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train.py
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train.py
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import time
import math
import copy
from pathlib import Path
import yaml
from tqdm import tqdm
import seaborn as sn
import torch
from torch import nn, optim
from torch.utils.tensorboard import SummaryWriter
from utils import parse_config
from models.build import build_model_from_config
from data_loader.data_loader import get_data_loaders_from_config
from losses import get_train_val_losses
from optimizers import get_optim_from_config, get_scheduler_from_config
from test import test_model
PHASES = ['train', 'val']
def train_model(model, dataloaders, losses, optimizer, scheduler, writer, config):
num_epochs = config['training']['num_epochs']
device = config['training']['device']
verbose_steps = config['training']['verbose_steps']
early_stopping_threshold = config['training']['early_stopping']
phases = ['train', 'val']
assert all([p in dataloaders.keys() for p in PHASES]),f'Dataloaders does not have all the needed phases {PHASES}.'
assert all([p in losses.keys() for p in PHASES]),f'Losses does not have all the needed phases {PHASES}.'
dataset_sizes = { s : len(dataloaders[s].dataset) for s in PHASES }
total_steps_per_epoch = dataset_sizes['train'] // dataloaders['train'].batch_size + 1
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0
best_loss = math.inf
best_epoch = 1
early_stopping_strike = 0
since = time.time()
try:
for epoch in range(num_epochs):
print('-' * 10)
print('Epoch {}/{}'.format(epoch+1, num_epochs))
print('-' * 10)
# Each epoch has a training and validation phase
for phase in PHASES:
if phase == 'train':
model.train()
else:
model.eval()
running_loss = 0.0
running_corrects = 0
criterion = losses[phase]
# Iterate over data.
for step, data in enumerate(dataloaders[phase]):
inputs, labels = data
inputs = inputs.to(device)
labels = labels.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward
# track history if only in train
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
# backward + optimize only if in training phase
if phase == 'train':
loss.backward()
optimizer.step()
global_step = epoch * total_steps_per_epoch + (step+1)
writer.add_scalar(f"Loss/{phase}", loss.item(), global_step)
# statistics
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
if phase == 'train' and (step+1) % verbose_steps == 0:
num_imgs_so_far = (step+1)*dataloaders['train'].batch_size
verbose_loss = running_loss / num_imgs_so_far
verbose_acc = running_corrects.double() /num_imgs_so_far
print('[{}] Step: {}/{} | Loss: {:.4f} Acc: {:.4f}'.format(phase, step+1, total_steps_per_epoch, verbose_loss, verbose_acc))
writer.flush()
if phase == 'train':
scheduler.step()
lr_now = scheduler.get_last_lr()
print('LR:', lr_now)
epoch_loss = running_loss / dataset_sizes[phase]
epoch_acc = running_corrects.double() / dataset_sizes[phase]
print('[{}] Loss: {:.4f} Acc: {:.4f}'.format(
phase, epoch_loss, epoch_acc))
writer.add_scalar(f"EpochLoss/{phase}", epoch_loss, epoch)
writer.add_scalar(f"EpochAccuracy/{phase}", epoch_acc, epoch)
writer.flush()
# checkpointing / early stoppping logic
if phase == 'val':
if epoch_loss < best_loss:
best_acc = epoch_acc
best_loss = epoch_loss
best_model_wts = copy.deepcopy(model.state_dict())
best_epoch = epoch + 1
early_stopping_strike = 0 # reset
print('Best val checkpointed.')
else:
early_stopping_strike += 1
print('Val not best, strike:{}/{}'.format(early_stopping_strike, early_stopping_threshold))
print()
if early_stopping_strike >= early_stopping_threshold:
print('Terminating training as val not best for {} strikes'.format(early_stopping_strike))
break
except KeyboardInterrupt:
print('Training interupted manually!')
finally:
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
print('Best val acc: {:4f}'.format(best_acc))
print('Best val loss: {:4f}'.format(best_loss))
print('achieved at epoch {}/{}'.format(best_epoch, epoch))
# load best model weights
model.load_state_dict(best_model_wts)
return model, best_acc, best_loss, best_epoch, epoch
def viz_to_tb(dataloader, writer, num_classes, display_num=4):
from collections import defaultdict
from torchvision.utils import make_grid
import numpy as np
from utils import tensor2numpy
labels_count = {k:0 for k in range(num_classes)}
imgs_dict = defaultdict(list)
dl_iter = iter(dataloader)
while all([v < display_num for v in labels_count.values()]):
inputs, labels = next(dl_iter)
for input_, label in zip(inputs, labels):
label = int(label)
if labels_count[label] < display_num:
imgs_dict[label].append(input_)
labels_count[label] += 1
for label, imgs in imgs_dict.items():
img_grid = make_grid(imgs)
img_grid = tensor2numpy(img_grid)
# img_grid = (img_grid * 255).astype(np.uint8)
writer.add_image(f'example image for label {label}', img_grid, dataformats='HWC')
def main(args):
config = parse_config(args.config)
out_dir = Path(config['training']['save_dir']) / config['training']['save_context']
out_dir.mkdir(exist_ok=True, parents=True)
config_out = out_dir / f"{config['training']['save_context']}_config.yaml"
with config_out.open('w') as wf:
yaml.dump(config, wf)
print(f'Training config saved to {config_out}.')
tb_logdir = out_dir / 'logdir'
writer = SummaryWriter(log_dir=tb_logdir)
model = build_model_from_config(config)
dataloaders, classes = get_data_loaders_from_config(config)
if config['datasets']['viz']:
viz_to_tb(dataloaders['train'], writer, config['datasets']['classes']['num_classes'])
losses = get_train_val_losses()
optimizer = get_optim_from_config(model.parameters(), config)
scheduler = get_scheduler_from_config(optimizer, config)
model, best_acc, best_loss, best_epoch, total_epoch = train_model(model, dataloaders, losses, optimizer, scheduler, writer, config)
weights_dir = out_dir / 'weights'
weights_dir.mkdir(parents=True, exist_ok=True)
save_path = weights_dir / f"{config['training']['save_context']}_bestval_loss{best_loss:0.3f}_acc{best_acc:0.3f}_ep{best_epoch}of{total_epoch}.pth"
torch.save(model.state_dict(), save_path)
print(f'Best val weights saved to {save_path}')
conf_mat, report, _, _, _ = test_model(model, dataloaders['test'], config, classes=classes)
test_dir = out_dir / 'test'
test_dir.mkdir(exist_ok=True, parents=True)
test_out = test_dir / f"{config['training']['save_context']}_clfreport.log"
with test_out.open('w') as wf:
wf.write(report)
sn_plot = sn.heatmap(conf_mat, annot=True, fmt='g', xticklabels=classes, yticklabels=classes)
test_out_cm = test_dir / f"{config['training']['save_context']}_confmat.jpg"
sn_plot.get_figure().savefig(test_out_cm)
if __name__ == '__main__':
import argparse
ap = argparse.ArgumentParser()
ap.add_argument('--config', help='Path to config file', default='configs/config.yaml')
args = ap.parse_args()
main(args)