[10, 20, 100, 200],
92
+ temperature=0.07,
93
+ batch_size=256,
94
+ n_per_class_list=[-1],
95
+ n_tries=1,
96
+ )
97
+ return parser
98
+
99
+
100
+ class KnnModule(torch.nn.Module):
101
+ """
102
+ Gets knn of test features from all processes on a chunk of the train features
103
+
104
+ Each rank gets a chunk of the train features as well as a chunk of the test features.
105
+ In `compute_neighbors`, for each rank one after the other, its chunk of test features
106
+ is sent to all devices, partial knns are computed with each chunk of train features
107
+ then collated back on the original device.
108
+ """
109
+
110
+ def __init__(self, train_features, train_labels, nb_knn, T, device, num_classes=1000):
111
+ super().__init__()
112
+
113
+ self.global_rank = distributed.get_global_rank()
114
+ self.global_size = distributed.get_global_size()
115
+
116
+ self.device = device
117
+ self.train_features_rank_T = train_features.chunk(self.global_size)[self.global_rank].T.to(self.device)
118
+ self.candidates = train_labels.chunk(self.global_size)[self.global_rank].view(1, -1).to(self.device)
119
+
120
+ self.nb_knn = nb_knn
121
+ self.max_k = max(self.nb_knn)
122
+ self.T = T
123
+ self.num_classes = num_classes
124
+
125
+ def _get_knn_sims_and_labels(self, similarity, train_labels):
126
+ topk_sims, indices = similarity.topk(self.max_k, largest=True, sorted=True)
127
+ neighbors_labels = torch.gather(train_labels, 1, indices)
128
+ return topk_sims, neighbors_labels
129
+
130
+ def _similarity_for_rank(self, features_rank, source_rank):
131
+ # Send the features from `source_rank` to all ranks
132
+ broadcast_shape = torch.tensor(features_rank.shape).to(self.device)
133
+ torch.distributed.broadcast(broadcast_shape, source_rank)
134
+
135
+ broadcasted = features_rank
136
+ if self.global_rank != source_rank:
137
+ broadcasted = torch.zeros(*broadcast_shape, dtype=features_rank.dtype, device=self.device)
138
+ torch.distributed.broadcast(broadcasted, source_rank)
139
+
140
+ # Compute the neighbors for `source_rank` among `train_features_rank_T`
141
+ similarity_rank = torch.mm(broadcasted, self.train_features_rank_T)
142
+ candidate_labels = self.candidates.expand(len(similarity_rank), -1)
143
+ return self._get_knn_sims_and_labels(similarity_rank, candidate_labels)
144
+
145
+ def _gather_all_knn_for_rank(self, topk_sims, neighbors_labels, target_rank):
146
+ # Gather all neighbors for `target_rank`
147
+ topk_sims_rank = retrieved_rank = None
148
+ if self.global_rank == target_rank:
149
+ topk_sims_rank = [torch.zeros_like(topk_sims) for _ in range(self.global_size)]
150
+ retrieved_rank = [torch.zeros_like(neighbors_labels) for _ in range(self.global_size)]
151
+
152
+ torch.distributed.gather(topk_sims, topk_sims_rank, dst=target_rank)
153
+ torch.distributed.gather(neighbors_labels, retrieved_rank, dst=target_rank)
154
+
155
+ if self.global_rank == target_rank:
156
+ # Perform a second top-k on the k * global_size retrieved neighbors
157
+ topk_sims_rank = torch.cat(topk_sims_rank, dim=1)
158
+ retrieved_rank = torch.cat(retrieved_rank, dim=1)
159
+ results = self._get_knn_sims_and_labels(topk_sims_rank, retrieved_rank)
160
+ return results
161
+ return None
162
+
163
+ def compute_neighbors(self, features_rank):
164
+ for rank in range(self.global_size):
165
+ topk_sims, neighbors_labels = self._similarity_for_rank(features_rank, rank)
166
+ results = self._gather_all_knn_for_rank(topk_sims, neighbors_labels, rank)
167
+ if results is not None:
168
+ topk_sims_rank, neighbors_labels_rank = results
169
+ return topk_sims_rank, neighbors_labels_rank
170
+
171
+ def forward(self, features_rank):
172
+ """
173
+ Compute the results on all values of `self.nb_knn` neighbors from the full `self.max_k`
174
+ """
175
+ assert all(k <= self.max_k for k in self.nb_knn)
176
+
177
+ topk_sims, neighbors_labels = self.compute_neighbors(features_rank)
178
+ batch_size = neighbors_labels.shape[0]
179
+ topk_sims_transform = softmax(topk_sims / self.T, 1)
180
+ matmul = torch.mul(
181
+ one_hot(neighbors_labels, num_classes=self.num_classes),
182
+ topk_sims_transform.view(batch_size, -1, 1),
183
+ )
184
+ probas_for_k = {k: torch.sum(matmul[:, :k, :], 1) for k in self.nb_knn}
185
+ return probas_for_k
186
+
187
+
188
+ class DictKeysModule(torch.nn.Module):
189
+ def __init__(self, keys):
190
+ super().__init__()
191
+ self.keys = keys
192
+
193
+ def forward(self, features_dict, targets):
194
+ for k in self.keys:
195
+ features_dict = features_dict[k]
196
+ return {"preds": features_dict, "target": targets}
197
+
198
+
199
+ def create_module_dict(*, module, n_per_class_list, n_tries, nb_knn, train_features, train_labels):
200
+ modules = {}
201
+ mapping = create_class_indices_mapping(train_labels)
202
+ for npc in n_per_class_list:
203
+ if npc < 0: # Only one try needed when using the full data
204
+ full_module = module(
205
+ train_features=train_features,
206
+ train_labels=train_labels,
207
+ nb_knn=nb_knn,
208
+ )
209
+ modules["full"] = ModuleDictWithForward({"1": full_module})
210
+ continue
211
+ all_tries = {}
212
+ for t in range(n_tries):
213
+ final_indices = filter_train(mapping, npc, seed=t)
214
+ k_list = list(set(nb_knn + [npc]))
215
+ k_list = sorted([el for el in k_list if el <= npc])
216
+ all_tries[str(t)] = module(
217
+ train_features=train_features[final_indices],
218
+ train_labels=train_labels[final_indices],
219
+ nb_knn=k_list,
220
+ )
221
+ modules[f"{npc} per class"] = ModuleDictWithForward(all_tries)
222
+
223
+ return ModuleDictWithForward(modules)
224
+
225
+
226
+ def filter_train(mapping, n_per_class, seed):
227
+ torch.manual_seed(seed)
228
+ final_indices = []
229
+ for k in mapping.keys():
230
+ index = torch.randperm(len(mapping[k]))[:n_per_class]
231
+ final_indices.append(mapping[k][index])
232
+ return torch.cat(final_indices).squeeze()
233
+
234
+
235
+ def create_class_indices_mapping(labels):
236
+ unique_labels, inverse = torch.unique(labels, return_inverse=True)
237
+ mapping = {unique_labels[i]: (inverse == i).nonzero() for i in range(len(unique_labels))}
238
+ return mapping
239
+
240
+
241
+ class ModuleDictWithForward(torch.nn.ModuleDict):
242
+ def forward(self, *args, **kwargs):
243
+ return {k: module(*args, **kwargs) for k, module in self._modules.items()}
244
+
245
+
246
+ def eval_knn(
247
+ model,
248
+ train_dataset,
249
+ val_dataset,
250
+ accuracy_averaging,
251
+ nb_knn,
252
+ temperature,
253
+ batch_size,
254
+ num_workers,
255
+ gather_on_cpu,
256
+ n_per_class_list=[-1],
257
+ n_tries=1,
258
+ ):
259
+ model = ModelWithNormalize(model)
260
+
261
+ logger.info("Extracting features for train set...")
262
+ train_features, train_labels = extract_features(
263
+ model, train_dataset, batch_size, num_workers, gather_on_cpu=gather_on_cpu
264
+ )
265
+ logger.info(f"Train features created, shape {train_features.shape}.")
266
+
267
+ val_dataloader = make_data_loader(
268
+ dataset=val_dataset,
269
+ batch_size=batch_size,
270
+ num_workers=num_workers,
271
+ sampler_type=SamplerType.DISTRIBUTED,
272
+ drop_last=False,
273
+ shuffle=False,
274
+ persistent_workers=True,
275
+ )
276
+ num_classes = train_labels.max() + 1
277
+ metric_collection = build_topk_accuracy_metric(accuracy_averaging, num_classes=num_classes)
278
+
279
+ device = torch.cuda.current_device()
280
+ partial_module = partial(KnnModule, T=temperature, device=device, num_classes=num_classes)
281
+ knn_module_dict = create_module_dict(
282
+ module=partial_module,
283
+ n_per_class_list=n_per_class_list,
284
+ n_tries=n_tries,
285
+ nb_knn=nb_knn,
286
+ train_features=train_features,
287
+ train_labels=train_labels,
288
+ )
289
+ postprocessors, metrics = {}, {}
290
+ for n_per_class, knn_module in knn_module_dict.items():
291
+ for t, knn_try in knn_module.items():
292
+ postprocessors = {
293
+ **postprocessors,
294
+ **{(n_per_class, t, k): DictKeysModule([n_per_class, t, k]) for k in knn_try.nb_knn},
295
+ }
296
+ metrics = {**metrics, **{(n_per_class, t, k): metric_collection.clone() for k in knn_try.nb_knn}}
297
+ model_with_knn = torch.nn.Sequential(model, knn_module_dict)
298
+
299
+ # ============ evaluation ... ============
300
+ logger.info("Start the k-NN classification.")
301
+ _, results_dict = evaluate(model_with_knn, val_dataloader, postprocessors, metrics, device)
302
+
303
+ # Averaging the results over the n tries for each value of n_per_class
304
+ for n_per_class, knn_module in knn_module_dict.items():
305
+ first_try = list(knn_module.keys())[0]
306
+ k_list = knn_module[first_try].nb_knn
307
+ for k in k_list:
308
+ keys = results_dict[(n_per_class, first_try, k)].keys() # keys are e.g. `top-1` and `top-5`
309
+ results_dict[(n_per_class, k)] = {
310
+ key: torch.mean(torch.stack([results_dict[(n_per_class, t, k)][key] for t in knn_module.keys()]))
311
+ for key in keys
312
+ }
313
+ for t in knn_module.keys():
314
+ del results_dict[(n_per_class, t, k)]
315
+
316
+ return results_dict
317
+
318
+
319
+ def eval_knn_with_model(
320
+ model,
321
+ output_dir,
322
+ train_dataset_str="ImageNet:split=TRAIN",
323
+ val_dataset_str="ImageNet:split=VAL",
324
+ nb_knn=(10, 20, 100, 200),
325
+ temperature=0.07,
326
+ autocast_dtype=torch.float,
327
+ accuracy_averaging=AccuracyAveraging.MEAN_ACCURACY,
328
+ transform=None,
329
+ gather_on_cpu=False,
330
+ batch_size=256,
331
+ num_workers=5,
332
+ n_per_class_list=[-1],
333
+ n_tries=1,
334
+ ):
335
+ transform = transform or make_classification_eval_transform()
336
+
337
+ train_dataset = make_dataset(
338
+ dataset_str=train_dataset_str,
339
+ transform=transform,
340
+ )
341
+ val_dataset = make_dataset(
342
+ dataset_str=val_dataset_str,
343
+ transform=transform,
344
+ )
345
+
346
+ with torch.cuda.amp.autocast(dtype=autocast_dtype):
347
+ results_dict_knn = eval_knn(
348
+ model=model,
349
+ train_dataset=train_dataset,
350
+ val_dataset=val_dataset,
351
+ accuracy_averaging=accuracy_averaging,
352
+ nb_knn=nb_knn,
353
+ temperature=temperature,
354
+ batch_size=batch_size,
355
+ num_workers=num_workers,
356
+ gather_on_cpu=gather_on_cpu,
357
+ n_per_class_list=n_per_class_list,
358
+ n_tries=n_tries,
359
+ )
360
+
361
+ results_dict = {}
362
+ if distributed.is_main_process():
363
+ for knn_ in results_dict_knn.keys():
364
+ top1 = results_dict_knn[knn_]["top-1"].item() * 100.0
365
+ top5 = results_dict_knn[knn_]["top-5"].item() * 100.0
366
+ results_dict[f"{knn_} Top 1"] = top1
367
+ results_dict[f"{knn_} Top 5"] = top5
368
+ logger.info(f"{knn_} classifier result: Top1: {top1:.2f} Top5: {top5:.2f}")
369
+
370
+ metrics_file_path = os.path.join(output_dir, "results_eval_knn.json")
371
+ with open(metrics_file_path, "a") as f:
372
+ for k, v in results_dict.items():
373
+ f.write(json.dumps({k: v}) + "\n")
374
+
375
+ if distributed.is_enabled():
376
+ torch.distributed.barrier()
377
+ return results_dict
378
+
379
+
380
+ def main(args):
381
+ model, autocast_dtype = setup_and_build_model(args)
382
+ eval_knn_with_model(
383
+ model=model,
384
+ output_dir=args.output_dir,
385
+ train_dataset_str=args.train_dataset_str,
386
+ val_dataset_str=args.val_dataset_str,
387
+ nb_knn=args.nb_knn,
388
+ temperature=args.temperature,
389
+ autocast_dtype=autocast_dtype,
390
+ accuracy_averaging=AccuracyAveraging.MEAN_ACCURACY,
391
+ transform=None,
392
+ gather_on_cpu=args.gather_on_cpu,
393
+ batch_size=args.batch_size,
394
+ num_workers=5,
395
+ n_per_class_list=args.n_per_class_list,
396
+ n_tries=args.n_tries,
397
+ )
398
+ return 0
399
+
400
+
401
+ if __name__ == "__main__":
402
+ description = "DINOv2 k-NN evaluation"
403
+ args_parser = get_args_parser(description=description)
404
+ args = args_parser.parse_args()
405
+ sys.exit(main(args))
torchhub/facebookresearch_dinov2_main/dinov2/eval/linear.py ADDED
@@ -0,0 +1,626 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import argparse
8
+ from functools import partial
9
+ import json
10
+ import logging
11
+ import os
12
+ import sys
13
+ from typing import List, Optional
14
+
15
+ import numpy as np
16
+ import torch
17
+ import torch.nn as nn
18
+ from torch.nn.parallel import DistributedDataParallel
19
+ from fvcore.common.checkpoint import Checkpointer, PeriodicCheckpointer
20
+
21
+ from dinov2.data import SamplerType, make_data_loader, make_dataset
22
+ from dinov2.data.transforms import make_classification_eval_transform, make_classification_train_transform
23
+ import dinov2.distributed as distributed
24
+ from dinov2.eval.metrics import MetricType, build_metric
25
+ from dinov2.eval.setup import get_args_parser as get_setup_args_parser
26
+ from dinov2.eval.setup import setup_and_build_model
27
+ from dinov2.eval.utils import ModelWithIntermediateLayers, evaluate
28
+ from dinov2.logging import MetricLogger
29
+
30
+
31
+ logger = logging.getLogger("dinov2")
32
+
33
+
34
+ def get_args_parser(
35
+ description: Optional[str] = None,
36
+ parents: Optional[List[argparse.ArgumentParser]] = None,
37
+ add_help: bool = True,
38
+ ):
39
+ parents = parents or []
40
+ setup_args_parser = get_setup_args_parser(parents=parents, add_help=False)
41
+ parents = [setup_args_parser]
42
+ parser = argparse.ArgumentParser(
43
+ description=description,
44
+ parents=parents,
45
+ add_help=add_help,
46
+ )
47
+ parser.add_argument(
48
+ "--train-dataset",
49
+ dest="train_dataset_str",
50
+ type=str,
51
+ help="Training dataset",
52
+ )
53
+ parser.add_argument(
54
+ "--val-dataset",
55
+ dest="val_dataset_str",
56
+ type=str,
57
+ help="Validation dataset",
58
+ )
59
+ parser.add_argument(
60
+ "--test-datasets",
61
+ dest="test_dataset_strs",
62
+ type=str,
63
+ nargs="+",
64
+ help="Test datasets, none to reuse the validation dataset",
65
+ )
66
+ parser.add_argument(
67
+ "--epochs",
68
+ type=int,
69
+ help="Number of training epochs",
70
+ )
71
+ parser.add_argument(
72
+ "--batch-size",
73
+ type=int,
74
+ help="Batch Size (per GPU)",
75
+ )
76
+ parser.add_argument(
77
+ "--num-workers",
78
+ type=int,
79
+ help="Number de Workers",
80
+ )
81
+ parser.add_argument(
82
+ "--epoch-length",
83
+ type=int,
84
+ help="Length of an epoch in number of iterations",
85
+ )
86
+ parser.add_argument(
87
+ "--save-checkpoint-frequency",
88
+ type=int,
89
+ help="Number of epochs between two named checkpoint saves.",
90
+ )
91
+ parser.add_argument(
92
+ "--eval-period-iterations",
93
+ type=int,
94
+ help="Number of iterations between two evaluations.",
95
+ )
96
+ parser.add_argument(
97
+ "--learning-rates",
98
+ nargs="+",
99
+ type=float,
100
+ help="Learning rates to grid search.",
101
+ )
102
+ parser.add_argument(
103
+ "--no-resume",
104
+ action="store_true",
105
+ help="Whether to not resume from existing checkpoints",
106
+ )
107
+ parser.add_argument(
108
+ "--val-metric-type",
109
+ type=MetricType,
110
+ choices=list(MetricType),
111
+ help="Validation metric",
112
+ )
113
+ parser.add_argument(
114
+ "--test-metric-types",
115
+ type=MetricType,
116
+ choices=list(MetricType),
117
+ nargs="+",
118
+ help="Evaluation metric",
119
+ )
120
+ parser.add_argument(
121
+ "--classifier-fpath",
122
+ type=str,
123
+ help="Path to a file containing pretrained linear classifiers",
124
+ )
125
+ parser.add_argument(
126
+ "--val-class-mapping-fpath",
127
+ type=str,
128
+ help="Path to a file containing a mapping to adjust classifier outputs",
129
+ )
130
+ parser.add_argument(
131
+ "--test-class-mapping-fpaths",
132
+ nargs="+",
133
+ type=str,
134
+ help="Path to a file containing a mapping to adjust classifier outputs",
135
+ )
136
+ parser.set_defaults(
137
+ train_dataset_str="ImageNet:split=TRAIN",
138
+ val_dataset_str="ImageNet:split=VAL",
139
+ test_dataset_strs=None,
140
+ epochs=10,
141
+ batch_size=128,
142
+ num_workers=8,
143
+ epoch_length=1250,
144
+ save_checkpoint_frequency=20,
145
+ eval_period_iterations=1250,
146
+ learning_rates=[1e-5, 2e-5, 5e-5, 1e-4, 2e-4, 5e-4, 1e-3, 2e-3, 5e-3, 1e-2, 2e-2, 5e-2, 0.1],
147
+ val_metric_type=MetricType.MEAN_ACCURACY,
148
+ test_metric_types=None,
149
+ classifier_fpath=None,
150
+ val_class_mapping_fpath=None,
151
+ test_class_mapping_fpaths=[None],
152
+ )
153
+ return parser
154
+
155
+
156
+ def has_ddp_wrapper(m: nn.Module) -> bool:
157
+ return isinstance(m, DistributedDataParallel)
158
+
159
+
160
+ def remove_ddp_wrapper(m: nn.Module) -> nn.Module:
161
+ return m.module if has_ddp_wrapper(m) else m
162
+
163
+
164
+ def _pad_and_collate(batch):
165
+ maxlen = max(len(targets) for image, targets in batch)
166
+ padded_batch = [
167
+ (image, np.pad(targets, (0, maxlen - len(targets)), constant_values=-1)) for image, targets in batch
168
+ ]
169
+ return torch.utils.data.default_collate(padded_batch)
170
+
171
+
172
+ def create_linear_input(x_tokens_list, use_n_blocks, use_avgpool):
173
+ intermediate_output = x_tokens_list[-use_n_blocks:]
174
+ output = torch.cat([class_token for _, class_token in intermediate_output], dim=-1)
175
+ if use_avgpool:
176
+ output = torch.cat(
177
+ (
178
+ output,
179
+ torch.mean(intermediate_output[-1][0], dim=1), # patch tokens
180
+ ),
181
+ dim=-1,
182
+ )
183
+ output = output.reshape(output.shape[0], -1)
184
+ return output.float()
185
+
186
+
187
+ class LinearClassifier(nn.Module):
188
+ """Linear layer to train on top of frozen features"""
189
+
190
+ def __init__(self, out_dim, use_n_blocks, use_avgpool, num_classes=1000):
191
+ super().__init__()
192
+ self.out_dim = out_dim
193
+ self.use_n_blocks = use_n_blocks
194
+ self.use_avgpool = use_avgpool
195
+ self.num_classes = num_classes
196
+ self.linear = nn.Linear(out_dim, num_classes)
197
+ self.linear.weight.data.normal_(mean=0.0, std=0.01)
198
+ self.linear.bias.data.zero_()
199
+
200
+ def forward(self, x_tokens_list):
201
+ output = create_linear_input(x_tokens_list, self.use_n_blocks, self.use_avgpool)
202
+ return self.linear(output)
203
+
204
+
205
+ class AllClassifiers(nn.Module):
206
+ def __init__(self, classifiers_dict):
207
+ super().__init__()
208
+ self.classifiers_dict = nn.ModuleDict()
209
+ self.classifiers_dict.update(classifiers_dict)
210
+
211
+ def forward(self, inputs):
212
+ return {k: v.forward(inputs) for k, v in self.classifiers_dict.items()}
213
+
214
+ def __len__(self):
215
+ return len(self.classifiers_dict)
216
+
217
+
218
+ class LinearPostprocessor(nn.Module):
219
+ def __init__(self, linear_classifier, class_mapping=None):
220
+ super().__init__()
221
+ self.linear_classifier = linear_classifier
222
+ self.register_buffer("class_mapping", None if class_mapping is None else torch.LongTensor(class_mapping))
223
+
224
+ def forward(self, samples, targets):
225
+ preds = self.linear_classifier(samples)
226
+ return {
227
+ "preds": preds[:, self.class_mapping] if self.class_mapping is not None else preds,
228
+ "target": targets,
229
+ }
230
+
231
+
232
+ def scale_lr(learning_rates, batch_size):
233
+ return learning_rates * (batch_size * distributed.get_global_size()) / 256.0
234
+
235
+
236
+ def setup_linear_classifiers(sample_output, n_last_blocks_list, learning_rates, batch_size, num_classes=1000):
237
+ linear_classifiers_dict = nn.ModuleDict()
238
+ optim_param_groups = []
239
+ for n in n_last_blocks_list:
240
+ for avgpool in [False, True]:
241
+ for _lr in learning_rates:
242
+ lr = scale_lr(_lr, batch_size)
243
+ out_dim = create_linear_input(sample_output, use_n_blocks=n, use_avgpool=avgpool).shape[1]
244
+ linear_classifier = LinearClassifier(
245
+ out_dim, use_n_blocks=n, use_avgpool=avgpool, num_classes=num_classes
246
+ )
247
+ linear_classifier = linear_classifier.cuda()
248
+ linear_classifiers_dict[
249
+ f"classifier_{n}_blocks_avgpool_{avgpool}_lr_{lr:.5f}".replace(".", "_")
250
+ ] = linear_classifier
251
+ optim_param_groups.append({"params": linear_classifier.parameters(), "lr": lr})
252
+
253
+ linear_classifiers = AllClassifiers(linear_classifiers_dict)
254
+ if distributed.is_enabled():
255
+ linear_classifiers = nn.parallel.DistributedDataParallel(linear_classifiers)
256
+
257
+ return linear_classifiers, optim_param_groups
258
+
259
+
260
+ @torch.no_grad()
261
+ def evaluate_linear_classifiers(
262
+ feature_model,
263
+ linear_classifiers,
264
+ data_loader,
265
+ metric_type,
266
+ metrics_file_path,
267
+ training_num_classes,
268
+ iteration,
269
+ prefixstring="",
270
+ class_mapping=None,
271
+ best_classifier_on_val=None,
272
+ ):
273
+ logger.info("running validation !")
274
+
275
+ num_classes = len(class_mapping) if class_mapping is not None else training_num_classes
276
+ metric = build_metric(metric_type, num_classes=num_classes)
277
+ postprocessors = {k: LinearPostprocessor(v, class_mapping) for k, v in linear_classifiers.classifiers_dict.items()}
278
+ metrics = {k: metric.clone() for k in linear_classifiers.classifiers_dict}
279
+
280
+ _, results_dict_temp = evaluate(
281
+ feature_model,
282
+ data_loader,
283
+ postprocessors,
284
+ metrics,
285
+ torch.cuda.current_device(),
286
+ )
287
+
288
+ logger.info("")
289
+ results_dict = {}
290
+ max_accuracy = 0
291
+ best_classifier = ""
292
+ for i, (classifier_string, metric) in enumerate(results_dict_temp.items()):
293
+ logger.info(f"{prefixstring} -- Classifier: {classifier_string} * {metric}")
294
+ if (
295
+ best_classifier_on_val is None and metric["top-1"].item() > max_accuracy
296
+ ) or classifier_string == best_classifier_on_val:
297
+ max_accuracy = metric["top-1"].item()
298
+ best_classifier = classifier_string
299
+
300
+ results_dict["best_classifier"] = {"name": best_classifier, "accuracy": max_accuracy}
301
+
302
+ logger.info(f"best classifier: {results_dict['best_classifier']}")
303
+
304
+ if distributed.is_main_process():
305
+ with open(metrics_file_path, "a") as f:
306
+ f.write(f"iter: {iteration}\n")
307
+ for k, v in results_dict.items():
308
+ f.write(json.dumps({k: v}) + "\n")
309
+ f.write("\n")
310
+
311
+ return results_dict
312
+
313
+
314
+ def eval_linear(
315
+ *,
316
+ feature_model,
317
+ linear_classifiers,
318
+ train_data_loader,
319
+ val_data_loader,
320
+ metrics_file_path,
321
+ optimizer,
322
+ scheduler,
323
+ output_dir,
324
+ max_iter,
325
+ checkpoint_period, # In number of iter, creates a new file every period
326
+ running_checkpoint_period, # Period to update main checkpoint file
327
+ eval_period,
328
+ metric_type,
329
+ training_num_classes,
330
+ resume=True,
331
+ classifier_fpath=None,
332
+ val_class_mapping=None,
333
+ ):
334
+ checkpointer = Checkpointer(linear_classifiers, output_dir, optimizer=optimizer, scheduler=scheduler)
335
+ start_iter = checkpointer.resume_or_load(classifier_fpath or "", resume=resume).get("iteration", -1) + 1
336
+
337
+ periodic_checkpointer = PeriodicCheckpointer(checkpointer, checkpoint_period, max_iter=max_iter)
338
+ iteration = start_iter
339
+ logger.info("Starting training from iteration {}".format(start_iter))
340
+ metric_logger = MetricLogger(delimiter=" ")
341
+ header = "Training"
342
+
343
+ for data, labels in metric_logger.log_every(
344
+ train_data_loader,
345
+ 10,
346
+ header,
347
+ max_iter,
348
+ start_iter,
349
+ ):
350
+ data = data.cuda(non_blocking=True)
351
+ labels = labels.cuda(non_blocking=True)
352
+
353
+ features = feature_model(data)
354
+ outputs = linear_classifiers(features)
355
+
356
+ losses = {f"loss_{k}": nn.CrossEntropyLoss()(v, labels) for k, v in outputs.items()}
357
+ loss = sum(losses.values())
358
+
359
+ # compute the gradients
360
+ optimizer.zero_grad()
361
+ loss.backward()
362
+
363
+ # step
364
+ optimizer.step()
365
+ scheduler.step()
366
+
367
+ # log
368
+ if iteration % 10 == 0:
369
+ torch.cuda.synchronize()
370
+ metric_logger.update(loss=loss.item())
371
+ metric_logger.update(lr=optimizer.param_groups[0]["lr"])
372
+ print("lr", optimizer.param_groups[0]["lr"])
373
+
374
+ if iteration - start_iter > 5:
375
+ if iteration % running_checkpoint_period == 0:
376
+ torch.cuda.synchronize()
377
+ if distributed.is_main_process():
378
+ logger.info("Checkpointing running_checkpoint")
379
+ periodic_checkpointer.save("running_checkpoint_linear_eval", iteration=iteration)
380
+ torch.cuda.synchronize()
381
+ periodic_checkpointer.step(iteration)
382
+
383
+ if eval_period > 0 and (iteration + 1) % eval_period == 0 and iteration != max_iter - 1:
384
+ _ = evaluate_linear_classifiers(
385
+ feature_model=feature_model,
386
+ linear_classifiers=remove_ddp_wrapper(linear_classifiers),
387
+ data_loader=val_data_loader,
388
+ metrics_file_path=metrics_file_path,
389
+ prefixstring=f"ITER: {iteration}",
390
+ metric_type=metric_type,
391
+ training_num_classes=training_num_classes,
392
+ iteration=iteration,
393
+ class_mapping=val_class_mapping,
394
+ )
395
+ torch.cuda.synchronize()
396
+
397
+ iteration = iteration + 1
398
+
399
+ val_results_dict = evaluate_linear_classifiers(
400
+ feature_model=feature_model,
401
+ linear_classifiers=remove_ddp_wrapper(linear_classifiers),
402
+ data_loader=val_data_loader,
403
+ metrics_file_path=metrics_file_path,
404
+ metric_type=metric_type,
405
+ training_num_classes=training_num_classes,
406
+ iteration=iteration,
407
+ class_mapping=val_class_mapping,
408
+ )
409
+ return val_results_dict, feature_model, linear_classifiers, iteration
410
+
411
+
412
+ def make_eval_data_loader(test_dataset_str, batch_size, num_workers, metric_type):
413
+ test_dataset = make_dataset(
414
+ dataset_str=test_dataset_str,
415
+ transform=make_classification_eval_transform(),
416
+ )
417
+ test_data_loader = make_data_loader(
418
+ dataset=test_dataset,
419
+ batch_size=batch_size,
420
+ num_workers=num_workers,
421
+ sampler_type=SamplerType.DISTRIBUTED,
422
+ drop_last=False,
423
+ shuffle=False,
424
+ persistent_workers=False,
425
+ collate_fn=_pad_and_collate if metric_type == MetricType.IMAGENET_REAL_ACCURACY else None,
426
+ )
427
+ return test_data_loader
428
+
429
+
430
+ def test_on_datasets(
431
+ feature_model,
432
+ linear_classifiers,
433
+ test_dataset_strs,
434
+ batch_size,
435
+ num_workers,
436
+ test_metric_types,
437
+ metrics_file_path,
438
+ training_num_classes,
439
+ iteration,
440
+ best_classifier_on_val,
441
+ prefixstring="",
442
+ test_class_mappings=[None],
443
+ ):
444
+ results_dict = {}
445
+ for test_dataset_str, class_mapping, metric_type in zip(test_dataset_strs, test_class_mappings, test_metric_types):
446
+ logger.info(f"Testing on {test_dataset_str}")
447
+ test_data_loader = make_eval_data_loader(test_dataset_str, batch_size, num_workers, metric_type)
448
+ dataset_results_dict = evaluate_linear_classifiers(
449
+ feature_model,
450
+ remove_ddp_wrapper(linear_classifiers),
451
+ test_data_loader,
452
+ metric_type,
453
+ metrics_file_path,
454
+ training_num_classes,
455
+ iteration,
456
+ prefixstring="",
457
+ class_mapping=class_mapping,
458
+ best_classifier_on_val=best_classifier_on_val,
459
+ )
460
+ results_dict[f"{test_dataset_str}_accuracy"] = 100.0 * dataset_results_dict["best_classifier"]["accuracy"]
461
+ return results_dict
462
+
463
+
464
+ def run_eval_linear(
465
+ model,
466
+ output_dir,
467
+ train_dataset_str,
468
+ val_dataset_str,
469
+ batch_size,
470
+ epochs,
471
+ epoch_length,
472
+ num_workers,
473
+ save_checkpoint_frequency,
474
+ eval_period_iterations,
475
+ learning_rates,
476
+ autocast_dtype,
477
+ test_dataset_strs=None,
478
+ resume=True,
479
+ classifier_fpath=None,
480
+ val_class_mapping_fpath=None,
481
+ test_class_mapping_fpaths=[None],
482
+ val_metric_type=MetricType.MEAN_ACCURACY,
483
+ test_metric_types=None,
484
+ ):
485
+ seed = 0
486
+
487
+ if test_dataset_strs is None:
488
+ test_dataset_strs = [val_dataset_str]
489
+ if test_metric_types is None:
490
+ test_metric_types = [val_metric_type] * len(test_dataset_strs)
491
+ else:
492
+ assert len(test_metric_types) == len(test_dataset_strs)
493
+ assert len(test_dataset_strs) == len(test_class_mapping_fpaths)
494
+
495
+ train_transform = make_classification_train_transform()
496
+ train_dataset = make_dataset(
497
+ dataset_str=train_dataset_str,
498
+ transform=train_transform,
499
+ )
500
+ training_num_classes = len(torch.unique(torch.Tensor(train_dataset.get_targets().astype(int))))
501
+ sampler_type = SamplerType.SHARDED_INFINITE
502
+ # sampler_type = SamplerType.INFINITE
503
+
504
+ n_last_blocks_list = [1, 4]
505
+ n_last_blocks = max(n_last_blocks_list)
506
+ autocast_ctx = partial(torch.cuda.amp.autocast, enabled=True, dtype=autocast_dtype)
507
+ feature_model = ModelWithIntermediateLayers(model, n_last_blocks, autocast_ctx)
508
+ sample_output = feature_model(train_dataset[0][0].unsqueeze(0).cuda())
509
+
510
+ linear_classifiers, optim_param_groups = setup_linear_classifiers(
511
+ sample_output,
512
+ n_last_blocks_list,
513
+ learning_rates,
514
+ batch_size,
515
+ training_num_classes,
516
+ )
517
+
518
+ optimizer = torch.optim.SGD(optim_param_groups, momentum=0.9, weight_decay=0)
519
+ max_iter = epochs * epoch_length
520
+ scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, max_iter, eta_min=0)
521
+ checkpointer = Checkpointer(linear_classifiers, output_dir, optimizer=optimizer, scheduler=scheduler)
522
+ start_iter = checkpointer.resume_or_load(classifier_fpath or "", resume=resume).get("iteration", -1) + 1
523
+ train_data_loader = make_data_loader(
524
+ dataset=train_dataset,
525
+ batch_size=batch_size,
526
+ num_workers=num_workers,
527
+ shuffle=True,
528
+ seed=seed,
529
+ sampler_type=sampler_type,
530
+ sampler_advance=start_iter,
531
+ drop_last=True,
532
+ persistent_workers=True,
533
+ )
534
+ val_data_loader = make_eval_data_loader(val_dataset_str, batch_size, num_workers, val_metric_type)
535
+
536
+ checkpoint_period = save_checkpoint_frequency * epoch_length
537
+
538
+ if val_class_mapping_fpath is not None:
539
+ logger.info(f"Using class mapping from {val_class_mapping_fpath}")
540
+ val_class_mapping = np.load(val_class_mapping_fpath)
541
+ else:
542
+ val_class_mapping = None
543
+
544
+ test_class_mappings = []
545
+ for class_mapping_fpath in test_class_mapping_fpaths:
546
+ if class_mapping_fpath is not None and class_mapping_fpath != "None":
547
+ logger.info(f"Using class mapping from {class_mapping_fpath}")
548
+ class_mapping = np.load(class_mapping_fpath)
549
+ else:
550
+ class_mapping = None
551
+ test_class_mappings.append(class_mapping)
552
+
553
+ metrics_file_path = os.path.join(output_dir, "results_eval_linear.json")
554
+ val_results_dict, feature_model, linear_classifiers, iteration = eval_linear(
555
+ feature_model=feature_model,
556
+ linear_classifiers=linear_classifiers,
557
+ train_data_loader=train_data_loader,
558
+ val_data_loader=val_data_loader,
559
+ metrics_file_path=metrics_file_path,
560
+ optimizer=optimizer,
561
+ scheduler=scheduler,
562
+ output_dir=output_dir,
563
+ max_iter=max_iter,
564
+ checkpoint_period=checkpoint_period,
565
+ running_checkpoint_period=epoch_length,
566
+ eval_period=eval_period_iterations,
567
+ metric_type=val_metric_type,
568
+ training_num_classes=training_num_classes,
569
+ resume=resume,
570
+ val_class_mapping=val_class_mapping,
571
+ classifier_fpath=classifier_fpath,
572
+ )
573
+ results_dict = {}
574
+ if len(test_dataset_strs) > 1 or test_dataset_strs[0] != val_dataset_str:
575
+ results_dict = test_on_datasets(
576
+ feature_model,
577
+ linear_classifiers,
578
+ test_dataset_strs,
579
+ batch_size,
580
+ 0, # num_workers,
581
+ test_metric_types,
582
+ metrics_file_path,
583
+ training_num_classes,
584
+ iteration,
585
+ val_results_dict["best_classifier"]["name"],
586
+ prefixstring="",
587
+ test_class_mappings=test_class_mappings,
588
+ )
589
+ results_dict["best_classifier"] = val_results_dict["best_classifier"]["name"]
590
+ results_dict[f"{val_dataset_str}_accuracy"] = 100.0 * val_results_dict["best_classifier"]["accuracy"]
591
+ logger.info("Test Results Dict " + str(results_dict))
592
+
593
+ return results_dict
594
+
595
+
596
+ def main(args):
597
+ model, autocast_dtype = setup_and_build_model(args)
598
+ run_eval_linear(
599
+ model=model,
600
+ output_dir=args.output_dir,
601
+ train_dataset_str=args.train_dataset_str,
602
+ val_dataset_str=args.val_dataset_str,
603
+ test_dataset_strs=args.test_dataset_strs,
604
+ batch_size=args.batch_size,
605
+ epochs=args.epochs,
606
+ epoch_length=args.epoch_length,
607
+ num_workers=args.num_workers,
608
+ save_checkpoint_frequency=args.save_checkpoint_frequency,
609
+ eval_period_iterations=args.eval_period_iterations,
610
+ learning_rates=args.learning_rates,
611
+ autocast_dtype=autocast_dtype,
612
+ resume=not args.no_resume,
613
+ classifier_fpath=args.classifier_fpath,
614
+ val_metric_type=args.val_metric_type,
615
+ test_metric_types=args.test_metric_types,
616
+ val_class_mapping_fpath=args.val_class_mapping_fpath,
617
+ test_class_mapping_fpaths=args.test_class_mapping_fpaths,
618
+ )
619
+ return 0
620
+
621
+
622
+ if __name__ == "__main__":
623
+ description = "DINOv2 linear evaluation"
624
+ args_parser = get_args_parser(description=description)
625
+ args = args_parser.parse_args()
626
+ sys.exit(main(args))
torchhub/facebookresearch_dinov2_main/dinov2/eval/log_regression.py ADDED
@@ -0,0 +1,445 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import argparse
8
+ import gc
9
+ import logging
10
+ import sys
11
+ import time
12
+ from typing import List, Optional
13
+
14
+ from cuml.linear_model import LogisticRegression
15
+ import torch
16
+ import torch.backends.cudnn as cudnn
17
+ import torch.distributed
18
+ from torch import nn
19
+ from torch.utils.data import TensorDataset
20
+ from torchmetrics import MetricTracker
21
+
22
+ from dinov2.data import make_dataset
23
+ from dinov2.data.transforms import make_classification_eval_transform
24
+ from dinov2.distributed import get_global_rank, get_global_size
25
+ from dinov2.eval.metrics import MetricType, build_metric
26
+ from dinov2.eval.setup import get_args_parser as get_setup_args_parser
27
+ from dinov2.eval.setup import setup_and_build_model
28
+ from dinov2.eval.utils import evaluate, extract_features
29
+ from dinov2.utils.dtype import as_torch_dtype
30
+
31
+
32
+ logger = logging.getLogger("dinov2")
33
+
34
+ DEFAULT_MAX_ITER = 1_000
35
+ C_POWER_RANGE = torch.linspace(-6, 5, 45)
36
+ _CPU_DEVICE = torch.device("cpu")
37
+
38
+
39
+ def get_args_parser(
40
+ description: Optional[str] = None,
41
+ parents: Optional[List[argparse.ArgumentParser]] = None,
42
+ add_help: bool = True,
43
+ ):
44
+ parents = parents or []
45
+ setup_args_parser = get_setup_args_parser(parents=parents, add_help=False)
46
+ parents = [setup_args_parser]
47
+ parser = argparse.ArgumentParser(
48
+ description=description,
49
+ parents=parents,
50
+ add_help=add_help,
51
+ )
52
+ parser.add_argument(
53
+ "--train-dataset",
54
+ dest="train_dataset_str",
55
+ type=str,
56
+ help="Training dataset",
57
+ )
58
+ parser.add_argument(
59
+ "--val-dataset",
60
+ dest="val_dataset_str",
61
+ type=str,
62
+ help="Validation dataset",
63
+ )
64
+ parser.add_argument(
65
+ "--finetune-dataset-str",
66
+ dest="finetune_dataset_str",
67
+ type=str,
68
+ help="Fine-tuning dataset",
69
+ )
70
+ parser.add_argument(
71
+ "--finetune-on-val",
72
+ action="store_true",
73
+ help="If there is no finetune dataset, whether to choose the "
74
+ "hyperparameters on the val set instead of 10%% of the train dataset",
75
+ )
76
+ parser.add_argument(
77
+ "--metric-type",
78
+ type=MetricType,
79
+ choices=list(MetricType),
80
+ help="Metric type",
81
+ )
82
+ parser.add_argument(
83
+ "--train-features-device",
84
+ type=str,
85
+ help="Device to gather train features (cpu, cuda, cuda:0, etc.), default: %(default)s",
86
+ )
87
+ parser.add_argument(
88
+ "--train-dtype",
89
+ type=str,
90
+ help="Data type to convert the train features to (default: %(default)s)",
91
+ )
92
+ parser.add_argument(
93
+ "--max-train-iters",
94
+ type=int,
95
+ help="Maximum number of train iterations (default: %(default)s)",
96
+ )
97
+ parser.set_defaults(
98
+ train_dataset_str="ImageNet:split=TRAIN",
99
+ val_dataset_str="ImageNet:split=VAL",
100
+ finetune_dataset_str=None,
101
+ metric_type=MetricType.MEAN_ACCURACY,
102
+ train_features_device="cpu",
103
+ train_dtype="float64",
104
+ max_train_iters=DEFAULT_MAX_ITER,
105
+ finetune_on_val=False,
106
+ )
107
+ return parser
108
+
109
+
110
+ class LogRegModule(nn.Module):
111
+ def __init__(
112
+ self,
113
+ C,
114
+ max_iter=DEFAULT_MAX_ITER,
115
+ dtype=torch.float64,
116
+ device=_CPU_DEVICE,
117
+ ):
118
+ super().__init__()
119
+ self.dtype = dtype
120
+ self.device = device
121
+ self.estimator = LogisticRegression(
122
+ penalty="l2",
123
+ C=C,
124
+ max_iter=max_iter,
125
+ output_type="numpy",
126
+ tol=1e-12,
127
+ linesearch_max_iter=50,
128
+ )
129
+
130
+ def forward(self, samples, targets):
131
+ samples_device = samples.device
132
+ samples = samples.to(dtype=self.dtype, device=self.device)
133
+ if self.device == _CPU_DEVICE:
134
+ samples = samples.numpy()
135
+ probas = self.estimator.predict_proba(samples)
136
+ return {"preds": torch.from_numpy(probas).to(samples_device), "target": targets}
137
+
138
+ def fit(self, train_features, train_labels):
139
+ train_features = train_features.to(dtype=self.dtype, device=self.device)
140
+ train_labels = train_labels.to(dtype=self.dtype, device=self.device)
141
+ if self.device == _CPU_DEVICE:
142
+ # both cuML and sklearn only work with numpy arrays on CPU
143
+ train_features = train_features.numpy()
144
+ train_labels = train_labels.numpy()
145
+ self.estimator.fit(train_features, train_labels)
146
+
147
+
148
+ def evaluate_model(*, logreg_model, logreg_metric, test_data_loader, device):
149
+ postprocessors = {"metrics": logreg_model}
150
+ metrics = {"metrics": logreg_metric}
151
+ return evaluate(nn.Identity(), test_data_loader, postprocessors, metrics, device)
152
+
153
+
154
+ def train_for_C(*, C, max_iter, train_features, train_labels, dtype=torch.float64, device=_CPU_DEVICE):
155
+ logreg_model = LogRegModule(C, max_iter=max_iter, dtype=dtype, device=device)
156
+ logreg_model.fit(train_features, train_labels)
157
+ return logreg_model
158
+
159
+
160
+ def train_and_evaluate(
161
+ *,
162
+ C,
163
+ max_iter,
164
+ train_features,
165
+ train_labels,
166
+ logreg_metric,
167
+ test_data_loader,
168
+ train_dtype=torch.float64,
169
+ train_features_device,
170
+ eval_device,
171
+ ):
172
+ logreg_model = train_for_C(
173
+ C=C,
174
+ max_iter=max_iter,
175
+ train_features=train_features,
176
+ train_labels=train_labels,
177
+ dtype=train_dtype,
178
+ device=train_features_device,
179
+ )
180
+ return evaluate_model(
181
+ logreg_model=logreg_model,
182
+ logreg_metric=logreg_metric,
183
+ test_data_loader=test_data_loader,
184
+ device=eval_device,
185
+ )
186
+
187
+
188
+ def sweep_C_values(
189
+ *,
190
+ train_features,
191
+ train_labels,
192
+ test_data_loader,
193
+ metric_type,
194
+ num_classes,
195
+ train_dtype=torch.float64,
196
+ train_features_device=_CPU_DEVICE,
197
+ max_train_iters=DEFAULT_MAX_ITER,
198
+ ):
199
+ if metric_type == MetricType.PER_CLASS_ACCURACY:
200
+ # If we want to output per-class accuracy, we select the hyperparameters with mean per class
201
+ metric_type = MetricType.MEAN_PER_CLASS_ACCURACY
202
+ logreg_metric = build_metric(metric_type, num_classes=num_classes)
203
+ metric_tracker = MetricTracker(logreg_metric, maximize=True)
204
+ ALL_C = 10**C_POWER_RANGE
205
+ logreg_models = {}
206
+
207
+ train_features = train_features.to(dtype=train_dtype, device=train_features_device)
208
+ train_labels = train_labels.to(device=train_features_device)
209
+
210
+ for i in range(get_global_rank(), len(ALL_C), get_global_size()):
211
+ C = ALL_C[i].item()
212
+ logger.info(
213
+ f"Training for C = {C:.5f}, dtype={train_dtype}, "
214
+ f"features: {train_features.shape}, {train_features.dtype}, "
215
+ f"labels: {train_labels.shape}, {train_labels.dtype}"
216
+ )
217
+ logreg_models[C] = train_for_C(
218
+ C=C,
219
+ max_iter=max_train_iters,
220
+ train_features=train_features,
221
+ train_labels=train_labels,
222
+ dtype=train_dtype,
223
+ device=train_features_device,
224
+ )
225
+
226
+ gather_list = [None for _ in range(get_global_size())]
227
+ torch.distributed.all_gather_object(gather_list, logreg_models)
228
+
229
+ logreg_models_gathered = {}
230
+ for logreg_dict in gather_list:
231
+ logreg_models_gathered.update(logreg_dict)
232
+
233
+ for i in range(len(ALL_C)):
234
+ metric_tracker.increment()
235
+ C = ALL_C[i].item()
236
+ evals = evaluate_model(
237
+ logreg_model=logreg_models_gathered[C],
238
+ logreg_metric=metric_tracker,
239
+ test_data_loader=test_data_loader,
240
+ device=torch.cuda.current_device(),
241
+ )
242
+ logger.info(f"Trained for C = {C:.5f}, accuracies = {evals}")
243
+
244
+ best_stats, which_epoch = metric_tracker.best_metric(return_step=True)
245
+ best_stats_100 = {k: 100.0 * v for k, v in best_stats.items()}
246
+ if which_epoch["top-1"] == i:
247
+ best_C = C
248
+ logger.info(f"Sweep best {best_stats_100}, best C = {best_C:.6f}")
249
+
250
+ return best_stats, best_C
251
+
252
+
253
+ def eval_log_regression(
254
+ *,
255
+ model,
256
+ train_dataset,
257
+ val_dataset,
258
+ finetune_dataset,
259
+ metric_type,
260
+ batch_size,
261
+ num_workers,
262
+ finetune_on_val=False,
263
+ train_dtype=torch.float64,
264
+ train_features_device=_CPU_DEVICE,
265
+ max_train_iters=DEFAULT_MAX_ITER,
266
+ ):
267
+ """
268
+ Implements the "standard" process for log regression evaluation:
269
+ The value of C is chosen by training on train_dataset and evaluating on
270
+ finetune_dataset. Then, the final model is trained on a concatenation of
271
+ train_dataset and finetune_dataset, and is evaluated on val_dataset.
272
+ If there is no finetune_dataset, the value of C is the one that yields
273
+ the best results on a random 10% subset of the train dataset
274
+ """
275
+
276
+ start = time.time()
277
+
278
+ train_features, train_labels = extract_features(
279
+ model, train_dataset, batch_size, num_workers, gather_on_cpu=(train_features_device == _CPU_DEVICE)
280
+ )
281
+ val_features, val_labels = extract_features(
282
+ model, val_dataset, batch_size, num_workers, gather_on_cpu=(train_features_device == _CPU_DEVICE)
283
+ )
284
+ val_data_loader = torch.utils.data.DataLoader(
285
+ TensorDataset(val_features, val_labels),
286
+ batch_size=batch_size,
287
+ drop_last=False,
288
+ num_workers=0,
289
+ persistent_workers=False,
290
+ )
291
+
292
+ if finetune_dataset is None and finetune_on_val:
293
+ logger.info("Choosing hyperparameters on the val dataset")
294
+ finetune_features, finetune_labels = val_features, val_labels
295
+ elif finetune_dataset is None and not finetune_on_val:
296
+ logger.info("Choosing hyperparameters on 10% of the train dataset")
297
+ torch.manual_seed(0)
298
+ indices = torch.randperm(len(train_features), device=train_features.device)
299
+ finetune_index = indices[: len(train_features) // 10]
300
+ train_index = indices[len(train_features) // 10 :]
301
+ finetune_features, finetune_labels = train_features[finetune_index], train_labels[finetune_index]
302
+ train_features, train_labels = train_features[train_index], train_labels[train_index]
303
+ else:
304
+ logger.info("Choosing hyperparameters on the finetune dataset")
305
+ finetune_features, finetune_labels = extract_features(
306
+ model, finetune_dataset, batch_size, num_workers, gather_on_cpu=(train_features_device == _CPU_DEVICE)
307
+ )
308
+ # release the model - free GPU memory
309
+ del model
310
+ gc.collect()
311
+ torch.cuda.empty_cache()
312
+ finetune_data_loader = torch.utils.data.DataLoader(
313
+ TensorDataset(finetune_features, finetune_labels),
314
+ batch_size=batch_size,
315
+ drop_last=False,
316
+ )
317
+
318
+ if len(train_labels.shape) > 1:
319
+ num_classes = train_labels.shape[1]
320
+ else:
321
+ num_classes = train_labels.max() + 1
322
+
323
+ logger.info("Using cuML for logistic regression")
324
+
325
+ best_stats, best_C = sweep_C_values(
326
+ train_features=train_features,
327
+ train_labels=train_labels,
328
+ test_data_loader=finetune_data_loader,
329
+ metric_type=metric_type,
330
+ num_classes=num_classes,
331
+ train_dtype=train_dtype,
332
+ train_features_device=train_features_device,
333
+ max_train_iters=max_train_iters,
334
+ )
335
+
336
+ if not finetune_on_val:
337
+ logger.info("Best parameter found, concatenating features")
338
+ train_features = torch.cat((train_features, finetune_features))
339
+ train_labels = torch.cat((train_labels, finetune_labels))
340
+
341
+ logger.info("Training final model")
342
+ logreg_metric = build_metric(metric_type, num_classes=num_classes)
343
+ evals = train_and_evaluate(
344
+ C=best_C,
345
+ max_iter=max_train_iters,
346
+ train_features=train_features,
347
+ train_labels=train_labels,
348
+ logreg_metric=logreg_metric.clone(),
349
+ test_data_loader=val_data_loader,
350
+ eval_device=torch.cuda.current_device(),
351
+ train_dtype=train_dtype,
352
+ train_features_device=train_features_device,
353
+ )
354
+
355
+ best_stats = evals[1]["metrics"]
356
+
357
+ best_stats["best_C"] = best_C
358
+
359
+ logger.info(f"Log regression evaluation done in {int(time.time() - start)}s")
360
+ return best_stats
361
+
362
+
363
+ def eval_log_regression_with_model(
364
+ model,
365
+ train_dataset_str="ImageNet:split=TRAIN",
366
+ val_dataset_str="ImageNet:split=VAL",
367
+ finetune_dataset_str=None,
368
+ autocast_dtype=torch.float,
369
+ finetune_on_val=False,
370
+ metric_type=MetricType.MEAN_ACCURACY,
371
+ train_dtype=torch.float64,
372
+ train_features_device=_CPU_DEVICE,
373
+ max_train_iters=DEFAULT_MAX_ITER,
374
+ ):
375
+ cudnn.benchmark = True
376
+
377
+ transform = make_classification_eval_transform(resize_size=224)
378
+ target_transform = None
379
+
380
+ train_dataset = make_dataset(dataset_str=train_dataset_str, transform=transform, target_transform=target_transform)
381
+ val_dataset = make_dataset(dataset_str=val_dataset_str, transform=transform, target_transform=target_transform)
382
+ if finetune_dataset_str is not None:
383
+ finetune_dataset = make_dataset(
384
+ dataset_str=finetune_dataset_str, transform=transform, target_transform=target_transform
385
+ )
386
+ else:
387
+ finetune_dataset = None
388
+
389
+ with torch.cuda.amp.autocast(dtype=autocast_dtype):
390
+ results_dict_logreg = eval_log_regression(
391
+ model=model,
392
+ train_dataset=train_dataset,
393
+ val_dataset=val_dataset,
394
+ finetune_dataset=finetune_dataset,
395
+ metric_type=metric_type,
396
+ batch_size=256,
397
+ num_workers=0, # 5,
398
+ finetune_on_val=finetune_on_val,
399
+ train_dtype=train_dtype,
400
+ train_features_device=train_features_device,
401
+ max_train_iters=max_train_iters,
402
+ )
403
+
404
+ results_dict = {
405
+ "top-1": results_dict_logreg["top-1"].cpu().numpy() * 100.0,
406
+ "top-5": results_dict_logreg.get("top-5", torch.tensor(0.0)).cpu().numpy() * 100.0,
407
+ "best_C": results_dict_logreg["best_C"],
408
+ }
409
+ logger.info(
410
+ "\n".join(
411
+ [
412
+ "Training of the supervised logistic regression on frozen features completed.\n"
413
+ "Top-1 test accuracy: {acc:.1f}".format(acc=results_dict["top-1"]),
414
+ "Top-5 test accuracy: {acc:.1f}".format(acc=results_dict["top-5"]),
415
+ "obtained for C = {c:.6f}".format(c=results_dict["best_C"]),
416
+ ]
417
+ )
418
+ )
419
+
420
+ torch.distributed.barrier()
421
+ return results_dict
422
+
423
+
424
+ def main(args):
425
+ model, autocast_dtype = setup_and_build_model(args)
426
+ eval_log_regression_with_model(
427
+ model=model,
428
+ train_dataset_str=args.train_dataset_str,
429
+ val_dataset_str=args.val_dataset_str,
430
+ finetune_dataset_str=args.finetune_dataset_str,
431
+ autocast_dtype=autocast_dtype,
432
+ finetune_on_val=args.finetune_on_val,
433
+ metric_type=args.metric_type,
434
+ train_dtype=as_torch_dtype(args.train_dtype),
435
+ train_features_device=torch.device(args.train_features_device),
436
+ max_train_iters=args.max_train_iters,
437
+ )
438
+ return 0
439
+
440
+
441
+ if __name__ == "__main__":
442
+ description = "DINOv2 logistic regression evaluation"
443
+ args_parser = get_args_parser(description=description)
444
+ args = args_parser.parse_args()
445
+ sys.exit(main(args))
torchhub/facebookresearch_dinov2_main/dinov2/eval/metrics.py ADDED
@@ -0,0 +1,114 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ from enum import Enum
8
+ import logging
9
+ from typing import Any, Dict, Optional
10
+
11
+ import torch
12
+ from torch import Tensor
13
+ from torchmetrics import Metric, MetricCollection
14
+ from torchmetrics.classification import MulticlassAccuracy
15
+ from torchmetrics.utilities.data import dim_zero_cat, select_topk
16
+
17
+
18
+ logger = logging.getLogger("dinov2")
19
+
20
+
21
+ class MetricType(Enum):
22
+ MEAN_ACCURACY = "mean_accuracy"
23
+ MEAN_PER_CLASS_ACCURACY = "mean_per_class_accuracy"
24
+ PER_CLASS_ACCURACY = "per_class_accuracy"
25
+ IMAGENET_REAL_ACCURACY = "imagenet_real_accuracy"
26
+
27
+ @property
28
+ def accuracy_averaging(self):
29
+ return getattr(AccuracyAveraging, self.name, None)
30
+
31
+ def __str__(self):
32
+ return self.value
33
+
34
+
35
+ class AccuracyAveraging(Enum):
36
+ MEAN_ACCURACY = "micro"
37
+ MEAN_PER_CLASS_ACCURACY = "macro"
38
+ PER_CLASS_ACCURACY = "none"
39
+
40
+ def __str__(self):
41
+ return self.value
42
+
43
+
44
+ def build_metric(metric_type: MetricType, *, num_classes: int, ks: Optional[tuple] = None):
45
+ if metric_type.accuracy_averaging is not None:
46
+ return build_topk_accuracy_metric(
47
+ average_type=metric_type.accuracy_averaging,
48
+ num_classes=num_classes,
49
+ ks=(1, 5) if ks is None else ks,
50
+ )
51
+ elif metric_type == MetricType.IMAGENET_REAL_ACCURACY:
52
+ return build_topk_imagenet_real_accuracy_metric(
53
+ num_classes=num_classes,
54
+ ks=(1, 5) if ks is None else ks,
55
+ )
56
+
57
+ raise ValueError(f"Unknown metric type {metric_type}")
58
+
59
+
60
+ def build_topk_accuracy_metric(average_type: AccuracyAveraging, num_classes: int, ks: tuple = (1, 5)):
61
+ metrics: Dict[str, Metric] = {
62
+ f"top-{k}": MulticlassAccuracy(top_k=k, num_classes=int(num_classes), average=average_type.value) for k in ks
63
+ }
64
+ return MetricCollection(metrics)
65
+
66
+
67
+ def build_topk_imagenet_real_accuracy_metric(num_classes: int, ks: tuple = (1, 5)):
68
+ metrics: Dict[str, Metric] = {f"top-{k}": ImageNetReaLAccuracy(top_k=k, num_classes=int(num_classes)) for k in ks}
69
+ return MetricCollection(metrics)
70
+
71
+
72
+ class ImageNetReaLAccuracy(Metric):
73
+ is_differentiable: bool = False
74
+ higher_is_better: Optional[bool] = None
75
+ full_state_update: bool = False
76
+
77
+ def __init__(
78
+ self,
79
+ num_classes: int,
80
+ top_k: int = 1,
81
+ **kwargs: Any,
82
+ ) -> None:
83
+ super().__init__(**kwargs)
84
+ self.num_classes = num_classes
85
+ self.top_k = top_k
86
+ self.add_state("tp", [], dist_reduce_fx="cat")
87
+
88
+ def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore
89
+ # preds [B, D]
90
+ # target [B, A]
91
+ # preds_oh [B, D] with 0 and 1
92
+ # select top K highest probabilities, use one hot representation
93
+ preds_oh = select_topk(preds, self.top_k)
94
+ # target_oh [B, D + 1] with 0 and 1
95
+ target_oh = torch.zeros((preds_oh.shape[0], preds_oh.shape[1] + 1), device=target.device, dtype=torch.int32)
96
+ target = target.long()
97
+ # for undefined targets (-1) use a fake value `num_classes`
98
+ target[target == -1] = self.num_classes
99
+ # fill targets, use one hot representation
100
+ target_oh.scatter_(1, target, 1)
101
+ # target_oh [B, D] (remove the fake target at index `num_classes`)
102
+ target_oh = target_oh[:, :-1]
103
+ # tp [B] with 0 and 1
104
+ tp = (preds_oh * target_oh == 1).sum(dim=1)
105
+ # at least one match between prediction and target
106
+ tp.clip_(max=1)
107
+ # ignore instances where no targets are defined
108
+ mask = target_oh.sum(dim=1) > 0
109
+ tp = tp[mask]
110
+ self.tp.append(tp) # type: ignore
111
+
112
+ def compute(self) -> Tensor:
113
+ tp = dim_zero_cat(self.tp) # type: ignore
114
+ return tp.float().mean()
torchhub/facebookresearch_dinov2_main/dinov2/eval/setup.py ADDED
@@ -0,0 +1,76 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import argparse
8
+ from typing import Any, List, Optional, Tuple
9
+
10
+ import torch
11
+ import torch.backends.cudnn as cudnn
12
+
13
+ from dinov2.models import build_model_from_cfg
14
+ from dinov2.utils.config import setup
15
+ import dinov2.utils.utils as dinov2_utils
16
+
17
+
18
+ def get_args_parser(
19
+ description: Optional[str] = None,
20
+ parents: Optional[List[argparse.ArgumentParser]] = None,
21
+ add_help: bool = True,
22
+ ):
23
+ parser = argparse.ArgumentParser(
24
+ description=description,
25
+ parents=parents or [],
26
+ add_help=add_help,
27
+ )
28
+ parser.add_argument(
29
+ "--config-file",
30
+ type=str,
31
+ help="Model configuration file",
32
+ )
33
+ parser.add_argument(
34
+ "--pretrained-weights",
35
+ type=str,
36
+ help="Pretrained model weights",
37
+ )
38
+ parser.add_argument(
39
+ "--output-dir",
40
+ default="",
41
+ type=str,
42
+ help="Output directory to write results and logs",
43
+ )
44
+ parser.add_argument(
45
+ "--opts",
46
+ help="Extra configuration options",
47
+ default=[],
48
+ nargs="+",
49
+ )
50
+ return parser
51
+
52
+
53
+ def get_autocast_dtype(config):
54
+ teacher_dtype_str = config.compute_precision.teacher.backbone.mixed_precision.param_dtype
55
+ if teacher_dtype_str == "fp16":
56
+ return torch.half
57
+ elif teacher_dtype_str == "bf16":
58
+ return torch.bfloat16
59
+ else:
60
+ return torch.float
61
+
62
+
63
+ def build_model_for_eval(config, pretrained_weights):
64
+ model, _ = build_model_from_cfg(config, only_teacher=True)
65
+ dinov2_utils.load_pretrained_weights(model, pretrained_weights, "teacher")
66
+ model.eval()
67
+ model.cuda()
68
+ return model
69
+
70
+
71
+ def setup_and_build_model(args) -> Tuple[Any, torch.dtype]:
72
+ cudnn.benchmark = True
73
+ config = setup(args)
74
+ model = build_model_for_eval(config, args.pretrained_weights)
75
+ autocast_dtype = get_autocast_dtype(config)
76
+ return model, autocast_dtype
torchhub/facebookresearch_dinov2_main/dinov2/eval/utils.py ADDED
@@ -0,0 +1,147 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import logging
8
+ from typing import Dict, Optional
9
+
10
+ import torch
11
+ from torch import nn
12
+ from torchmetrics import MetricCollection
13
+
14
+ from dinov2.data import DatasetWithEnumeratedTargets, SamplerType, make_data_loader
15
+ import dinov2.distributed as distributed
16
+ from dinov2.logging import MetricLogger
17
+
18
+
19
+ logger = logging.getLogger("dinov2")
20
+
21
+
22
+ class ModelWithNormalize(torch.nn.Module):
23
+ def __init__(self, model):
24
+ super().__init__()
25
+ self.model = model
26
+
27
+ def forward(self, samples):
28
+ return nn.functional.normalize(self.model(samples), dim=1, p=2)
29
+
30
+
31
+ class ModelWithIntermediateLayers(nn.Module):
32
+ def __init__(self, feature_model, n_last_blocks, autocast_ctx):
33
+ super().__init__()
34
+ self.feature_model = feature_model
35
+ self.feature_model.eval()
36
+ self.n_last_blocks = n_last_blocks
37
+ self.autocast_ctx = autocast_ctx
38
+
39
+ def forward(self, images):
40
+ with torch.inference_mode():
41
+ with self.autocast_ctx():
42
+ features = self.feature_model.get_intermediate_layers(
43
+ images, self.n_last_blocks, return_class_token=True
44
+ )
45
+ return features
46
+
47
+
48
+ @torch.inference_mode()
49
+ def evaluate(
50
+ model: nn.Module,
51
+ data_loader,
52
+ postprocessors: Dict[str, nn.Module],
53
+ metrics: Dict[str, MetricCollection],
54
+ device: torch.device,
55
+ criterion: Optional[nn.Module] = None,
56
+ ):
57
+ model.eval()
58
+ if criterion is not None:
59
+ criterion.eval()
60
+
61
+ for metric in metrics.values():
62
+ metric = metric.to(device)
63
+
64
+ metric_logger = MetricLogger(delimiter=" ")
65
+ header = "Test:"
66
+
67
+ for samples, targets, *_ in metric_logger.log_every(data_loader, 10, header):
68
+ outputs = model(samples.to(device))
69
+ targets = targets.to(device)
70
+
71
+ if criterion is not None:
72
+ loss = criterion(outputs, targets)
73
+ metric_logger.update(loss=loss.item())
74
+
75
+ for k, metric in metrics.items():
76
+ metric_inputs = postprocessors[k](outputs, targets)
77
+ metric.update(**metric_inputs)
78
+
79
+ metric_logger.synchronize_between_processes()
80
+ logger.info(f"Averaged stats: {metric_logger}")
81
+
82
+ stats = {k: metric.compute() for k, metric in metrics.items()}
83
+ metric_logger_stats = {k: meter.global_avg for k, meter in metric_logger.meters.items()}
84
+ return metric_logger_stats, stats
85
+
86
+
87
+ def all_gather_and_flatten(tensor_rank):
88
+ tensor_all_ranks = torch.empty(
89
+ distributed.get_global_size(),
90
+ *tensor_rank.shape,
91
+ dtype=tensor_rank.dtype,
92
+ device=tensor_rank.device,
93
+ )
94
+ tensor_list = list(tensor_all_ranks.unbind(0))
95
+ torch.distributed.all_gather(tensor_list, tensor_rank.contiguous())
96
+ return tensor_all_ranks.flatten(end_dim=1)
97
+
98
+
99
+ def extract_features(model, dataset, batch_size, num_workers, gather_on_cpu=False):
100
+ dataset_with_enumerated_targets = DatasetWithEnumeratedTargets(dataset)
101
+ sample_count = len(dataset_with_enumerated_targets)
102
+ data_loader = make_data_loader(
103
+ dataset=dataset_with_enumerated_targets,
104
+ batch_size=batch_size,
105
+ num_workers=num_workers,
106
+ sampler_type=SamplerType.DISTRIBUTED,
107
+ drop_last=False,
108
+ shuffle=False,
109
+ )
110
+ return extract_features_with_dataloader(model, data_loader, sample_count, gather_on_cpu)
111
+
112
+
113
+ @torch.inference_mode()
114
+ def extract_features_with_dataloader(model, data_loader, sample_count, gather_on_cpu=False):
115
+ gather_device = torch.device("cpu") if gather_on_cpu else torch.device("cuda")
116
+ metric_logger = MetricLogger(delimiter=" ")
117
+ features, all_labels = None, None
118
+ for samples, (index, labels_rank) in metric_logger.log_every(data_loader, 10):
119
+ samples = samples.cuda(non_blocking=True)
120
+ labels_rank = labels_rank.cuda(non_blocking=True)
121
+ index = index.cuda(non_blocking=True)
122
+ features_rank = model(samples).float()
123
+
124
+ # init storage feature matrix
125
+ if features is None:
126
+ features = torch.zeros(sample_count, features_rank.shape[-1], device=gather_device)
127
+ labels_shape = list(labels_rank.shape)
128
+ labels_shape[0] = sample_count
129
+ all_labels = torch.full(labels_shape, fill_value=-1, device=gather_device)
130
+ logger.info(f"Storing features into tensor of shape {features.shape}")
131
+
132
+ # share indexes, features and labels between processes
133
+ index_all = all_gather_and_flatten(index).to(gather_device)
134
+ features_all_ranks = all_gather_and_flatten(features_rank).to(gather_device)
135
+ labels_all_ranks = all_gather_and_flatten(labels_rank).to(gather_device)
136
+
137
+ # update storage feature matrix
138
+ if len(index_all) > 0:
139
+ features.index_copy_(0, index_all, features_all_ranks)
140
+ all_labels.index_copy_(0, index_all, labels_all_ranks)
141
+
142
+ logger.info(f"Features shape: {tuple(features.shape)}")
143
+ logger.info(f"Labels shape: {tuple(all_labels.shape)}")
144
+
145
+ assert torch.all(all_labels > -1)
146
+
147
+ return features, all_labels
torchhub/facebookresearch_dinov2_main/dinov2/fsdp/__init__.py ADDED
@@ -0,0 +1,158 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import os
8
+ from typing import Any
9
+
10
+ import torch
11
+ import dinov2.distributed as distributed
12
+ from functools import partial
13
+ from fvcore.common.checkpoint import Checkpointer
14
+ from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
15
+ from torch.distributed.fsdp import ShardingStrategy
16
+ from torch.distributed.fsdp import MixedPrecision
17
+ from torch.distributed.fsdp import StateDictType
18
+ from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
19
+ from torch.distributed.fsdp.wrap import ModuleWrapPolicy
20
+ from torch.distributed.fsdp._runtime_utils import _reshard
21
+
22
+
23
+ def get_fsdp_wrapper(model_cfg, modules_to_wrap=set()):
24
+ sharding_strategy_dict = {
25
+ "NO_SHARD": ShardingStrategy.NO_SHARD,
26
+ "SHARD_GRAD_OP": ShardingStrategy.SHARD_GRAD_OP,
27
+ "FULL_SHARD": ShardingStrategy.FULL_SHARD,
28
+ }
29
+
30
+ dtype_dict = {
31
+ "fp32": torch.float32,
32
+ "fp16": torch.float16,
33
+ "bf16": torch.bfloat16,
34
+ }
35
+
36
+ mixed_precision_config = MixedPrecision(
37
+ param_dtype=dtype_dict[model_cfg.mixed_precision.param_dtype],
38
+ reduce_dtype=dtype_dict[model_cfg.mixed_precision.reduce_dtype],
39
+ buffer_dtype=dtype_dict[model_cfg.mixed_precision.buffer_dtype],
40
+ )
41
+
42
+ sharding_strategy_config = sharding_strategy_dict[model_cfg.sharding_strategy]
43
+
44
+ local_rank = distributed.get_local_rank()
45
+
46
+ fsdp_wrapper = partial(
47
+ FSDP,
48
+ sharding_strategy=sharding_strategy_config,
49
+ mixed_precision=mixed_precision_config,
50
+ device_id=local_rank,
51
+ sync_module_states=True,
52
+ use_orig_params=True,
53
+ auto_wrap_policy=ModuleWrapPolicy(modules_to_wrap),
54
+ )
55
+ return fsdp_wrapper
56
+
57
+
58
+ def is_fsdp(x):
59
+ return isinstance(x, FSDP)
60
+
61
+
62
+ def is_sharded_fsdp(x):
63
+ return is_fsdp(x) and x.sharding_strategy is not ShardingStrategy.NO_SHARD
64
+
65
+
66
+ def free_if_fsdp(x):
67
+ if is_sharded_fsdp(x):
68
+ handles = x._handles
69
+ true_list = [True for h in handles]
70
+ _reshard(x, handles, true_list)
71
+
72
+
73
+ def get_fsdp_modules(x):
74
+ return FSDP.fsdp_modules(x)
75
+
76
+
77
+ def reshard_fsdp_model(x):
78
+ for m in get_fsdp_modules(x):
79
+ free_if_fsdp(m)
80
+
81
+
82
+ def rankstr():
83
+ return f"rank_{distributed.get_global_rank()}"
84
+
85
+
86
+ class FSDPCheckpointer(Checkpointer):
87
+ def save(self, name: str, **kwargs: Any) -> None:
88
+ """
89
+ Dump model and checkpointables to a file.
90
+
91
+ Args:
92
+ name (str): name of the file.
93
+ kwargs (dict): extra arbitrary data to save.
94
+ """
95
+ if not self.save_dir or not self.save_to_disk:
96
+ return
97
+
98
+ data = {}
99
+ with FSDP.state_dict_type(self.model, StateDictType.LOCAL_STATE_DICT):
100
+ data["model"] = self.model.state_dict()
101
+
102
+ # data["model"] = self.model.state_dict()
103
+ for key, obj in self.checkpointables.items():
104
+ data[key] = obj.state_dict()
105
+ data.update(kwargs)
106
+
107
+ basename = f"{name}.{rankstr()}.pth"
108
+ save_file = os.path.join(self.save_dir, basename)
109
+ assert os.path.basename(save_file) == basename, basename
110
+ self.logger.info("Saving checkpoint to {}".format(save_file))
111
+ with self.path_manager.open(save_file, "wb") as f:
112
+ torch.save(data, f)
113
+ self.tag_last_checkpoint(basename)
114
+
115
+ def load(self, *args, **kwargs):
116
+ with FSDP.state_dict_type(self.model, StateDictType.LOCAL_STATE_DICT):
117
+ return super().load(*args, **kwargs)
118
+
119
+ def has_checkpoint(self) -> bool:
120
+ """
121
+ Returns:
122
+ bool: whether a checkpoint exists in the target directory.
123
+ """
124
+ save_file = os.path.join(self.save_dir, f"last_checkpoint.{rankstr()}")
125
+ return self.path_manager.exists(save_file)
126
+
127
+ def get_checkpoint_file(self) -> str:
128
+ """
129
+ Returns:
130
+ str: The latest checkpoint file in target directory.
131
+ """
132
+ save_file = os.path.join(self.save_dir, f"last_checkpoint.{rankstr()}")
133
+ try:
134
+ with self.path_manager.open(save_file, "r") as f:
135
+ last_saved = f.read().strip()
136
+ except IOError:
137
+ # if file doesn't exist, maybe because it has just been
138
+ # deleted by a separate process
139
+ return ""
140
+ # pyre-fixme[6]: For 2nd param expected `Union[PathLike[str], str]` but got
141
+ # `Union[bytes, str]`.
142
+ return os.path.join(self.save_dir, last_saved)
143
+
144
+ def tag_last_checkpoint(self, last_filename_basename: str) -> None:
145
+ """
146
+ Tag the last checkpoint.
147
+
148
+ Args:
149
+ last_filename_basename (str): the basename of the last filename.
150
+ """
151
+ if distributed.is_enabled():
152
+ torch.distributed.barrier()
153
+ save_file = os.path.join(self.save_dir, f"last_checkpoint.{rankstr()}")
154
+ with self.path_manager.open(save_file, "w") as f:
155
+ f.write(last_filename_basename) # pyre-ignore
156
+
157
+
158
+ ShardedGradScaler = ShardedGradScaler
torchhub/facebookresearch_dinov2_main/dinov2/layers/.DS_Store ADDED
Binary file (6.15 kB). View file
 
torchhub/facebookresearch_dinov2_main/dinov2/layers/__init__.py ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ from .dino_head import DINOHead
8
+ from .mlp import Mlp
9
+ from .patch_embed import PatchEmbed
10
+ from .swiglu_ffn import SwiGLUFFN, SwiGLUFFNFused
11
+ from .block import NestedTensorBlock
12
+ from .attention import MemEffAttention
torchhub/facebookresearch_dinov2_main/dinov2/layers/attention.py ADDED
@@ -0,0 +1,81 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ # References:
8
+ # https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
9
+ # https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
10
+
11
+ import logging
12
+
13
+ from torch import Tensor
14
+ from torch import nn
15
+
16
+
17
+ logger = logging.getLogger("dinov2")
18
+
19
+
20
+ try:
21
+ from xformers.ops import memory_efficient_attention, unbind, fmha
22
+
23
+ XFORMERS_AVAILABLE = True
24
+ except ImportError:
25
+ logger.warning("xFormers not available")
26
+ XFORMERS_AVAILABLE = False
27
+
28
+
29
+ class Attention(nn.Module):
30
+ def __init__(
31
+ self,
32
+ dim: int,
33
+ num_heads: int = 8,
34
+ qkv_bias: bool = False,
35
+ proj_bias: bool = True,
36
+ attn_drop: float = 0.0,
37
+ proj_drop: float = 0.0,
38
+ ) -> None:
39
+ super().__init__()
40
+ self.num_heads = num_heads
41
+ head_dim = dim // num_heads
42
+ self.scale = head_dim**-0.5
43
+
44
+ self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
45
+ self.attn_drop = nn.Dropout(attn_drop)
46
+ self.proj = nn.Linear(dim, dim, bias=proj_bias)
47
+ self.proj_drop = nn.Dropout(proj_drop)
48
+
49
+ def forward(self, x: Tensor) -> Tensor:
50
+ B, N, C = x.shape
51
+ qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
52
+
53
+ q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]
54
+ attn = q @ k.transpose(-2, -1)
55
+
56
+ attn = attn.softmax(dim=-1)
57
+ attn = self.attn_drop(attn)
58
+
59
+ x = (attn @ v).transpose(1, 2).reshape(B, N, C)
60
+ x = self.proj(x)
61
+ x = self.proj_drop(x)
62
+ return x
63
+
64
+
65
+ class MemEffAttention(Attention):
66
+ def forward(self, x: Tensor, attn_bias=None) -> Tensor:
67
+ if not XFORMERS_AVAILABLE:
68
+ assert attn_bias is None, "xFormers is required for nested tensors usage"
69
+ return super().forward(x)
70
+
71
+ B, N, C = x.shape
72
+ qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
73
+
74
+ q, k, v = unbind(qkv, 2)
75
+
76
+ x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
77
+ x = x.reshape([B, N, C])
78
+
79
+ x = self.proj(x)
80
+ x = self.proj_drop(x)
81
+ return x
torchhub/facebookresearch_dinov2_main/dinov2/layers/block.py ADDED
@@ -0,0 +1,252 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ # References:
8
+ # https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
9
+ # https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py
10
+
11
+ import logging
12
+ from typing import Callable, List, Any, Tuple, Dict
13
+
14
+ import torch
15
+ from torch import nn, Tensor
16
+
17
+ from .attention import Attention, MemEffAttention
18
+ from .drop_path import DropPath
19
+ from .layer_scale import LayerScale
20
+ from .mlp import Mlp
21
+
22
+
23
+ logger = logging.getLogger("dinov2")
24
+
25
+
26
+ try:
27
+ from xformers.ops import fmha
28
+ from xformers.ops import scaled_index_add, index_select_cat
29
+
30
+ XFORMERS_AVAILABLE = True
31
+ except ImportError:
32
+ logger.warning("xFormers not available")
33
+ XFORMERS_AVAILABLE = False
34
+
35
+
36
+ class Block(nn.Module):
37
+ def __init__(
38
+ self,
39
+ dim: int,
40
+ num_heads: int,
41
+ mlp_ratio: float = 4.0,
42
+ qkv_bias: bool = False,
43
+ proj_bias: bool = True,
44
+ ffn_bias: bool = True,
45
+ drop: float = 0.0,
46
+ attn_drop: float = 0.0,
47
+ init_values=None,
48
+ drop_path: float = 0.0,
49
+ act_layer: Callable[..., nn.Module] = nn.GELU,
50
+ norm_layer: Callable[..., nn.Module] = nn.LayerNorm,
51
+ attn_class: Callable[..., nn.Module] = Attention,
52
+ ffn_layer: Callable[..., nn.Module] = Mlp,
53
+ ) -> None:
54
+ super().__init__()
55
+ # print(f"biases: qkv: {qkv_bias}, proj: {proj_bias}, ffn: {ffn_bias}")
56
+ self.norm1 = norm_layer(dim)
57
+ self.attn = attn_class(
58
+ dim,
59
+ num_heads=num_heads,
60
+ qkv_bias=qkv_bias,
61
+ proj_bias=proj_bias,
62
+ attn_drop=attn_drop,
63
+ proj_drop=drop,
64
+ )
65
+ self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
66
+ self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
67
+
68
+ self.norm2 = norm_layer(dim)
69
+ mlp_hidden_dim = int(dim * mlp_ratio)
70
+ self.mlp = ffn_layer(
71
+ in_features=dim,
72
+ hidden_features=mlp_hidden_dim,
73
+ act_layer=act_layer,
74
+ drop=drop,
75
+ bias=ffn_bias,
76
+ )
77
+ self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
78
+ self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
79
+
80
+ self.sample_drop_ratio = drop_path
81
+
82
+ def forward(self, x: Tensor) -> Tensor:
83
+ def attn_residual_func(x: Tensor) -> Tensor:
84
+ return self.ls1(self.attn(self.norm1(x)))
85
+
86
+ def ffn_residual_func(x: Tensor) -> Tensor:
87
+ return self.ls2(self.mlp(self.norm2(x)))
88
+
89
+ if self.training and self.sample_drop_ratio > 0.1:
90
+ # the overhead is compensated only for a drop path rate larger than 0.1
91
+ x = drop_add_residual_stochastic_depth(
92
+ x,
93
+ residual_func=attn_residual_func,
94
+ sample_drop_ratio=self.sample_drop_ratio,
95
+ )
96
+ x = drop_add_residual_stochastic_depth(
97
+ x,
98
+ residual_func=ffn_residual_func,
99
+ sample_drop_ratio=self.sample_drop_ratio,
100
+ )
101
+ elif self.training and self.sample_drop_ratio > 0.0:
102
+ x = x + self.drop_path1(attn_residual_func(x))
103
+ x = x + self.drop_path1(ffn_residual_func(x)) # FIXME: drop_path2
104
+ else:
105
+ x = x + attn_residual_func(x)
106
+ x = x + ffn_residual_func(x)
107
+ return x
108
+
109
+
110
+ def drop_add_residual_stochastic_depth(
111
+ x: Tensor,
112
+ residual_func: Callable[[Tensor], Tensor],
113
+ sample_drop_ratio: float = 0.0,
114
+ ) -> Tensor:
115
+ # 1) extract subset using permutation
116
+ b, n, d = x.shape
117
+ sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1)
118
+ brange = (torch.randperm(b, device=x.device))[:sample_subset_size]
119
+ x_subset = x[brange]
120
+
121
+ # 2) apply residual_func to get residual
122
+ residual = residual_func(x_subset)
123
+
124
+ x_flat = x.flatten(1)
125
+ residual = residual.flatten(1)
126
+
127
+ residual_scale_factor = b / sample_subset_size
128
+
129
+ # 3) add the residual
130
+ x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor)
131
+ return x_plus_residual.view_as(x)
132
+
133
+
134
+ def get_branges_scales(x, sample_drop_ratio=0.0):
135
+ b, n, d = x.shape
136
+ sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1)
137
+ brange = (torch.randperm(b, device=x.device))[:sample_subset_size]
138
+ residual_scale_factor = b / sample_subset_size
139
+ return brange, residual_scale_factor
140
+
141
+
142
+ def add_residual(x, brange, residual, residual_scale_factor, scaling_vector=None):
143
+ if scaling_vector is None:
144
+ x_flat = x.flatten(1)
145
+ residual = residual.flatten(1)
146
+ x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor)
147
+ else:
148
+ x_plus_residual = scaled_index_add(
149
+ x, brange, residual.to(dtype=x.dtype), scaling=scaling_vector, alpha=residual_scale_factor
150
+ )
151
+ return x_plus_residual
152
+
153
+
154
+ attn_bias_cache: Dict[Tuple, Any] = {}
155
+
156
+
157
+ def get_attn_bias_and_cat(x_list, branges=None):
158
+ """
159
+ this will perform the index select, cat the tensors, and provide the attn_bias from cache
160
+ """
161
+ batch_sizes = [b.shape[0] for b in branges] if branges is not None else [x.shape[0] for x in x_list]
162
+ all_shapes = tuple((b, x.shape[1]) for b, x in zip(batch_sizes, x_list))
163
+ if all_shapes not in attn_bias_cache.keys():
164
+ seqlens = []
165
+ for b, x in zip(batch_sizes, x_list):
166
+ for _ in range(b):
167
+ seqlens.append(x.shape[1])
168
+ attn_bias = fmha.BlockDiagonalMask.from_seqlens(seqlens)
169
+ attn_bias._batch_sizes = batch_sizes
170
+ attn_bias_cache[all_shapes] = attn_bias
171
+
172
+ if branges is not None:
173
+ cat_tensors = index_select_cat([x.flatten(1) for x in x_list], branges).view(1, -1, x_list[0].shape[-1])
174
+ else:
175
+ tensors_bs1 = tuple(x.reshape([1, -1, *x.shape[2:]]) for x in x_list)
176
+ cat_tensors = torch.cat(tensors_bs1, dim=1)
177
+
178
+ return attn_bias_cache[all_shapes], cat_tensors
179
+
180
+
181
+ def drop_add_residual_stochastic_depth_list(
182
+ x_list: List[Tensor],
183
+ residual_func: Callable[[Tensor, Any], Tensor],
184
+ sample_drop_ratio: float = 0.0,
185
+ scaling_vector=None,
186
+ ) -> Tensor:
187
+ # 1) generate random set of indices for dropping samples in the batch
188
+ branges_scales = [get_branges_scales(x, sample_drop_ratio=sample_drop_ratio) for x in x_list]
189
+ branges = [s[0] for s in branges_scales]
190
+ residual_scale_factors = [s[1] for s in branges_scales]
191
+
192
+ # 2) get attention bias and index+concat the tensors
193
+ attn_bias, x_cat = get_attn_bias_and_cat(x_list, branges)
194
+
195
+ # 3) apply residual_func to get residual, and split the result
196
+ residual_list = attn_bias.split(residual_func(x_cat, attn_bias=attn_bias)) # type: ignore
197
+
198
+ outputs = []
199
+ for x, brange, residual, residual_scale_factor in zip(x_list, branges, residual_list, residual_scale_factors):
200
+ outputs.append(add_residual(x, brange, residual, residual_scale_factor, scaling_vector).view_as(x))
201
+ return outputs
202
+
203
+
204
+ class NestedTensorBlock(Block):
205
+ def forward_nested(self, x_list: List[Tensor]) -> List[Tensor]:
206
+ """
207
+ x_list contains a list of tensors to nest together and run
208
+ """
209
+ assert isinstance(self.attn, MemEffAttention)
210
+
211
+ if self.training and self.sample_drop_ratio > 0.0:
212
+
213
+ def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
214
+ return self.attn(self.norm1(x), attn_bias=attn_bias)
215
+
216
+ def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
217
+ return self.mlp(self.norm2(x))
218
+
219
+ x_list = drop_add_residual_stochastic_depth_list(
220
+ x_list,
221
+ residual_func=attn_residual_func,
222
+ sample_drop_ratio=self.sample_drop_ratio,
223
+ scaling_vector=self.ls1.gamma if isinstance(self.ls1, LayerScale) else None,
224
+ )
225
+ x_list = drop_add_residual_stochastic_depth_list(
226
+ x_list,
227
+ residual_func=ffn_residual_func,
228
+ sample_drop_ratio=self.sample_drop_ratio,
229
+ scaling_vector=self.ls2.gamma if isinstance(self.ls1, LayerScale) else None,
230
+ )
231
+ return x_list
232
+ else:
233
+
234
+ def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
235
+ return self.ls1(self.attn(self.norm1(x), attn_bias=attn_bias))
236
+
237
+ def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
238
+ return self.ls2(self.mlp(self.norm2(x)))
239
+
240
+ attn_bias, x = get_attn_bias_and_cat(x_list)
241
+ x = x + attn_residual_func(x, attn_bias=attn_bias)
242
+ x = x + ffn_residual_func(x)
243
+ return attn_bias.split(x)
244
+
245
+ def forward(self, x_or_x_list):
246
+ if isinstance(x_or_x_list, Tensor):
247
+ return super().forward(x_or_x_list)
248
+ elif isinstance(x_or_x_list, list):
249
+ assert XFORMERS_AVAILABLE, "Please install xFormers for nested tensors usage"
250
+ return self.forward_nested(x_or_x_list)
251
+ else:
252
+ raise AssertionError
torchhub/facebookresearch_dinov2_main/dinov2/layers/dino_head.py ADDED
@@ -0,0 +1,59 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import torch
8
+ import torch.nn as nn
9
+ from torch.nn.init import trunc_normal_
10
+ from torch.nn.utils import weight_norm
11
+
12
+
13
+ class DINOHead(nn.Module):
14
+ def __init__(
15
+ self,
16
+ in_dim,
17
+ out_dim,
18
+ use_bn=False,
19
+ nlayers=3,
20
+ hidden_dim=2048,
21
+ bottleneck_dim=256,
22
+ mlp_bias=True,
23
+ ):
24
+ super().__init__()
25
+ nlayers = max(nlayers, 1)
26
+ self.mlp = _build_mlp(nlayers, in_dim, bottleneck_dim, hidden_dim=hidden_dim, use_bn=use_bn, bias=mlp_bias)
27
+ self.apply(self._init_weights)
28
+ self.last_layer = weight_norm(nn.Linear(bottleneck_dim, out_dim, bias=False))
29
+ self.last_layer.weight_g.data.fill_(1)
30
+
31
+ def _init_weights(self, m):
32
+ if isinstance(m, nn.Linear):
33
+ trunc_normal_(m.weight, std=0.02)
34
+ if isinstance(m, nn.Linear) and m.bias is not None:
35
+ nn.init.constant_(m.bias, 0)
36
+
37
+ def forward(self, x):
38
+ x = self.mlp(x)
39
+ eps = 1e-6 if x.dtype == torch.float16 else 1e-12
40
+ x = nn.functional.normalize(x, dim=-1, p=2, eps=eps)
41
+ x = self.last_layer(x)
42
+ return x
43
+
44
+
45
+ def _build_mlp(nlayers, in_dim, bottleneck_dim, hidden_dim=None, use_bn=False, bias=True):
46
+ if nlayers == 1:
47
+ return nn.Linear(in_dim, bottleneck_dim, bias=bias)
48
+ else:
49
+ layers = [nn.Linear(in_dim, hidden_dim, bias=bias)]
50
+ if use_bn:
51
+ layers.append(nn.BatchNorm1d(hidden_dim))
52
+ layers.append(nn.GELU())
53
+ for _ in range(nlayers - 2):
54
+ layers.append(nn.Linear(hidden_dim, hidden_dim, bias=bias))
55
+ if use_bn:
56
+ layers.append(nn.BatchNorm1d(hidden_dim))
57
+ layers.append(nn.GELU())
58
+ layers.append(nn.Linear(hidden_dim, bottleneck_dim, bias=bias))
59
+ return nn.Sequential(*layers)
torchhub/facebookresearch_dinov2_main/dinov2/layers/drop_path.py ADDED
@@ -0,0 +1,35 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+ #
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ # References:
8
+ # https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
9
+ # https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/drop.py
10
+
11
+
12
+ from torch import nn
13
+
14
+
15
+ def drop_path(x, drop_prob: float = 0.0, training: bool = False):
16
+ if drop_prob == 0.0 or not training:
17
+ return x
18
+ keep_prob = 1 - drop_prob
19
+ shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
20
+ random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
21
+ if keep_prob > 0.0:
22
+ random_tensor.div_(keep_prob)
23
+ output = x * random_tensor
24
+ return output
25
+
26
+
27
+ class DropPath(nn.Module):
28
+ """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
29
+
30
+ def __init__(self, drop_prob=None):
31
+ super(DropPath, self).__init__()
32
+ self.drop_prob = drop_prob
33
+
34
+ def forward(self, x):
35
+ return drop_path(x, self.drop_prob, self.training)