121 lines
3.5 KiB
Python
121 lines
3.5 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 utils
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import argparse
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import numpy as np
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import paddle.fluid as fluid
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from ppcls.modeling import architectures
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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("-i", "--image_file", type=str)
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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("--use_gpu", type=str2bool, default=True)
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return parser.parse_args()
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def create_predictor(args):
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def create_input():
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image = fluid.data(
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name='image', shape=[None, 3, 224, 224], dtype='float32')
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return image
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def create_model(args, model, input, class_dim=1000):
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if args.model == "GoogLeNet":
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out, _, _ = model.net(input=input, class_dim=class_dim)
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else:
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out = model.net(input=input, class_dim=class_dim)
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out = fluid.layers.softmax(out)
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return out
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model = architectures.__dict__[args.model]()
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place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
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exe = fluid.Executor(place)
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startup_prog = fluid.Program()
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infer_prog = fluid.Program()
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with fluid.program_guard(infer_prog, startup_prog):
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with fluid.unique_name.guard():
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image = create_input()
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out = create_model(args, model, image)
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infer_prog = infer_prog.clone(for_test=True)
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fluid.load(
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program=infer_prog, model_path=args.pretrained_model, executor=exe)
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return exe, infer_prog, [image.name], [out.name]
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def create_operators():
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size = 224
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img_mean = [0.485, 0.456, 0.406]
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img_std = [0.229, 0.224, 0.225]
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img_scale = 1.0 / 255.0
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decode_op = utils.DecodeImage()
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resize_op = utils.ResizeImage(resize_short=256)
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crop_op = utils.CropImage(size=(size, size))
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normalize_op = utils.NormalizeImage(
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scale=img_scale, mean=img_mean, std=img_std)
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totensor_op = utils.ToTensor()
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return [decode_op, resize_op, crop_op, normalize_op, totensor_op]
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def preprocess(fname, ops):
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data = open(fname, 'rb').read()
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for op in ops:
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data = op(data)
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return data
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def postprocess(outputs, topk=5):
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output = outputs[0]
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prob = np.array(output).flatten()
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index = prob.argsort(axis=0)[-topk:][::-1].astype('int32')
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return zip(index, prob[index])
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def main():
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args = parse_args()
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operators = create_operators()
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exe, program, feed_names, fetch_names = create_predictor(args)
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data = preprocess(args.image_file, operators)
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data = np.expand_dims(data, axis=0)
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outputs = exe.run(program,
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feed={feed_names[0]: data},
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fetch_list=fetch_names,
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return_numpy=False)
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probs = postprocess(outputs)
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for idx, prob in probs:
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print("class id: {:d}, probability: {:.4f}".format(idx, prob))
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if __name__ == "__main__":
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main()
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