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train_net.py
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train_net.py
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"""
This code allows you to train single domain learning networks.
"""
import os
import sys
import torch
import numpy as np
import tensorflow as tf
from tqdm import tqdm
from torch.utils.tensorboard import SummaryWriter
from data.meta_dataset_reader import (MetaDatasetBatchReader,
MetaDatasetEpisodeReader)
from models.losses import cross_entropy_loss, prototype_loss
from models.model_utils import (CheckPointer, UniformStepLR,
CosineAnnealRestartLR, ExpDecayLR)
from models.model_helpers import get_model, get_optimizer
from utils import Accumulator
from config import args
def train():
# initialize datasets and loaders
trainsets, valsets, testsets = args['data.train'], args['data.val'], args['data.test']
train_loader = MetaDatasetBatchReader('train', trainsets, valsets, testsets,
batch_size=args['train.batch_size'])
val_loader = MetaDatasetEpisodeReader('val', trainsets, valsets, testsets)
# initialize model and optimizer
num_train_classes = train_loader.num_classes('train')
model = get_model(num_train_classes, args)
optimizer = get_optimizer(model, args, params=model.get_parameters())
# restoring the last checkpoint
checkpointer = CheckPointer(args, model, optimizer=optimizer)
if os.path.isfile(checkpointer.last_ckpt) and args['train.resume']:
start_iter, best_val_loss, best_val_acc =\
checkpointer.restore_model(ckpt='last')
else:
print('No checkpoint restoration')
best_val_loss = 999999999
best_val_acc = start_iter = 0
# define learning rate policy
if args['train.lr_policy'] == "step":
lr_manager = UniformStepLR(optimizer, args, start_iter)
elif "exp_decay" in args['train.lr_policy']:
lr_manager = ExpDecayLR(optimizer, args, start_iter)
elif "cosine" in args['train.lr_policy']:
lr_manager = CosineAnnealRestartLR(optimizer, args, start_iter)
# defining the summary writer
writer = SummaryWriter(checkpointer.out_path)
# Training loop
max_iter = args['train.max_iter']
epoch_loss = {name: [] for name in trainsets}
epoch_acc = {name: [] for name in trainsets}
epoch_val_loss = {name: [] for name in valsets}
epoch_val_acc = {name: [] for name in valsets}
config = tf.compat.v1.ConfigProto()
# config.gpu_options.allow_growth = True
config.gpu_options.allow_growth = False
with tf.compat.v1.Session(config=config) as session:
for i in tqdm(range(max_iter)):
if i < start_iter:
continue
optimizer.zero_grad()
sample = train_loader.get_train_batch(session)
logits = model.forward(sample['images'])
if len(logits.size()) < 2:
logits = logits.unsqueeze(0)
batch_loss, stats_dict, _ = cross_entropy_loss(logits, sample['labels'])
batch_dataset = sample['dataset_name']
epoch_loss[batch_dataset].append(stats_dict['loss'])
epoch_acc[batch_dataset].append(stats_dict['acc'])
batch_loss.backward()
optimizer.step()
lr_manager.step(i)
if (i + 1) % 200 == 0:
for dataset_name in trainsets:
writer.add_scalar(f"loss/{dataset_name}-train_acc",
np.mean(epoch_loss[dataset_name]), i)
writer.add_scalar(f"accuracy/{dataset_name}-train_acc",
np.mean(epoch_acc[dataset_name]), i)
epoch_loss[dataset_name], epoch_acc[dataset_name] = [], []
writer.add_scalar('learning_rate',
optimizer.param_groups[0]['lr'], i)
# Evaluation inside the training loop
if (i + 1) % args['train.eval_freq'] == 0:
model.eval()
dataset_accs, dataset_losses = [], []
for valset in valsets:
val_losses, val_accs = [], []
for j in tqdm(range(args['train.eval_size'])):
with torch.no_grad():
sample = val_loader.get_validation_task(session, valset)
context_features = model.embed(sample['context_images'])
target_features = model.embed(sample['target_images'])
context_labels = sample['context_labels']
target_labels = sample['target_labels']
_, stats_dict, _ = prototype_loss(context_features, context_labels,
target_features, target_labels)
val_losses.append(stats_dict['loss'])
val_accs.append(stats_dict['acc'])
# write summaries per validation set
dataset_acc, dataset_loss = np.mean(val_accs) * 100, np.mean(val_losses)
dataset_accs.append(dataset_acc)
dataset_losses.append(dataset_loss)
epoch_val_loss[valset].append(dataset_loss)
epoch_val_acc[valset].append(dataset_acc)
writer.add_scalar(f"loss/{valset}/val_loss", dataset_loss, i)
writer.add_scalar(f"accuracy/{valset}/val_acc", dataset_acc, i)
print(f"{valset}: val_acc {dataset_acc:.2f}%, val_loss {dataset_loss:.3f}")
# write summaries averaged over datasets
avg_val_loss, avg_val_acc = np.mean(dataset_losses), np.mean(dataset_accs)
writer.add_scalar(f"loss/avg_val_loss", avg_val_loss, i)
writer.add_scalar(f"accuracy/avg_val_acc", avg_val_acc, i)
# saving checkpoints
if avg_val_acc > best_val_acc:
best_val_loss, best_val_acc = avg_val_loss, avg_val_acc
is_best = True
print('Best model so far!')
else:
is_best = False
extra_dict = {'epoch_loss': epoch_loss, 'epoch_acc': epoch_acc, 'epoch_val_loss': epoch_val_loss, 'epoch_val_acc': epoch_val_acc}
checkpointer.save_checkpoint(i, best_val_acc, best_val_loss,
is_best, optimizer=optimizer,
state_dict=model.get_state_dict(), extra=extra_dict)
model.train()
print(f"Trained and evaluated at {i}")
writer.close()
if start_iter < max_iter:
print(f"""Done training with best_mean_val_loss: {best_val_loss:.3f}, best_avg_val_acc: {best_val_acc:.2f}%""")
else:
print(f"""No training happened. Loaded checkpoint at {start_iter}, while max_iter was {max_iter}""")
if __name__ == '__main__':
train()