#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you 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.
#

# the code is modified from
# https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py

from singa import autograd
from singa import tensor
from singa import device
from singa import opt

import numpy as np
from tqdm import trange


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


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


class BasicBlock(autograd.Layer):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = autograd.BatchNorm2d(planes)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = autograd.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def __call__(self, x):
        residual = x

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

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

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

        out = autograd.add(out, residual)
        out = autograd.relu(out)

        return out


class Bottleneck(autograd.Layer):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = autograd.Conv2d(
            inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = autograd.BatchNorm2d(planes)
        self.conv2 = autograd.Conv2d(planes, planes, kernel_size=3,
                                     stride=stride,
                                     padding=1, bias=False)
        self.bn2 = autograd.BatchNorm2d(planes)
        self.conv3 = autograd.Conv2d(
            planes, planes * self.expansion, kernel_size=1, bias=False)
        self.bn3 = autograd.BatchNorm2d(planes * self.expansion)

        self.downsample = downsample
        self.stride = stride

    def __call__(self, x):
        residual = x

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

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

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

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

        out = autograd.add(out, residual)
        out = autograd.relu(out)

        return out


class ResNet(autograd.Layer):

    def __init__(self, block, layers, num_classes=1000):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = autograd.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                                     bias=False)
        self.bn1 = autograd.BatchNorm2d(64)
        self.maxpool = autograd.MaxPool2d(
            kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = autograd.AvgPool2d(7, stride=1)
        self.fc = autograd.Linear(512 * block.expansion, num_classes)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            conv = autograd.Conv2d(self.inplanes, planes * block.expansion,
                                   kernel_size=1, stride=stride, bias=False)
            bn = autograd.BatchNorm2d(planes * block.expansion)

            def downsample(x):
                return bn(conv(x))

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        def forward(x):
            for layer in layers:
                x = layer(x)
            return x
        return forward

    def __call__(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = autograd.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 = autograd.flatten(x)
        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)

    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)

    return model


def resnet50(pretrained=False, **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)

    return model


def resnet101(pretrained=False, **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)

    return model


def resnet152(pretrained=False, **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)

    return model


if __name__ == '__main__':
    model = resnet18()
    print('Start intialization............')
    dev = device.create_cuda_gpu_on(0)
    #dev = device.create_cuda_gpu()
    niters = 200
    batch_size = 16
    IMG_SIZE = 224
    sgd = opt.SGD(lr=0.1, momentum=0.9, weight_decay=1e-5)

    tx = tensor.Tensor((batch_size, 3, IMG_SIZE, IMG_SIZE), dev)
    ty = tensor.Tensor((batch_size,), dev, tensor.int32)
    autograd.training = True
    x = np.random.randn(batch_size, 3, IMG_SIZE, IMG_SIZE).astype(np.float32)
    y = np.random.randint(0, 1000, batch_size, dtype=np.int32)
    tx.copy_from_numpy(x)
    ty.copy_from_numpy(y)

    with trange(niters) as t:
        for b in t:
            x = model(tx)
            loss = autograd.softmax_cross_entropy(x, ty)
            for p, g in autograd.backward(loss):
                # print(p.shape, g.shape)
                sgd.update(p, g)
                #pass