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83 lines
2.8 KiB
83 lines
2.8 KiB
3 years ago
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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3 years ago
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from . import prune
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from . import quant
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from . import distill
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from . import unstructured_prune
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from .prune import *
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from .quant import *
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from .distill import *
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from .unstructured_prune import *
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import yaml
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3 years ago
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from paddlers.models.ppdet.core.workspace import load_config
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from paddlers.models.ppdet.utils.checkpoint import load_pretrain_weight
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3 years ago
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def build_slim_model(cfg, slim_cfg, mode='train'):
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with open(slim_cfg) as f:
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slim_load_cfg = yaml.load(f, Loader=yaml.Loader)
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if mode != 'train' and slim_load_cfg['slim'] == 'Distill':
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return cfg
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if slim_load_cfg['slim'] == 'Distill':
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model = DistillModel(cfg, slim_cfg)
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cfg['model'] = model
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elif slim_load_cfg['slim'] == 'DistillPrune':
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if mode == 'train':
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model = DistillModel(cfg, slim_cfg)
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pruner = create(cfg.pruner)
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pruner(model.student_model)
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else:
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model = create(cfg.architecture)
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weights = cfg.weights
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load_config(slim_cfg)
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pruner = create(cfg.pruner)
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model = pruner(model)
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load_pretrain_weight(model, weights)
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cfg['model'] = model
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cfg['slim_type'] = cfg.slim
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elif slim_load_cfg['slim'] == 'PTQ':
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model = create(cfg.architecture)
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load_config(slim_cfg)
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load_pretrain_weight(model, cfg.weights)
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slim = create(cfg.slim)
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cfg['slim_type'] = cfg.slim
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cfg['model'] = slim(model)
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cfg['slim'] = slim
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elif slim_load_cfg['slim'] == 'UnstructuredPruner':
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load_config(slim_cfg)
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slim = create(cfg.slim)
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cfg['slim_type'] = cfg.slim
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cfg['slim'] = slim
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cfg['unstructured_prune'] = True
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else:
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load_config(slim_cfg)
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model = create(cfg.architecture)
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if mode == 'train':
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load_pretrain_weight(model, cfg.pretrain_weights)
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slim = create(cfg.slim)
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cfg['slim_type'] = cfg.slim
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# TODO: fix quant export model in framework.
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if mode == 'test' and slim_load_cfg['slim'] == 'QAT':
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slim.quant_config['activation_preprocess_type'] = None
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cfg['model'] = slim(model)
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cfg['slim'] = slim
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if mode != 'train':
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load_pretrain_weight(cfg['model'], cfg.weights)
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return cfg
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