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train_clothing_dmi.py
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train_clothing_dmi.py
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import os
import math
import argparse
import numpy as np
import torch
import torch.optim as optim
from torchvision import transforms
from utils import get_softmax_out
from ops import train, train_soft, test, val_test
from dataset import Clothing1M, Clothing1M_soft
from networks.resnet import resnet50
def main():
# Settings
parser = argparse.ArgumentParser(description='PyTorch Clothing1M')
parser.add_argument('--batch_size', type=int, default=256, help='input batch size for training (default: 256)')
parser.add_argument('--test_batch_size', type=int, default=256, help='input batch size for testing (default: 256)')
parser.add_argument('--epochs', type=int, default=10, help='number of epochs to train (default: 120)')
parser.add_argument('--gpu_id', type=int, default=0, help='index of gpu to use (default: 0)')
parser.add_argument('--lr', type=float, default=0.001, help='init learning rate (default: 0.1)')
parser.add_argument('--seed', type=int, default=0, help='random seed (default: 0)')
parser.add_argument('--save', action='store_true', default=False, help='For saving softmax_out_avg')
parser.add_argument('--SEAL', type=int, default=0, help='Phase of self-evolution')
args = parser.parse_args()
torch.manual_seed(args.seed)
device = torch.device('cuda:'+str(args.gpu_id) if torch.cuda.is_available() else 'cpu')
# Datasets
root = './data/Clothing1M'
num_classes = 14
kwargs = {'num_workers': 32, 'pin_memory': True} if torch.cuda.is_available() else {}
transform_train = transforms.Compose([transforms.Resize((256, 256)),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
transform_test = transforms.Compose([transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
train_dataset = Clothing1M(root, mode='train', transform=transform_train)
val_dataset = Clothing1M(root, mode='val', transform=transform_test)
test_dataset = Clothing1M(root, mode='test', transform=transform_test)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, **kwargs)
softmax_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.test_batch_size, shuffle=False, **kwargs)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=args.test_batch_size, shuffle=False, **kwargs)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=args.test_batch_size, shuffle=False, **kwargs)
def learning_rate(lr_init, epoch):
optim_factor = 0
if(epoch > 5):
optim_factor = 1
return lr_init*math.pow(0.1, optim_factor)
def load_pretrain(num_classes, device):
model_pre = resnet50(num_classes=1000, pretrained=True) # imagenet pretrained, numclasses=1000
if num_classes==1000:
return model_pre.to(device)
else:
model = resnet50(num_classes=num_classes, pretrained=False)
params_pre = model_pre.state_dict().copy()
params = model.state_dict()
for i in params_pre:
if not i.startswith('fc'):
params[i] = params_pre[i]
model.load_state_dict(params)
return model.to(device)
# results
results_root = os.path.join('results', 'clothing')
if not os.path.isdir(results_root):
os.makedirs(results_root)
""" Test model """
if args.SEAL==-1:
model = resnet50().to(device)
model.load_state_dict(torch.load(os.path.join(results_root, 'seed0_clothing_normal.pt')))
test(args, model, device, test_loader)
""" Get softmax_out_avg - normal training on noisy labels """
if args.SEAL==0:
print('The DMI model is trained using the official pytorch implemention of L_DMI <https://github.com/Newbeeer/L_DMI>.\n')
""" Self Evolution - training on softmax_out_avg from DMI model """
if args.SEAL==1:
# Loading softmax_out_avg of last phase
softmax_root = os.path.join(results_root, 'softmax_out_dmi.npy')
softmax_out_avg = np.load(softmax_root).reshape([-1, len(train_dataset), num_classes])
softmax_out_avg = softmax_out_avg[:5].mean(axis=0) # We found that the DMI model may not converged in the last 5 epochs.
print('softmax_out_avg loaded from', softmax_root, ', shape: ', softmax_out_avg.shape)
# Dataset with soft targets
train_dataset_soft = Clothing1M_soft(root, targets_soft=torch.Tensor(softmax_out_avg.copy()), mode='train', transform=transform_train)
train_loader_soft = torch.utils.data.DataLoader(train_dataset_soft, batch_size=args.batch_size, shuffle=True, **kwargs)
# Building model
model = load_pretrain(num_classes, device)
model = torch.nn.DataParallel(model, device_ids=[0,1,2,3])
model.load_state_dict(torch.load(os.path.join(results_root, 'clothing_dmi.pt')))
print('Initialize the model using DMI model.')
# Training
best_val_acc = 0
save_path = os.path.join(results_root, 'seed'+str(args.seed)+'_clothing_dmi_SEAL1.pt')
softmax_out = []
for epoch in range(1, args.epochs + 1):
optimizer = optim.SGD(model.parameters(), lr=learning_rate(args.lr, epoch), momentum=0.9, weight_decay=1e-3)
train_soft(args, model, device, train_loader_soft, optimizer, epoch)
best_val_acc = val_test(args, model, device, val_loader, test_loader, best_val_acc, save_path)
softmax_out.append(get_softmax_out(model, softmax_loader, device))
if args.save:
softmax_root = os.path.join(results_root, 'seed'+str(args.seed)+'_softmax_out_dmi_SEAL1.npy')
softmax_out = np.concatenate(softmax_out)
np.save(softmax_root, softmax_out)
print('new softmax_out saved to', softmax_root, ', shape: ', softmax_out.shape)
if args.SEAL>=2:
# Loading softmax_out_avg of last phase
softmax_root = os.path.join(results_root, 'seed'+str(args.seed)+'_softmax_out_dmi_SEAL'+str(args.SEAL-1)+'.npy')
softmax_out_avg = np.load(softmax_root).reshape([-1, len(train_dataset), num_classes])
softmax_out_avg = softmax_out_avg.mean(axis=0)
print('softmax_out_avg loaded from', softmax_root, ', shape: ', softmax_out_avg.shape)
# Dataset with soft targets
train_dataset_soft = Clothing1M_soft(root, targets_soft=torch.Tensor(softmax_out_avg.copy()), mode='train', transform=transform_train)
train_loader_soft = torch.utils.data.DataLoader(train_dataset_soft, batch_size=args.batch_size, shuffle=True, **kwargs)
# Building model
model = load_pretrain(num_classes, device)
model = torch.nn.DataParallel(model, device_ids=[0,1,2,3])
model_path = os.path.join(results_root, 'seed'+str(args.seed)+'_clothing_dmi_SEAL'+str(args.SEAL-1)+'.pt')
model.load_state_dict(torch.load(model_path))
print('Initialize the model using {}.'.format(model_path))
# Training
best_val_acc = 0
save_path = os.path.join(results_root, 'seed'+str(args.seed)+'_clothing_dmi_SEAL'+str(args.SEAL)+'.pt')
softmax_out = []
for epoch in range(1, args.epochs + 1):
optimizer = optim.SGD(model.parameters(), lr=learning_rate(args.lr, epoch), momentum=0.9, weight_decay=1e-3)
train_soft(args, model, device, train_loader_soft, optimizer, epoch)
best_val_acc = val_test(args, model, device, val_loader, test_loader, best_val_acc, save_path)
softmax_out.append(get_softmax_out(model, softmax_loader, device))
if args.save:
softmax_root = os.path.join(results_root, 'seed'+str(args.seed)+'_softmax_out_dmi_SEAL'+str(args.SEAL)+'.npy')
softmax_out = np.concatenate(softmax_out)
np.save(softmax_root, softmax_out)
print('new softmax_out saved to', softmax_root, ', shape: ', softmax_out.shape)
if __name__ == '__main__':
main()