from __future__ import print_function

import os
import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
from torchvision import datasets, transforms
import torchvision.models as models
import matplotlib.pyplot as plt
import numpy as np


CurrentPath = os.path.split(os.path.realpath(__file__))[0]+"/"
sys.path.append(CurrentPath+'../tools')
sys.path.append(CurrentPath+'../')

from tools import UniModule
from tools import utils, Train, Loader, WebVisual


class MINISTCNN(UniModule.ModuleBase):
    def __init__(self):
        super(MINISTCNN, self).__init__()
        self.layer1 = nn.Sequential(  # 28-4=24  /2=12
            nn.Conv2d(1, 16, kernel_size=5),
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.layer2 = nn.Sequential(  # 12-2=10  /2=5
            nn.Conv2d(16, 32, kernel_size=3),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.layer3 = nn.Sequential(  # 5-2=3
            nn.Conv2d(32, 64, kernel_size=3),
            nn.BatchNorm2d(64),
            nn.ReLU()
        )
        self.fc = nn.Sequential(
            nn.Linear(64*3*3, 512),
            # nn.ReLU(),
            nn.Linear(512, 10))

    def forward(self, x):
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return F.log_softmax(x, dim=1)



WebVisual.InitVisdom()
window = WebVisual.LineWin()
linePre = WebVisual.Line(window, "linePre")

traindata, testdata = Loader.MNIST(
    32, num_workers=4, trainsize=60000, shuffle=True)

modelMinist = utils.SetDevice(MINISTCNN())

# model = utils.LoadModel(model, CurrentPath+"/checkpointEntropySearch.pkl")

optimizer = optim.SGD(modelMinist.parameters(), lr=0.0005)
Train.TrainEpochs(modelMinist, traindata, optimizer,
                  testdata, 100, 1, linePre)
utils.SaveModel(modelMinist, CurrentPath+"/checkpointMnistTrain.pkl")