PaddleX/dygraph/paddlex/ppcls/modeling/architectures/shufflenet_v2.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 paddle
from paddle import ParamAttr, reshape, transpose, concat, split
from paddle.nn import Layer, Conv2D, MaxPool2D, AdaptiveAvgPool2D, BatchNorm, Linear
from paddle.nn.initializer import KaimingNormal
from paddle.nn.functional import swish

__all__ = [
    "ShuffleNetV2_x0_25", "ShuffleNetV2_x0_33", "ShuffleNetV2_x0_5",
    "ShuffleNetV2_x1_0", "ShuffleNetV2_x1_5", "ShuffleNetV2_x2_0",
    "ShuffleNetV2_swish"
]


def channel_shuffle(x, groups):
    batch_size, num_channels, height, width = x.shape[0:4]
    channels_per_group = num_channels // groups

    # reshape
    x = reshape(
        x=x, shape=[batch_size, groups, channels_per_group, height, width])

    # transpose
    x = transpose(x=x, perm=[0, 2, 1, 3, 4])

    # flatten
    x = reshape(x=x, shape=[batch_size, num_channels, height, width])
    return x


class ConvBNLayer(Layer):
    def __init__(
            self,
            in_channels,
            out_channels,
            kernel_size,
            stride,
            padding,
            groups=1,
            act=None,
            name=None, ):
        super(ConvBNLayer, self).__init__()
        self._conv = Conv2D(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            groups=groups,
            weight_attr=ParamAttr(
                initializer=KaimingNormal(), name=name + "_weights"),
            bias_attr=False)

        self._batch_norm = BatchNorm(
            out_channels,
            param_attr=ParamAttr(name=name + "_bn_scale"),
            bias_attr=ParamAttr(name=name + "_bn_offset"),
            act=act,
            moving_mean_name=name + "_bn_mean",
            moving_variance_name=name + "_bn_variance")

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


class InvertedResidual(Layer):
    def __init__(self,
                 in_channels,
                 out_channels,
                 stride,
                 act="relu",
                 name=None):
        super(InvertedResidual, self).__init__()
        self._conv_pw = ConvBNLayer(
            in_channels=in_channels // 2,
            out_channels=out_channels // 2,
            kernel_size=1,
            stride=1,
            padding=0,
            groups=1,
            act=act,
            name='stage_' + name + '_conv1')
        self._conv_dw = ConvBNLayer(
            in_channels=out_channels // 2,
            out_channels=out_channels // 2,
            kernel_size=3,
            stride=stride,
            padding=1,
            groups=out_channels // 2,
            act=None,
            name='stage_' + name + '_conv2')
        self._conv_linear = ConvBNLayer(
            in_channels=out_channels // 2,
            out_channels=out_channels // 2,
            kernel_size=1,
            stride=1,
            padding=0,
            groups=1,
            act=act,
            name='stage_' + name + '_conv3')

    def forward(self, inputs):
        x1, x2 = split(
            inputs,
            num_or_sections=[inputs.shape[1] // 2, inputs.shape[1] // 2],
            axis=1)
        x2 = self._conv_pw(x2)
        x2 = self._conv_dw(x2)
        x2 = self._conv_linear(x2)
        out = concat([x1, x2], axis=1)
        return channel_shuffle(out, 2)


class InvertedResidualDS(Layer):
    def __init__(self,
                 in_channels,
                 out_channels,
                 stride,
                 act="relu",
                 name=None):
        super(InvertedResidualDS, self).__init__()

        # branch1
        self._conv_dw_1 = ConvBNLayer(
            in_channels=in_channels,
            out_channels=in_channels,
            kernel_size=3,
            stride=stride,
            padding=1,
            groups=in_channels,
            act=None,
            name='stage_' + name + '_conv4')
        self._conv_linear_1 = ConvBNLayer(
            in_channels=in_channels,
            out_channels=out_channels // 2,
            kernel_size=1,
            stride=1,
            padding=0,
            groups=1,
            act=act,
            name='stage_' + name + '_conv5')
        # branch2
        self._conv_pw_2 = ConvBNLayer(
            in_channels=in_channels,
            out_channels=out_channels // 2,
            kernel_size=1,
            stride=1,
            padding=0,
            groups=1,
            act=act,
            name='stage_' + name + '_conv1')
        self._conv_dw_2 = ConvBNLayer(
            in_channels=out_channels // 2,
            out_channels=out_channels // 2,
            kernel_size=3,
            stride=stride,
            padding=1,
            groups=out_channels // 2,
            act=None,
            name='stage_' + name + '_conv2')
        self._conv_linear_2 = ConvBNLayer(
            in_channels=out_channels // 2,
            out_channels=out_channels // 2,
            kernel_size=1,
            stride=1,
            padding=0,
            groups=1,
            act=act,
            name='stage_' + name + '_conv3')

    def forward(self, inputs):
        x1 = self._conv_dw_1(inputs)
        x1 = self._conv_linear_1(x1)
        x2 = self._conv_pw_2(inputs)
        x2 = self._conv_dw_2(x2)
        x2 = self._conv_linear_2(x2)
        out = concat([x1, x2], axis=1)

        return channel_shuffle(out, 2)


class ShuffleNet(Layer):
    def __init__(self, class_dim=1000, scale=1.0, act="relu"):
        super(ShuffleNet, self).__init__()
        self.scale = scale
        self.class_dim = class_dim
        stage_repeats = [4, 8, 4]

        if scale == 0.25:
            stage_out_channels = [-1, 24, 24, 48, 96, 512]
        elif scale == 0.33:
            stage_out_channels = [-1, 24, 32, 64, 128, 512]
        elif scale == 0.5:
            stage_out_channels = [-1, 24, 48, 96, 192, 1024]
        elif scale == 1.0:
            stage_out_channels = [-1, 24, 116, 232, 464, 1024]
        elif scale == 1.5:
            stage_out_channels = [-1, 24, 176, 352, 704, 1024]
        elif scale == 2.0:
            stage_out_channels = [-1, 24, 224, 488, 976, 2048]
        else:
            raise NotImplementedError("This scale size:[" + str(scale) +
                                      "] is not implemented!")
        # 1. conv1
        self._conv1 = ConvBNLayer(
            in_channels=3,
            out_channels=stage_out_channels[1],
            kernel_size=3,
            stride=2,
            padding=1,
            act=act,
            name='stage1_conv')
        self._max_pool = MaxPool2D(kernel_size=3, stride=2, padding=1)

        # 2. bottleneck sequences
        self._block_list = []
        for stage_id, num_repeat in enumerate(stage_repeats):
            for i in range(num_repeat):
                if i == 0:
                    block = self.add_sublayer(
                        name=str(stage_id + 2) + '_' + str(i + 1),
                        sublayer=InvertedResidualDS(
                            in_channels=stage_out_channels[stage_id + 1],
                            out_channels=stage_out_channels[stage_id + 2],
                            stride=2,
                            act=act,
                            name=str(stage_id + 2) + '_' + str(i + 1)))
                else:
                    block = self.add_sublayer(
                        name=str(stage_id + 2) + '_' + str(i + 1),
                        sublayer=InvertedResidual(
                            in_channels=stage_out_channels[stage_id + 2],
                            out_channels=stage_out_channels[stage_id + 2],
                            stride=1,
                            act=act,
                            name=str(stage_id + 2) + '_' + str(i + 1)))
                self._block_list.append(block)
        # 3. last_conv
        self._last_conv = ConvBNLayer(
            in_channels=stage_out_channels[-2],
            out_channels=stage_out_channels[-1],
            kernel_size=1,
            stride=1,
            padding=0,
            act=act,
            name='conv5')
        # 4. pool
        self._pool2d_avg = AdaptiveAvgPool2D(1)
        self._out_c = stage_out_channels[-1]
        # 5. fc
        self._fc = Linear(
            stage_out_channels[-1],
            class_dim,
            weight_attr=ParamAttr(name='fc6_weights'),
            bias_attr=ParamAttr(name='fc6_offset'))

    def forward(self, inputs):
        y = self._conv1(inputs)
        y = self._max_pool(y)
        for inv in self._block_list:
            y = inv(y)
        y = self._last_conv(y)
        y = self._pool2d_avg(y)
        y = paddle.flatten(y, start_axis=1, stop_axis=-1)
        y = self._fc(y)
        return y


def ShuffleNetV2_x0_25(**args):
    model = ShuffleNet(scale=0.25, **args)
    return model


def ShuffleNetV2_x0_33(**args):
    model = ShuffleNet(scale=0.33, **args)
    return model


def ShuffleNetV2_x0_5(**args):
    model = ShuffleNet(scale=0.5, **args)
    return model


def ShuffleNetV2_x1_0(**args):
    model = ShuffleNet(scale=1.0, **args)
    return model


def ShuffleNetV2_x1_5(**args):
    model = ShuffleNet(scale=1.5, **args)
    return model


def ShuffleNetV2_x2_0(**args):
    model = ShuffleNet(scale=2.0, **args)
    return model


def ShuffleNetV2_swish(**args):
    model = ShuffleNet(scale=1.0, act="swish", **args)
    return model