diff --git a/paddle/fluid/inference/analysis/CMakeLists.txt b/paddle/fluid/inference/analysis/CMakeLists.txt index 9faf5bb303677..50835784440bf 100644 --- a/paddle/fluid/inference/analysis/CMakeLists.txt +++ b/paddle/fluid/inference/analysis/CMakeLists.txt @@ -15,3 +15,9 @@ cc_test(test_subgraph_splitter DEPS analysis paddle_fluid tensor ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model) set_tests_properties(test_subgraph_splitter PROPERTIES DEPENDS test_word2vec) + +cc_test(test_dfg_graphviz_draw_pass + SRCS dfg_graphviz_draw_pass_tester.cc + DEPS analysis + ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model) +set_tests_properties(test_dfg_graphviz_draw_pass PROPERTIES DEPENDS test_word2vec) diff --git a/paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h b/paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h new file mode 100644 index 0000000000000..41d4475382bef --- /dev/null +++ b/paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h @@ -0,0 +1,68 @@ +/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. + +Licensed 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. */ + +/* + * This file create an DFG_GraphvizDrawPass which helps to draw a data flow + * graph's structure using graphviz. + */ + +#pragma once + +#include +#include +#include "paddle/fluid/inference/analysis/pass.h" + +namespace paddle { +namespace inference { +namespace analysis { + +/* + * Output a dot file and write to some place. + */ +class DFG_GraphvizDrawPass : public DataFlowGraphPass { + public: + DFG_GraphvizDrawPass(const std::string& dir, const std::string& id) + : dir_(dir), id_(id) {} + + bool Initialize() override { return Pass::Initialize(); } + void Run(DataFlowGraph* graph) override { + auto content = Draw(graph); + std::ofstream file(GenDotPath()); + file.write(content.c_str(), content.size()); + file.close(); + LOG(INFO) << "draw dot to " << GenDotPath(); + } + + bool Finalize() override { return Pass::Finalize(); } + + Pass* CreatePrinterPass(std::ostream& os, + const std::string& banner) const override { + return nullptr; + } + + private: + // Path of the dot file to output. + std::string GenDotPath() const { + return dir_ + "/" + "graph_" + id_ + ".dot"; + } + + std::string Draw(DataFlowGraph* graph) { return graph->DotString(); } + + std::string dir_; + std::string id_; +}; + +} // namespace analysis +} // namespace inference +} // namespace paddle diff --git a/paddle/fluid/inference/analysis/dfg_graphviz_draw_pass_tester.cc b/paddle/fluid/inference/analysis/dfg_graphviz_draw_pass_tester.cc new file mode 100644 index 0000000000000..3fc1cc18b8554 --- /dev/null +++ b/paddle/fluid/inference/analysis/dfg_graphviz_draw_pass_tester.cc @@ -0,0 +1,46 @@ +/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. + +Licensed 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. */ + +#include "paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h" + +#include +#include +#include +#include "paddle/fluid/inference/analysis/ut_helper.h" + +namespace paddle { +namespace inference { +namespace analysis { + +TEST_F(DFG_Tester, dfg_graphviz_draw_pass_tester) { + auto dfg = ProgramDescToDFG(desc); + DFG_GraphvizDrawPass pass("./", "test"); + pass.Initialize(); + pass.Run(&dfg); + + // test content + std::ifstream file("./graph_test.dot"); + ASSERT_TRUE(file.is_open()); + + std::string line; + int no{0}; + while (std::getline(file, line)) { + no++; + } + ASSERT_EQ(no, 82); +} + +} // namespace analysis +} // namespace inference +} // namespace paddle diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index bd6ed0f30e4d7..56f6f26803919 100644 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -81,6 +81,8 @@ 'label_smooth', 'roi_pool', 'dice_loss', + 'image_resize', + 'image_resize_short', 'resize_bilinear', 'gather', 'random_crop', @@ -3929,22 +3931,25 @@ def dice_loss(input, label, epsilon=0.00001): return reduce_mean(dice_score) -def resize_bilinear(input, out_shape=None, scale=None, name=None): +def image_resize(input, + out_shape=None, + scale=None, + name=None, + resample='BILINEAR'): """ - The mathematical meaning of resize bilinear layer is - Bilinear interpolation. - Bilinear interpolation is an extension of linear interpolation for - interpolating functions of two variables (e.g. H-direction and - W-direction in this layer) on a rectilinear 2D grid. + Resize a batch of images. - For details, please refer to Wikipedia: - https://en.wikipedia.org/wiki/Bilinear_interpolation + The input must be a tensor of the shape (num_batches, channels, in_h, in_w), + and the resizing only applies on the last two dimensions(hight and width). + + Supporting resample methods: + 'BILINEAR' : Bilinear interpolation Args: - input (Variable): The input tensor of resize bilinear layer, + input (Variable): The input tensor of image resize layer, This is a 4-D tensor of the shape (num_batches, channels, in_h, in_w). - out_shape(list|tuple|Variable|None): Output shape of resize bilinear + out_shape(list|tuple|Variable|None): Output shape of image resize layer, the shape is (out_h, out_w). Default: None scale(float|None): The multiplier for the input height or width. @@ -3953,6 +3958,8 @@ def resize_bilinear(input, out_shape=None, scale=None, name=None): Default: None name(str|None): A name for this layer(optional). If set None, the layer will be named automatically. + resample(str): The resample method. It can only be 'BILINEAR' currently. + Default: 'BILINEAR' Returns: out (Variable): The output is a 4-D tensor of the shape @@ -3961,8 +3968,12 @@ def resize_bilinear(input, out_shape=None, scale=None, name=None): Examples: .. code-block:: python - out = fluid.layers.resize_bilinear(input, out_shape=[12, 12]) + out = fluid.layers.image_resize(input, out_shape=[12, 12]) """ + resample_methods = {'BILINEAR': 'bilinear_interp'} + if resample not in resample_methods: + raise ValueError( + "The 'resample' of image_resize can only be 'BILINEAR' currently.") if out_shape is None and scale is None: raise ValueError("One of out_shape and scale must not be None") helper = LayerHelper('bilinear_interp', **locals()) @@ -3990,7 +4001,7 @@ def _is_list_or_turple_(data): out = helper.create_tmp_variable(dtype) helper.append_op( - type="bilinear_interp", + type=resample_methods[resample], inputs=inputs, outputs={"Out": out}, attrs={"out_h": out_h, @@ -3998,6 +4009,55 @@ def _is_list_or_turple_(data): return out +def resize_bilinear(input, out_shape=None, scale=None, name=None): + """ + This is an alias of layer 'image_resize' with bilinear interpolation. + + The mathematical meaning of resize bilinear layer is + Bilinear interpolation. + Bilinear interpolation is an extension of linear interpolation for + interpolating functions of two variables (e.g. H-direction and + W-direction in this layer) on a rectilinear 2D grid. + + For details, please refer to Wikipedia: + https://en.wikipedia.org/wiki/Bilinear_interpolation + """ + + return image_resize(input, out_shape, scale, name, 'BILINEAR') + + +def image_resize_short(input, out_short_len, resample='BILINEAR'): + """ + Resize a batch of images. The short edge of input images will be + resized to the given 'out_short_len'. The long edge of input images + will be resized proportionately to make images' length-width ratio + constant. + + Args: + input (Variable): The input tensor of image resize layer, + This is a 4-D tensor of the shape + (num_batches, channels, in_h, in_w). + out_short_len(int): The length of output images' short edge. + + Returns: + out (Variable): The output is a 4-D tensor of the shape + (num_batches, channls, out_h, out_w). + """ + in_shape = input.shape + if len(in_shape) != 4: + raise ValueError( + "The rank of input must be 4 (num_batches, channels, in_h, in_w).") + hw = in_shape[2:4] + short_idx = hw.index(min(hw)) + long_idx = 1 - short_idx + out_shape = list(hw) + out_shape[short_idx] = out_short_len + out_shape[long_idx] = int( + float(out_shape[long_idx]) * (float(out_short_len) / float(hw[ + short_idx])) + 0.5) + return image_resize(input=input, out_shape=out_shape, resample=resample) + + def gather(input, index): """ Output is obtained by gathering entries of the outer-most dimension @@ -4005,7 +4065,7 @@ def gather(input, index): .. math:: - Out = X[Index] + Out = X[Index] .. code-block:: text @@ -4013,8 +4073,8 @@ def gather(input, index): Given: - X = [[1, 2], - [3, 4], + X = [[1, 2], + [3, 4], [5, 6]] Index = [1, 2]