"""
loss_func = nn.MSELoss()
print("loading view_data")
view_data_in = img_to_tensorVariable(
os.path.join(dir_input,'test/'), namelist_input, 1, N_TEST_IMG
)
view_data_out = img_to_tensorVariable(
os.path.join(dir_output,'test/'), namelist_output, 1, N_TEST_IMG
)
print("loading complete")
print("loading train_loader")
folder_input = folder2.ImageFolder(
root = dir_input,
transform = trans_comp,
loader = folder2.pil_loader
)
train_loader_input = torch.utils.data.DataLoader(
folder_input, batch_size = BATCH_SIZE, shuffle = False)
folder_output = folder2.ImageFolder(
root = dir_output,
transform = trans_comp,
loader = folder2.pil_loader
)
train_loader_output = torch.utils.data.DataLoader(
folder_output, batch_size = BATCH_SIZE, shuffle = False)
print("loading complete")
print("loading test_loader")
recon_loss_func = nn.MSELoss()
gen_optimizer = torch.optim.Adam(generator.parameters(), lr=lr)
# training
img_dir_train = "/home/powergkrry/lv_challenge/data/dataset/dataset06/i/train/"
img_dir_train_out = "/home/powergkrry/lv_challenge/data/dataset/dataset06/i/train_output/"
img_dir_test = "/home/powergkrry/lv_challenge/data/dataset/dataset06/i/test/"
img_dir_test_out = "/home/powergkrry/lv_challenge/data/dataset/dataset06/i/test_output/"
dir_list = os.listdir(img_dir_train)
dir_list.sort()
img_list = os.listdir(img_dir_test_out + "o/output_o/")
img_list.sort()
for flip in dir_list:
img_data_arr_test = folder2.ImageFolder(root=img_dir_test + flip,
transform=transforms.Compose([
transforms.ToTensor(),
]))
img_batch_arr_test = data.DataLoader(img_data_arr_test,
batch_size=batch_size,
num_workers=2)
img_output_arr_test = folder2.ImageFolder(root=img_dir_test_out + flip,
transform=transforms.Compose([
transforms.ToTensor(),
]))
img_output_batch_arr_test = data.DataLoader(img_output_arr_test,
batch_size=batch_size,
num_workers=2)
for traintest in dir_list_traintest:
os.mkdir(out_dir + iop + traintest)
img_name_list = os.listdir(img_dir + iop + traintest + "o/original_o/")
#img_name_list = os.listdir(img_dir + iop + traintest + "o/1_o/")
#img_name_list = os.listdir(img_dir + iop + traintest + "o/0.1_o/")
img_name_list.sort()
img_num = len(img_name_list)
for fliprot in dir_list_fliprot:
os.mkdir(out_dir + iop + traintest + fliprot)
img_folder = folder2.ImageFolder(
root=img_dir + iop + traintest + fliprot,
transform=transforms.Compose([
#transforms.CenterCrop(128),
transforms.ToTensor(),
transforms.Normalize([0.2481], [0.3109]),
]))
img_dataloader = data.DataLoader(
img_folder,
batch_size=1,
#num_workers = 2,
)
for idx in range(img_num):
tensor_arr = []
for i_batch, (img, cat) in enumerate(img_dataloader):
if (i_batch % img_num == idx):
tensor_arr.append(img)
all_diffusion = torch.cat(tensor_arr, dim=1)
"""
loss_func = nn.MSELoss()
"""
print("loading view_data")
view_data_in = img_to_tensorVariable(
os.path.join(dir_input,'test/'), namelist_input, 1, N_TEST_IMG
)
view_data_out = img_to_tensorVariable(
os.path.join(dir_output,'test/'), namelist_output, 1, N_TEST_IMG
)
print("loading complete")
"""
print("loading train_loader")
folder_output = folder2.ImageFolder(root=dir_clean,
transform=trans_comp,
loader=folder2.pil_loader)
train_loader_clean = torch.utils.data.DataLoader(folder_output,
batch_size=BATCH_SIZE,
shuffle=False)
print("loading complete")
print("loading test_loader")
folder_test_clean = folder2.ImageFolder(root=dir_test_clean,
transform=trans_comp,
loader=folder2.pil_loader)
test_loader_clean = torch.utils.data.DataLoader(folder_test_clean,
batch_size=test_BATCH_SIZE,
shuffle=False)
print("loading complete")
optimizer = torch.optim.Adam(autoencoder.parameters(), lr=LR)
loss_func = nn.MSELoss()
print("loading view_data")
view_data_in = img_to_tensorVariable(os.path.join(dir_input, 'test/'),
namelist_input, 1, N_TEST_IMG)
view_data_out = img_to_tensorVariable(os.path.join(dir_output, 'test/'),
namelist_output, 1, N_TEST_IMG)
print("loading complete")
start_time = time.time()
print("loading train_loader")
#train_loader_input = img_to_tensorVariable(dir_input, namelist_input, 1, 10000)
folder_input = folder2.ImageFolder(root=dir_input,
transform=ToTensor(),
loader=folder2.pil_loader)
train_loader_input = torch.utils.data.DataLoader(folder_input,
batch_size=BATCH_SIZE,
shuffle=False)
folder_output = folder2.ImageFolder(root=dir_output,
transform=ToTensor(),
loader=folder2.pil_loader)
train_loader_output = torch.utils.data.DataLoader(folder_output,
batch_size=BATCH_SIZE,
shuffle=False)
"""
view_data_in = torch.utils.data.DataLoader(
folder_input[0][:N_TEST_IMG], batch_size = N_TEST_IMG, shuffle = False)
view_data_out = torch.utils.data.DataLoader(
def return_mean_std(tensor):
mean_t = tensor.mean()
std_t = tensor.std()
return transforms.Normalize([mean_t], [std_t])
#input
img_folder = folder2.ImageFolder(
root=img_dir,
transform=transforms.Compose([
#transforms.CenterCrop(128),
transforms.ToTensor(),
#transforms.Lambda(lambda tensor: transforms.Normalize(str([x.mean()]) + ", " + str([x.std()]) for x in tensor)),
transforms.Lambda(lambda tensor: save_t=tensor,
mean_t=save_t.mean(),
std_t=save_t.std(),
transforms.Normalize([mean_t], [std_t])),
]))
img_dataloader = data.DataLoader(
img_folder,
batch_size=1,
#num_workers = 2,
)
for i, (img_tensor, label) in enumerate(img_dataloader):
out_dir_name = out_dir + "ioriginal_png/" + input_dir[
label[0]] + "/" + img_name_list[i % len(img_name_list)][:3] + ".pt"
# hyperparameters
batch_size = args.batch_size
lr = args.lr
epoch = args.epoch
img_size = 256
train_error = []
test_error = []
# input pipeline
img_dir = "/home/powergkrry/lv_challenge/data/dataset/dataset02/p/train/"
dir_list = os.listdir(img_dir)
dir_list.remove('output')
img_data_arr = [
folder2.ImageFolder(root=img_dir + dirname,
transform=transforms.Compose([
transforms.ToTensor(),
])) for dirname in dir_list
]
img_batch_arr = [
data.DataLoader(img_data, batch_size=batch_size, num_workers=2)
for img_data in img_data_arr
]
img_output_arr = folder2.ImageFolder(
root="/home/powergkrry/lv_challenge/data/dataset/dataset02/p/train/output",
transform=transforms.Compose([
transforms.ToTensor(),
]))
img_output_batch_arr = data.DataLoader(img_output_arr,
return result
if __name__ == '__main__':
dir_clean = '/home/powergkrry/MURA/MURA_TRAIN_RESIZE/'
trans_comp = transforms.Compose([
#transforms.CenterCrop(100),
FullCrop((32, 35)),
transforms.Lambda(lambda crops: torch.stack(
[transforms.ToTensor()(crop) for crop in crops]))
])
folder_output = folder2.ImageFolder(root=dir_clean,
transform=trans_comp,
loader=folder2.pil_loader)
folder_output2 = folder2.ImageFolder(root=dir_clean,
transform=transforms.ToTensor(),
loader=folder2.pil_loader)
train_loader_clean = torch.utils.data.DataLoader(folder_output,
batch_size=1,
shuffle=False)
train_loader_clean2 = torch.utils.data.DataLoader(folder_output2,
batch_size=1,
shuffle=False)
print(train_loader_clean)
it = iter(train_loader_clean)
img, _ = it.next()
print(p.size())
optimizer = optim.Adam(model.parameters(), lr=0.001)
data_transform = transforms.Compose([
transforms.RandomSizedCrop(150),
#transforms.RandomHorizontalFlip(),
transforms.ToTensor()
])
model.train()
test_accu = []
i = 0
for epoch in range(10000):
ddsm_dataset = folder2.ImageFolder(
root='/hoem04/powergkrry/CBIS-DDSM_data/Test1data/Test1data_PNG',
transform=data_transform,
loader=folder2.pil_loader)
train_loader = torch.utils.data.DataLoader(ddsm_dataset,
batch_size=8,
shuffle=True)
print("dataset :")
print(ddsm_dataset)
for data, target in train_loader:
data, target = Variable(data).cuda(), Variable(target).cuda()
#print(data)
#print(target)
optimizer.zero_grad()
output = model(data)
batch_size = 1,
#num_workers = 2,
)
for i, (img_tensor, label) in enumerate(img_dataloader):
out_dir_name = out_dir + "ioriginal_png/" + input_dir[label[0]] + "/" + img_name_list[i % len(img_name_list)][:3] + ".pt"
torch.save(img_tensor.view(-1, 1, 192, 192), out_dir_name)
print(out_dir_name)
"""
#output
img_folder = folder2.ImageFolder(
root=img_dir,
transform=transforms.Compose([
transforms.CenterCrop(192),
transforms.ToTensor(),
#transforms.FiveCrop(64),
#transforms.Lambda(lambda crops: torch.stack([transforms.ToTensor()(crop) for crop in crops])),
]))
img_dataloader = data.DataLoader(
img_folder,
batch_size=1,
#num_workers = 2,
)
for i, (img_tensor, label) in enumerate(img_dataloader):
out_dir_name = out_dir + "ioriginal_png/" + input_dir[
label[0]] + "/" + img_name_list[i % len(img_name_list)][:3] + ".pt"
torch.save(img_tensor.view(-1, 1, 192, 192), out_dir_name)
print(out_dir_name)
r_train_error = []
test_error = []
r_test_error = []
flip = ['_o', '_h', '_v', 'hv']
# input pipeline
img_dir = "/home/yeonjee/lv_challenge/data/dataset/dataset01/p/train/"
# 디렉토리를 4개로 분리시키는 건 어떨까?
dir_list = [[
dir_name for dir_name in os.listdir(img_dir) if f == dir_name[-2:]
] for f in flip]
for lst in dir_list:
lst.sort()
img_data_arr = [[
folder2.ImageFolder(root=img_dir + dirname,
transform=transforms.ToTensor()) for dirname in dir_l
] for dir_l in dir_list]
img_batch_arr = [[
data.DataLoader(img_data, batch_size=batch_size, num_workers=2)
for img_data in img_data_a
] for img_data_a in img_data_arr]
img_output_arr = folder2.ImageFolder(
root="/home/yeonjee/lv_challenge/data/dataset/dataset01/p/train/output",
transform=transforms.Compose([
fourflip(),
transforms.Lambda(lambda crops: torch.stack(
[transforms.ToTensor()(crop) for crop in crops]))
]))
f, a = plt.subplots(3, N_TEST_IMG, figsize=(5,3))
plt.ion()
for i in range(N_TEST_IMG):
a[0][i].imshow(view_data_in.data.cpu().numpy()[i].reshape(256,256), cmap='gray')
a[0][i].set_xticks(())
a[0][i].set_yticks(())
a[1][i].imshow(view_data_out.data.cpu().numpy()[i].reshape(256,256), cmap='gray')
a[1][i].set_xticks(())
a[1][i].set_yticks(())
plt.show()
"""
for dir_input_location in os.listdir(dir_input):
print("loading train_loader")
folder_input = folder2.ImageFolder(root=os.path.join(
dir_input, dir_input_location),
transform=trans_comp,
loader=folder2.pil_loader)
train_loader_input = torch.utils.data.DataLoader(folder_input,
batch_size=BATCH_SIZE,
shuffle=False)
folder_output = folder2.ImageFolder(root=dir_output,
transform=trans_comp,
loader=folder2.pil_loader)
train_loader_output = torch.utils.data.DataLoader(folder_output,
batch_size=BATCH_SIZE,
shuffle=False)
print("loading complete")
