[GroupScale(net.scale_size), GroupCenterCrop(net.input_size)]) elif args.test_crops == 10: cropping = torchvision.transforms.Compose( [GroupOverSample(net.input_size, net.scale_size)]) else: raise ValueError( "Only 1 and 10 crops are supported while we got {}".format( args.test_crops)) if not args.use_reference and not args.use_kinetics_reference: checkpoint = torch.load(args.weights) else: model_url = get_reference_model_url( args.dataset, args.modality, "ImageNet" if args.use_reference else "Kinetics", args.arch, ) checkpoint = model_zoo.load_url(model_url) print(("using reference model: {}".format(model_url))) print(("model epoch {} loss: {}".format(checkpoint["epoch"], checkpoint["best_loss"]))) base_dict = { ".".join(k.split(".")[1:]): v for k, v in list(checkpoint["state_dict"].items()) } stats = checkpoint["reg_stats"].numpy() dataset = SSNDataSet( "",
if args.test_crops == 1: cropping = torchvision.transforms.Compose([ GroupScale(net.scale_size), GroupCenterCrop(net.input_size), ]) elif args.test_crops == 10: cropping = torchvision.transforms.Compose([ GroupOverSample(net.input_size, net.scale_size) ]) else: raise ValueError("Only 1 and 10 crops are supported while we got {}".format(args.test_crops)) if not args.use_reference and not args.use_kinetics_reference: checkpoint = torch.load(args.weights) else: model_url = get_reference_model_url(args.dataset, args.modality, 'ImageNet' if args.use_reference else 'Kinetics', args.arch) checkpoint = model_zoo.load_url(model_url) print("using reference model: {}".format(model_url)) print("model epoch {} loss: {}".format(checkpoint['epoch'], checkpoint['best_loss'])) base_dict = {'.'.join(k.split('.')[1:]): v for k, v in list(checkpoint['state_dict'].items())} stats = checkpoint['reg_stats'].numpy() dataset = SSNDataSet("", test_prop_file, new_length=data_length, modality=args.modality, aug_seg=2, body_seg=5, image_tmpl="img_{:05d}.jpg" if args.modality in ["RGB", "RGBDiff"] else args.flow_pref + "{}_{:05d}.jpg", test_mode=True, test_interval=args.frame_interval, transform=torchvision.transforms.Compose([
GroupScale(net.scale_size), GroupCenterCrop(net.input_size), ]) elif args.test_crops == 10: cropping = torchvision.transforms.Compose( [GroupOverSample(net.input_size, net.scale_size)]) else: raise ValueError( "Only 1 and 10 crops are supported while we got {}".format( args.test_crops)) if not args.use_reference and not args.use_kinetics_reference: checkpoint = torch.load(args.weights) else: model_url = get_reference_model_url( args.dataset, args.modality, 'ImageNet' if args.use_reference else 'Kinetics', args.arch) checkpoint = model_zoo.load_url(model_url) print("using reference model: {}".format(model_url)) print("model epoch {} loss: {}".format(checkpoint['epoch'], checkpoint['best_loss'])) base_dict = { '.'.join(k.split('.')[1:]): v for k, v in list(checkpoint['state_dict'].items()) } stats = checkpoint['reg_stats'].numpy() dataset = SSNDataSet( "", test_prop_file,