Source code

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# 2019/07/10

# ZTorchAlexNetModel.py

# This is based on AlexNet: https://github.com/icpm/pytorch-cifar10/blob/master/models/AlexNet.py
# See: https://github.com/vinhkhuc/PyTorch-Mini-Tutorials/blob/master/5_convolutional_net.py

# encodig: utf-8

import sys
import os
import time
import traceback

import torch
import torchvision
import torch.nn as nn
import torch.nn.init as init
import torch.optim as optim
import torch.nn.functional as F
import torchvision.transforms as transforms
from torch.autograd import Variable


sys.path.append('../')
from SOL4Py.torch.ZTorchSimpleModel    import *


## 
# ZTorchAlexNetModel

class ZTorchAlexNetModel(ZTorchSimpleModel):
  #
  # Constructor
  def __init__(self, image_size, n_classes, model_filename):
    super(ZTorchAlexNetModel, self).__init__(image_size, n_classes, model_filename)

    self.features = nn.Sequential(
        nn.Conv2d(3, 64, kernel_size=3, padding=1),
        nn.ReLU(inplace=True),
        nn.MaxPool2d(kernel_size=2, stride=2),
        nn.Conv2d(64, 192, kernel_size=5, padding=2),
        nn.ReLU(inplace=True),
        nn.MaxPool2d(kernel_size=2, stride=2),
        nn.Conv2d(192, 384, kernel_size=3, padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(384, 256, kernel_size=3, padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(256, 256, kernel_size=3, padding=1),
        nn.ReLU(inplace=True),
        nn.MaxPool2d(kernel_size=2, stride=2),
        )
 
    self.classifier = nn.Sequential(
        nn.Dropout(),
        nn.Linear(256 * 4 * 4, 4096),
        nn.ReLU(inplace=True),
        nn.Dropout(),
        nn.Linear(4096, 4096),
        nn.ReLU(inplace=True),
        nn.Linear(4096, n_classes)
        )


  def forward(self, input):
    output = self.features(input)
    output = output.view(output.size(0), 256 * 4 * 4)
    output = self.classifier(output)
    return output