def local_conv2d_cpu(fgraph, node):
if not isinstance(node.op, AbstractConv2d) or node.inputs[0].dtype == "float16":
return None
img, kern = node.inputs
if not isinstance(img.type, TensorType) or not isinstance(kern.type, TensorType):
return None
if node.op.border_mode not in ["full", "valid"]:
return None
if not node.op.filter_flip:
# Not tested yet
return None
if node.op.num_groups > 1 or node.op.unshared:
return None
if node.op.filter_dilation != (1, 1):
return None
rval = conv2d(
img,
kern,
node.op.imshp,
node.op.kshp,
border_mode=node.op.border_mode,
subsample=node.op.subsample,
)
copy_stack_trace(node.outputs[0], rval)
return [rval]
def sym_conv2d(input, filters):
# define aesara graph and function
input.name = "input"
filters.name = "filters"
rval = conv.conv2d(
input,
filters,
image_shape,
filter_shape,
border_mode,
subsample,
unroll_batch=unroll_batch,
unroll_kern=unroll_kern,
unroll_patch=unroll_patch,
)
rval.name = "conv_output"
return rval
def speed(self):
n_calls = 20000
print("n_calls", n_calls)
for border_mode in ["valid", "full"]:
print()
print(border_mode)
for openmp in [False, True]:
print("OpenMP", openmp)
image_shapes = [(1, 5, 6, 6), (10, 5, 6, 6)
# (10, 10, 16, 16),
# (10, 10, 32, 32)]
]
print("image_shape", image_shapes)
for image_shape in image_shapes:
filter_shapes = [(1, 5, 4, 4), (2, 5, 4, 4), (5, 5, 4, 4)]
print("filter_shapes", filter_shapes)
for filter_shape in filter_shapes:
input = aesara.shared(np.random.random(image_shape))
filters = aesara.shared(np.random.random(filter_shape))
with pytest.warns(DeprecationWarning):
output = conv.conv2d(
input,
filters,
image_shape,
filter_shape,
border_mode,
unroll_patch=True,
openmp=openmp,
)
mode = Mode(linker=aesara.link.vm.VMLinker(
allow_gc=False, use_cloop=True))
aesara_conv = aesara.function([], output, mode=mode)
t1 = time.time()
aesara_conv.vm(n_calls=n_calls)
t2 = time.time()
print(t2 - t1, end=" ")
print()
def test_infer_shape(self):
# Note: infer_shape is incomplete and thus input and filter shapes
# must be provided explicitly
rng = np.random.default_rng(280284)
def rand(*shape):
r = np.asarray(rng.random(shape), dtype="float64")
return r * 2 - 1
adtens = dtensor4()
bdtens = dtensor4()
aivec_val = [4, 5, 6, 3]
bivec_val = [7, 5, 3, 2]
adtens_val = rand(*aivec_val)
bdtens_val = rand(*bivec_val)
with pytest.warns(DeprecationWarning):
self._compile_and_check(
[adtens, bdtens],
[
conv.conv2d(adtens,
bdtens,
aivec_val,
bivec_val,
border_mode="valid")
],
[adtens_val, bdtens_val],
conv.ConvOp,
excluding=["conv_gemm"],
)
with pytest.warns(DeprecationWarning):
self._compile_and_check(
[adtens, bdtens],
[
conv.conv2d(adtens,
bdtens,
aivec_val,
bivec_val,
border_mode="full")
],
[adtens_val, bdtens_val],
conv.ConvOp,
excluding=["conv_gemm"],
)
aivec_val = [6, 2, 8, 3]
bivec_val = [4, 2, 5, 3]
adtens_val = rand(*aivec_val)
bdtens_val = rand(*bivec_val)
with pytest.warns(DeprecationWarning):
self._compile_and_check(
[adtens, bdtens],
[
conv.conv2d(adtens,
bdtens,
aivec_val,
bivec_val,
border_mode="valid")
],
[adtens_val, bdtens_val],
conv.ConvOp,
excluding=["conv_gemm"],
)
with pytest.warns(DeprecationWarning):
self._compile_and_check(
[adtens, bdtens],
[
conv.conv2d(adtens,
bdtens,
aivec_val,
bivec_val,
border_mode="full")
],
[adtens_val, bdtens_val],
conv.ConvOp,
excluding=["conv_gemm"],
)
aivec_val = [3, 6, 7, 5]
bivec_val = [5, 6, 3, 2]
adtens_val = rand(*aivec_val)
bdtens_val = rand(*bivec_val)
with pytest.warns(DeprecationWarning):
self._compile_and_check(
[adtens, bdtens],
[
conv.conv2d(adtens,
bdtens,
aivec_val,
bivec_val,
border_mode="valid")
],
[adtens_val, bdtens_val],
conv.ConvOp,
excluding=["conv_gemm"],
)
with pytest.warns(DeprecationWarning):
self._compile_and_check(
[adtens, bdtens],
[
conv.conv2d(adtens,
bdtens,
aivec_val,
bivec_val,
border_mode="full")
],
[adtens_val, bdtens_val],
conv.ConvOp,
excluding=["conv_gemm"],
)
aivec_val = [3, 6, 7, 5]
bivec_val = [5, 6, 2, 3]
adtens_val = rand(*aivec_val)
bdtens_val = rand(*bivec_val)
with pytest.warns(DeprecationWarning):
self._compile_and_check(
[adtens, bdtens],
[
conv.conv2d(adtens,
bdtens,
aivec_val,
bivec_val,
border_mode="valid")
],
[adtens_val, bdtens_val],
conv.ConvOp,
excluding=["conv_gemm"],
)
with pytest.warns(DeprecationWarning):
self._compile_and_check(
[adtens, bdtens],
[
conv.conv2d(adtens,
bdtens,
aivec_val,
bivec_val,
border_mode="full")
],
[adtens_val, bdtens_val],
conv.ConvOp,
excluding=["conv_gemm"],
)
aivec_val = [5, 2, 4, 3]
bivec_val = [6, 2, 4, 3]
adtens_val = rand(*aivec_val)
bdtens_val = rand(*bivec_val)
with pytest.warns(DeprecationWarning):
self._compile_and_check(
[adtens, bdtens],
[
conv.conv2d(adtens,
bdtens,
aivec_val,
bivec_val,
border_mode="valid")
],
[adtens_val, bdtens_val],
conv.ConvOp,
excluding=["conv_gemm"],
)
with pytest.warns(DeprecationWarning):
self._compile_and_check(
[adtens, bdtens],
[
conv.conv2d(adtens,
bdtens,
aivec_val,
bivec_val,
border_mode="full")
],
[adtens_val, bdtens_val],
conv.ConvOp,
excluding=["conv_gemm"],
)