Merge remote-tracking branch 'ups/develop' into multithreads

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)

paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h

+/* 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 <fstream>
+#include <string>
+#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

paddle/fluid/inference/analysis/dfg_graphviz_draw_pass_tester.cc

+/* 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 <gtest/gtest.h>
+#include <fstream>
+#include <string>
+#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

python/paddle/fluid/layers/nn.py

@@ -81,6 +81,8 @@ __all__ = [
     '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 resize_bilinear(input, out_shape=None, scale=None, name=None):
 
     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 resize_bilinear(input, out_shape=None, scale=None, name=None):
     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