<reponame>JanAlexanderPersonal/covid19_weak_supervision
"""Dilated ResNet"""
import math
import torch
import torch.utils.model_zoo as model_zoo
import torch.nn as nn

__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
           'resnet152', 'BasicBlock', 'Bottleneck']

model_urls = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
}


def conv3x3(in_planes, out_planes, stride=1):
    "3x3 convolution with padding"
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class BasicBlock(nn.Module):
    """ResNet BasicBlock
    """
    expansion = 1
    def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, previous_dilation=1,
                 norm_layer=None):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride,
                               padding=dilation, dilation=dilation, bias=False)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1,
                               padding=previous_dilation, dilation=previous_dilation, bias=False)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    """ResNet Bottleneck
    """
    # pylint: disable=unused-argument
    expansion = 4
    def __init__(self, inplanes, planes, stride=1, dilation=1,
                 downsample=None, previous_dilation=1, norm_layer=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = norm_layer(planes)
        self.conv2 = nn.Conv2d(
            planes, planes, kernel_size=3, stride=stride,
            padding=dilation, dilation=dilation, bias=False)
        self.bn2 = norm_layer(planes)
        self.conv3 = nn.Conv2d(
            planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = norm_layer(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.dilation = dilation
        self.stride = stride

    def _sum_each(self, x, y):
        assert(len(x) == len(y))
        z = []
        for i in range(len(x)):
            z.append(x[i]+y[i])
        return z

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class ResNet(nn.Module):
    """Dilated Pre-trained ResNet Model, which preduces the stride of 8 featuremaps at conv5.

    Parameters
    ----------
    block : Block
        Class for the residual block. Options are BasicBlockV1, BottleneckV1.
    layers : list of int
        Numbers of layers in each block
    classes : int, default 1000
        Number of classification classes.
    dilated : bool, default False
        Applying dilation strategy to pretrained ResNet yielding a stride-8 model,
        typically used in Semantic Segmentation.
    norm_layer : object
        Normalization layer used in backbone network (default: :class:`mxnet.gluon.nn.BatchNorm`;
        for Synchronized Cross-GPU BachNormalization).

    Reference:

        - <NAME>, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.

        - <NAME>, and <NAME>. "Multi-scale context aggregation by dilated convolutions."
    """
    # pylint: disable=unused-variable
    def __init__(self, block, layers, num_classes=1000, dilated=True,
                 deep_base=True, norm_layer=nn.BatchNorm2d):
        self.inplanes = 128 if deep_base else 64
        super(ResNet, self).__init__()
        if deep_base:
            self.conv1 = nn.Sequential(
                nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False),
                norm_layer(64),
                nn.ReLU(inplace=True),
                nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
                norm_layer(64),
                nn.ReLU(inplace=True),
                nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1, bias=False),
            )
        else:
            self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                                   bias=False)
        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0], norm_layer=norm_layer)
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2, norm_layer=norm_layer)
        if dilated:
            self.layer3 = self._make_layer(block, 256, layers[2], stride=1,
                                           dilation=2, norm_layer=norm_layer)
            self.layer4 = self._make_layer(block, 512, layers[3], stride=1,
                                           dilation=4, norm_layer=norm_layer)
        else:
            asds
            self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                           norm_layer=norm_layer)
            self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                           norm_layer=norm_layer)
        self.avgpool = nn.AvgPool2d(7, stride=1)
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        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))
            elif isinstance(m, norm_layer):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1, dilation=1, norm_layer=None):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                norm_layer(planes * block.expansion),
            )

        layers = []
        if dilation == 1 or dilation == 2:
            layers.append(block(self.inplanes, planes, stride, dilation=1,
                                downsample=downsample, previous_dilation=dilation, norm_layer=norm_layer))
        elif dilation == 4:
            layers.append(block(self.inplanes, planes, stride, dilation=2,
                                downsample=downsample, previous_dilation=dilation, norm_layer=norm_layer))
        else:
            raise RuntimeError("=> unknown dilation size: {}".format(dilation))

        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, dilation=dilation, previous_dilation=dilation,
                                norm_layer=norm_layer))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x


def resnet18(pretrained=False, **kwargs):
    """Constructs a ResNet-18 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
    return model


def resnet34(pretrained=False, **kwargs):
    """Constructs a ResNet-34 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
    return model


def resnet50(pretrained=False, root='/mnt/projects/counting/models', **kwargs):
    """Constructs a ResNet-50 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    
    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
    if pretrained:
        model.load_state_dict(torch.load(
            get_model_file('resnet50', root=root)), strict=False)
    return model


def resnet101(pretrained=False, root='/mnt/projects/counting/models', **kwargs):
    """Constructs a ResNet-101 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
    if pretrained:
        model.load_state_dict(torch.load(
            get_model_file('resnet101', root=root)), strict=False)
    return model


def resnet152(pretrained=False, root='/mnt/projects/counting/models', **kwargs):
    """Constructs a ResNet-152 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
    if pretrained:
        from ..models.model_store import get_model_file
        model.load_state_dict(torch.load(
            get_model_file('resnet152', root=root)), strict=False)
    return model

import os
import requests
import errno
import shutil
import hashlib
from tqdm import tqdm
import torch

_model_sha1 = {name: checksum for checksum, name in [
    ('ebb6acbbd1d1c90b7f446ae59d30bf70c74febc1', 'resnet50'),
    ('2a57e44de9c853fa015b172309a1ee7e2d0e4e2a', 'resnet101'),
    ('0d43d698c66aceaa2bc0309f55efdd7ff4b143af', 'resnet152'),
    ('2e22611a7f3992ebdee6726af169991bc26d7363', 'deepten_minc'),
    ('662e979de25a389f11c65e9f1df7e06c2c356381', 'fcn_resnet50_ade'),
    ('eeed8e582f0fdccdba8579e7490570adc6d85c7c', 'fcn_resnet50_pcontext'),
    ('54f70c772505064e30efd1ddd3a14e1759faa363', 'psp_resnet50_ade'),
    ('075195c5237b778c718fd73ceddfa1376c18dfd0', 'deeplab_resnet50_ade'),
    ('5ee47ee28b480cc781a195d13b5806d5bbc616bf', 'encnet_resnet101_coco'),
    ('4de91d5922d4d3264f678b663f874da72e82db00', 'encnet_resnet50_pcontext'),
    ('9f27ea13d514d7010e59988341bcbd4140fcc33d', 'encnet_resnet101_pcontext'),
    ('07ac287cd77e53ea583f37454e17d30ce1509a4a', 'encnet_resnet50_ade'),
    ('<KEY>', 'encnet_resnet101_ade'),
    ]}
import zipfile
def get_model_file(name, root=os.path.join('~', '.encoding', 'models')):
    r"""Return location for the pretrained on local file system.
    This function will download from online model zoo when model cannot be found or has mismatch.
    The root directory will be created if it doesn't exist.
    Parameters
    ----------
    name : str
        Name of the model.
    root : str, default '~/.encoding/models'
        Location for keeping the model parameters.
    Returns
    -------
    file_path
        Path to the requested pretrained model file.
    """
    file_name = '{name}-{short_hash}'.format(name=name, short_hash=short_hash(name))
    root = os.path.expanduser(root)
    file_path = os.path.join(root, file_name+'.pth')
    sha1_hash = _model_sha1[name]
    if os.path.exists(file_path):
        if check_sha1(file_path, sha1_hash):
            return file_path
        else:
            print('Mismatch in the content of model file {} detected.' +
                  ' Downloading again.'.format(file_path))
    else:
        print('Model file {} is not found. Downloading.'.format(file_path))

    if not os.path.exists(root):
        os.makedirs(root)

    zip_file_path = os.path.join(root, file_name+'.zip')
    repo_url = os.environ.get('ENCODING_REPO', encoding_repo_url)
    if repo_url[-1] != '/':
        repo_url = repo_url + '/'
    download(_url_format.format(repo_url=repo_url, file_name=file_name),
             path=zip_file_path,
             overwrite=True)
    with zipfile.ZipFile(zip_file_path) as zf:
        zf.extractall(root)
    os.remove(zip_file_path)

    if check_sha1(file_path, sha1_hash):
        return file_path
    else:
        raise ValueError('Downloaded file has different hash. Please try again.')

def purge(root=os.path.join('~', '.encoding', 'models')):
    r"""Purge all pretrained model files in local file store.
    Parameters
    ----------
    root : str, default '~/.encoding/models'
        Location for keeping the model parameters.
    """
    root = os.path.expanduser(root)
    files = os.listdir(root)
    for f in files:
        if f.endswith(".pth"):
            os.remove(os.path.join(root, f))

def pretrained_model_list():
    return list(_model_sha1.keys())


encoding_repo_url = 'https://hangzh.s3.amazonaws.com/'
_url_format = '{repo_url}encoding/models/{file_name}.zip'

def short_hash(name):
    if name not in _model_sha1:
        raise ValueError('Pretrained model for {name} is not available.'.format(name=name))
    return _model_sha1[name][:8]
__all__ = ['save_checkpoint', 'download', 'mkdir', 'check_sha1']

def save_checkpoint(state, args, is_best, filename='checkpoint.pth.tar'):
    """Saves checkpoint to disk"""
    directory = "runs/%s/%s/%s/"%(args.dataset, args.model, args.checkname)
    if not os.path.exists(directory):
        os.makedirs(directory)
    filename = directory + filename
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, directory + 'model_best.pth.tar')


def download(url, path=None, overwrite=False, sha1_hash=None):
    """Download an given URL
    Parameters
    ----------
    url : str
        URL to download
    path : str, optional
        Destination path to store downloaded file. By default stores to the
        current directory with same name as in url.
    overwrite : bool, optional
        Whether to overwrite destination file if already exists.
    sha1_hash : str, optional
        Expected sha1 hash in hexadecimal digits. Will ignore existing file when hash is specified
        but doesn't match.
    Returns
    -------
    str
        The file path of the downloaded file.
    """
    if path is None:
        fname = url.split('/')[-1]
    else:
        path = os.path.expanduser(path)
        if os.path.isdir(path):
            fname = os.path.join(path, url.split('/')[-1])
        else:
            fname = path

    if overwrite or not os.path.exists(fname) or (sha1_hash and not check_sha1(fname, sha1_hash)):
        dirname = os.path.dirname(os.path.abspath(os.path.expanduser(fname)))
        if not os.path.exists(dirname):
            os.makedirs(dirname)

        print('Downloading %s from %s...'%(fname, url))
        r = requests.get(url, stream=True)
        if r.status_code != 200:
            raise RuntimeError("Failed downloading url %s"%url)
        total_length = r.headers.get('content-length')
        with open(fname, 'wb') as f:
            if total_length is None: # no content length header
                for chunk in r.iter_content(chunk_size=1024):
                    if chunk: # filter out keep-alive new chunks
                        f.write(chunk)
            else:
                total_length = int(total_length)
                for chunk in tqdm(r.iter_content(chunk_size=1024),
                                  total=int(total_length / 1024. + 0.5),
                                  unit='KB', unit_scale=False, dynamic_ncols=True):
                    f.write(chunk)

        if sha1_hash and not check_sha1(fname, sha1_hash):
            raise UserWarning('File {} is downloaded but the content hash does not match. ' \
                              'The repo may be outdated or download may be incomplete. ' \
                              'If the "repo_url" is overridden, consider switching to ' \
                              'the default repo.'.format(fname))

    return fname


def check_sha1(filename, sha1_hash):
    """Check whether the sha1 hash of the file content matches the expected hash.
    Parameters
    ----------
    filename : str
        Path to the file.
    sha1_hash : str
        Expected sha1 hash in hexadecimal digits.
    Returns
    -------
    bool
        Whether the file content matches the expected hash.
    """
    sha1 = hashlib.sha1()
    with open(filename, 'rb') as f:
        while True:
            data = f.read(1048576)
            if not data:
                break
            sha1.update(data)

    return sha1.hexdigest() == sha1_hash


def mkdir(path):
    """make dir exists okay"""
    try:
        os.makedirs(path)
    except OSError as exc:  # Python >2.5
        if exc.errno == errno.EEXIST and os.path.isdir(path):
            pass
        else:
            raise