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main.py
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main.py
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import torch
import torchvision
import torchvision.transforms as transforms
import glob
import cv2
import numpy as np
from torch.utils.data import Dataset, DataLoader
import os
classes = ('photos', 'memes')
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Assuming that we are on a CUDA machine, this should print a CUDA device:
print(device)
class MemesPhotosDataset(Dataset):
def __init__(self, path):
self.data_path = path
file_list = glob.glob(self.data_path + "*")
print(file_list)
self.data = []
for class_path in file_list:
class_name = class_path.split("/")[-1]
for img_path in glob.glob(class_path + "/*.jpg"):
self.data.append([img_path, class_name])
print(self.data)
self.class_map = {"photos" : 0, "memes" : 1}
self.img_dim = (512, 512)
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
img_path, class_name = self.data[idx]
img = cv2.imread(img_path)
img = np.flip(img, 2)
img = cv2.resize(img, self.img_dim)
class_id = self.class_map[class_name]
img_tensor = torch.from_numpy(img)
img_tensor = img_tensor.permute(2, 0, 1)
class_id = torch.tensor([class_id])
# class_id = class_id.view(-1,)
# print(class_id)
return img_tensor.float(), class_id
dataset = MemesPhotosDataset("photos_memes_dataset/")
print(len(dataset))
from torch.utils.data import random_split
train, test = random_split(dataset, [1119, 373])
trainloader = DataLoader(train, batch_size=4, shuffle=True)
testloader = DataLoader(test, batch_size=4, shuffle=False)
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16*125*125, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 2)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = torch.flatten(x, 1) # flatten all dimensions except batch
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
net.to(device)
import torch.optim as optim
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.000001, momentum=0.9)
for epoch in range(2): # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
# get the inputs; data is a list of [inputs, labels]
inputs, labels = data[0].to(device), data[1].to(device)
# print(inputs.shape)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
labels = labels.view(-1,)
# print(labels)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
if i % 50 == 49: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 50))
running_loss = 0.0
print('Finished Training')
PATH = './photomeme_net.pth'
torch.save(net.state_dict(), PATH)
PATH = './photomeme_net.pth'
net = Net()
net.load_state_dict(torch.load(PATH))
net.to(device)
# prepare to count predictions for each class
correct_pred = {classname: 0 for classname in classes}
total_pred = {classname: 0 for classname in classes}
correct = 0
total = 0
# again no gradients needed
with torch.no_grad():
for data in testloader:
images, labels = data[0].to(device), data[1].to(device)
outputs = net(images)
_, predictions = torch.max(outputs, 1)
# collect the correct predictions for each class
for label, prediction in zip(labels, predictions):
if label == prediction:
correct_pred[classes[label]] += 1
correct += 1
total_pred[classes[label]] += 1
total += 1
# print accuracy for each class
print('Accuracy of the network on the test images: %d %%' % (
100 * correct / total))
for classname, correct_count in correct_pred.items():
accuracy = 100 * float(correct_count) / total_pred[classname]
print("Accuracy for class {:5s} is: {:.1f} %".format(classname,
accuracy))