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
