#!/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.

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.cuda.profiler as profiler
import torch.optim as optim

import pyprof
pyprof.init()


class LeNet5(nn.Module):

    def __init__(self):
        super(LeNet5, self).__init__()
        # 1 input image channel, 6 output channels, 5x5 square convolution
        # kernel
        self.conv1 = nn.Conv2d(1, 6, 5)
        self.conv2 = nn.Conv2d(6, 16, 5)
        # an affine operation: y = Wx + b
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        # Max pooling over a (2, 2) window
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
        # If the size is a square you can only specify a single number
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
        x = x.view(-1, self.num_flat_features(x))
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

    def num_flat_features(self, x):
        size = x.size()[1:]  # all dimensions except the batch dimension
        num_features = 1
        for s in size:
            num_features *= s
        return num_features


with torch.autograd.profiler.emit_nvtx():

    net = LeNet5().cuda()

    input = torch.randn(1, 1, 32, 32).cuda()
    out = net(input)

    target = torch.randn(10)  # a dummy target, for example
    target = target.view(1, -1).cuda()  # make it the same shape as output
    criterion = nn.MSELoss()

    # create your optimizer
    optimizer = optim.SGD(net.parameters(), lr=0.01)

    # in your training loop:
    optimizer.zero_grad()  # zero the gradient buffers

    profiler.start()
    output = net(input)
    loss = criterion(output, target)
    loss.backward()
    optimizer.step()  # Does the update
    profiler.stop()
