def array_like_conv_output(self, inputs_shape, filters_shape, border_mode,
subsample, dilation, dtype):
# Return a random array with inferred convolution output shape.
out_shp = get_conv_output_shape(inputs_shape, filters_shape,
border_mode, subsample, dilation)
out_shp = assert_conv_shape(out_shp)
return np.random.random(out_shp).astype(dtype)
def get_if_valid_conv_output_shape(case_tuple):
# Filter function to keep only cases that produce valid convolution output shapes.
out_shp = get_conv_output_shape(
case_tuple[0], # input shape
case_tuple[1], # filter shape
case_tuple[4], # border mode
case_tuple[2], # subsample
case_tuple[3],
) # dilation
try:
return assert_conv_shape(out_shp)
except ValueError:
return False
def local_abstractconv_cudnn_alt(fgraph, node):
if not isinstance(node.op, (AbstractConv2d, AbstractConv2d_gradWeights,
AbstractConv2d_gradInputs)):
return
if version(raises=False) < 6000 and node.op.filter_dilation != (1, 1):
return None
if node.op.unshared:
return None
if isinstance(node.op.border_mode, tuple) and any(
isinstance(p, tuple) for p in node.op.border_mode):
# Asymmetric padding not yet supported
return None
inp1 = node.inputs[0]
inp2 = node.inputs[1]
if not dnn_available(inp1.type.context_name):
return
op = node.op
border_mode = node.op.border_mode
subsample = node.op.subsample
filter_dilation = node.op.filter_dilation
num_groups = node.op.num_groups
precision, _ = get_precision(None, [inp1, inp2])
if node.op.filter_flip:
conv_mode = "conv"
else:
conv_mode = "cross"
if isinstance(op, AbstractConv2d):
if border_mode == "half" or subsample != (1, 1) or num_groups != 1:
return None
if border_mode == "full":
direction_hint = "bprop inputs"
elif border_mode == "valid" and filter_dilation == (1, 1):
direction_hint = "bprop weights"
else:
return None
rval = dnn_conv(
inp1,
inp2,
border_mode=border_mode,
subsample=subsample,
dilation=filter_dilation,
direction_hint=direction_hint,
conv_mode=conv_mode,
num_groups=num_groups,
)
elif isinstance(op, AbstractConv2d_gradWeights):
if (border_mode == "valid" and subsample == (1, 1)
and filter_dilation == (1, 1) and num_groups == 1):
img = gpu_contiguous(inp1)
topgrad = gpu_contiguous(inp2)
ctx_name = infer_context_name(img, topgrad)
img = gpu_contiguous(img.dimshuffle(1, 0, 2, 3))
topgrad = gpu_contiguous(topgrad.dimshuffle(1, 0, 2, 3))
ishape = [shape_i_op(i)(img) for i in range(img.ndim)]
tshape = [shape_i_op(i)(topgrad) for i in range(topgrad.ndim)]
out_shp = get_conv_output_shape(
ishape,
tshape,
border_mode=border_mode,
subsample=subsample,
filter_dilation=filter_dilation,
)
out_shp = assert_conv_shape(out_shp)
out = GpuAllocEmpty(dtype=img.dtype,
context_name=ctx_name)(*out_shp)
desc = GpuDnnConvDesc(
border_mode=border_mode,
subsample=subsample,
dilation=filter_dilation,
conv_mode="cross",
precision=precision,
)(out.shape)
conv = GpuDnnConv(algo=None, num_groups=num_groups)(img, topgrad,
out, desc)
if conv_mode == "conv":
conv = conv[:, :, ::-1, ::-1]
rval = as_gpuarray_variable(conv.dimshuffle(1, 0, 2, 3), ctx_name)
else:
return None
elif isinstance(op, AbstractConv2d_gradInputs):
if border_mode == "valid" and subsample == (1, 1) and num_groups == 1:
kerns = gpu_contiguous(inp1.dimshuffle(1, 0, 2, 3))
topgrad = gpu_contiguous(inp2)
ctx_name = infer_context_name(kerns, topgrad)
conv_mode = "cross" if conv_mode == "conv" else "conv"
desc = GpuDnnConvDesc(
border_mode="full",
subsample=subsample,
dilation=filter_dilation,
conv_mode=conv_mode,
precision=precision,
)(kerns.shape)
tshape = [shape_i_op(i)(topgrad) for i in range(topgrad.ndim)]
kshape = [shape_i_op(i)(kerns) for i in range(kerns.ndim)]
shape = get_conv_output_shape(
tshape,
kshape,
border_mode="full",
subsample=subsample,
filter_dilation=filter_dilation,
)
shape = assert_conv_shape(shape)
out = GpuAllocEmpty(dtype=topgrad.dtype,
context_name=ctx_name)(*shape)
rval = GpuDnnConv(algo=None, num_groups=num_groups)(topgrad, kerns,
out, desc)
else:
return None
return [rval]