# Parse arguments parser = ArgumentParser(description=('Sample images from the generative component of a ' 'cGAN learned on the LFW/LFWcrop dataset.')) parser.add_argument('-s', '--conditional-sampler', default='fix_random', choices=sampler.conditional_samplers.values(), type=lambda k: sampler.conditional_samplers[k]) parser.add_argument('-e', '--embedding-file') parser.add_argument('--show-nearest-training', default=False, action='store_true') parser.add_argument('model_path') args = parser.parse_args() m, n = 4, 5 topo_samples, _ = sampler.get_conditional_topo_samples(args.model_path, m, n, args.conditional_sampler, embedding_file=(args.embedding_file if args.embedding_file is not None else sampler.DEFAULT_EMBEDDING_FILE)) pv = PatchViewer(grid_shape=(m, (n + 1 if args.show_nearest_training else n)), patch_shape=(32,32), is_color=True) # Optionally load dataset for --show-nearest-training dataset = None if args.show_nearest_training: model = serial.load(args.model_path) # Shape: b * (0 * 1 * c) # (topo view) dataset = yaml_parse.load(model.dataset_yaml_src) for i in xrange(topo_samples.shape[0]):
parser.add_argument('-n', type=int, default=1000, help='Number of images to generate') parser.add_argument('model_path') parser.add_argument('output_directory') args = parser.parse_args() if os.path.exists(args.output_directory): print 'Warning: output directory %s exists' % args.output_directory if os.path.isfile(args.output_directory): raise ValueError("Provided output directory %s is a file" % args.output_directory) else: try: os.mkdirs(args.output_directory) except AttributeError: os.mkdir(args.output_directory) samples, cond_data = sampler.get_conditional_topo_samples(args.model_path, args.n, 1, args.conditional_sampler) for i, sample in enumerate(samples): img = make_image_from_sample(sample) path = os.path.join(args.output_directory, '%04i.png' % i) img.save(path) print >> sys.stderr, "Saved %i images to %s." % (args.n, args.output_directory) np.save(os.path.join(args.output_directory, 'conditional_data'), cond_data) scipy.io.savemat(os.path.join(args.output_directory, 'conditional_data.mat'), {'x': cond_data}) print >> sys.stderr, 'Saved conditional data matrix.'
'--conditional-sampler', default='fix_random', choices=sampler.conditional_samplers.values(), type=lambda k: sampler.conditional_samplers[k]) parser.add_argument('-e', '--embedding-file') parser.add_argument('--show-nearest-training', default=False, action='store_true') parser.add_argument('model_path') args = parser.parse_args() m, n = 4, 5 topo_samples, _ = sampler.get_conditional_topo_samples( args.model_path, m, n, args.conditional_sampler, embedding_file=(args.embedding_file if args.embedding_file is not None else sampler.DEFAULT_EMBEDDING_FILE)) pv = PatchViewer(grid_shape=(m, (n + 1 if args.show_nearest_training else n)), patch_shape=(32, 32), is_color=True) # Optionally load dataset for --show-nearest-training dataset = None if args.show_nearest_training: model = serial.load(args.model_path) # Shape: b * (0 * 1 * c) # (topo view)
help='Number of images to generate') parser.add_argument('model_path') parser.add_argument('output_directory') args = parser.parse_args() if os.path.exists(args.output_directory): print 'Warning: output directory %s exists' % args.output_directory if os.path.isfile(args.output_directory): raise ValueError("Provided output directory %s is a file" % args.output_directory) else: try: os.mkdirs(args.output_directory) except AttributeError: os.mkdir(args.output_directory) samples, cond_data = sampler.get_conditional_topo_samples( args.model_path, args.n, 1, args.conditional_sampler) for i, sample in enumerate(samples): img = make_image_from_sample(sample) path = os.path.join(args.output_directory, '%04i.png' % i) img.save(path) print >> sys.stderr, "Saved %i images to %s." % (args.n, args.output_directory) np.save(os.path.join(args.output_directory, 'conditional_data'), cond_data) scipy.io.savemat(os.path.join(args.output_directory, 'conditional_data.mat'), {'x': cond_data}) print >> sys.stderr, 'Saved conditional data matrix.'