300 - Convolution network, gradient, tweaks, with pytorch

Links: notebook, html, PDF, python, slides, GitHub

Object detection on CIFAR10.

Note: install tqdm if not installed: !pip install tqdm

import time
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
print("torch", torch.__version__)
from torchvision import datasets, transforms
from tqdm import tqdm

torch 1.5.0+cpu

%matplotlib inline

BATCH_SIZE = 64
TEST_BATCH_SIZE = 64
DATA_DIR = 'data/'
USE_CUDA = False  # switch to True if you have GPU
N_EPOCHS = 2 # 5

train_loader = torch.utils.data.DataLoader(
    datasets.CIFAR10(DATA_DIR, train=True, download=True,
                   transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])),
    batch_size=BATCH_SIZE, shuffle=True)
test_loader = torch.utils.data.DataLoader(
    datasets.CIFAR10(DATA_DIR, train=False, transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])),
    batch_size=TEST_BATCH_SIZE, shuffle=True)

Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to data/cifar-10-python.tar.gz

HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))

Extracting data/cifar-10-python.tar.gz to data/

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(500, 50)
        self.fc2 = nn.Linear(50, 64)

    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, 500)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=-1)

model = Net()
if USE_CUDA: model = model.cuda()

optimizer = optim.Adam(model.parameters())

def train(epoch, verbose=True):
    model.train()
    losses = []
    loader = tqdm(train_loader, total=len(train_loader))
    for batch_idx, (data, target) in enumerate(loader):
        if USE_CUDA:
            data, target = data.cuda(), target.cuda()
        data, target = Variable(data), Variable(target)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        losses.append(float(loss.item()))
        if verbose and batch_idx % 100 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))
    return np.mean(losses)

def test(verbose=True):
    model.eval()
    test_loss = 0
    correct = 0
    for data, target in test_loader:
        if USE_CUDA: data, target = data.cuda(), target.cuda()
        with torch.no_grad():
            data = Variable(data)
        target = Variable(target)
        output = model(data)
        test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss
        pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
        correct += pred.eq(target.data.view_as(pred)).cpu().sum().item()

    test_loss /= len(test_loader.dataset)
    if verbose:
        print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
            test_loss, correct, len(test_loader.dataset),
            100. * correct / len(test_loader.dataset)))
    return [float(test_loss), correct]

perfs = []
for epoch in range(1, N_EPOCHS + 1):
    t0 = time.time()
    train_loss = train(epoch, verbose=False)
    test_loss, correct = test(verbose=False)
    perfs.append([epoch, train_loss, test_loss, correct, len(test_loader.dataset), time.time() - t0])
    print("epoch {}: train loss {:.4f}, test loss {:.4f}, accuracy {}/{} in {:.2f}s".format(*perfs[-1]))

100%|██████████| 782/782 [00:45<00:00, 17.31it/s]

epoch 1: train loss 2.1118, test loss 1.6829, accuracy 3942/10000 in 49.14s

100%|██████████| 782/782 [00:41<00:00, 18.71it/s]

epoch 2: train loss 1.8141, test loss 1.5836, accuracy 4203/10000 in 45.54s

df_perfs = pd.DataFrame(perfs, columns=["epoch", "train_loss", "test_loss", "accuracy", "n_test", "time"])
df_perfs

	epoch	train_loss	test_loss	accuracy	n_test	time
0	1	2.111783	1.682875	3942	10000	49.143509
1	2	1.814102	1.583610	4203	10000	45.540153

df_perfs[["train_loss", "test_loss"]].plot();