import test_utils FLAGS = test_utils.parse_common_options( datadir='/tmp/mnist-data', batch_size=128, momentum=0.5, lr=0.01, target_accuracy=98.0, num_epochs=18) from common_utils import TestCase, run_tests import os import shutil import time import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms import torch_xla import torch_xla_py.data_parallel as dp import torch_xla_py.utils as xu import torch_xla_py.xla_model as xm import unittest class MNIST(nn.Module): def __init__(self): super(MNIST, self).__init__() self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
import test_utils FLAGS = test_utils.parse_common_options(datadir='../cifar-data', batch_size=125, num_epochs=254, momentum=0.9, lr=0.8) import os import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import torch_xla import torch_xla_py.data_parallel as dp import torch_xla_py.utils as xu import torch_xla_py.xla_model as xm import torchvision import torchvision.transforms as transforms # Import utilities and models from torch.optim.lr_scheduler import MultiStepLR from utilities import Cutout, RandomPixelPad, CosineAnnealingRestartsLR from models import WRN_McDonnell def train_cifar(): print('==> Preparing data..') transform_train = transforms.Compose([ transforms.Lambda(lambda x: RandomPixelPad(x, padding=4)),
'vgg16_bn', 'vgg19', 'vgg19_bn' ] MODEL_OPTS = { '--model': { 'choices': SUPPORTED_MODELS, 'default': 'resnet50', } } FLAGS = test_utils.parse_common_options( datadir='/tmp/imagenet', batch_size=None, num_epochs=None, momentum=None, lr=None, target_accuracy=None, opts=MODEL_OPTS.items(), ) from common_utils import TestCase, run_tests import os import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import torchvision import torchvision.transforms as transforms import torch_xla import torch_xla_py.data_parallel as dp
import test_utils FLAGS = test_utils.parse_common_options(datadir='/tmp/mnist-data', batch_size=256, target_accuracy=98.0) from common_utils import TestCase, run_tests import shutil import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms import torch_xla import torch_xla_py.xla_model as xm import unittest class MNIST(nn.Module): def __init__(self): super(MNIST, self).__init__() self.conv1 = nn.Conv2d(1, 10, kernel_size=5) self.bn1 = nn.BatchNorm2d(10) self.conv2 = nn.Conv2d(10, 20, kernel_size=5) self.bn2 = nn.BatchNorm2d(20) 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 = self.bn1(x)
import test_utils FLAGS = test_utils.parse_common_options( datadir='/tmp/imagenet', batch_size=128, num_epochs=15, target_accuracy=0.0) from common_utils import TestCase, run_tests import os import shutil import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import torchvision import torchvision.transforms as transforms import torch_xla import torch_xla_py.xla_model as xm import unittest def _cross_entropy_loss_eval_fn(cross_entropy_loss): def eval_fn(output, target): loss = cross_entropy_loss(output, target).item() # Get the index of the max log-probability. pred = output.max(1, keepdim=True)[1] correct = pred.eq(target.view_as(pred)).sum().item() return loss, correct return eval_fn
import test_utils FLAGS = test_utils.parse_common_options(datadir='/tmp/cifar-data', batch_size=128, num_epochs=20, momentum=0.9, lr=0.2, target_accuracy=80.0) from common_utils import TestCase, run_tests import shutil import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import torch_xla import torch_xla_py.data_parallel as dp import torch_xla_py.utils as xu import torch_xla_py.xla_model as xm import torchvision import torchvision.transforms as transforms import unittest class BasicBlock(nn.Module): expansion = 1 def __init__(self, in_planes, planes, stride=1): super(BasicBlock, self).__init__() self.conv1 = nn.Conv2d(in_planes, planes,
import test_utils FLAGS = test_utils.parse_common_options(datadir='/tmp/cifar-data', batch_size=128, num_epochs=15, target_accuracy=80.0) from common_utils import TestCase, run_tests import shutil import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import torch_xla import torch_xla_py.utils as xu import torch_xla_py.xla_model as xm import torchvision import torchvision.transforms as transforms import unittest class BasicBlock(nn.Module): expansion = 1 def __init__(self, in_planes, planes, stride=1): super(BasicBlock, self).__init__() self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1,