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train.py
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train.py
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import argparse
import logging
import os
import sys
import pickle
import torch
import torch.nn as nn
from torch import optim
from tqdm import tqdm
from eval import eval_net
from unet import UNet
from utils.dataset import PsdDatasetWithClass
from torch.utils.data import DataLoader, random_split, RandomSampler
dir_img = 'data/imgs/'
dir_mask = 'data/masks/'
dir_checkpoint = 'checkpoints_3/'
dir_mixture = 'datasets/dataset_0426_14000_128x20/mixture_dataset_multiple/mixture_data_14000.pickle'
dir_list_label = ['datasets/dataset_0426_14000_128x20/component/Blt.mat.pickle',
'datasets/dataset_0426_14000_128x20/component/Zigbee.mat.pickle',
'datasets/dataset_0426_14000_128x20/component/ZigbeeASK.mat.pickle',
'datasets/dataset_0426_14000_128x20/component/ZigbeeBPSK.mat.pickle']
dir_train_sample_pickle = 'datasets/dataset_0426_14000_128x20/train_set.pickle'
dir_val_sample_pickle = 'datasets/dataset_0426_14000_128x20/val_set.pickle'
training_set_visualization_file_path = 'train_set_visualization.pickle'
loss_storage_file_path = 'scoreFile.pickle'
loss_storage_file_path_val = 'scoreFileVal.pickle'
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
def train_net(net,
device,
epochs=5,
batch_size=10,
lr=0.01,
val_percent=0.001,
save_cp=True,
img_scale=0.5,
dir_mixture=dir_mixture,
dir_list_label=dir_list_label,
override=False):
dataset = PsdDatasetWithClass(dir_mixture, dir_list_label)
n_val = int(len(dataset) * val_percent)
n_train = len(dataset) - n_val
train, val = random_split(dataset, [n_train, n_val])
all_scores_train = []
all_scores_val = []
if not override:
print(f'Regenerate random dataset from files!\n'
f'mixture file path:{dir_mixture}')
train_loader = DataLoader(train, batch_size=batch_size, num_workers=8, pin_memory=True,
sampler=RandomSampler(train, replacement=True, num_samples=2000))
val_loader = DataLoader(val, batch_size=batch_size, num_workers=8, pin_memory=True, drop_last=True,
sampler=RandomSampler(val,replacement=True, num_samples=200))
train_sample_file = open(dir_train_sample_pickle, 'wb')
val_sample_file = open(dir_val_sample_pickle, 'wb')
pickle.dump(train_loader, train_sample_file)
pickle.dump(val_loader, val_sample_file)
train_sample_file.close()
val_sample_file.close()
else:
print(f'Load dataset from previous generation!\n'
f'training set path:{dir_train_sample_pickle}\n'
f'validation set path:{dir_val_sample_pickle}')
train_sample_file = open(dir_train_sample_pickle, 'rb')
val_sample_file = open(dir_val_sample_pickle, 'rb')
train_loader = pickle.load(train_sample_file)
val_loader = pickle.load(val_sample_file)
train_sample_file.close()
val_sample_file.close()
#writer = SummaryWriter(comment=f'LR_{lr}_BS_{batch_size}_SCALE_{img_scale}')
logging.info(f'''Starting training:
Epochs: {epochs}
Batch size: {batch_size}
Learning rate: {lr}
Training size: {n_train}
Validation size: {n_val}
Checkpoints: {save_cp}
Device: {device.type}
Images scaling: {img_scale}
''')
optimizer = optim.Adam(net.parameters(), lr=lr, weight_decay=1e-5)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min' if net.n_classes > 1 else 'max', patience=2)
if net.n_classes > 1:
criterion = nn.CrossEntropyLoss()
else:
criterion = nn.MSELoss()
batch_index = 0
for epoch in range(epochs):
net.train()
epoch_loss = 0
batch_num = 2000/100
with tqdm(total=n_train, desc=f'Epoch {epoch + 1}/{epochs}', unit='samples') as pbar:
for batch in train_loader:
batch_index += 1
imgs = batch['mixture'].unsqueeze(1)
true_masks = batch['source_labels'][:, 3, :, :].unsqueeze(1)
assert imgs.shape[1] == net.n_channels, \
f'Network has been defined with {net.n_channels} input channels, ' \
f'but loaded images have {imgs.shape[1]} channels. Please check that ' \
'the images are loaded correctly.'
imgs = imgs.to(device=device, dtype=torch.float32)
mask_type = torch.float32 if net.n_classes == 1 else torch.long
true_masks = true_masks.to(device=device, dtype=mask_type)
masks_pred = net(imgs)
loss = criterion(masks_pred, true_masks)
epoch_loss += loss.item()
if batch_index == 5:
pickle_file = open(training_set_visualization_file_path, 'wb')
pickle.dump({'component_output': masks_pred,
'class_output': None,
'mixture': imgs,
'component_label': batch['source_labels'],
'class_label': None,
'classify_loss': None,
'component_loss': loss.item()}, pickle_file)
pickle_file.close()
print(f'training visualization file stored! File path:{training_set_visualization_file_path}')
pbar.set_postfix(**{'loss (batch)': loss.item()})
optimizer.zero_grad()
loss.backward()
nn.utils.clip_grad_value_(net.parameters(), 0.1)
optimizer.step()
pbar.update(imgs.shape[0])
val_score = eval_net(net, val_loader, device)
scheduler.step(val_score)
print('batch_num:', batch_num)
all_scores_train.append(epoch_loss/batch_num)
all_scores_val.append(val_score)
if net.n_classes > 1:
logging.info('Validation cross entropy: {}'.format(val_score))
else:
logging.info('Validation Dice Coeff: {}'.format(val_score))
# Store all training loss
score_file_train = open(loss_storage_file_path, 'wb')
score_file_val = open(loss_storage_file_path_val, 'wb')
pickle.dump(all_scores_train, score_file_train)
pickle.dump(all_scores_val, score_file_val)
score_file_train.close()
score_file_val.close()
print(f'Training Score file saved! Location:{loss_storage_file_path}\n'
f'Validation Score file saved! Location:{loss_storage_file_path_val}')
if len(all_scores_val) > 3 and all_scores_val[-1] > all_scores_val[-2] > all_scores_val[-3]:
logging.info(f'Validation threshold reached! Current val loss:{all_scores_val[-1]}, '
f'current batch num:{len(all_scores_val)}')
break
if save_cp:
try:
os.mkdir(dir_checkpoint)
logging.info('Created checkpoint directory')
except OSError:
pass
torch.save(net.state_dict(),
dir_checkpoint + f'CP_epoch{epoch + 1}.pth')
logging.info(f'Checkpoint {epoch + 1} saved !')
def get_args():
parser = argparse.ArgumentParser(description='Train the UNet on images and target masks',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('-e', '--epochs', metavar='E', type=int, default=5,
help='Number of epochs', dest='epochs')
parser.add_argument('-b', '--batch-size', metavar='B', type=int, nargs='?', default=1,
help='Batch size', dest='batchsize')
parser.add_argument('-l', '--learning-rate', metavar='LR', type=float, nargs='?', default=0.1,
help='Learning rate', dest='lr')
parser.add_argument('-f', '--load', dest='load', type=str, default=False,
help='Load model from a .pth file')
parser.add_argument('-s', '--scale', dest='scale', type=float, default=0.5,
help='Downscaling factor of the images')
parser.add_argument('-v', '--validation', dest='val', type=float, default=10.0,
help='Percent of the data that is used as validation (0-100)')
return parser.parse_args()
if __name__ == '__main__':
logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
args = get_args()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
logging.info(f'Using device {device}')
# Change here to adapt to your data
# n_channels=3 for RGB images
# n_classes is the number of probabilities you want to get per pixel
# - For 1 class and background, use n_classes=1
# - For 2 classes, use n_classes=1
# - For N > 2 classes, use n_classes=N
#Changed channel number to 1 for STFT images
net = UNet(n_channels=1, n_classes=1, bilinear=True)
logging.info(f'Network:\n'
f'\t{net.n_channels} input channels\n'
f'\t{net.n_classes} output channels (classes)\n'
f'\t{"Bilinear" if net.bilinear else "Transposed conv"} upscaling')
if args.load:
net.load_state_dict(
torch.load(args.load, map_location=device)
)
logging.info(f'Model loaded from {args.load}')
net.to(device=device)
# faster convolutions, but more memory
# cudnn.benchmark = True
try:
train_net(net=net,
epochs=args.epochs,
batch_size=args.batchsize,
lr=args.lr,
device=device,
img_scale=args.scale,
val_percent=args.val / 100,
override=True)
except KeyboardInterrupt:
torch.save(net.state_dict(), 'INTERRUPTED.pth')
logging.info('Saved interrupt')
try:
sys.exit(0)
except SystemExit:
os._exit(0)