def __init__(self, op, load_dic): ConvNet.__init__(self, op, load_dic) self.summary_dir = self.save_file + '_summary' if not os.path.exists(self.summary_dir): os.makedirs(self.summary_dir) ''' here, (train,test,pred) corresponds to (train,validation,testing)''' self.write_features = [self.write_features_train, self.write_features_test, self.write_features_pred]
def __init__(self, op, load_dic): ConvNet.__init__(self, op, load_dic) self.summary_dir = self.save_file + '_summary' if not os.path.exists(self.summary_dir): os.makedirs(self.summary_dir) ''' here, (train,test,pred) corresponds to (train,validation,testing)''' self.write_features = [ self.write_features_train, self.write_features_test, self.write_features_pred ]
def __init__(self, model_path, data_processor, gpu, layers): op = ConvNetPredict.get_options_parser() op.set_value('load_file', model_path) op.set_value('gpu', str(gpu)) load_dic = IGPUModel.load_checkpoint(model_path) old_op = load_dic["op"] old_op.merge_from(op) op = old_op op.eval_expr_defaults() ConvNet.__init__(self, op, load_dic) self.dp = data_processor self.ftr_layer_idx = map(self.get_layer_idx, layers)
def __init__(self, op, load_dic): ConvNet.__init__(self, op, load_dic)
def __init__(self, op, load_dic): ConvNet.__init__(self, op, load_dic) self.op.set_value('data_path', '/home/komet/software/cuda-convnet/data/cifar-10-py-colmajor')
def __init__(self, op, load_dic): ConvNet.__init__(self, op, load_dic) self.op.set_value( 'data_path', '/home/komet/software/cuda-convnet/data/cifar-10-py-colmajor')
def __init__(self, op, load_dict): ConvNet.__init__(self, op, load_dic) self.statistics = dict() self.temp_data = dict()
def __init__(self, op, load_dic, dp_params): ConvNet.__init__(self, op, load_dic, dp_params)
def __init__(self, op, load_dict): ConvNet.__init__(self, op, load_dic) self.statistics = dict() self.temp_data = dict() self.cropper_dict = {'faceubd': IFaceUbdCropImage}
def __init__(self, op, load_dic): ConvNet.__init__(self, op, load_dic) print self.save_figure
def __init__(self, op, load_dic, dp_params={}): filename_options = [] self.regular_test_outputs = [] ConvNet.__init__(self, op, load_dic, dp_params=dp_params) self.model_state['regular_test_outputs'] = self.regular_test_outputs
def __init__(self, op, load_dict): ConvNet.__init__(self, op, load_dic) self.statistics = dict() self.temp_data = dict() self.cropper_dict = {'faceubd':IFaceUbdCropImage}