From 69bf236c8ded35ca8482ccc5b78791eeb77e6248 Mon Sep 17 00:00:00 2001 From: Jake Lee Date: Fri, 1 Feb 2019 14:53:12 -0800 Subject: [PATCH] Revert "Export resize and support batch size (#14014)" This reverts commit 2a4634b983be26cecdd5018b29b4f78e602dba2f. --- python/mxnet/gluon/data/vision/transforms.py | 34 +-- src/io/image_io.cc | 14 +- src/operator/contrib/bilinear_resize-inl.cuh | 184 --------------- src/operator/contrib/bilinear_resize.cu | 79 ++++++- src/operator/image/image_random-inl.h | 8 +- src/operator/image/image_utils.h | 59 ----- src/operator/image/resize-inl.h | 218 ------------------ src/operator/image/resize.cc | 83 ------- src/operator/image/resize.cu | 77 ------- tests/python/gpu/test_gluon_transforms.py | 61 +---- .../python/unittest/test_gluon_data_vision.py | 40 +--- 11 files changed, 113 insertions(+), 744 deletions(-) delete mode 100644 src/operator/contrib/bilinear_resize-inl.cuh delete mode 100644 src/operator/image/image_utils.h delete mode 100644 src/operator/image/resize-inl.h delete mode 100644 src/operator/image/resize.cc delete mode 100644 src/operator/image/resize.cu diff --git a/python/mxnet/gluon/data/vision/transforms.py b/python/mxnet/gluon/data/vision/transforms.py index aa4a3e3d8957..2f557f591f60 100644 --- a/python/mxnet/gluon/data/vision/transforms.py +++ b/python/mxnet/gluon/data/vision/transforms.py @@ -262,8 +262,8 @@ def forward(self, x): return image.center_crop(x, *self._args)[0] -class Resize(HybridBlock): - """Resize an image or a batch of image NDArray to the given size. +class Resize(Block): + """Resize an image to the given size. Should be applied before `mxnet.gluon.data.vision.transforms.ToTensor`. Parameters @@ -276,17 +276,13 @@ class Resize(HybridBlock): interpolation : int Interpolation method for resizing. By default uses bilinear interpolation. See OpenCV's resize function for available choices. - Note that the Resize on gpu use contrib.bilinearResize2D operator - which only support bilinear interpolation(1). The result would be slightly - different on gpu compared to cpu. OpenCV tend to align center while bilinearResize2D - use algorithm which aligns corner. Inputs: - - **data**: input tensor with (H x W x C) or (N x H x W x C) shape. + - **data**: input tensor with (Hi x Wi x C) shape. Outputs: - - **out**: output tensor with (H x W x C) or (N x H x W x C) shape. + - **out**: output tensor with (H x W x C) shape. Examples -------- @@ -294,9 +290,6 @@ class Resize(HybridBlock): >>> image = mx.nd.random.uniform(0, 255, (224, 224, 3)).astype(dtype=np.uint8) >>> transformer(image) - >>> image = mx.nd.random.uniform(0, 255, (3, 224, 224, 3)).astype(dtype=np.uint8) - >>> transformer(image) - """ def __init__(self, size, keep_ratio=False, interpolation=1): super(Resize, self).__init__() @@ -304,8 +297,23 @@ def __init__(self, size, keep_ratio=False, interpolation=1): self._size = size self._interpolation = interpolation - def hybrid_forward(self, F, x): - return F.image.resize(x, self._size, self._keep, self._interpolation) + def forward(self, x): + if isinstance(self._size, numeric_types): + if not self._keep: + wsize = self._size + hsize = self._size + else: + h, w, _ = x.shape + if h > w: + wsize = self._size + hsize = int(h * wsize / w) + else: + hsize = self._size + wsize = int(w * hsize / h) + else: + wsize, hsize = self._size + return image.imresize(x, wsize, hsize, self._interpolation) + class RandomFlipLeftRight(HybridBlock): """Randomly flip the input image left to right with a probability diff --git a/src/io/image_io.cc b/src/io/image_io.cc index 44fcdb8321de..b3f7c40b2b1a 100644 --- a/src/io/image_io.cc +++ b/src/io/image_io.cc @@ -38,7 +38,6 @@ #include #include "../operator/elemwise_op_common.h" -#include "../operator/image/resize-inl.h" #if MXNET_USE_OPENCV #include @@ -286,8 +285,19 @@ inline void Imresize(const nnvm::NodeAttrs& attrs, const std::vector &inputs, const std::vector &req, const std::vector &outputs) { +#if MXNET_USE_OPENCV + CHECK_NE(inputs[0].type_flag_, mshadow::kFloat16) << "imresize doesn't support fp16"; + const int DTYPE[] = {CV_32F, CV_64F, -1, CV_8U, CV_32S}; + int cv_type = CV_MAKETYPE(DTYPE[inputs[0].type_flag_], inputs[0].shape_[2]); const auto& param = nnvm::get(attrs.parsed); - op::image::ResizeImpl(inputs, outputs, param.h, param.w, param.interp); + cv::Mat buf(inputs[0].shape_[0], inputs[0].shape_[1], cv_type, inputs[0].dptr_); + cv::Mat dst(outputs[0].shape_[0], outputs[0].shape_[1], cv_type, outputs[0].dptr_); + cv::resize(buf, dst, cv::Size(param.w, param.h), 0, 0, param.interp); + CHECK(!dst.empty()); + CHECK_EQ(static_cast(dst.ptr()), outputs[0].dptr_); +#else + LOG(FATAL) << "Build with USE_OPENCV=1 for image io."; +#endif // MXNET_USE_OPENCV } diff --git a/src/operator/contrib/bilinear_resize-inl.cuh b/src/operator/contrib/bilinear_resize-inl.cuh deleted file mode 100644 index b8dacb1c4f31..000000000000 --- a/src/operator/contrib/bilinear_resize-inl.cuh +++ /dev/null @@ -1,184 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, - * software distributed under the License is distributed on an - * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY - * KIND, either express or implied. See the License for the - * specific language governing permissions and limitations - * under the License. - */ -/*! - * Copyright (c) 2019 by Contributors - * \file bilinear_resize-inl.cuh - * \brief bilinear resize operator cuda implementation - * \author Hang Zhang, Jake Lee -*/ - -#ifndef MXNET_OPERATOR_CONTRIB_BILINEAR_RESIZE_CUH_ -#define MXNET_OPERATOR_CONTRIB_BILINEAR_RESIZE_CUH_ - -#include -#include - -namespace mxnet { -namespace op { - -using namespace mshadow; - -enum ImageLayout { - HWC, - NHWC, - NCHW -}; - -template -struct ScalarConvert { - static __host__ __device__ __forceinline__ Out to(const In v) { return (Out) v; } -}; - -// The maximum number of threads in a block -static const unsigned MAX_BLOCK_SIZE = 512U; - -// Number of threads in a block given an input size up to MAX_BLOCK_SIZE -static unsigned getNumThreads(int nElem, const bool smaller) { - unsigned threadSizes[5] = {32, 64, 128, 256, MAX_BLOCK_SIZE}; - const int maxi = smaller ? 4 : 5; - for (int i = 0; i != maxi; ++i) { - if (static_cast(nElem) <= threadSizes[i]) { - return threadSizes[i]; - } - } - return smaller ? (MAX_BLOCK_SIZE >> 1) : MAX_BLOCK_SIZE; -} - -// caffe_gpu_interp2_kernel overloading with Tensor -template -__global__ void caffe_gpu_interp2_kernel(const int n, - const Acctype rheight, const Acctype rwidth, - const Tensor data1, - Tensor data2, - ImageLayout layout) { - int index = threadIdx.x + blockIdx.x * blockDim.x; - const int channels = data1.size(2); - const int height1 = data1.size(0); - const int width1 = data1.size(1); - const int height2 = data2.size(0); - const int width2 = data2.size(1); - - if (index < n) { - const int w2 = index % width2; // 0:width2-1 - const int h2 = index / width2; // 0:height2-1 - // special case: just copy - if (height1 == height2 && width1 == width2) { - const int h1 = h2; - const int w1 = w2; - for (int c = 0; c < channels; ++c) { - const Dtype val = data1[h1][w1][c]; - data2[h2][w2][c] = val; - } - return; - } - // - const Acctype h1r = rheight * h2; - const int h1 = h1r; - const int h1p = (h1 < height1 - 1) ? 1 : 0; - const Acctype h1lambda = h1r - h1; - const Acctype h0lambda = Acctype(1) - h1lambda; - // - const Acctype w1r = rwidth * w2; - const int w1 = w1r; - const int w1p = (w1 < width1 - 1) ? 1 : 0; - const Acctype w1lambda = w1r - w1; - const Acctype w0lambda = Acctype(1) - w1lambda; - for (int c = 0; c < channels; ++c) { - const Acctype val = h0lambda * (w0lambda * data1[h1][w1][c] - + w1lambda * data1[h1][w1+w1p][c]) - + h1lambda * (w0lambda * data1[h1+h1p][w1][c] - + w1lambda * data1[h1+h1p][w1+w1p][c]); - data2[h2][w2][c] = ScalarConvert::to(val); - } - } -} - -// caffe_gpu_interp2_kernel overloading with Tensor -template -__global__ void caffe_gpu_interp2_kernel(const int n, - const Acctype rheight, const Acctype rwidth, - const Tensor data1, - Tensor data2, - ImageLayout layout) { - int index = threadIdx.x + blockIdx.x * blockDim.x; - int batch_size = (layout == NHWC) ? data1.size(0) : data1.size(0); - int channels = (layout == NHWC) ? data1.size(3) : data1.size(1); - int height1 = (layout == NHWC) ? data1.size(1) : data1.size(2); - int width1 = (layout == NHWC) ? data1.size(2) : data1.size(3); - int height2 = (layout == NHWC) ? data2.size(1) : data2.size(2); - int width2 = (layout == NHWC) ? data2.size(2): data2.size(3); - - if (index < n) { - const int w2 = index % width2; // 0:width2-1 - const int h2 = index / width2; // 0:height2-1 - // special case: just copy - if (height1 == height2 && width1 == width2) { - const int h1 = h2; - const int w1 = w2; - - for (int n = 0; n < batch_size; ++n) { - for (int c = 0; c < channels; ++c) { - if (layout == NHWC) { - const Dtype val = data1[n][h1][w1][c]; - data2[n][h2][w2][c] = val; - } else { - const Dtype val = data1[n][c][h1][w1]; - data2[n][c][h2][w2] = val; - } - } - } - return; - } - // - const Acctype h1r = rheight * h2; - const int h1 = h1r; - const int h1p = (h1 < height1 - 1) ? 1 : 0; - const Acctype h1lambda = h1r - h1; - const Acctype h0lambda = Acctype(1) - h1lambda; - // - const Acctype w1r = rwidth * w2; - const int w1 = w1r; - const int w1p = (w1 < width1 - 1) ? 1 : 0; - const Acctype w1lambda = w1r - w1; - const Acctype w0lambda = Acctype(1) - w1lambda; - - for (auto n = 0; n < batch_size; ++n) { - for (int c = 0; c < channels; ++c) { - if (layout == NHWC) { - const Acctype val = h0lambda * (w0lambda * data1[n][h1][w1][c] - + w1lambda * data1[n][h1][w1+w1p][c]) - + h1lambda * (w0lambda * data1[n][h1+h1p][w1][c] - + w1lambda * data1[n][h1+h1p][w1+w1p][c]); - data2[n][h2][w2][c] = ScalarConvert::to(val); - } else { - const Acctype val = h0lambda * (w0lambda * data1[n][c][h1][w1] - + w1lambda * data1[n][c][h1][w1+w1p]) - + h1lambda * (w0lambda * data1[n][c][h1+h1p][w1] - + w1lambda * data1[n][c][h1+h1p][w1+w1p]); - data2[n][c][h2][w2] = ScalarConvert::to(val); - } - } - } - } -} - -} // namespace op -} // namespace mxnet - -#endif // MXNET_OPERATOR_CONTRIB_BILINEAR_RESIZE_CUH_ \ No newline at end of file diff --git a/src/operator/contrib/bilinear_resize.cu b/src/operator/contrib/bilinear_resize.cu index b0a4c4b316d9..f01c9c2fa132 100644 --- a/src/operator/contrib/bilinear_resize.cu +++ b/src/operator/contrib/bilinear_resize.cu @@ -25,13 +25,86 @@ #include #include #include "bilinear_resize-inl.h" -#include "bilinear_resize-inl.cuh" namespace mxnet { namespace op { using namespace mshadow; +template +struct ScalarConvert { + static __host__ __device__ __forceinline__ Out to(const In v) { return (Out) v; } +}; + + +// The maximum number of threads in a block +static const unsigned MAX_BLOCK_SIZE = 512U; + +// Number of threads in a block given an input size up to MAX_BLOCK_SIZE +static unsigned getNumThreads(int nElem, const bool smaller) { + unsigned threadSizes[5] = {32, 64, 128, 256, MAX_BLOCK_SIZE}; + const int maxi = smaller ? 4 : 5; + for (int i = 0; i != maxi; ++i) { + if (static_cast(nElem) <= threadSizes[i]) { + return threadSizes[i]; + } + } + return smaller ? (MAX_BLOCK_SIZE >> 1) : MAX_BLOCK_SIZE; +} + +template +__global__ void caffe_gpu_interp2_kernel(const int n, + const Acctype rheight, const Acctype rwidth, + const Tensor data1, + Tensor data2) { + int index = threadIdx.x + blockIdx.x * blockDim.x; + const int batchsize = data1.size(0); + const int channels = data1.size(1); + const int height1 = data1.size(2); + const int width1 = data1.size(3); + const int height2 = data2.size(2); + const int width2 = data2.size(3); + + if (index < n) { + const int w2 = index % width2; // 0:width2-1 + const int h2 = index / width2; // 0:height2-1 + // special case: just copy + if (height1 == height2 && width1 == width2) { + const int h1 = h2; + const int w1 = w2; + for (int n = 0; n < batchsize ; n++) { + for (int c = 0; c < channels; ++c) { + const Dtype val = data1[n][c][h1][w1]; + data2[n][c][h2][w2] = val; + } + } + return; + } + // + const Acctype h1r = rheight * h2; + const int h1 = h1r; + const int h1p = (h1 < height1 - 1) ? 1 : 0; + const Acctype h1lambda = h1r - h1; + const Acctype h0lambda = Acctype(1) - h1lambda; + // + const Acctype w1r = rwidth * w2; + const int w1 = w1r; + const int w1p = (w1 < width1 - 1) ? 1 : 0; + const Acctype w1lambda = w1r - w1; + const Acctype w0lambda = Acctype(1) - w1lambda; + // + for (int n = 0; n < batchsize ; n++) { + for (int c = 0; c < channels; ++c) { + const Acctype val = h0lambda * (w0lambda * data1[n][c][h1][w1] + + w1lambda * data1[n][c][h1][w1+w1p]) + + h1lambda * (w0lambda * data1[n][c][h1+h1p][w1] + + w1lambda * data1[n][c][h1+h1p][w1+w1p]); + data2[n][c][h2][w2] = ScalarConvert::to(val); + } + } + } +} + // Backward (adjoint) operation 1 <- 2 (accumulates) template __global__ void caffe_gpu_interp2_kernel_backward(const int n, @@ -108,10 +181,9 @@ void SpatialUpSamplingBilinearUpdateOutput(mshadow::Stream *s, dim3 blocks(static_cast(num_kernels / num_threads) + 1); dim3 threads(num_threads); cudaStream_t stream = mshadow::Stream::GetStream(s); - ImageLayout layout = NCHW; caffe_gpu_interp2_kernel <<>>( - num_kernels, rheight, rwidth, idata, odata, layout); + num_kernels, rheight, rwidth, idata, odata); MSHADOW_CUDA_POST_KERNEL_CHECK(SpatialUpSamplingBilinearUpdateOutput); } @@ -143,5 +215,6 @@ NNVM_REGISTER_OP(_contrib_BilinearResize2D) NNVM_REGISTER_OP(_backward_contrib_BilinearResize2D) .set_attr("FCompute", BilinearSampleOpBackward); + } // namespace op } // namespace mxnet diff --git a/src/operator/image/image_random-inl.h b/src/operator/image/image_random-inl.h index aeea0bcf9fec..74807b9b681e 100644 --- a/src/operator/image/image_random-inl.h +++ b/src/operator/image/image_random-inl.h @@ -26,18 +26,14 @@ #define MXNET_OPERATOR_IMAGE_IMAGE_RANDOM_INL_H_ +#include #include +#include #include #include -#include #include -#include -#include "mxnet/base.h" #include "../mxnet_op.h" #include "../operator_common.h" -#if MXNET_USE_OPENCV - #include -#endif // MXNET_USE_OPENCV namespace mxnet { namespace op { diff --git a/src/operator/image/image_utils.h b/src/operator/image/image_utils.h deleted file mode 100644 index a7155345c967..000000000000 --- a/src/operator/image/image_utils.h +++ /dev/null @@ -1,59 +0,0 @@ -/* -* Licensed to the Apache Software Foundation (ASF) under one -* or more contributor license agreements. See the NOTICE file -* distributed with this work for additional information -* regarding copyright ownership. The ASF licenses this file -* to you under the Apache License, Version 2.0 (the -* "License"); you may not use this file except in compliance -* with the License. You may obtain a copy of the License at -* -* http://www.apache.org/licenses/LICENSE-2.0 -* -* Unless required by applicable law or agreed to in writing, -* software distributed under the License is distributed on an -* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -* KIND, either express or implied. See the License for the -* specific language governing permissions and limitations -* under the License. -*/ - -/*! - * Copyright (c) 2019 by Contributors - * \file image_utils.h - * \brief the image operator utility function implementation - * \author Jake Lee - */ - -#ifndef MXNET_OPERATOR_IMAGE_IMAGE_UTILS_H_ -#define MXNET_OPERATOR_IMAGE_IMAGE_UTILS_H_ - -#include -#if MXNET_USE_OPENCV - #include -#endif // MXNET_USE_OPENCV - -namespace mxnet { -namespace op { -namespace image { - -enum ImageLayout {H, W, C}; -enum BatchImageLayout {N, kH, kW, kC}; - -struct SizeParam { - int height; - int width; - SizeParam() { - height = 0; - width = 0; - } - SizeParam(int height_, int width_) { - height = height_; - width = width_; - } -}; // struct SizeParam - -} // namespace image -} // namespace op -} // namespace mxnet - -#endif // MXNET_OPERATOR_IMAGE_IMAGE_UTILS_H_ diff --git a/src/operator/image/resize-inl.h b/src/operator/image/resize-inl.h deleted file mode 100644 index 3e1310068073..000000000000 --- a/src/operator/image/resize-inl.h +++ /dev/null @@ -1,218 +0,0 @@ -/* -* Licensed to the Apache Software Foundation (ASF) under one -* or more contributor license agreements. See the NOTICE file -* distributed with this work for additional information -* regarding copyright ownership. The ASF licenses this file -* to you under the Apache License, Version 2.0 (the -* "License"); you may not use this file except in compliance -* with the License. You may obtain a copy of the License at -* -* http://www.apache.org/licenses/LICENSE-2.0 -* -* Unless required by applicable law or agreed to in writing, -* software distributed under the License is distributed on an -* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -* KIND, either express or implied. See the License for the -* specific language governing permissions and limitations -* under the License. -*/ -/*! -* \file resize-inl.h -* \brief image resize operator using opencv and only support bilinear resize -* \author Jake Lee -*/ -#ifndef MXNET_OPERATOR_IMAGE_RESIZE_INL_H_ -#define MXNET_OPERATOR_IMAGE_RESIZE_INL_H_ - -#include -#include - -#include "../mxnet_op.h" -#include "../operator_common.h" -#include "image_utils.h" - -#if MXNET_USE_OPENCV - #include -#endif // MXNET_USE_OPENCV - -namespace mxnet { -namespace op { -namespace image { - -using namespace mshadow; - -#if MXNET_USE_CUDA -template -void ResizeImplCUDA(Stream *s, - const T input, - const T output); -#endif // MXNET_USE_CUDA - -struct ResizeParam : public dmlc::Parameter { - nnvm::Tuple size; - bool keep_ratio; - int interp; - DMLC_DECLARE_PARAMETER(ResizeParam) { - DMLC_DECLARE_FIELD(size) - .set_default(nnvm::Tuple()) - .describe("Size of new image. Could be (width, height) or (size)"); - DMLC_DECLARE_FIELD(keep_ratio) - .describe("Whether to resize the short edge or both edges to `size`, " - "if size is give as an integer.") - .set_default(false); - DMLC_DECLARE_FIELD(interp) - .set_default(1) - .describe("Interpolation method for resizing. By default uses bilinear interpolation" - "Options are INTER_NEAREST - a nearest-neighbor interpolation" - "INTER_LINEAR - a bilinear interpolation" - "INTER_AREA - resampling using pixel area relation" - "INTER_CUBIC - a bicubic interpolation over 4x4 pixel neighborhood" - "INTER_LANCZOS4 - a Lanczos interpolation over 8x8 pixel neighborhood" - "Note that the GPU version only support bilinear interpolation(1)" - " and the result on cpu would be slightly different from gpu." - "It uses opencv resize function which tend to align center on cpu" - "while using contrib.bilinearResize2D which aligns corner on gpu"); - } -}; -// handle the keep ratio param -inline SizeParam GetHeightAndWidth(int data_h, - int data_w, - const ResizeParam& param) { - CHECK((param.size.ndim() == 1) || (param.size.ndim() == 2)) - << "Input size dimension must be 1 or 2, but got " - << param.size.ndim(); - int resized_h; - int resized_w; - if (param.size.ndim() == 1) { - CHECK_GT(param.size[0], 0) - << "Input size should be greater than 0, but got " - << param.size[0]; - if (!param.keep_ratio) { - resized_h = param.size[0]; - resized_w = param.size[0]; - } else { - if (data_h > data_w) { - resized_w = param.size[0]; - resized_h = static_cast(data_h * resized_w / data_w); - } else { - resized_h = param.size[0]; - resized_w = static_cast(data_w * resized_h / data_h); - } - } - } else { - CHECK_GT(param.size[0], 0) - << "Input width should be greater than 0, but got " - << param.size[0]; - CHECK_GT(param.size[1], 0) - << "Input height should be greater than 0, but got " - << param.size[1]; - resized_h = param.size[1]; - resized_w = param.size[0]; - } - return SizeParam(resized_h, resized_w); -} - -inline bool ResizeShape(const nnvm::NodeAttrs& attrs, - std::vector *in_attrs, - std::vector *out_attrs) { - // input attrs should only be (h, w, c) or (n, h, w, c) - CHECK((in_attrs->at(0).ndim() == 3U) || (in_attrs->at(0).ndim() == 4U)) - << "Input image dimension should be 3 or 4 but got " - << in_attrs->at(0).ndim(); - const auto& ishape = (*in_attrs)[0]; - const ResizeParam& param = nnvm::get(attrs.parsed); - SizeParam size; - if (ishape.ndim() == 3) { - size = GetHeightAndWidth(ishape[H], ishape[W], param); - SHAPE_ASSIGN_CHECK(*out_attrs, 0, TShape({size.height, size.width, ishape[C]})); - } else { - size = GetHeightAndWidth(ishape[kH], ishape[kW], param); - SHAPE_ASSIGN_CHECK(*out_attrs, 0, - TShape({ishape[N], size.height, size.width, ishape[kC]})); - } - return true; -} - -inline void ResizeImpl(const std::vector &inputs, - const std::vector &outputs, - const int height, - const int width, - const int interp, - const int input_index = 0, - const int output_index = 0) { -#if MXNET_USE_OPENCV - CHECK_NE(inputs[0].type_flag_, mshadow::kFloat16) << "opencv image mat doesn't support fp16"; - CHECK((inputs[0].type_flag_ != mshadow::kInt32) || (inputs[0].type_flag_ != mshadow::kInt64)) - << "opencv resize doesn't support int32, int64"; - // mapping to opencv matrix element type according to channel - const int DTYPE[] = {CV_32F, CV_64F, -1, CV_8U, CV_32S}; - if (inputs[0].ndim() == 3) { - const int cv_type = CV_MAKETYPE(DTYPE[inputs[0].type_flag_], inputs[0].shape_[C]); - cv::Mat buf(inputs[0].shape_[H], inputs[0].shape_[W], cv_type, inputs[0].dptr_); - cv::Mat dst(outputs[0].shape_[H], outputs[0].shape_[W], cv_type, outputs[0].dptr_); - cv::resize(buf, dst, cv::Size(width, height), 0, 0, interp); - CHECK(!dst.empty()); - CHECK_EQ(static_cast(dst.ptr()), outputs[0].dptr_); - } else { - const int cv_type = CV_MAKETYPE(DTYPE[inputs[0].type_flag_], inputs[0].shape_[kC]); - MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, { - cv::Mat buf(inputs[0].shape_[kH], inputs[0].shape_[kW], cv_type, - inputs[0].dptr() + input_index); - cv::Mat dst(outputs[0].shape_[kH], outputs[0].shape_[kW], cv_type, - outputs[0].dptr() + output_index); - cv::resize(buf, dst, cv::Size(width, height), 0, 0, interp); - CHECK(!dst.empty()); - CHECK_EQ(static_cast(dst.ptr()), outputs[0].dptr() + output_index); - }); - } -#else - LOG(FATAL) << "Build with USE_OPENCV=1 for image resize operator."; -#endif // MXNET_USE_OPENCV -} - -template -inline void Resize(const nnvm::NodeAttrs &attrs, - const OpContext &ctx, - const std::vector &inputs, - const std::vector &req, - const std::vector &outputs) { - CHECK_EQ(outputs.size(), 1U); - const ResizeParam& param = nnvm::get(attrs.parsed); - SizeParam size; - if (std::is_same::value) { -#if MXNET_USE_CUDA - CHECK(param.interp == 1) << "interp should be 1 for using Resize on GPU."; - mshadow::Stream *s = ctx.get_stream(); - MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, { - if (inputs[0].ndim() == 3) { - Tensor input = inputs[0].get(s); - Tensor output = outputs[0].get(s); - ResizeImplCUDA, float>(s, input, output); - } else { - Tensor input = inputs[0].get(s); - Tensor output = outputs[0].get(s); - ResizeImplCUDA, float>(s, input, output); - } - }); -#endif // MXNET_USE_CUDA - } else if (inputs[0].ndim() == 3) { - size = GetHeightAndWidth(inputs[0].shape_[H], inputs[0].shape_[W], param); - ResizeImpl(inputs, outputs, size.height, size.width, param.interp); - } else { - size = GetHeightAndWidth(inputs[0].shape_[kH], inputs[0].shape_[kW], param); - const auto batch_size = inputs[0].shape_[N]; - const auto input_step = inputs[0].shape_[kH] * inputs[0].shape_[kW] * inputs[0].shape_[kC]; - const auto output_step = size.height * size.width * inputs[0].shape_[kC]; - #pragma omp parallel for - for (auto i = 0; i < batch_size; ++i) { - ResizeImpl(inputs, outputs, size.height, size.width, - param.interp, i * input_step, i * output_step); - } - } -} - -} // namespace image -} // namespace op -} // namespace mxnet - -#endif // MXNET_OPERATOR_IMAGE_RESIZE_INL_H_ diff --git a/src/operator/image/resize.cc b/src/operator/image/resize.cc deleted file mode 100644 index d3b28f08008f..000000000000 --- a/src/operator/image/resize.cc +++ /dev/null @@ -1,83 +0,0 @@ -/* -* Licensed to the Apache Software Foundation (ASF) under one -* or more contributor license agreements. See the NOTICE file -* distributed with this work for additional information -* regarding copyright ownership. The ASF licenses this file -* to you under the Apache License, Version 2.0 (the -* "License"); you may not use this file except in compliance -* with the License. You may obtain a copy of the License at -* -* http://www.apache.org/licenses/LICENSE-2.0 -* -* Unless required by applicable law or agreed to in writing, -* software distributed under the License is distributed on an -* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -* KIND, either express or implied. See the License for the -* specific language governing permissions and limitations -* under the License. -*/ -/*! - * Copyright (c) 2019 by Contributors - * \file resize.cc - * \brief resize operator cpu - * \author Jake Lee -*/ -#include -#include "./resize-inl.h" -#include "../operator_common.h" -#include "../elemwise_op_common.h" - -namespace mxnet { -namespace op { -namespace image { - -DMLC_REGISTER_PARAMETER(ResizeParam); - -NNVM_REGISTER_OP(_image_resize) -.describe(R"code(Resize an image NDArray of shape (H x W x C) or (N x H x W x C) -to the given size -Example: - .. code-block:: python - image = mx.nd.random.uniform(0, 255, (4, 2, 3)).astype(dtype=np.uint8) - mx.nd.image.resize(image, (3, 3)) - [[[124 111 197] - [158 80 155] - [193 50 112]] - - [[110 100 113] - [134 165 148] - [157 231 182]] - - [[202 176 134] - [174 191 149] - [147 207 164]]] - - image = mx.nd.random.uniform(0, 255, (2, 4, 2, 3)).astype(dtype=np.uint8) - mx.nd.image.resize(image, (2, 2)) - [[[[ 59 133 80] - [187 114 153]] - - [[ 38 142 39] - [207 131 124]]] - - - [[[117 125 136] - [191 166 150]] - - [[129 63 113] - [182 109 48]]]] - -)code" ADD_FILELINE) -.set_num_inputs(1) -.set_num_outputs(1) -.set_attr_parser(ParamParser) -.set_attr("FInferShape", ResizeShape) -.set_attr("FInferType", ElemwiseType<1, 1>) -.set_attr("FCompute", Resize) -.set_attr("FGradient", ElemwiseGradUseNone{ "_copy" }) -.add_argument("data", "NDArray-or-Symbol", "The input.") -.add_arguments(ResizeParam::__FIELDS__()); - -} // namespace image -} // namespace op -} // namespace mxnet diff --git a/src/operator/image/resize.cu b/src/operator/image/resize.cu deleted file mode 100644 index f045f3b238ea..000000000000 --- a/src/operator/image/resize.cu +++ /dev/null @@ -1,77 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, - * software distributed under the License is distributed on an - * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY - * KIND, either express or implied. See the License for the - * specific language governing permissions and limitations - * under the License. - */ -/*! - * Copyright (c) 2019 by Contributors - * \file bilinear_resize.cu - * \brief bilinear resize operator - * \author Hang Zhang, Jake Lee -*/ -#include -#include "./resize-inl.h" -#include "../contrib/bilinear_resize-inl.cuh" - -namespace mxnet { -namespace op { -namespace image { - -using namespace mshadow; - -template -void ResizeImplCUDA(mshadow::Stream *s, - const T input, - const T output) { - int outputHeight; - int outputWidth; - int inputHeight; - int inputWidth; - mxnet::op::ImageLayout layout; - if (std::is_same>::value) { - layout = HWC; - outputHeight = output.size(0); - outputWidth = output.size(1); - inputHeight = input.size(0); - inputWidth = input.size(1); - } else { - layout = NHWC; - outputHeight = output.size(1); - outputWidth = output.size(2); - inputHeight = input.size(1); - inputWidth = input.size(2); - } - const AccReal rheight = (outputHeight > 1) ? (AccReal)(inputHeight - 1)/ - (outputHeight - 1) : AccReal(0); - const AccReal rwidth = (outputWidth > 1) ? (AccReal)(inputWidth - 1)/ - (outputWidth - 1) : AccReal(0); - const int num_kernels = outputHeight * outputWidth; - const int num_threads = getNumThreads(inputHeight * inputWidth, false); - dim3 blocks(static_cast(num_kernels / num_threads) + 1); - dim3 threads(num_threads); - cudaStream_t stream = mshadow::Stream::GetStream(s); - caffe_gpu_interp2_kernel - <<>>( - num_kernels, rheight, rwidth, input, output, layout); - MSHADOW_CUDA_POST_KERNEL_CHECK(caffe_gpu_interp2_kernel); -} - -NNVM_REGISTER_OP(_image_resize) -.set_attr("FCompute", Resize); - -} // namespace image -} // namespace op -} // namespace mxnet diff --git a/tests/python/gpu/test_gluon_transforms.py b/tests/python/gpu/test_gluon_transforms.py index 4a1017b538ac..c7afc762bd80 100644 --- a/tests/python/gpu/test_gluon_transforms.py +++ b/tests/python/gpu/test_gluon_transforms.py @@ -69,63 +69,4 @@ def test_normalize(): # Invalid Input - Channel neither 1 or 3 invalid_data_in = nd.random.uniform(0, 1, (5, 4, 300, 300)) normalize_transformer = transforms.Normalize(mean=(0, 1, 2), std=(3, 2, 1)) - assertRaises(MXNetError, normalize_transformer, invalid_data_in) - - -@with_seed() -def test_resize(): - # Test with normal case 3D input float type - data_in_3d = nd.random.uniform(0, 255, (300, 300, 3)) - out_nd_3d = transforms.Resize((100, 100))(data_in_3d) - data_in_4d_nchw = nd.moveaxis(nd.expand_dims(data_in_3d, axis=0), 3, 1) - data_expected_3d = (nd.moveaxis(nd.contrib.BilinearResize2D(data_in_4d_nchw, 100, 100), 1, 3))[0] - assert_almost_equal(out_nd_3d.asnumpy(), data_expected_3d.asnumpy()) - - # Test with normal case 4D input float type - data_in_4d = nd.random.uniform(0, 255, (2, 300, 300, 3)) - out_nd_4d = transforms.Resize((100, 100))(data_in_4d) - data_in_4d_nchw = nd.moveaxis(data_in_4d, 3, 1) - data_expected_4d = nd.moveaxis(nd.contrib.BilinearResize2D(data_in_4d_nchw, 100, 100), 1, 3) - assert_almost_equal(out_nd_4d.asnumpy(), data_expected_4d.asnumpy()) - - # Test invalid interp - data_in_3d = nd.random.uniform(0, 255, (300, 300, 3)) - invalid_transform = transforms.Resize(-150, keep_ratio=False, interpolation=2) - assertRaises(MXNetError, invalid_transform, data_in_3d) - - # Credited to Hang Zhang - def py_bilinear_resize_nhwc(x, outputHeight, outputWidth): - batch, inputHeight, inputWidth, channel = x.shape - if outputHeight == inputHeight and outputWidth == inputWidth: - return x - y = np.empty([batch, outputHeight, outputWidth, channel]).astype('uint8') - rheight = 1.0 * (inputHeight - 1) / (outputHeight - 1) if outputHeight > 1 else 0.0 - rwidth = 1.0 * (inputWidth - 1) / (outputWidth - 1) if outputWidth > 1 else 0.0 - for h2 in range(outputHeight): - h1r = 1.0 * h2 * rheight - h1 = int(np.floor(h1r)) - h1lambda = h1r - h1 - h1p = 1 if h1 < (inputHeight - 1) else 0 - for w2 in range(outputWidth): - w1r = 1.0 * w2 * rwidth - w1 = int(np.floor(w1r)) - w1lambda = w1r - w1 - w1p = 1 if w1 < (inputHeight - 1) else 0 - for b in range(batch): - for c in range(channel): - y[b][h2][w2][c] = (1-h1lambda)*((1-w1lambda)*x[b][h1][w1][c] + \ - w1lambda*x[b][h1][w1+w1p][c]) + \ - h1lambda*((1-w1lambda)*x[b][h1+h1p][w1][c] + \ - w1lambda*x[b][h1+h1p][w1+w1p][c]) - return y - - # Test with normal case 3D input int8 type - data_in_4d = nd.random.uniform(0, 255, (1, 300, 300, 3)).astype('uint8') - out_nd_3d = transforms.Resize((100, 100))(data_in_4d[0]) - assert_almost_equal(out_nd_3d.asnumpy(), py_bilinear_resize_nhwc(data_in_4d.asnumpy(), 100, 100)[0], atol=1.0) - - # Test with normal case 4D input int8 type - data_in_4d = nd.random.uniform(0, 255, (2, 300, 300, 3)).astype('uint8') - out_nd_4d = transforms.Resize((100, 100))(data_in_4d) - assert_almost_equal(out_nd_4d.asnumpy(), py_bilinear_resize_nhwc(data_in_4d.asnumpy(), 100, 100), atol=1.0) - + assertRaises(MXNetError, normalize_transformer, invalid_data_in) \ No newline at end of file diff --git a/tests/python/unittest/test_gluon_data_vision.py b/tests/python/unittest/test_gluon_data_vision.py index f10f0ae4fe19..c83778fefc65 100644 --- a/tests/python/unittest/test_gluon_data_vision.py +++ b/tests/python/unittest/test_gluon_data_vision.py @@ -17,7 +17,7 @@ from __future__ import print_function import mxnet as mx import mxnet.ndarray as nd -from mxnet.base import MXNetError +import numpy as np from mxnet import gluon from mxnet.base import MXNetError from mxnet.gluon.data.vision import transforms @@ -25,7 +25,6 @@ from mxnet.test_utils import almost_equal from common import assertRaises, setup_module, with_seed, teardown -import numpy as np @with_seed() def test_to_tensor(): @@ -69,43 +68,6 @@ def test_normalize(): assertRaises(MXNetError, normalize_transformer, invalid_data_in) -@with_seed() -def test_resize(): - def _test_resize_with_diff_type(dtype): - # test normal case - data_in = nd.random.uniform(0, 255, (300, 200, 3)).astype(dtype) - out_nd = transforms.Resize(200)(data_in) - data_expected = mx.image.imresize(data_in, 200, 200, 1) - assert_almost_equal(out_nd.asnumpy(), data_expected.asnumpy()) - # test 4D input - data_bath_in = nd.random.uniform(0, 255, (3, 300, 200, 3)).astype(dtype) - out_batch_nd = transforms.Resize(200)(data_bath_in) - for i in range(len(out_batch_nd)): - assert_almost_equal(mx.image.imresize(data_bath_in[i], 200, 200, 1).asnumpy(), - out_batch_nd[i].asnumpy()) - # test interp = 2 - out_nd = transforms.Resize(200, interpolation=2)(data_in) - data_expected = mx.image.imresize(data_in, 200, 200, 2) - assert_almost_equal(out_nd.asnumpy(), data_expected.asnumpy()) - # test height not equals to width - out_nd = transforms.Resize((200, 100))(data_in) - data_expected = mx.image.imresize(data_in, 200, 100, 1) - assert_almost_equal(out_nd.asnumpy(), data_expected.asnumpy()) - # test keep_ratio - out_nd = transforms.Resize(150, keep_ratio=True)(data_in) - data_expected = mx.image.imresize(data_in, 150, 225, 1) - assert_almost_equal(out_nd.asnumpy(), data_expected.asnumpy()) - # test size below zero - invalid_transform = transforms.Resize(-150, keep_ratio=True) - assertRaises(MXNetError, invalid_transform, data_in) - # test size more than 2: - invalid_transform = transforms.Resize((100, 100, 100), keep_ratio=True) - assertRaises(MXNetError, invalid_transform, data_in) - - for dtype in ['uint8', 'float32', 'float64']: - _test_resize_with_diff_type(dtype) - - @with_seed() def test_flip_left_right(): data_in = np.random.uniform(0, 255, (300, 300, 3)).astype(dtype=np.uint8)