diff --git a/CMakeLists.txt b/CMakeLists.txt index 15a7c6b07417adfacd461e95c0b92f658e1e11cc..fdc62b31511c424b2944d05be46d029a6d4bfc8b 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -13,7 +13,7 @@ # limitations under the License cmake_minimum_required(VERSION 3.0) - +SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -ldl -lpthread") set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/cmake") set(PROJ_ROOT ${CMAKE_CURRENT_SOURCE_DIR}) set(PROJ_BINARY_ROOT ${CMAKE_CURRENT_BINARY_DIR}) diff --git a/paddle/function/CropOp.cpp b/paddle/function/CropOp.cpp index 39e06fc120aae434a3a6a0946743ee14eb5a32af..f12ee43e3d72f9ac776eaff93914228850694dd2 100644 --- a/paddle/function/CropOp.cpp +++ b/paddle/function/CropOp.cpp @@ -22,11 +22,10 @@ template <> void Crop(real* outputs, const real* inputs, const TensorShape inShape, + const TensorShape outShape, const FuncConfig& conf) { std::vector crop_corner = conf.get>("crop_corner"); - std::vector crop_shape = - conf.get>("crop_shape"); int cCrop = crop_corner[1]; int hCrop = crop_corner[2]; int wCrop = crop_corner[3]; @@ -36,9 +35,9 @@ void Crop(real* outputs, int inH = inShape[2]; int inW = inShape[3]; - int outC = crop_shape[1]; - int outH = crop_shape[2]; - int outW = crop_shape[3]; + int outC = outShape[1]; + int outH = outShape[2]; + int outW = outShape[3]; for (int n = 0; n < num; n++) { for (int c = 0; c < outC; c++) { @@ -54,12 +53,11 @@ void Crop(real* outputs, template <> void CropGrad(const real* inGrad, real* outGrad, + const TensorShape inShape, const TensorShape outShape, const FuncConfig& conf) { std::vector crop_corner = conf.get>("crop_corner"); - std::vector crop_shape = - conf.get>("crop_shape"); int cCrop = crop_corner[1]; int hCrop = crop_corner[2]; int wCrop = crop_corner[3]; @@ -69,9 +67,9 @@ void CropGrad(const real* inGrad, int outH = outShape[2]; int outW = outShape[3]; - int inC = crop_shape[1]; - int inH = crop_shape[2]; - int inW = crop_shape[3]; + int inC = inShape[1]; + int inH = inShape[2]; + int inW = inShape[3]; for (int n = 0; n < num; n++) { for (int c = 0; c < inC; c++) { @@ -123,9 +121,13 @@ public: CHECK_EQ(outputs[0].getArgType(), ASSIGN_TO); TensorShape inShape = inputs[0].shape(); + TensorShape outShape = outputs[0].shape(); - Crop( - outputs[0].data(), inputs[0].data(), inShape, conf_); + Crop(outputs[0].data(), + inputs[0].data(), + inShape, + outShape, + conf_); } private: @@ -152,9 +154,13 @@ public: CHECK_EQ(outputs[0].getArgType(), ADD_TO); TensorShape outShape = outputs[0].shape(); + TensorShape inShape = inputs[0].shape(); - CropGrad( - inputs[0].data(), outputs[0].data(), outShape, conf_); + CropGrad(inputs[0].data(), + outputs[0].data(), + inShape, + outShape, + conf_); } private: diff --git a/paddle/function/CropOp.h b/paddle/function/CropOp.h index 71e8c4c00ee3cfd5093b1305cefa442ec8adb8ae..87986fbdc7e33aeb24d947e82a5d67ba23f532de 100644 --- a/paddle/function/CropOp.h +++ b/paddle/function/CropOp.h @@ -31,6 +31,7 @@ template void Crop(real* outputs, const real* inputs, const TensorShape inShape, + const TensorShape outShape, const FuncConfig& conf); /** @@ -45,5 +46,6 @@ template void CropGrad(const real* inGrad, real* outGrad, const TensorShape inShape, + const TensorShape outShape, const FuncConfig& conf); } // namespace paddle diff --git a/paddle/function/CropOpGpu.cu b/paddle/function/CropOpGpu.cu index cadb58b6e995db9160cde44c0f5ab7e1f474117c..37ce6de0647e5e06a231710b5a53089533de2407 100644 --- a/paddle/function/CropOpGpu.cu +++ b/paddle/function/CropOpGpu.cu @@ -37,9 +37,9 @@ template <> void Crop(real* outputs, const real* inputs, const TensorShape inShape, + const TensorShape outShape, const FuncConfig& conf) { std::vector crop_corner = conf.get>("crop_corner"); - std::vector crop_shape = conf.get>("crop_shape"); int cropC = crop_corner[1]; int cropH = crop_corner[2]; int cropW = crop_corner[3]; @@ -49,14 +49,14 @@ void Crop(real* outputs, int inH = inShape[2]; int inW = inShape[3]; - int outC = crop_shape[1]; - int outH = crop_shape[2]; - int outW = crop_shape[3]; - + int outC = outShape[1]; + int outH = outShape[2]; + int outW = outShape[3]; + size_t nth = num * outC * outH * outW; int blockSize = 1024; int gridSize = (nth + blockSize - 1) / blockSize; - + KeCrop<<>> (outputs, inputs, inC, inH, inW, cropC, cropH, cropW, outC, outH, outW, nth); @@ -75,7 +75,7 @@ __global__ void KeCropDiff(const real* inGrad, real* outGrad, const int n = idx / inW / inH / inC; const int off = ((n * outC + c + cropC) * outH + h + cropH) * outW + cropW + w; - + outGrad[off] += inGrad[idx]; } } @@ -83,10 +83,10 @@ __global__ void KeCropDiff(const real* inGrad, real* outGrad, template <> void CropGrad(const real* inGrad, real* outGrad, + const TensorShape inShape, const TensorShape outShape, const FuncConfig& conf) { std::vector crop_corner = conf.get>("crop_corner"); - std::vector crop_shape = conf.get>("crop_shape"); int cropC = crop_corner[1]; int cropH = crop_corner[2]; int cropW = crop_corner[3]; @@ -96,10 +96,10 @@ void CropGrad(const real* inGrad, int outH = outShape[2]; int outW = outShape[3]; - int inC = crop_shape[1]; - int inH = crop_shape[2]; - int inW = crop_shape[3]; - + int inC = inShape[1]; + int inH = inShape[2]; + int inW = inShape[3]; + size_t nth = num * inC * inH * inW; int blockSize = 1024; int gridSize = (nth + blockSize - 1) / blockSize; diff --git a/paddle/gserver/layers/CropLayer.cpp b/paddle/gserver/layers/CropLayer.cpp index b2fa17b400caf3760aef7736d495421fce346ee2..69ad913420bdb6e1b2ed0618b7f9b78d7477be99 100644 --- a/paddle/gserver/layers/CropLayer.cpp +++ b/paddle/gserver/layers/CropLayer.cpp @@ -22,7 +22,8 @@ bool CropLayer::init(const LayerMap& layerMap, const ParameterMap& parameterMap) { /* Initialize the basic parent class */ Layer::init(layerMap, parameterMap); - + CHECK_LE(static_cast(inputLayers_.size()), 2); + CHECK_GE(static_cast(inputLayers_.size()), 1); crop_axis_ = config_.axis(); for (int i = 0; i < config_.offset_size(); i++) { crop_offsets_.push_back(config_.offset(i)); @@ -36,8 +37,14 @@ bool CropLayer::init(const LayerMap& layerMap, ? input0_img_conf.img_size_y() : input0_img_conf.img_size(), input0_img_conf.img_size()}); - // 2. get output shape from input_1 or crop shap conf - if (config_.inputs_size() == 2) { + // 2. get target dims from config + if (config_.inputs_size() == 1) { + targetDims_ = TensorShape({config_.shape(0), + config_.shape(1), + config_.shape(2), + config_.shape(3)}); + } else { + // 2. get input_1 shape auto& input1_img_conf = config_.inputs(1).image_conf(); targetDims_ = TensorShape({0, input1_img_conf.channels(), @@ -45,24 +52,10 @@ bool CropLayer::init(const LayerMap& layerMap, ? input1_img_conf.img_size_y() : input1_img_conf.img_size(), input1_img_conf.img_size()}); - } else { - targetDims_ = TensorShape({config_.shape(0), - config_.shape(1), - config_.shape(2), - config_.shape(3)}); } - // 3. get final crop shape + // 3. get final crop corner int dimSize = 4; - for (int i = 0; i < dimSize; i++) { - if (i >= crop_axis_) { - crop_shape_.push_back(targetDims_[i]); - } else { - crop_shape_.push_back(inDims_[i]); - } - } - - // 4. get final crop corner crop_corner_ = {0, 0, 0, 0}; for (int i = 0; i < dimSize; i++) { if (i >= crop_axis_) { @@ -75,43 +68,61 @@ bool CropLayer::init(const LayerMap& layerMap, } outDims_ = TensorShape(4); - setOutDims(0); - - createFunction(forward_, - "Crop", - FuncConfig() - .set("crop_corner", crop_corner_) - .set("crop_shape", crop_shape_)); - createFunction(backward_, - "CropGrad", - FuncConfig() - .set("crop_corner", crop_corner_) - .set("crop_shape", crop_shape_)); + + createFunction( + forward_, "Crop", FuncConfig().set("crop_corner", crop_corner_)); + createFunction( + backward_, "CropGrad", FuncConfig().set("crop_corner", crop_corner_)); return true; } -void CropLayer::setOutDims(const size_t batchSize) { - outDims_.reshape({batchSize, crop_shape_[1], crop_shape_[2], crop_shape_[3]}); +void CropLayer::setOutDims() { + MatrixPtr input = inputLayers_[1]->getOutputValue(); + size_t batchSize = input->getHeight(); + // get target dims from input_1 + if (config_.inputs_size() == 2) { + targetDims_.setDim(0, batchSize); + int ch = config_.inputs(0).image_conf().channels(); + if (ch != 0) targetDims_.setDim(1, ch); + int h = inputLayers_[1]->getOutput().getFrameHeight(); + if (h != 0) targetDims_.setDim(2, h); + int w = inputLayers_[1]->getOutput().getFrameWidth(); + if (w != 0) targetDims_.setDim(3, w); + } + // get final crop shape from target dims and crop axis + std::vector crop_shape; + int dimSize = 4; + for (int i = 0; i < dimSize; i++) { + if (i >= crop_axis_) { + crop_shape.push_back(targetDims_[i]); + } else { + crop_shape.push_back(inDims_[i]); + } + } + + outDims_.reshape( + {crop_shape[0], crop_shape[1], crop_shape[2], crop_shape[3]}); + output_.setFrameHeight(crop_shape[2]); + output_.setFrameWidth(crop_shape[3]); } -void CropLayer::setTensorDim(const size_t batchSize) { - CHECK_EQ(static_cast(inputLayers_.size()), 2); +void CropLayer::setInDims() { + MatrixPtr input = inputLayers_[0]->getOutputValue(); + size_t batchSize = input->getHeight(); inDims_.setDim(0, batchSize); int h = inputLayers_[0]->getOutput().getFrameHeight(); if (h != 0) inDims_.setDim(2, h); int w = inputLayers_[0]->getOutput().getFrameWidth(); if (w != 0) inDims_.setDim(3, w); - setOutDims(batchSize); } void CropLayer::forward(PassType passType) { Layer::forward(passType); - MatrixPtr input = inputLayers_[0]->getOutputValue(); - size_t batchSize = input->getHeight(); - setTensorDim(batchSize); + setInDims(); + setOutDims(); int size = outDims_[1] * outDims_[2] * outDims_[3]; - resetOutput(batchSize, size); + resetOutput(outDims_[0], size); MatrixPtr outV = getOutputValue(); REGISTER_TIMER_INFO("CropForward", getName().c_str()); diff --git a/paddle/gserver/layers/CropLayer.h b/paddle/gserver/layers/CropLayer.h index 23cede1c3fe91d348dc5f2e313972c4aa49d1fcb..6b6202621023575c1c83049ecbd019656c726e3f 100644 --- a/paddle/gserver/layers/CropLayer.h +++ b/paddle/gserver/layers/CropLayer.h @@ -39,13 +39,12 @@ public: void backward(const UpdateCallback& callback = nullptr) override; protected: - void setOutDims(const size_t batchSize); - void setTensorDim(const size_t batchSize); + void setOutDims(); + void setInDims(); int32_t crop_axis_; std::vector crop_offsets_; std::vector crop_corner_; - std::vector crop_shape_; TensorShape inDims_; TensorShape targetDims_; TensorShape outDims_; diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index e599fa85ffc8f53c1442f35c4453304ab3ed1489..6b50d9cbf7d80016562035c2cd7990c890b51b86 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -2005,29 +2005,6 @@ class CropLayer(LayerBase): image_conf.img_size_y = input_layer.height image_conf.channels = input_layer.size / (input_layer.width * input_layer.height) - out_ch = image_conf.channels - out_h = image_conf.img_size - out_w = image_conf.img_size_y - if len(self.inputs) == 2: - # get channels, width and height from input_1 layer - input_layer = self.get_input_layer(1) - image_conf = self.config.inputs[1].image_conf - image_conf.img_size = input_layer.width - image_conf.img_size_y = input_layer.height - image_conf.channels = input_layer.size / (input_layer.width * - input_layer.height) - out_ch = image_conf.channels - out_h = image_conf.img_size_y - out_w = image_conf.img_size - else: - # set channels, width and heigth of current layer - if len(shape) > 2: - out_ch = shape[-3] - if len(shape) > 1: - out_h = shape[-2] - if len(shape) > 0: - out_w = shape[-1] - self.set_cnn_layer(name, out_h, out_w, out_ch) @config_layer('batch_norm') diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index b42cb02bff5fc517fe380a556b5a8fcac4c7c2cd..5a7e91dd398eb5ec033308c5431ba04f9822b9fe 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -5881,9 +5881,9 @@ def prelu_layer(input, @wrap_name_default() @layer_support() -def crop_layer(input, axis, offset, shape=None, name=None, layer_attr=None): +def crop_layer(input, offset, axis=2, shape=None, name=None, layer_attr=None): """ - The crop layer crop images by offset and shape. User can set crop shape by + The crop layer crops images by offset and shape. User can set crop shape by args 'shape' explicitly or by reference input layer. @@ -5896,16 +5896,16 @@ def crop_layer(input, axis, offset, shape=None, name=None, layer_attr=None): :param input: The input layer.If two inputs were setted, the second input will be regarded as reference input :type input: LayerOutput or Sequence + :param offset: The crop offset + :type offset: Sequence :param axis: start axis to be cropped. To image input layer: - 0: batch size - 1: channels - 2: height - 3: width :type partial_sum: int - :param offset: The crop offset - :type offset: Sequence :param shape: The shape to be cropped. Default is None. - :type shape: Sqquence | None + :type shape: Sequence | None :param name: Name of this layer. :type name: basestring :return: LayerOutput object. @@ -5913,8 +5913,6 @@ def crop_layer(input, axis, offset, shape=None, name=None, layer_attr=None): """ if isinstance(input, LayerOutput): input = [input] - elif isinstance(input, Projection): - input = [input] else: assert isinstance(input, collections.Sequence) l = Layer( diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_crop.py b/python/paddle/trainer_config_helpers/tests/configs/test_crop.py new file mode 100644 index 0000000000000000000000000000000000000000..8314a7e9a5586647c70ff010156817110919c72b --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/test_crop.py @@ -0,0 +1,21 @@ +from paddle.trainer_config_helpers import * + +settings(batch_size=1000, learning_rate=1e-5) + +data = data_layer(name='data', size=2016, height=48, width=42) +refernce_data = data_layer(name='data', size=768, height=16, width=16) + +conv = img_conv_layer( + input=data, + filter_size=3, + num_channels=1, + num_filters=16, + padding=1, + act=LinearActivation(), + bias_attr=True) + +pool = img_pool_layer(input=conv, pool_size=2, stride=2, pool_type=MaxPooling()) + +crop = crop_layer(input=[pool, refernce_data], axis=2) + +outputs(pad)