def forward(ctx,
input_features,
kernel,
tensor_stride=1,
stride=1,
kernel_size=-1,
dilation=1,
region_type=0,
region_offset=None,
generate_new_coords=False,
in_coords_key=None,
out_coords_key=None,
coords_manager=None):
"""
region_type=0 HyperCube
"""
# Prep arguments
# Kernel shape (n_spatial_kernels, in_nfeat, out_nfeat)
assert input_features.shape[1] == kernel.shape[1], \
"The input shape " + str(list(input_features.shape)) + \
" does not match the kernel shape " + str(list(kernel.shape))
if out_coords_key is None:
out_coords_key = CoordsKey(in_coords_key.D)
assert in_coords_key.D == out_coords_key.D
assert input_features.type() == kernel.type(), \
f"Type mismatch input: {input_features.type()} != kernel: {kernel.type()}"
if not input_features.is_contiguous():
input_features = input_features.contiguous()
tensor_stride, stride, kernel_size, dilation, region_type = prep_args(
tensor_stride, stride, kernel_size, dilation, region_type,
in_coords_key.D)
if region_offset is None:
region_offset = torch.IntTensor()
ctx.in_feat = input_features
ctx.kernel = kernel
ctx = save_ctx(ctx, tensor_stride, stride, kernel_size, dilation,
region_type, in_coords_key, out_coords_key,
coords_manager)
D = in_coords_key.D
out_feat = input_features.new()
fw_fn = getattr(
MEB, 'ConvolutionTransposeForward' + get_postfix(input_features))
fw_fn(ctx.in_feat, out_feat, kernel,
convert_to_int_list(ctx.tensor_stride, D),
convert_to_int_list(ctx.stride, D),
convert_to_int_list(ctx.kernel_size, D),
convert_to_int_list(ctx.dilation, D), region_type, region_offset,
ctx.in_coords_key.CPPCoordsKey, ctx.out_coords_key.CPPCoordsKey,
ctx.coords_man.CPPCoordsManager, generate_new_coords)
return out_feat
def forward(ctx,
input_features,
kernel,
tensor_stride=1,
stride=1,
kernel_size=-1,
dilation=1,
region_type=0,
region_offset=None,
in_coords_key=None,
out_coords_key=None,
coords_manager=None):
"""
region_type=0 HyperCube
"""
# Prep arguments
# Kernel shape (n_spatial_kernels, in_nfeat, out_nfeat)
assert input_features.shape[1] == kernel.shape[1]
if out_coords_key is None:
out_coords_key = CoordsKey(in_coords_key.D)
assert in_coords_key.D == out_coords_key.D
assert input_features.type() == kernel.type()
tensor_stride, stride, kernel_size, dilation, region_type = prep_args(
tensor_stride, stride, kernel_size, dilation, region_type,
in_coords_key.D)
if region_offset is None:
region_offset = torch.IntTensor()
ctx.in_feat = input_features
ctx.kernel = kernel
ctx = save_ctx(ctx, tensor_stride, stride, kernel_size, dilation,
region_type, in_coords_key, out_coords_key,
coords_manager)
D = in_coords_key.D
out_feat = input_features.new()
fw_fn = getattr(MEB,
'ConvolutionForward' + get_postfix(input_features))
fw_fn(D, ctx.in_feat, out_feat, kernel,
convert_to_int_list(ctx.tensor_stride, D),
convert_to_int_list(ctx.stride, D),
convert_to_int_list(ctx.kernel_size, D),
convert_to_int_list(ctx.dilation, D), region_type, region_offset,
ctx.in_coords_key.CPPCoordsKey, ctx.out_coords_key.CPPCoordsKey,
ctx.coords_man.CPPCoordsManager)
return out_feat
def forward(ctx,
input_features,
tensor_stride=1,
stride=1,
kernel_size=-1,
dilation=1,
region_type=0,
region_offset=None,
in_coords_key=None,
out_coords_key=None,
coords_manager=None):
assert isinstance(region_type, RegionType)
if out_coords_key is None:
out_coords_key = CoordsKey(in_coords_key.D)
assert in_coords_key.D == out_coords_key.D
if not input_features.is_contiguous():
input_features = input_features.contiguous()
tensor_stride, stride, kernel_size, dilation, region_type = prep_args(
tensor_stride, stride, kernel_size, dilation, region_type,
in_coords_key.D)
if region_offset is None:
region_offset = torch.IntTensor()
ctx.in_feat = input_features
ctx = save_ctx(ctx, tensor_stride, stride, kernel_size, dilation,
region_type, in_coords_key, out_coords_key,
coords_manager)
D = in_coords_key.D
out_feat = input_features.new()
max_index = input_features.new().int()
ctx.max_index = max_index
fw_fn = getattr(MEB, 'MaxPoolingForward' + get_postfix(input_features))
fw_fn(input_features, out_feat, max_index,
convert_to_int_list(ctx.tensor_stride, D),
convert_to_int_list(ctx.stride, D),
convert_to_int_list(ctx.kernel_size, D),
convert_to_int_list(ctx.dilation, D), region_type, region_offset,
ctx.in_coords_key.CPPCoordsKey, ctx.out_coords_key.CPPCoordsKey,
ctx.coords_man.CPPCoordsManager)
return out_feat
def forward(ctx,
input_features,
tensor_stride=1,
stride=1,
kernel_size=-1,
dilation=1,
region_type=0,
region_offset=None,
average=True,
in_coords_key=None,
out_coords_key=None,
coords_manager=None):
assert isinstance(region_type, RegionType)
if out_coords_key is None:
out_coords_key = CoordsKey(in_coords_key.D)
assert in_coords_key.D == out_coords_key.D
tensor_stride, stride, kernel_size, dilation, region_type = prep_args(
tensor_stride, stride, kernel_size, dilation, region_type,
in_coords_key.D)
if region_offset is None:
region_offset = torch.IntTensor()
ctx.in_feat = input_features
ctx = save_ctx(ctx, tensor_stride, stride, kernel_size, dilation,
region_type, in_coords_key, out_coords_key,
coords_manager)
ctx.use_avg = average
D = in_coords_key.D
out_feat = input_features.new()
ctx.num_nonzero = input_features.new()
fw_fn = getattr(MEB, 'AvgPoolingForward' + get_postfix(input_features))
fw_fn(D, ctx.in_feat, out_feat, ctx.num_nonzero,
convert_to_int_list(ctx.tensor_stride, D),
convert_to_int_list(ctx.stride, D),
convert_to_int_list(ctx.kernel_size, D),
convert_to_int_list(ctx.dilation, D), region_type, region_offset,
ctx.in_coords_key.CPPCoordsKey, ctx.out_coords_key.CPPCoordsKey,
ctx.coords_man.CPPCoordsManager, ctx.use_avg)
return out_feat
