Merge branch 'develop' of github.com:PaddlePaddle/Paddle into overlap_memcpy_with_dist

paddle/fluid/framework/CMakeLists.txt

@@ -87,7 +87,7 @@ cc_library(executor SRCS executor.cc DEPS op_registry device_context scope
 framework_proto glog lod_rank_table feed_fetch_method)
 
 
-cc_library(parallel_executor SRCS parallel_executor.cc DEPS multi_devices_graph_builder threaded_ssa_graph_executor)
+cc_library(parallel_executor SRCS parallel_executor.cc DEPS multi_devices_graph_builder threaded_ssa_graph_executor scope_buffered_ssa_graph_executor)
 
 cc_library(prune SRCS prune.cc DEPS framework_proto)
 cc_test(prune_test SRCS prune_test.cc DEPS op_info prune recurrent_op device_context)

paddle/fluid/framework/details/CMakeLists.txt

@@ -36,5 +36,6 @@ cc_test(broadcast_op_test SRCS broadcast_op_handle_test.cc DEPS var_handle op_ha
         device_context broadcast_op_handle)
 cc_test(gather_op_test SRCS gather_op_handle_test.cc DEPS var_handle op_handle_base scope ddim memory
         device_context gather_op_handle)
+cc_library(scope_buffered_ssa_graph_executor SRCS scope_buffered_ssa_graph_executor.cc DEPS ssa_graph_executor)
 #cc_test(reduce_op_handle_test SRCS reduce_op_handle_test.cc DEPS var_handle op_handle_base scope ddim memory
 #        device_context reduce_op_handle )

paddle/fluid/framework/details/execution_strategy.h

@@ -22,6 +22,7 @@ struct ExecutionStrategy {
   size_t num_threads_{0};
   bool use_event_{true};
   bool allow_op_delay_{false};
+  size_t num_iteration_per_drop_scope_{100};
 };
 
 }  //  namespace details

paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.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/framework/details/scope_buffered_ssa_graph_executor.h"
+#include <string>
+#include <vector>
+#include "paddle/fluid/framework/executor.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+ScopeBufferedSSAGraphExecutor::ScopeBufferedSSAGraphExecutor(
+    ExecutionStrategy strategy, std::vector<Scope *> local_scopes,
+    std::vector<VariableInfo> var_infos, std::vector<platform::Place> places,
+    std::unique_ptr<SSAGraphExecutor> &&underlying_executor)
+    : strategy_(std::move(strategy)),
+      underlying_executor_(std::move(underlying_executor)),
+      local_scopes_(std::move(local_scopes)),
+      var_infos_(std::move(var_infos)),
+      places_(std::move(places)) {}
+
+FeedFetchList ScopeBufferedSSAGraphExecutor::Run(
+    const std::vector<std::string> &fetch_tensors) {
+  if (drop_scope_counter_ == 0) {
+    // Create local scopes.
+    for (auto it = local_scopes_.rbegin(); it != local_scopes_.rend(); ++it) {
+      auto &scope = *it;
+      Scope &local_scope = scope->NewScope();
+      *scope->Var(details::kLocalExecScopeName)->GetMutable<Scope *>() =
+          &local_scope;
+
+      for (auto &info : var_infos_) {
+        if (scope->FindVar(info.name_) != nullptr) {
+          continue;
+        }
+
+        if (info.persistable_) {  // Persistable
+          InitializeVariable(scope->Var(info.name_), info.type_);
+        } else {
+          InitializeVariable(local_scope.Var(info.name_), info.type_);
+        }
+      }
+    }
+  }
+
+  auto fetch_data = underlying_executor_->Run(fetch_tensors);
+  drop_scope_counter_ += 1;
+  if (!fetch_tensors.empty() ||
+      drop_scope_counter_ == strategy_.num_iteration_per_drop_scope_) {
+    drop_scope_counter_ = 0;
+    // Wait All computational streams
+    for (auto p : places_) {
+      platform::DeviceContextPool::Instance().Get(p)->Wait();
+    }
+    for (auto &scope : local_scopes_) {
+      auto &local_scope =
+          *scope->Var(details::kLocalExecScopeName)->GetMutable<Scope *>();
+      scope->DeleteScope(local_scope);
+    }
+  }
+  return fetch_data;
+}
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.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.
+
+#pragma once
+
+#include <memory>
+#include <string>
+#include <vector>
+#include "paddle/fluid/framework/details/execution_strategy.h"
+#include "paddle/fluid/framework/details/ssa_graph_executor.h"
+#include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/platform/place.h"
+namespace paddle {
+namespace framework {
+namespace details {
+
+struct VariableInfo {
+  std::string name_;
+  proto::VarType::Type type_;
+  bool persistable_;
+};
+
+class ScopeBufferedSSAGraphExecutor : public SSAGraphExecutor {
+ public:
+  ScopeBufferedSSAGraphExecutor(
+      ExecutionStrategy strategy, std::vector<Scope*> local_scopes,
+      std::vector<VariableInfo> var_infos, std::vector<platform::Place> places,
+      std::unique_ptr<SSAGraphExecutor>&& underlying_executor);
+  FeedFetchList Run(const std::vector<std::string>& fetch_tensors) override;
+
+ private:
+  size_t drop_scope_counter_{0};
+
+  ExecutionStrategy strategy_;
+  std::unique_ptr<SSAGraphExecutor> underlying_executor_;
+  std::vector<Scope*> local_scopes_;
+  std::vector<VariableInfo> var_infos_;
+  std::vector<platform::Place> places_;
+};
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/ssa_graph_executor.cc

@@ -17,10 +17,6 @@
 namespace paddle {
 namespace framework {
 namespace details {
-
-SSAGraphExecutor::SSAGraphExecutor(std::unique_ptr<SSAGraph> &&graph)
-    : graph_(std::move(graph)) {}
-
 SSAGraphExecutor::~SSAGraphExecutor() {}
 
 }  // namespace details

paddle/fluid/framework/details/ssa_graph_executor.h

@@ -28,15 +28,11 @@ class SSAGraphExecutor {
   DISABLE_COPY_AND_ASSIGN(SSAGraphExecutor);
 
  public:
-  // Steal graph inside
-  explicit SSAGraphExecutor(std::unique_ptr<SSAGraph> &&graph);
+  SSAGraphExecutor() {}
 
   virtual ~SSAGraphExecutor();
 
   virtual FeedFetchList Run(const std::vector<std::string> &fetch_tensors) = 0;
-
- protected:
-  std::unique_ptr<SSAGraph> graph_;
 };
 }  // namespace details
 }  // namespace framework

paddle/fluid/framework/details/threaded_ssa_graph_executor.cc

@@ -21,7 +21,7 @@ ThreadedSSAGraphExecutor::ThreadedSSAGraphExecutor(
     const ExecutionStrategy &strategy, const std::vector<Scope *> &local_scopes,
     const std::vector<platform::Place> &places,
     std::unique_ptr<SSAGraph> &&graph)
-    : SSAGraphExecutor(std::move(graph)),
+    : graph_(std::move(graph)),
       pool_(strategy.num_threads_ >= 2 ? new ::ThreadPool(strategy.num_threads_)
                                        : nullptr),
       local_scopes_(local_scopes),
@@ -189,7 +189,9 @@ void ThreadedSSAGraphExecutor::RunOp(
     BlockingQueue<VarHandleBase *> *ready_var_q, details::OpHandleBase *op) {
   auto op_run = [ready_var_q, op, this] {
     try {
+      if (VLOG_IS_ON(10)) {
         VLOG(10) << op << " " << op->Name() << " : " << op->DebugString();
+      }
       op->Run(strategy_.use_event_);
       VLOG(10) << op << " " << op->Name() << " Done ";
       running_ops_--;

paddle/fluid/framework/details/threaded_ssa_graph_executor.h

@@ -51,6 +51,7 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
              details::OpHandleBase *op);
 
  private:
+  std::unique_ptr<SSAGraph> graph_;
   std::unique_ptr<::ThreadPool> pool_;
   std::vector<Scope *> local_scopes_;
   std::vector<platform::Place> places_;

paddle/fluid/framework/parallel_executor.cc

@@ -22,6 +22,7 @@ limitations under the License. */
 #include "paddle/fluid/platform/nccl_helper.h"
 #endif
 
+#include "paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h"
 #include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
 #include "paddle/fluid/platform/profiler.h"
 
@@ -41,8 +42,6 @@ class ParallelExecutorPrivate {
 #ifdef PADDLE_WITH_CUDA
   std::unique_ptr<platform::NCCLContextMap> nccl_ctxs_;
 #endif
-
-  std::vector<std::tuple<std::string, proto::VarType::Type, bool>> var_types_;
   bool own_local_scope;
 };
 
@@ -91,9 +90,18 @@ ParallelExecutor::ParallelExecutor(
       local_scopes.empty()) {  // Is CUDA
     BCastParamsToGPUs(bcast_vars);
   }
-// Startup Program has been run. All local scopes has correct parameters.
+  // Startup Program has been run. All local scopes has correct parameters.
 
-// Step 2. Convert main_program to SSA form and dependency graph. Also, insert
+  // Step 2. Create vars in each scope;
+  std::vector<details::VariableInfo> var_infos;
+  for (auto *var : main_program.Block(0).AllVars()) {
+    var_infos.emplace_back();
+    var_infos.back().name_ = var->Name();
+    var_infos.back().type_ = var->GetType();
+    var_infos.back().persistable_ = var->Persistable();
+  }
+
+// Step 3. Convert main_program to SSA form and dependency graph. Also, insert
 // ncclOp
 #ifdef PADDLE_WITH_CUDA
   builder_.reset(new details::MultiDevSSAGraphBuilder(
@@ -106,16 +114,14 @@ ParallelExecutor::ParallelExecutor(
       build_strategy));
 
 #endif
-  auto graph = builder_.get()->Build(main_program);
+  auto graph = builder_->Build(main_program);
 
   member_->executor_.reset(new details::ThreadedSSAGraphExecutor(
       exec_strategy, member_->local_scopes_, places, std::move(graph)));
 
-  // Step 3. Create vars in each scope;
-  for (auto *var : main_program.Block(0).AllVars()) {
-    member_->var_types_.emplace_back(var->Name(), var->GetType(),
-                                     var->Persistable());
-  }
+  member_->executor_.reset(new details::ScopeBufferedSSAGraphExecutor(
+      exec_strategy, member_->local_scopes_, std::move(var_infos),
+      member_->places_, std::move(member_->executor_)));
 }
 
 void ParallelExecutor::BCastParamsToGPUs(
@@ -178,42 +184,9 @@ void ParallelExecutor::BCastParamsToGPUs(
 void ParallelExecutor::Run(const std::vector<std::string> &fetch_tensors,
                            const std::string &fetched_var_name) {
   platform::RecordBlock b(0);
-  // Create local scopes.
-  for (auto it = member_->local_scopes_.rbegin();
-       it != member_->local_scopes_.rend(); ++it) {
-    auto &scope = *it;
-    Scope &local_scope = scope->NewScope();
-    *scope->Var(details::kLocalExecScopeName)->GetMutable<Scope *>() =
-        &local_scope;
-
-    for (auto &name_type_pair : member_->var_types_) {
-      if (scope->FindVar(std::get<0>(name_type_pair)) != nullptr) {
-        continue;
-      }
-
-      if (std::get<2>(name_type_pair)) {  // Persistable
-        InitializeVariable(scope->Var(std::get<0>(name_type_pair)),
-                           std::get<1>(name_type_pair));
-      } else {
-        InitializeVariable(local_scope.Var(std::get<0>(name_type_pair)),
-                           std::get<1>(name_type_pair));
-      }
-    }
-  }
-
   auto fetch_data = member_->executor_->Run(fetch_tensors);
   *member_->global_scope_->Var(fetched_var_name)->GetMutable<FeedFetchList>() =
       fetch_data;
-
-  // Wait All computational streams
-  for (auto p : member_->places_) {
-    platform::DeviceContextPool::Instance().Get(p)->Wait();
-  }
-  for (auto &scope : member_->local_scopes_) {
-    auto &local_scope =
-        *scope->Var(details::kLocalExecScopeName)->GetMutable<Scope *>();
-    scope->DeleteScope(local_scope);
-  }
 }
 
 void ParallelExecutor::FeedTensorsIntoLocalScopes(

paddle/fluid/platform/device_context.cc

@@ -175,7 +175,6 @@ CUDADeviceContext::~CUDADeviceContext() {
 Place CUDADeviceContext::GetPlace() const { return place_; }
 
 void CUDADeviceContext::Wait() const {
-  std::lock_guard<std::recursive_mutex> guard(mutex_);
   PADDLE_ENFORCE(cudaStreamSynchronize(stream_));
   PADDLE_ENFORCE(cudaGetLastError());
 }

paddle/fluid/platform/device_context.h

@@ -100,7 +100,6 @@ class CUDADeviceContext : public DeviceContext {
 
   template <typename Callback>
   void RecordEvent(cudaEvent_t ev, Callback callback) {
-    std::lock_guard<std::recursive_mutex> guard(mutex_);
     callback();
     PADDLE_ENFORCE(cudaEventRecord(ev, stream_));
   }
@@ -110,8 +109,6 @@ class CUDADeviceContext : public DeviceContext {
 
   std::unique_ptr<Eigen::GpuDevice> eigen_device_;
   std::unique_ptr<EigenCudaStreamDevice> eigen_stream_;
-
-  mutable std::recursive_mutex mutex_;
   cudaStream_t stream_;
   cudnnHandle_t cudnn_handle_;
   cublasHandle_t cublas_handle_;

paddle/fluid/platform/dynload/cublas.h

@@ -45,7 +45,7 @@ extern void *cublas_dso_handle;
       std::call_once(cublas_dso_flag, []() {                                 \
         cublas_dso_handle = paddle::platform::dynload::GetCublasDsoHandle(); \
       });                                                                    \
-      void *p_##__name = dlsym(cublas_dso_handle, #__name);                  \
+      static void *p_##__name = dlsym(cublas_dso_handle, #__name);           \
       return reinterpret_cast<FUNC_TYPE>(p_##__name)(args...);               \
     }                                                                        \
   };                                                                         \

paddle/fluid/platform/dynload/cudnn.h

@@ -39,7 +39,7 @@ extern void EnforceCUDNNLoaded(const char* fn_name);
         cudnn_dso_handle = paddle::platform::dynload::GetCUDNNDsoHandle(); \
       });                                                                  \
       EnforceCUDNNLoaded(#__name);                                         \
-      void* p_##__name = dlsym(cudnn_dso_handle, #__name);                 \
+      static void* p_##__name = dlsym(cudnn_dso_handle, #__name);          \
       return reinterpret_cast<cudnn_func>(p_##__name)(args...);            \
     }                                                                      \
   };                                                                       \

paddle/fluid/platform/dynload/cupti.h

@@ -45,7 +45,7 @@ extern void *cupti_dso_handle;
       std::call_once(cupti_dso_flag, []() {                                \
         cupti_dso_handle = paddle::platform::dynload::GetCUPTIDsoHandle(); \
       });                                                                  \
-      void *p_##__name = dlsym(cupti_dso_handle, #__name);                 \
+      static void *p_##__name = dlsym(cupti_dso_handle, #__name);          \
       return reinterpret_cast<cuptiFunc>(p_##__name)(args...);             \
     }                                                                      \
   };                                                                       \

paddle/fluid/platform/dynload/curand.h

@@ -34,7 +34,7 @@ extern void *curand_dso_handle;
       std::call_once(curand_dso_flag, []() {                                 \
         curand_dso_handle = paddle::platform::dynload::GetCurandDsoHandle(); \
       });                                                                    \
-      void *p_##__name = dlsym(curand_dso_handle, #__name);                  \
+      static void *p_##__name = dlsym(curand_dso_handle, #__name);           \
       return reinterpret_cast<curandFunc>(p_##__name)(args...);              \
     }                                                                        \
   };                                                                         \

paddle/fluid/platform/dynload/nccl.h

@@ -37,7 +37,7 @@ extern void* nccl_dso_handle;
       std::call_once(nccl_dso_flag, []() {                               \
         nccl_dso_handle = paddle::platform::dynload::GetNCCLDsoHandle(); \
       });                                                                \
-      void* p_##__name = dlsym(nccl_dso_handle, #__name);                \
+      static void* p_##__name = dlsym(nccl_dso_handle, #__name);         \
       return reinterpret_cast<nccl_func>(p_##__name)(args...);           \
     }                                                                    \
   };                                                                     \

paddle/fluid/platform/dynload/tensorrt.h

@@ -40,7 +40,7 @@ extern void* tensorrt_dso_handle;
             paddle::platform::dynload::GetTensorRtDsoHandle();          \
         PADDLE_ENFORCE(tensorrt_dso_handle, "load tensorrt so failed"); \
       });                                                               \
-      void* p_##__name = dlsym(tensorrt_dso_handle, #__name);           \
+      static void* p_##__name = dlsym(tensorrt_dso_handle, #__name);    \
       PADDLE_ENFORCE(p_##__name, "load %s failed", #__name);            \
       return reinterpret_cast<tensorrt_func>(p_##__name)(args...);      \
     }                                                                   \

paddle/fluid/platform/dynload/warpctc.h

@@ -40,7 +40,7 @@ extern void* warpctc_dso_handle;
       std::call_once(warpctc_dso_flag, []() {                                  \
         warpctc_dso_handle = paddle::platform::dynload::GetWarpCTCDsoHandle(); \
       });                                                                      \
-      void* p_##_name = dlsym(warpctc_dso_handle, #__name);                    \
+      static void* p_##_name = dlsym(warpctc_dso_handle, #__name);             \
       return reinterpret_cast<warpctcFunc>(p_##_name)(args...);                \
     }                                                                          \
   };                                                                           \

paddle/fluid/pybind/pybind.cc

@@ -519,6 +519,14 @@ All parameter, weight, gradient are variables in Paddle.
           [](const ExecutionStrategy &self) { return self.allow_op_delay_; },
           [](ExecutionStrategy &self, bool allow_op_delay) {
             self.allow_op_delay_ = allow_op_delay;
+          })
+      .def_property(
+          "num_iteration_per_drop_scope",
+          [](const ExecutionStrategy &self) {
+            return self.num_iteration_per_drop_scope_;
+          },
+          [](ExecutionStrategy &self, size_t num_iteration_per_drop_scope) {
+            self.num_iteration_per_drop_scope_ = num_iteration_per_drop_scope;
           });
   py::class_<BuildStrategy> build_strategy(pe, "BuildStrategy");
 

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