86 lines
2.6 KiB
Python
86 lines
2.6 KiB
Python
# Copyright (c) 2020 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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import argparse
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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(__dir__)
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sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
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from ppcls.modeling import architectures
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from ppcls.utils.save_load import load_dygraph_pretrain
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import paddle
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import paddle.nn.functional as F
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from paddle.jit import to_static
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def parse_args():
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def str2bool(v):
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return v.lower() in ("true", "t", "1")
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parser = argparse.ArgumentParser()
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parser.add_argument("-m", "--model", type=str)
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parser.add_argument("-p", "--pretrained_model", type=str)
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parser.add_argument("-o", "--output_path", type=str, default="./inference")
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parser.add_argument("--class_dim", type=int, default=1000)
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parser.add_argument("--load_static_weights", type=str2bool, default=False)
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parser.add_argument("--img_size", type=int, default=224)
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return parser.parse_args()
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class Net(paddle.nn.Layer):
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def __init__(self, net, class_dim, model):
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super(Net, self).__init__()
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self.pre_net = net(class_dim=class_dim)
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self.model = model
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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, inputs):
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x = self.pre_net(inputs)
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if self.model == "GoogLeNet":
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x = x[0]
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x = F.softmax(x)
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return x
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def main():
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args = parse_args()
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net = architectures.__dict__[args.model]
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model = Net(net, args.class_dim, args.model)
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load_dygraph_pretrain(
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model.pre_net,
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path=args.pretrained_model,
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load_static_weights=args.load_static_weights)
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model.eval()
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model = 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, 3, args.img_size, args.img_size], dtype='float32')
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])
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paddle.jit.save(model, os.path.join(args.output_path, "inference"))
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if __name__ == "__main__":
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main()
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