def make_dataflow_demo(env): ensure_load() # return feed_dict, extra_info def split_data(img, label): return dict(img=img[np.newaxis].astype('float32')) df = _mnist[1] # use validation set actually df = flow.DictOfArrayDataFlow(df) df = flow.tools.cycle(df) df = flow.tools.ssmap(split_data, df) return df
def make_dataflow_inference(env): ensure_load() batch_size = get_env('inference.batch_size') epoch_size = get_env('inference.epoch_size') df = _mnist[1] # use validation set actually df = flow.DictOfArrayDataFlow(df) df = flow.tools.cycle(df) df = flow.BatchDataFlow(df, batch_size, sample_dict={'img': np.empty(shape=(batch_size, 28, 28, 1), dtype='float32'), }) df = flow.EpochDataFlow(df, epoch_size) return df
def make_dataflow_demo(env): num_classes = get_env('dataset.nr_classes') ensure_load(num_classes) # return feed_dict, extra_info def split_data(img, label): return dict(img=img[np.newaxis].astype('float32')), dict(label=label) df = _cifar[1] # use validation set actually df = flow.DictOfArrayDataFlow(df) df = flow.tools.cycle(df) df = flow.tools.ssmap(split_data, df) return df
def main_demo_infogan(env, func): net = env.network samples = net.zc_distrib.numerical_sample(net.zc_distrib_num_prior) df = {'zc': samples.reshape(samples.shape[0], 1, -1)} df = flow.DictOfArrayDataFlow(df) all_outputs = [] for data in tqdm.tqdm(df, total=len(df), **get_tqdm_defaults()): res = func(**data) all_outputs.append(res['output'][0, :, :, 0]) grid_desc = get_env('demo.infogan.grid_desc') final = image.image_grid(all_outputs, grid_desc) final = (final * 255).astype('uint8') image.imwrite('infogan.png', final)
def make_dataflow_demo(env): reconstruct = get_env('demo.is_reconstruct', False) if reconstruct: ensure_load() # return feed_dict, extra_info def split_data(img, label): return dict(img=img[np.newaxis].astype('float32')) df = _mnist[1] # use validation set actually df = flow.DictOfArrayDataFlow(df) df = flow.tools.cycle(df) df = flow.tools.ssmap(split_data, df) else: df = flow.EmptyDictDataFlow() return df
def make_dataflow_inference(env): num_classes = get_env('dataset.nr_classes') ensure_load(num_classes) batch_size = get_env('inference.batch_size') epoch_size = get_env('inference.epoch_size') df = _cifar[1] # use validation set actually df = flow.DictOfArrayDataFlow(df) df = flow.tools.cycle(df) df = flow.BatchDataFlow(df, batch_size, sample_dict={ 'img': np.empty(shape=(batch_size, _cifar_img_dim, _cifar_img_dim, 3), dtype='float32'), 'label': np.empty(shape=(batch_size, ), dtype='int32') }) df = flow.EpochDataFlow(df, epoch_size) return df