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gender_model.py
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gender_model.py
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import torch.nn as nn
import torch.nn.functional as F
import torch
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, 34) # output: 34 x 142 x 142
self.conv2 = conv_block(34, 64, pool=True) # output: 64 x 71 x 71 ; as pool is true
self.res1 = nn.Sequential(conv_block(64, 64), conv_block(64, 64))
self.conv3 = conv_block(64, 128) # output: 128 x 71 x 71
self.conv4 = conv_block(128,256, pool=True) # output: 256 x 35 x 35
self.res2 = nn.Sequential(conv_block(256,256), conv_block(256,256))
self.conv5 = conv_block(256,512) # output: 512 x 35 x 35
self.conv6 = conv_block(512, 512, pool=True) # output: 512 x 17 x 17
self.res3 = nn.Sequential(conv_block(512, 512), conv_block(512, 512))
self.classifier = nn.Sequential(nn.AdaptiveMaxPool2d(1), # output: 512 x 17/(17) x 17/(17)
nn.Flatten(),
nn.Dropout(0.2),
nn.Linear(512, num_classes)) # output: 512 x 1 x 1
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.conv5(out)
out = self.conv6(out)
out = self.res3(out) + out
out = self.classifier(out)
return out
# for getting devices
def get_default_device():
"""Pick GPU if available, else CPU"""
if torch.cuda.is_available():
return torch.device('cuda')
else:
return torch.device('cpu')
def to_device(data, device):
"""Move tensor(s) to chosen device"""
if isinstance(data, (list,tuple)):
return [to_device(x, device) for x in data]
return data.to(device, non_blocking=True)