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dataset.py
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dataset.py
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import numpy as np # Matrix Operations (Matlab of Python)
import pandas as pd # Work with Datasources
import matplotlib.pyplot as plt # Drawing Library
from PIL import Image
import torch # Like a numpy but we could work with GPU by pytorch library
import torch.nn as nn # Nural Network Implimented with pytorch
import torchvision # A library for work with pretrained model and datasets
from torchvision import transforms
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import torch.nn.functional as F
import glob
import os
#get_ipython().magic('matplotlib inline')
image_size = (100, 100)
image_row_size = image_size[0] * image_size[1]
# In[4]:
class CatDogDataset(Dataset):
def __init__(self, path, transform=None):
self.classes = os.listdir(path)
self.path = [f"{path}/{className}" for className in self.classes]
self.file_list = [glob.glob(f"{x}/*") for x in self.path]
self.transform = transform
files = []
for i, className in enumerate(self.classes):
for fileName in self.file_list[i]:
files.append([i, className, fileName])
self.file_list = files
files = None
def __len__(self):
return len(self.file_list)
def __getitem__(self, idx):
fileName = self.file_list[idx][2]
classCategory = self.file_list[idx][0]
im = Image.open(fileName)
if self.transform:
im = self.transform(im)
return im, classCategory
# In[ ]:
#CatDogDataset = CatDogDataset('abc')