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4 years ago

Imports

import torch
import torchvision
import numpy as np
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.nn.functional as F
from torchvision.datasets import FashionMNIST
from torchvision.transforms import ToTensor
from torchvision.utils import make_grid
from torch.utils.data.dataloader import DataLoader
from torch.utils.data import random_split
import os
from torchvision.datasets import ImageFolder
import torchvision.transforms as tt
from tqdm import tqdm
from torch.utils.data.dataloader import DataLoader
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
%matplotlib inline
DataProcessing
data_dir = './asl'

print(os.listdir(data_dir))
classes = os.listdir(data_dir + "/asl_alphabet_train/asl_alphabet_train")
print(classes)

image_size = 32*32
['asl_alphabet_test', 'asl_alphabet_train'] ['A', 'B', 'C', 'D', 'del', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'nothing', 'O', 'P', 'Q', 'R', 'S', 'space', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'] 
transform = tt.Compose(
    [
        tt.Resize(32),
        tt.ToTensor(),
        tt.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
    ]
)
dataset = ImageFolder(data_dir+"/asl_alphabet_train/asl_alphabet_train", transform)