def __init__(self, options, path):
self.options = options
self.path = path
self.device = options['device']
print('Starting to prepare network and data...')
self.net = nn.DataParallel(self._net_choice(
self.options['net_choice'])).to(self.device)
#self.net.load_state_dict(torch.load('/home/zhangyongshun/se_base_model/model_save/ResNet/backup/epoch120/ResNet50-finetune_fc_cub.pkl'))
print('Network is as follows:')
print(self.net)
#print(self.net.state_dict())
self.criterion = nn.CrossEntropyLoss()
self.solver = torch.optim.SGD(
self.net.parameters(),
lr=self.options['base_lr'],
momentum=self.options['momentum'],
weight_decay=self.options['weight_decay'])
self.schedule = torch.optim.lr_scheduler.StepLR(self.solver,
step_size=30,
gamma=0.1)
#self.schedule = torch.optim.lr_scheduler.ReduceLROnPlateau(
# self.solver, mode='max', factor=0.1, patience=3, verbose=True, threshold=1e-4
#)
train_transform_list = [
transforms.RandomResizedCrop(self.options['img_size']),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225))
]
test_transforms_list = [
transforms.Resize(int(self.options['img_size'] / 0.875)),
transforms.CenterCrop(self.options['img_size']),
transforms.ToTensor(),
transforms.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225))
]
train_data = cub200(self.path['data'],
train=True,
transform=transforms.Compose(train_transform_list))
test_data = cub200(self.path['data'],
train=False,
transform=transforms.Compose(test_transforms_list))
self.train_loader = torch.utils.data.DataLoader(
train_data,
batch_size=self.options['batch_size'],
shuffle=True,
num_workers=4,
pin_memory=True)
self.test_loader = torch.utils.data.DataLoader(test_data,
batch_size=16,
shuffle=False,
num_workers=4,
pin_memory=True)
def __init__(self, options, path):
self.options = options
self.path = path
self.device = options['device']
print('Starting to prepare network and data...')
net_params = BCNN(is_all=True, num_classes=200)
self.net = nn.DataParallel(net_params).to(self.device)
if stage == "train_all":
self.net.load_state_dict(torch.load('./models/saved/VGG/VGG_bilinear_wonorm_train_last_layer.pkl'))
1
else:
for param in net_params.parameters():
param.requires_grad = False
for param in net_params.classifier.parameters():
param.requires_grad = True
print('Network is as follows:')
print(self.net)
#print(self.net.state_dict())
self.criterion = nn.CrossEntropyLoss()
if stage == "train_all":
self.solver = torch.optim.SGD(self.net.parameters(), lr=self.options['base_lr'], momentum=self.options['momentum'], weight_decay=self.options['weight_decay'])
else:
self.solver = torch.optim.SGD(filter(lambda p: p.requires_grad, self.net.parameters()), lr=self.options['base_lr'], momentum=self.options['momentum'], weight_decay=self.options['weight_decay'])
# self.schedule = torch.optim.lr_scheduler.StepLR(self.solver, step_size=50, gamma=0.25)
self.schedule = torch.optim.lr_scheduler.ReduceLROnPlateau(
self.solver, mode='max', factor=0.25, patience=5, verbose=True,
threshold=1e-4)
if stage == "train_all":
self.schedule = torch.optim.lr_scheduler.ReduceLROnPlateau(
self.solver, mode='max', factor=0.1, patience=5, verbose=True,
threshold=1e-4)
train_transform_list = [
transforms.Resize(512),
transforms.RandomCrop(448),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225))
]
test_transforms_list = [
transforms.Resize(512),
transforms.CenterCrop(self.options['img_size']),
transforms.ToTensor(),
transforms.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225))
]
train_data = cub200(self.path['data'], train=True, transform=transforms.Compose(train_transform_list))
test_data = cub200(self.path['data'], train=False, transform=transforms.Compose(test_transforms_list))
self.train_loader = torch.utils.data.DataLoader(
train_data, batch_size=self.options['batch_size'], shuffle=True, num_workers=4, pin_memory=True
)
self.test_loader = torch.utils.data.DataLoader(
test_data, batch_size=16, shuffle=False, num_workers=4, pin_memory=True
)
def __init__(self, options, path):
self.options = options
self.path = path
self.device = options['device']
print('Starting to prepare network and data...')
# print(self.device)
# exit()
self.net = self._net_choice(self.options['net_choice']).to(self.device)
if self.options["test"]:
load_data = torch.load('./model_save/ResNet/ResNet50.pkl', map_location = self.device)
self.net.load_state_dict(load_data)
print('Network is as follows:')
print(self.net)
#print(self.net.state_dict())
self.criterion = nn.CrossEntropyLoss()
self.solver = torch.optim.SGD(
self.net.parameters(), lr=self.options['base_lr'], momentum=self.options['momentum'], weight_decay=self.options['weight_decay']
)
self.schedule = torch.optim.lr_scheduler.StepLR(self.solver, step_size=30, gamma=0.1)
#self.schedule = torch.optim.lr_scheduler.ReduceLROnPlateau(
# self.solver, mode='max', factor=0.1, patience=3, verbose=True, threshold=1e-4
#)
train_transform_list = [
transforms.RandomResizedCrop(self.options['img_size']),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225))
]
test_transforms_list = [
transforms.Resize(int(self.options['img_size']/0.875)),
transforms.CenterCrop(self.options['img_size']),
transforms.ToTensor(),
transforms.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225))
]
if (self.options['dataset'] == "cub"):
train_data = cub200(self.path['data'], train=True, transform=transforms.Compose(train_transform_list))
test_data = cub200(self.path['data'], train=False, transform=transforms.Compose(test_transforms_list))
elif (self.options['dataset'] == "flo"):
print("flower dataset")
train_data = flower(self.path['data'], train=True, transform=transforms.Compose(train_transform_list))
test_data = flower(self.path['data'], train=False, transform=transforms.Compose(test_transforms_list))
exit()
self.train_loader = torch.utils.data.DataLoader(
train_data, batch_size=self.options['batch_size'], shuffle=True, num_workers=4, pin_memory=True
)
self.test_loader = torch.utils.data.DataLoader(
test_data, batch_size=16, shuffle=False, num_workers=4, pin_memory=True
)
self.activation = {}