def get_data_loaders(batch_size):
data_dir = os.path.join(
list(get_data_paths().values())[0], 'pytorch', 'mnist')
train_dataset = datasets.MNIST(root=data_dir,
train=True,
download=True,
transform=transforms.ToTensor())
x_train_mnist = train_dataset.train_data.type(torch.FloatTensor)
y_train_mnist = train_dataset.train_labels
test_dataset = datasets.MNIST(root=data_dir,
train=False,
download=True,
transform=transforms.ToTensor())
x_test_mnist = test_dataset.test_data.type(torch.FloatTensor)
y_test_mnist = test_dataset.test_labels
logging.info('Training Data Size: ', x_train_mnist.size(), '-',
y_train_mnist.size())
logging.info('Testing Data Size: ', x_test_mnist.size(), '-',
y_test_mnist.size())
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=batch_size,
shuffle=False)
return train_loader, train_dataset, test_loader, test_dataset
'--batch-norm-decay',
type=float,
default=0.997,
help='Decay for batch norm.')
parser.add_argument(
'--batch-norm-epsilon',
type=float,
default=1e-5,
help='Epsilon for batch norm.')
args = parser.parse_args()
if args.num_gpus < 0:
raise ValueError(
'Invalid GPU count: \"--num-gpus\" must be 0 or a positive integer.')
if args.num_gpus == 0 and args.variable_strategy == 'GPU':
raise ValueError('num-gpus=0, CPU must be used as parameter server. Set'
'--variable-strategy=CPU.')
if (args.num_layers - 2) % 6 != 0:
raise ValueError('Invalid --num-layers parameter.')
if args.num_gpus != 0 and args.train_batch_size % args.num_gpus != 0:
raise ValueError('--train-batch-size must be multiple of --num-gpus.')
if args.num_gpus != 0 and args.eval_batch_size % args.num_gpus != 0:
raise ValueError('--eval-batch-size must be multiple of --num-gpus.')
data_dir = os.path.join(list(get_data_paths().values())[0], 'cifar-10-data')
# We create data for the project if it does not exists
if not os.path.exists(os.path.join(data_dir, 'train.tfrecords')):
generate_data(data_dir)
train(job_dir=get_outputs_path(), data_dir=data_dir, **vars(args))
default=10,
metavar='N',
help='how many batches to wait before logging training status')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
model = Network()
if args.cuda:
model.cuda()
data_dir = os.path.join(
list(get_data_paths().values())[0], 'pytorch', 'mnist')
logging.info('Downloading data ...')
train_loader = get_train_loader(data_dir, args.batch_size, args.cuda)
test_loader = get_test_loader(data_dir, args.test_batch_size, args.cuda)
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum)
logging.info('Start training ...')
for epoch in range(1, args.epochs + 1):
train(model=model,
train_loader=train_loader,
epoch=epoch,
cuda=args.cuda,
optimizer=optimizer,
log_interval=args.log_interval)
import Model
from config import config
import matplotlib.pyplot as plt
from Data import DataContainer
import CostFunction
import scipy
import cv2
import matplotlib
import os
import warnings
import argparse
import time
try:
from polyaxon_helper import get_data_paths
config.data_path = os.path.join(
get_data_paths()['data1'],
'Micromanipulator/NeedleTracking3/40xdatasets')
except:
warnings.warn("You are Running on the local Machine", UserWarning)
#print(os.listdir(get_data_paths()['data1']))
#print(get_data_paths()[0] )
print()
#print(config.data_path )
parser = argparse.ArgumentParser()
parser.add_argument('--out', default='/media/hossam/Projects/NeedleDetection/')
args = parser.parse_args()
config.Project_DIR = vars(args)['out'] + "/"
FLAGS = config()
FLAGS.batch_size = 2