def _init(self):
"""
Inner method for children's classes for model specific initialization.
As baseline, checks device support and puts model on it.
:return:
"""
self.model, self.device = UtilsFactory.prepare_model(self.model)
def main(args):
global IMG_SIZE
IMG_SIZE = (args.img_size, args.img_size)
model = ResnetEncoder(arch=args.arch, pooling=args.pooling)
model = model.eval()
model, device = UtilsFactory.prepare_model(model)
images_df = pd.read_csv(args.in_csv)
images_df = images_df.reset_index().drop("index", axis=1)
images_df = list(images_df.to_dict("index").values())
open_fn = ImageReader(row_key=args.img_col,
dict_key="image",
datapath=args.datapath)
dataloader = UtilsFactory.create_loader(images_df,
open_fn,
batch_size=args.batch_size,
workers=args.n_workers,
dict_transform=dict_transformer)
features = []
dataloader = tqdm(dataloader) if args.verbose else dataloader
with torch.no_grad():
for batch in dataloader:
features_ = model(batch["image"].to(device))
features_ = features_.cpu().detach().numpy()
features.append(features_)
features = np.concatenate(features, axis=0)
np.save(args.out_npy, features)
def main(args):
model = models.__dict__[args.arch](pretrained=True)
model = model.eval()
model, device = UtilsFactory.prepare_model(model)
labels = json.loads(open(args.labels).read())
i2k = Images2Keywords(model, args.n_keywords, labels)
images_df = pd.read_csv(args.in_csv)
images_df = images_df.reset_index().drop("index", axis=1)
images_df = list(images_df.to_dict("index").values())
open_fn = ImageReader(row_key=args.img_col,
dict_key="image",
datapath=args.datapath)
dataloader = UtilsFactory.create_loader(images_df,
open_fn,
batch_size=args.batch_size,
workers=args.n_workers,
dict_transform=dict_transformer)
keywords = []
dataloader = tqdm(dataloader) if args.verbose else dataloader
with torch.no_grad():
for batch in dataloader:
keywords_batch = i2k(batch["image"].to(device))
keywords += keywords_batch
input_csv = pd.read_csv(args.in_csv)
input_csv[args.keywords_col] = keywords
input_csv.to_csv(args.out_csv, index=False)