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mnist.py
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mnist.py
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"""
Modified version of the PyTorch MNIST example to log outputs for OverBoard
"""
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from overboard_logger import Logger
try:
import matplotlib.pyplot as plt
except ModuleNotFoundError:
print('MatPlotLib not found.')
plt = None
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x, dim=1)
def confusion_matrix(name, predictions, labels):
"""Show the confusion matrix as a MatPlotLib figure.
Note this is only executed by the OverBoard GUI, not by the training code."""
num_labels = labels.max() + 1
# create a confusion matrix
matrix = torch.zeros((num_labels, num_labels))
for (i, j) in zip(labels.tolist(), predictions.tolist()):
matrix[i, j] += 1
matrix = matrix.numpy()
# create a figure, and show the matrix
(figure, ax) = plt.subplots()
ax.imshow(matrix)
# add axis labels and tick marks
plt.xlabel('Predictions')
plt.ylabel('Ground-truth labels')
plt.xticks(list(range(num_labels)), fontsize=8)
plt.yticks(list(range(num_labels)), fontsize=8)
figure.subplots_adjust(bottom=0.15) # make space for bottom label
# add a text annotation to each matrix cell
for i in range(num_labels):
for j in range(num_labels):
ax.text(j, i, int(matrix[i, j]), ha="center", va="center", color="w", fontsize=8)
# important: return the figure (or a list of figures) instead of showing it
return figure
def train(args, model, device, train_loader, optimizer, epoch, logger):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability
accuracy = pred.eq(target.view_as(pred)).double().mean()
# log the loss and accuracy
logger.update_average({'train.loss': loss.item(), 'train.accuracy': accuracy.item()})
logger.print(prefix='train')
if logger.rate_limit(seconds=10):
# show the images once in a while
logger.tensor('Images', data, grayscale=True)
# also show conv1's filters
parameters = dict(model.named_parameters())
logger.tensor('Conv1 filters', parameters['conv1.weight'])
# show a confusion matrix (custom MatPlotLib plot)
logger.visualize(confusion_matrix, 'Confusion matrix (batch)', pred.detach(), target)
def test(args, model, device, test_loader, logger):
model.eval()
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
loss = F.nll_loss(output, target, reduction='sum')
pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability
accuracy = pred.eq(target.view_as(pred)).double().mean()
# log the loss and accuracy
logger.update_average({'val.loss': loss.item(), 'val.accuracy': accuracy.item()})
# display final values in console
logger.print(prefix='val')
def main():
# Training settings
parser = argparse.ArgumentParser()
parser.add_argument('--batch-size', type=int, default=64, metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs', type=int, default=10, metavar='N',
help='number of epochs to train (default: 10)')
parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
help='learning rate (default: 0.01)')
parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
help='SGD momentum (default: 0.5)')
parser.add_argument('--device', type=str, default='cuda',
help='Device (cuda:0 for the first GPU, cuda:1 for the next, etc, or cpu)')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--datadir', type=str, default='/data/mnist/',
help='MNIST data directory')
parser.add_argument('--outputdir', type=str, default='/data/mnist-experiments/',
help='output directory')
args = parser.parse_args()
device = args.device
if not torch.cuda.is_available():
device = 'cpu'
torch.manual_seed(args.seed)
kwargs = {'num_workers': 1, 'pin_memory': True} if device != 'cpu' else {}
train_loader = torch.utils.data.DataLoader(
datasets.MNIST(args.datadir, train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=args.batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST(args.datadir, train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=args.test_batch_size, shuffle=True, **kwargs)
model = Net().to(device)
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
# open logging stream
with Logger(args.outputdir, meta=args) as logger:
# do training
for epoch in range(1, args.epochs + 1):
train(args, model, device, train_loader, optimizer, epoch, logger)
test(args, model, device, test_loader, logger)
# record average statistics collected over this epoch (with logger.update_average)
logger.append()
if __name__ == '__main__':
main()