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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 LocalCL
from trainers.train_global_cl import augment_and_concat, save
from trainers.utils import (TensorboardLogger, compare_configs, seed_everything, crop_batch)
def calculate_loss_elements(logits, batch_size, n_regions, diag_offset):
pos_mask = torch.zeros((batch_size * n_regions*2, batch_size * n_regions*2), device=logits.device) + \
torch.diag(torch.ones(batch_size * n_regions * 2 - abs(-n_regions * batch_size + diag_offset), device=logits.device), diagonal=-n_regions * batch_size + diag_offset) +\
torch.diag(torch.ones(batch_size * n_regions * 2 - abs(n_regions * batch_size + diag_offset), device=logits.device), diagonal=n_regions * batch_size + diag_offset)
#torch.diag(torch.ones(batch_size*n_regions-diag_offset, device=logits.device), diagonal= batch_size*n_regions + diag_offset) + \
#torch.diag(torch.ones(batch_size*n_regions+diag_offset, device=logits.device), diagonal=-batch_size*n_regions + diag_offset)
pos_mask[:batch_size*n_regions, :batch_size*n_regions] = 0
pos_mask[batch_size*n_regions:, batch_size*n_regions:] = 0
neg_mask = torch.zeros((batch_size * n_regions*2, batch_size * n_regions*2), device=logits.device)
for region in range(-2*n_regions+1,2*n_regions):
neg_mask += torch.diag(torch.ones(batch_size * n_regions * 2 - abs(region * batch_size + diag_offset), device=logits.device), diagonal=region * batch_size + diag_offset)
neg_mask[:batch_size*n_regions, :batch_size*n_regions] = 0
neg_mask[batch_size*n_regions:, batch_size*n_regions:] = 0
#neg_mask = torch.diag(torch.ones(batch_size - abs(diag_offset), device=logits.device), diagonal=diag_offset).repeat(n_regions * 2, n_regions * 2)
#neg_mask -= torch.diag(torch.ones(batch_size * n_regions * 2 - abs(diag_offset), device=logits.device), diag_offset)
pos_logits = (logits*pos_mask).sum(1)
neg_logits = torch.exp(logits*neg_mask).mean(1)
return pos_logits[pos_mask.sum(1).bool()], neg_logits[pos_mask.sum(1).bool()]
def calculate_loss(features, batch_size, tau):
n_regions = 20 # sample 15x15 regions from each image
# sample 15x15 3x3 regions from each image
x_center_samples = torch.randperm(features.shape[2]-2).to(features.device)[:n_regions]+1
y_center_samples = torch.randperm(features.shape[3]-2).to(features.device)[:n_regions]+1
# one sample
regions = torch.stack([features[:,:,x_center_samples[i]-1:x_center_samples[i]+2, y_center_samples[i]-1:y_center_samples[i]+2] for i in range(n_regions)], dim=1) # (n_views x b) x n_regions x emb_dim x 3 x 3
regions = rearrange(regions, 'bn r c h w -> bn r (c h w)') # (n_views x b) x n_regions x (emb_dim x 3 x 3)
norm_regions = regions / regions.norm(dim=2, keepdim=True)
contrast_feature = torch.cat(torch.unbind(norm_regions, dim=1), dim=0) # b_1.reg1, b_1.reg2, ..., b_2.reg1, b_2.reg2, ...
# compute logits - note: no numerical stability tricks here
logits = torch.div(
torch.matmul(contrast_feature, contrast_feature.T), tau
)
loss = 0
for diag_offset in range(-batch_size + 1, batch_size):
pos_logits, neg_logits = calculate_loss_elements(logits, batch_size, n_regions, diag_offset)
loss += (- pos_logits + neg_logits).mean()
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
)
pbar = tqdm(total=config.val_freq, desc='Training')
if step >= config.max_steps or config.debug:
return model
def load(config, path):
raise NotImplementedError
def main(config):
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)
partial_model, optimizer, step = load(config, config.resume_path)
else:
partial_model = LocalCL(
img_size=config.img_size,
dim=config.dim,
dim_mults=config.dim_mults,
channels=config.channels,
out_dim=config.out_channels)
state_dict = torch.load(config.global_model_path, map_location='cpu')['model_state_dict']
partial_model.load_state_dict(state_dict=state_dict, strict=False)
params_to_optimise = []
names_to_optimise = []
for name, param in partial_model.ups[:partial_model.l].named_parameters():
params_to_optimise.append(param)
names_to_optimise.append(name)
optimizer = torch.optim.Adam(params_to_optimise, lr=config.lr) # , betas=config.adam_betas)
# freeze the remaining layers
names_to_optimise = [f'ups.{n}' for n in names_to_optimise]
for name, param in partial_model.named_parameters():
if name not in names_to_optimise:
param.requires_grad = False
step = 0
partial_model.to(config.device)
partial_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, partial_model, optimizer, train_dl, val_dl, logger, scaler, step) |