├── .gitignore ├── LICENSE ├── README.md ├── convert.py ├── download.py ├── resnet.py └── verify.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # SimCLRv2-Pytorch 2 | Pretrained SimCLRv2 models in Pytorch 3 | 4 | ```python 5 | python download.py r152_3x_sk1 6 | python convert.py r152_3x_sk1/model.ckpt-250228 [--ema] 7 | python verify.py r152_3x_sk1.pth 8 | ``` 9 | 10 | | Model | Pytorch | TF | 11 | | :-------------: |:-------------:| :-----:| 12 | | r50_1x_sk0 | 70.97 | 71.7 | 13 | | r50_1x_sk1 | 73.79 | 74.6 | 14 | | r152_3x_sk1 | 79.12 | 79.8 | 15 | -------------------------------------------------------------------------------- /convert.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | import torch 4 | import numpy as np 5 | import torch.nn as nn 6 | import tensorflow as tf 7 | 8 | from resnet import get_resnet, name_to_params 9 | 10 | parser = argparse.ArgumentParser(description='SimCLR converter') 11 | parser.add_argument('tf_path', type=str, help='path of the input tensorflow file (ex: model.ckpt-250228)') 12 | parser.add_argument('--ema', action='store_true') 13 | parser.add_argument('--supervised', action='store_true') 14 | args = parser.parse_args() 15 | 16 | 17 | def main(): 18 | use_ema_model = args.ema 19 | prefix = ('ema_model/' if use_ema_model else '') + 'base_model/' 20 | head_prefix = ('ema_model/' if use_ema_model else '') + 'head_contrastive/' 21 | # 1. read tensorflow weight into a python dict 22 | vars_list = [] 23 | contrastive_vars = [] 24 | for v in tf.train.list_variables(args.tf_path): 25 | if v[0].startswith(prefix) and not v[0].endswith('/Momentum'): 26 | vars_list.append(v[0]) 27 | elif v[0] in {'head_supervised/linear_layer/dense/bias', 'head_supervised/linear_layer/dense/kernel'}: 28 | vars_list.append(v[0]) 29 | elif v[0].startswith(head_prefix) and not v[0].endswith('/Momentum'): 30 | contrastive_vars.append(v[0]) 31 | 32 | sd = {} 33 | ckpt_reader = tf.train.load_checkpoint(args.tf_path) 34 | for v in vars_list: 35 | sd[v] = ckpt_reader.get_tensor(v) 36 | 37 | split_idx = 2 if use_ema_model else 1 38 | # 2. convert the state_dict to PyTorch format 39 | conv_keys = [k for k in sd.keys() if k.split('/')[split_idx].split('_')[0] == 'conv2d'] 40 | conv_idx = [] 41 | for k in conv_keys: 42 | mid = k.split('/')[split_idx] 43 | if len(mid) == 6: 44 | conv_idx.append(0) 45 | else: 46 | conv_idx.append(int(mid[7:])) 47 | arg_idx = np.argsort(conv_idx) 48 | conv_keys = [conv_keys[idx] for idx in arg_idx] 49 | 50 | bn_keys = list(set([k.split('/')[split_idx] for k in sd.keys() 51 | if k.split('/')[split_idx].split('_')[0] == 'batch'])) 52 | bn_idx = [] 53 | for k in bn_keys: 54 | if len(k.split('_')) == 2: 55 | bn_idx.append(0) 56 | else: 57 | bn_idx.append(int(k.split('_')[2])) 58 | arg_idx = np.argsort(bn_idx) 59 | bn_keys = [bn_keys[idx] for idx in arg_idx] 60 | 61 | depth, width, sk_ratio = name_to_params(args.tf_path) 62 | model, head = get_resnet(depth, width, sk_ratio) 63 | 64 | conv_op = [] 65 | bn_op = [] 66 | for m in model.modules(): 67 | if isinstance(m, nn.Conv2d): 68 | conv_op.append(m) 69 | elif isinstance(m, nn.BatchNorm2d): 70 | bn_op.append(m) 71 | assert len(vars_list) == (len(conv_op) + len(bn_op) * 4 + 2) # 2 for fc 72 | 73 | for i_conv in range(len(conv_keys)): 74 | m = conv_op[i_conv] 75 | w = torch.from_numpy(sd[conv_keys[i_conv]]).permute(3, 2, 0, 1) 76 | assert w.shape == m.weight.shape, f'size mismatch {w.shape} <> {m.weight.shape}' 77 | m.weight.data = w 78 | 79 | for i_bn in range(len(bn_keys)): 80 | m = bn_op[i_bn] 81 | gamma = torch.from_numpy(sd[prefix + bn_keys[i_bn] + '/gamma']) 82 | assert m.weight.shape == gamma.shape, f'size mismatch {gamma.shape} <> {m.weight.shape}' 83 | m.weight.data = gamma 84 | m.bias.data = torch.from_numpy(sd[prefix + bn_keys[i_bn] + '/beta']) 85 | m.running_mean = torch.from_numpy(sd[prefix + bn_keys[i_bn] + '/moving_mean']) 86 | m.running_var = torch.from_numpy(sd[prefix + bn_keys[i_bn] + '/moving_variance']) 87 | 88 | w = torch.from_numpy(sd['head_supervised/linear_layer/dense/kernel']).t() 89 | assert model.fc.weight.shape == w.shape 90 | model.fc.weight.data = w 91 | b = torch.from_numpy(sd['head_supervised/linear_layer/dense/bias']) 92 | assert model.fc.bias.shape == b.shape 93 | model.fc.bias.data = b 94 | 95 | if args.supervised: 96 | save_location = f'r{depth}_{width}x_sk{1 if sk_ratio != 0 else 0}{"_ema" if use_ema_model else ""}.pth' 97 | torch.save({'resnet': model.state_dict(), 'head': head.state_dict()}, save_location) 98 | return 99 | sd = {} 100 | for v in contrastive_vars: 101 | sd[v] = ckpt_reader.get_tensor(v) 102 | linear_op = [] 103 | bn_op = [] 104 | for m in head.modules(): 105 | if isinstance(m, nn.Linear): 106 | linear_op.append(m) 107 | elif isinstance(m, nn.BatchNorm1d): 108 | bn_op.append(m) 109 | for i, (l, m) in enumerate(zip(linear_op, bn_op)): 110 | l.weight.data = torch.from_numpy(sd[f'{head_prefix}nl_{i}/dense/kernel']).t() 111 | common_prefix = f'{head_prefix}nl_{i}/batch_normalization/' 112 | m.weight.data = torch.from_numpy(sd[f'{common_prefix}gamma']) 113 | if i != 2: 114 | m.bias.data = torch.from_numpy(sd[f'{common_prefix}beta']) 115 | m.running_mean = torch.from_numpy(sd[f'{common_prefix}moving_mean']) 116 | m.running_var = torch.from_numpy(sd[f'{common_prefix}moving_variance']) 117 | 118 | # 3. dump the PyTorch weights. 119 | save_location = f'r{depth}_{width}x_sk{1 if sk_ratio != 0 else 0}{"_ema" if use_ema_model else ""}.pth' 120 | torch.save({'resnet': model.state_dict(), 'head': head.state_dict()}, save_location) 121 | 122 | 123 | if __name__ == '__main__': 124 | main() 125 | -------------------------------------------------------------------------------- /download.py: -------------------------------------------------------------------------------- 1 | import os 2 | import argparse 3 | from math import ceil 4 | 5 | import requests 6 | from tqdm import tqdm 7 | 8 | available_simclr_models = ['r50_1x_sk0', 'r50_1x_sk1', 'r50_2x_sk0', 'r50_2x_sk1', 9 | 'r101_1x_sk0', 'r101_1x_sk1', 'r101_2x_sk0', 'r101_2x_sk1', 10 | 'r152_1x_sk0', 'r152_1x_sk1', 'r152_2x_sk0', 'r152_2x_sk1', 'r152_3x_sk1'] 11 | simclr_base_url = 'https://storage.googleapis.com/simclr-checkpoints/simclrv2/{category}/{model}/' 12 | files = ['checkpoint', 'graph.pbtxt', 'model.ckpt-{category}.data-00000-of-00001', 13 | 'model.ckpt-{category}.index', 'model.ckpt-{category}.meta'] 14 | simclr_categories = {'finetuned_100pct': 37535, 'finetuned_10pct': 3754, 15 | 'finetuned_1pct': 751, 'pretrained': 250228, 'supervised': 28151} 16 | chunk_size = 1024 * 8 17 | 18 | 19 | def download(url, destination): 20 | if os.path.exists(destination): 21 | return 22 | response = requests.get(url, stream=True) 23 | save_response_content(response, destination) 24 | 25 | 26 | def save_response_content(response, destination): 27 | if 'Content-length' in response.headers: 28 | total = int(ceil(int(response.headers['Content-length']) / chunk_size)) 29 | else: 30 | total = None 31 | with open(destination, 'wb') as f: 32 | for data in tqdm(response.iter_content(chunk_size=chunk_size), leave=False, total=total): 33 | f.write(data) 34 | 35 | 36 | def run(): 37 | parser = argparse.ArgumentParser(description='Model Downloader') 38 | parser.add_argument('model', type=str, choices=available_simclr_models) 39 | parser.add_argument('--simclr_category', type=str, choices=list(simclr_categories.keys()), default='pretrained') 40 | args = parser.parse_args() 41 | model = args.model 42 | os.makedirs(model, exist_ok=True) 43 | url = simclr_base_url.format(model=model, category=args.simclr_category) 44 | model_category = simclr_categories[args.simclr_category] 45 | for file in tqdm(files): 46 | f = file.format(category=model_category) 47 | download(url + f, os.path.join(model, f)) 48 | 49 | 50 | if __name__ == '__main__': 51 | run() 52 | -------------------------------------------------------------------------------- /resnet.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | 5 | BATCH_NORM_EPSILON = 1e-5 6 | BATCH_NORM_DECAY = 0.9 # == pytorch's default value as well 7 | 8 | 9 | class BatchNormRelu(nn.Sequential): 10 | def __init__(self, num_channels, relu=True): 11 | super().__init__(nn.BatchNorm2d(num_channels, eps=BATCH_NORM_EPSILON), nn.ReLU() if relu else nn.Identity()) 12 | 13 | 14 | def conv(in_channels, out_channels, kernel_size=3, stride=1, bias=False): 15 | return nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, 16 | stride=stride, padding=(kernel_size - 1) // 2, bias=bias) 17 | 18 | 19 | class SelectiveKernel(nn.Module): 20 | def __init__(self, in_channels, out_channels, stride, sk_ratio, min_dim=32): 21 | super().__init__() 22 | assert sk_ratio > 0.0 23 | self.main_conv = nn.Sequential(conv(in_channels, 2 * out_channels, stride=stride), 24 | BatchNormRelu(2 * out_channels)) 25 | mid_dim = max(int(out_channels * sk_ratio), min_dim) 26 | self.mixing_conv = nn.Sequential(conv(out_channels, mid_dim, kernel_size=1), BatchNormRelu(mid_dim), 27 | conv(mid_dim, 2 * out_channels, kernel_size=1)) 28 | 29 | def forward(self, x): 30 | x = self.main_conv(x) 31 | x = torch.stack(torch.chunk(x, 2, dim=1), dim=0) # 2, B, C, H, W 32 | g = x.sum(dim=0).mean(dim=[2, 3], keepdim=True) 33 | m = self.mixing_conv(g) 34 | m = torch.stack(torch.chunk(m, 2, dim=1), dim=0) # 2, B, C, 1, 1 35 | return (x * F.softmax(m, dim=0)).sum(dim=0) 36 | 37 | 38 | class Projection(nn.Module): 39 | def __init__(self, in_channels, out_channels, stride, sk_ratio=0): 40 | super().__init__() 41 | if sk_ratio > 0: 42 | self.shortcut = nn.Sequential(nn.ZeroPad2d((0, 1, 0, 1)), 43 | # kernel_size = 2 => padding = 1 44 | nn.AvgPool2d(kernel_size=2, stride=stride, padding=0), 45 | conv(in_channels, out_channels, kernel_size=1)) 46 | else: 47 | self.shortcut = conv(in_channels, out_channels, kernel_size=1, stride=stride) 48 | self.bn = BatchNormRelu(out_channels, relu=False) 49 | 50 | def forward(self, x): 51 | return self.bn(self.shortcut(x)) 52 | 53 | 54 | class BottleneckBlock(nn.Module): 55 | expansion = 4 56 | 57 | def __init__(self, in_channels, out_channels, stride, sk_ratio=0, use_projection=False): 58 | super().__init__() 59 | if use_projection: 60 | self.projection = Projection(in_channels, out_channels * 4, stride, sk_ratio) 61 | else: 62 | self.projection = nn.Identity() 63 | ops = [conv(in_channels, out_channels, kernel_size=1), BatchNormRelu(out_channels)] 64 | if sk_ratio > 0: 65 | ops.append(SelectiveKernel(out_channels, out_channels, stride, sk_ratio)) 66 | else: 67 | ops.append(conv(out_channels, out_channels, stride=stride)) 68 | ops.append(BatchNormRelu(out_channels)) 69 | ops.append(conv(out_channels, out_channels * 4, kernel_size=1)) 70 | ops.append(BatchNormRelu(out_channels * 4, relu=False)) 71 | self.net = nn.Sequential(*ops) 72 | 73 | def forward(self, x): 74 | shortcut = self.projection(x) 75 | return F.relu(shortcut + self.net(x)) 76 | 77 | 78 | class Blocks(nn.Module): 79 | def __init__(self, num_blocks, in_channels, out_channels, stride, sk_ratio=0): 80 | super().__init__() 81 | self.blocks = nn.ModuleList([BottleneckBlock(in_channels, out_channels, stride, sk_ratio, True)]) 82 | self.channels_out = out_channels * BottleneckBlock.expansion 83 | for _ in range(num_blocks - 1): 84 | self.blocks.append(BottleneckBlock(self.channels_out, out_channels, 1, sk_ratio)) 85 | 86 | def forward(self, x): 87 | for b in self.blocks: 88 | x = b(x) 89 | return x 90 | 91 | 92 | class Stem(nn.Sequential): 93 | def __init__(self, sk_ratio, width_multiplier): 94 | ops = [] 95 | channels = 64 * width_multiplier // 2 96 | if sk_ratio > 0: 97 | ops.append(conv(3, channels, stride=2)) 98 | ops.append(BatchNormRelu(channels)) 99 | ops.append(conv(channels, channels)) 100 | ops.append(BatchNormRelu(channels)) 101 | ops.append(conv(channels, channels * 2)) 102 | else: 103 | ops.append(conv(3, channels * 2, kernel_size=7, stride=2)) 104 | ops.append(BatchNormRelu(channels * 2)) 105 | ops.append(nn.MaxPool2d(kernel_size=3, stride=2, padding=1)) 106 | super().__init__(*ops) 107 | 108 | 109 | class ResNet(nn.Module): 110 | def __init__(self, layers, width_multiplier, sk_ratio): 111 | super().__init__() 112 | ops = [Stem(sk_ratio, width_multiplier)] 113 | channels_in = 64 * width_multiplier 114 | ops.append(Blocks(layers[0], channels_in, 64 * width_multiplier, 1, sk_ratio)) 115 | channels_in = ops[-1].channels_out 116 | ops.append(Blocks(layers[1], channels_in, 128 * width_multiplier, 2, sk_ratio)) 117 | channels_in = ops[-1].channels_out 118 | ops.append(Blocks(layers[2], channels_in, 256 * width_multiplier, 2, sk_ratio)) 119 | channels_in = ops[-1].channels_out 120 | ops.append(Blocks(layers[3], channels_in, 512 * width_multiplier, 2, sk_ratio)) 121 | channels_in = ops[-1].channels_out 122 | self.channels_out = channels_in 123 | self.net = nn.Sequential(*ops) 124 | self.fc = nn.Linear(channels_in, 1000) 125 | 126 | def forward(self, x, apply_fc=False): 127 | h = self.net(x).mean(dim=[2, 3]) 128 | if apply_fc: 129 | h = self.fc(h) 130 | return h 131 | 132 | 133 | class ContrastiveHead(nn.Module): 134 | def __init__(self, channels_in, out_dim=128, num_layers=3): 135 | super().__init__() 136 | self.layers = nn.ModuleList() 137 | for i in range(num_layers): 138 | if i != num_layers - 1: 139 | dim, relu = channels_in, True 140 | else: 141 | dim, relu = out_dim, False 142 | self.layers.append(nn.Linear(channels_in, dim, bias=False)) 143 | bn = nn.BatchNorm1d(dim, eps=BATCH_NORM_EPSILON, affine=True) 144 | if i == num_layers - 1: 145 | nn.init.zeros_(bn.bias) 146 | self.layers.append(bn) 147 | if relu: 148 | self.layers.append(nn.ReLU()) 149 | 150 | def forward(self, x): 151 | for b in self.layers: 152 | x = b(x) 153 | return x 154 | 155 | 156 | def get_resnet(depth=50, width_multiplier=1, sk_ratio=0): # sk_ratio=0.0625 is recommended 157 | layers = {50: [3, 4, 6, 3], 101: [3, 4, 23, 3], 152: [3, 8, 36, 3], 200: [3, 24, 36, 3]}[depth] 158 | resnet = ResNet(layers, width_multiplier, sk_ratio) 159 | return resnet, ContrastiveHead(resnet.channels_out) 160 | 161 | 162 | def name_to_params(checkpoint): 163 | sk_ratio = 0.0625 if '_sk1' in checkpoint else 0 164 | if 'r50_' in checkpoint: 165 | depth = 50 166 | elif 'r101_' in checkpoint: 167 | depth = 101 168 | elif 'r152_' in checkpoint: 169 | depth = 152 170 | else: 171 | raise NotImplementedError 172 | 173 | if '_1x_' in checkpoint: 174 | width = 1 175 | elif '_2x_' in checkpoint: 176 | width = 2 177 | elif '_3x_' in checkpoint: 178 | width = 3 179 | else: 180 | raise NotImplementedError 181 | 182 | return depth, width, sk_ratio 183 | -------------------------------------------------------------------------------- /verify.py: -------------------------------------------------------------------------------- 1 | import os 2 | import argparse 3 | from collections import Counter 4 | 5 | import torch 6 | from PIL import Image 7 | from tqdm import tqdm 8 | import torchvision.transforms as transforms 9 | from torch.utils.data import Dataset, DataLoader 10 | 11 | from resnet import get_resnet, name_to_params 12 | 13 | 14 | class ImagenetValidationDataset(Dataset): 15 | def __init__(self, val_path): 16 | super().__init__() 17 | self.val_path = val_path 18 | self.transform = transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor()]) 19 | with open(os.path.join(val_path, 'ILSVRC2012_validation_ground_truth.txt')) as f: 20 | self.labels = [int(l) - 1 for l in f.readlines()] 21 | 22 | def __len__(self): 23 | return len(self.labels) 24 | 25 | def __getitem__(self, item): 26 | img = Image.open(os.path.join(self.val_path, f'ILSVRC2012_val_{item + 1:08d}.JPEG')).convert('RGB') 27 | return self.transform(img), self.labels[item] 28 | 29 | 30 | def accuracy(output, target, topk=(1,)): 31 | maxk = max(topk) 32 | _, pred = output.topk(maxk, 1, True, True) 33 | pred = pred.t().cpu() 34 | correct = pred.eq(target.view(1, -1).expand_as(pred)) 35 | res = [] 36 | for k in topk: 37 | correct_k = correct[:k].view(-1).float().sum().item() 38 | res.append(correct_k) 39 | return res 40 | 41 | 42 | @torch.no_grad() 43 | def run(pth_path): 44 | device = 'cuda' 45 | dataset = ImagenetValidationDataset('./val/') 46 | data_loader = DataLoader(dataset, batch_size=64, shuffle=False, pin_memory=True, num_workers=8) 47 | model, _ = get_resnet(*name_to_params(pth_path)) 48 | model.load_state_dict(torch.load(pth_path)['resnet']) 49 | model = model.to(device).eval() 50 | preds = [] 51 | target = [] 52 | for images, labels in tqdm(data_loader): 53 | _, pred = model(images.to(device), apply_fc=True).topk(1, dim=1) 54 | preds.append(pred.squeeze(1).cpu()) 55 | target.append(labels) 56 | p = torch.cat(preds).numpy() 57 | t = torch.cat(target).numpy() 58 | all_counters = [Counter() for i in range(1000)] 59 | for i in range(50000): 60 | all_counters[t[i]][p[i]] += 1 61 | total_correct = 0 62 | for i in range(1000): 63 | total_correct += all_counters[i].most_common(1)[0][1] 64 | print(f'ACC: {total_correct / 50000 * 100}') 65 | 66 | 67 | if __name__ == '__main__': 68 | parser = argparse.ArgumentParser(description='SimCLR verifier') 69 | parser.add_argument('pth_path', type=str, help='path of the input checkpoint file') 70 | args = parser.parse_args() 71 | run(args.pth_path) 72 | --------------------------------------------------------------------------------