Hi, I have implemented the following model:
# Solution for CIFAR-10 dataset, using NN with one hidden layer
import torch
import torchvision
import torch.nn as nn
import matplotlib.pyplot as plt
import torch.nn.functional as F
import torchvision.transforms as transforms
from torch.backends import cudnn
from torchvision.datasets import CIFAR10
from torch.utils.data import random_split
from torch.utils.data import DataLoader
dtype = torch.cuda.FloatTensor
cudnn.benchmark = True
# Hyperparmeters
batch_size = 100
learning_rate1 = 0.01
learning_rate2 = 0.003
epochs1 = 7
epochs2 = 10
# Other constants
input_size = 32*32*3
hidden_size = 64
num_classes = 10
# Computing on GPU (if possible)
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)
class DeviceDataLoader():
"""Wrap a dataloader to move data to a device"""
def __init__(self, dl, device):
self.dl = dl
self.device = device
def __iter__(self):
"""Yield a batch of data after moving it to device"""
for b in self.dl:
yield to_device(b, self.device)
def __len__(self):
"""Number of batches"""
return len(self.dl)
# Download dataset and split for validation
dataset = CIFAR10(root='data/', train=True, transform=transforms.ToTensor(), download=True)
test_ds = CIFAR10(root='data/', train=False, transform=transforms.ToTensor(), download=True)
train_ds, val_ds = random_split(dataset, [40000, 10000])
train_dl = DeviceDataLoader(DataLoader(train_ds, batch_size, shuffle=True, pin_memory=True), get_default_device())
val_dl = DeviceDataLoader(DataLoader(val_ds, batch_size, shuffle=True, pin_memory=True), get_default_device())
# Define accuracy function (for programmer):
def accuracy(outputs, labels):
_, preds = torch.max(outputs, dim=1)
return torch.tensor(torch.sum(preds == labels).item() / len(preds))
# Define the model:
class CifarModel(nn.Module):
def __init__(self):
super().__init__()
self.linear1 = nn.Linear(input_size, hidden_size)
self.linear2 = nn.Linear(hidden_size, num_classes)
def forward(self, xb):
xb = xb.view(xb.size(0), -1)
out = self.linear1(xb)
# activate function:
out = F.relu(out)
out = self.linear2(out)
return out
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.detach()}
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 [{}], val_loss: {:.4f}, val_acc: {:.4f}".format(epoch, result['val_loss'], result['val_acc']))
model = CifarModel()
to_device(model, get_default_device())
# Training functions:
def evaluate(model, val_dl):
outputs = [model.validation_step(batch) for batch in val_dl]
return model.validation_epoch_end(outputs)
def fit(epochs, lr, model, train_dl, val_dl, opt_func=torch.optim.SGD):
history = []
optimizer = opt_func(model.parameters(), lr)
for epoch in range(epochs):
# Training Phase
for batch in train_dl:
loss = model.training_step(batch)
loss.backward()
optimizer.step()
optimizer.zero_grad()
# Validation phase
result = evaluate(model, val_dl)
model.epoch_end(epoch, result)
history.append(result)
return history
history = fit(epochs1, learning_rate1, model, train_dl, val_dl)
history += fit(epochs2, learning_rate2, model, train_dl, val_dl)
print(history)
and I have checked that indeed all the data has moved to the GPU. however, when I run that code I still have a 100% CPU utilization and 4% GPU.
I have used this tutorial .
Thanks.
