Extend Crop to N-D, changed CropParameter.

include/caffe/layers/crop_layer.hpp

@@ -41,9 +41,27 @@ class CropLayer : public Layer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
-  int crop_h_, crop_w_;
-};
+  vector<int> offsets;
 
+ private:
+  void crop_copy(const vector<Blob<Dtype>*>& bottom,
+               const vector<Blob<Dtype>*>& top,
+               const vector<int>& offsets,
+               vector<int> indices,
+               int cur_dim,
+               const Dtype* src_data,
+               Dtype* dest_data,
+               bool is_forward);
+
+  void crop_copy_gpu(const vector<Blob<Dtype>*>& bottom,
+                const vector<Blob<Dtype>*>& top,
+                const vector<int>& offsets,
+                vector<int> indices,
+                int cur_dim,
+                const Dtype* src_data,
+                Dtype* dest_data,
+                bool is_forward);
+};
 }  // namespace caffe
 
 #endif  // CAFFE_CROP_LAYER_HPP_

src/caffe/layers/crop_layer.cpp

@@ -1,8 +1,10 @@
 #include <algorithm>
+#include <functional>
 #include <map>
 #include <set>
 #include <vector>
 
+
 #include "caffe/layer.hpp"
 #include "caffe/layers/crop_layer.hpp"
 #include "caffe/net.hpp"
@@ -13,40 +15,108 @@ namespace caffe {
 template <typename Dtype>
 void CropLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
-  const CropParameter& param = this->layer_param_.crop_param();
   CHECK_EQ(bottom.size(), 2) << "Wrong number of bottom blobs.";
-  CHECK_EQ(bottom[0]->num_axes(), 4) << "Only works with 4D blobs.";
-  CHECK_EQ(bottom[1]->num_axes(), 4) << "Only works with 4D blobs.";
-  crop_h_ = param.offset_height();
-  crop_w_ = param.offset_width();
+  // parameter setup moved to Reshape because it depends on size.
 }
 
 template <typename Dtype>
 void CropLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
-  // Check that the image we are cropping minus the margin is bigger than the
-  // destination image.
-  CHECK_GT(bottom[0]->height()-crop_h_, bottom[1]->height())
-      << "invalid offset";
-  CHECK_GT(bottom[0]->width()-crop_w_, bottom[1]->width()) << "invalid offset";
-  top[0]->Reshape(bottom[0]->num(), bottom[0]->channels(), bottom[1]->height(),
-      bottom[1]->width());
+  const CropParameter& param = this->layer_param_.crop_param();
+  // bottom[0] supplies the data
+  // bottom[1] supplies the size
+  int input_dim = bottom[0]->num_axes();
+  CHECK_LT(param.axis(), input_dim) << "crop axis bigger than input dim";
+  // initialize all offsets to 0
+  offsets = vector<int>(input_dim, 0);
+  // initialize new shape to bottom[0]
+  vector<int> new_shape(bottom[0]->shape());
+
+  if (param.offset_size() > 1) {
+    // the number of crop values specified must be equal to the number
+    // of dimensions following axis
+    CHECK_EQ(param.axis() + param.offset_size(), input_dim)
+      << "number of crop values specified must be equal to the number of "
+      << "dimensions following axis.";
+  }
+  // apply crops
+  for (int i = 0; i < input_dim; ++i) {
+    int crop_offset = 0;
+    int new_size    = bottom[0]->shape(i);
+    if (i >= param.axis() && param.offset_size() == 1) {
+      // if only one crop value is supplied, crop all dimensions after axis
+      // by this crop value
+      crop_offset = param.offset(0);
+      new_size = bottom[1]->shape(i);
+    } else if (i >= param.axis() && param.offset_size() > 1) {
+      // crop values specified must be equal to the number of dimensions
+      // following axis
+      crop_offset = param.offset(i - param.axis());
+      new_size = bottom[1]->shape(i);
+    }
+    // Check that the image we are cropping minus the margin is bigger
+    // than the destination image.
+    CHECK_GE(bottom[0]->shape(i) - crop_offset,
+             bottom[1]->shape(i))
+        << "invalid crop parameters in dimension: " << i;
+    // Now set new size and offsets
+    new_shape[i] = new_size;
+    offsets[i] = crop_offset;
+  }
+  top[0]->Reshape(new_shape);
+}
+
+// recursive copy function
+template <typename Dtype>
+void CropLayer<Dtype>::crop_copy(const vector<Blob<Dtype>*>& bottom,
+             const vector<Blob<Dtype>*>& top,
+             const vector<int>& offsets,
+             vector<int> indices,
+             int cur_dim,
+             const Dtype* src_data,
+             Dtype* dest_data,
+             bool is_forward) {
+  if (cur_dim + 1 < top[0]->num_axes()) {
+    // We are not yet at the final dimension, call copy recursivley
+    for (int i = 0; i < top[0]->shape(cur_dim); ++i) {
+      indices[cur_dim] = i;
+      crop_copy(bottom, top, offsets, indices, cur_dim+1,
+                src_data, dest_data, is_forward);
+    }
+  } else {
+    // We are at the last dimensions, which is stored continously in memory
+    for (int i = 0; i < top[0]->shape(cur_dim); ++i) {
+      // prepare index vector reduced(red) and with offsets(off)
+      std::vector<int> ind_red(cur_dim, 0);
+      std::vector<int> ind_off(cur_dim+1, 0);
+      for (int j = 0; j < cur_dim; ++j) {
+          ind_red[j] = indices[j];
+          ind_off[j] = indices[j] + offsets[j];
+      }
+      ind_off[cur_dim] = offsets[cur_dim];
+      // do the copy
+      if (is_forward) {
+        caffe_copy(top[0]->shape(cur_dim),
+            src_data + bottom[0]->offset(ind_off),
+            dest_data + top[0]->offset(ind_red));
+      } else {
+        // in the backwards pass the src_data is top_diff
+        // and the dest_data is bottom_diff
+        caffe_copy(top[0]->shape(cur_dim),
+            src_data + top[0]->offset(ind_red),
+            dest_data + bottom[0]->offset(ind_off));
+      }
+    }
+  }
 }
 
 template <typename Dtype>
 void CropLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
+  std::vector<int> indices(top[0]->num_axes(), 0);
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = top[0]->mutable_cpu_data();
-  for (int n = 0; n < top[0]->num(); ++n) {
-    for (int c = 0; c < top[0]->channels(); ++c) {
-      for (int h = 0; h < top[0]->height(); ++h) {
-        caffe_copy(top[0]->width(),
-            bottom_data + bottom[0]->offset(n, c, crop_h_ + h, crop_w_),
-            top_data + top[0]->offset(n, c, h));
-      }
-    }
-  }
+  crop_copy(bottom, top, offsets, indices, 0, bottom_data, top_data, true);
 }
 
 template <typename Dtype>
@@ -54,17 +124,11 @@ void CropLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->cpu_diff();
   Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+
   if (propagate_down[0]) {
     caffe_set(bottom[0]->count(), static_cast<Dtype>(0), bottom_diff);
-    for (int n = 0; n < top[0]->num(); ++n) {
-      for (int c = 0; c < top[0]->channels(); ++c) {
-        for (int h = 0; h < top[0]->height(); ++h) {
-          caffe_copy(top[0]->width(),
-              top_diff + top[0]->offset(n, c, h),
-              bottom_diff + bottom[0]->offset(n, c, crop_h_ + h, crop_w_));
-        }
-      }
-    }
+    std::vector<int> indices(top[0]->num_axes(), 0);
+    crop_copy(bottom, top, offsets, indices, 0, top_diff, bottom_diff, false);
   }
 }
 

src/caffe/layers/crop_layer.cu

@@ -22,19 +22,90 @@ __global__ void copy_kernel(const int n, const int height, const int width,
   }
 }
 
+// recursive copy function, this function is similar to crop_copy but loops
+// over all but the last two dimensions. It is implemented this way to allow
+// for ND cropping while still relying on a CUDA kernel for the innermost
+// two dimensions for performance reasons.
+// An alternative way to implement ND cropping relying more on the kernel
+// would require passing offsets to the kernel, which is a bit problematic
+// because it is of variable length. Since in the standard (N,C,W,H) case
+// N,C are usually not cropped a speedup could be achieved by not looping
+// the application of the copy_kernel around these dimensions.
+template <typename Dtype>
+void CropLayer<Dtype>::crop_copy_gpu(const vector<Blob<Dtype>*>& bottom,
+             const vector<Blob<Dtype>*>& top,
+             const vector<int>& offsets,
+             vector<int> indices,
+             int cur_dim,
+             const Dtype* src_data,
+             Dtype* dest_data,
+             bool is_forward) {
+  if (cur_dim + 2 < top[0]->num_axes()) {
+    // We are not yet at the final dimension, call copy recursivley
+    for (int i = 0; i < top[0]->shape(cur_dim); ++i) {
+      indices[cur_dim] = i;
+      crop_copy_gpu(bottom, top, offsets, indices, cur_dim+1,
+                src_data, dest_data, is_forward);
+    }
+  } else {
+    // We are at the last two dimensions, which are stored continously in memory
+    // With (N,C,H,W)
+    //      (0,1,2,3) cur_dim   -> H
+    //                cur_dim+1 -> W
+    const int lines = top[0]->shape(cur_dim);
+    const int height = top[0]->shape(cur_dim);
+    const int width = top[0]->shape(cur_dim+1);
+    std::vector<int> ind_off(cur_dim+2, 0);
+    for (int j = 0; j < cur_dim; ++j) {
+        ind_off[j] = indices[j] + offsets[j];
+    }
+    ind_off[cur_dim] = offsets[cur_dim];
+    ind_off[cur_dim+1] = offsets[cur_dim+1];
+    // Compute copy strides
+    const int src_outer_stride =
+        bottom[0]->shape(cur_dim)*bottom[0]->shape(cur_dim+1);
+    const int src_inner_stride = bottom[0]->shape(cur_dim+1);
+    const int dest_outer_stride =
+        top[0]->shape(cur_dim)*top[0]->shape(cur_dim+1);
+    const int dest_inner_stride = top[0]->shape(cur_dim+1);
+
+    if (is_forward) {
+      const Dtype* bottom_data = bottom[0]->gpu_data() +
+          bottom[0]->offset(ind_off);
+      Dtype* top_data = top[0]->mutable_gpu_data() +
+          top[0]->offset(indices);
+      // NOLINT_NEXT_LINE(whitespace/operators)
+      copy_kernel<<<CAFFE_GET_BLOCKS(lines), CAFFE_CUDA_NUM_THREADS>>>(
+          lines, height, width,
+          src_outer_stride, src_inner_stride,
+          dest_outer_stride, dest_inner_stride,
+          bottom_data, top_data);
+
+    } else {
+      const Dtype* top_diff = top[0]->gpu_diff() +
+          top[0]->offset(indices);
+      Dtype* bottom_diff = bottom[0]->mutable_gpu_diff() +
+          bottom[0]->offset(ind_off);
+      // NOLINT_NEXT_LINE(whitespace/operators)
+      copy_kernel<<<CAFFE_GET_BLOCKS(lines), CAFFE_CUDA_NUM_THREADS>>>(
+          lines, height, width,
+          dest_outer_stride, dest_inner_stride,
+          src_outer_stride, src_inner_stride,
+          top_diff, bottom_diff);
+    }
+  }
+}
+
 template <typename Dtype>
 void CropLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
+  std::vector<int> indices(top[0]->num_axes(), 0);
+  // This works because crop_copy uses caffe_copy which calls cudaMemcpy.
+  // My intuition is that calling this thousands of times is probably less
+  // efficient than writing a custom kernel.
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
-  const int lines = top[0]->count() / top[0]->width();
-
-  // NOLINT_NEXT_LINE(whitespace/operators)
-  copy_kernel<<<CAFFE_GET_BLOCKS(lines), CAFFE_CUDA_NUM_THREADS>>>(
-      lines, top[0]->height(), top[0]->width(),
-      bottom[0]->height() * bottom[0]->width(), bottom[0]->width(),
-      top[0]->height() * top[0]->width(), top[0]->width(),
-      bottom_data + bottom[0]->offset(0, 0, crop_h_, crop_w_), top_data);
+  crop_copy_gpu(bottom, top, offsets, indices, 0, bottom_data, top_data, true);
 }
 
 template <typename Dtype>
@@ -42,16 +113,12 @@ void CropLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->gpu_diff();
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
-  const int lines = top[0]->count() / top[0]->width();
 
   if (propagate_down[0]) {
     caffe_gpu_set(bottom[0]->count(), static_cast<Dtype>(0), bottom_diff);
-    // NOLINT_NEXT_LINE(whitespace/operators)
-    copy_kernel<<<CAFFE_GET_BLOCKS(lines), CAFFE_CUDA_NUM_THREADS>>>(
-        lines, top[0]->height(), top[0]->width(),
-        top[0]->height() * top[0]->width(), top[0]->width(),
-        bottom[0]->height() * bottom[0]->width(), bottom[0]->width(),
-        top_diff, bottom_diff + bottom[0]->offset(0, 0, crop_h_, crop_w_));
+    std::vector<int> indices(top[0]->num_axes(), 0);
+    crop_copy_gpu(bottom, top, offsets, indices, 0, top_diff, bottom_diff,
+                  false);
   }
 }
 

src/caffe/proto/caffe.proto

@@ -600,10 +600,19 @@ message ConvolutionParameter {
 }
 
 message CropParameter {
-  // Assumes standard dimensions: ( N,C,H,W )
-  // This could possibly be extended to  use "optional BlobShape offsets"
-  optional uint32 offset_height = 1[default = 0];
-  optional uint32 offset_width = 2[default = 0];
+  // To crop, elements of the first bottom are selected to fit the dimensions
+  // of the second, reference bottom. The crop is configured by
+  // - the crop `axis` to pick the dimensions for cropping
+  // - the crop `offset` to set the shift for all/each dimension
+  // to align the cropped bottom with the reference bottom.
+  // All dimensions up to but excluding `axis` are preserved, while
+  // the dimensions including and trailing `axis` are cropped.
+  // If only one `offset` is set, then all dimensions are offset by this amount.
+  // Otherwise, the number of offsets must equal the number of cropped axes to
+  // shift the crop in each dimension accordingly.
+  // Note: standard dimensions are N,C,H,W so the default is a spatial crop.
+  optional uint32 axis = 1 [default = 2];
+  repeated uint32 offset = 2;
 }
 
 message DataParameter {
@@ -680,7 +689,7 @@ message EltwiseParameter {
 // Message that stores parameters used by ELULayer
 message ELUParameter {
   // Described in:
-  // Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate 
+  // Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate
   // Deep Network Learning by Exponential Linear Units (ELUs). arXiv
   optional float alpha = 1 [default = 1];
 }