[Codemod][CppQualityCommon] Fix CQS signal readability-braces-around-statements in fbcode/pytorch/vision

Reviewed By: dtolnay

Differential Revision: D78881813

fbshipit-source-id: f5d56101748bdfec69ab77fbda21ce29ec131323

Author: generatedunixname89002005287564

torchvision/csrc/io/image/cpu/decode_png.cpp

@@ -104,8 +104,9 @@ torch::Tensor decode_png(
 
   int channels = png_get_channels(png_ptr, info_ptr);
 
-  if (color_type == PNG_COLOR_TYPE_GRAY && bit_depth < 8)
+  if (color_type == PNG_COLOR_TYPE_GRAY && bit_depth < 8) {
     png_set_expand_gray_1_2_4_to_8(png_ptr);
+  }
 
   int number_of_passes;
   if (interlace_type == PNG_INTERLACE_ADAM7) {

torchvision/csrc/io/image/cpu/encode_png.cpp

@@ -47,13 +47,15 @@ void torch_png_write_data(
   size_t nsize = p->size + length;
 
   /* allocate or grow buffer */
-  if (p->buffer)
+  if (p->buffer) {
     p->buffer = (char*)realloc(p->buffer, nsize);
-  else
+  } else {
     p->buffer = (char*)malloc(nsize);
+  }
 
-  if (!p->buffer)
+  if (!p->buffer) {
     png_error(png_ptr, "Write Error");
+  }
 
   /* copy new bytes to end of buffer */
   memcpy(p->buffer + p->size, data, length);

torchvision/csrc/io/image/cuda/decode_jpegs_cuda.cpp

@@ -89,9 +89,9 @@ std::vector<torch::Tensor> decode_jpegs_cuda(
   at::cuda::CUDAGuard device_guard(device);
 
   if (cudaJpegDecoder == nullptr || device != cudaJpegDecoder->target_device) {
-    if (cudaJpegDecoder != nullptr)
+    if (cudaJpegDecoder != nullptr) {
       cudaJpegDecoder.reset(new CUDAJpegDecoder(device));
-    else {
+    } else {
       cudaJpegDecoder = std::make_unique<CUDAJpegDecoder>(device);
       std::atexit([]() { cudaJpegDecoder.reset(); });
     }

torchvision/csrc/io/image/cuda/encode_jpegs_cuda.cpp

@@ -46,8 +46,9 @@ std::vector<torch::Tensor> encode_jpegs_cuda(
   // so we reuse it across calls. NB: the cached structures are device specific
   // and cannot be reused across devices
   if (cudaJpegEncoder == nullptr || device != cudaJpegEncoder->target_device) {
-    if (cudaJpegEncoder != nullptr)
+    if (cudaJpegEncoder != nullptr) {
       delete cudaJpegEncoder.release();
+    }
 
     cudaJpegEncoder = std::make_unique<CUDAJpegEncoder>(device);
 

torchvision/csrc/ops/cpu/nms_kernel.cpp

@@ -17,8 +17,9 @@ at::Tensor nms_kernel_impl(
       dets.scalar_type() == scores.scalar_type(),
       "dets should have the same type as scores");
 
-  if (dets.numel() == 0)
+  if (dets.numel() == 0) {
     return at::empty({0}, dets.options().dtype(at::kLong));
+  }
 
   auto x1_t = dets.select(1, 0).contiguous();
   auto y1_t = dets.select(1, 1).contiguous();
@@ -47,8 +48,9 @@ at::Tensor nms_kernel_impl(
 
   for (int64_t _i = 0; _i < ndets; _i++) {
     auto i = order[_i];
-    if (suppressed[i] == 1)
+    if (suppressed[i] == 1) {
       continue;
+    }
     keep[num_to_keep++] = i;
     auto ix1 = x1[i];
     auto iy1 = y1[i];
@@ -58,8 +60,9 @@ at::Tensor nms_kernel_impl(
 
     for (int64_t _j = _i + 1; _j < ndets; _j++) {
       auto j = order[_j];
-      if (suppressed[j] == 1)
+      if (suppressed[j] == 1) {
         continue;
+      }
       auto xx1 = std::max(ix1, x1[j]);
       auto yy1 = std::max(iy1, y1[j]);
       auto xx2 = std::min(ix2, x2[j]);
@@ -69,8 +72,9 @@ at::Tensor nms_kernel_impl(
       auto h = std::max(static_cast<scalar_t>(0), yy2 - yy1);
       auto inter = w * h;
       auto ovr = inter / (iarea + areas[j] - inter);
-      if (ovr > iou_threshold)
+      if (ovr > iou_threshold) {
         suppressed[j] = 1;
+      }
     }
   }
   return keep_t.narrow(/*dim=*/0, /*start=*/0, /*length=*/num_to_keep);

torchvision/csrc/ops/cpu/ps_roi_align_kernel.cpp

@@ -20,10 +20,12 @@ T bilinear_interpolate(
     return 0;
   }
 
-  if (y <= 0)
+  if (y <= 0) {
     y = 0;
-  if (x <= 0)
+  }
+  if (x <= 0) {
     x = 0;
+  }
 
   int y_low = (int)y;
   int x_low = (int)x;
@@ -163,10 +165,12 @@ void bilinear_interpolate_gradient(
     return;
   }
 
-  if (y <= 0)
+  if (y <= 0) {
     y = 0;
-  if (x <= 0)
+  }
+  if (x <= 0) {
     x = 0;
+  }
 
   y_low = (int)y;
   x_low = (int)x;

torchvision/csrc/ops/cpu/roi_align_kernel.cpp

@@ -130,10 +130,12 @@ void bilinear_interpolate_gradient(
     return;
   }
 
-  if (y <= 0)
+  if (y <= 0) {
     y = 0;
-  if (x <= 0)
+  }
+  if (x <= 0) {
     x = 0;
+  }
 
   y_low = (int)y;
   x_low = (int)x;
@@ -304,8 +306,9 @@ at::Tensor roi_align_forward_kernel(
   at::Tensor output = at::zeros(
       {num_rois, channels, pooled_height, pooled_width}, input.options());
 
-  if (output.numel() == 0)
+  if (output.numel() == 0) {
     return output;
+  }
 
   auto input_ = input.contiguous(), rois_ = rois.contiguous();
   AT_DISPATCH_FLOATING_TYPES_AND_HALF(

torchvision/csrc/ops/quantized/cpu/qnms_kernel.cpp

@@ -17,8 +17,9 @@ at::Tensor qnms_kernel_impl(
       dets.scalar_type() == scores.scalar_type(),
       "dets should have the same type as scores");
 
-  if (dets.numel() == 0)
+  if (dets.numel() == 0) {
     return at::empty({0}, dets.options().dtype(at::kLong));
+  }
 
   const auto ndets = dets.size(0);
 
@@ -56,8 +57,9 @@ at::Tensor qnms_kernel_impl(
 
   for (int64_t _i = 0; _i < ndets; _i++) {
     auto i = order[_i];
-    if (suppressed[i] == 1)
+    if (suppressed[i] == 1) {
       continue;
+    }
     keep[num_to_keep++] = i;
 
     // We explicitly cast coordinates to float so that the code can be
@@ -70,8 +72,9 @@ at::Tensor qnms_kernel_impl(
 
     for (int64_t _j = _i + 1; _j < ndets; _j++) {
       auto j = order[_j];
-      if (suppressed[j] == 1)
+      if (suppressed[j] == 1) {
         continue;
+      }
       float xx1 = std::max(ix1val, (float)x1[j].val_);
       float yy1 = std::max(iy1val, (float)y1[j].val_);
       float xx2 = std::min(ix2val, (float)x2[j].val_);
@@ -81,8 +84,9 @@ at::Tensor qnms_kernel_impl(
       auto h = std::max(0.f, yy2 - yy1); // * scale (gets canceled below)
       auto inter = w * h;
       auto ovr = inter / (iarea + areas[j] - inter);
-      if (ovr > iou_threshold)
+      if (ovr > iou_threshold) {
         suppressed[j] = 1;
+      }
     }
   }
   return keep_t.narrow(/*dim=*/0, /*start=*/0, /*length=*/num_to_keep);

torchvision/csrc/ops/quantized/cpu/qroi_align_kernel.cpp

@@ -257,8 +257,9 @@ at::Tensor qroi_align_forward_kernel(
       input.q_scale(),
       input.q_zero_point());
 
-  if (output.numel() == 0)
+  if (output.numel() == 0) {
     return output;
+  }
 
   AT_DISPATCH_QINT_TYPES(input.scalar_type(), "qroi_align_forward_kernel", [&] {
     qroi_align_forward_kernel_impl<scalar_t>(