def init_data_providers(self):
class Dummy:
def advance_batch(self):
pass
if self.need_gpu:
ConvNet.init_data_providers(self)
else:
self.train_data_provider = self.test_data_provider = Dummy()
def init_data_providers(self):
self.need_gpu = self.op.get_value('show_preds')
class Dummy:
def advance_batch(self):
pass
if self.need_gpu:
ConvNet.init_data_providers(self)
else:
self.train_data_provider = self.test_data_provider = Dummy()
def init_data_providers(self):
self.need_gpu = self.op.get_value('show_preds')
class Dummy:
def advance_batch(self):
pass
if self.need_gpu:
ConvNet.init_data_providers(self)
else:
self.train_data_provider = self.test_data_provider = Dummy()
def init_data_providers(self):
class Dummy:
def advance_batch(self):
pass
if self.need_gpu:
if self.dp_type == "imagenet":
self.train_data_provider = ImageNetDataProvider(self.data_path, self.train_batch_range, test=False, show=True)
self.test_data_provider = ImageNetDataProvider(self.data_path, self.test_batch_range, test=True, show=True)
else:
ConvNet.init_data_providers(self)
else:
self.train_data_provider = self.test_data_provider = Dummy()
def init_data_providers(self):
class Dummy:
def advance_batch(self):
pass
if self.need_gpu:
ConvNet.init_data_providers(self)
if self.op.get_value("write_features_pred") or self.op.get_value("show_preds") == 2:
self.pred_data_provider = DataProvider.get_instance(self.libmodel, self.data_path, self.pred_batch_range,
type=self.dp_type, dp_params=self.dp_params, test=DataProvider.DP_PREDICT)
else:
self.train_data_provider = self.test_data_provider = Dummy()
def init_data_providers(self):
ConvNet.init_data_providers(self)
if self.noise_level == 0:
return
self.noise_W = n.load(self.noise_Qpath)
self.noise_W = self.noise_level * self.noise_W + (
1 - self.noise_level) * n.eye(self.noise_W.shape[0])
for d in self.train_data_provider.data_dic:
d['labels'] = mix_labels(self.noise_W, d['labels'])
d['labels'] = n.require(d['labels'].reshape(
(1, d['data'].shape[1])),
dtype=n.single,
requirements='C')
def init_data_providers(self):
class Dummy:
def advance_batch(self):
pass
if self.need_gpu:
ConvNet.init_data_providers(self)
if self.op.get_value("write_features_pred") or self.op.get_value(
"show_preds") == 2:
self.pred_data_provider = DataProvider.get_instance(
self.libmodel,
self.data_path,
self.pred_batch_range,
type=self.dp_type,
dp_params=self.dp_params,
test=DataProvider.DP_PREDICT)
else:
self.train_data_provider = self.test_data_provider = Dummy()
