# 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, fixed_input_shape=None): 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) if 'status' in info: status = info['status'] elif 'save_method' in info: # 兼容老版本PaddleX status = info['save_method'] 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']) model.fixed_input_shape = fixed_input_shape if status == "Normal" or \ status == "Prune" or status == "fluid.save": 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" or status == "fluid.save_inference_model": [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') # 固定模型的输入shape fix_input_shape(info, fixed_input_shape=fixed_input_shape) if transforms_mode == 'RGB': to_rgb = True else: to_rgb = False if 'BatchTransforms' in info: # 兼容老版本PaddleX模型 model.test_transforms = build_transforms_v1( model.model_type, info['Transforms'], info['BatchTransforms']) model.eval_transforms = copy.deepcopy(model.test_transforms) else: 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'])) model.trainable = False return model def fix_input_shape(info, fixed_input_shape=None): if fixed_input_shape is not None: resize = {'ResizeByShort': {}} padding = {'Padding': {}} if info['_Attributes']['model_type'] == 'classifier': crop_size = 0 for transform in info['Transforms']: if 'CenterCrop' in transform: crop_size = transform['CenterCrop']['crop_size'] break assert crop_size == fixed_input_shape[ 0], "fixed_input_shape must == CenterCrop:crop_size:{}".format( crop_size) assert crop_size == fixed_input_shape[ 1], "fixed_input_shape must == CenterCrop:crop_size:{}".format( crop_size) if crop_size == 0: logging.warning( "fixed_input_shape must == input shape when trainning") else: print("*" * 10) resize['ResizeByShort']['short_size'] = min(fixed_input_shape) resize['ResizeByShort']['max_size'] = max(fixed_input_shape) padding['Padding']['target_size'] = list(fixed_input_shape) info['Transforms'].append(resize) info['Transforms'].append(padding) 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 def build_transforms_v1(model_type, transforms_info, batch_transforms_info): """ 老版本模型加载，仅支持PaddleX前端导出的模型 """ logging.debug("Use build_transforms_v1 to reconstruct transforms") 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 = op_info[0] op_attr = op_info[1] if op_name == 'DecodeImage': continue if op_name == 'Permute': continue if op_name == 'ResizeByShort': op_attr_new = dict() if 'short_size' in op_attr: op_attr_new['short_size'] = op_attr['short_size'] else: op_attr_new['short_size'] = op_attr['target_size'] op_attr_new['max_size'] = op_attr.get('max_size', -1) op_attr = op_attr_new if op_name.startswith('Arrange'): continue 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)) if model_type == "detector" and len(batch_transforms_info) > 0: op_name = batch_transforms_info[0][0] op_attr = batch_transforms_info[0][1] assert op_name == "PaddingMiniBatch", "Only PaddingMiniBatch transform is supported for batch transform" padding = T.Padding(coarsest_stride=op_attr['coarsest_stride']) transforms.append(padding) eval_transforms = T.Compose(transforms) return eval_transforms