# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # 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 from paddlex.cv.transforms import build_transforms, build_transforms_v1 def load_model(model_dir, fixed_input_shape=None): model_scope = fluid.Scope() if not osp.exists(model_dir): logging.error("model_dir '{}' is not exists!".format(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) 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 'model_name' in info['_init_params']: del info['_init_params']['model_name'] model = getattr(paddlex.cv.models, info['Model'])(**info['_init_params']) model.fixed_input_shape = fixed_input_shape if '_Attributes' in info: if 'fixed_input_shape' in info['_Attributes']: fixed_input_shape = info['_Attributes']['fixed_input_shape'] if fixed_input_shape is not None: logging.info("Model already has fixed_input_shape with {}". format(fixed_input_shape)) model.fixed_input_shape = fixed_input_shape if info['Model'].count('RCNN') > 0: if info['_init_params']['with_fpn']: if model.fixed_input_shape[0] % 32 > 0: raise Exception( "The first value in fixed_input_shape must be a multiple of 32, but recieved {}.". format(model.fixed_input_shape[0])) if model.fixed_input_shape[1] % 32 > 0: raise Exception( "The second value in fixed_input_shape must be a multiple of 32, but recieved {}.". format(model.fixed_input_shape[1])) with fluid.scope_guard(model_scope): 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], scope=model_scope) 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=model.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.scope = model_scope model.trainable = False model.status = status return model def fix_input_shape(info, fixed_input_shape=None): if fixed_input_shape is not None: input_channel = 3 if 'input_channel' in info['_init_params']: input_channel = info['_init_params']['input_channel'] resize = {'ResizeByShort': {}} padding = {'Padding': {}} if info['_Attributes']['model_type'] == 'classifier': pass elif info['Model'].count('YOLO') > 0: resize_op_index = None for i in range(len(info['Transforms'])): if list(info['Transforms'][i].keys())[0] == 'Resize': resize_op_index = i if resize_op_index is not None: info['Transforms'][resize_op_index]['Resize'][ 'target_size'] = fixed_input_shape[0] elif info['Model'].count('RCNN') > 0: resize_op_index = None for i in range(len(info['Transforms'])): if list(info['Transforms'][i].keys())[0] == 'ResizeByShort': resize_op_index = i if resize_op_index is not None: info['Transforms'][resize_op_index]['ResizeByShort'][ 'short_size'] = min(fixed_input_shape) info['Transforms'][resize_op_index]['ResizeByShort'][ 'max_size'] = max(fixed_input_shape) else: resize['ResizeByShort']['short_size'] = min(fixed_input_shape) resize['ResizeByShort']['max_size'] = max(fixed_input_shape) info['Transforms'].append(resize) padding_op_index = None for i in range(len(info['Transforms'])): if list(info['Transforms'][i].keys())[0] == 'Padding': padding_op_index = i if padding_op_index is not None: info['Transforms'][padding_op_index]['Padding'][ 'target_size'] = list(fixed_input_shape) else: padding['Padding']['target_size'] = list(fixed_input_shape) info['Transforms'].append(padding) elif info['_Attributes']['model_type'] == 'segmenter': resize['ResizeByShort']['short_size'] = min(fixed_input_shape) resize['ResizeByShort']['max_size'] = max(fixed_input_shape) padding['Padding']['target_size'] = list(fixed_input_shape) padding['Padding']['im_padding_value'] = [0.] * input_channel info['Transforms'].append(resize) info['Transforms'].append(padding)