def test_spatial_convolution_transpose_with_output(input_size, conv_size, result, device_id, precision):
dt = PRECISION_TO_TYPE[precision]
dev = cntk_device(device_id)
# fill input operand with a sequence 1,2,3,... til total size and then
# resize to input_size
total_size = np.prod(input_size)
x = np.arange(total_size, dtype=dt)
input_operand = x.reshape(input_size)
a = C.input_variable(shape=input_operand.shape[1:],
dtype=sanitize_dtype_cntk(precision),
needs_gradient=False,
name='a')
# do the same for convolution kernel
total_size = np.prod(conv_size)
y = np.arange(total_size, dtype=dt)
conv_map = constant(value=y.reshape(conv_size), device=dev)
from cntk import convolution_transpose
input_op = convolution_transpose(conv_map, a, auto_padding=[True], strides=2, output_shape=(1,5,6), reduction_rank=0)
forward_input = {a: input_operand}
expected_forward = AA(result)
unittest_helper(input_op, forward_input, expected_forward,
None, device_id=device_id, precision=precision)
def test_convolution_transpose(input_size, conv_size, result, device_id,
precision):
dt = PRECISION_TO_TYPE[precision]
dev = cntk_device(device_id)
# fill input operand with a sequence 1,2,3,... til total size and then
# resize to input_size
total_size = np.prod(input_size)
x = np.arange(total_size, dtype=dt)
input_operand = x.reshape(input_size)
a = C.input_variable(shape=input_operand.shape[1:],
dtype=sanitize_dtype_cntk(precision),
needs_gradient=False,
name='a')
# do the same for convolution kernel
total_size = np.prod(conv_size)
y = np.arange(total_size, dtype=dt)
conv_map = constant(value=y.reshape(conv_size), device=dev)
from cntk import convolution_transpose
input_op = convolution_transpose(conv_map, a, auto_padding=[False])
forward_input = {a: input_operand}
expected_forward = AA(result)
unittest_helper(input_op,
forward_input,
expected_forward,
None,
device_id=device_id,
precision=precision)
def test_convolution_transpose(tmpdir, dtype, device_id):
pytest.skip('Needs to be fixed after removal of batch axis change.')
if device_id == -1 and dtype == np.float16:
pytest.skip('Test only runs on GPU')
device = cntk_device(device_id)
with C.default_options(dtype=dtype):
img_shape = (1, 3, 3)
img = np.asarray(np.random.uniform(-1, 1, img_shape), dtype=dtype)
x = C.input_variable(img.shape)
filter = np.reshape(np.array([2, -1, -1, 2], dtype=dtype), (1, 2, 2))
kernel = C.constant(value=filter)
root_node = C.convolution_transpose(kernel,
x,
auto_padding=[False],
output_shape=(1, 4, 4))
filename = os.path.join(str(tmpdir), R'conv_transpose.onnx')
root_node.save(filename, format=C.ModelFormat.ONNX)
loaded_node = C.Function.load(filename, format=C.ModelFormat.ONNX)
assert root_node.shape == loaded_node.shape
x_ = loaded_node.arguments[0]
assert np.allclose(loaded_node.eval({x_: [img]}, device=device),
root_node.eval({x: [img]}, device=device))
def test_ConvTranspose(tmpdir, dtype, device_id):
if device_id == -1 and dtype == np.float16:
pytest.skip('Test is skipped on CPU with float16 data')
device = cntk_device(device_id)
with C.default_options(dtype=dtype):
# Keep the shapes below as they are, because this tests an earlier bug.
input_shape = (48, 16, 16)
img = np.reshape(np.arange(np.prod(input_shape), dtype=dtype),
input_shape)
x = C.input_variable(input_shape)
kernel_shape = (
48, 32, 3, 3
) # For convolution_transpose the shape is (I x O x W x H)
kernel = C.constant(value=np.ones(shape=(kernel_shape), dtype=dtype))
conv_trans_model = C.convolution_transpose(
kernel,
x,
strides=(2, 2),
output_shape=(32, 32, 32),
auto_padding=[False, True, True])
verify_one_input(conv_trans_model, img, tmpdir, 'ConvTranspose_0',
device)
def test_ConvTranspose(tmpdir):
# Keep the shapes below as they are, because this tests an earlier bug.
input_shape = (48, 16, 16)
img = np.reshape(np.arange(np.prod(input_shape), dtype = np.float32), input_shape)
x = C.input_variable(input_shape)
kernel_shape = (48, 32, 3, 3) # For convolution_transpose the shape is (I x O x W x H)
kernel = C.constant(value = np.ones(shape=(kernel_shape), dtype = np.float32))
conv_trans_model = C.convolution_transpose(kernel, x, strides=(2, 2), output_shape=(32, 32, 32), auto_padding = [False, True, True])
verify_one_input(conv_trans_model, img, tmpdir, 'ConvTranspose_0')
def test_convolution_transpose_attributes():
x = C.input_variable( (1, 5, 5) )
filter = np.reshape(np.array([2, -1, -1, 2], dtype = np.float32), (1, 2, 2))
kernel = C.constant(value = filter)
f = C.convolution_transpose(kernel , x, auto_padding = [False])
d = f.root_function.attributes
expected = {'autoPadding': [False, False, False],
'sharing': [True, True, True],
'strides': (1, 1, 1),
'maxTempMemSizeInSamples': 0,
'upperPad': (0, 0, 0),
'lowerPad': (0, 0, 0),
'transpose': True,
'outputShape': (0,)
}
_check(expected, d)
def test_convolution_transpose_attributes():
x = C.input( (1, 5, 5) )
filter = np.reshape(np.array([2, -1, -1, 2], dtype = np.float32), (1, 2, 2))
kernel = C.constant(value = filter)
f = C.convolution_transpose(kernel , x, auto_padding = [False])
d = f.root_function.attributes
expected = {'autoPadding': [False, False, False],
'sharing': [True, True, True],
'strides': (1, 1, 1),
'maxTempMemSizeInSamples': 0,
'upperPad': (0, 0, 0),
'lowerPad': (0, 0, 0),
'transpose': True,
'outputShape': (0,)
}
_check(expected, d)
def test_ConvTranspose(tmpdir, dtype, device_id):
if device_id == -1 and dtype == np.float16:
pytest.skip('Test is skipped on CPU with float16 data')
device = cntk_device(device_id)
with C.default_options(dtype=dtype):
# Keep the shapes below as they are, because this tests an earlier bug.
input_shape = (48, 16, 16)
img = np.reshape(np.arange(np.prod(input_shape), dtype = dtype), input_shape)
x = C.input_variable(input_shape)
kernel_shape = (48, 32, 3, 3) # For convolution_transpose the shape is (I x O x W x H)
kernel = C.constant(value = np.ones(shape=(kernel_shape), dtype = dtype))
conv_trans_model = C.convolution_transpose(kernel, x, strides=(2, 2), output_shape=(32, 32, 32), auto_padding = [False, True, True])
verify_one_input(conv_trans_model, img, tmpdir, 'ConvTranspose_0', device)
def test_convolution_transpose(tmpdir):
img_shape = (1, 3, 3)
img = np.asarray(np.random.uniform(-1, 1, img_shape), dtype=np.float32)
x = C.input_variable(img.shape)
filter = np.reshape(np.array([2, -1, -1, 2], dtype = np.float32), (1, 2, 2))
kernel = C.constant(value = filter)
root_node = C.convolution_transpose(kernel, x, auto_padding=[False], output_shape=(1, 4, 4))
filename = os.path.join(str(tmpdir), R'conv_transpose.onnx')
root_node.save(filename, format=C.ModelFormat.ONNX)
loaded_node = C.Function.load(filename, format=C.ModelFormat.ONNX)
assert root_node.shape == loaded_node.shape
x_ = loaded_node.arguments[0]
assert np.allclose(loaded_node.eval({x_:[img]}), root_node.eval({x:[img]}))
def test_convolution_transpose(tmpdir):
img_shape = (1, 3, 3)
img = np.asarray(np.random.uniform(-1, 1, img_shape), dtype=np.float32)
x = C.input_variable(img.shape)
filter = np.reshape(np.array([2, -1, -1, 2], dtype = np.float32), (1, 2, 2))
kernel = C.constant(value = filter)
root_node = C.convolution_transpose(kernel, x, auto_padding=[False], output_shape=(1, 4, 4))
filename = os.path.join(str(tmpdir), R'conv_transpose.onnx')
root_node.save(filename, format=C.ModelFormat.ONNX)
loaded_node = C.Function.load(filename, format=C.ModelFormat.ONNX)
assert root_node.shape == loaded_node.shape
x_ = loaded_node.arguments[0]
assert np.allclose(loaded_node.eval({x_:[img]}), root_node.eval({x:[img]}))
def test_convolution_transpose(tmpdir, dtype, device_id):
if device_id == -1 and dtype == np.float16:
pytest.skip('Test only runs on GPU')
device = cntk_device(device_id)
with C.default_options(dtype=dtype):
img_shape = (1, 3, 3)
img = np.asarray(np.random.uniform(-1, 1, img_shape), dtype=dtype)
x = C.input_variable(img.shape)
filter = np.reshape(np.array([2, -1, -1, 2], dtype=dtype), (1, 2, 2))
kernel = C.constant(value = filter)
root_node = C.convolution_transpose(kernel, x, auto_padding=[False], output_shape=(1, 4, 4))
filename = os.path.join(str(tmpdir), R'conv_transpose.onnx')
root_node.save(filename, format=C.ModelFormat.ONNX)
loaded_node = C.Function.load(filename, format=C.ModelFormat.ONNX)
assert root_node.shape == loaded_node.shape
x_ = loaded_node.arguments[0]
assert np.allclose(loaded_node.eval({x_:[img]}, device=device), root_node.eval({x:[img]}, device=device))
