resnext.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 numpy as np
import paddle
from paddle import ParamAttr
import paddle.nn as nn
from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
from paddle.nn.initializer import Uniform

import math

__all__ = [
    "ResNeXt50_32x4d", "ResNeXt50_64x4d", "ResNeXt101_32x4d",
    "ResNeXt101_64x4d", "ResNeXt152_32x4d", "ResNeXt152_64x4d"
]


class ConvBNLayer(nn.Layer):
    def __init__(self,
                 num_channels,
                 num_filters,
                 filter_size,
                 stride=1,
                 groups=1,
                 act=None,
                 name=None):
        super(ConvBNLayer, self).__init__()

        self._conv = Conv2d(
            in_channels=num_channels,
            out_channels=num_filters,
            kernel_size=filter_size,
            stride=stride,
            padding=(filter_size - 1) // 2,
            groups=groups,
            weight_attr=ParamAttr(name=name + "_weights"),
            bias_attr=False)
        if name == "conv1":
            bn_name = "bn_" + name
        else:
            bn_name = "bn" + name[3:]
        self._batch_norm = BatchNorm(
            num_filters,
            act=act,
            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')

    def forward(self, inputs):
        y = self._conv(inputs)
        y = self._batch_norm(y)
        return y


class BottleneckBlock(nn.Layer):
    def __init__(self,
                 num_channels,
                 num_filters,
                 stride,
                 cardinality,
                 shortcut=True,
                 name=None):
        super(BottleneckBlock, self).__init__()

        self.conv0 = ConvBNLayer(
            num_channels=num_channels,
            num_filters=num_filters,
            filter_size=1,
            act='relu',
            name=name + "_branch2a")
        self.conv1 = ConvBNLayer(
            num_channels=num_filters,
            num_filters=num_filters,
            filter_size=3,
            groups=cardinality,
            stride=stride,
            act='relu',
            name=name + "_branch2b")
        self.conv2 = ConvBNLayer(
            num_channels=num_filters,
            num_filters=num_filters * 2 if cardinality == 32 else num_filters,
            filter_size=1,
            act=None,
            name=name + "_branch2c")

        if not shortcut:
            self.short = ConvBNLayer(
                num_channels=num_channels,
                num_filters=num_filters * 2
                if cardinality == 32 else num_filters,
                filter_size=1,
                stride=stride,
                name=name + "_branch1")

        self.shortcut = shortcut

    def forward(self, inputs):
        y = self.conv0(inputs)
        conv1 = self.conv1(y)
        conv2 = self.conv2(conv1)

        if self.shortcut:
            short = inputs
        else:
            short = self.short(inputs)

        y = paddle.elementwise_add(x=short, y=conv2, act='relu')
        return y


class ResNeXt(nn.Layer):
    def __init__(self, layers=50, class_dim=1000, cardinality=32):
        super(ResNeXt, self).__init__()

        self.layers = layers
        self.cardinality = cardinality
        supported_layers = [50, 101, 152]
        assert layers in supported_layers, \
            "supported layers are {} but input layer is {}".format(
                supported_layers, layers)
        supported_cardinality = [32, 64]
        assert cardinality in supported_cardinality, \
            "supported cardinality is {} but input cardinality is {}" \
            .format(supported_cardinality, cardinality)
        if layers == 50:
            depth = [3, 4, 6, 3]
        elif layers == 101:
            depth = [3, 4, 23, 3]
        elif layers == 152:
            depth = [3, 8, 36, 3]
        num_channels = [64, 256, 512, 1024]
        num_filters = [128, 256, 512,
                       1024] if cardinality == 32 else [256, 512, 1024, 2048]

        self.conv = ConvBNLayer(
            num_channels=3,
            num_filters=64,
            filter_size=7,
            stride=2,
            act='relu',
            name="res_conv1")
        self.pool2d_max = MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.block_list = []
        for block in range(len(depth)):
            shortcut = False
            for i in range(depth[block]):
                if layers in [101, 152] and block == 2:
                    if i == 0:
                        conv_name = "res" + str(block + 2) + "a"
                    else:
                        conv_name = "res" + str(block + 2) + "b" + str(i)
                else:
                    conv_name = "res" + str(block + 2) + chr(97 + i)
                bottleneck_block = self.add_sublayer(
                    'bb_%d_%d' % (block, i),
                    BottleneckBlock(
                        num_channels=num_channels[block] if i == 0 else
                        num_filters[block] * int(64 // self.cardinality),
                        num_filters=num_filters[block],
                        stride=2 if i == 0 and block != 0 else 1,
                        cardinality=self.cardinality,
                        shortcut=shortcut,
                        name=conv_name))
                self.block_list.append(bottleneck_block)
                shortcut = True

        self.pool2d_avg = AdaptiveAvgPool2d(1)

        self.pool2d_avg_channels = num_channels[-1] * 2

        stdv = 1.0 / math.sqrt(self.pool2d_avg_channels * 1.0)

        self.out = Linear(
            self.pool2d_avg_channels,
            class_dim,
            weight_attr=ParamAttr(
                initializer=Uniform(-stdv, stdv), name="fc_weights"),
            bias_attr=ParamAttr(name="fc_offset"))

    def forward(self, inputs):
        y = self.conv(inputs)
        y = self.pool2d_max(y)
        for block in self.block_list:
            y = block(y)
        y = self.pool2d_avg(y)
        y = paddle.reshape(y, shape=[-1, self.pool2d_avg_channels])
        y = self.out(y)
        return y


def ResNeXt50_32x4d(**args):
    model = ResNeXt(layers=50, cardinality=32, **args)
    return model


def ResNeXt50_64x4d(**args):
    model = ResNeXt(layers=50, cardinality=64, **args)
    return model


def ResNeXt101_32x4d(**args):
    model = ResNeXt(layers=101, cardinality=32, **args)
    return model


def ResNeXt101_64x4d(**args):
    model = ResNeXt(layers=101, cardinality=64, **args)
    return model


def ResNeXt152_32x4d(**args):
    model = ResNeXt(layers=152, cardinality=32, **args)
    return model


def ResNeXt152_64x4d(**args):
    model = ResNeXt(layers=152, cardinality=64, **args)
    return model