106 lines
3.5 KiB
Python
106 lines
3.5 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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 __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import sys
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(os.path.abspath(os.path.join(__dir__, '../')))
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import paddle
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import paddle.nn as nn
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from ppcls.utils import config
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from ppcls.utils.logger import init_logger
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from ppcls.utils.config import print_config
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from ppcls.arch import build_model, RecModel, DistillationModel
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from ppcls.utils.save_load import load_dygraph_pretrain
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from ppcls.arch.gears.identity_head import IdentityHead
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class ExportModel(nn.Layer):
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"""
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ExportModel: add softmax onto the model
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"""
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def __init__(self, config):
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super().__init__()
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self.base_model = build_model(config)
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# we should choose a final model to export
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if isinstance(self.base_model, DistillationModel):
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self.infer_model_name = config["infer_model_name"]
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else:
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self.infer_model_name = None
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self.infer_output_key = config.get("infer_output_key", None)
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if self.infer_output_key == "features" and isinstance(self.base_model,
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RecModel):
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self.base_model.head = IdentityHead()
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if config.get("infer_add_softmax", True):
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self.softmax = nn.Softmax(axis=-1)
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else:
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self.softmax = None
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def eval(self):
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self.training = False
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for layer in self.sublayers():
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layer.training = False
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layer.eval()
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def forward(self, x):
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x = self.base_model(x)
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if self.infer_model_name is not None:
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x = x[self.infer_model_name]
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if self.infer_output_key is not None:
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x = x[self.infer_output_key]
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if self.softmax is not None:
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x = self.softmax(x)
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return x
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if __name__ == "__main__":
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args = config.parse_args()
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config = config.get_config(
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args.config, overrides=args.override, show=False)
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log_file = os.path.join(config['Global']['output_dir'],
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config["Arch"]["name"], "export.log")
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init_logger(name='root', log_file=log_file)
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print_config(config)
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# set device
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assert config["Global"]["device"] in ["cpu", "gpu", "xpu"]
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device = paddle.set_device(config["Global"]["device"])
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model = ExportModel(config["Arch"])
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if config["Global"]["pretrained_model"] is not None:
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load_dygraph_pretrain(model.base_model,
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config["Global"]["pretrained_model"])
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model.eval()
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model = paddle.jit.to_static(
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model,
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input_spec=[
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paddle.static.InputSpec(
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shape=[None] + config["Global"]["image_shape"],
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dtype='float32')
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])
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paddle.jit.save(model,
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os.path.join(config["Global"]["save_inference_dir"],
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"inference"))
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