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rasbt/vgg16.py

Created March 3, 2019 06:34
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Speed comparison DataLoader vs in-memory
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vgg16.py
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets
from torchvision import transforms
from torch.utils.data import DataLoader
if torch.cuda.is_available():
torch.backends.cudnn.deterministic = True
# Device
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print('Device:', device)
# Hyperparameters
random_seed = 1
learning_rate = 0.001
num_epochs = 50
batch_size = 128
# Architecture
num_features = 784
num_classes = 10
train_dataset = datasets.CIFAR10(root='data',
train=True,
transform=transforms.ToTensor(),
download=True)
train_loader = DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True)
class VGG16(torch.nn.Module):
def __init__(self, num_features, num_classes):
super(VGG16, self).__init__()
# calculate same padding:
# (w - k + 2*p)/s + 1 = o
# => p = (s(o-1) - w + k)/2
self.block_1 = nn.Sequential(
nn.Conv2d(in_channels=3,
out_channels=64,
kernel_size=(3, 3),
stride=(1, 1),
# (1(32-1)- 32 + 3)/2 = 1
padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=64,
out_channels=64,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(2, 2),
stride=(2, 2))
)
self.block_2 = nn.Sequential(
nn.Conv2d(in_channels=64,
out_channels=128,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=128,
out_channels=128,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(2, 2),
stride=(2, 2))
)
self.block_3 = nn.Sequential(
nn.Conv2d(in_channels=128,
out_channels=256,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=256,
out_channels=256,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=256,
out_channels=256,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(2, 2),
stride=(2, 2))
)
self.block_4 = nn.Sequential(
nn.Conv2d(in_channels=256,
out_channels=512,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=512,
out_channels=512,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=512,
out_channels=512,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(2, 2),
stride=(2, 2))
)
self.block_5 = nn.Sequential(
nn.Conv2d(in_channels=512,
out_channels=512,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=512,
out_channels=512,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=512,
out_channels=512,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(2, 2),
stride=(2, 2))
)
self.classifier = nn.Sequential(
nn.Linear(512, 4096),
nn.Linear(4096, 4096),
nn.Linear(4096, num_classes)
)
for m in self.modules():
if isinstance(m, torch.nn.Conv2d):
#n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
#m.weight.data.normal_(0, np.sqrt(2. / n))
m.weight.detach().normal_(0, 0.05)
if m.bias is not None:
m.bias.detach().zero_()
elif isinstance(m, torch.nn.Linear):
m.weight.detach().normal_(0, 0.05)
m.bias.detach().detach().zero_()
def forward(self, x):
x = self.block_1(x)
x = self.block_2(x)
x = self.block_3(x)
x = self.block_4(x)
x = self.block_5(x)
logits = self.classifier(x.view(-1, 512))
probas = F.softmax(logits, dim=1)
return logits, probas
torch.manual_seed(random_seed)
model = VGG16(num_features=num_features,
num_classes=num_classes)
model = model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
start_time = time.time()
for epoch in range(num_epochs):
model.train()
for batch_idx, (features, targets) in enumerate(train_loader):
features = features.to(device)
targets = targets.to(device)
### FORWARD AND BACK PROP
logits, probas = model(features)
cost = F.cross_entropy(logits, targets)
optimizer.zero_grad()
cost.backward()
### UPDATE MODEL PARAMETERS
optimizer.step()
### LOGGING
if not batch_idx % 50:
print ('Epoch: %03d/%03d | Batch %04d/%04d | Cost: %.4f'
%(epoch+1, num_epochs, batch_idx,
len(train_loader), cost))
print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))
print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))
Raw
vgg16_inmemory.py
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets
from torchvision import transforms
from torch.utils.data import DataLoader
if torch.cuda.is_available():
torch.backends.cudnn.deterministic = True
# Device
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print('Device:', device)
# Hyperparameters
random_seed = 1
learning_rate = 0.001
num_epochs = 50
batch_size = 128
# Architecture
num_features = 784
num_classes = 10
train_dataset = datasets.CIFAR10(root='data',
train=True,
transform=transforms.ToTensor(),
download=True)
train_loader = DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True)
all_features = torch.zeros([len(train_dataset), 3, 32, 32]).float()
all_targets = torch.zeros(len(train_dataset)).long()
all_batch_start, all_batch_end = [], []
for batch_idx, (features, targets) in enumerate(train_loader):
batch_start = batch_idx*targets.size(0)
batch_end = (batch_idx+1)*targets.size(0)
all_batch_start.append(batch_start)
all_batch_end.append(batch_end)
all_features[batch_start: batch_end] = features
all_targets[batch_start: batch_end] = targets
class VGG16(torch.nn.Module):
def __init__(self, num_features, num_classes):
super(VGG16, self).__init__()
# calculate same padding:
# (w - k + 2*p)/s + 1 = o
# => p = (s(o-1) - w + k)/2
self.block_1 = nn.Sequential(
nn.Conv2d(in_channels=3,
out_channels=64,
kernel_size=(3, 3),
stride=(1, 1),
# (1(32-1)- 32 + 3)/2 = 1
padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=64,
out_channels=64,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(2, 2),
stride=(2, 2))
)
self.block_2 = nn.Sequential(
nn.Conv2d(in_channels=64,
out_channels=128,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=128,
out_channels=128,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(2, 2),
stride=(2, 2))
)
self.block_3 = nn.Sequential(
nn.Conv2d(in_channels=128,
out_channels=256,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=256,
out_channels=256,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=256,
out_channels=256,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(2, 2),
stride=(2, 2))
)
self.block_4 = nn.Sequential(
nn.Conv2d(in_channels=256,
out_channels=512,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=512,
out_channels=512,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=512,
out_channels=512,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(2, 2),
stride=(2, 2))
)
self.block_5 = nn.Sequential(
nn.Conv2d(in_channels=512,
out_channels=512,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=512,
out_channels=512,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=512,
out_channels=512,
kernel_size=(3, 3),
stride=(1, 1),
padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(2, 2),
stride=(2, 2))
)
self.classifier = nn.Sequential(
nn.Linear(512, 4096),
nn.Linear(4096, 4096),
nn.Linear(4096, num_classes)
)
for m in self.modules():
if isinstance(m, torch.nn.Conv2d):
#n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
#m.weight.data.normal_(0, np.sqrt(2. / n))
m.weight.detach().normal_(0, 0.05)
if m.bias is not None:
m.bias.detach().zero_()
elif isinstance(m, torch.nn.Linear):
m.weight.detach().normal_(0, 0.05)
m.bias.detach().detach().zero_()
def forward(self, x):
x = self.block_1(x)
x = self.block_2(x)
x = self.block_3(x)
x = self.block_4(x)
x = self.block_5(x)
logits = self.classifier(x.view(-1, 512))
probas = F.softmax(logits, dim=1)
return logits, probas
torch.manual_seed(random_seed)
model = VGG16(num_features=num_features,
num_classes=num_classes)
model = model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
start_time = time.time()
for epoch in range(num_epochs):
shuffle_idx = torch.randperm(len(train_dataset))
all_features = all_features[shuffle_idx]
all_targets = all_targets[shuffle_idx]
model.train()
for batch_idx, (start, end) in enumerate(zip(all_batch_start, all_batch_end)):
features = all_features[start:end]
targets = all_targets[start:end]
features = features.to(device)
targets = targets.to(device)
### FORWARD AND BACK PROP
logits, probas = model(features)
cost = F.cross_entropy(logits, targets)
optimizer.zero_grad()
cost.backward()
### UPDATE MODEL PARAMETERS
optimizer.step()
### LOGGING
if not batch_idx % 50:
print ('Epoch: %03d/%03d | Batch %04d/%04d | Cost: %.4f'
%(epoch+1, num_epochs, batch_idx,
len(train_loader), cost))
print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))
print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))
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rasbt commented Mar 3, 2019

vgg16.py in PyTorch 1.0.1 with RTX2080Ti:

Epoch: 001/050 | Batch 0000/0391 | Cost: 302.2942
Epoch: 001/050 | Batch 0050/0391 | Cost: 2.3336
Epoch: 001/050 | Batch 0100/0391 | Cost: 2.1539
Epoch: 001/050 | Batch 0150/0391 | Cost: 2.0014
Epoch: 001/050 | Batch 0200/0391 | Cost: 1.9107
Epoch: 001/050 | Batch 0250/0391 | Cost: 1.8304
Epoch: 001/050 | Batch 0300/0391 | Cost: 1.9485
Epoch: 001/050 | Batch 0350/0391 | Cost: 1.5591
Time elapsed: 0.23 min
...
Epoch: 050/050 | Batch 0000/0391 | Cost: 0.1005
Epoch: 050/050 | Batch 0050/0391 | Cost: 0.1097
Epoch: 050/050 | Batch 0100/0391 | Cost: 0.0284
Epoch: 050/050 | Batch 0150/0391 | Cost: 0.1244
Epoch: 050/050 | Batch 0200/0391 | Cost: 0.0822
Epoch: 050/050 | Batch 0250/0391 | Cost: 0.0908
Epoch: 050/050 | Batch 0300/0391 | Cost: 0.1248
Epoch: 050/050 | Batch 0350/0391 | Cost: 0.0926
Time elapsed: 11.68 min

vgg16_inmemory-py in PyTorch 1.0.1 with RTX2080Ti (this one loads the dataset into memory and fetches the batches from there:

Epoch: 001/050 | Batch 0000/0391 | Cost: 331.2111
Epoch: 001/050 | Batch 0050/0391 | Cost: 2.3051
Epoch: 001/050 | Batch 0100/0391 | Cost: 2.1995
Epoch: 001/050 | Batch 0150/0391 | Cost: 2.0644
Epoch: 001/050 | Batch 0200/0391 | Cost: 1.8679
Epoch: 001/050 | Batch 0250/0391 | Cost: 1.7329
Epoch: 001/050 | Batch 0300/0391 | Cost: 1.8466
Epoch: 001/050 | Batch 0350/0391 | Cost: 1.8419
Time elapsed: 0.23 min
...
Epoch: 050/050 | Batch 0000/0391 | Cost: 2.3039
Epoch: 050/050 | Batch 0050/0391 | Cost: 2.3029
Epoch: 050/050 | Batch 0100/0391 | Cost: 2.3163
Epoch: 050/050 | Batch 0150/0391 | Cost: 2.3132
Epoch: 050/050 | Batch 0200/0391 | Cost: 2.3173
Epoch: 050/050 | Batch 0250/0391 | Cost: 2.3053
Epoch: 050/050 | Batch 0300/0391 | Cost: 2.3009
Epoch: 050/050 | Batch 0350/0391 | Cost: 2.3057
Time elapsed: 11.61 min

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