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model.py
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model.py
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from torchvision import models, transforms
import torch
from PIL import Image
import torch.nn as nn
def accuracy(outputs, labels):
_, preds = torch.max(outputs, dim=1)
return torch.tensor(torch.sum(preds == labels).item() / len(preds))
class ImageClassificationBase(nn.Module):
def training_step(self, batch):
images, labels = batch
out = self(images) # Generate predictions
loss = F.cross_entropy(out, labels) # Calculate loss
return loss
def validation_step(self, batch):
images, labels = batch
out = self(images) # Generate predictions
loss = F.cross_entropy(out, labels) # Calculate loss
acc = accuracy(out, labels) # Calculate accuracy
return {'val_loss': loss.detach(), 'val_acc': acc}
def validation_epoch_end(self, outputs):
batch_losses = [x['val_loss'] for x in outputs]
epoch_loss = torch.stack(batch_losses).mean() # Combine losses
batch_accs = [x['val_acc'] for x in outputs]
epoch_acc = torch.stack(batch_accs).mean() # Combine accuracies
return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item()}
def epoch_end(self, epoch, result):
print("Epoch [{}], last_lr: {:.5f}, train_loss: {:.4f}, val_loss: {:.4f}, val_acc: {:.4f}".format(
epoch, result['lrs'][-1], result['train_loss'], result['val_loss'], result['val_acc']))
def conv_block(in_channels, out_channels, pool=False):
layers = [nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)]
if pool: layers.append(nn.MaxPool2d(2))
return nn.Sequential(*layers)
class ResNet9(ImageClassificationBase):
def __init__(self, in_channels, num_classes):
super().__init__()
self.conv1 = conv_block(in_channels, 64)
self.conv2 = conv_block(64, 128, pool=True)
self.res1 = nn.Sequential(conv_block(128, 128), conv_block(128, 128))
self.conv3 = conv_block(128, 256, pool=True)
self.conv4 = conv_block(256, 512, pool=True)
self.res2 = nn.Sequential(conv_block(512, 512), conv_block(512, 512))
self.classifier = nn.Sequential(nn.MaxPool2d(4),
nn.Flatten(),
nn.Linear(512, num_classes))
def forward(self, xb):
out = self.conv1(xb)
out = self.conv2(out)
out = self.res1(out) + out
out = self.conv3(out)
out = self.conv4(out)
out = self.res2(out) + out
out = self.classifier(out)
return out
def predict(image_path):
resnet = torch.load("starter2.pth", map_location = "cpu")
transform = (transforms.Compose([transforms.Resize((32,32)),transforms.ToTensor()]))
img = Image.open(image_path)
batch_t = torch.unsqueeze(transform(img), 0)
resnet.eval()
out = resnet(batch_t)
with open('image_classes.txt') as f:
classes = [line.strip() for line in f.readlines()]
prob = torch.nn.functional.softmax(out, dim=1)[0] * 100
_, indices = torch.sort(out, descending=True)
return [(classes[idx], prob[idx].item()) for idx in indices[0][:3]]