Spaces:
Runtime error
Runtime error
File size: 7,582 Bytes
a2dba58 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 |
import argparse
import os
from pathlib import Path
import torch
from torch import autocast, Tensor
from torch.cuda.amp import GradScaler
from tqdm import tqdm
from config import parser
from einops import rearrange
from dataloaders.CXR14 import build_dataloaders
from models.global_local_cl import GlobalCL
from trainers.utils import (TensorboardLogger, compare_configs, seed_everything, crop_batch)
def save(model, optimizer, config, path, step):
torch.save({
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'config': config,
'step': step
}, path)
def augment(x, img_size, batch_size):
x = crop_batch([x], img_size, batch_size) # random crop
brightness = torch.rand((batch_size, 1, 1, 1), device=x.device)*.6 - .3 # random brightness adjustment between [-.3, .3]
contrast = torch.rand((batch_size, 1, 1, 1), device=x.device)*.6 + .7 # random contrast adjustment between [.7, 1.3]
x = (x + brightness) * contrast # apply brightness and contrast
return x
def augment_and_concat(x, img_size, batch_size):
x_1 = augment(x, img_size, batch_size)
x_2 = augment(x, img_size, batch_size)
return torch.cat((x_1, x_2), dim=0) # 2b x c x h x w
def calculate_loss(features, batch_size, tau):
norm_features = features / features.norm(dim=1, keepdim=True)
similarity_matrix = torch.exp(norm_features @ norm_features.T / tau) # 2b x 2b [[b_1xb_1, b_1xb_2], [b_2xb_1, b_2xb_2]]
positive_term_1 = torch.diagonal(similarity_matrix[:batch_size, batch_size:])
negative_term_1 = similarity_matrix[:batch_size].sum(-1) - torch.diagonal(similarity_matrix[:batch_size, :batch_size]) - torch.diagonal(similarity_matrix[:batch_size, batch_size:]) # (b x 2b).sum(1) - (b_1 x b_1).diag() - (b_1 x b_2).diag() = b
positive_term_2 = torch.diagonal(similarity_matrix[batch_size:, :batch_size])
negative_term_2 = similarity_matrix[batch_size:].sum(-1) - torch.diagonal(similarity_matrix[batch_size:, batch_size:]) - torch.diagonal(similarity_matrix[batch_size:, :batch_size]) # (b x 2b).sum(1) - (b_2 x b_2).diag() - (b_2 x b_1).diag() = b
loss = (-torch.log(positive_term_1 / negative_term_1).mean() - torch.log(positive_term_2 / negative_term_2).mean())/2
return loss
@torch.no_grad()
def validate(config, model, val_loader):
model.eval()
losses = []
for i, x in tqdm(enumerate(val_loader), desc='Validating'):
batch_size = x.shape[0]
x = x.to(config.device)
x = augment_and_concat(x, config.img_size, batch_size) # 2b x c x h x w
with autocast(device_type=config.device, enabled=config.mixed_precision):
features = model(x) # 2b x emb_dim
loss = calculate_loss(features, batch_size, config.tau)
losses.append(loss.item())
if i + 1 == config.max_val_steps or config.debug:
break
avg_loss = sum(losses) / len(losses)
print(f'Validation loss: {avg_loss:.4f}')
model.train()
return {
'val/loss': avg_loss,
}
def train(config, model, optimizer, train_dl, val_dl, logger, scaler, step):
best_val_loss = float('inf')
train_losses = []
pbar = tqdm(total=config.val_freq, desc='Training')
while True:
for x in train_dl:
pbar.update(1)
step += 1
x = x.to(config.device)
batch_size = x.shape[0]
x = augment_and_concat(x, config.img_size, batch_size) # 2b x c x h x w
optimizer.zero_grad()
with autocast(device_type=config.device, enabled=config.mixed_precision):
features = model(x) # 2b x emb_dim
loss = calculate_loss(features, batch_size, config.tau)
scaler.scale(loss).backward()
optimizer.step()
train_losses.append(loss.item())
pbar.set_description(f'Training loss: {loss.item():.4f}')
if step % config.log_freq == 0 or config.debug:
avg_train_loss = sum(train_losses) / len(train_losses)
print(f'Step {step} - Train loss: {avg_train_loss:.4f}')
logger.log({'train/loss': avg_train_loss}, step=step)
if step % config.val_freq == 0 or config.debug:
val_results = validate(config, model, val_dl)
logger.log(val_results, step=step)
if val_results['val/loss'] < best_val_loss and not config.debug:
print(f'Step {step} - New best validation loss: '
f'{val_results["val/loss"]:.4f}, saving model '
f'in {config.log_dir}')
best_val_loss = val_results['val/loss']
save(
model,
optimizer,
config,
config.log_dir / 'best_model.pt',
step
)
if step >= config.max_steps or config.debug:
return model
# implementing
def load(new_config, path):
checkpoint = torch.load(path, map_location=torch.device(new_config.device))
old_config = checkpoint['config']
compare_configs(old_config, new_config)
model = GlobalCL(
img_size=old_config.img_size,
dim=old_config.dim,
dim_mults=old_config.dim_mults,
channels=old_config.channels,
out_dim=old_config.out_channels).to(new_config.device)
model.load_state_dict(checkpoint['model_state_dict'])
optimizer = torch.optim.Adam(model.parameters(), lr=new_config.lr)
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
step = checkpoint['step']
return model, optimizer, step
def main(config):
# adjust logdir to include experiment name
config.log_dir = Path(config.log_dir).parent / Path(config.log_dir).name
os.makedirs(config.log_dir, exist_ok=True)
# save config namespace into logdir
with open(config.log_dir / 'config.txt', 'w') as f:
for k, v in vars(config).items():
if type(v) not in [str, int, float, bool]:
f.write(f'{k}: {str(v)}\n')
else:
f.write(f'{k}: {v}\n')
# Random seed
seed_everything(config.seed)
# Init model and optimizer
if config.resume_path is not None:
print('Loading model from', config.resume_path)
encoder_model, optimizer, step = load(config, config.resume_path)
else:
encoder_model = GlobalCL(
img_size=config.img_size,
dim=config.dim,
dim_mults=config.dim_mults,
channels=config.channels,
out_dim=config.out_channels)
optimizer = torch.optim.Adam(encoder_model.parameters(), lr=config.lr) # , betas=config.adam_betas)
step = 0
encoder_model.to(config.device)
encoder_model.train()
scaler = GradScaler()
# Load data
dataloaders = build_dataloaders(
config.data_dir,
config.img_size,
config.batch_size,
config.num_workers,
)
train_dl = dataloaders['train']
val_dl = dataloaders['val']
print('Train dataset size:', len(train_dl.dataset))
print('Validation dataset size:', len(val_dl.dataset))
# Logger
logger = TensorboardLogger(config.log_dir, enabled=not config.debug)
train(config, encoder_model, optimizer, train_dl, val_dl, logger, scaler, step) |