def score(
result_folder,
epoch=None,
use_cpu=False):
"""Test a network on the dataset."""
if use_cpu:
bnet.set_mode_cpu()
else:
bnet.set_mode_gpu()
_LOGGER.info("Loading data...")
tr_data, _ = training_data()
te_data, te_labels = test_data()
from data import _MEAN
_LOGGER.info("Loading network...")
# Load the model.
model, _, _, _ = _model(result_folder,
tr_data.shape[0],
epoch=epoch,
no_solver=True)
_LOGGER.info("Predicting...")
results = model.predict(te_data,
test_callbacks=[
RandCropMonitor('data', _MEAN),
mnt.ProgressIndicator()
],
out_blob_names=['score'])
_LOGGER.info("Accuracy: %f.",
accuracy_score(te_labels,
np.argmax(np.array(results), axis=1)))
def test_image(
result_folder,
epoch=None,
image_idx=0,
use_cpu=False):
"""Test a network on one test image."""
if use_cpu:
bnet.set_mode_cpu()
else:
bnet.set_mode_gpu()
_LOGGER.info("Loading data...")
tr_data, _ = training_data()
te_data, _ = test_data()
from data import _MEAN
_LOGGER.info("Loading network...")
# Load the model for training.
model, _, _, _ = _model(result_folder,
tr_data.shape[0],
epoch=epoch)
_LOGGER.info("Predicting...")
results = model.predict(te_data[:image_idx + 1],
test_callbacks=[
RandCropMonitor('data', _MEAN),
mnt.ProgressIndicator()
],
out_blob_names=['score'])
_LOGGER.info("Prediction for image %d: %s.",
image_idx, str(results[image_idx]))
def test_image(result_folder, epoch=None, image_idx=0, use_cpu=False):
"""Test a network on one test image."""
if use_cpu:
bnet.set_mode_cpu()
else:
bnet.set_mode_gpu()
_LOGGER.info("Loading data...")
tr_data, _ = training_data()
te_data, _ = test_data()
_LOGGER.info("Loading network...")
# Load the model for training.
model, _, _, _ = _model(result_folder, tr_data.shape[0], epoch=epoch)
_LOGGER.info("Predicting...")
results = model.predict(te_data, test_callbacks=[mnt.ProgressIndicator()])
_LOGGER.info("Prediction for image %d: %s.", image_idx,
str(results[image_idx]))
def test_image(
result_folder,
epoch=None,
image_idx=0,
use_cpu=False):
"""Test a network on one test image."""
if use_cpu:
bnet.set_mode_cpu()
else:
bnet.set_mode_gpu()
_LOGGER.info("Loading data...")
tr_data, _ = training_data()
te_data, _ = test_data()
_LOGGER.info("Loading network...")
# Load the model for training.
model, _, _, _ = _model(result_folder,
tr_data.shape[0],
epoch=epoch)
_LOGGER.info("Predicting...")
results = model.predict(te_data,
test_callbacks=[mnt.ProgressIndicator()])
_LOGGER.info("Prediction for image %d: %s.",
image_idx, str(results[image_idx]))
def cli(result_folder,
model_name=None,
epoch=None,
num_epoch=150,
optimizer_name='sgd',
lr_param=0.1,
lr_decay_sched='90,135',
lr_decay_ratio=0.1,
mom_param=0.9,
wd_param=0.0001,
monitor=False,
allow_overwrite=False,
use_cpu=False):
"""Train a model."""
print("Parameters: ", sys.argv)
if use_cpu:
bnet.set_mode_cpu()
else:
bnet.set_mode_gpu()
# Load the data.
tr_data, tr_labels = training_data()
te_data, te_labels = test_data()
from data import _MEAN
# Setup the output folder, including logging.
model, optimizer, out_folder, base_iter = _model(
result_folder, tr_data.shape[0], model_name, epoch, 10, optimizer_name,
lr_param, lr_decay_sched, lr_decay_ratio, mom_param, wd_param, False,
allow_overwrite)
batch_size = model.blobs['data'].shape[0]
logger = mnt.JSONLogger(str(out_folder),
'model', {
'train': ['train_loss', 'train_accuracy'],
'test': ['test_loss', 'test_accuracy']
},
base_iter=base_iter,
write_every=round_to_mbsize(10000, batch_size),
create_plot=monitor)
progr_ind = mnt.ProgressIndicator()
cropper = RandCropMonitor('data', _MEAN)
if monitor:
extra_monitors = [
mnt.ActivationMonitor(round_to_mbsize(10000, batch_size),
os.path.join(str(out_folder),
'visualizations' + os.sep),
selected_blobs=['resblock3_out', 'avpool'],
sample={'data': tr_data[0]}),
mnt.FilterMonitor(round_to_mbsize(10000, batch_size),
os.path.join(str(out_folder),
'visualizations' + os.sep),
selected_parameters={
'resblock1_conv1': [0],
'resblock3_conv1': [0],
'resblock7_conv1': [0]
}),
mnt.GradientMonitor(round_to_mbsize(10000, batch_size),
os.path.join(str(out_folder),
'visualizations' + os.sep),
relative=True,
selected_parameters={
'resblock1_conv1': [0, 1],
'resblock3_conv1': [0, 1],
'resblock7_conv1': [0, 1]
}),
]
else:
extra_monitors = []
model.fit(round_to_mbsize(num_epoch * 50000, batch_size),
optimizer,
X={
'data': tr_data,
'labels': tr_labels
},
X_val={
'data': te_data,
'labels': te_labels
},
test_interval=round_to_mbsize(50000, batch_size),
train_callbacks=[
progr_ind,
logger,
mnt.RotatingMirroringMonitor({'data': 0}, 0, 0.5),
cropper,
mnt.Checkpointer(os.path.join(str(out_folder), 'model'),
round_to_mbsize(50000 * 10, batch_size),
base_iterations=base_iter),
] + extra_monitors,
test_callbacks=[progr_ind, cropper, logger],
shuffle=True)
def cli(result_folder,
model_name=None,
epoch=None,
num_epoch=3,
optimizer_name='sgd',
lr_param=0.001,
lr_decay_sched='90,135',
lr_decay_ratio=0.1,
mom_param=0.9,
wd_param=0.0001,
monitor=False,
allow_overwrite=False,
use_cpu=False):
"""Train a model."""
print("Parameters: ", sys.argv)
if use_cpu:
bnet.set_mode_cpu()
else:
bnet.set_mode_gpu()
# Load the data.
tr_data, tr_labels = training_data()
te_data, te_labels = test_data()
# Setup the output folder, including logging.
model, optimizer, out_folder, base_iter = _model(
result_folder,
tr_data.shape[0],
model_name,
epoch,
1,
optimizer_name,
lr_param,
lr_decay_sched,
lr_decay_ratio,
mom_param,
wd_param,
False,
allow_overwrite)
batch_size = model.blobs['data'].shape[0]
logger = mnt.JSONLogger(str(out_folder),
'model',
{'train': ['train_loss', 'train_accuracy'],
'test': ['test_loss', 'test_accuracy']},
base_iter=base_iter,
write_every=round_to_mbsize(50000, batch_size),
create_plot=monitor)
progr_ind = mnt.ProgressIndicator()
if monitor:
extra_monitors = [
mnt.ActivationMonitor(round_to_mbsize(10000, batch_size),
os.path.join(str(out_folder),
'visualizations' + os.sep),
sample={'data': tr_data[0]}),
mnt.FilterMonitor(round_to_mbsize(10000, batch_size),
os.path.join(str(out_folder),
'visualizations' + os.sep)),
mnt.GradientMonitor(round_to_mbsize(10000, batch_size),
os.path.join(str(out_folder),
'visualizations' + os.sep),
relative=True),
]
else:
extra_monitors = []
model.fit(round_to_mbsize(num_epoch * tr_data.shape[0], batch_size),
optimizer,
X={'data': tr_data, 'labels': tr_labels},
X_val={'data': te_data, 'labels': te_labels},
test_interval=round_to_mbsize(tr_data.shape[0], batch_size),
train_callbacks=[
progr_ind,
logger,
mnt.Checkpointer(os.path.join(str(out_folder),
'model'),
round_to_mbsize(tr_data.shape[0], batch_size),
base_iterations=base_iter),
] + extra_monitors,
test_callbacks=[
progr_ind,
logger])
def cli(
result_folder,
model_name=None,
epoch=None,
num_epoch=150,
optimizer_name="sgd",
lr_param=0.1,
lr_decay_sched="90,135",
lr_decay_ratio=0.1,
mom_param=0.9,
wd_param=0.0001,
monitor=False,
allow_overwrite=False,
use_cpu=False,
):
"""Train a model."""
print("Parameters: ", sys.argv)
if use_cpu:
bnet.set_mode_cpu()
else:
bnet.set_mode_gpu()
# Load the data.
tr_data, tr_labels = training_data()
te_data, te_labels = test_data()
from data import _MEAN
# Setup the output folder, including logging.
model, optimizer, out_folder, base_iter = _model(
result_folder,
tr_data.shape[0],
model_name,
epoch,
10,
optimizer_name,
lr_param,
lr_decay_sched,
lr_decay_ratio,
mom_param,
wd_param,
False,
allow_overwrite,
)
batch_size = model.blobs["data"].shape[0]
logger = mnt.JSONLogger(
str(out_folder),
"model",
{"train": ["train_loss", "train_accuracy"], "test": ["test_loss", "test_accuracy"]},
base_iter=base_iter,
write_every=round_to_mbsize(10000, batch_size),
create_plot=monitor,
)
progr_ind = mnt.ProgressIndicator()
cropper = RandCropMonitor("data", _MEAN)
if monitor:
extra_monitors = [
mnt.ActivationMonitor(
round_to_mbsize(10000, batch_size),
os.path.join(str(out_folder), "visualizations" + os.sep),
selected_blobs=["resblock3_out", "avpool"],
sample={"data": tr_data[0]},
),
mnt.FilterMonitor(
round_to_mbsize(10000, batch_size),
os.path.join(str(out_folder), "visualizations" + os.sep),
selected_parameters={"resblock1_conv1": [0], "resblock3_conv1": [0], "resblock7_conv1": [0]},
),
mnt.GradientMonitor(
round_to_mbsize(10000, batch_size),
os.path.join(str(out_folder), "visualizations" + os.sep),
relative=True,
selected_parameters={"resblock1_conv1": [0, 1], "resblock3_conv1": [0, 1], "resblock7_conv1": [0, 1]},
),
]
else:
extra_monitors = []
model.fit(
round_to_mbsize(num_epoch * 50000, batch_size),
optimizer,
X={"data": tr_data, "labels": tr_labels},
X_val={"data": te_data, "labels": te_labels},
test_interval=round_to_mbsize(50000, batch_size),
train_callbacks=[
progr_ind,
logger,
mnt.RotatingMirroringMonitor({"data": 0}, 0, 0.5),
cropper,
mnt.Checkpointer(
os.path.join(str(out_folder), "model"),
round_to_mbsize(50000 * 10, batch_size),
base_iterations=base_iter,
),
]
+ extra_monitors,
test_callbacks=[progr_ind, cropper, logger],
shuffle=True,
)