def test_compute_flat():
"""Tests that the Concat layer correctly computes.
Uses concat_along = FLAT
"""
batch = 15
n_inputs = 1025
fullyconnected_outputs = 2046
inputs = np.random.uniform(size=(batch, n_inputs)).astype(np.float32)
weights = np.random.uniform(size=(n_inputs, fullyconnected_outputs))
weights = weights.astype(np.float32)
biases = np.random.uniform(size=(fullyconnected_outputs))
biases = biases.astype(np.float32)
true_fullyconnected_outputs = np.matmul(inputs, weights) + biases
means = np.random.uniform(size=(n_inputs)).astype(np.float32)
stds = np.random.uniform(size=(n_inputs)).astype(np.float32)
true_normalize_outputs = (inputs - means) / stds
true_outputs = np.concatenate([true_fullyconnected_outputs,
true_normalize_outputs],
axis=1)
fullyconnected_layer = FullyConnectedLayer(weights, biases)
normalize_layer = NormalizeLayer(means, stds)
concat_layer = ConcatLayer([fullyconnected_layer, normalize_layer],
ConcatAlong.FLAT)
assert np.allclose(concat_layer.compute(inputs), true_outputs)
torch_inputs = torch.FloatTensor(inputs)
torch_outputs = concat_layer.compute(torch_inputs).numpy()
assert np.allclose(torch_outputs, true_outputs)
def test_compute_invalid():
"""Tests that the Concat layer fails on an invalid concat_along.
"""
batch = 15
n_inputs = 1025
fullyconnected_outputs = 2046
inputs = np.random.uniform(size=(batch, n_inputs)).astype(np.float32)
weights = np.random.uniform(size=(n_inputs, fullyconnected_outputs))
weights = weights.astype(np.float32)
biases = np.random.uniform(size=(fullyconnected_outputs))
biases = biases.astype(np.float32)
fullyconnected_layer = FullyConnectedLayer(weights, biases)
means = np.random.uniform(size=(n_inputs)).astype(np.float32)
stds = np.random.uniform(size=(n_inputs)).astype(np.float32)
normalize_layer = NormalizeLayer(means, stds)
concat_layer = ConcatLayer([fullyconnected_layer, normalize_layer], None)
try:
concat_layer.compute(inputs)
assert False
except NotImplementedError:
assert True
def test_compute_channels():
"""Tests that the Concat layer correctly computes.
Uses concat_along = CHANNELS
"""
batch = 15
height, width, channels = (32, 32, 3)
out_channels = 6
inputs = np.random.uniform(size=(batch, height * width * channels))
inputs = inputs.astype(np.float32)
filters = np.random.uniform(size=(2, 2, channels, out_channels))
filters = filters.astype(np.float32)
biases = np.random.uniform(size=(out_channels)).astype(np.float32)
conv_window_data = StridedWindowData((height, width, channels), (2, 2),
(2, 2), (0, 0), out_channels)
conv2d_layer = Conv2DLayer(conv_window_data, filters, biases)
conv2d_outputs = conv2d_layer.compute(inputs)
pool_window_data = StridedWindowData((height, width, channels), (2, 2),
(2, 2), (0, 0), channels)
averagepool_layer = AveragePoolLayer(pool_window_data)
pool_outputs = averagepool_layer.compute(inputs)
true_outputs = np.concatenate([
conv2d_outputs.reshape((-1, out_channels)),
pool_outputs.reshape((-1, channels))
],
axis=1).reshape((batch, -1))
concat_layer = ConcatLayer([conv2d_layer, averagepool_layer],
ConcatAlong.CHANNELS)
assert np.allclose(concat_layer.compute(inputs), true_outputs)
torch_inputs = torch.FloatTensor(inputs)
torch_outputs = concat_layer.compute(torch_inputs).numpy()
assert np.allclose(torch_outputs, true_outputs)