提交 70e44732 编写于 作者: W wangyang59

added convTrans test and python components

上级 5c88f072
...@@ -5,4 +5,6 @@ build/ ...@@ -5,4 +5,6 @@ build/
.vscode .vscode
.idea .idea
.project .project
.cproject
.pydevproject .pydevproject
Makefile
...@@ -26,6 +26,14 @@ add_unittest_without_exec(test_ActivationGrad ...@@ -26,6 +26,14 @@ add_unittest_without_exec(test_ActivationGrad
TestUtil.cpp) TestUtil.cpp)
add_test(NAME test_ActivationGrad add_test(NAME test_ActivationGrad
COMMAND test_ActivationGrad) COMMAND test_ActivationGrad)
################# test_ConvTrans #######################
add_unittest_without_exec(test_ConvTrans
test_ConvTrans.cpp
LayerGradUtil.cpp
TestUtil.cpp)
add_test(NAME test_ConvTrans
COMMAND test_ConvTrans)
################## test_Evaluator ####################### ################## test_Evaluator #######################
add_unittest(test_Evaluator add_unittest(test_Evaluator
......
/* 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 "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);
TEST(Layer, convTransLayerFwd) {
TestConfig configt;
configt.biasSize = 3;
configt.layerConfig.set_type("exconvt");
configt.layerConfig.set_num_filters(3);
configt.layerConfig.set_partial_sum(1);
configt.layerConfig.set_shared_biases(true);
configt.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 288});
LayerInputConfig* input = configt.layerConfig.add_inputs();
ConvConfig* conv = input->mutable_conv_conf();
conv->set_filter_size(2);
conv->set_filter_size_y(3);
conv->set_channels(16);
conv->set_padding(0);
conv->set_padding_y(1);
conv->set_stride(2);
conv->set_stride_y(2);
conv->set_groups(1);
conv->set_filter_channels(3 / conv->groups());
conv->set_img_size(16);
conv->set_output_x(
(2 * conv->padding() + conv->img_size() - conv->filter_size()) /
((float)conv->stride()) +
1.5);
configt.layerConfig.set_size(conv->img_size() * conv->img_size() *
configt.layerConfig.num_filters());
configt.layerConfig.set_name("convTrans");
// data layer initialize
std::vector<DataLayerPtr> dataLayers;
LayerMap layerMap;
vector<Argument> datas;
initDataLayer(configt, &dataLayers, &datas, &layerMap, "convTrans",
100, false, useGpu);
// test layer initialize
std::vector<ParameterPtr> parameters;
LayerPtr convtLayer;
initTestLayer(configt, &layerMap, &parameters, &convtLayer);
convtLayer->getBiasParameter()->zeroMem();
convtLayer->forward(PASS_GC);
TestConfig config;
config.biasSize = 16;
config.layerConfig.set_type("exconv");
config.layerConfig.set_num_filters(16);
config.layerConfig.set_partial_sum(1);
config.layerConfig.set_shared_biases(true);
config.inputDefs.push_back({INPUT_DATA, "layer_1", 768, 288});
input = config.layerConfig.add_inputs();
conv = input->mutable_conv_conf();
conv->set_filter_size(2);
conv->set_filter_size_y(3);
conv->set_channels(3);
conv->set_padding(0);
conv->set_padding_y(1);
conv->set_stride(2);
conv->set_stride_y(2);
conv->set_groups(1);
conv->set_filter_channels(conv->channels() / conv->groups());
conv->set_img_size(16);
conv->set_output_x(
(2 * conv->padding() + conv->img_size() - conv->filter_size()) /
((float)conv->stride()) +
1.5);
config.layerConfig.set_size(conv->output_x() * conv->output_x() *
config.layerConfig.num_filters());
config.layerConfig.set_name("conv");
// data layer initialize
std::vector<DataLayerPtr> dataLayers2;
LayerMap layerMap2;
vector<Argument> datas2;
initDataLayer(config, &dataLayers2, &datas2, &layerMap2, "conv",
100, false, useGpu);
// test layer initialize
std::vector<ParameterPtr> parameters2;
LayerPtr convLayer;
initTestLayer(config, &layerMap2, &parameters2, &convLayer);
convLayer->getBiasParameter()->zeroMem();
convLayer->getParameters()[0]->getBuf(PARAMETER_VALUE)->copyFrom(
*(convtLayer->getParameters()[0]->getBuf(PARAMETER_VALUE)));
convLayer->forward(PASS_GC);
convLayer->getOutput().grad->copyFrom(*(dataLayers[0]->getOutputValue()));
vector<int> callbackFlags(parameters2.size(), 0);
auto callback = [&](Parameter* para) { ++callbackFlags[para->getID()]; };
convLayer->backward(callback);
checkMatrixEqual(convtLayer->getOutputValue(),
dataLayers2[0]->getOutputGrad());
}
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();
}
...@@ -1106,6 +1106,37 @@ def parse_conv(conv, input_layer_name, conv_conf): ...@@ -1106,6 +1106,37 @@ def parse_conv(conv, input_layer_name, conv_conf):
conv_conf.padding, conv_conf.stride, conv_conf.padding, conv_conf.stride,
conv_conf.caffe_mode) conv_conf.caffe_mode)
def parse_convt(conv, input_layer_name, conv_conf):
conv_conf.filter_size = conv.filter_size
conv_conf.filter_size_y = conv.filter_size_y
conv_conf.channels = conv.channels
conv_conf.padding = conv.padding
conv_conf.padding_y = conv.padding_y
conv_conf.stride = conv.stride
conv_conf.stride_y = conv.stride_y
conv_conf.groups = conv.groups
conv_conf.filter_channels = conv.channels / conv.groups
conv_conf.caffe_mode = conv.caffe_mode
outputSize = g_layer_map[input_layer_name].size / conv.channels
print('channels=%d size=%d'%(conv.channels,
g_layer_map[input_layer_name].size))
conv_conf.output_x = int(outputSize ** 0.5)
config_assert((conv_conf.output_x ** 2) == outputSize,
("Input layer %s: Incorrect input image size %d for input "
+ "image pixels %d")
% (input_layer_name, conv_conf.img_size, img_pixels))
if conv.caffe_mode:
conv_conf.img_size = \
(conv_conf.output_x - 1) * conv.stride \
+ conv.filter_size - 2 * conv.padding
else:
conv_conf.img_size = \
(conv_conf.output_x - 1) * conv.stride \
+ conv.filter_size - 2 * conv.padding + 1
def parse_block_expand(block_expand, input_layer_name, block_expand_conf): def parse_block_expand(block_expand, input_layer_name, block_expand_conf):
block_expand_conf.channels = block_expand.channels block_expand_conf.channels = block_expand.channels
block_expand_conf.stride_x = block_expand.stride_x block_expand_conf.stride_x = block_expand.stride_x
...@@ -1612,6 +1643,70 @@ class ConvLayer(ConvLayerBase): ...@@ -1612,6 +1643,70 @@ class ConvLayer(ConvLayerBase):
class ConvLayer(ConvLayerBase): class ConvLayer(ConvLayerBase):
layer_type = 'cudnn_conv' layer_type = 'cudnn_conv'
@config_layer('convt')
class ConvTransLayerBase(LayerBase):
layer_type = 'convt'
def __init__(
self,
name,
inputs=[],
bias=True,
num_filters=None,
shared_biases=False,
**xargs):
super(ConvLayerBase, self).__init__(
name, self.layer_type, 0, inputs=inputs, **xargs)
if num_filters is not None:
self.config.num_filters = num_filters
use_gpu = int(g_command_config_args.get("use_gpu", 0))
parallel_nn = int(g_command_config_args.get("parallel_nn", 0))
# Automatically select cudnn_type for GPU and exconv for CPU
# if set type=conv, but still reserve the way user specify
# exconv or cudnn_conv manually.
if self.layer_type == "cudnn_convt":
config_assert(use_gpu, "cudnn_convt only support GPU")
if (use_gpu == 1 and self.layer_type != "exconvt" and
(parallel_nn == 0 or self.config.device > -1)):
self.layer_type = "cudnn_convt"
else:
self.layer_type = "exconvt"
# need to specify layer in config
self.config.type = self.layer_type
if shared_biases is not None:
self.config.shared_biases = shared_biases
for input_index in xrange(len(self.inputs)):
input_layer = self.get_input_layer(input_index)
parse_convt(
self.inputs[input_index].conv,
input_layer.name,
self.config.inputs[input_index].conv_conf)
conv_conf = self.config.inputs[input_index].conv_conf
psize = self.calc_parameter_size(conv_conf)
print("output size for %s is %d " % (name, conv_conf.output_x))
self.create_input_parameter(input_index, psize)
self.set_layer_size(
(conv_conf.img_size ** 2) * self.config.num_filters)
psize = self.config.size
if shared_biases:
psize = self.config.num_filters
self.create_bias_parameter(bias, psize, [psize, 1])
def calc_parameter_size(self, conv_conf):
return conv_conf.channels() * conv_conf.filter_channels \
* (conv_conf.filter_size * conv_conf.filter_size_y)
@config_layer('exconvt')
class ConvTransLayer(ConvTransLayerBase):
layer_type = 'exconvt'
@config_layer('norm') @config_layer('norm')
class NormLayer(LayerBase): class NormLayer(LayerBase):
def __init__( def __init__(
......
...@@ -78,6 +78,7 @@ class LayerType(object): ...@@ -78,6 +78,7 @@ class LayerType(object):
COSINE_SIM = 'cos' COSINE_SIM = 'cos'
HSIGMOID = 'hsigmoid' HSIGMOID = 'hsigmoid'
CONV_LAYER = "conv" CONV_LAYER = "conv"
CONVTRANS_LAYER = "convt"
POOL_LAYER = "pool" POOL_LAYER = "pool"
BATCH_NORM_LAYER = 'batch_norm' BATCH_NORM_LAYER = 'batch_norm'
NORM_LAYER = 'norm' NORM_LAYER = 'norm'
...@@ -1625,6 +1626,128 @@ def img_conv_layer(input, filter_size, num_filters, ...@@ -1625,6 +1626,128 @@ def img_conv_layer(input, filter_size, num_filters,
return LayerOutput(name, LayerType.CONV_LAYER, parents=[input], return LayerOutput(name, LayerType.CONV_LAYER, parents=[input],
activation=act, num_filters=num_filters) activation=act, num_filters=num_filters)
@wrap_name_default("convt")
@wrap_param_attr_default()
@wrap_bias_attr_default()
@wrap_act_default(act=ReluActivation())
@layer_support(DROPOUT)
def img_convTrans_layer(input, filter_size, num_filters,
name=None, num_channels=None,
act=None, groups=1, stride=1, padding=0, bias_attr=None,
param_attr=None, shared_biases=True, layer_attr=None,
filter_size_y=None, stride_y=None, padding_y=None):
"""
Convolution Transpose (deconv) layer for image. Paddle only support square
input currently and thus input image's width equals height.
The details of convolution transpose layer,
please refer to the following explanation and references therein
<http://datascience.stackexchange.com/questions/6107/
what-are-deconvolutional-layers/>`_ .
The num_channel means input image's channel number. It may be 1 or 3 when
input is raw pixels of image(mono or RGB), or it may be the previous layer's
num_filters * num_group.
There are several group of filter in PaddlePaddle implementation.
Each group will process some channel of the inputs. For example, if an input
num_channel = 256, group = 4, num_filter=32, the PaddlePaddle will create
32*4 = 128 filters to process inputs. The channels will be split into 4
pieces. First 256/4 = 64 channels will process by first 32 filters. The
rest channels will be processed by rest group of filters.
:param name: Layer name.
:type name: basestring
:param input: Layer Input.
:type input: LayerOutput
:param filter_size: The x dimension of a filter kernel. Or input a tuple for
two image dimension.
:type filter_size: int|tuple|list
:param filter_size_y: The y dimension of a filter kernel. Since PaddlePaddle
currently supports rectangular filters, the filter's
shape will be (filter_size, filter_size_y).
:type filter_size_y: int|None
:param num_filters: Each filter group's number of filter
:param act: Activation type. Default is tanh
:type act: BaseActivation
:param groups: Group size of filters.
:type groups: int
:param stride: The x dimension of the stride. Or input a tuple for two image
dimension.
:type stride: int|tuple|list
:param stride_y: The y dimension of the stride.
:type stride_y: int
:param padding: The x dimension of the padding. Or input a tuple for two
image dimension
:type padding: int|tuple|list
:param padding_y: The y dimension of the padding.
:type padding_y: int
:param bias_attr: Convolution bias attribute. None means default bias.
False means no bias.
:type bias_attr: ParameterAttribute|False
:param num_channels: number of input channels. If None will be set
automatically from previous output.
:type num_channels: int
:param param_attr: Convolution param attribute. None means default attribute
:type param_attr: ParameterAttribute
:param shared_biases: Is biases will be shared between filters or not.
:type shared_biases: bool
:param layer_attr: Layer Extra Attribute.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
if num_channels is None:
assert input.num_filters is not None
num_channels = input.num_filters
if filter_size_y is None:
if isinstance(filter_size, collections.Sequence):
assert len(filter_size) == 2
filter_size, filter_size_y = filter_size
else:
filter_size_y = filter_size
if stride_y is None:
if isinstance(stride, collections.Sequence):
assert len(stride) == 2
stride, stride_y = stride
else:
stride_y = stride
if padding_y is None:
if isinstance(padding, collections.Sequence):
assert len(padding) == 2
padding, padding_y = padding
else:
padding_y = padding
if param_attr.attr.get('initial_smart'):
# special initial for conv layers.
init_w = (2.0 / (filter_size ** 2 * num_channels)) ** 0.5
param_attr.attr["initial_mean"] = 0.0
param_attr.attr["initial_std"] = init_w
param_attr.attr["initial_strategy"] = 0
param_attr.attr["initial_smart"] = False
Layer(
name=name,
inputs=Input(input.name, conv=Conv(
filter_size=filter_size, padding=padding, stride=stride,
channels=num_channels, groups=groups,
filter_size_y=filter_size_y, padding_y=padding_y,
stride_y=stride_y),
**param_attr.attr),
active_type=act.name,
num_filters=num_filters,
bias=ParamAttr.to_bias(bias_attr),
shared_biases=shared_biases,
type=LayerType.CONVTRANS_LAYER,
**ExtraLayerAttribute.to_kwargs(layer_attr)
)
return LayerOutput(name, LayerType.CONVTRANS_LAYER, parents=[input],
activation=act, num_filters=num_filters)
@wrap_name_default("pool") @wrap_name_default("pool")
@layer_support() @layer_support()
......
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