def test_binary_layer_config_qconv(bits,
weight_quantization,
input_shape=(1, 3, 7, 7),
channel=2):
d = np.random.uniform(-1, 1, input_shape)
in_data = mx.nd.array(d)
in_data.attach_grad()
qconv = nn.QConv2D(channel,
3,
bits=bits,
quantization=weight_quantization,
in_channels=input_shape[1])
qconv.initialize(mx.init.Xavier(magnitude=2))
with nn.set_binary_layer_config(bits=bits,
weight_quantization=weight_quantization):
qconv_config = nn.QConv2D(channel,
3,
params=qconv.collect_params(),
in_channels=input_shape[1])
grad, y = forward(in_data, qconv)
grad_, y_ = forward(in_data, qconv_config)
np.testing.assert_almost_equal(y, y_)
np.testing.assert_almost_equal(grad, grad_)
def test_binary_layer_config_qact(grad_cancel,
bits_a,
activation,
input_shape=(1, 2, 4, 4)):
d = np.random.uniform(-1, 1, input_shape)
in_data = mx.nd.array(d)
in_data.attach_grad()
qact = nn.QActivation(bits=bits_a,
gradient_cancel_threshold=grad_cancel,
method=activation)
with nn.set_binary_layer_config(grad_cancel=grad_cancel,
bits_a=bits_a,
activation=activation):
qact_config = nn.QActivation()
grad, y = forward(in_data, qact)
grad_, y_ = forward(in_data, qact_config)
np.testing.assert_almost_equal(y, y_)
np.testing.assert_almost_equal(grad, grad_)
def test_binary_layer_config_scaling():
assert isinstance(nn.activated_conv(3), nn.BinaryConvolution)
with nn.set_binary_layer_config(approximation="xnor"):
assert isinstance(nn.activated_conv(3), nn.ScaledBinaryConv)
assert isinstance(nn.activated_conv(3), nn.BinaryConvolution)