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nsfw_clf.py
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nsfw_clf.py
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import numpy as np
import torch
from torch import nn
from torch import optim
import torch.nn.functional as F
from torchvision import datasets, transforms
import torchvision.models as models
def load_split_train(valid_size=.2):
train_transforms = transforms.Compose([transforms.Resize(224),
transforms.RandomResizedCrop(224),
transforms.ToTensor(),
])
test_transforms = transforms.Compose([transforms.Resize(224),
transforms.RandomResizedCrop(224),
transforms.ToTensor(),
])
train_data = datasets.ImageFolder('./',
transform=train_transforms)
test_data = datasets.ImageFolder('./',
transform=test_transforms)
num_train = len(train_data)
indices = list(range(num_train))
split = int(np.floor(valid_size * num_train))
np.random.shuffle(indices)
from torch.utils.data.sampler import SubsetRandomSampler
train_idx, test_idx = indices[split:], indices[:split]
train_sampler = SubsetRandomSampler(train_idx)
test_sampler = SubsetRandomSampler(test_idx)
trainloader = torch.utils.data.DataLoader(train_data,
sampler=train_sampler, batch_size=64)
testloader = torch.utils.data.DataLoader(test_data,
sampler=test_sampler, batch_size=64)
return trainloader, testloader
if __name__ =='__main__':
trainloader, testloader = load_split_train(.2)
model = models.resnet18(pretrained=True)
for param in model.parameters():
param.requires_grad = False
model.fc = nn.Sequential(nn.Linear(512, 64),
nn.ReLU(),
nn.Dropout(0.2),
nn.Linear(64, 64),
nn.LogSoftmax(dim=1))
criterion = nn.NLLLoss()
optimizer = optim.Adam(model.fc.parameters(), lr=0.05)
device = torch.device("cpu")
model.to(device)
epochs = 1
steps = 0
running_loss = 0
print_every = 1
train_losses, test_losses = [], []
for epoch in range(epochs):
for inputs, labels in trainloader:
steps += 1
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
logps = model.forward(inputs)
loss = criterion(logps, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if steps % print_every == 0:
test_loss = 0
accuracy = 0
model.eval()
with torch.no_grad():
for inputs, labels in testloader:
inputs, labels = inputs.to(device), labels.to(device)
logps = model.forward(inputs)
batch_loss = criterion(logps, labels)
test_loss += batch_loss.item()
ps = torch.exp(logps)
top_p, top_class = ps.topk(1, dim=1)
equals = top_class == labels.view(*top_class.shape)
accuracy += torch.mean(equals.type(torch.FloatTensor)).item()
train_losses.append(running_loss / len(trainloader))
test_losses.append(test_loss / len(testloader))
print(f"Epoch {epoch+1}/{epochs}.. "
f"Train loss: {running_loss/print_every:.3f}.. "
f"Test loss: {test_loss/len(testloader):.3f}.. "
f"Test accuracy: {accuracy/len(testloader):.3f}")
running_loss = 0
model.train()
torch.save(model, 'nsfw_class.pth')