resnet.py

# 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 *
"""
paper: https://arxiv.org/abs/1512.03385
"""
is_test = get_config_arg("is_test", bool, False)
is_predict = get_config_arg("is_predict", bool, False)
data_provider = get_config_arg("data_provider", bool, True)
layer_num = get_config_arg("layer_num", int, 50)

if not is_predict and data_provider:
    train_list = 'train.list' if not is_test else None
    # mean.meta is mean file of ImageNet dataset.
    # mean.meta size : 3 x 224 x 224.
    # If you use three mean value, set like:
    # "mean_value:103.939,116.779,123.68;"
    args = {
        'mean_meta': "model/mean_meta_224/mean.meta",
        'image_size': 224,
        'crop_size': 224,
        'color': True,
        'swap_channel:': [2, 1, 0]
    }
    define_py_data_sources2(
        train_list,
        'example/test.list',
        module="example.image_list_provider",
        obj="processData",
        args=args)

batch_size = 1
learning_rate = 0.1 / batch_size
momentum = 0.9
weight_decay = 0.0001 * batch_size
default_momentum(momentum)
default_decay_rate(weight_decay)

Settings(
    algorithm='sgd',
    batch_size=batch_size,
    learning_rate=learning_rate,

    # set the appropriate parameters according your schedule
    learning_method='momentum',
    learning_rate_decay_a=0.5,
    learning_rate_decay_b=1200000 * 10,
    learning_rate_schedule="discexp", )


def conv_bn_layer(name,
                  input,
                  filter_size,
                  num_filters,
                  stride,
                  padding,
                  channels=None,
                  active_type=ReluActivation()):
    """
    A wrapper for conv layer with batch normalization layers.
    Note:
    conv layer has no activation.
    """

    tmp = img_conv_layer(
        name=name + "_conv",
        input=input,
        filter_size=filter_size,
        num_channels=channels,
        num_filters=num_filters,
        stride=stride,
        padding=padding,
        act=LinearActivation(),
        bias_attr=False)
    return batch_norm_layer(
        name=name + "_bn", input=tmp, act=active_type, use_global_stats=is_test)


def bottleneck_block(name, input, num_filters1, num_filters2):
    """
    A wrapper for bottlenect building block in ResNet.
    Last conv_bn_layer has no activation.
    Addto layer has activation of relu.
    """
    last_name = conv_bn_layer(
        name=name + '_branch2a',
        input=input,
        filter_size=1,
        num_filters=num_filters1,
        stride=1,
        padding=0)
    last_name = conv_bn_layer(
        name=name + '_branch2b',
        input=last_name,
        filter_size=3,
        num_filters=num_filters1,
        stride=1,
        padding=1)
    last_name = conv_bn_layer(
        name=name + '_branch2c',
        input=last_name,
        filter_size=1,
        num_filters=num_filters2,
        stride=1,
        padding=0,
        active_type=LinearActivation())

    return addto_layer(
        name=name + "_addto", input=[input, last_name], act=ReluActivation())


def mid_projection(name, input, num_filters1, num_filters2, stride=2):
    """
    A wrapper for middile projection in ResNet.
    projection shortcuts are used for increasing dimensions,
    and other shortcuts are identity
    branch1: projection shortcuts are used for increasing
    dimensions, has no activation.
    branch2x: bottleneck building block, shortcuts are identity.
    """
    # stride = 2
    branch1 = conv_bn_layer(
        name=name + '_branch1',
        input=input,
        filter_size=1,
        num_filters=num_filters2,
        stride=stride,
        padding=0,
        active_type=LinearActivation())

    last_name = conv_bn_layer(
        name=name + '_branch2a',
        input=input,
        filter_size=1,
        num_filters=num_filters1,
        stride=stride,
        padding=0)
    last_name = conv_bn_layer(
        name=name + '_branch2b',
        input=last_name,
        filter_size=3,
        num_filters=num_filters1,
        stride=1,
        padding=1)

    last_name = conv_bn_layer(
        name=name + '_branch2c',
        input=last_name,
        filter_size=1,
        num_filters=num_filters2,
        stride=1,
        padding=0,
        active_type=LinearActivation())

    return addto_layer(
        name=name + "_addto", input=[branch1, last_name], act=ReluActivation())


def deep_res_net(res2_num=3, res3_num=4, res4_num=6, res5_num=3):
    """
    A wrapper for 50,101,152 layers of ResNet.
    res2_num: number of blocks stacked in conv2_x
    res3_num: number of blocks stacked in conv3_x
    res4_num: number of blocks stacked in conv4_x
    res5_num: number of blocks stacked in conv5_x
    """
    # For ImageNet
    # conv1: 112x112
    img = data_layer(name='input', size=224 * 224 * 3)
    tmp = conv_bn_layer(
        "conv1",
        img,
        filter_size=7,
        channels=3,
        num_filters=64,
        stride=2,
        padding=3)
    tmp = img_pool_layer(name="pool1", input=tmp, pool_size=3, stride=2)

    # conv2_x: 56x56
    tmp = mid_projection(
        name="res2_1", input=tmp, num_filters1=64, num_filters2=256, stride=1)
    for i in xrange(2, res2_num + 1, 1):
        tmp = bottleneck_block(
            name="res2_" + str(i), input=tmp, num_filters1=64, num_filters2=256)

    # conv3_x: 28x28
    tmp = mid_projection(
        name="res3_1", input=tmp, num_filters1=128, num_filters2=512)
    for i in xrange(2, res3_num + 1, 1):
        tmp = bottleneck_block(
            name="res3_" + str(i),
            input=tmp,
            num_filters1=128,
            num_filters2=512)

    # conv4_x: 14x14
    tmp = mid_projection(
        name="res4_1", input=tmp, num_filters1=256, num_filters2=1024)
    for i in xrange(2, res4_num + 1, 1):
        tmp = bottleneck_block(
            name="res4_" + str(i),
            input=tmp,
            num_filters1=256,
            num_filters2=1024)

    # conv5_x: 7x7
    tmp = mid_projection(
        name="res5_1", input=tmp, num_filters1=512, num_filters2=2048)
    for i in xrange(2, res5_num + 1, 1):
        tmp = bottleneck_block(
            name="res5_" + str(i),
            input=tmp,
            num_filters1=512,
            num_filters2=2048)

    tmp = img_pool_layer(
        name='avgpool',
        input=tmp,
        pool_size=7,
        stride=1,
        pool_type=AvgPooling())

    output = fc_layer(
        name='output', input=tmp, size=1000, act=SoftmaxActivation())

    if not is_predict:
        classification_cost(
            input=output, label=data_layer(
                name='label', size=1))


def res_net_50():
    deep_res_net(3, 4, 6, 3)


def res_net_101():
    deep_res_net(3, 4, 23, 3)


def res_net_152():
    deep_res_net(3, 8, 36, 3)


if not is_predict:
    Inputs("input", "label")
else:
    Inputs("input")
# Outputs("cost-softmax" if not is_predict else "output")
Outputs("res5_3_branch2c_conv", "res5_3_branch2c_bn")

if layer_num == 50:
    res_net_50()
elif layer_num == 101:
    res_net_101()
elif layer_num == 152:
    res_net_152()
else:
    print("Wrong layer number.")