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from __future__ import print_function | |
import time | |
import math | |
import argparse | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
import torch.optim as optim | |
import numpy as np | |
from torchvision import datasets, transforms | |
#import matplotlib.pyplot as plt | |
#from large_margin_softmax_linear import LargeMarginSoftmaxLinear | |
#from experiment import LargeMarginSoftmaxLinear | |
torch.set_printoptions(precision=20) | |
tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120), | |
(44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150), | |
(148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148), | |
(227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199), | |
(188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)] | |
for i in range(len(tableau20)): | |
r, g, b = tableau20[i] | |
tableau20[i] = (r / 255., g / 255., b / 255.) | |
# Training settings | |
parser = argparse.ArgumentParser(description='PyTorch MNIST Example') | |
parser.add_argument('--batch-size', type=int, default=256, metavar='N', | |
help='input batch size for training (default: 256)') | |
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=30, metavar='N', | |
help='number of epochs to train (default: 30)') | |
parser.add_argument('--lr', type=float, default=0.01, metavar='LR', | |
help='learning rate (default: 0.01)') | |
parser.add_argument('--momentum', type=float, default=0.9, metavar='M', | |
help='SGD momentum (default: 0.9)') | |
parser.add_argument('--weight_decay', type=float, default=0.0005, metavar='W', | |
help='SGD weight decay (default: 0.0005)') | |
parser.add_argument('--no-cuda', action='store_true', default=False, | |
help='disables CUDA training') | |
parser.add_argument('--seed', type=int, default=1, metavar='S', | |
help='random seed (default: 1)') | |
parser.add_argument('--log-interval', type=int, default=50, metavar='N', | |
help='how many batches to wait before logging training status') | |
parser.add_argument('--vis_path', type=str, default="visualizations/color6", metavar='S', | |
help='path to save your visualization figures') | |
args = parser.parse_args() | |
#use_cuda = False | |
use_cuda = not args.no_cuda and torch.cuda.is_available() | |
print(use_cuda) | |
torch.manual_seed(args.seed) | |
device = torch.device("cuda" if use_cuda else "cpu") | |
torch.backends.cudnn.benchmark = True if use_cuda else False | |
print(device) | |
kwargs = {'num_workers': 6, 'pin_memory': True} if use_cuda else {} | |
train_loader = torch.utils.data.DataLoader( | |
datasets.MNIST('../data', 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('../data', train=False, transform=transforms.Compose([ | |
transforms.ToTensor(), | |
transforms.Normalize((0.1307,), (0.3081,)) | |
])), | |
batch_size=args.test_batch_size, shuffle=True, **kwargs) | |
def weights_init(m): | |
if isinstance(m, nn.Conv2d): | |
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels | |
m.weight.data.normal_(0, math.sqrt(2. / n)) | |
#nn.init.xavier_uniform(m.weight.batch_data) | |
#elif isinstance(m, nn.BatchNorm2d): | |
# m.weight.batch_data.fill_(1) | |
# m.bias.batch_data.zero_() | |
#elif isinstance(m, nn.BatchNorm1d): | |
# m.weight.batch_data.fill_(1) | |
# m.bias.batch_data.zero_() | |
class Net(nn.Module): | |
def __init__(self): | |
super(Net, self).__init__() | |
self.conv1 = nn.Conv2d(1, 32, kernel_size=5, padding=2) | |
#self.bn1 = nn.BatchNorm2d(32) | |
self.prelu1 = nn.PReLU() | |
self.conv2 = nn.Conv2d(32, 32, kernel_size=5, padding=2) | |
#self.bn2 = nn.BatchNorm2d(32) | |
self.prelu2 = nn.PReLU() | |
self.conv3 = nn.Conv2d(32, 64, kernel_size=5, padding=2) | |
#self.bn3 = nn.BatchNorm2d(64) | |
self.prelu3 = nn.PReLU() | |
self.conv4 = nn.Conv2d(64, 64, kernel_size=5, padding=2) | |
#self.bn4 = nn.BatchNorm2d(64) | |
self.prelu4 = nn.PReLU() | |
self.conv5 = nn.Conv2d(64, 128, kernel_size=5, padding=2) | |
#self.bn5 = nn.BatchNorm2d(128) | |
self.prelu5 = nn.PReLU() | |
self.conv6 = nn.Conv2d(128, 128, kernel_size=5, padding=2) | |
#self.bn6 = nn.BatchNorm2d(128) | |
self.prelu6 = nn.PReLU() | |
self.fc1 = nn.Linear(1152, 2) | |
#self.bn7 = nn.BatchNorm1d(2) | |
self.prelu7 = nn.PReLU() | |
self.fc2 = nn.Linear(2, 10) | |
self.loss = nn.CrossEntropyLoss() | |
#self.fc2 = LargeMarginSoftmaxLinear(2, 10, 2, 0) | |
#self.fc2 = LargeMarginSoftmaxLinear(2, 10, 2, 0, use_cuda, device) | |
def forward(self, x, y): | |
x = self.prelu1(self.conv1(x)) | |
x = F.max_pool2d(self.prelu2(self.conv2(x)), 2, stride=2) | |
x = self.prelu3(self.conv3(x)) | |
x = F.max_pool2d(self.prelu4(self.conv4(x)), 2, stride=2) | |
x = self.prelu5(self.conv5(x)) | |
x = F.max_pool2d(self.prelu6(self.conv6(x)), 2, stride=2) | |
#x = F.relu(self.bn1(self.conv1(x))) | |
#x = F.max_pool2d(F.relu(self.bn2(self.conv2(x))), 2, stride=2) | |
#x = F.relu(self.bn3(self.conv3(x))) | |
#x = F.max_pool2d(F.relu(self.bn4(self.conv4(x))), 2, stride=2) | |
#x = F.relu(self.bn5(self.conv5(x))) | |
#x = F.max_pool2d(F.relu(self.bn6(self.conv6(x))), 2, stride=2) | |
x = x.view(-1, 1152) | |
#print(x.shape) | |
features = self.fc1(x) | |
x = self.prelu7(features) | |
#x = F.relu(self.bn7(self.fc1(x))) | |
#x = self.fc2(x, y) | |
x = self.fc2(x) | |
#print(x) | |
return self.loss(x, y) | |
model = Net().to(device) | |
model.apply(weights_init) | |
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay) | |
def visualize(features, labels, epoch, vis_path): | |
plt.clf() | |
for i in range(10): | |
plt.scatter(features[(labels == i), 0], features[(labels == i), 1], s=3, c=tableau20[i], alpha=0.8) | |
plt.savefig(vis_path + "/epoch_" + str(epoch) + ".jpg") | |
return | |
def train(epoch): | |
model.train() | |
#features = [] | |
#predictions = [] | |
#labels = [] | |
for batch_idx, (batch_data, batch_labels) in enumerate(train_loader): | |
batch_data, batch_labels = batch_data.to(device), batch_labels.to(device) | |
#print(batch_data.dtype) | |
#print(batch_labels.dtype) | |
#print(batch_data.size()) | |
#print(batch_labels.size()) | |
optimizer.zero_grad() | |
enabled = batch_idx == 10 | |
with torch.autograd.profiler.profile(enabled=enabled, use_cuda=True) as prof: | |
loss = model(batch_data, batch_labels) | |
#loss = F.nll_loss(batch_scores, batch_labels) | |
#print(loss) | |
loss.backward() | |
optimizer.step() | |
if enabled: | |
print(prof.key_averages().table(sort_by='cuda_time_total')) | |
import pdb | |
pdb.set_trace() | |
if batch_idx % args.log_interval == 0: | |
print('Train Epoch: {} [{}/{} ({:.2f}%)]\tLoss: {:.6f}'.format( | |
epoch, batch_idx * len(batch_data), len(train_loader.dataset), | |
100. * batch_idx / len(train_loader), loss.item())) | |
#features.append(batch_features) | |
#batch_predictions = batch_scores.max(1, keepdim=True)[1] | |
#predictions.append(batch_predictions) | |
#labels.append(batch_labels) | |
#features = torch.cat(features, 0).data.to('cpu').numpy() | |
#predictions = torch.cat(predictions, 0).data.cpu().numpy() | |
#labels = torch.cat(labels, 0).data.to('cpu').numpy() | |
#visualize(features, labels, epoch, args.vis_path) | |
def test(): | |
model.eval() | |
test_loss = 0 | |
correct = 0 | |
with torch.no_grad(): | |
for batch_data, batch_labels in test_loader: | |
batch_data, batch_labels = batch_data.to(device), batch_labels.to(device) | |
batch_scores, batch_features = model(batch_data) | |
test_loss += F.nll_loss(batch_scores, batch_labels, size_average=False).item() # sum up batch loss | |
pred = batch_scores.max(1, keepdim=True)[1] # get the index of the max log-probability | |
correct += pred.eq(batch_labels.view_as(pred)).sum().item() | |
test_loss /= len(test_loader.dataset) | |
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\n'.format( | |
test_loss, correct, len(test_loader.dataset), | |
100. * correct / len(test_loader.dataset))) | |
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1) | |
for epoch in range(1, args.epochs + 1): | |
scheduler.step() | |
#lr = [] | |
#for param_group in optimizer.param_groups: | |
# lr += [param_group['lr']] | |
#print(lr) | |
torch.cuda.synchronize() | |
start = time.perf_counter() | |
train(epoch) | |
torch.cuda.synchronize() | |
end = time.perf_counter() | |
#print(end-start) | |
print('Total time taken: {:.2f}s\n'.format(end-start)) | |
#test() |
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