Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Update to clang-format 11 #3254

Merged
merged 1 commit into from
Jan 14, 2021
Merged

Update to clang-format 11 #3254

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
13 changes: 7 additions & 6 deletions torchvision/csrc/ops/autograd/ps_roi_align_kernel.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -62,12 +62,13 @@ class PSROIAlignFunction
input_shape[2],
input_shape[3]);

return {grad_in,
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable()};
return {
grad_in,
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable()};
}
};

Expand Down
11 changes: 6 additions & 5 deletions torchvision/csrc/ops/autograd/ps_roi_pool_kernel.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -53,11 +53,12 @@ class PSROIPoolFunction : public torch::autograd::Function<PSROIPoolFunction> {
input_shape[2],
input_shape[3]);

return {grad_in,
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable()};
return {
grad_in,
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable()};
}
};

Expand Down
15 changes: 8 additions & 7 deletions torchvision/csrc/ops/autograd/roi_align_kernel.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -57,13 +57,14 @@ class ROIAlignFunction : public torch::autograd::Function<ROIAlignFunction> {
input_shape[3],
ctx->saved_data["sampling_ratio"].toInt(),
ctx->saved_data["aligned"].toBool());
return {grad_in,
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable()};
return {
grad_in,
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable()};
}
};

Expand Down
11 changes: 6 additions & 5 deletions torchvision/csrc/ops/autograd/roi_pool_kernel.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -53,11 +53,12 @@ class ROIPoolFunction : public torch::autograd::Function<ROIPoolFunction> {
input_shape[2],
input_shape[3]);

return {grad_in,
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable()};
return {
grad_in,
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable()};
}
};

Expand Down
189 changes: 103 additions & 86 deletions torchvision/csrc/ops/cpu/deform_conv2d_kernel.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -634,44 +634,50 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> backward_gradient_inputs(
input = input.reshape(
{batch_sz / n_parallel_imgs, n_parallel_imgs, n_in_channels, in_h, in_w});

grad_offset = grad_offset.reshape({batch_sz / n_parallel_imgs,
n_parallel_imgs,
n_offset_grps * 2 * weight_h * weight_w,
out_h,
out_w});
offset = offset.reshape({batch_sz / n_parallel_imgs,
n_parallel_imgs,
n_offset_grps * 2 * weight_h * weight_w,
out_h,
out_w});
grad_offset = grad_offset.reshape(
{batch_sz / n_parallel_imgs,
n_parallel_imgs,
n_offset_grps * 2 * weight_h * weight_w,
out_h,
out_w});
offset = offset.reshape(
{batch_sz / n_parallel_imgs,
n_parallel_imgs,
n_offset_grps * 2 * weight_h * weight_w,
out_h,
out_w});

if (use_mask) {
grad_mask = grad_mask.reshape({batch_sz / n_parallel_imgs,
n_parallel_imgs,
n_offset_grps * weight_h * weight_w,
out_h,
out_w});
mask = mask.reshape({batch_sz / n_parallel_imgs,
n_parallel_imgs,
n_offset_grps * weight_h * weight_w,
out_h,
out_w});
grad_mask = grad_mask.reshape(
{batch_sz / n_parallel_imgs,
n_parallel_imgs,
n_offset_grps * weight_h * weight_w,
out_h,
out_w});
mask = mask.reshape(
{batch_sz / n_parallel_imgs,
n_parallel_imgs,
n_offset_grps * weight_h * weight_w,
out_h,
out_w});
}

grad_out = grad_out
.reshape({batch_sz / n_parallel_imgs,
n_parallel_imgs,
n_weight_grps,
n_out_channels / n_weight_grps,
out_h,
out_w})
.reshape(
{batch_sz / n_parallel_imgs,
n_parallel_imgs,
n_weight_grps,
n_out_channels / n_weight_grps,
out_h,
out_w})
.permute({0, 2, 3, 1, 4, 5});

weight = weight.reshape({n_weight_grps,
weight.size(0) / n_weight_grps,
weight.size(1),
weight.size(2),
weight.size(3)});
weight = weight.reshape(
{n_weight_grps,
weight.size(0) / n_weight_grps,
weight.size(1),
weight.size(2),
weight.size(3)});

columns = columns.view(
{n_weight_grps, columns.size(0) / n_weight_grps, columns.size(1)});
Expand Down Expand Up @@ -775,37 +781,41 @@ at::Tensor backward_gradient_parameters(
}

at::Tensor grad_out_buf = grad_out
.reshape({batch_sz / n_parallel_imgs,
n_parallel_imgs,
n_weight_grps,
n_out_channels / n_weight_grps,
out_h,
out_w})
.reshape(
{batch_sz / n_parallel_imgs,
n_parallel_imgs,
n_weight_grps,
n_out_channels / n_weight_grps,
out_h,
out_w})
.permute({0, 2, 3, 1, 4, 5})
.contiguous();

input = input.reshape(
{batch_sz / n_parallel_imgs, n_parallel_imgs, n_in_channels, in_h, in_w});

offset = offset.reshape({batch_sz / n_parallel_imgs,
n_parallel_imgs,
n_offset_grps * 2 * weight_h * weight_w,
out_h,
out_w});
offset = offset.reshape(
{batch_sz / n_parallel_imgs,
n_parallel_imgs,
n_offset_grps * 2 * weight_h * weight_w,
out_h,
out_w});

if (use_mask) {
mask = mask.reshape({batch_sz / n_parallel_imgs,
n_parallel_imgs,
n_offset_grps * weight_h * weight_w,
out_h,
out_w});
mask = mask.reshape(
{batch_sz / n_parallel_imgs,
n_parallel_imgs,
n_offset_grps * weight_h * weight_w,
out_h,
out_w});
}

grad_weight = grad_weight.view({n_weight_grps,
grad_weight.size(0) / n_weight_grps,
grad_weight.size(1),
grad_weight.size(2),
grad_weight.size(3)});
grad_weight = grad_weight.view(
{n_weight_grps,
grad_weight.size(0) / n_weight_grps,
grad_weight.size(1),
grad_weight.size(2),
grad_weight.size(3)});

auto columns = at::empty(
{n_weight_grps,
Expand Down Expand Up @@ -846,10 +856,11 @@ at::Tensor backward_gradient_parameters(
}
}

grad_weight = grad_weight.view({grad_weight.size(0) * grad_weight.size(1),
grad_weight.size(2),
grad_weight.size(3),
grad_weight.size(4)});
grad_weight = grad_weight.view(
{grad_weight.size(0) * grad_weight.size(1),
grad_weight.size(2),
grad_weight.size(3),
grad_weight.size(4)});
return grad_weight;
}

Expand Down Expand Up @@ -976,26 +987,29 @@ at::Tensor deform_conv2d_forward_kernel(
}

// Separate batches into blocks
out = out.view({batch_sz / n_parallel_imgs,
n_parallel_imgs,
out_channels,
out_h,
out_w});
out = out.view(
{batch_sz / n_parallel_imgs,
n_parallel_imgs,
out_channels,
out_h,
out_w});
input_c = input_c.view(
{batch_sz / n_parallel_imgs, n_parallel_imgs, n_in_channels, in_h, in_w});

offset_c = offset_c.view({batch_sz / n_parallel_imgs,
n_parallel_imgs,
n_offset_grps * 2 * weight_h * weight_w,
out_h,
out_w});
offset_c = offset_c.view(
{batch_sz / n_parallel_imgs,
n_parallel_imgs,
n_offset_grps * 2 * weight_h * weight_w,
out_h,
out_w});

if (use_mask) {
mask_c = mask_c.view({batch_sz / n_parallel_imgs,
n_parallel_imgs,
n_offset_grps * weight_h * weight_w,
out_h,
out_w});
mask_c = mask_c.view(
{batch_sz / n_parallel_imgs,
n_parallel_imgs,
n_offset_grps * weight_h * weight_w,
out_h,
out_w});
}

at::Tensor out_buf = at::zeros(
Expand All @@ -1006,16 +1020,18 @@ at::Tensor deform_conv2d_forward_kernel(
out.options());

// Separate channels into convolution groups
out_buf = out_buf.view({out_buf.size(0),
n_weight_grps,
out_buf.size(1) / n_weight_grps,
out_buf.size(2),
out_buf.size(3)});
weight_c = weight_c.view({n_weight_grps,
weight_c.size(0) / n_weight_grps,
weight_c.size(1),
weight_c.size(2),
weight_c.size(3)});
out_buf = out_buf.view(
{out_buf.size(0),
n_weight_grps,
out_buf.size(1) / n_weight_grps,
out_buf.size(2),
out_buf.size(3)});
weight_c = weight_c.view(
{n_weight_grps,
weight_c.size(0) / n_weight_grps,
weight_c.size(1),
weight_c.size(2),
weight_c.size(3)});

// Sample points and perform convolution
auto columns = at::zeros(
Expand Down Expand Up @@ -1056,11 +1072,12 @@ at::Tensor deform_conv2d_forward_kernel(
columns.view({columns.size(0) * columns.size(1), columns.size(2)});
}

out_buf = out_buf.view({batch_sz / n_parallel_imgs,
out_channels,
n_parallel_imgs,
out_h,
out_w});
out_buf = out_buf.view(
{batch_sz / n_parallel_imgs,
out_channels,
n_parallel_imgs,
out_h,
out_w});
out_buf.transpose_(1, 2);
out.copy_(out_buf);
out = out.view({batch_sz, out_channels, out_h, out_w});
Expand Down
Loading