ROIPool: Support for all datatypes (pytorch#632)

* Use of torch7 naming scheme for ROIAlign forward and backward

* use common cuda helpers in ROIAlign

* use .options() in favor of .type() where applicable

* Added tests for forward pass of ROIAlign, as well as more consistent naming scheme for CPU vs CUDA

* working ROIAlign cuda backwards pass

* working ROIAlign backwards pass for CPU

* added relevant headers for ROIAlign backwards

* tests for ROIAlign layer

* replace .type() with .options() for tensor initialization in ROIAlign layers

* support for Half types in ROIAlign

* gradcheck tests for ROIAlign

* updated ROIPool on CPU to work with all datatypes

* updated and cleaned tests for ROI Pooling

Author: varunagrawal

test/test_layers.py

@@ -7,12 +7,13 @@
 #endif
 
 // Interface for Python
-at::Tensor ROIAlign_forward(const at::Tensor& input,
-                            const at::Tensor& rois,
-                            const float spatial_scale,
-                            const int pooled_height,
-                            const int pooled_width,
-                            const int sampling_ratio) {
+at::Tensor ROIAlign_forward(const at::Tensor& input,    // Input feature map.
+                            const at::Tensor& rois,     // List of ROIs to pool over.
+                            const float spatial_scale,  // The scale of the image features. ROIs will be scaled to this.
+                            const int pooled_height,    // The height of the pooled feature map.
+                            const int pooled_width,     // The width of the pooled feature
+                            const int sampling_ratio)   // The number of points to sample in each bin along each axis.
+{                   
   if (input.type().is_cuda()) {
 #ifdef WITH_CUDA
     return ROIAlign_forward_cuda(input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio);
@@ -40,6 +41,6 @@ at::Tensor ROIAlign_backward(const at::Tensor& grad,
     AT_ERROR("Not compiled with GPU support");
 #endif
   }
-  AT_ERROR("Not implemented on the CPU");
+  return ROIAlign_backward_cpu(grad, rois, spatial_scale, pooled_height, pooled_width, batch_size, channels, height, width, sampling_ratio);
 }
 

torchvision/csrc/ROIAlign.h

@@ -110,46 +110,37 @@ void pre_calc_for_bilinear_interpolate(
 }
 
 template <typename T>
-void ROIAlignForward_cpu_kernel(
+void ROIAlignForward(
     const int nthreads,
-    const T* bottom_data,
+    const T* input,
     const T& spatial_scale,
     const int channels,
     const int height,
     const int width,
     const int pooled_height,
     const int pooled_width,
     const int sampling_ratio,
-    const T* bottom_rois,
-    //int roi_cols,
-    T* top_data) {
-  //AT_ASSERT(roi_cols == 4 || roi_cols == 5);
-  int roi_cols = 5;
-
+    const T* rois,
+    T* output) {
   int n_rois = nthreads / channels / pooled_width / pooled_height;
   // (n, c, ph, pw) is an element in the pooled output
   // can be parallelized using omp
   // #pragma omp parallel for num_threads(32)
   for (int n = 0; n < n_rois; n++) {
     int index_n = n * channels * pooled_width * pooled_height;
 
-    // roi could have 4 or 5 columns
-    const T* offset_bottom_rois = bottom_rois + n * roi_cols;
-    int roi_batch_ind = 0;
-    if (roi_cols == 5) {
-      roi_batch_ind = offset_bottom_rois[0];
-      offset_bottom_rois++;
-    }
+    const T* offset_rois = rois + n * 5;
+    int roi_batch_ind = offset_rois[0];
 
     // Do not using rounding; this implementation detail is critical
-    T roi_start_w = offset_bottom_rois[0] * spatial_scale;
-    T roi_start_h = offset_bottom_rois[1] * spatial_scale;
-    T roi_end_w = offset_bottom_rois[2] * spatial_scale;
-    T roi_end_h = offset_bottom_rois[3] * spatial_scale;
-    // T roi_start_w = round(offset_bottom_rois[0] * spatial_scale);
-    // T roi_start_h = round(offset_bottom_rois[1] * spatial_scale);
-    // T roi_end_w = round(offset_bottom_rois[2] * spatial_scale);
-    // T roi_end_h = round(offset_bottom_rois[3] * spatial_scale);
+    T roi_start_w = offset_rois[1] * spatial_scale;
+    T roi_start_h = offset_rois[2] * spatial_scale;
+    T roi_end_w = offset_rois[3] * spatial_scale;
+    T roi_end_h = offset_rois[4] * spatial_scale;
+    // T roi_start_w = round(offset_rois[0] * spatial_scale);
+    // T roi_start_h = round(offset_rois[1] * spatial_scale);
+    // T roi_end_w = round(offset_rois[2] * spatial_scale);
+    // T roi_end_h = round(offset_rois[3] * spatial_scale);
 
     // Force malformed ROIs to be 1x1
     T roi_width = std::max(roi_end_w - roi_start_w, (T)1.);
@@ -188,8 +179,8 @@ void ROIAlignForward_cpu_kernel(
 
       for (int c = 0; c < channels; c++) {
       int index_n_c = index_n + c * pooled_width * pooled_height;
-      const T* offset_bottom_data =
-          bottom_data + (roi_batch_ind * channels + c) * height * width;
+      const T* offset_input =
+          input + (roi_batch_ind * channels + c) * height * width;
       int pre_calc_index = 0;
 
       for (int ph = 0; ph < pooled_height; ph++) {
@@ -200,46 +191,186 @@ void ROIAlignForward_cpu_kernel(
           for (int iy = 0; iy < roi_bin_grid_h; iy++) {
             for (int ix = 0; ix < roi_bin_grid_w; ix++) {
               PreCalc<T> pc = pre_calc[pre_calc_index];
-              output_val += pc.w1 * offset_bottom_data[pc.pos1] +
-                  pc.w2 * offset_bottom_data[pc.pos2] +
-                  pc.w3 * offset_bottom_data[pc.pos3] +
-                  pc.w4 * offset_bottom_data[pc.pos4];
+              output_val += pc.w1 * offset_input[pc.pos1] +
+                  pc.w2 * offset_input[pc.pos2] +
+                  pc.w3 * offset_input[pc.pos3] +
+                  pc.w4 * offset_input[pc.pos4];
 
               pre_calc_index += 1;
             }
           }
           output_val /= count;
 
-          top_data[index] = output_val;
+          output[index] = output_val;
         } // for pw
       } // for ph
     } // for c
   } // for n
 }
 
+template <typename T>
+void bilinear_interpolate_gradient(
+    const int height, const int width,
+    T y, T x,
+    T& w1, T& w2, T& w3, T& w4,
+    int& x_low, int& x_high, int& y_low, int& y_high,
+    const int index /* index for debug only*/) {
+
+  // deal with cases that inverse elements are out of feature map boundary
+  if (y < -1.0 || y > height || x < -1.0 || x > width) {
+    // empty
+    w1 = w2 = w3 = w4 = 0.;
+    x_low = x_high = y_low = y_high = -1;
+    return;
+  }
+
+  if (y <= 0) y = 0;
+  if (x <= 0) x = 0;
+
+  y_low = (int)y;
+  x_low = (int)x;
+
+  if (y_low >= height - 1) {
+    y_high = y_low = height - 1;
+    y = (T)y_low;
+  } else {
+    y_high = y_low + 1;
+  }
+
+  if (x_low >= width - 1) {
+    x_high = x_low = width - 1;
+    x = (T)x_low;
+  } else {
+    x_high = x_low + 1;
+  }
+
+  T ly = y - y_low;
+  T lx = x - x_low;
+  T hy = 1. - ly, hx = 1. - lx;
+
+  // reference in forward
+  // T v1 = input[y_low * width + x_low];
+  // T v2 = input[y_low * width + x_high];
+  // T v3 = input[y_high * width + x_low];
+  // T v4 = input[y_high * width + x_high];
+  // T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
+
+  w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
+
+  return;
+}
+
+template <class T>
+inline void add(T* address, const T& val) {
+  *address += val;
+}
+
+template <typename T>
+void ROIAlignBackward(
+    const int nthreads,
+    const T* grad_output,
+    const T& spatial_scale,
+    const int channels,
+    const int height, 
+    const int width,
+    const int pooled_height, 
+    const int pooled_width,
+    const int sampling_ratio,
+    T* grad_input,
+    const T* rois,
+    const int n_stride, const int c_stride,
+    const int h_stride, const int w_stride) {
+  for (int index = 0; index < nthreads; index++) {
+    // (n, c, ph, pw) is an element in the pooled output
+    int pw = index % pooled_width;
+    int ph = (index / pooled_width) % pooled_height;
+    int c = (index / pooled_width / pooled_height) % channels;
+    int n = index / pooled_width / pooled_height / channels;
+
+    const T* offset_rois = rois + n * 5;
+    int roi_batch_ind  = offset_rois[0];
+    
+    // Do not using rounding; this implementation detail is critical
+    T roi_start_w = offset_rois[1] * spatial_scale;
+    T roi_start_h = offset_rois[2] * spatial_scale;
+    T roi_end_w = offset_rois[3] * spatial_scale;
+    T roi_end_h = offset_rois[4] * spatial_scale;
+    
+    // Force malformed ROIs to be 1x1
+    T roi_width = std::max(roi_end_w - roi_start_w, (T)1.);
+    T roi_height = std::max(roi_end_h - roi_start_h, (T)1.);
+    T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
+    T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
+
+    T* offset_grad_input = grad_input + ((roi_batch_ind * channels + c) * height * width);
+
+    int output_offset = n*n_stride + c*c_stride;
+    const T* offset_grad_output = grad_output + output_offset;
+    const T grad_output_this_bin = offset_grad_output[ph*h_stride + pw*w_stride];
+
+    // We use roi_bin_grid to sample the grid and mimic integral
+    int roi_bin_grid_h = (sampling_ratio > 0) ? sampling_ratio : ceil(roi_height / pooled_height); // e.g., = 2
+    int roi_bin_grid_w = (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width);
+
+    // We do average (integral) pooling inside a bin
+    const T count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4
+
+    for (int iy = 0; iy < roi_bin_grid_h; iy++) 
+    {
+      const T y = roi_start_h + ph * bin_size_h + static_cast<T>(iy + .5f) * bin_size_h / static_cast<T>(roi_bin_grid_h); // e.g., 0.5, 1.5
+      for (int ix = 0; ix < roi_bin_grid_w; ix++) 
+      {
+        const T x = roi_start_w + pw * bin_size_w + static_cast<T>(ix + .5f) * bin_size_w / static_cast<T>(roi_bin_grid_w);
+
+        T w1, w2, w3, w4;
+        int x_low, x_high, y_low, y_high;
+
+        bilinear_interpolate_gradient(height, width, y, x,
+            w1, w2, w3, w4,
+            x_low, x_high, y_low, y_high,
+            index);
+
+        T g1 = grad_output_this_bin * w1 / count;
+        T g2 = grad_output_this_bin * w2 / count;
+        T g3 = grad_output_this_bin * w3 / count;
+        T g4 = grad_output_this_bin * w4 / count;
+
+        if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) {
+          // atomic add is not needed for now since it is single threaded
+          add(offset_grad_input + y_low * width + x_low, static_cast<T>(g1));
+          add(offset_grad_input + y_low * width + x_high, static_cast<T>(g2));
+          add(offset_grad_input + y_high * width + x_low, static_cast<T>(g3));
+          add(offset_grad_input + y_high * width + x_high, static_cast<T>(g4));
+        } // if
+      } // ix
+    } // iy
+  } // for
+} // ROIAlignBackward
+
+
 at::Tensor ROIAlign_forward_cpu(const at::Tensor& input,
                                 const at::Tensor& rois,
                                 const float spatial_scale,
                                 const int pooled_height,
                                 const int pooled_width,
                                 const int sampling_ratio) {
-  AT_ASSERTM(!input.type().is_cuda(), "input must be a CPU tensor");
-  AT_ASSERTM(!rois.type().is_cuda(), "rois must be a CPU tensor");
+  AT_ASSERTM(input.device().is_cpu(), "input must be a CPU tensor");
+  AT_ASSERTM(rois.device().is_cpu(), "rois must be a CPU tensor");
 
   auto num_rois = rois.size(0);
   auto channels = input.size(1);
   auto height = input.size(2);
   auto width = input.size(3);
 
-  at::Tensor output = at::zeros({num_rois, channels, pooled_height, pooled_width}, input.type());
+  at::Tensor output = at::zeros({num_rois, channels, pooled_height, pooled_width}, input.options());
 
   auto output_size = num_rois * pooled_height * pooled_width * channels;
 
   if (output.numel() == 0)
     return output;
 
-  AT_DISPATCH_FLOATING_TYPES(input.type(), "ROIAlign_forward", [&] {
-    ROIAlignForward_cpu_kernel<scalar_t>(
+  AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.type(), "ROIAlign_forward", [&] {
+    ROIAlignForward<scalar_t>(
          output_size,
          input.data<scalar_t>(),
          spatial_scale,
@@ -254,3 +385,52 @@ at::Tensor ROIAlign_forward_cpu(const at::Tensor& input,
   });
   return output;
 }
+
+
+at::Tensor ROIAlign_backward_cpu(const at::Tensor& grad,
+                                 const at::Tensor& rois,
+                                 const float spatial_scale,
+                                 const int pooled_height,
+                                 const int pooled_width,
+                                 const int batch_size,
+                                 const int channels,
+                                 const int height,
+                                 const int width,
+                                 const int sampling_ratio) {
+  AT_ASSERTM(grad.device().is_cpu(), "grad must be a CPU tensor");
+  AT_ASSERTM(rois.device().is_cpu(), "rois must be a CPU tensor");
+
+  at::Tensor grad_input = at::zeros({batch_size, channels, height, width}, grad.options());
+
+  // handle possibly empty gradients
+  if (grad.numel() == 0)
+  {
+    return grad_input;
+  }
+
+  // get stride values to ensure indexing into gradients is correct.
+  int n_stride = grad.stride(0);
+  int c_stride = grad.stride(1);
+  int h_stride = grad.stride(2);
+  int w_stride = grad.stride(3);
+
+  AT_DISPATCH_FLOATING_TYPES_AND_HALF(grad.type(), "ROIAlign_forward", [&] {
+    ROIAlignBackward<scalar_t>(
+        grad.numel(),
+        grad.data<scalar_t>(),
+        spatial_scale,
+        channels,
+        height, 
+        width,
+        pooled_height, 
+        pooled_width,
+        sampling_ratio,
+        grad_input.data<scalar_t>(),
+        rois.data<scalar_t>(),
+        n_stride, 
+        c_stride,
+        h_stride, 
+        w_stride);
+  });
+  return grad_input;
+}