Source code for mmagic.models.editors.deblurganv2.deblurganv2_util

# Copyright (c) OpenMMLab. All rights reserved.
from __future__ import absolute_import, division, print_function
import functools
import math

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils import model_zoo

pretrained_settings = {
    'inceptionresnetv2': {
        'imagenet': {
            'url':
            'https://download.openxlab.org.cn/models/xiaomile/DeblurGANv2/'
            'weight/inceptionresnetv2-520b38e4.pth',
            'input_space': 'RGB',
            'input_size': [3, 299, 299],
            'input_range': [0, 1],
            'mean': [0.5, 0.5, 0.5],
            'std': [0.5, 0.5, 0.5],
            'num_classes': 1000
        },
        'imagenet+background': {
            'url':
            'https://download.openxlab.org.cn/models/xiaomile/DeblurGANv2/'
            'weight/inceptionresnetv2-520b38e4.pth',
            'input_space': 'RGB',
            'input_size': [3, 299, 299],
            'input_range': [0, 1],
            'mean': [0.5, 0.5, 0.5],
            'std': [0.5, 0.5, 0.5],
            'num_classes': 1001
        }
    }
}


class BasicConv2d(nn.Module):

    def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
        super(BasicConv2d, self).__init__()
        self.conv = nn.Conv2d(
            in_planes,
            out_planes,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            bias=False)  # verify bias false
        self.bn = nn.BatchNorm2d(
            out_planes,
            eps=0.001,  # value found in tensorflow
            momentum=0.1,  # default pytorch value
            affine=True)
        self.relu = nn.ReLU(inplace=False)

    def forward(self, x):
        """Forward function.

        Args:
            x (torch.Tensor ): You can directly input a ``torch.Tensor``.

        Returns:
            torch.Tensor : ``torch.tensor`` will be returned.
        """
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x


class Mixed_5b(nn.Module):

    def __init__(self):
        super(Mixed_5b, self).__init__()

        self.branch0 = BasicConv2d(192, 96, kernel_size=1, stride=1)

        self.branch1 = nn.Sequential(
            BasicConv2d(192, 48, kernel_size=1, stride=1),
            BasicConv2d(48, 64, kernel_size=5, stride=1, padding=2))

        self.branch2 = nn.Sequential(
            BasicConv2d(192, 64, kernel_size=1, stride=1),
            BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1),
            BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1))

        self.branch3 = nn.Sequential(
            nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
            BasicConv2d(192, 64, kernel_size=1, stride=1))

    def forward(self, x):
        """Forward function.

        Args:
            x (torch.Tensor ): You can directly input a ``torch.Tensor``.

        Returns:
            torch.Tensor : ``torch.tensor`` will be returned.
        """
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        x3 = self.branch3(x)
        out = torch.cat((x0, x1, x2, x3), 1)
        return out


class Block35(nn.Module):

    def __init__(self, scale=1.0):
        super(Block35, self).__init__()

        self.scale = scale

        self.branch0 = BasicConv2d(320, 32, kernel_size=1, stride=1)

        self.branch1 = nn.Sequential(
            BasicConv2d(320, 32, kernel_size=1, stride=1),
            BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1))

        self.branch2 = nn.Sequential(
            BasicConv2d(320, 32, kernel_size=1, stride=1),
            BasicConv2d(32, 48, kernel_size=3, stride=1, padding=1),
            BasicConv2d(48, 64, kernel_size=3, stride=1, padding=1))

        self.conv2d = nn.Conv2d(128, 320, kernel_size=1, stride=1)
        self.relu = nn.ReLU(inplace=False)

    def forward(self, x):
        """Forward function.

        Args:
            x (torch.Tensor ): You can directly input a ``torch.Tensor``.

        Returns:
            torch.Tensor : ``torch.tensor`` will be returned.
        """
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        out = torch.cat((x0, x1, x2), 1)
        out = self.conv2d(out)
        out = out * self.scale + x
        out = self.relu(out)
        return out


class Mixed_6a(nn.Module):

    def __init__(self):
        super(Mixed_6a, self).__init__()

        self.branch0 = BasicConv2d(320, 384, kernel_size=3, stride=2)

        self.branch1 = nn.Sequential(
            BasicConv2d(320, 256, kernel_size=1, stride=1),
            BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1),
            BasicConv2d(256, 384, kernel_size=3, stride=2))

        self.branch2 = nn.MaxPool2d(3, stride=2)

    def forward(self, x):
        """Forward function.

        Args:
            x (torch.Tensor ): You can directly input a ``torch.Tensor``.

        Returns:
            torch.Tensor : ``torch.tensor`` will be returned.
        """
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        out = torch.cat((x0, x1, x2), 1)
        return out


class Block17(nn.Module):

    def __init__(self, scale=1.0):
        super(Block17, self).__init__()

        self.scale = scale

        self.branch0 = BasicConv2d(1088, 192, kernel_size=1, stride=1)

        self.branch1 = nn.Sequential(
            BasicConv2d(1088, 128, kernel_size=1, stride=1),
            BasicConv2d(
                128, 160, kernel_size=(1, 7), stride=1, padding=(0, 3)),
            BasicConv2d(
                160, 192, kernel_size=(7, 1), stride=1, padding=(3, 0)))

        self.conv2d = nn.Conv2d(384, 1088, kernel_size=1, stride=1)
        self.relu = nn.ReLU(inplace=False)

    def forward(self, x):
        """Forward function.

        Args:
            x (torch.Tensor ): You can directly input a ``torch.Tensor``.

        Returns:
            torch.Tensor : ``torch.tensor`` will be returned.
        """
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        out = torch.cat((x0, x1), 1)
        out = self.conv2d(out)
        out = out * self.scale + x
        out = self.relu(out)
        return out


class Mixed_7a(nn.Module):

    def __init__(self):
        super(Mixed_7a, self).__init__()

        self.branch0 = nn.Sequential(
            BasicConv2d(1088, 256, kernel_size=1, stride=1),
            BasicConv2d(256, 384, kernel_size=3, stride=2))

        self.branch1 = nn.Sequential(
            BasicConv2d(1088, 256, kernel_size=1, stride=1),
            BasicConv2d(256, 288, kernel_size=3, stride=2))

        self.branch2 = nn.Sequential(
            BasicConv2d(1088, 256, kernel_size=1, stride=1),
            BasicConv2d(256, 288, kernel_size=3, stride=1, padding=1),
            BasicConv2d(288, 320, kernel_size=3, stride=2))

        self.branch3 = nn.MaxPool2d(3, stride=2)

    def forward(self, x):
        """Forward function.

        Args:
            x (torch.Tensor ): You can directly input a ``torch.Tensor``.

        Returns:
            torch.Tensor : ``torch.tensor`` will be returned.
        """
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        x3 = self.branch3(x)
        out = torch.cat((x0, x1, x2, x3), 1)
        return out


class Block8(nn.Module):

    def __init__(self, scale=1.0, noReLU=False):
        super(Block8, self).__init__()

        self.scale = scale
        self.noReLU = noReLU

        self.branch0 = BasicConv2d(2080, 192, kernel_size=1, stride=1)

        self.branch1 = nn.Sequential(
            BasicConv2d(2080, 192, kernel_size=1, stride=1),
            BasicConv2d(
                192, 224, kernel_size=(1, 3), stride=1, padding=(0, 1)),
            BasicConv2d(
                224, 256, kernel_size=(3, 1), stride=1, padding=(1, 0)))

        self.conv2d = nn.Conv2d(448, 2080, kernel_size=1, stride=1)
        if not self.noReLU:
            self.relu = nn.ReLU(inplace=False)

    def forward(self, x):
        """Forward function.

        Args:
            x (torch.Tensor ): You can directly input a ``torch.Tensor``.

        Returns:
            torch.Tensor : ``torch.tensor`` will be returned.
        """
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        out = torch.cat((x0, x1), 1)
        out = self.conv2d(out)
        out = out * self.scale + x
        if not self.noReLU:
            out = self.relu(out)
        return out


class InceptionResNetV2(nn.Module):
    """Define a inceptionresnetv2 model."""

    def __init__(self, num_classes=1001):
        super(InceptionResNetV2, self).__init__()
        # Special attributes
        self.input_space = None
        self.input_size = (299, 299, 3)
        self.mean = None
        self.std = None
        # Modules
        self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2)
        self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1)
        self.conv2d_2b = BasicConv2d(
            32, 64, kernel_size=3, stride=1, padding=1)
        self.maxpool_3a = nn.MaxPool2d(3, stride=2)
        self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1)
        self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1)
        self.maxpool_5a = nn.MaxPool2d(3, stride=2)
        self.mixed_5b = Mixed_5b()
        self.repeat = nn.Sequential(
            Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17),
            Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17),
            Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17),
            Block35(scale=0.17))
        self.mixed_6a = Mixed_6a()
        self.repeat_1 = nn.Sequential(
            Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10),
            Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10),
            Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10),
            Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10),
            Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10),
            Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10),
            Block17(scale=0.10), Block17(scale=0.10))
        self.mixed_7a = Mixed_7a()
        self.repeat_2 = nn.Sequential(
            Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20),
            Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20),
            Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20))
        self.block8 = Block8(noReLU=True)
        self.conv2d_7b = BasicConv2d(2080, 1536, kernel_size=1, stride=1)
        self.avgpool_1a = nn.AvgPool2d(8, count_include_pad=False)
        self.last_linear = nn.Linear(1536, num_classes)

    def features(self, input):
        """Get network features.

        Args:
            input (torch.tensor): You can directly input a ``torch.Tensor``.
        """
        x = self.conv2d_1a(input)
        x = self.conv2d_2a(x)
        x = self.conv2d_2b(x)
        x = self.maxpool_3a(x)
        x = self.conv2d_3b(x)
        x = self.conv2d_4a(x)
        x = self.maxpool_5a(x)
        x = self.mixed_5b(x)
        x = self.repeat(x)
        x = self.mixed_6a(x)
        x = self.repeat_1(x)
        x = self.mixed_7a(x)
        x = self.repeat_2(x)
        x = self.block8(x)
        x = self.conv2d_7b(x)
        return x

    def logits(self, features):
        """Get features logits.

        Args:
            features (torch.tensor): You can directly input a ``torch.Tensor``.
        """
        x = self.avgpool_1a(features)
        x = x.view(x.size(0), -1)
        x = F.dropout(x, training=self.training)
        x = self.last_linear(x)
        return x

    def forward(self, input):
        """Forward function.

        Args:
            input (torch.Tensor ): You can directly input a ``torch.Tensor``.

        Returns:
            torch.Tensor : ``torch.tensor`` will be returned.
        """
        x = self.features(input)
        x = self.logits(x)
        return x


def inceptionresnetv2(num_classes=1000, pretrained='imagenet'):
    """return a inceptionresnetv2 network."""
    if pretrained:
        settings = pretrained_settings['inceptionresnetv2'][pretrained]
        model = InceptionResNetV2(num_classes=1001)
        model.load_state_dict(model_zoo.load_url(settings['url']))

        if pretrained == 'imagenet':
            new_last_linear = nn.Linear(1536, 1000)
            new_last_linear.weight.data = model.last_linear.weight.data[1:]
            new_last_linear.bias.data = model.last_linear.bias.data[1:]
            model.last_linear = new_last_linear

        model.input_space = settings['input_space']
        model.input_size = settings['input_size']
        model.input_range = settings['input_range']

        model.mean = settings['mean']
        model.std = settings['std']
    else:
        model = InceptionResNetV2(num_classes=num_classes)
    return model


def conv_bn(inp, oup, stride):
    return nn.Sequential(
        nn.Conv2d(inp, oup, 3, stride, 1, bias=False), nn.BatchNorm2d(oup),
        nn.ReLU6(inplace=True))


def conv_1x1_bn(inp, oup):
    return nn.Sequential(
        nn.Conv2d(inp, oup, 1, 1, 0, bias=False), nn.BatchNorm2d(oup),
        nn.ReLU6(inplace=True))


class InvertedResidual(nn.Module):

    def __init__(self, inp, oup, stride, expand_ratio):
        super(InvertedResidual, self).__init__()
        self.stride = stride
        assert stride in [1, 2]

        hidden_dim = round(inp * expand_ratio)
        self.use_res_connect = self.stride == 1 and inp == oup

        if expand_ratio == 1:
            self.conv = nn.Sequential(
                # dw
                nn.Conv2d(
                    hidden_dim,
                    hidden_dim,
                    3,
                    stride,
                    1,
                    groups=hidden_dim,
                    bias=False),
                nn.BatchNorm2d(hidden_dim),
                nn.ReLU6(inplace=True),
                # pw-linear
                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup),
            )
        else:
            self.conv = nn.Sequential(
                # pw
                nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
                nn.BatchNorm2d(hidden_dim),
                nn.ReLU6(inplace=True),
                # dw
                nn.Conv2d(
                    hidden_dim,
                    hidden_dim,
                    3,
                    stride,
                    1,
                    groups=hidden_dim,
                    bias=False),
                nn.BatchNorm2d(hidden_dim),
                nn.ReLU6(inplace=True),
                # pw-linear
                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup),
            )

    def forward(self, x):
        """Forward function.

        Args:
            x (torch.Tensor ): You can directly input a ``torch.Tensor``.

        Returns:
            torch.Tensor : ``torch.tensor`` will be returned.
        """
        if self.use_res_connect:
            return x + self.conv(x)
        else:
            return self.conv(x)


class MobileNetV2(nn.Module):

    def __init__(self, n_class=1000, input_size=224, width_mult=1.):
        super(MobileNetV2, self).__init__()
        block = InvertedResidual
        input_channel = 32
        last_channel = 1280
        interverted_residual_setting = [
            # t, c, n, s
            [1, 16, 1, 1],
            [6, 24, 2, 2],
            [6, 32, 3, 2],
            [6, 64, 4, 2],
            [6, 96, 3, 1],
            [6, 160, 3, 2],
            [6, 320, 1, 1],
        ]

        # building first layer
        assert input_size % 32 == 0
        input_channel = int(input_channel * width_mult)
        self.last_channel = int(
            last_channel * width_mult) if width_mult > 1.0 else last_channel
        self.features = [conv_bn(3, input_channel, 2)]
        # building inverted residual blocks
        for t, c, n, s in interverted_residual_setting:
            output_channel = int(c * width_mult)
            for i in range(n):
                if i == 0:
                    self.features.append(
                        block(
                            input_channel, output_channel, s, expand_ratio=t))
                else:
                    self.features.append(
                        block(
                            input_channel, output_channel, 1, expand_ratio=t))
                input_channel = output_channel
        # building last several layers
        self.features.append(conv_1x1_bn(input_channel, self.last_channel))
        # make it nn.Sequential
        self.features = nn.Sequential(*self.features)

        # building classifier
        self.classifier = nn.Sequential(
            nn.Dropout(0.2),
            nn.Linear(self.last_channel, n_class),
        )

        self._initialize_weights()

    def forward(self, x):
        """Forward function.

        Args:
            x (torch.Tensor ): You can directly input a ``torch.Tensor``.

        Returns:
            torch.Tensor : ``torch.tensor`` will be returned.
        """
        x = self.features(x)
        x = x.mean(3).mean(2)
        x = self.classifier(x)
        return x

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
                if m.bias is not None:
                    m.bias.data.zero_()
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                n = m.weight.size(1)
                m.weight.data.normal_(0, 0.01)
                m.bias.data.zero_()


def get_norm_layer(norm_type='instance'):
    """Returns a norm layer of the specified type.

    Args:
        norm_type (Str): norm layer type
    """
    if norm_type == 'batch':
        norm_layer = functools.partial(nn.BatchNorm2d, affine=True)
    elif norm_type == 'instance':
        norm_layer = functools.partial(
            nn.InstanceNorm2d, affine=False, track_running_stats=True)
    else:
        raise NotImplementedError('normalization layer [%s] is not found' %
                                  norm_type)
    return norm_layer