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): 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()