def test_loader():
parser = NeonArgparser(__doc__)
args = parser.parse_args()
train_dir = os.path.join(args.data_dir, 'macrotrain')
test_dir = os.path.join(args.data_dir, 'macrotest')
write_batches(args, train_dir, trainimgs, 0)
write_batches(args, test_dir, testimgs, 1)
train = ImageLoader(set_name='train', do_transforms=False, inner_size=32,
repo_dir=train_dir)
test = ImageLoader(set_name='validation', do_transforms=False, inner_size=32,
repo_dir=test_dir)
err = run(args, train, test)
return err
def test_loader():
print('Testing image loader')
parser = NeonArgparser(__doc__)
args = parser.parse_args()
train_archive = os.path.join(args.data_dir, traindir + '-ingested')
test_archive = os.path.join(args.data_dir, testdir + '-ingested')
write_batches(args, train_archive, traindir, 0)
write_batches(args, test_archive, testdir, 1)
train = ImageLoader(set_name='train', do_transforms=False, inner_size=32,
scale_range=0, repo_dir=train_archive)
test = ImageLoader(set_name='validation', do_transforms=False, inner_size=32,
scale_range=0, repo_dir=test_archive)
err = run(args, train, test)
return err
def test_loader():
parser = NeonArgparser(__doc__)
args = parser.parse_args()
train_dir = os.path.join(args.data_dir, 'macrotrain')
test_dir = os.path.join(args.data_dir, 'macrotest')
write_batches(args, train_dir, trainimgs, 0)
write_batches(args, test_dir, testimgs, 1)
train = ImageLoader(set_name='train', do_transforms=False, inner_size=32,
repo_dir=train_dir)
test = ImageLoader(set_name='validation', do_transforms=False, inner_size=32,
repo_dir=test_dir)
train.init_batch_provider()
test.init_batch_provider()
err = run(args, train, test)
test.exit_batch_provider()
train.exit_batch_provider()
return err
from neon.optimizers import GradientDescentMomentum, MultiOptimizer
from neon.transforms import Rectlin, Softmax, CrossEntropyMulti, TopKMisclassification
from neon.models import Model
from neon.data import ImageLoader
parser = NeonArgparser(__doc__)
parser.add_argument('--subset_pct', type=float, default=100,
help='subset of training dataset to use (percentage)')
parser.add_argument('--test_only', action='store_true',
help='skip fitting - evaluate metrics on trained model weights')
args = parser.parse_args()
# setup data provider
img_set_options = dict(repo_dir=args.data_dir, inner_size=224,
dtype=args.datatype, subset_pct=args.subset_pct)
test = ImageLoader(set_name='validation', scale_range=(256, 256),
do_transforms=False, **img_set_options)
init1 = Xavier(local=False)
initx = Xavier(local=True)
bias = Constant(val=0.20)
relu = Rectlin()
common = dict(activation=relu, init=initx, bias=bias)
commonp1 = dict(activation=relu, init=initx, bias=bias, padding=1)
commonp2 = dict(activation=relu, init=initx, bias=bias, padding=2)
pool3s1p1 = dict(fshape=3, padding=1, strides=1)
pool3s2p1 = dict(fshape=3, padding=1, strides=2, op='max')
def inception(kvals):
(p1, p2, p3, p4) = kvals
parser.add_argument(
'--test_only',
action='store_true',
help='skip fitting - evaluate metrics on trained model weights')
args = parser.parse_args()
if args.test_only:
if args.model_file is None:
raise ValueError('To test model, trained weights need to be provided')
# setup data provider
img_set_options = dict(repo_dir=args.data_dir,
inner_size=224,
subset_pct=args.subset_pct)
train = ImageLoader(set_name='train',
scale_range=(256, 256),
shuffle=True,
**img_set_options)
test = ImageLoader(set_name='validation',
scale_range=(256, 256),
do_transforms=False,
**img_set_options)
init_g1 = Gaussian(scale=0.01)
init_g2 = Gaussian(scale=0.005)
relu = Rectlin()
layers = []
layers.append(
Conv((11, 11, 96),
padding=0,
be = gen_backend(backend=args.backend,
rng_seed=1,
batch_size=batch_size,
device_id=0,
deterministic_update=True)
data_dir = '/usr/local/data/I1K/macrobatches'
assert os.path.isdir(data_dir), 'Data dir is missing'
# subset pct is set to make sure that every epoch has the same mb count
img_set_options = dict(repo_dir=data_dir,
inner_size=224,
dtype=np.float32,
subset_pct=0.09990891117239205)
train = ImageLoader(set_name='train', scale_range=(256, 256), shuffle=False,
do_transforms=False, **img_set_options)
init1 = Xavier(local=False)
initx = Xavier(local=True)
bias = Constant(val=0.20)
relu = Rectlin()
common = dict(activation=relu, init=initx, bias=bias)
commonp1 = dict(activation=relu, init=initx, bias=bias, padding=1)
commonp2 = dict(activation=relu, init=initx, bias=bias, padding=2)
pool3s1p1 = dict(fshape=3, padding=1, strides=1)
pool3s2p1 = dict(fshape=3, padding=1, strides=2, op='max')
def inception(kvals, name):
(p1, p2, p3, p4) = kvals
help='first run without this and then run with so resume from saved file')
args = parser.parse_args()
if args.backend == 'mgpu':
batch_size = 32 * 8 # for some reason bs 128 does not work with bias layers
else:
batch_size = 32
# subset pct is set to make sure that every epoch has the same mb count
img_set_options = dict(repo_dir=args.data_dir,
inner_size=224,
dtype=np.float32,
subset_pct=0.09990891117239205)
train = ImageLoader(set_name='train',
scale_range=(256, 256),
shuffle=False,
do_transforms=False,
**img_set_options)
init1 = Xavier(local=False)
initx = Xavier(local=True)
bias = Constant(val=0.20)
relu = Rectlin()
common = dict(activation=relu, init=initx, bias=bias)
commonp1 = dict(activation=relu, init=initx, bias=bias, padding=1)
commonp2 = dict(activation=relu, init=initx, bias=bias, padding=2)
pool3s1p1 = dict(fshape=3, padding=1, strides=1)
pool3s2p1 = dict(fshape=3, padding=1, strides=2, op='max')
parser = NeonArgparser(__doc__)
args = parser.parse_args()
args.backend = 'gpu'
args.datatype = np.float16
be = gen_backend(backend=args.backend,
batch_size=128,
rng_seed=123,
device_id=args.device_id,
datatype=args.datatype)
options = dict(repo_dir='../dataset/Cifar10',
inner_size=32,
subset_pct=100,
dtype=args.datatype)
train_set = ImageLoader(set_name='train',
scale_range=32,
shuffle=True,
**options)
valid_set = ImageLoader(set_name='validation',
scale_range=32,
do_transforms=False,
**options)
# define model
nfilters = [96, 192, 256]
init_w = Gaussian(scale=0.01)
relu = Rectlin()
common_params = dict(init=init_w, activation=relu)
convp1 = dict(padding=1, **common_params)
layers = [
Conv((3, 3, nfilters[0]), bias=Constant(0.1), **convp1),
Conv((3, 3, nfilters[0]), bias=Constant(0.1), **convp1),
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ----------------------------------------------------------------------------
from neon.util.argparser import NeonArgparser
from neon.util.persist import load_obj
from neon.transforms import Misclassification
from neon.models import Model
from neon.data import ImageLoader
# parse the command line arguments (generates the backend)
parser = NeonArgparser(__doc__)
args = parser.parse_args()
# setup data provider
test_set = ImageLoader(set_name='validation',
repo_dir=args.data_dir,
inner_size=32,
scale_range=40,
do_transforms=False)
model = Model(load_obj(args.model_file))
print 'Accuracy: %.1f %% (Top-1)' % (
1.0 - model.eval(test_set, metric=Misclassification()) * 100)
default=9,
help='depth of each stage (network depth will be 9n+2)')
parser.add_argument('--subset_pct',
type=float,
default=100,
help='subset of training dataset to use (percentage)')
args = parser.parse_args()
# setup data provider
imgset_options = dict(inner_size=32,
scale_range=40,
aspect_ratio=110,
repo_dir=args.data_dir,
subset_pct=args.subset_pct)
train = ImageLoader(set_name='train',
shuffle=True,
do_transforms=True,
**imgset_options)
test = ImageLoader(set_name='validation',
shuffle=False,
do_transforms=False,
**imgset_options)
tune_set = ImageLoader(set_name='train',
shuffle=False,
do_transforms=False,
inner_size=32,
scale_range=40,
repo_dir=args.data_dir,
subset_pct=20)
def conv_params(fsize, nfm, stride=1, relu=True, batch_norm=True):
# hyperparameters
batch_size = 64
# setup backend
be = gen_backend(backend=args.backend,
batch_size=batch_size,
rng_seed=args.rng_seed,
device_id=args.device_id,
datatype=args.datatype)
# setup data provider
img_set_options = dict(repo_dir=args.data_dir,
inner_size=224,
dtype=args.datatype,
subset_pct=100)
train = ImageLoader(set_name='train', **img_set_options)
test = ImageLoader(set_name='validation',
do_transforms=False,
**img_set_options)
relu = Rectlin()
init_uni = GlorotUniform()
# The parameters below are straight out of [Springenberg2014]
opt_gdm = GradientDescentMomentum(learning_rate=0.01,
schedule=Schedule(step_config=[10],
change=0.1),
momentum_coef=0.9,
wdecay=.0005)
stages = (3, 8, 36, 3)
elif args.depth in (4, 98, 138):
stages = (3, 7, 35, 3)
else:
neon_logger.display("Bad depth parameter")
sys.exit()
# setup data provider
img_set_options = dict(repo_dir=args.data_dir,
inner_size=224,
subset_pct=args.subset_pct)
train = ImageLoader(set_name='train',
scale_range={
'min_area_pct': 8,
'max_area_pct': 100
},
aspect_ratio=133,
contrast_range=(60, 140),
shuffle=True,
**img_set_options)
test = ImageLoader(set_name='validation',
scale_range=256,
do_transforms=False,
**img_set_options)
tune = ImageLoader(set_name='train',
scale_range=256,
do_transforms=False,
repo_dir=args.data_dir,
inner_size=224,
subset_pct=10)
type=float,
default=100,
help='subset of training dataset to use (percentage)')
parser.add_argument(
'--test_only',
action='store_true',
help='skip fitting - evaluate metrics on trained model weights')
args = parser.parse_args()
# setup data provider
img_set_options = dict(repo_dir=args.data_dir,
inner_size=224,
dtype=args.datatype,
subset_pct=args.subset_pct)
test = ImageLoader(set_name='validation',
scale_range=(256, 256),
do_transforms=False,
**img_set_options)
init1 = Xavier(local=False)
initx = Xavier(local=True)
bias = Constant(val=0.20)
relu = Rectlin()
common = dict(activation=relu, init=initx, bias=bias)
commonp1 = dict(activation=relu, init=initx, bias=bias, padding=1)
commonp2 = dict(activation=relu, init=initx, bias=bias, padding=2)
pool3s1p1 = dict(fshape=3, padding=1, strides=1)
pool3s2p1 = dict(fshape=3, padding=1, strides=2, op='max')
def inception(kvals):
parser = NeonArgparser(__doc__)
parser.add_argument('--network',
default='plain',
choices=['plain', 'resnet'],
help='type of network to create (plain or resnet)')
parser.add_argument('--depth',
type=int,
default=9,
help='depth of each stage (network depth will be 6n+2)')
args = parser.parse_args()
# setup data provider
imgset_options = dict(inner_size=112, scale_range=140, repo_dir=args.data_dir)
train = ImageLoader(set_name='train',
shuffle=True,
do_transforms=True,
inner_size=112,
scale_range=(128, 240),
repo_dir=args.data_dir)
test = ImageLoader(set_name='validation',
shuffle=False,
do_transforms=False,
inner_size=112,
scale_range=0,
repo_dir=args.data_dir)
def conv_params(fsize, nfm, stride=1, relu=True):
return dict(fshape=(fsize, fsize, nfm),
strides=stride,
padding=(1 if fsize > 1 else 0),
from neon.models import Model
from neon.data import ImageLoader
from neon.callbacks.callbacks import Callbacks
# parse the command line arguments (generates the backend)
parser = NeonArgparser(__doc__)
parser.add_argument('--depth',
type=int,
default=9,
help='depth of each stage (network depth will be 6n+2)')
args = parser.parse_args()
# setup data provider
imgset_options = dict(inner_size=32, scale_range=40, repo_dir=args.data_dir)
train = ImageLoader(set_name='train',
shuffle=True,
do_transforms=True,
**imgset_options)
test = ImageLoader(set_name='validation',
shuffle=False,
do_transforms=False,
**imgset_options)
def conv_params(fsize, nfm, stride=1, relu=True):
return dict(fshape=(fsize, fsize, nfm),
strides=stride,
padding=(1 if fsize > 1 else 0),
activation=(Rectlin() if relu else None),
init=Kaiming(local=True),
batch_norm=True)
scales = [112, 128, 160, 240]
for scale in scales:
print scale
layers = []
layers += [Conv(**conv_params(7, 32, 2))]
for nfm, stride in zip(nfms, strides):
layers.append(module_factory(nfm, stride))
layers.append(Pooling(7, op='avg'))
layers.append(
Conv(fshape=(1, 1, 100), init=Kaiming(local=True), batch_norm=True))
layers.append(Pooling(fshape='all', op='avg'))
layers.append(Activation(Softmax()))
model = Model(layers=layers)
test = ImageLoader(set_name='validation',
shuffle=False,
do_transforms=False,
inner_size=scale,
scale_range=scale,
repo_dir=args.data_dir)
model.load_params("/home/users/hunter/bigfeat_dropout.pkl")
softmaxes = model.get_outputs(test)
from neon.util.persist import save_obj
save_obj(softmaxes, "bigfeat_dropout_SM_{}.pkl".format(scale))
