103 lines
2.8 KiB
Python
103 lines
2.8 KiB
Python
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import torch
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import torchvision
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import torchvision.transforms as transforms
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import torch.nn as nn
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import torch.nn.functional as f
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import torch.optim as optim
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import numpy as np
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from matplotlib.pyplot import imshow
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import os
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import PIL
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import numpy as np
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from matplotlib.pyplot import imshow
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def to_negative(img):
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img = PIL.ImageOps.invert(img)
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return img
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class Negative(object):
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def __init__(self):
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pass
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def __call__(self, img):
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return to_negative(img)
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def plotdigit(image):
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img = np.reshape(image, (-1, 100))
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imshow(img, cmap='Greys')
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transform = transforms.Compose([Negative(), transforms.ToTensor()])
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train_set = torchvision.datasets.ImageFolder(root='train', transform=transform)
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classes = ("apple", "potato")
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BATCH_SIZE = 2
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train_loader = torch.utils.data.DataLoader(train_set, batch_size=BATCH_SIZE, shuffle=True, num_workers=0)
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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class Net(nn.Module):
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def __init__(self):
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super(Net, self).__init__()
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self.flatten = nn.Flatten()
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self.linear_relu_stack = nn.Sequential(
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nn.Linear(3*100*100, 512),
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nn.ReLU(),
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nn.Linear(512, 512),
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nn.ReLU(),
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nn.Linear(512, 2),
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nn.ReLU()
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)
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self.linear_relu_stack = self.linear_relu_stack.to(device)
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def forward(self, x):
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x = self.flatten(x).to(device)
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logits = self.linear_relu_stack(x).to(device)
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return logits
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def training_network():
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net = Net()
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net = net.to(device)
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
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for epoch in range(4):
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running_loss = 0.0
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for i, data in enumerate(train_loader, 0):
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inputs, labels = data[0].to(device), data[1].to(device)
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optimizer.zero_grad()
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outputs = net(inputs.to(device))
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loss = criterion(outputs, labels)
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loss.backward()
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optimizer.step()
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running_loss += loss.item()
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if i % 2000 == 1999:
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print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss))
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running_loss = 0.0
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print("Finished training")
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save_network_to_file(net)
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def result_from_network(net, loaded_image):
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image = PIL.Image.open(loaded_image)
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pil_to_tensor = transforms.ToTensor()(image.convert("RGB")).unsqueeze_(0)
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outputs = net(pil_to_tensor.to(device))
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return classes[torch.max(outputs, 1)[1]]
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def save_network_to_file(network):
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torch.save(network.state_dict(), 'network_model.pth')
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print("Network saved to file")
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def load_network_from_structure(network):
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network.load_state_dict(torch.load('network_model.pth'))
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if __name__ == "__main__":
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print(torch.cuda.is_available())
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training_network()
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