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import torch.nn as nn
import torch
import pytorch_lightning as pl
import torch.nn.functional as F
import timm
class CustomModelMain_Old(nn.Module):
def __init__(self, problem_type, n_classes):
super().__init__()
if problem_type == 'Classification' and n_classes == 1:
output = nn.Sigmoid()
elif problem_type == 'Regression' and n_classes == 1:
output = nn.ReLU()
elif problem_type == 'Classification' and n_classes > 1:
output = nn.Softmax(dim = 1)
self.conv1 = nn.Conv2d(in_channels = 3, out_channels = 32, kernel_size = 3, padding = 1)
self.pool1 = nn.MaxPool2d(kernel_size = 2, stride = 2)
self.conv2 = nn.Conv2d(in_channels = 32, out_channels = 64, kernel_size = 3, padding = 1)
self.pool2 = nn.MaxPool2d(kernel_size = 2, stride = 2)
self.conv3 = nn.Conv2d(in_channels = 64, out_channels = 64, kernel_size = 3, padding = 1)
self.pool3 = nn.MaxPool2d(kernel_size = 2, stride = 2)
self.flatten = nn.Flatten()
self.fc1 = nn.Linear(64 * 25 * 25 , 128)
self.relu = nn.ReLU()
self.dropout = nn.Dropout(p = 0.5)
self.fc2 = nn.Linear(128, n_classes)
self.output = output
def forward(self, x):
x = self.conv1(x)
x = self.pool1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.pool2(x)
x = self.relu(x)
x = self.conv3(x)
x = self.pool3(x)
x = self.relu(x)
x = self.flatten(x)
x = self.fc1(x)
x = self.relu(x)
x = self.dropout(x)
x = self.fc2(x)
x = self.output(x)
return x
class CustomModelMain_New(nn.Module):
def __init__(self, problem_type, n_classes):
super().__init__()
if problem_type == 'Classification' and n_classes == 1:
output = nn.Sigmoid()
elif problem_type == 'Regression' and n_classes == 1:
output = nn.ReLU()
elif problem_type == 'Classification' and n_classes > 1:
output = nn.Softmax(dim = 1)
self.backbone = timm.create_model('efficientnet_b0', pretrained = True, num_classes = n_classes)
for name, param in self.backbone.named_parameters():
if name.startswith('blocks'):
if not 'blocks.5' in name:
param.requires_grad = False
else:
param.requires_grad = True
num_features = self.backbone.classifier.in_features
self.backbone.classifier = nn.Sequential(
nn.Linear(num_features, 256),
nn.ReLU(),
nn.Linear(256, n_classes),
output
)
def forward(self, x):
x = self.backbone(x)
return x
class age_lightning(pl.LightningModule):
def __init__(self):
super().__init__()
self.model = CustomModelMain_New('Regression', 1)
def forward(self, x):
return self.model(x)
def training_step(self, batch, batch_idx):
x, y = batch
y = y[:, 0]
y_hat = self(x)
loss = F.mse_loss(y_hat, y.unsqueeze(-1).float())
acc = torch.eq((y_hat > 0.5).int().to(torch.int64), y.unsqueeze(-1).int()).all(dim=1).sum() / len(y)
self.log('train loss', loss, prog_bar = True)
return loss
def validation_step(self, batch, batch_idx):
x, y = batch
y_val = y[:, 0]
y_hat = self(x)
loss = F.mse_loss(y_hat, y_val.unsqueeze(-1).float())
self.log('valid loss', loss, prog_bar = True)
def configure_optimizers(self):
return torch.optim.Adam(self.parameters(), lr=1e-4)
class gender_lightning(pl.LightningModule):
def __init__(self):
super().__init__()
self.model = CustomModelMain_New('Classification', 1)
def forward(self, x):
return self.model(x)
def training_step(self, batch, batch_idx):
x, y = batch
y = y[:, 1]
y_hat = self(x)
loss = F.binary_cross_entropy(y_hat, y.unsqueeze(-1).float())
acc = torch.eq((y_hat > 0.5).int().to(torch.int64), y.unsqueeze(-1).int()).all(dim=1).sum() / len(y)
self.log('train loss', loss, prog_bar = True)
self.log('accuracy', acc, prog_bar = True)
return loss
def validation_step(self, batch, batch_idx):
x, y = batch
y_val = y[:, 1]
y_hat = self(x)
loss = F.binary_cross_entropy_with_logits(y_hat, y_val.unsqueeze(-1).float())
acc = torch.eq((y_hat > 0.5).int().to(torch.int64), y_val.unsqueeze(-1).int()).all(dim=1).sum() / len(y_val)
self.log('valid loss', loss, prog_bar = True)
self.log('val accuracy', acc, prog_bar = True)
def configure_optimizers(self):
return torch.optim.Adam(self.parameters(), lr=1e-4)
class race_lightning(pl.LightningModule):
def __init__(self):
super().__init__()
self.model = CustomModelMain_New('Classification', 5)
def forward(self, x):
return self.model(x)
def training_step(self, batch, batch_idx):
x, y = batch
y = y[:, 2]
y_hat = self(x)
y_oh = F.one_hot(y, num_classes = 5)
loss = F.cross_entropy(y_hat.log(), y_oh.float())
preds = y_hat.argmax(dim = 1)
acc = torch.eq(y, preds).float().mean()
self.log('train loss', loss, prog_bar = True)
self.log('accuracy', acc, prog_bar = True)
return loss
def validation_step(self, batch, batch_idx):
x, y = batch
y_val = y[:, 2]
y_hat = self(x)
y_oh = F.one_hot(y_val, num_classes = 5)
loss = F.cross_entropy(y_hat.log(), y_oh.float())
preds = y_hat.argmax(dim = 1)
acc = torch.eq(y_val, preds).float().mean()
self.log('valid loss', loss, prog_bar = True)
self.log('val accuracy', acc, prog_bar = True)
def configure_optimizers(self):
return torch.optim.Adam(self.parameters(), lr=1e-4)
class Ultimate_Lightning(pl.LightningModule):
def __init__(self):
super().__init__()
self.age_model = CustomModelMain_New('Regression', 1)
self.gender_model = CustomModelMain_New('Classification', 1)
self.race_model = CustomModelMain_New('Classification', 5)
def forward(self, x):
return self.age_model(x), self.gender_model(x), self.race_model(x)
def training_step(self, batch, batch_idx):
x, y = batch
y_age, y_gender, y_race = y[:, 0], y[:, 1], y[:, 2]
y_hat_age, y_hat_gender, y_hat_race = self(x)
age_loss = F.mse_loss(y_hat_age, y_age.unsqueeze(-1).float())
age_acc = torch.eq((y_hat_age > 0.5).int().to(torch.int64), y_age.unsqueeze(-1).int()).all(dim=1).sum() / len(y_age)
gender_loss = F.binary_cross_entropy(y_hat_gender, y_gender.unsqueeze(-1).float())
gender_acc = torch.eq((y_hat_gender > 0.5).int().to(torch.int64), y_gender.unsqueeze(-1).int()).all(dim=1).sum() / len(y_gender)
y_race_oh = F.one_hot(y_race, num_classes = 5)
race_loss = F.cross_entropy(y_hat_race.log(), y_race_oh.float())
race_preds = y_hat_race.argmax(dim = 1)
race_acc = torch.eq(y_race, race_preds).float().mean()
total_loss = (0.001 * age_loss) + gender_loss + race_loss
self.log('age loss', age_loss, prog_bar = True)
self.log('gender loss', gender_loss, prog_bar = True)
self.log('race loss', race_loss, prog_bar = True)
self.log('gender acc', gender_acc, prog_bar = True)
self.log('race acc', race_acc, prog_bar = True)
self.log('total loss', total_loss, prog_bar = True)
return total_loss
def validation_step(self, batch, batch_idx):
x, y = batch
y_age, y_gender, y_race = y[:, 0], y[:, 1], y[:, 2]
y_hat_age, y_hat_gender, y_hat_race = self(x)
age_loss = F.mse_loss(y_hat_age, y_age.unsqueeze(-1).float())
age_acc = torch.eq((y_hat_age > 0.5).int().to(torch.int64), y_age.unsqueeze(-1).int()).all(dim=1).sum() / len(y_age)
gender_loss = F.binary_cross_entropy(y_hat_gender, y_gender.unsqueeze(-1).float())
gender_acc = torch.eq((y_hat_gender > 0.5).int().to(torch.int64), y_gender.unsqueeze(-1).int()).all(dim=1).sum() / len(y_gender)
y_race_oh = F.one_hot(y_race, num_classes = 5)
race_loss = F.cross_entropy(y_hat_race.log(), y_race_oh.float())
race_preds = y_hat_race.argmax(dim = 1)
race_acc = torch.eq(y_race, race_preds).float().mean()
total_loss = (0.001 * age_loss) + gender_loss + race_loss
self.log('val age loss', age_loss, prog_bar = True)
self.log('val gender acc', gender_acc, prog_bar = True)
self.log('val race acc', race_acc, prog_bar = True)
def configure_optimizers(self):
return torch.optim.Adam(self.parameters(), lr=1e-4) |