mirror of https://github.com/sthalles/SimCLR.git
108 lines
3.4 KiB
Python
108 lines
3.4 KiB
Python
import torch
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import yaml
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print(torch.__version__)
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import torch.optim as optim
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import os
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from torch.utils.data import DataLoader
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from torchvision import datasets
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from torch.utils.tensorboard import SummaryWriter
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import torch.nn.functional as F
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import matplotlib.pyplot as plt
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from models.resnet_simclr import ResNetSimCLR
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from utils import get_negative_mask, get_similarity_function
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from data_aug.data_transform import DataTransform, get_data_transform_opes
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torch.manual_seed(0)
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config = yaml.load(open("config.yaml", "r"), Loader=yaml.FullLoader)
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batch_size = config['batch_size']
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out_dim = config['out_dim']
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temperature = config['temperature']
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use_cosine_similarity = config['use_cosine_similarity']
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data_augment = get_data_transform_opes(s=config['s'], crop_size=96)
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train_dataset = datasets.STL10('./data', split='unlabeled', download=True, transform=DataTransform(data_augment))
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train_loader = DataLoader(train_dataset, batch_size=batch_size, num_workers=config['num_workers'], drop_last=True,
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shuffle=True)
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# model = Encoder(out_dim=out_dim)
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model = ResNetSimCLR(base_model=config["base_convnet"], out_dim=out_dim)
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train_gpu = torch.cuda.is_available()
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print("Is gpu available:", train_gpu)
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# moves the model parameters to gpu
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if train_gpu:
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model.cuda()
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criterion = torch.nn.CrossEntropyLoss(reduction='sum')
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optimizer = optim.Adam(model.parameters(), 3e-4)
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train_writer = SummaryWriter()
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sim_func_dim1, sim_func_dim2 = get_similarity_function(use_cosine_similarity)
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# Mask to remove positive examples from the batch of negative samples
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negative_mask = get_negative_mask(batch_size)
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n_iter = 0
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for e in range(config['epochs']):
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for step, ((xis, xjs), _) in enumerate(train_loader):
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optimizer.zero_grad()
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if train_gpu:
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xis = xis.cuda()
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xjs = xjs.cuda()
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# get the representations and the projections
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ris, zis = model(xis) # [N,C]
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train_writer.add_histogram("xi_repr", ris, global_step=n_iter)
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train_writer.add_histogram("xi_latent", zis, global_step=n_iter)
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# get the representations and the projections
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rjs, zjs = model(xjs) # [N,C]
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train_writer.add_histogram("xj_repr", rjs, global_step=n_iter)
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train_writer.add_histogram("xj_latent", zjs, global_step=n_iter)
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# normalize projection feature vectors
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zis = F.normalize(zis, dim=1)
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zjs = F.normalize(zjs, dim=1)
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l_pos = sim_func_dim1(zis, zjs).view(batch_size, 1)
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l_pos /= temperature
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negatives = torch.cat([zjs, zis], dim=0)
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loss = 0
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for positives in [zis, zjs]:
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l_neg = sim_func_dim2(positives, negatives)
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labels = torch.zeros(batch_size, dtype=torch.long)
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if train_gpu:
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labels = labels.cuda()
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l_neg = l_neg[negative_mask].view(l_neg.shape[0], -1)
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l_neg /= temperature
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logits = torch.cat([l_pos, l_neg], dim=1) # [N,K+1]
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loss += criterion(logits, labels)
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loss = loss / (2 * batch_size)
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train_writer.add_scalar('loss', loss, global_step=n_iter)
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loss.backward()
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optimizer.step()
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n_iter += 1
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model_checkpoints_folder = os.path.join(train_writer.log_dir, 'checkpoints')
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if not os.path.exists(model_checkpoints_folder):
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os.makedirs(model_checkpoints_folder)
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torch.save(model.state_dict(), os.path.join(model_checkpoints_folder, 'model.pth'))
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