def test_random_rotation():
# test exceptions for probability input outside of [0,1]
assertRaises(ValueError, transforms.RandomRotation, [-10, 10.], rotate_with_proba=1.1)
assertRaises(ValueError, transforms.RandomRotation, [-10, 10.], rotate_with_proba=-0.3)
# test `forward`
transformer = transforms.RandomRotation([-10, 10.])
assertRaises(TypeError, transformer, mx.nd.ones((3, 30, 60), dtype='uint8'))
single_image = mx.nd.ones((3, 30, 60), dtype='float32')
single_output = transformer(single_image)
assert same(single_output.shape, (3, 30, 60))
batch_image = mx.nd.ones((3, 3, 30, 60), dtype='float32')
batch_output = transformer(batch_image)
assert same(batch_output.shape, (3, 3, 30, 60))
# test identity (rotate_with_proba = 0)
transformer = transforms.RandomRotation([-100., 100.], rotate_with_proba=0.0)
data = mx.nd.random_normal(shape=(3, 30, 60))
assert_almost_equal(data, transformer(data))
def test_transformer():
from mxnet.gluon.data.vision import transforms
transform = transforms.Compose([
transforms.Resize(300),
transforms.Resize(300, keep_ratio=True),
transforms.CenterCrop(256),
transforms.RandomResizedCrop(224),
transforms.RandomFlipLeftRight(),
transforms.RandomColorJitter(0.1, 0.1, 0.1, 0.1),
transforms.RandomBrightness(0.1),
transforms.RandomContrast(0.1),
transforms.RandomSaturation(0.1),
transforms.RandomHue(0.1),
transforms.RandomLighting(0.1),
transforms.ToTensor(),
transforms.RandomRotation([-10., 10.]),
transforms.Normalize([0, 0, 0], [1, 1, 1])])
transform(mx.nd.ones((245, 480, 3), dtype='uint8')).wait_to_read()
def prepare_data_mxnet(batch_size=256):
train_transforms = transforms.Compose([
transforms.Resize((32, 32)),
transforms.ToTensor(),
transforms.RandomRotation((-30, 30)),
transforms.Normalize(0.1307, 0.3081),
])
test_transforms = transforms.Compose([
transforms.Resize((32, 32)),
transforms.ToTensor(),
transforms.Normalize(0.1307, 0.3081),
])
train_data = gluon.data.DataLoader(
datasets.MNIST(train=True).transform_first(train_transforms),
batch_size=batch_size,
shuffle=True,
num_workers=8)
test_data = gluon.data.DataLoader(
datasets.MNIST(train=False).transform_first(test_transforms),
batch_size=batch_size,
shuffle=False,
num_workers=8)
return train_data, test_data
# Data augmentation definitions
transform_train = transforms.Compose([
# Randomly crop an area, and then resize it to be 32x32
transforms.RandomResizedCrop(opt.crop_size,scale=(0.6,1.)),# test also with 0.6
# Randomly flip the image horizontally/vertically
transforms.RandomFlipLeftRight(),
transforms.RandomFlipTopBottom(),
# Randomly jitter the brightness, contrast and saturation of the image
transforms.RandomColorJitter(brightness=0.9, contrast=0.9, saturation=0.9), #NEW hue
# Transpose the image from height*width*num_channels to num_channels*height*width
# and map values from [0, 255] to [0,1]
#transforms.RandomGray(p=0.35), # Random gray scale NEW
transforms.ToTensor(),
transforms.RandomRotation(angle_limits=(-90,90),zoom_in=True), # Random rotation
# Normalize the image with mean and standard deviation calculated across all images
transforms.Normalize([11.663384, 10.260227, 7.65015 ], [21.421959, 18.044296, 15.494861])
])
transform_test = transforms.Compose([
transforms.Resize(opt.crop_size),
transforms.ToTensor(),
transforms.Normalize([11.663384, 10.260227, 7.65015 ], [21.421959, 18.044296, 15.494861])
])
# Datasets/DataLoaders
from GalaxyZoo.src.GZooDataset import *
dataset_train = GZooData(root=os.path.join(opt.root,'GalaxyZoo'), transform=transform_train)
datagen_train = gluon.data.DataLoader(dataset_train,batch_size=opt.batch_size,shuffle=True,last_batch='rollover',pin_memory=True,num_workers=16)
