def load_data(): inpath = get_data_directory(__file__) X_np, Y = multi_mnist.load(inpath) X_np = X_np.astype(np.float32) X_np /= 255.0 X = torch.from_numpy(X_np) counts = torch.FloatTensor([len(objs) for objs in Y]) return X, counts
def load_data(): inpath = get_data_directory(__file__) X_np, Y = multi_mnist.load(inpath) X_np = X_np.astype(np.float32) X_np /= 255.0 X = torch.from_numpy(X_np) # Using FloatTensor to allow comparison with values sampled from # Bernoulli. counts = torch.FloatTensor([len(objs) for objs in Y]) return X, counts
def load_data(): inpath = get_data_directory(__file__) # WL: edited to fix a bug. ===== #(X_np, Y), _ = multi_mnist(inpath, max_digits=2, canvas_size=50, seed=42) X_np, Y = multi_mnist.load(inpath) # ============================== X_np = X_np.astype(np.float32) X_np /= 255.0 X = torch.from_numpy(X_np) counts = torch.FloatTensor([len(objs) for objs in Y]) return X, counts