PaddleRS/paddlers/models/ppcls/arch/backbone/model_zoo/squeezenet.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.

import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D

from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url

MODEL_URLS = {
    "SqueezeNet1_0":
    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_0_pretrained.pdparams",
    "SqueezeNet1_1":
    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_1_pretrained.pdparams",
}

__all__ = list(MODEL_URLS.keys())


class MakeFireConv(nn.Layer):
    def __init__(self,
                 input_channels,
                 output_channels,
                 filter_size,
                 padding=0,
                 name=None):
        super(MakeFireConv, self).__init__()
        self._conv = Conv2D(
            input_channels,
            output_channels,
            filter_size,
            padding=padding,
            weight_attr=ParamAttr(name=name + "_weights"),
            bias_attr=ParamAttr(name=name + "_offset"))

    def forward(self, x):
        x = self._conv(x)
        x = F.relu(x)
        return x


class MakeFire(nn.Layer):
    def __init__(self,
                 input_channels,
                 squeeze_channels,
                 expand1x1_channels,
                 expand3x3_channels,
                 name=None):
        super(MakeFire, self).__init__()
        self._conv = MakeFireConv(
            input_channels, squeeze_channels, 1, name=name + "_squeeze1x1")
        self._conv_path1 = MakeFireConv(
            squeeze_channels, expand1x1_channels, 1, name=name + "_expand1x1")
        self._conv_path2 = MakeFireConv(
            squeeze_channels,
            expand3x3_channels,
            3,
            padding=1,
            name=name + "_expand3x3")

    def forward(self, inputs):
        x = self._conv(inputs)
        x1 = self._conv_path1(x)
        x2 = self._conv_path2(x)
        return paddle.concat([x1, x2], axis=1)


class SqueezeNet(nn.Layer):
    def __init__(self, version, class_num=1000):
        super(SqueezeNet, self).__init__()
        self.version = version

        if self.version == "1.0":
            self._conv = Conv2D(
                3,
                96,
                7,
                stride=2,
                weight_attr=ParamAttr(name="conv1_weights"),
                bias_attr=ParamAttr(name="conv1_offset"))
            self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0)
            self._conv1 = MakeFire(96, 16, 64, 64, name="fire2")
            self._conv2 = MakeFire(128, 16, 64, 64, name="fire3")
            self._conv3 = MakeFire(128, 32, 128, 128, name="fire4")

            self._conv4 = MakeFire(256, 32, 128, 128, name="fire5")
            self._conv5 = MakeFire(256, 48, 192, 192, name="fire6")
            self._conv6 = MakeFire(384, 48, 192, 192, name="fire7")
            self._conv7 = MakeFire(384, 64, 256, 256, name="fire8")

            self._conv8 = MakeFire(512, 64, 256, 256, name="fire9")
        else:
            self._conv = Conv2D(
                3,
                64,
                3,
                stride=2,
                padding=1,
                weight_attr=ParamAttr(name="conv1_weights"),
                bias_attr=ParamAttr(name="conv1_offset"))
            self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0)
            self._conv1 = MakeFire(64, 16, 64, 64, name="fire2")
            self._conv2 = MakeFire(128, 16, 64, 64, name="fire3")

            self._conv3 = MakeFire(128, 32, 128, 128, name="fire4")
            self._conv4 = MakeFire(256, 32, 128, 128, name="fire5")

            self._conv5 = MakeFire(256, 48, 192, 192, name="fire6")
            self._conv6 = MakeFire(384, 48, 192, 192, name="fire7")
            self._conv7 = MakeFire(384, 64, 256, 256, name="fire8")
            self._conv8 = MakeFire(512, 64, 256, 256, name="fire9")

        self._drop = Dropout(p=0.5, mode="downscale_in_infer")
        self._conv9 = Conv2D(
            512,
            class_num,
            1,
            weight_attr=ParamAttr(name="conv10_weights"),
            bias_attr=ParamAttr(name="conv10_offset"))
        self._avg_pool = AdaptiveAvgPool2D(1)

    def forward(self, inputs):
        x = self._conv(inputs)
        x = F.relu(x)
        x = self._pool(x)
        if self.version == "1.0":
            x = self._conv1(x)
            x = self._conv2(x)
            x = self._conv3(x)
            x = self._pool(x)
            x = self._conv4(x)
            x = self._conv5(x)
            x = self._conv6(x)
            x = self._conv7(x)
            x = self._pool(x)
            x = self._conv8(x)
        else:
            x = self._conv1(x)
            x = self._conv2(x)
            x = self._pool(x)
            x = self._conv3(x)
            x = self._conv4(x)
            x = self._pool(x)
            x = self._conv5(x)
            x = self._conv6(x)
            x = self._conv7(x)
            x = self._conv8(x)
        x = self._drop(x)
        x = self._conv9(x)
        x = F.relu(x)
        x = self._avg_pool(x)
        x = paddle.squeeze(x, axis=[2, 3])
        return x


def _load_pretrained(pretrained, model, model_url, use_ssld=False):
    if pretrained is False:
        pass
    elif pretrained is True:
        load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
    elif isinstance(pretrained, str):
        load_dygraph_pretrain(model, pretrained)
    else:
        raise RuntimeError(
            "pretrained type is not available. Please use `string` or `boolean` type."
        )


def SqueezeNet1_0(pretrained=False, use_ssld=False, **kwargs):
    model = SqueezeNet(version="1.0", **kwargs)
    _load_pretrained(
        pretrained, model, MODEL_URLS["SqueezeNet1_0"], use_ssld=use_ssld)
    return model


def SqueezeNet1_1(pretrained=False, use_ssld=False, **kwargs):
    model = SqueezeNet(version="1.1", **kwargs)
    _load_pretrained(
        pretrained, model, MODEL_URLS["SqueezeNet1_1"], use_ssld=use_ssld)
    return model