/
cnn_runner_pth.py
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/
cnn_runner_pth.py
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import argparse
from tqdm import tqdm
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.autograd import Variable
import torch.backends.cudnn as cudnn
import models
import utils
from utils import RunningMean, use_gpu, mixup_data, mixup_criterion
from misc import FurnitureDataset, preprocess, preprocess_with_augmentation, NB_CLASSES, preprocess_hflip, preprocess_five_crop, preprocess_five_crop_hflip
from FocalLoss import FocalLoss
import numpy as np
BATCH_SIZE = 10
def get_model(name):
print('[+] loading model... ', end='', flush=True)
if name == 'densenet201':
model = models.densenet201_finetune(NB_CLASSES)
elif name == 'inceptionresnetv2':
model = models.inceptionresnetv2_finetune(NB_CLASSES)
if name == 'senet154':
model = models.senet154_finetune(NB_CLASSES)
if name == 'nasnetlarge':
model = models.nasnetlarge_finetune(NB_CLASSES)
if name == 'inceptionv4':
model = models.inceptionv4_finetune(NB_CLASSES)
if name == 'se_resnext101_32x4d':
model = models.se_resnext101_32x4d_finetune(NB_CLASSES)
if use_gpu:
model.cuda()
print('done')
return model
def predict(args):
model = get_model(args.name)
model.load_state_dict(torch.load('models_trained/{}_{}_{}/best_val_weight_{}.pth'.format(args.name, args.aug, args.alpha, args.name)))
model.eval()
#tta_preprocess = [preprocess_five_crop, preprocess_five_crop_hflip]
tta_preprocess = [preprocess, preprocess_hflip]
data_loaders = []
for transform in tta_preprocess:
test_dataset = FurnitureDataset('test', transform=transform)
data_loader = DataLoader(dataset=test_dataset, num_workers=1,
batch_size=BATCH_SIZE,
shuffle=False)
data_loaders.append(data_loader)
lx, px = utils.predict_tta(model, data_loaders)
data = {
'lx': lx.cpu(),
'px': px.cpu(),
}
torch.save(data, 'models_trained/{}_{}_{}/test_prediction_{}.pth'.format(args.name, args.aug, args.alpha, args.name))
data_loaders = []
for transform in tta_preprocess:
test_dataset = FurnitureDataset('val', transform=transform)
data_loader = DataLoader(dataset=test_dataset, num_workers=1,
batch_size=BATCH_SIZE,
shuffle=False)
data_loaders.append(data_loader)
lx, px = utils.predict_tta(model, data_loaders)
data = {
'lx': lx.cpu(),
'px': px.cpu(),
}
torch.save(data, 'models_trained/{}_{}_{}/val_prediction_{}.pth'.format(args.name, args.aug, args.alpha, args.name))
def train(args):
train_dataset = FurnitureDataset('train', transform=preprocess_with_augmentation)
val_dataset = FurnitureDataset('val', transform=preprocess)
training_data_loader = DataLoader(dataset=train_dataset, num_workers=8,
batch_size=BATCH_SIZE,
shuffle=True)
validation_data_loader = DataLoader(dataset=val_dataset, num_workers=1,
batch_size=BATCH_SIZE,
shuffle=False)
model = get_model(args.name)
class_weight = np.load('./class_weight.npy')
#criterion = nn.CrossEntropyLoss(weight=torch.FloatTensor(class_weight)).cuda()
criterion = nn.CrossEntropyLoss().cuda()
#criterion = FocalLoss(alpha=alpha, gamma=0).cuda()
nb_learnable_params = sum(p.numel() for p in model.fresh_params())
print(f'[+] nb learnable params {nb_learnable_params}')
min_loss = float("inf")
lr = 0
patience = 0
for epoch in range(30):
print(f'epoch {epoch}')
if epoch == 1:
lr = 0.00003
print(f'[+] set lr={lr}')
if patience == 2:
patience = 0
model.load_state_dict(torch.load('models_trained/{}_{}_{}/best_val_weight_{}.pth'.format(args.name, args.aug, args.alpha, args.name)))
lr = lr / 10
if lr < 3e-6:
lr = 3e-6
print(f'[+] set lr={lr}')
if epoch == 0:
lr = 0.001
print(f'[+] set lr={lr}')
optimizer = torch.optim.Adam(model.fresh_params(), lr=lr)
else:
optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=0.0001)
running_loss = RunningMean()
running_score = RunningMean()
model.train()
pbar = tqdm(training_data_loader, total=len(training_data_loader))
for inputs, labels in pbar:
batch_size = inputs.size(0)
inputs = Variable(inputs)
labels = Variable(labels)
if use_gpu:
inputs = inputs.cuda()
labels = labels.cuda()
optimizer.zero_grad()
if args.aug:
inputs, targets_a, targets_b, lam = mixup_data(inputs, labels, args.alpha, use_gpu)
outputs = model(inputs)
if args.aug:
loss_func = mixup_criterion(targets_a, targets_b, lam)
loss = loss_func(criterion, outputs)
else:
loss = criterion(outputs, labels)
_, preds = torch.max(outputs.data, dim=1)
running_loss.update(loss.data[0], 1)
if args.aug:
running_score.update(batch_size - lam * preds.eq(targets_a.data).cpu().sum() - (1 - lam) * preds.eq(targets_b.data).cpu().sum(), batch_size)
else:
running_score.update(torch.sum(preds != labels.data), batch_size)
loss.backward()
optimizer.step()
pbar.set_description(f'{running_loss.value:.5f} {running_score.value:.3f}')
print(f'[+] epoch {epoch} {running_loss.value:.5f} {running_score.value:.3f}')
lx, px = utils.predict(model, validation_data_loader)
log_loss = criterion(Variable(px), Variable(lx))
log_loss = log_loss.data[0]
_, preds = torch.max(px, dim=1)
accuracy = torch.mean((preds != lx).float())
print(f'[+] val {log_loss:.5f} {accuracy:.3f}')
if log_loss < min_loss:
torch.save(model.state_dict(), 'models_trained/{}_{}_{}/best_val_weight_{}.pth'.format(args.name, args.aug, args.alpha, args.name))
print(f'[+] val score improved from {min_loss:.5f} to {log_loss:.5f}. Saved!')
min_loss = log_loss
patience = 0
else:
patience += 1
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('mode', choices=['train', 'predict'])
parser.add_argument('name', choices=['densenet201', 'inceptionresnetv2', 'senet154', 'nasnetlarge', 'inceptionv4', 'se_resnext101_32x4d'])
parser.add_argument('aug', type=bool)
parser.add_argument('alpha', type=float)
args = parser.parse_args()
print(f'[+] start `{args.mode}` using `{args.name}` augmentation `{args.aug}` alpha `{args.alpha}`')
cudnn.benchmark = True
print(f'[+] cudnn `{cudnn.benchmark}`')
if args.mode == 'train':
train(args)
elif args.mode == 'predict':
predict(args)