SOL4Py Samples

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# 2019/07/23

#  TorchMNISTDenoisingAutoEncoder.py

# encodig: utf-8

import sys
import os
import time
import traceback

import matplotlib.pyplot as plt
import numpy as np

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

import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST, FashionMNIST

sys.path.append('../../')


from SOL4Py.ZMLModel import *
from SOL4Py.ZMain    import *

from SOL4Py.torch.ZTorchSimpleAutoEncoderModel import ZTorchSimpleAutoEncoderModel
from SOL4Py.torch.ZTorchEpochChangeNotifier import ZTorchEpochChangeNotifier
from SOL4Py.torch.ZTorchModelCheckPoint import ZTorchModelCheckPoint
from SOL4Py.ZPILGaussianNoise import ZPILGaussianNoise

sys.path.append('../')
from MNISTAutoEncoder.TorchMNISTAutoEncoder import *


# We define TorchMNISTDenosingAutoEncoder class derived from TorchMNISTAutoEncoder,
# because there are quite similar interfaces between them.

class TorchMNISTDenosingAutoEncoder(TorchMNISTAutoEncoder):

  ##
  # Constructor

  def __init__(self, dataset_id, epochs, mainv=None, ipaddress="127.0.0.1", port=8888):
    super(TorchMNISTDenosingAutoEncoder, self).__init__(dataset_id, epochs, mainv, ipaddress, port)
    
    self.model_filename  = self.__class__.__name__ + "_" + str(self.dataset_id) + ".pt"


  # Create training and validation transformers with ZPILGaussianNoise.
  def create_image_transformer(self):
    self.train_transformer = transforms.Compose([
                        ZPILGaussianNoise(sigma=40),
                        transforms.ToTensor(),])

    self.valid_transformer = transforms.Compose([
                        ZPILGaussianNoise(sigma=40),
                        transforms.ToTensor(),])


#################################################
#
if main(__name__):

  try:
    app_name  = os.path.basename(sys.argv[0])

    dataset_id  = 0
    
    epochs     = 10
    if len(sys.argv) >= 2:
      dataset_id = int(sys.argv[1])

    if len(sys.argv) == 3:
      epochs = int(sys.argv[2])

    model = TorchMNISTDenosingAutoEncoder(dataset_id, epochs)
    model.build()
    
    sampling = 10
    decoded_images = model.predict(model.valid_loader,    n_sampling=sampling)
    model.show_images(model.valid_loader, decoded_images, n_sampling=sampling)
   
  except:
    traceback.print_exc()