Yang Yu
8 years ago
commit
2ac5772970
@ -0,0 +1,213 @@
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#!/usr/bin/env python
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from paddle.trainer_config_helpers import *
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height = 224
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width = 224
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num_class = 1000
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batch_size = get_config_arg('batch_size', int, 64)
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layer_num = get_config_arg("layer_num", int, 50)
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is_test = get_config_arg("is_test", bool, False)
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args = {'height': height, 'width': width, 'color': True, 'num_class': num_class}
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define_py_data_sources2(
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"train.list", None, module="provider", obj="process", args=args)
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settings(
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batch_size=batch_size,
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learning_rate=0.01 / batch_size,
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learning_method=MomentumOptimizer(0.9),
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regularization=L2Regularization(0.0005 * batch_size))
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#######################Network Configuration #############
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def conv_bn_layer(name,
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input,
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filter_size,
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num_filters,
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stride,
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padding,
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channels=None,
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active_type=ReluActivation()):
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"""
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A wrapper for conv layer with batch normalization layers.
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Note:
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conv layer has no activation.
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"""
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tmp = img_conv_layer(
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name=name + "_conv",
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input=input,
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filter_size=filter_size,
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num_channels=channels,
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num_filters=num_filters,
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stride=stride,
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padding=padding,
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act=LinearActivation(),
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bias_attr=False)
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return batch_norm_layer(
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name=name + "_bn", input=tmp, act=active_type, use_global_stats=is_test)
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def bottleneck_block(name, input, num_filters1, num_filters2):
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"""
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A wrapper for bottlenect building block in ResNet.
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Last conv_bn_layer has no activation.
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Addto layer has activation of relu.
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"""
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last_name = conv_bn_layer(
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name=name + '_branch2a',
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input=input,
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filter_size=1,
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num_filters=num_filters1,
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stride=1,
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padding=0)
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last_name = conv_bn_layer(
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name=name + '_branch2b',
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input=last_name,
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filter_size=3,
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num_filters=num_filters1,
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stride=1,
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padding=1)
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last_name = conv_bn_layer(
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name=name + '_branch2c',
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input=last_name,
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filter_size=1,
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num_filters=num_filters2,
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stride=1,
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padding=0,
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active_type=LinearActivation())
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return addto_layer(
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name=name + "_addto", input=[input, last_name], act=ReluActivation())
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def mid_projection(name, input, num_filters1, num_filters2, stride=2):
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"""
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A wrapper for middile projection in ResNet.
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projection shortcuts are used for increasing dimensions,
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and other shortcuts are identity
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branch1: projection shortcuts are used for increasing
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dimensions, has no activation.
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branch2x: bottleneck building block, shortcuts are identity.
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"""
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# stride = 2
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branch1 = conv_bn_layer(
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name=name + '_branch1',
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input=input,
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filter_size=1,
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num_filters=num_filters2,
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stride=stride,
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padding=0,
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active_type=LinearActivation())
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last_name = conv_bn_layer(
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name=name + '_branch2a',
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input=input,
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filter_size=1,
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num_filters=num_filters1,
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stride=stride,
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padding=0)
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last_name = conv_bn_layer(
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name=name + '_branch2b',
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input=last_name,
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filter_size=3,
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num_filters=num_filters1,
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stride=1,
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padding=1)
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last_name = conv_bn_layer(
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name=name + '_branch2c',
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input=last_name,
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filter_size=1,
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num_filters=num_filters2,
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stride=1,
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padding=0,
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active_type=LinearActivation())
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return addto_layer(
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name=name + "_addto", input=[branch1, last_name], act=ReluActivation())
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img = data_layer(name='image', size=height * width * 3)
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def deep_res_net(res2_num=3, res3_num=4, res4_num=6, res5_num=3):
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"""
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A wrapper for 50,101,152 layers of ResNet.
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res2_num: number of blocks stacked in conv2_x
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res3_num: number of blocks stacked in conv3_x
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res4_num: number of blocks stacked in conv4_x
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res5_num: number of blocks stacked in conv5_x
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"""
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# For ImageNet
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# conv1: 112x112
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tmp = conv_bn_layer(
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"conv1",
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input=img,
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filter_size=7,
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channels=3,
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num_filters=64,
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stride=2,
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padding=3)
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tmp = img_pool_layer(name="pool1", input=tmp, pool_size=3, stride=2)
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# conv2_x: 56x56
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tmp = mid_projection(
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name="res2_1", input=tmp, num_filters1=64, num_filters2=256, stride=1)
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for i in xrange(2, res2_num + 1, 1):
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tmp = bottleneck_block(
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name="res2_" + str(i), input=tmp, num_filters1=64, num_filters2=256)
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# conv3_x: 28x28
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tmp = mid_projection(
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name="res3_1", input=tmp, num_filters1=128, num_filters2=512)
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for i in xrange(2, res3_num + 1, 1):
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tmp = bottleneck_block(
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name="res3_" + str(i),
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input=tmp,
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num_filters1=128,
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num_filters2=512)
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# conv4_x: 14x14
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tmp = mid_projection(
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name="res4_1", input=tmp, num_filters1=256, num_filters2=1024)
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for i in xrange(2, res4_num + 1, 1):
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tmp = bottleneck_block(
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name="res4_" + str(i),
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input=tmp,
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num_filters1=256,
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num_filters2=1024)
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# conv5_x: 7x7
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tmp = mid_projection(
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name="res5_1", input=tmp, num_filters1=512, num_filters2=2048)
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for i in xrange(2, res5_num + 1, 1):
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tmp = bottleneck_block(
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name="res5_" + str(i),
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input=tmp,
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num_filters1=512,
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num_filters2=2048)
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tmp = img_pool_layer(
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name='avgpool',
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input=tmp,
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pool_size=7,
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stride=1,
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pool_type=AvgPooling())
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return fc_layer(input=tmp, size=num_class, act=SoftmaxActivation())
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if layer_num == 50:
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resnet = deep_res_net(3, 4, 6, 3)
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elif layer_num == 101:
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resnet = deep_res_net(3, 4, 23, 3)
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elif layer_num == 152:
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resnet = deep_res_net(3, 8, 36, 3)
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else:
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print("Wrong layer number.")
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lbl = data_layer(name="label", size=num_class)
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loss = cross_entropy(name='loss', input=resnet, label=lbl)
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inputs(img, lbl)
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outputs(loss)
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@ -0,0 +1,152 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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|
See the License for the specific language governing permissions and
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|
limitations under the License. */
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#include <numeric>
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#include "paddle/framework/lod_rank_table.h"
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#include "paddle/framework/lod_tensor_array.h"
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#include "paddle/framework/op_registry.h"
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#include "paddle/memory/memcpy.h"
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namespace paddle {
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namespace operators {
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using LoD = framework::LoD;
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class ArrayToLoDTensorOp : public framework::OperatorBase {
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public:
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ArrayToLoDTensorOp(const std::string &type,
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const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs)
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: OperatorBase(type, inputs, outputs, attrs) {}
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void Run(const framework::Scope &scope,
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const platform::DeviceContext &dev_ctx) const override {
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auto &x = scope.FindVar(Input("X"))->Get<framework::LoDTensorArray>();
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auto &rank_table =
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scope.FindVar(Input("RankTable"))->Get<framework::LoDRankTable>();
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auto *out =
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scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensor>();
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// Check dims, place and data type of input's elements and infer output's
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// dim
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PADDLE_ENFORCE(!x.empty(), "There's no element in the input array.");
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int rank = x[0].dims().size();
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platform::Place place = x[0].place();
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std::type_index data_type = x[0].type();
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framework::DDim ins_dims = framework::slice_ddim(x[0].dims(), 1, rank);
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int64_t batch_size = x[0].dims()[0];
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for (size_t i = 1; i < x.size(); ++i) {
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PADDLE_ENFORCE_EQ(framework::slice_ddim(x[i].dims(), 1, rank), ins_dims,
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"The dimension of the %zu'th element in LoDTensorArray "
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"differs from previous ones.",
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|
i);
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PADDLE_ENFORCE(platform::places_are_same_class(x[i].place(), place),
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"The place class of the %zu'th element in LoDTensorArray "
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|
"differs from previous ones.",
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|
i);
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PADDLE_ENFORCE(x[i].type() == data_type,
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"The date type of the %zu'th element in LoDTensorArray "
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|
"differs from previous ones.",
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i);
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batch_size += x[i].dims()[0];
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}
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auto ins_dim_vec = framework::vectorize(ins_dims);
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ins_dim_vec.insert(ins_dim_vec.begin(), batch_size);
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framework::DDim out_dims = framework::make_ddim(ins_dim_vec);
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out->Resize(out_dims);
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out->mutable_data(place, data_type);
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auto &table_items = rank_table.items();
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std::vector<size_t> table_item_idx(table_items.size());
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// table_item_idx = range(table_items_idx.size())
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std::iota(table_item_idx.begin(), table_item_idx.end(), 0);
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std::sort(table_item_idx.begin(), table_item_idx.end(),
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[&](size_t a, size_t b) {
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return table_items[a].index < table_items[b].index;
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|
});
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|
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// Build LoDTensor `out`
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framework::LoD *out_lod = out->mutable_lod();
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|
out_lod->clear();
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size_t out_offset = 0;
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auto prefix_lod = rank_table.coarse_lod();
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prefix_lod.emplace_back();
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auto &cur_level_lod = prefix_lod.back();
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|
cur_level_lod.push_back(0);
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|
for (size_t idx : table_item_idx) {
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cur_level_lod.push_back(cur_level_lod.back() + table_items[idx].length);
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for (size_t x_idx = 0; x_idx < table_items[idx].length; ++x_idx) {
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|
auto lod_and_offset = framework::GetSubLoDAndAbsoluteOffset(
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|
x[x_idx].lod(), idx, idx + 1, 0);
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|
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|
auto &lod_length = lod_and_offset.first;
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|
framework::AppendLoD(out_lod, lod_length);
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|
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|
size_t start_offset = lod_and_offset.second.first;
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|
size_t end_offset = lod_and_offset.second.second;
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|
VLOG(10) << "idx=" << idx << " x_idx=" << x_idx << " ["
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|
<< ", " << end_offset << "]";
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|
// Copy data
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|
PADDLE_ENFORCE_GE(end_offset, start_offset);
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|
size_t len = end_offset - start_offset;
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|
if (len == 0) {
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|
continue;
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|
}
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|
out->Slice(out_offset, out_offset + len)
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|
.CopyFrom(x[x_idx].Slice(start_offset, end_offset), place, dev_ctx);
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|
out_offset += len;
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|
}
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|
}
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|
out_lod->insert(out_lod->begin(), prefix_lod.begin(), prefix_lod.end());
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|
}
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|
};
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|
|
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|
class ArrayToLoDTensorOpProtoMaker : public framework::OpProtoAndCheckerMaker {
|
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|
public:
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|
ArrayToLoDTensorOpProtoMaker(framework::OpProto *proto,
|
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|
framework::OpAttrChecker *op_checker)
|
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|
: OpProtoAndCheckerMaker(proto, op_checker) {
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|
AddInput("X",
|
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|
"(std::vector<LodTensor>) A vector of tensors that is going to "
|
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|
"be casted to a big LoDTensor.");
|
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|
AddInput("RankTable",
|
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|
"(LoDRankTable) RankTable provides the coarse lod infomation to "
|
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|
"build the output LoDTensor. See "
|
||||||
|
"'paddle/framework/lod_rank_table.h' for more details.");
|
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|
AddOutput("Out", "(LoDTensor) The LoDTensor formed by input tensor array.");
|
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|
AddComment(
|
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|
R"DOC(This Op build a big LoDTensor from a std::vector<LoDTensor>
|
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|
and a LoDRankTable. It is supposed to be used in getting dynamic RNN's
|
||||||
|
outputs back to a normal LoDTensor. The std::vector<LoDTensor>
|
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|
would be the output of RNN Op and the LoDRankTable would be build
|
||||||
|
with RNN's input.)DOC");
|
||||||
|
}
|
||||||
|
};
|
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|
|
||||||
|
class ArrayToLoDTensorInferShape : public framework::InferShapeBase {
|
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|
public:
|
||||||
|
void operator()(framework::InferShapeContext *context) const override {
|
||||||
|
PADDLE_ENFORCE(context->HasInput("X"),
|
||||||
|
"ArrayToLoDTensorOp must has input X.");
|
||||||
|
PADDLE_ENFORCE(context->HasInput("RankTable"),
|
||||||
|
"ArrayToLoDTensorOp must has input RankTable.");
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
} // namespace operators
|
||||||
|
} // namespace paddle
|
||||||
|
|
||||||
|
namespace ops = paddle::operators;
|
||||||
|
REGISTER_OPERATOR(array_to_lod_tensor, ops::ArrayToLoDTensorOp,
|
||||||
|
ops::ArrayToLoDTensorOpProtoMaker,
|
||||||
|
ops::ArrayToLoDTensorInferShape);
|
||||||
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