Spaces:
Configuration error
Configuration error
File size: 6,665 Bytes
1ab1a09 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 |
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import contextlib
import filelock
import os
import tempfile
import numpy as np
import random
from urllib.parse import urlparse, unquote
import paddle
from paddleseg.utils import logger, seg_env
from paddleseg.utils.download import download_file_and_uncompress
@contextlib.contextmanager
def generate_tempdir(directory: str=None, **kwargs):
'''Generate a temporary directory'''
directory = seg_env.TMP_HOME if not directory else directory
with tempfile.TemporaryDirectory(dir=directory, **kwargs) as _dir:
yield _dir
def load_entire_model(model, pretrained):
if pretrained is not None:
load_pretrained_model(model, pretrained)
else:
logger.warning('Not all pretrained params of {} are loaded, ' \
'training from scratch or a pretrained backbone.'.format(model.__class__.__name__))
def download_pretrained_model(pretrained_model):
"""
Download pretrained model from url.
Args:
pretrained_model (str): the url of pretrained weight
Returns:
str: the path of pretrained weight
"""
assert urlparse(pretrained_model).netloc, "The url is not valid."
pretrained_model = unquote(pretrained_model)
savename = pretrained_model.split('/')[-1]
if not savename.endswith(('tgz', 'tar.gz', 'tar', 'zip')):
savename = pretrained_model.split('/')[-2]
else:
savename = savename.split('.')[0]
with generate_tempdir() as _dir:
with filelock.FileLock(os.path.join(seg_env.TMP_HOME, savename)):
pretrained_model = download_file_and_uncompress(
pretrained_model,
savepath=_dir,
extrapath=seg_env.PRETRAINED_MODEL_HOME,
extraname=savename)
pretrained_model = os.path.join(pretrained_model, 'model.pdparams')
return pretrained_model
def load_pretrained_model(model, pretrained_model):
if pretrained_model is not None:
logger.info('Loading pretrained model from {}'.format(pretrained_model))
if urlparse(pretrained_model).netloc:
pretrained_model = download_pretrained_model(pretrained_model)
if os.path.exists(pretrained_model):
para_state_dict = paddle.load(pretrained_model)
model_state_dict = model.state_dict()
keys = model_state_dict.keys()
num_params_loaded = 0
for k in keys:
if k not in para_state_dict:
logger.warning("{} is not in pretrained model".format(k))
elif list(para_state_dict[k].shape) != list(model_state_dict[k]
.shape):
logger.warning(
"[SKIP] Shape of pretrained params {} doesn't match.(Pretrained: {}, Actual: {})"
.format(k, para_state_dict[k].shape, model_state_dict[k]
.shape))
else:
model_state_dict[k] = para_state_dict[k]
num_params_loaded += 1
model.set_dict(model_state_dict)
logger.info("There are {}/{} variables loaded into {}.".format(
num_params_loaded,
len(model_state_dict), model.__class__.__name__))
else:
raise ValueError('The pretrained model directory is not Found: {}'.
format(pretrained_model))
else:
logger.info(
'No pretrained model to load, {} will be trained from scratch.'.
format(model.__class__.__name__))
def resume(model, optimizer, resume_model):
if resume_model is not None:
logger.info('Resume model from {}'.format(resume_model))
if os.path.exists(resume_model):
resume_model = os.path.normpath(resume_model)
ckpt_path = os.path.join(resume_model, 'model.pdparams')
para_state_dict = paddle.load(ckpt_path)
ckpt_path = os.path.join(resume_model, 'model.pdopt')
opti_state_dict = paddle.load(ckpt_path)
model.set_state_dict(para_state_dict)
optimizer.set_state_dict(opti_state_dict)
iter = resume_model.split('_')[-1]
iter = int(iter)
return iter
else:
raise ValueError(
'Directory of the model needed to resume is not Found: {}'.
format(resume_model))
else:
logger.info('No model needed to resume.')
def worker_init_fn(worker_id):
np.random.seed(random.randint(0, 100000))
def get_image_list(image_path):
"""Get image list"""
valid_suffix = [
'.JPEG', '.jpeg', '.JPG', '.jpg', '.BMP', '.bmp', '.PNG', '.png'
]
image_list = []
image_dir = None
if os.path.isfile(image_path):
if os.path.splitext(image_path)[-1] in valid_suffix:
image_list.append(image_path)
else:
image_dir = os.path.dirname(image_path)
with open(image_path, 'r') as f:
for line in f:
line = line.strip()
if len(line.split()) > 1:
line = line.split()[0]
image_list.append(os.path.join(image_dir, line))
elif os.path.isdir(image_path):
image_dir = image_path
for root, dirs, files in os.walk(image_path):
for f in files:
if '.ipynb_checkpoints' in root:
continue
if f.startswith('.'):
continue
if os.path.splitext(f)[-1] in valid_suffix:
image_list.append(os.path.join(root, f))
else:
raise FileNotFoundError(
'`--image_path` is not found. it should be a path of image, or a file list containing image paths, or a directory including images.'
)
if len(image_list) == 0:
raise RuntimeError(
'There are not image file in `--image_path`={}'.format(image_path))
return image_list, image_dir
|