PaddleClas/ppcls/modeling/architectures/csp_resnet.py

# copyright (c) 2020 PaddlePaddle Authors. 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import math

import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr

__all__ = ["CSPNetNet50", ]


class CSPNetNet():
    def __init__(self, layers=50):
        self.layers = layers

    def net(self, input, class_dim=1000, data_format="NCHW"):
        layers = self.layers
        supported_layers = [18, 34, 50, 101, 152]
        assert layers in supported_layers, \
            "supported layers are {} but input layer is {}".format(
                supported_layers, layers)

        if layers == 18:
            depth = [2, 2, 2, 2]
        elif layers == 34 or layers == 50:
            depth = [3, 3, 5, 3]
        elif layers == 101:
            depth = [3, 4, 23, 3]
        elif layers == 152:
            depth = [3, 8, 36, 3]
        num_filters = [64, 128, 256, 512]

        conv = self.conv_bn_layer(
            input=input,
            num_filters=64,
            filter_size=7,
            stride=2,
            act='relu',
            name="conv1",
            data_format=data_format)
        conv = fluid.layers.pool2d(
            input=conv,
            pool_size=3,
            pool_stride=2,
            pool_padding=1,
            pool_type='max',
            data_format=data_format)

        if layers >= 50:
            for block in range(len(depth)):
                conv_name = "res" + str(block + 2) + chr(97)
                if block != 0:
                    conv = self.conv_bn_layer(
                        input=conv,
                        num_filters=num_filters[block] * 2,
                        filter_size=3,
                        stride=2,
                        act="leaky_relu",
                        name=conv_name + "_downsample",
                        data_format=data_format)

                # layer warp
                #                 left = conv
                #                 right = conv
                left, right = fluid.layers.split(
                    conv,
                    num_or_sections=[conv.shape[1] // 2, conv.shape[1] // 2],
                    dim=1)
                right = self.conv_bn_layer(
                    input=right,
                    num_filters=num_filters[block] * 4,
                    filter_size=1,
                    act="leaky_relu",
                    name=conv_name + "_right_first_route",
                    data_format=data_format)

                for i in range(depth[block]):
                    conv_name = "res" + str(block + 2) + chr(97 + i)

                    right = self.bottleneck_block(
                        input=right,
                        num_filters=num_filters[block],
                        stride=1,
                        name=conv_name,
                        data_format=data_format)

                # route
                left = self.conv_bn_layer(
                    input=left,
                    num_filters=num_filters[block] * 2,
                    filter_size=1,
                    act="leaky_relu",
                    name=conv_name + "_left_route",
                    data_format=data_format)
                right = self.conv_bn_layer(
                    input=right,
                    num_filters=num_filters[block] * 2,
                    filter_size=1,
                    act="leaky_relu",
                    name=conv_name + "_right_route",
                    data_format=data_format)
                conv = fluid.layers.concat([left, right], axis=1)

                conv = self.conv_bn_layer(
                    input=conv,
                    num_filters=num_filters[block] * 2,
                    filter_size=1,
                    stride=1,
                    act="leaky_relu",
                    name=conv_name + "_merged_transition",
                    data_format=data_format)
        else:
            assert False, "not implemented now!!!"

        pool = fluid.layers.pool2d(
            input=conv,
            pool_type='avg',
            global_pooling=True,
            data_format=data_format)
        stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
        out = fluid.layers.fc(
            input=pool,
            size=class_dim,
            param_attr=fluid.param_attr.ParamAttr(
                name="fc_0.w_0",
                initializer=fluid.initializer.Uniform(-stdv, stdv)),
            bias_attr=ParamAttr(name="fc_0.b_0"))
        return out

    def conv_bn_layer(self,
                      input,
                      num_filters,
                      filter_size,
                      stride=1,
                      groups=1,
                      act=None,
                      name=None,
                      data_format='NCHW'):
        conv = fluid.layers.conv2d(
            input=input,
            num_filters=num_filters,
            filter_size=filter_size,
            stride=stride,
            padding=(filter_size - 1) // 2,
            groups=groups,
            act=act,
            param_attr=ParamAttr(name=name + "_weights"),
            bias_attr=False,
            name=name + '.conv2d.output.1',
            data_format=data_format)

        if name == "conv1":
            bn_name = "bn_" + name
        else:
            bn_name = "bn" + name[3:]
        bn = fluid.layers.batch_norm(
            input=conv,
            act=None,
            name=bn_name + '.output.1',
            param_attr=ParamAttr(name=bn_name + '_scale'),
            bias_attr=ParamAttr(bn_name + '_offset'),
            moving_mean_name=bn_name + '_mean',
            moving_variance_name=bn_name + '_variance',
            data_layout=data_format)
        return bn

    def shortcut(self, input, ch_out, stride, is_first, name, data_format):
        if data_format == 'NCHW':
            ch_in = input.shape[1]
        else:
            ch_in = input.shape[-1]
        if ch_in != ch_out or stride != 1 or is_first == True:
            return self.conv_bn_layer(
                input, ch_out, 1, stride, name=name, data_format=data_format)
        else:
            return input

    def bottleneck_block(self, input, num_filters, stride, name, data_format):
        conv0 = self.conv_bn_layer(
            input=input,
            num_filters=num_filters,
            filter_size=1,
            act="leaky_relu",
            name=name + "_branch2a",
            data_format=data_format)
        conv1 = self.conv_bn_layer(
            input=conv0,
            num_filters=num_filters,
            filter_size=3,
            stride=stride,
            act="leaky_relu",
            name=name + "_branch2b",
            data_format=data_format)
        conv2 = self.conv_bn_layer(
            input=conv1,
            num_filters=num_filters * 2,
            filter_size=1,
            act=None,
            name=name + "_branch2c",
            data_format=data_format)

        short = self.shortcut(
            input,
            num_filters * 2,
            stride,
            is_first=False,
            name=name + "_branch1",
            data_format=data_format)

        ret = short + conv2
        ret = fluid.layers.leaky_relu(ret, alpha=0.1)
        return ret


def CSPNetNet50():
    model = CSPNetNet(layers=50)
    return model