def main():
size = 224
mean = (0.485, 0.456, 0.406)
std = (0.229, 0.224, 0.225)
train_list = make_datapath_list(phase="train")
val_list = make_datapath_list(phase="val")
train_dataset = HymenopteraDataset(file_list=train_list,
transform=ImageTransform(
size, mean, std),
phase='train')
val_dataset = HymenopteraDataset(file_list=val_list,
transform=ImageTransform(size, mean, std),
phase='val')
batch_size = 32
train_dataloader = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True)
val_dataloader = torch.utils.data.DataLoader(val_dataset,
batch_size=batch_size,
shuffle=False)
dataloaders_dict = {"train": train_dataloader, "val": val_dataloader}
num_epochs = 2
train_model(net,
dataloaders_dict,
criterion,
optimizer,
num_epochs=num_epochs)
def main():
G = Generator(z_dim=20)
D = Discriminator(z_dim=20)
E = Encoder(z_dim=20)
G.apply(weights_init)
D.apply(weights_init)
E.apply(weights_init)
train_img_list=make_datapath_list(num=200)
mean = (0.5,)
std = (0.5,)
train_dataset = GAN_Img_Dataset(file_list=train_img_list, transform=ImageTransform(mean, std))
batch_size = 64
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
num_epochs = 1500
G_update, D_update, E_update = train_model(G, D, E, dataloader=train_dataloader, num_epochs=num_epochs, save_model_name='Efficient_GAN')
G = torch.nn.DataParallel(G)
D = torch.nn.DataParallel(D)
print("parallel mode")
batch_size = 8
z_dim = 20
fixed_z = torch.randn(batch_size, z_dim)
fixed_z = fixed_z.view(fixed_z.size(0), fixed_z.size(1), 1, 1)
fake_images = G(fixed_z.to(device))
train_img_list = make_datapath_list(num=1000)
mean = (0.5, )
std = (0.5, )
train_dataset = GAN_Img_Dataset(file_list=train_img_list,
transform=ImageTransform(mean, std))
train_dataloader = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True)
batch_iterator = iter(train_dataloader)
# fetch first element
images = next(batch_iterator)
fig = plt.figure(figsize=(15, 6))
for i in range(0, 5):
#train is upside
plt.subplot(2, 5, i + 1)
plt.imshow(images[i][0].cpu().detach().numpy(), 'gray')
def main():
train_list = make_datapath_list(phase="train")
val_list = make_datapath_list(phase="val")
size, mean, std = 224, (0.485, 0.456, 0.406), (0.229, 0.224, 0.225)
train_dataset = HymenopteraDataset(file_list=train_list,
transform=ImageTransform(
size, mean, std),
phase="train")
val_dataset = HymenopteraDataset(file_list=val_list,
transform=ImageTransform(size, mean, std),
phase="val")
batch_size = 32
train_dataloader = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True)
val_dataloader = torch.utils.data.DataLoader(val_dataset,
batch_size=batch_size,
shuffle=False)
dataloaders_dict = {"train": train_dataloader, "val": val_dataloader}
use_pretrained = True
net = models.vgg16(pretrained=use_pretrained)
net.classifier[6] = nn.Linear(in_features=4096, out_features=2)
net.train()
criterion = nn.CrossEntropyLoss()
#store params in params_to_update for fine tuning
params_to_update_1 = []
params_to_update_2 = []
params_to_update_3 = []
update_param_names_1 = ["features"]
update_param_names_2 = [
"classifier.0.weight", "classifier.0.bias", "classifier.3.weight",
"classifier.3.bias"
]
update_param_names_3 = ["classifier.6.weight", "classifier.6.bias"]
for name, param in net.named_parameters():
if update_param_names_1[0] in name:
param.requires_grad = True
params_to_update_1.append(param)
elif name in update_param_names_2:
param.requires_grad = True
params_to_update_2.append(param)
elif name in update_param_names_3:
param.requires_grad = True
params_to_update_3.append(param)
else:
param.requires_grad = False
optimizer = optim.SGD([{
'params': params_to_update_1,
'lr': 1e-4
}, {
'params': params_to_update_2,
'lr': 5e-4
}, {
'params': params_to_update_3,
'lr': 1e-3
}],
momentum=0.9)
num_epochs = 2
train_model(net,
dataloaders_dict,
criterion,
optimizer,
num_epochs=num_epochs)
G.to(device)
D.to(device)
"""use GPU in parallel"""
if device == 'cuda':
G = torch.nn.DataParallel(G)
D = torch.nn.DataParallel(D)
print("parallel mode")
batch_size = 5
train_img_list = make_datapath_list(num=1000)
mean = (0.5,)
std = (0.5,)
train_dataset = GAN_Img_Dataset(file_list=train_img_list, transform=ImageTransform(mean, std))
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=False)
batch_iterator = iter(train_dataloader)
# fetch first element
images = next(batch_iterator)
x = images[0:5]
x = x.to(device)
z = torch.randn(5, 20).to(device)
z = z.view(z.size(0), z.size(1), 1, 1)