# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. # # Licensed under the Apache License, Version 2.0 (the "License"); # 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. import yaml import os.path as osp import six import copy from collections import OrderedDict import paddle.fluid as fluid from paddle.fluid.framework import Parameter import paddlex import paddlex.utils.logging as logging def load_model(model_dir): if not osp.exists(osp.join(model_dir, "model.yml")): raise Exception("There's not model.yml in {}".format(model_dir)) with open(osp.join(model_dir, "model.yml")) as f: info = yaml.load(f.read(), Loader=yaml.Loader) status = info['status'] if not hasattr(paddlex.cv.models, info['Model']): raise Exception("There's no attribute {} in paddlex.cv.models".format( info['Model'])) if info['_Attributes']['model_type'] == 'classifier': model = paddlex.cv.models.BaseClassifier(**info['_init_params']) else: model = getattr(paddlex.cv.models, info['Model'])(**info['_init_params']) if status == "Normal" or \ status == "Prune": startup_prog = fluid.Program() model.test_prog = fluid.Program() with fluid.program_guard(model.test_prog, startup_prog): with fluid.unique_name.guard(): model.test_inputs, model.test_outputs = model.build_net( mode='test') model.test_prog = model.test_prog.clone(for_test=True) model.exe.run(startup_prog) if status == "Prune": from .slim.prune import update_program model.test_prog = update_program(model.test_prog, model_dir, model.places[0]) import pickle with open(osp.join(model_dir, 'model.pdparams'), 'rb') as f: load_dict = pickle.load(f) fluid.io.set_program_state(model.test_prog, load_dict) elif status == "Infer" or \ status == "Quant": [prog, input_names, outputs] = fluid.io.load_inference_model( model_dir, model.exe, params_filename='__params__') model.test_prog = prog test_outputs_info = info['_ModelInputsOutputs']['test_outputs'] model.test_inputs = OrderedDict() model.test_outputs = OrderedDict() for name in input_names: model.test_inputs[name] = model.test_prog.global_block().var(name) for i, out in enumerate(outputs): var_desc = test_outputs_info[i] model.test_outputs[var_desc[0]] = out if 'Transforms' in info: transforms_mode = info.get('TransformsMode', 'RGB') if transforms_mode == 'RGB': to_rgb = True else: to_rgb = False model.test_transforms = build_transforms(model.model_type, info['Transforms'], to_rgb) model.eval_transforms = copy.deepcopy(model.test_transforms) if '_Attributes' in info: for k, v in info['_Attributes'].items(): if k in model.__dict__: model.__dict__[k] = v logging.info("Model[{}] loaded.".format(info['Model'])) return model def build_transforms(model_type, transforms_info, to_rgb=True): if model_type == "classifier": import paddlex.cv.transforms.cls_transforms as T elif model_type == "detector": import paddlex.cv.transforms.det_transforms as T elif model_type == "segmenter": import paddlex.cv.transforms.seg_transforms as T transforms = list() for op_info in transforms_info: op_name = list(op_info.keys())[0] op_attr = op_info[op_name] if not hasattr(T, op_name): raise Exception( "There's no operator named '{}' in transforms of {}".format( op_name, model_type)) transforms.append(getattr(T, op_name)(**op_attr)) eval_transforms = T.Compose(transforms) eval_transforms.to_rgb = to_rgb return eval_transforms