'img_indices': [373, 413, 428, 468],
'cnnid': 26,
'iterations': 100,
'lr': 0.01,
'octave_scale': 1.2,
'num_octaves': 10,
'device': 'cuda'
}
args = argparse.Namespace(**args)
# build model
model = Model(
make_layers([
32, 32, 32, 'M',
64, 64, 64, 'M',
128, 128, 128, 'M',
256, 256, 256, 256, 'M',
512, 512, 512, 512, 'M'
])
).to(args.device)
# load checkpoint
checkpoint = torch.load(args.ckptpath)
model.load_state_dict(checkpoint['model_state_dict'])
# prepare dataset
valid_paths, valid_labels = get_paths_labels(os.path.join(args.dataset_dir, 'validation'))
valid_set = ImgDataset(valid_paths, valid_labels, 512, data_transforms['test'])
# dream & deep_dream
layer_activations = None
'dataset_dir': sys.argv[1],
'output_dir': sys.argv[2],
'img_indices': [373, 413, 428, 468],
'cnnid': 26,
'iterations': 100,
'lr': 0.01,
'octave_scale': 1.2,
'num_octaves': 10,
'device': 'cuda'
}
args = argparse.Namespace(**args)
# build model
model = Model(
make_layers([
32, 32, 32, 'M', 64, 64, 64, 'M', 128, 128, 128, 'M', 256, 256, 256,
256, 'M', 512, 512, 512, 512, 'M'
])).to(args.device)
# load checkpoint
checkpoint = torch.load(args.ckptpath)
model.load_state_dict(checkpoint['model_state_dict'])
# prepare dataset
valid_paths, valid_labels = get_paths_labels(
os.path.join(args.dataset_dir, 'validation'))
valid_set = ImgDataset(valid_paths, valid_labels, 512, data_transforms['test'])
# dream & deep_dream
layer_activations = None