def forward(self, input):
assert isinstance(input, spconv.SparseConvTensor)
features = input.features
device = features.device
indices = input.indices
spatial_shape = input.spatial_shape
batch_size = input.batch_size
if not self.subm:
out_spatial_shape = ops.get_conv_output_size(
spatial_shape, self.kernel_size, self.stride, self.padding,
self.dilation)
else:
out_spatial_shape = spatial_shape
outids, indice_pairs, indice_pairs_num = ops.get_indice_pairs(
indices, batch_size, spatial_shape, self.kernel_size, self.stride,
self.padding, self.dilation, 0, self.subm)
out_features = Fsp.indice_maxpool(features, indice_pairs.to(device),
indice_pairs_num.to(device),
outids.shape[0])
out_tensor = spconv.SparseConvTensor(out_features, outids,
out_spatial_shape, batch_size)
out_tensor.indice_dict = input.indice_dict
out_tensor.grid = input.grid
return out_tensor
def forward(self, input):
assert isinstance(input, spconv.SparseConvTensor)
features = input.features
device = features.device
indices = input.indices
spatial_shape = input.spatial_shape
batch_size = input.batch_size
if not self.subm:
if self.transposed:
out_spatial_shape = ops.get_deconv_output_size(
spatial_shape, self.kernel_size, self.stride, self.padding,
self.dilation, self.output_padding)
else:
out_spatial_shape = ops.get_conv_output_size(
spatial_shape, self.kernel_size, self.stride, self.padding,
self.dilation)
else:
out_spatial_shape = spatial_shape
# input.update_grid(out_spatial_shape)
# t = time.time()
## Weight Standardization
weight = self.weight
weight_mean = weight.mean(dim=1, keepdim=True).mean(dim=2,
keepdim=True).mean(
dim=3,
keepdim=True)
weight = weight - weight_mean
std = weight.view(weight.size(0), -1).std(dim=1).view(-1, 1, 1,
1) + 1e-5
weight = weight / std.expand_as(weight)
if self.conv1x1:
features = torch.mm(
input.features, weight.view(self.in_channels,
self.out_channels))
if self.bias is not None:
features += self.bias
out_tensor = spconv.SparseConvTensor(features, input.indices,
input.spatial_shape,
input.batch_size)
out_tensor.indice_dict = input.indice_dict
out_tensor.grid = input.grid
return out_tensor
datas = input.find_indice_pair(self.indice_key)
if self.inverse:
assert datas is not None and self.indice_key is not None
_, outids, indice_pairs, indice_pair_num, out_spatial_shape = datas
assert indice_pair_num.shape[0] == np.prod(
self.kernel_size
), "inverse conv must have same kernel size as its couple conv"
else:
if self.indice_key is not None and datas is not None:
outids, _, indice_pairs, indice_pair_num, _ = datas
else:
outids, indice_pairs, indice_pair_num = ops.get_indice_pairs(
indices,
batch_size,
spatial_shape,
self.kernel_size,
self.stride,
self.padding,
self.dilation,
self.output_padding,
self.subm,
self.transposed,
grid=input.grid,
use_hash=self.use_hash)
input.indice_dict[self.indice_key] = (outids, indices,
indice_pairs,
indice_pair_num,
spatial_shape)
if self.fused_bn:
assert self.bias is not None
out_features = ops.fused_indice_conv(features, weight, self.bias,
indice_pairs.to(device),
indice_pair_num,
outids.shape[0], self.inverse,
self.subm)
else:
if self.subm:
out_features = Fsp.indice_subm_conv(features, weight,
indice_pairs.to(device),
indice_pair_num,
outids.shape[0], self.algo)
else:
if self.inverse:
out_features = Fsp.indice_inverse_conv(
features, weight, indice_pairs.to(device),
indice_pair_num, outids.shape[0], self.algo)
else:
out_features = Fsp.indice_conv(features, weight,
indice_pairs.to(device),
indice_pair_num,
outids.shape[0], self.algo)
if self.bias is not None:
out_features += self.bias
out_tensor = spconv.SparseConvTensor(out_features, outids,
out_spatial_shape, batch_size)
out_tensor.indice_dict = input.indice_dict
out_tensor.grid = input.grid
return out_tensor
def forward(self, input):
assert isinstance(input, spconv.SparseConvTensor)
features = input.features
device = features.device
indices = input.indices
spatial_shape = input.spatial_shape
batch_size = input.batch_size
if not self.subm:
if self.transposed:
out_spatial_shape = ops.get_deconv_output_size(
spatial_shape, self.kernel_size, self.stride, self.padding,
self.dilation, self.output_padding)
else:
out_spatial_shape = ops.get_conv_output_size(
spatial_shape, self.kernel_size, self.stride, self.padding,
self.dilation)
else:
out_spatial_shape = spatial_shape
# input.update_grid(out_spatial_shape)
# t = time.time()
if self.conv1x1:
input.features = torch.mm(
input.features,
self.weight.view(self.in_channels, self.out_channels))
if self.bias:
input.features += self.bias
return input
datas = input.find_indice_pair(self.indice_key)
if self.inverse:
assert datas is not None and self.indice_key is not None
_, outids, indice_pairs, indice_pair_num, out_spatial_shape = datas
assert indice_pairs.shape[0] == np.prod(
self.kernel_size
), "inverse conv must have same kernel size as its couple conv"
else:
if self.indice_key is not None and datas is not None:
outids, _, indice_pairs, indice_pair_num, _ = datas
else:
outids, indice_pairs, indice_pair_num = ops.get_indice_pairs(
indices,
batch_size,
spatial_shape,
self.kernel_size,
self.stride,
self.padding,
self.dilation,
self.output_padding,
self.subm,
self.transposed,
grid=input.grid)
input.indice_dict[self.indice_key] = (outids, indices,
indice_pairs,
indice_pair_num,
spatial_shape)
if self.subm:
out_features = Fsp.indice_subm_conv(features, self.weight,
indice_pairs.to(device),
indice_pair_num,
outids.shape[0])
else:
if self.inverse:
out_features = Fsp.indice_inverse_conv(features, self.weight,
indice_pairs.to(device),
indice_pair_num,
outids.shape[0])
else:
out_features = Fsp.indice_conv(features, self.weight,
indice_pairs.to(device),
indice_pair_num,
outids.shape[0])
if self.bias:
out_features += self.bias
out_tensor = spconv.SparseConvTensor(out_features, outids,
out_spatial_shape, batch_size)
out_tensor.indice_dict = input.indice_dict
out_tensor.grid = input.grid
return out_tensor
