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train.py
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train.py
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import torch
import torch.nn as nn
from torch.utils.data import DataLoader, random_split
import os
import torchsummary
from torch.utils.tensorboard import SummaryWriter
# from torchviz import make_dot
from dataset import FaceLandmarksDatasetWithMediapipe
from model import LierDetectModel_v3 as LierDetectModel
from model import LierDetectModelWithCNN as CNN_MODEL
from utils import create_directory
def train_loop(dataloader, model, loss_fn, optimizer):
size = len(dataloader.dataset)
num_batches = len(dataloader)
train_loss = 0
for batch, (X, y) in enumerate(dataloader):
landmark, heart_rate = X
landmark =landmark.reshape(landmark.shape[0], 3, -1)
pred = model(landmark, heart_rate)
loss = loss_fn(pred, y)
# BackPropagation
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item()
if batch % 100 == 0:
_loss, current = loss.item(), batch * len(X)
print(f"loss: {_loss:>7f} [{current:>5d}/{size:>5d}]")
train_loss /= num_batches
return train_loss
def test_loop(dataloader, model, loss_fn):
model.eval()
size = len(dataloader.dataset)
num_batches = len(dataloader)
test_loss, correct = 0, 0
with torch.no_grad():
for (landmark, heart_rate), y in dataloader:
pred = model(landmark, heart_rate)
test_loss += loss_fn(pred, y).item()
correct += (pred >= torch.FloatTensor([0.5])).float().sum().item()
test_loss /= num_batches
correct /= size * num_batches
# print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
return correct
def main_v1():
dataset = FaceLandmarksDatasetWithMediapipe(csv_file="./data.csv")
dataset_size = len(dataset)
train_size = int(dataset_size * 0.8)
test_size = dataset_size - train_size
# validation_size = dataset_size - train_size - validation_size
train_dataset, test_dataset = random_split(dataset, [train_size, test_size])
train_dataloader = DataLoader(train_dataset, batch_size=8, shuffle=True, drop_last=True)
# validation_dataloader = DataLoader(validation_dataset, batch_size=4, shuffle=True, drop_last=True)
test_dataloader = DataLoader(test_dataset, batch_size=4, shuffle=True, drop_last=True)
n_epochs = 50
learning_rate = 0.01
model = LierDetectModel()
# TensorBoard Start
writer = SummaryWriter()
x1 = torch.zeros(1, 478, 3)
x1 = x1.reshape(x1.shape[0], 3, -1)
x2 = torch.zeros(1, 10)
writer.add_graph(model, [x1, x2])
print(model)
loss_fn = nn.BCELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
for epoch in range(1, n_epochs):
running_loss = train_loop(train_dataloader, model, loss_fn, optimizer)
accuracy = test_loop(test_dataloader, model, loss_fn)
print(f"Epoch [{epoch+1}/{n_epochs}], Loss: {running_loss:.5f}, Accuracy: {accuracy:.5f}")
writer.add_scalar('training loss', running_loss, epoch+1)
writer.add_scalar('test accuracy', accuracy, epoch+1)
print("Done!")
PATH = ["./weights"]
create_directory(PATH)
torch.save(model, os.path.join(PATH[0], "model_v1-4.pt"))
# writer.close()
def train_loop_v2(dataloader, model, loss_fn, optimizer):
size = len(dataloader.dataset)
num_batches = len(dataloader)
train_loss = 0
for batch, (X, y) in enumerate(dataloader):
landmark, heart_rate = X
landmark =landmark.reshape(landmark.shape[0], 3, -1)
pred = model(landmark, heart_rate)
loss = loss_fn(pred, y)
# BackPropagation
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item()
# if batch % 100 == 0:
# _loss, current = loss.item(), batch * len(X)
# print(f"loss: {_loss:>7f} [{current:>5d}/{size:>5d}]")
train_loss /= num_batches
return train_loss
def test_loop_v2(dataloader, model, loss_fn):
size = len(dataloader.dataset)
num_batches = len(dataloader)
test_loss, correct = 0, 0
with torch.no_grad():
for (landmark, heart_rate), y in dataloader:
landmark =landmark.reshape(landmark.shape[0], 3, -1)
pred = model(landmark, heart_rate)
test_loss += loss_fn(pred, y).item()
correct += (pred >= torch.FloatTensor([0.5])).float().sum().item()
test_loss /= num_batches
correct /= size * num_batches
print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
return correct
def main_v2():
dataset = FaceLandmarksDatasetWithMediapipe(csv_file="./data.csv")
dataset_size = len(dataset)
train_size = int(dataset_size * 0.8)
test_size = dataset_size - train_size
# validation_size = dataset_size - train_size - validation_size
train_dataset, test_dataset = random_split(dataset, [train_size, test_size])
train_dataloader = DataLoader(train_dataset, batch_size=8, shuffle=True, drop_last=True)
# validation_dataloader = DataLoader(validation_dataset, batch_size=4, shuffle=True, drop_last=True)
test_dataloader = DataLoader(test_dataset, batch_size=4, shuffle=True, drop_last=True)
n_epochs = 20
learning_rate = 0.1
model = CNN_MODEL()
writer = SummaryWriter()
x1 = torch.zeros(1, 478, 3)
x1 = x1.reshape(x1.shape[0], 3, -1)
x2 = torch.zeros(1, 10)
writer.add_graph(model, [x1, x2])
print(model)
loss_fn = nn.BCELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
for epoch in range(0, n_epochs):
running_loss = train_loop_v2(train_dataloader, model, loss_fn, optimizer)
accuracy = test_loop_v2(test_dataloader, model, loss_fn)
print(f"Epoch [{epoch+1}/{n_epochs}], Loss: {running_loss:.5f}, Accuracy: {accuracy:.5f}")
writer.add_scalar('training loss', running_loss, epoch+1)
writer.add_scalar('test accuracy', accuracy, epoch+1)
print("Done!")
PATH = "./weights"
if not os.path.exists(PATH):
os.makedirs(PATH)
torch.save(model, os.path.join(PATH, "model_v2.pt"))
if __name__ == "__main__":
main_v1()