225 lines
7.1 KiB
Python
225 lines
7.1 KiB
Python
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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 yaml
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import os
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from argparse import ArgumentParser, RawDescriptionHelpFormatter
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def override(dl, ks, v):
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"""
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Recursively replace dict of list
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Args:
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dl(dict or list): dict or list to be replaced
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ks(list): list of keys
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v(str): value to be replaced
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"""
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def str2num(v):
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try:
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return eval(v)
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except Exception:
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return v
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assert isinstance(dl, (list, dict)), ("{} should be a list or a dict")
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assert len(ks) > 0, ('lenght of keys should larger than 0')
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if isinstance(dl, list):
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k = str2num(ks[0])
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if len(ks) == 1:
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assert k < len(dl), ('index({}) out of range({})'.format(k, dl))
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dl[k] = str2num(v)
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else:
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override(dl[k], ks[1:], v)
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else:
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if len(ks) == 1:
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#assert ks[0] in dl, ('{} is not exist in {}'.format(ks[0], dl))
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if not ks[0] in dl:
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logger.warning('A new filed ({}) detected!'.format(ks[0], dl))
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dl[ks[0]] = str2num(v)
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else:
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assert ks[0] in dl, (
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'({}) doesn\'t exist in {}, a new dict field is invalid'.
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format(ks[0], dl))
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override(dl[ks[0]], ks[1:], v)
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def override_config(config, options=None):
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"""
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Recursively override the config
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Args:
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config(dict): dict to be replaced
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options(list): list of pairs(key0.key1.idx.key2=value)
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such as: [
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'topk=2',
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'VALID.transforms.1.ResizeImage.resize_short=300'
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]
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Returns:
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config(dict): replaced config
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"""
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if options is not None:
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for opt in options:
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assert isinstance(opt, str), (
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"option({}) should be a str".format(opt))
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assert "=" in opt, (
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"option({}) should contain a ="
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"to distinguish between key and value".format(opt))
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pair = opt.split('=')
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assert len(pair) == 2, ("there can be only a = in the option")
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key, value = pair
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keys = key.split('.')
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override(config, keys, value)
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return config
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class ArgsParser(ArgumentParser):
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def __init__(self):
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super(ArgsParser, self).__init__(
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formatter_class=RawDescriptionHelpFormatter)
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self.add_argument("-c", "--config", help="configuration file to use")
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self.add_argument(
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"-t", "--tag", default="0", help="tag for marking worker")
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self.add_argument(
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'-o',
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'--override',
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action='append',
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default=[],
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help='config options to be overridden')
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self.add_argument(
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"--style_image", default="examples/style_images/1.jpg", help="tag for marking worker")
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self.add_argument(
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"--text_corpus", default="PaddleOCR", help="tag for marking worker")
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self.add_argument(
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"--language", default="en", help="tag for marking worker")
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def parse_args(self, argv=None):
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args = super(ArgsParser, self).parse_args(argv)
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assert args.config is not None, \
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"Please specify --config=configure_file_path."
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return args
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def load_config(file_path):
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"""
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Load config from yml/yaml file.
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Args:
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file_path (str): Path of the config file to be loaded.
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Returns: config
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"""
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ext = os.path.splitext(file_path)[1]
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assert ext in ['.yml', '.yaml'], "only support yaml files for now"
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with open(file_path, 'rb') as f:
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config = yaml.load(f, Loader=yaml.Loader)
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return config
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def gen_config():
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base_config = {
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"Global": {
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"algorithm": "SRNet",
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"use_gpu": True,
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"start_epoch": 1,
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"stage1_epoch_num": 100,
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"stage2_epoch_num": 100,
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"log_smooth_window": 20,
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"print_batch_step": 2,
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"save_model_dir": "./output/SRNet",
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"use_visualdl": False,
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"save_epoch_step": 10,
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"vgg_pretrain": "./pretrained/VGG19_pretrained",
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"vgg_load_static_pretrain": True
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},
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"Architecture": {
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"model_type": "data_aug",
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"algorithm": "SRNet",
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"net_g": {
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"name": "srnet_net_g",
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"encode_dim": 64,
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"norm": "batch",
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"use_dropout": False,
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"init_type": "xavier",
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"init_gain": 0.02,
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"use_dilation": 1
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},
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# input_nc, ndf, netD,
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# n_layers_D=3, norm='instance', use_sigmoid=False, init_type='normal', init_gain=0.02, gpu_id='cuda:0'
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"bg_discriminator": {
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"name": "srnet_bg_discriminator",
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"input_nc": 6,
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"ndf": 64,
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"netD": "basic",
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"norm": "none",
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"init_type": "xavier",
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},
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"fusion_discriminator": {
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"name": "srnet_fusion_discriminator",
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"input_nc": 6,
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"ndf": 64,
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"netD": "basic",
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"norm": "none",
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"init_type": "xavier",
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}
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},
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"Loss": {
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"lamb": 10,
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"perceptual_lamb": 1,
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"muvar_lamb": 50,
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"style_lamb": 500
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},
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"Optimizer": {
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"name": "Adam",
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"learning_rate": {
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"name": "lambda",
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"lr": 0.0002,
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"lr_decay_iters": 50
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},
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"beta1": 0.5,
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"beta2": 0.999,
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},
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"Train": {
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"batch_size_per_card": 8,
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"num_workers_per_card": 4,
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"dataset": {
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"delimiter": "\t",
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"data_dir": "/",
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"label_file": "tmp/label.txt",
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"transforms": [{
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"DecodeImage": {
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"to_rgb": True,
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"to_np": False,
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"channel_first": False
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}
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}, {
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"NormalizeImage": {
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"scale": 1. / 255.,
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"mean": [0.485, 0.456, 0.406],
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"std": [0.229, 0.224, 0.225],
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"order": None
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}
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}, {
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"ToCHWImage": None
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}]
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}
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}
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}
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with open("config.yml", "w") as f:
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yaml.dump(base_config, f)
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if __name__ == '__main__':
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gen_config()
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