args = parser.parse_args() if string is None else parser.parse_args(string)
return args
if __name__=='__main__':
args = parse_args()
wandb.init(config=args,
project=f'dlcv_naive_{args.source}2{args.target}')
size = 64
t0 = transforms.Compose([
transforms.Resize(size),
transforms.ColorJitter(),
transforms.RandomRotation(15, fill=(0,)),
transforms.Grayscale(3),
transforms.ToTensor(),
transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))
])
t1 = transforms.Compose([
transforms.Resize(size),
transforms.Grayscale(3),
transforms.ToTensor(),
transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))
])
root = '../hw3_data/digits/'
# dataset
source, target = args.source, args.target
train_source_dataset = Digits_Dataset(root+f'{source}/train', source, t0)
train_target_dataset = Digits_Dataset(root+f'{target}/train', target, t0)
def inference(args):
if args.target=='mnistm':
args.source = 'usps'
elif args.target=='usps':
args.source = 'svhn'
elif args.target=='svhn':
args.source = 'mnistm'
else:
raise NotImplementedError(f"{args.target}: not implemented!")
size = args.img_size
t1 = transforms.Compose([
transforms.Resize(size),
transforms.Grayscale(3),
transforms.ToTensor(),
transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))
])
valid_target_dataset = Digits_Dataset_Test(args.dataset_path, t1)
valid_target_dataloader = DataLoader(valid_target_dataset,
batch_size=512,
num_workers=6)
load = torch.load(
f"./p3/result/3_2/{args.source}2{args.target}/best_model.pth",
map_location='cpu')
feature_extractor = FeatureExtractor()
feature_extractor.load_state_dict(load['F'])
feature_extractor.cuda()
feature_extractor.eval()
label_predictor = LabelPredictor()
label_predictor.load_state_dict(load['C'])
label_predictor.cuda()
label_predictor.eval()
out_preds = []
out_fnames = []
count=0
for i,(imgs, fnames) in enumerate(valid_target_dataloader):
bsize = imgs.size(0)
imgs = imgs.cuda()
features = feature_extractor(imgs)
class_output = label_predictor(features)
_, preds = class_output.max(1)
preds = preds.detach().cpu()
out_preds.append(preds)
out_fnames += fnames
count+=bsize
print(f"\t [{count}/{len(valid_target_dataloader.dataset)}]",
end=" \r")
out_preds = torch.cat(out_preds)
out_preds = out_preds.cpu().numpy()
d = {'image_name':out_fnames, 'label':out_preds}
df = pd.DataFrame(data=d)
df = df.sort_values('image_name')
df.to_csv(args.out_csv, index=False)
print(f' [Info] finish predicting {args.dataset_path}')