def backward_pass(m, in_tensor, weight, bias=None):
grad_out_feat = in_tensor
with torch.no_grad():
if not grad_out_feat.is_contiguous():
grad_out_feat = grad_out_feat.contiguous()
bw_fn = get_minkowski_function('ConvolutionTransposeBackward',
grad_out_feat)
grad_in_feat, grad_kernel = bw_fn(
m.in_tensor, grad_out_feat, m.kernel,
m.kernel_generator.kernel_size,
m.kernel_generator.kernel_stride,
m.kernel_generator.kernel_dilation,
m.kernel_generator.region_type,
m.kernel_generator.region_offsets, m.convolution_mode,
m.in_coordinate_map_key, m.out_coordinate_map_key,
m.coordinate_manager._manager)
grad_in_feat.detach()
assert grad_in_feat.size() == m.in_shape, (grad_in_feat.size(),
m.in_shape(),
m.out_shape,
m.in_coords.size())
return grad_in_feat
def backward_pass(m, tensor, weight):
with torch.no_grad():
grad_out_feat = tensor.contiguous()
bw_fn = get_minkowski_function('GlobalPoolingBackward',
grad_out_feat)
grad_in_feat = bw_fn(m.in_tensor, grad_out_feat, m.num_nonzero,
m.pooling_mode, m.in_coordinate_map_key,
m.out_coordinate_map_key,
m.coordinate_manager._manager)
return grad_in_feat.detach()
def forward_pass(m, in_tensor, weight, bias=None):
with torch.no_grad():
input_features = in_tensor.contiguous()
fw_fn = get_minkowski_function('GlobalPoolingForward',
input_features)
out_feat, num_nonzero = fw_fn(input_features, m.pooling_mode,
m.in_coordinate_map_key,
m.out_coordinate_map_key,
m.coordinate_manager._manager)
setattr(m, "num_nonzero", num_nonzero)
return out_feat.detach()
def forward_pass(m, in_tensor, weight, bias=None, scale_groups=1):
input_features = in_tensor
if not input_features.is_contiguous():
input_features = input_features.contiguous()
fw_fn = get_minkowski_function('ConvolutionTransposeForward',
input_features)
out_feat = fw_fn(
input_features, m.kernel, m.kernel_generator.kernel_size,
m.kernel_generator.kernel_stride,
m.kernel_generator.kernel_dilation, m.kernel_generator.region_type,
m.kernel_generator.region_offsets,
m.kernel_generator.expand_coordinates, m.convolution_mode,
m.in_coordinate_map_key, m.out_coordinate_map_key,
m.coordinate_manager._manager)
out_feat.detach()
return out_feat