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training.py
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training.py
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import torch, torch.nn as nn, torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import StepLR, CosineAnnealingLR
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import ConcatDataset
from datetime import datetime
from models import FCMNIST, CNNMNIST
from BitNetMCU import BitLinear, BitConv2d
import time
import random
import argparse
import yaml
from torchsummary import summary
#----------------------------------------------
# BitNetMCU training
# cpldcpu 2024-03
#----------------------------------------------
def create_run_name(hyperparameters):
runname = hyperparameters["runtag"] + hyperparameters["scheduler"] + '_lr' + str(hyperparameters["learning_rate"]) + ('_Aug' if hyperparameters["augmentation"] else '') + '_BitMnist_' + hyperparameters["WScale"] + "_" +hyperparameters["QuantType"] + "_" + hyperparameters["NormType"] + "_width" + str(hyperparameters["network_width1"]) + "_" + str(hyperparameters["network_width2"]) + "_" + str(hyperparameters["network_width3"]) + "_bs" + str(hyperparameters["batch_size"]) + "_epochs" + str(hyperparameters["num_epochs"])
hyperparameters["runname"] = runname
return runname
def train_model(model, device, hyperparameters, train_data, test_data):
num_epochs = hyperparameters["num_epochs"]
learning_rate = hyperparameters["learning_rate"]
step_size = hyperparameters["step_size"]
lr_decay = hyperparameters["lr_decay"]
runname = create_run_name(hyperparameters)
# define dataloaders
batch_size = hyperparameters["batch_size"] # Define your batch size
# ON-the-fly augmentation requires using the (slow) dataloader. Without augmentation, we can load the entire dataset into GPU for speedup
if hyperparameters["augmentation"]:
train_loader = DataLoader(
train_data, batch_size=batch_size, shuffle=True,
num_workers=4, pin_memory=True)
else:
# load entire dataset into GPU for 5x speedup
train_loader = DataLoader(train_data, batch_size=len(train_data), shuffle=False) # shuffling will be done separately
entire_dataset = next(iter(train_loader))
all_train_images, all_train_labels = entire_dataset[0].to(device), entire_dataset[1].to(device)
# Test dataset is always in GPU
test_loader = DataLoader(test_data, batch_size=len(test_data), shuffle=False)
entire_dataset = next(iter(test_loader))
all_test_images, all_test_labels = entire_dataset[0].to(device), entire_dataset[1].to(device)
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
if hyperparameters["scheduler"] == "StepLR":
scheduler = StepLR(optimizer, step_size=step_size, gamma=lr_decay)
elif hyperparameters["scheduler"] == "Cosine":
scheduler = CosineAnnealingLR(optimizer, T_max=num_epochs, eta_min=0)
criterion = nn.CrossEntropyLoss()
# tensorboard writer
now_str = datetime.now().strftime("%Y%m%d-%H%M%S")
writer = SummaryWriter(log_dir=f'runs/{runname}-{now_str}')
train_loss=[]
test_loss = []
# Train the CNN
for epoch in range(num_epochs):
correct = 0
train_loss=[]
start_time = time.time()
if hyperparameters["augmentation"]:
for i, (images, labels) in enumerate(train_loader):
images, labels = images.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
train_loss.append(loss.item())
correct += (predicted == labels).sum().item()
else:
# Shuffle images (important!)
indices = list(range(len(all_train_images)))
random.shuffle(indices)
for i in range(len(indices) // batch_size):
batch_indices = indices[i * batch_size:(i + 1) * batch_size]
images = torch.stack([all_train_images[i] for i in batch_indices])
labels = torch.stack([all_train_labels[i] for i in batch_indices])
optimizer.zero_grad()
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
train_loss.append(loss.item())
correct += (predicted == labels).sum().item()
scheduler.step()
trainaccuracy = correct / len(train_loader.dataset) * 100
correct = 0
total = 0
test_loss = []
with torch.no_grad():
for i in range(len(all_test_images) // batch_size):
images = all_test_images[i * batch_size:(i + 1) * batch_size]
labels = all_test_labels[i * batch_size:(i + 1) * batch_size]
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
loss = criterion(outputs, labels)
test_loss.append(loss.item())
total += labels.size(0)
correct += (predicted == labels).sum().item()
end_time = time.time()
epoch_time = end_time - start_time
testaccuracy = correct / total * 100
print(f'Epoch [{epoch+1}/{num_epochs}], LTrain:{np.mean(train_loss):.6f} ATrain: {trainaccuracy:.2f}% LTest:{np.mean(test_loss):.6f} ATest: {correct / total * 100:.2f}% Time[s]: {epoch_time:.2f} w_clip/entropy[bits]: ', end='')
# update clipping scalars once per epoch
totalbits = 0
for i, layer in enumerate(model.modules()):
if isinstance(layer, BitLinear) or isinstance(layer, BitConv2d):
# update clipping scalar
if epoch < hyperparameters['maxw_update_until_epoch']:
layer.update_clipping_scalar(layer.weight, hyperparameters['maxw_algo'], hyperparameters['maxw_quantscale'])
# calculate entropy of weights
w_quant, _, _ = layer.weight_quant(layer.weight)
_, counts = np.unique(w_quant.cpu().detach().numpy(), return_counts=True)
probabilities = counts / np.sum(counts)
entropy = -np.sum(probabilities * np.log2(probabilities))
print(f'{layer.s.item():.3f}/{entropy:.2f}', end=' ')
totalbits += layer.weight.numel() * layer.bpw
print()
writer.add_scalar('Loss/train', np.mean(train_loss), epoch+1)
writer.add_scalar('Accuracy/train', trainaccuracy, epoch+1)
writer.add_scalar('Loss/test', np.mean(test_loss), epoch+1)
writer.add_scalar('Accuracy/test', testaccuracy, epoch+1)
writer.add_scalar('learning_rate', optimizer.param_groups[0]['lr'], epoch+1)
writer.flush()
numofweights = sum(p.numel() for p in model.parameters() if p.requires_grad)
# totalbits = numofweights * hyperparameters['BPW']
print(f'TotalBits: {totalbits} TotalBytes: {totalbits/8.0} ')
writer.add_hparams(hyperparameters, {'Parameters': numofweights, 'Totalbits': totalbits, 'Accuracy/train': trainaccuracy, 'Accuracy/test': testaccuracy, 'Loss/train': np.mean(train_loss), 'Loss/test': np.mean(test_loss)})
writer.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Training script')
parser.add_argument('--params', type=str, help='Name of the parameter file', default='trainingparameters.yaml')
args = parser.parse_args()
if args.params:
paramname = args.params
else:
paramname = 'trainingparameters.yaml'
print(f'Load parameters from file: {paramname}')
with open(paramname) as f:
hyperparameters = yaml.safe_load(f)
runname= create_run_name(hyperparameters)
print(runname)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load the MNIST dataset
transform = transforms.Compose([
transforms.Resize((16, 16)), # Resize images to 16x16
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
train_data = datasets.MNIST(root='data', train=True, transform=transform, download=True)
test_data = datasets.MNIST(root='data', train=False, transform=transform)
if hyperparameters["augmentation"]:
# Data augmentation for training data
augmented_transform = transforms.Compose([
# 10,10 seems to be best combination
transforms.RandomRotation(degrees=hyperparameters["rotation1"]),
transforms.RandomAffine(degrees=hyperparameters["rotation2"], translate=(0.1, 0.1), scale=(0.9, 1.1)), # both are needed for best results.
transforms.Resize((16, 16)), # Resize images to 16x16
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
augmented_train_data = datasets.MNIST(root='data', train=True, transform=augmented_transform)
train_data = ConcatDataset([train_data, augmented_train_data])
# Initialize the network and optimizer
model = FCMNIST(
# model = CNNMNIST(
network_width1=hyperparameters["network_width1"],
network_width2=hyperparameters["network_width2"],
network_width3=hyperparameters["network_width3"],
QuantType=hyperparameters["QuantType"],
NormType=hyperparameters["NormType"],
WScale=hyperparameters["WScale"],
).to(device)
summary(model, input_size=(1, 16, 16)) # Assuming the input size is (1, 16, 16)
print('training...')
train_model(model, device, hyperparameters, train_data, test_data)
print('saving model...')
torch.save(model.state_dict(), f'modeldata/{runname}.pth')