def test_convolution_layer_tfl(self):
iX = XLayer(type=['Input'],
name='in1',
shapes=[1, 3, 3, 2],
sizes=[32],
bottoms=[],
tops=[],
targets=[])
kX = XLayer(type=['Constant'],
name='kernel',
shapes=[4, 3, 3, 2],
sizes=[54],
data=[
np.transpose(np.ones((4, 2, 3, 3), dtype=np.float32),
(0, 2, 3, 1))
],
bottoms=[],
tops=[],
targets=[])
X = xlf.get_xop_factory_func('Convolution')(op_name='conv1',
kernel_size=[3, 3],
strides=[1, 1],
padding_hw=[1, 1],
dilation=[1, 1],
groups=1,
channels=4,
data_layout='NHWC',
kernel_layout='OHWI',
input_layer=iX,
weights_layer=kX)
assert X.type[0] == 'Convolution'
assert X.shapes == [1, 3, 3, 4]
assert X.attrs['padding'] == [[0, 0], [1, 1], [1, 1], [0, 0]]
assert X.attrs['data_layout'] == 'NHWC'
assert X.attrs['kernel_layout'] == 'OIHW'
assert X.attrs['shape'] == [1, 3, 3, 4]
assert X.attrs['kernel_size'] == [3, 3]
assert X.attrs['strides'] == [1, 1]
assert X.attrs['groups'] == 1
assert X.attrs['dilation'] == [1, 1]
assert X.attrs['channels'] == [2, 4]
np.testing.assert_array_equal(X.data.weights,
np.ones((4, 2, 3, 3), dtype=np.float32))
np.testing.assert_array_equal(X.data.biases,
np.zeros((4), dtype=np.float32))
from pyxir.graph.ops.l2_convolution import \
conv2d_layout_transform
conv2d_layout_transform(X, target_layout='NCHW')
assert X.type[0] == 'Convolution'
assert X.shapes == [1, 4, 3, 3]
assert X.attrs['data_layout'] == 'NCHW'
assert X.attrs['padding'] == [[0, 0], [0, 0], [1, 1], [1, 1]]
def conv2d_test_util(
in_shape,
weight_shape,
out_shape,
padding=(0, 0),
strides=(1, 1),
dilation=(1, 1),
groups=1,
data_layout="NCHW",
kernel_layout="OIHW",
target_kernel_layout="OIHW",
conv_transpose=False,
):
iX = px.ops.input("in", shape=list(in_shape))
kX = px.ops.constant("kernel", np.ones(weight_shape, dtype=np.float32))
kernel_size = [
weight_shape[kernel_layout.index("H")],
weight_shape[kernel_layout.index("W")],
]
if not conv_transpose:
in_ch = weight_shape[kernel_layout.index("I")] * groups
channels = weight_shape[kernel_layout.index("O")]
expected_type = "Convolution"
X = px.ops.conv2d(
op_name="conv",
input_layer=iX,
weights_layer=kX,
kernel_size=kernel_size,
strides=list(strides),
padding_hw=list(padding),
dilation=list(dilation),
groups=groups,
channels=channels,
data_layout=data_layout,
kernel_layout=kernel_layout,
target_kernel_layout=target_kernel_layout,
)
else:
in_ch = weight_shape[kernel_layout.index("I")] * groups
channels = weight_shape[kernel_layout.index("O")]
expected_type = "Conv2DTranspose"
X = px.ops.conv2d_transpose(
op_name="conv",
input_layer=iX,
weights_layer=kX,
kernel_size=kernel_size,
strides=list(strides),
padding_hw=list(padding),
dilation=list(dilation),
groups=groups,
channels=channels,
data_layout=data_layout,
kernel_layout=kernel_layout,
target_kernel_layout=target_kernel_layout,
)
# OIHW
weight_shape = (
weight_shape[1],
weight_shape[0],
weight_shape[2],
weight_shape[3],
)
layout_idx = tuple(["NCHW".index(e) for e in data_layout])
padding_nchw = [
[0, 0],
[0, 0],
[padding[0], padding[1]],
[padding[2], padding[3]],
]
padding = [padding_nchw[e] for e in layout_idx]
assert X.type[0] == expected_type
assert X.shapes == list(
out_shape), "Expected out shape: {0}, but got: {1}".format(
out_shape, X.shapes)
assert X.attrs[
"padding"] == padding, "Expected padding: {0}, but got: {1}".format(
padding, X.attrs["padding"])
assert X.attrs["data_layout"] == data_layout
assert X.attrs["kernel_layout"] == target_kernel_layout
assert X.attrs["shape"] == list(out_shape)
assert X.attrs["kernel_size"] == kernel_size
assert X.attrs["strides"] == list(strides)
assert X.attrs["groups"] == groups
assert X.attrs["dilation"] == list(dilation)
assert X.attrs["channels"] == [in_ch, channels]
np.testing.assert_array_equal(X.data.weights,
np.ones(weight_shape, dtype=np.float32))
np.testing.assert_array_equal(
X.data.biases,
np.zeros((channels, ), dtype=np.float32),
)
conv2d_layout_transform(X, target_layout="NHWC")
layout_idx = tuple([data_layout.index(e) for e in "NHWC"])
trans_layout_idx = tuple(["NCHW".index(e) for e in "NHWC"])
trans_out_shape = [out_shape[e] for e in layout_idx]
assert X.type[0] == expected_type
assert X.shapes == trans_out_shape, "Expected out shape: {0}, but got: {1}".format(
trans_out_shape, X.shapes)
assert X.attrs["data_layout"] == "NHWC"
padding = [padding_nchw[e] for e in trans_layout_idx]
assert X.attrs[
"padding"] == padding, "Expected padding: {0}, but got: {1}".format(
padding, X.attrs["padding"])