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,
