#!/usr/bin/env python3
# -*- coding: utf-8 -*-

# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# 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.
"""
An example showing use of nested NVTX markers.
"""

import torch
import torch.nn as nn

import torch.cuda.profiler as profiler
import torch.cuda.nvtx as nvtx
import pyprof
pyprof.init()


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


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class Bottleneck(nn.Module):
    expansion = 4
    count = 1

    def __init__(
        self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None
    ):
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.)) * groups
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

        self.id = Bottleneck.count
        Bottleneck.count += 1

    def forward(self, x):
        identity = x

        nvtx.range_push("layer:Bottleneck_{}".format(self.id))

        nvtx.range_push("layer:Conv1")
        out = self.conv1(x)
        nvtx.range_pop()

        nvtx.range_push("layer:BN1")
        out = self.bn1(out)
        nvtx.range_pop()

        nvtx.range_push("layer:ReLU")
        out = self.relu(out)
        nvtx.range_pop()

        nvtx.range_push("layer:Conv2")
        out = self.conv2(out)
        nvtx.range_pop()

        nvtx.range_push("layer:BN2")
        out = self.bn2(out)
        nvtx.range_pop()

        nvtx.range_push("layer:ReLU")
        out = self.relu(out)
        nvtx.range_pop()

        nvtx.range_push("layer:Conv3")
        out = self.conv3(out)
        nvtx.range_pop()

        nvtx.range_push("layer:BN3")
        out = self.bn3(out)
        nvtx.range_pop()

        if self.downsample is not None:
            nvtx.range_push("layer:Downsample")
            identity = self.downsample(x)
            nvtx.range_pop()

        nvtx.range_push("layer:Residual")
        out += identity
        nvtx.range_pop()

        nvtx.range_push("layer:ReLU")
        out = self.relu(out)
        nvtx.range_pop()

        nvtx.range_pop()

        return out


class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=1000, groups=1, width_per_group=64, norm_layer=None):
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = 64
        self.dilation = 1

        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(3, self.inplanes, 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])
        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 = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(
            block(
                self.inplanes, planes, stride, downsample, self.groups, self.base_width, previous_dilation, norm_layer
            )
        )
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(
                block(
                    self.inplanes, planes, groups=self.groups, base_width=self.base_width, dilation=self.dilation,
                    norm_layer=norm_layer
                )
            )

        return nn.Sequential(*layers)

    def forward(self, x):

        nvtx.range_push("layer:conv1_x")
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        nvtx.range_pop()

        nvtx.range_push("layer:conv2_x")
        x = self.layer1(x)
        nvtx.range_pop()

        nvtx.range_push("layer:conv3_x")
        x = self.layer2(x)
        nvtx.range_pop()

        nvtx.range_push("layer:conv4_x")
        x = self.layer3(x)
        nvtx.range_pop()

        nvtx.range_push("layer:conv5_x")
        x = self.layer4(x)
        nvtx.range_pop()

        x = self.avgpool(x)
        x = torch.flatten(x, 1)

        nvtx.range_push("layer:FC")
        x = self.fc(x)
        nvtx.range_pop()

        return x


def resnet50():
    return ResNet(Bottleneck, [3, 4, 6, 3])


#Create model
net = resnet50().cuda().half()
net.train()

#Create optimizer
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(net.parameters(), lr=0.01, momentum=0.9)

#Create synthetic input and label
x = torch.rand(32, 3, 224, 224).cuda().half()
target = torch.empty(32, dtype=torch.long).random_(1000).cuda()

with torch.autograd.profiler.emit_nvtx():
    profiler.start()
    output = net(x)
    loss = criterion(output, target)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    profiler.stop()
