default='cuda',
help="Device: 'cuda' or 'cpu'")
args = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available()
and args.device == 'cuda' else "cpu")
# attributes variable contains labels for the categories in the dataset and mapping between string names and IDs
attributes = AttributesDataset(args.attributes_file)
# during validation we use only tensor and normalization transforms
val_transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize(mean, std)])
test_dataset = FashionDataset('./val.csv', attributes, val_transform)
test_dataloader = DataLoader(test_dataset,
batch_size=64,
shuffle=False,
num_workers=8)
model = MultiOutputModel(
n_color_classes=attributes.num_colors,
n_gender_classes=attributes.num_genders,
n_article_classes=attributes.num_articles).to(device)
# Visualization of the trained model
visualize_grid(model,
test_dataloader,
attributes,
device,
checkpoint=args.checkpoint)
train_dataset = FashionDataset('./fashion-product-images/train.csv',
attributes, train_transform)
train_dataloader = DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers)
val_dataset = FashionDataset('./fashion-product-images/val.csv',
attributes, val_transform)
val_dataloader = DataLoader(val_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
model = MultiOutputModel(n_color_classes=attributes.num_colors,
n_gender_classes=attributes.num_genders,
n_article_classes=attributes.num_articles)\
.to(device)
optimizer = torch.optim.Adam(model.parameters())
logdir = os.path.join('./logs/', get_cur_time())
savedir = os.path.join('./checkpoints/', get_cur_time())
os.makedirs(logdir, exist_ok=True)
os.makedirs(savedir, exist_ok=True)
logger = SummaryWriter(logdir)
n_train_samples = len(train_dataloader)
# Uncomment rows below to see example images with ground truth labels in val dataset and all the labels:
visualize_grid(model,
val_dataloader,
proba = model.predict(image)[0]
idxs = np.argsort(proba)[::-1][:2]
for (i, j) in enumerate(idxs):
label = "{}".format(mlb.classes_[j])
print(label)
if __name__ == '__main__':
checkpoint = '/checkpoint-000050.pth'
attributes_file = '/styles.csv'
device = 'cpu'
#imagg=open(args.inputimage())
attributes = AttributesDataset(attributes_file)
model = MultiOutputModel(
n_color_classes=attributes.num_colors,
n_gender_classes=attributes.num_genders,
n_article_classes=attributes.num_articles).to(device)
#enter path to input image
res = DATASET_PATH
val_transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize(mean, std)])
img = Image.open(res)
img = val_transform(img)
img = img.view(-1, 3, img.shape[0], img.shape[1])
name = checkpoint
print('Restoring checkpoint: {}'.format(name))
model.load_state_dict(torch.load(name, map_location='cpu'))
#checkpoint_load(model, checkpoint=args.checkpoint)
model.eval()