def test_layers_conv_pool_unpool_deconv():
pass
inC, inH, inW = 1,4,4
y = input((inC,inH, inW))
cMap = 1
zero_pad = True
conv_init = 1
filter_shape = (2,2)
pooling_strides = (2,2)
dat = np.arange(0,16, dtype=np.float32).reshape(1,1,4,4)
conv = Convolution(filter_shape, cMap, pad=zero_pad, init=conv_init,activation=None)(y)
pool = MaxPooling(filter_shape, pooling_strides)(conv)
unpool = MaxUnpooling(filter_shape, pooling_strides)(pool, conv)
z = ConvolutionTranspose(filter_shape, cMap, init=conv_init, pad=zero_pad)(unpool)
assert z.shape == y.shape
res = z(dat)
expected_res = np.asarray([[30, 64, 34], [76, 160, 84], [46, 96, 50]], np.float32)
np.testing.assert_array_almost_equal(res[0][0][1:,1:], expected_res, decimal=6,
err_msg="Wrong values in conv/pooling/unpooling/conv_transposed")
def test_layers_convolution_transpose():
import pytest
inC, inH, inW = 1, 3, 3
in_filter_shape = (3, 3)
out_num_filters = 1
dat = np.ones([1, inC, inH, inW], dtype = np.float32)
y = input((inC, inH, inW))
##########################################################
# Test convolutional layer for correctness (p=False s = 1)
##########################################################
zeropad = False
in_strides = 1
model = ConvolutionTranspose(in_filter_shape,
num_filters=out_num_filters,
activation=None,
pad=zeropad,
strides=in_strides, name='foo')
res = model(y).eval({y: dat})
# Extract the W weight matrix
expected_res = np.sum(model.W.value)
np.testing.assert_array_almost_equal(res[0][0][2][2], expected_res, decimal=6, \
err_msg="Error in convolution transpose computation with stride = 1 and zeropad = False")
##########################################################
# Test convolutional transpose layer for correctness (p=False s = 2)
##########################################################
zeropad = False
in_strides = 2
model = ConvolutionTranspose(in_filter_shape,
num_filters=out_num_filters,
activation=None,
pad=zeropad,
strides=in_strides, name='foo')
res = model(y).eval({y: dat})
# Extract the W weight matrix
expected_res = model.W.value[0][0][:2,:2]
np.testing.assert_array_almost_equal(res[0][0][:2,:2], expected_res, decimal=6, \
err_msg="Error in convolution transpose computation with stride = 2 and zeropad = False")
##########################################################
# Test convolutional transpose layer for correctness (p=True s = 1)
##########################################################
zeropad = True
in_strides = 1
model = ConvolutionTranspose(in_filter_shape,
num_filters=out_num_filters,
activation=None,
pad=zeropad,
strides=in_strides, name='foo')
res = model(y).eval({y: dat})
expected_res = np.sum(model.W.value)
np.testing.assert_array_almost_equal(res[0][0][1][1], expected_res, decimal=6, \
err_msg="Error in convolution transpose computation with stride = 1 and zeropad = True")
##########################################################
# Test convolutional transpose layer for correctness (p=True s = 2)
##########################################################
zeropad = True
in_strides = 2
model = ConvolutionTranspose(in_filter_shape,
num_filters=out_num_filters,
activation=None,
pad=zeropad,
strides=in_strides, name='foo')
res = model(y).eval({y: dat})
expected_res = model.W.value[0][0][1][1]
np.testing.assert_array_almost_equal(res[0][0][0][0], expected_res, decimal=6,
err_msg="Error in convolution transpose computation with stride = 1 and zeropad = True")
def test_failing_convolution_transpose():
with pytest.raises(ValueError):
conv = ConvolutionTranspose((3,3), 1)
conv.update_signature(5)
def test_failing_convolution_transpose():
with pytest.raises(ValueError):
conv = ConvolutionTranspose((3,3), 1)
conv.update_signature(5)
