def load_and_preprocess(image_file, size=256):
img = Image.open(image_file).convert('RGB')
transform = transforms.Compose([
transforms.Resize([size, size]),
transforms.ToTensor(),
tensor_normalizer()
])
img_tensor = transform(img).unsqueeze(0)
return img_tensor
# torch.cuda.manual_seed(SEED)
# torch.set_default_tensor_type('torch.cuda.FloatTensor')
# kwargs = {'num_workers': 4, 'pin_memory': True}
# else:
# torch.set_default_tensor_type('torch.FloatTensor')
# kwargs = {}
kwargs = {'num_workers': 4, 'pin_memory': True}
IMAGE_SIZE = 256
BATCH_SIZE = 4
DATASET = "./coco/"
transform = transforms.Compose([
transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)),
transforms.CenterCrop(IMAGE_SIZE),
transforms.ToTensor(),
tensor_normalizer()
])
# http://pytorch.org/docs/master/torchvision/datasets.html#imagefolder
train_dataset = datasets.ImageFolder(DATASET, transform)
# http://pytorch.org/docs/master/data.html#torch.utils.data.DataLoader
train_loader = DataLoader(train_dataset,
batch_size=BATCH_SIZE,
shuffle=True,
**kwargs)
# LossOutput = namedtuple("LossOutput", ["conv2_0", "conv2_1", "conv2_2", "conv2_3"])
#
#
# # https://discuss.pytorch.org/t/how-to-extract-features-of-an-image-from-a-trained-model/119/3
# class LossNetwork(torch.nn.Module):
# def __init__(self, resnet_model):
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
torch.set_default_tensor_type('torch.cuda.FloatTensor')
kwargs = {'num_workers': 4, 'pin_memory': True}
else:
torch.set_default_tensor_type('torch.FloatTensor')
kwargs = {}
# Data Loading
data_transform = transforms.Compose([
transforms.Scale(args.image_size),
transforms.CenterCrop(args.image_size),
transforms.ToTensor(),
tensor_normalizer()
])
print('=====> Load train images')
train_dataset = datasets.ImageFolder(args.data, data_transform)
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
print('Number of train images:', len(train_dataset))
vgg_model = models.vgg16(pretrained=True)
if args.cuda:
vgg_model.cuda()
vgg = Vgg16(vgg_model)
######################################################################
SEED = 1080
np.random.seed(SEED)
torch.manual_seed(SEED)
##################################
# build transforms for ImageLoader
##################################
IMAGE_SIZE = 256
transform_list = []
# (1) cut out the extra parts of pictures
transform = transforms.Compose([
transforms.Resize(IMAGE_SIZE),
transforms.CenterCrop(IMAGE_SIZE),
transforms.ToTensor(),
tensor_normalizer()
])
transform_list.append(transform)
# (2) padding zeros in the edges of picture
transform = transforms.Compose(
[ZeroPadding(IMAGE_SIZE),
transforms.ToTensor(),
tensor_normalizer()])
transform_list.append(transform)
print('transforms done')
####################################
# construct ImageLoaders and dataloader
####################################
DATADIR = os.path.join('..', 'Datasets')
BATCHSIZE = 4
def load_and_preprocess(image_file):
img = Image.open(image_file).convert('RGB')
transform = transforms.Compose(
[transforms.ToTensor(), tensor_normalizer()])
img_tensor = transform(img).unsqueeze(0)
return Variable(img_tensor, volatile=True)
# torch.cuda.manual_seed(SEED)
# torch.set_default_tensor_type('torch.cuda.FloatTensor')
# kwargs = {'num_workers': 4, 'pin_memory': True}
# else:
# torch.set_default_tensor_type('torch.FloatTensor')
# kwargs = {}
kwargs = {'num_workers': 4, 'pin_memory': True}
IMAGE_SIZE = 256
BATCH_SIZE = 4
DATASET = "./coco/"
transform = transforms.Compose([
transforms.Resize(IMAGE_SIZE),
transforms.CenterCrop(IMAGE_SIZE),
transforms.ToTensor(),
tensor_normalizer()
])
# http://pytorch.org/docs/master/torchvision/datasets.html#imagefolder
train_dataset = datasets.ImageFolder(DATASET, transform)
# http://pytorch.org/docs/master/data.html#torch.utils.data.DataLoader
train_loader = DataLoader(train_dataset,
batch_size=BATCH_SIZE,
shuffle=True,
**kwargs)
LossOutput = namedtuple(
"LossOutput", ["conv0_0", "conv1_2", "conv2_3", "conv3_5", "conv4_2"])
# https://discuss.pytorch.org/t/how-to-extract-features-of-an-image-from-a-trained-model/119/3
class LossNetwork(torch.nn.Module):