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(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]
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)