def test_serialize():
"""Tests that it correctly [de]serializes.
"""
in_shape = (32, 64, np.random.randint(1, 128))
out_channels = np.random.randint(1, 128)
window_shape = (4, 2)
strides = (3, 1)
pad = (1, 3)
window_data = StridedWindowData(in_shape, window_shape, strides, pad,
out_channels)
serialized = window_data.serialize()
assert serialized.in_height == 32
assert serialized.in_width == 64
assert serialized.in_channels == in_shape[2]
assert serialized.window_height == 4
assert serialized.window_width == 2
assert serialized.out_channels == out_channels
assert serialized.stride_height == 3
assert serialized.stride_width == 1
assert serialized.pad_height == 1
assert serialized.pad_width == 3
assert StridedWindowData.deserialize(serialized).serialize() == serialized
def deserialize(cls, serialized):
"""Deserializes the layer from the Protobuf format.
"""
if serialized.WhichOneof("layer_data") == "maxpool_data":
window_data = StridedWindowData.deserialize(
serialized.maxpool_data.window_data)
return cls(window_data)
return None
def deserialize(cls, serialized):
"""Deserializes from the Protobuf format.
"""
if serialized.WhichOneof("layer_data") == "conv2d_data":
window_data = StridedWindowData.deserialize(
serialized.conv2d_data.window_data)
filters = np.array(serialized.conv2d_data.filters)
filters = filters.reshape(window_data.window_shape +
(window_data.input_shape[2],
window_data.out_channels,))
biases = np.array(serialized.conv2d_data.biases)
return cls(window_data, filters, biases)
return None