提交 8e6965be 编写于 作者: Y Yi Wang

Resolve conflict with upstream/develop

构建PaddlePaddle Docker Image
===========================
PaddlePaddle的Docker Image构建源码放置在 :code:`${源码根目录}/paddle/scripts/docker/`目录下。
该Image基于ubuntu 14.04。该目录下有两个文件,Dockerfile和build.sh。其中:
* Dockerfile是docker image的主要描述文件。描述了Docker image的构建步骤、各种参数和维护
人员等等。
* build.sh是docker image的主要构建步骤。
该image的构建在docker 1.12版本测试通过, 低于docker 1.12版本的情况下并没有测试。主要由于旧版本
的docker可能缺乏 :code:`--build-arg` 参数,从而不能在运行编译命令的时候接受参数。
同时,该构建脚本充分考虑了网络不稳定的情况,对于cuda的Toolkit有断点续传和传输速度过小重启下载的
简单优化。
使用脚本构建PaddlePaddle Docker Image
-------------------------------------------
该脚本的使用方法是,进入该源码目录,执行 :code:`docker build .` 命令。可以使用
:code:`--build-arg` 传入的配置参数包括:
* LOWEST\_DL\_SPEED\: 多线程下载过程中,最低线程的下载速度(默认单位是Bytes,可以传入10K,
10M,或者10G这样的单位)。如果小于这个下载速度,那么这个下载线程将会关闭。所有的下载线程关闭时,
下载进程会重启。
* WITH\_GPU\: ON or OFF。是否开启GPU功能。注意,编译PaddlePaddle的GPU版本并不需要一定在具有GPU
的机器上进行。但是,运行PaddlePaddle的GPU版本一定要在具有CUDA的机器上运行。
简单的使用样例为\:
.. code-block:: bash
cd ${源码根目录}/paddle/scripts/docker/
docker build --build-arg LOWEST_DL_SPEED=50K\
--build-arg WITH_GPU=ON \
--tag paddle_gpu:latest .
即可在本地编译出PaddlePaddle的镜像。
构建PaddlePaddle的Docker Image
==============================
PaddlePaddle的Docker Image构建源码放置在 ``${源码根目录}/paddle/scripts/docker/`` 目录下。该目录有三类文件:
- Dockerfile:Docker Image的描述文件,包括构建步骤、各种参数和维护人员等。
- 一共维护了12个Dockerfile,Dockerfile.m4是它们的模板。
- PaddlePaddle中所有的Image都基于ubuntu 14.04。
- build.sh:Docker Image的构建脚本,使用方式见下一小节。
- generate.sh:通过Dockerfile.m4模板生成不同的Dockerfile。
使用脚本构建Docker Image
------------------------
进入源码目录,执行 ``docker build`` 命令,即可在本地编译出PaddlePaddle的镜像。简单的使用样例为
.. code-block:: bash
cd ${源码根目录}/paddle/scripts/docker/
docker build --build-arg LOWEST_DL_SPEED=50K\
--build-arg WITH_GPU=ON \
--tag paddle_gpu:latest .
其中,``--build-arg`` 传入的配置参数包括:
- LOWEST\_DL\_SPEED\: 在多线程下载过程中,设置下线线程的最低速度。
- 默认单位是Bytes,但可以传入10K、10M、或10G等这样的单位。
- 如果小于这个速度,那么这个线程将会关闭。当所有的线程都关闭了,那么下载进程将会重启。
- WITH\_GPU\: ON or OFF,是否开启GPU功能。注意,
- **编译** PaddlePaddle的GPU版本 **不一定** 要在具有GPU的机器上进行。
- **运行** PaddlePaddle的GPU版本 **一定** 要在具有GPU的机器上运行。
注意:所有Image的构建在Docker 1.12版本测试通过, 低于1.12的版本并没有测试。原因是旧版本可能缺乏 ``--build-arg`` 参数,从而不能在运行编译命令的时候接受参数。
...@@ -16,6 +16,7 @@ PaddlePaddle文档 ...@@ -16,6 +16,7 @@ PaddlePaddle文档
-------- --------
* `新写Layer <../doc/dev/new_layer/index.html>`_ * `新写Layer <../doc/dev/new_layer/index.html>`_
* `如何贡献文档 <howto/how_to_write_docs/index.html>`_ * `如何贡献文档 <howto/how_to_write_docs/index.html>`_
* `如何构建Docker Image <howto/build_docker_image.html>`_
算法教程 算法教程
-------- --------
......
...@@ -59,7 +59,8 @@ void ConvProjection::getConvParams() { ...@@ -59,7 +59,8 @@ void ConvProjection::getConvParams() {
void ConvProjection::initCudnn() { void ConvProjection::initCudnn() {
hl_create_filter_descriptor( hl_create_filter_descriptor(
&filterDesc_, channels_, numFilters_, filterH_, filterW_); &filterDesc_, channels_ / groups_, numFilters_ / groups_,
filterH_, filterW_);
hl_create_tensor_descriptor(&inputDesc_); hl_create_tensor_descriptor(&inputDesc_);
hl_create_tensor_descriptor(&outputDesc_); hl_create_tensor_descriptor(&outputDesc_);
hl_create_convolution_descriptor(&convDesc_, hl_create_convolution_descriptor(&convDesc_,
...@@ -86,7 +87,7 @@ void ConvProjection::initCudnn() { ...@@ -86,7 +87,7 @@ void ConvProjection::initCudnn() {
void ConvProjection::reshapeTensorDesc(int batchSize) { void ConvProjection::reshapeTensorDesc(int batchSize) {
hl_tensor_reshape(inputDesc_, hl_tensor_reshape(inputDesc_,
batchSize, batchSize,
channels_, channels_ / groups_,
imageH_, imageH_,
imageW_, imageW_,
channels_ * imageH_ * imageW_, channels_ * imageH_ * imageW_,
...@@ -115,7 +116,7 @@ void ConvProjection::reshapeTensorDesc(int batchSize) { ...@@ -115,7 +116,7 @@ void ConvProjection::reshapeTensorDesc(int batchSize) {
hl_tensor_reshape(outputDesc_, hl_tensor_reshape(outputDesc_,
batchSize, batchSize,
numFilters_, numFilters_ / groups_,
outputH_, outputH_,
outputW_, outputW_,
nStride, nStride,
......
...@@ -145,7 +145,7 @@ void ExpandConvBaseLayer::expandFwdOnce(MatrixPtr image, ...@@ -145,7 +145,7 @@ void ExpandConvBaseLayer::expandFwdOnce(MatrixPtr image,
real *expInData = expandInput_->getData(); real *expInData = expandInput_->getData();
for (int g = 0; g < groups_[inIdx]; ++g) { for (int g = 0; g < groups_[inIdx]; ++g) {
MatrixPtr A = MatrixPtr A =
Matrix::create(wgtData, subK, subM, true, useGpu_); // mark transpose Matrix::create(wgtData, subM, subK, false, useGpu_); // mark transpose
MatrixPtr B = Matrix::create(expInData, subK, subN, false, useGpu_); MatrixPtr B = Matrix::create(expInData, subK, subN, false, useGpu_);
MatrixPtr C = Matrix::create(outData, subM, subN, false, useGpu_); MatrixPtr C = Matrix::create(outData, subM, subN, false, useGpu_);
C->mul(A, B, 1, 1); C->mul(A, B, 1, 1);
...@@ -182,7 +182,7 @@ void ExpandConvBaseLayer::bpropActs(MatrixPtr out, ...@@ -182,7 +182,7 @@ void ExpandConvBaseLayer::bpropActs(MatrixPtr out,
// create temporary matrix // create temporary matrix
MatrixPtr C = Matrix::create(expandInData, subK, subN, false, useGpu_); MatrixPtr C = Matrix::create(expandInData, subK, subN, false, useGpu_);
MatrixPtr B = Matrix::create(localGradData, subM, subN, false, useGpu_); MatrixPtr B = Matrix::create(localGradData, subM, subN, false, useGpu_);
MatrixPtr A = Matrix::create(wgtData, subK, subM, false, useGpu_); MatrixPtr A = Matrix::create(wgtData, subM, subK, true, useGpu_);
C->mul(A, B); // mul C->mul(A, B); // mul
// clear the temporary matrix // clear the temporary matrix
...@@ -247,10 +247,10 @@ void ExpandConvBaseLayer::bpropWeights(MatrixPtr image, ...@@ -247,10 +247,10 @@ void ExpandConvBaseLayer::bpropWeights(MatrixPtr image,
// expand-mul one-group by one // expand-mul one-group by one
for (int g = 0; g < groups_[inpIdx]; g++) { for (int g = 0; g < groups_[inpIdx]; g++) {
MatrixPtr A = Matrix::create(expandInData, subK, subN, false, useGpu_); MatrixPtr A = Matrix::create(expandInData, subK, subN, true, useGpu_);
MatrixPtr B = Matrix::create(gradData, subM, subN, true, useGpu_); MatrixPtr B = Matrix::create(gradData, subM, subN, false, useGpu_);
MatrixPtr C = Matrix::create(wGradData, subK, subM, false, useGpu_); MatrixPtr C = Matrix::create(wGradData, subM, subK, false, useGpu_);
C->mul(A, B, 1, 1); C->mul(B, A, 1, 1);
A->clear(); A->clear();
B->clear(); B->clear();
......
...@@ -34,7 +34,14 @@ add_unittest_without_exec(test_ConvTrans ...@@ -34,7 +34,14 @@ add_unittest_without_exec(test_ConvTrans
add_test(NAME test_ConvTrans add_test(NAME test_ConvTrans
COMMAND test_ConvTrans) COMMAND test_ConvTrans)
################# test_ConvUnify #######################
add_unittest_without_exec(test_ConvUnify
test_ConvUnify.cpp
LayerGradUtil.cpp
TestUtil.cpp)
add_test(NAME test_ConvUnify
COMMAND test_ConvUnify)
################## test_Evaluator ####################### ################## test_Evaluator #######################
add_unittest(test_Evaluator add_unittest(test_Evaluator
test_Evaluator.cpp test_Evaluator.cpp
......
...@@ -34,6 +34,7 @@ conv = img_conv_layer(input=data, filter_size=1, filter_size_y=1, ...@@ -34,6 +34,7 @@ conv = img_conv_layer(input=data, filter_size=1, filter_size_y=1,
num_channels=8, num_channels=8,
num_filters=16, stride=1, num_filters=16, stride=1,
bias_attr=True, bias_attr=True,
act=LinearActivation()) act=LinearActivation(),
groups=2)
outputs(concat, conv) outputs(concat, conv)
...@@ -24,7 +24,7 @@ proj2 = conv_projection(input=data, filter_size=1, filter_size_y=1, ...@@ -24,7 +24,7 @@ proj2 = conv_projection(input=data, filter_size=1, filter_size_y=1,
concat = concat_layer(input=[proj1, proj2], bias_attr=False, act=ReluActivation()) concat = concat_layer(input=[proj1, proj2], bias_attr=False, act=ReluActivation())
proj = conv_projection(input=data, filter_size=1, filter_size_y=1, proj = conv_projection(input=data, filter_size=1, filter_size_y=1,
num_channels=8, num_filters=16, stride=1) num_channels=8, num_filters=16, stride=1, groups=2)
with mixed_layer(bias_attr=True, act=LinearActivation()) as conv: with mixed_layer(bias_attr=True, act=LinearActivation()) as conv:
conv += proj conv += proj
......
#edit-mode: -*- python -*-
# Copyright (c) 2016 Baidu, Inc. 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.
from paddle.trainer_config_helpers import *
settings(batch_size=10)
data = data_layer(name ="input", size=8*16*16)
conv1 = img_conv_layer(input=data, filter_size=1, filter_size_y=1,
num_channels=8,
num_filters=16, stride=1,
bias_attr=False,
act=ReluActivation(),
layer_type="exconv")
conv2 = img_conv_layer(input=data, filter_size=1, filter_size_y=1,
num_channels=8,
num_filters=16, stride=1,
bias_attr=False,
act=ReluActivation(),
layer_type="exconv")
concat = concat_layer(input=[conv1, conv2])
conv = img_conv_layer(input=data, filter_size=1, filter_size_y=1,
num_channels=8,
num_filters=16, stride=1,
bias_attr=True,
act=LinearActivation(),
groups=2,
layer_type="exconv")
outputs(concat, conv)
/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.
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 <gtest/gtest.h>
#include <vector>
#include <string>
#include "paddle/gserver/layers/DataLayer.h"
#include "ModelConfig.pb.h"
#include "paddle/trainer/Trainer.h"
#include "paddle/utils/GlobalConstants.h"
#include "paddle/gserver/layers/ExpandConvTransLayer.h"
#include "paddle/math/MathUtils.h"
#include "TestUtil.h"
#include "LayerGradUtil.h"
using namespace paddle; // NOLINT
using namespace std; // NOLINT
P_DECLARE_bool(use_gpu);
P_DECLARE_int32(gpu_id);
P_DECLARE_double(checkgrad_eps);
P_DECLARE_bool(thread_local_rand_use_global_seed);
P_DECLARE_bool(prev_batch_state);
// Do one forward pass of convTrans layer and check to see if its output
// matches the given result
MatrixPtr doOneConvTest(size_t imgSize, size_t output_x, size_t stride,
size_t padding, size_t filter_size, size_t channel,
size_t numfilters, size_t groups, MatrixPtr& inputData,
real* param, bool useGpu) {
TestConfig config;
config.biasSize = numfilters;
if (useGpu) {
config.layerConfig.set_type("cudnn_conv");
} else {
config.layerConfig.set_type("exconv");
}
config.layerConfig.set_num_filters(numfilters);
config.layerConfig.set_partial_sum(1);
config.layerConfig.set_shared_biases(true);
size_t weightSize = channel* filter_size * filter_size *
config.layerConfig.num_filters() / groups;
config.inputDefs.push_back({INPUT_DATA, "layer_0",
imgSize * imgSize * channel,
weightSize});
LayerInputConfig* input = config.layerConfig.add_inputs();
ConvConfig* conv = input->mutable_conv_conf();
conv->set_filter_size(filter_size);
conv->set_filter_size_y(filter_size);
conv->set_channels(channel);
conv->set_padding(padding);
conv->set_padding_y(padding);
conv->set_stride(stride);
conv->set_stride_y(stride);
conv->set_groups(groups);
conv->set_filter_channels(channel/groups);
conv->set_img_size(imgSize);
conv->set_output_x(output_x);
config.layerConfig.set_size(conv->output_x() * conv->output_x() *
config.layerConfig.num_filters());
config.layerConfig.set_name("conv");
std::vector<DataLayerPtr> dataLayers;
LayerMap layerMap;
vector<Argument> datas;
initDataLayer(config, &dataLayers, &datas, &layerMap, "conv",
1, false, useGpu);
dataLayers[0]->getOutputValue()->zeroMem();
dataLayers[0]->getOutputValue()->copyFrom(*inputData);
// test layer initialize
std::vector<ParameterPtr> parameters;
LayerPtr convLayer;
initTestLayer(config, &layerMap, &parameters, &convLayer);
convLayer->getBiasParameter()->zeroMem();
convLayer->getParameters()[0]->zeroMem();
convLayer->getParameters()[0]->getBuf(PARAMETER_VALUE)->copyFrom(param,
weightSize);
convLayer->forward(PASS_GC);
return convLayer->getOutputValue();
}
TEST(Layer, convParaUnified) {
#ifndef PADDLE_ONLY_CPU
MatrixPtr input, resultCpu, resultGpu;
input = Matrix::create(1, 4 * 4, false, false);
float inputData[] = {1, 2, 3, 4,
5, 6, 7, 8,
9, 10, 11, 12,
13, 14, 15, 16};
float param[] = {1, 2, 3, 4, 5, 6, 7, 8, 9,
9, 8, 7, 6, 5, 4, 3, 2, 1};
input->setData(inputData);
resultCpu = doOneConvTest(/* imgSize */ 4,
/* output_x */ 2,
/* stride */ 1,
/* padding */ 0,
/* filter_size */ 3,
/*channel*/ 1,
/*numfilters*/ 2,
/*groups*/ 1,
input, param, false);
resultGpu = doOneConvTest(/* imgSize */ 4,
/* output_x */ 2,
/* stride */ 1,
/* padding */ 0,
/* filter_size */ 3,
/*channel*/ 1,
/*numfilters*/ 2,
/*groups*/ 1,
input, param, true);
checkMatrixEqual(resultCpu, resultGpu);
input = Matrix::create(1, 3 * 3 * 2, false, false);
float inputData2[] = {1, 2, 3,
4, 5, 6,
7, 8, 9,
10, 11, 12,
13, 14, 15,
16, 17, 18};
float param2[] = {1, 2, 3, 4, 5, 6, 7, 8,
8, 7, 6, 5, 4, 3, 2, 1};
input->setData(inputData2);
resultCpu = doOneConvTest(/* imgSize */ 3,
/* output_x */ 2,
/* stride */ 1,
/* padding */ 0,
/* filter_size */ 2,
/*channel*/ 2,
/*numfilters*/ 2,
/*groups*/ 1,
input, param2, false);
resultGpu = doOneConvTest(/* imgSize */ 3,
/* output_x */ 2,
/* stride */ 1,
/* padding */ 0,
/* filter_size */ 2,
/*channel*/ 2,
/*numfilters*/ 2,
/*groups*/ 1,
input, param2, true);
checkMatrixEqual(resultCpu, resultGpu);
float param3[] = {1, 2, 3, 4,
4, 3, 2, 1};
resultCpu = doOneConvTest(/* imgSize */ 3,
/* output_x */ 2,
/* stride */ 1,
/* padding */ 0,
/* filter_size */ 2,
/*channel*/ 2,
/*numfilters*/ 2,
/*groups*/ 2,
input, param3, false);
resultGpu = doOneConvTest(/* imgSize */ 3,
/* output_x */ 2,
/* stride */ 1,
/* padding */ 0,
/* filter_size */ 2,
/*channel*/ 2,
/*numfilters*/ 2,
/*groups*/ 2,
input, param3, true);
checkMatrixEqual(resultCpu, resultGpu);
#endif
}
int main(int argc, char** argv) {
testing::InitGoogleTest(&argc, argv);
initMain(argc, argv);
FLAGS_thread_local_rand_use_global_seed = true;
srand(1);
return RUN_ALL_TESTS();
}
...@@ -166,9 +166,8 @@ TEST(Projection, scaling) { ...@@ -166,9 +166,8 @@ TEST(Projection, scaling) {
} }
} }
#ifndef PADDLE_ONLY_CPU void testProjectionConv(size_t groups) {
TEST(Projection, conv) { const int NUM_FILTERS = 18;
const int NUM_FILTERS = 16;
const int FILTER_SIZE = 2; const int FILTER_SIZE = 2;
const int FILTER_SIZE_Y = 3; const int FILTER_SIZE_Y = 3;
const int CHANNELS = 3; const int CHANNELS = 3;
...@@ -186,7 +185,7 @@ TEST(Projection, conv) { ...@@ -186,7 +185,7 @@ TEST(Projection, conv) {
conv->set_padding_y(1); conv->set_padding_y(1);
conv->set_stride(2); conv->set_stride(2);
conv->set_stride_y(2); conv->set_stride_y(2);
conv->set_groups(1); conv->set_groups(groups);
conv->set_filter_channels(conv->channels() / conv->groups()); conv->set_filter_channels(conv->channels() / conv->groups());
conv->set_img_size(IMAGE_SIZE); conv->set_img_size(IMAGE_SIZE);
int output_x = outputSize(conv->img_size(), int output_x = outputSize(conv->img_size(),
...@@ -206,13 +205,20 @@ TEST(Projection, conv) { ...@@ -206,13 +205,20 @@ TEST(Projection, conv) {
testProjectionGrad( testProjectionGrad(
conf, conf,
INPUT_DATA, INPUT_DATA,
/* parameterSize */ NUM_FILTERS * CHANNELS * FILTER_SIZE * FILTER_SIZE_Y, /* parameterSize */ NUM_FILTERS * CHANNELS * FILTER_SIZE * FILTER_SIZE_Y
/ groups,
/* batchSize */ 100, /* batchSize */ 100,
true, true,
false, false,
NUM_FILTERS, NUM_FILTERS,
true); true);
} }
#ifndef PADDLE_ONLY_CPU
TEST(Projection, conv) {
testProjectionConv(1);
testProjectionConv(3);
}
#endif #endif
TEST(Layer, BilinearInterpLayer) { TEST(Layer, BilinearInterpLayer) {
......
...@@ -255,6 +255,16 @@ TEST(Compare, img_conv) { ...@@ -255,6 +255,16 @@ TEST(Compare, img_conv) {
compareNetwork(config_file_a, config_file_b); compareNetwork(config_file_a, config_file_b);
FLAGS_use_gpu = useGpu; FLAGS_use_gpu = useGpu;
} }
// Test cudnn_conv and exconv give the same result
TEST(Compare, img_conv2) {
std::string config_file_a = "./gserver/tests/img_conv_a.conf";
std::string config_file_b = "./gserver/tests/img_conv_c.conf";
bool useGpu = FLAGS_use_gpu;
FLAGS_use_gpu = true;
compareNetwork(config_file_a, config_file_b);
FLAGS_use_gpu = useGpu;
}
#endif #endif
P_DEFINE_string(config_file_a, "", "config of one network to compare"); P_DEFINE_string(config_file_a, "", "config of one network to compare");
......
...@@ -698,7 +698,8 @@ class ConvProjection(Projection): ...@@ -698,7 +698,8 @@ class ConvProjection(Projection):
ci = self.proj_conf.conv_conf.channels ci = self.proj_conf.conv_conf.channels
fh = self.proj_conf.conv_conf.filter_size fh = self.proj_conf.conv_conf.filter_size
fw = self.proj_conf.conv_conf.filter_size_y fw = self.proj_conf.conv_conf.filter_size_y
return co * ci * fh * fw gr = self.proj_conf.conv_conf.groups
return co * ci * fh * fw / gr
def calc_bias_size(self): def calc_bias_size(self):
return self.proj_conf.num_filters return self.proj_conf.num_filters
......
...@@ -129,6 +129,9 @@ class LayerType(object): ...@@ -129,6 +129,9 @@ class LayerType(object):
HSIGMOID = 'hsigmoid' HSIGMOID = 'hsigmoid'
CONV_LAYER = "conv" CONV_LAYER = "conv"
CONVTRANS_LAYER = "convt" CONVTRANS_LAYER = "convt"
EXCONV_LAYER = "exconv"
EXCONVTRANS_LAYER = "exconvt"
CUDNNCONV_LAYER = "cudnn_conv"
POOL_LAYER = "pool" POOL_LAYER = "pool"
BATCH_NORM_LAYER = 'batch_norm' BATCH_NORM_LAYER = 'batch_norm'
NORM_LAYER = 'norm' NORM_LAYER = 'norm'
...@@ -1762,7 +1765,8 @@ def img_conv_layer(input, ...@@ -1762,7 +1765,8 @@ def img_conv_layer(input,
filter_size_y=None, filter_size_y=None,
stride_y=None, stride_y=None,
padding_y=None, padding_y=None,
trans=False): trans=False,
layer_type=None):
""" """
Convolution layer for image. Paddle only support square input currently and Convolution layer for image. Paddle only support square input currently and
thus input image's width equals height. thus input image's width equals height.
...@@ -1829,6 +1833,10 @@ def img_conv_layer(input, ...@@ -1829,6 +1833,10 @@ def img_conv_layer(input,
:type layer_attr: ExtraLayerAttribute :type layer_attr: ExtraLayerAttribute
:param trans: true if it is a convTransLayer, false if it is a convLayer :param trans: true if it is a convTransLayer, false if it is a convLayer
:type trans: bool :type trans: bool
:param layer_type: specify the layer_type, default is None. If trans=True,
layer_type has to be "exconvt", otherwise layer_type
has to be either "exconv" or "cudnn_conv"
:type layer_type: String
:return: LayerOutput object. :return: LayerOutput object.
:rtype: LayerOutput :rtype: LayerOutput
""" """
...@@ -1865,6 +1873,13 @@ def img_conv_layer(input, ...@@ -1865,6 +1873,13 @@ def img_conv_layer(input,
param_attr.attr["initial_strategy"] = 0 param_attr.attr["initial_strategy"] = 0
param_attr.attr["initial_smart"] = False param_attr.attr["initial_smart"] = False
if layer_type:
if trans:
assert layer_type in ["exconvt"]
else:
assert layer_type in ["exconv", "cudnn_conv"]
lt = layer_type
else:
lt = LayerType.CONVTRANS_LAYER if trans else LayerType.CONV_LAYER lt = LayerType.CONVTRANS_LAYER if trans else LayerType.CONV_LAYER
l = Layer( l = Layer(
......
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