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未验证 提交 931564e5 编写于 作者: Q qingqing01 提交者： GitHub
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Merge pull request #91 from qingqing01/clean_code 

Remove hapi and move examples/* to root dir
上级 224515a6 e4c55582
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Showing with 82 addition and 13925 deletion
examples/bert/readme.md → bert/readme.md
浏览文件 @ 931564e5
......@@ -6,4 +6,4 @@
4. unzip pretrained parameters: tar -zvxf bert_uncased_L-12_H-768_A-12.tar.gz
4. bash run_classifier_single_gpu.sh
4. bash run_classifier_single_gpu.sh
examples/bert_leveldb/readme.md → bert_leveldb/readme.md
浏览文件 @ 931564e5
......@@ -8,4 +8,4 @@
4. unzip pretrained parameters: tar -zvxf bert_uncased_L-12_H-768_A-12.tar.gz
4. bash run_classifier_single_gpu.sh
4. bash run_classifier_single_gpu.sh
examples/bmn/bmn_utils.py → bmn/bmn_utils.py
浏览文件 @ 931564e5
......@@ -160,5 +160,3 @@ def bmn_post_processing(video_dict, subset, output_path, result_path):
json.dump(output_dict, outfile)
outfile.close()
hapi/__init__.py 已删除 100644 → 0
浏览文件 @ 224515a6
# 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.
from hapi import logger
from hapi.configure import Config
from hapi import callbacks
from hapi import datasets
from hapi import distributed
from hapi import download
from hapi import metrics
from hapi import model
from hapi import progressbar
from hapi import text
from hapi import vision
from hapi import loss
logger.setup_logger()
__all__ = [
'Config', 'callbacks', 'datasets', 'distributed', 'download', 'metrics',
'model', 'progressbar', 'text', 'vision', 'loss'
]
hapi/callbacks.py 已删除 100644 → 0
浏览文件 @ 224515a6
# 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 six
import copy
from .progressbar import ProgressBar
from paddle.fluid.dygraph.parallel import ParallelEnv
def config_callbacks(callbacks=None,
model=None,
batch_size=None,
epochs=None,
steps=None,
log_freq=2,
verbose=2,
save_freq=1,
save_dir=None,
metrics=None,
mode='train'):
cbks = callbacks or []
cbks = cbks if isinstance(cbks, (list, tuple)) else [cbks]
if not any(isinstance(k, ProgBarLogger) for k in cbks) and verbose:
cbks = [ProgBarLogger(log_freq, verbose=verbose)] + cbks
if not any(isinstance(k, ModelCheckpoint) for k in cbks):
cbks = cbks + [ModelCheckpoint(save_freq, save_dir)]
cbk_list = CallbackList(cbks)
cbk_list.set_model(model)
metrics = metrics or [] if mode != 'test' else []
params = {
'batch_size': batch_size,
'epochs': epochs,
'steps': steps,
'verbose': verbose,
'metrics': metrics,
}
cbk_list.set_params(params)
return cbk_list
class CallbackList(object):
def __init__(self, callbacks=None):
# copy
self.callbacks = [c for c in callbacks]
self.params = {}
self.model = None
def append(self, callback):
self.callbacks.append(callback)
def __iter__(self):
return iter(self.callbacks)
def set_params(self, params):
for c in self.callbacks:
c.set_params(params)
def set_model(self, model):
for c in self.callbacks:
c.set_model(model)
def _call(self, name, *args):
for c in self.callbacks:
func = getattr(c, name)
func(*args)
def _check_mode(self, mode):
assert mode in ['train', 'eval', 'test'], \
'mode should be train, eval or test'
def on_begin(self, mode, logs=None):
self._check_mode(mode)
name = 'on_{}_begin'.format(mode)
self._call(name, logs)
def on_end(self, mode, logs=None):
self._check_mode(mode)
name = 'on_{}_end'.format(mode)
self._call(name, logs)
def on_epoch_begin(self, epoch=None, logs=None):
self._call('on_epoch_begin', epoch, logs)
def on_epoch_end(self, epoch=None, logs=None):
self._call('on_epoch_end', epoch, logs)
def on_batch_begin(self, mode, step=None, logs=None):
self._check_mode(mode)
name = 'on_{}_batch_begin'.format(mode)
self._call(name, step, logs)
def on_batch_end(self, mode, step=None, logs=None):
self._check_mode(mode)
name = 'on_{}_batch_end'.format(mode)
self._call(name, step, logs)
class Callback(object):
"""Base class used to build new callbacks.
"""
def __init__(self):
self.model = None
self.params = {}
def set_params(self, params):
self.params = params
def set_model(self, model):
self.model = model
def on_train_begin(self, logs=None):
"""Called at the start of training.
"""
def on_train_end(self, logs=None):
"""Called at the end of training.
"""
def on_eval_begin(self, logs=None):
"""Called at the start of evaluation.
"""
def on_eval_end(self, logs=None):
"""Called at the end of evaluation.
"""
def on_test_begin(self, logs=None):
"""Called at the beginning of predict.
"""
def on_test_end(self, logs=None):
"""Called at the end of predict.
"""
def on_epoch_begin(self, epoch, logs=None):
"""Called at the beginning of each epoch.
"""
def on_epoch_end(self, epoch, logs=None):
"""Called at the end of each epoch.
"""
def on_train_batch_begin(self, step, logs=None):
"""Called at the beginning of each batch in training.
"""
def on_train_batch_end(self, step, logs=None):
"""Called at the end of each batch in training.
"""
def on_eval_batch_begin(self, step, logs=None):
"""Called at the beginning of each batch in evaluation.
"""
def on_eval_batch_end(self, step, logs=None):
"""Called at the end of each batch in evaluation.
"""
def on_test_batch_begin(self, step, logs=None):
"""Called at the beginning of each batch in predict.
"""
def on_test_batch_end(self, step, logs=None):
"""Called at the end of each batch in predict.
"""
class ProgBarLogger(Callback):
"""Logger callback function
Args:
log_freq (int): The frequency, in number of steps, the logs such as `loss`,
`metrics` are printed. Default: 1.
verbose (int): The verbosity mode, should be 0, 1, or 2.
0 = silent, 1 = progress bar, 2 = one line per epoch. Default: 2.
Examples:
.. code-block:: python
import numpy as np
from paddle import fluid
from hapi.metrics import Accuracy
from hapi.loss import CrossEntropy
from hapi.datasets import MNIST
from hapi.vision.transforms import Compose, Resize
from hapi.vision.models import LeNet
from hapi.callbacks import ProgBarLogger
from hapi.model import Input, set_device
inputs = [Input([-1, 1, 28, 28], 'float32', name='image')]
labels = [Input([None, 1], 'int64', name='label')]
train_dataset = MNIST(mode='train')
model = LeNet()
optim = fluid.optimizer.Adam(0.001)
model.prepare(optimizer=optim,
loss_function=CrossEntropy(),
metrics=Accuracy(),
inputs=inputs,
labels=labels)
callback = ProgBarLogger(log_freq=10)
model.fit(train_dataset, batch_size=64, callbacks=callback)
"""
def __init__(self, log_freq=1, verbose=2):
self.epochs = None
self.steps = None
self.progbar = None
self.verbose = verbose
self.log_freq = log_freq
def _is_print(self):
return self.verbose and ParallelEnv().local_rank == 0
def on_train_begin(self, logs=None):
self.epochs = self.params['epochs']
assert self.epochs
self.train_metrics = self.params['metrics']
assert self.train_metrics
def on_epoch_begin(self, epoch=None, logs=None):
self.steps = self.params['steps']
self.epoch = epoch
self.train_step = 0
if self.epochs and self._is_print():
print('Epoch %d/%d' % (epoch + 1, self.epochs))
self.train_progbar = ProgressBar(num=self.steps, verbose=self.verbose)
def _updates(self, logs, mode):
values = []
metrics = getattr(self, '%s_metrics' % (mode))
progbar = getattr(self, '%s_progbar' % (mode))
steps = getattr(self, '%s_step' % (mode))
for k in metrics:
if k in logs:
values.append((k, logs[k]))
progbar.update(steps, values)
def on_train_batch_end(self, step, logs=None):
logs = logs or {}
self.train_step += 1
if self._is_print() and self.train_step % self.log_freq == 0:
if self.steps is None or self.train_step < self.steps:
self._updates(logs, 'train')
def on_epoch_end(self, epoch, logs=None):
logs = logs or {}
if self._is_print() and (self.steps is not None):
self._updates(logs, 'train')
def on_eval_begin(self, logs=None):
self.eval_steps = logs.get('steps', None)
self.eval_metrics = logs.get('metrics_name', [])
self.eval_step = 0
self.evaled_samples = 0
self.eval_progbar = ProgressBar(
num=self.eval_steps, verbose=self.verbose)
if self._is_print():
print('Eval begin...')
def on_eval_batch_end(self, step, logs=None):
logs = logs or {}
self.eval_step += 1
samples = logs.get('batch_size', 1)
self.evaled_samples += samples
if self._is_print() and self.eval_step % self.log_freq == 0:
if self.eval_steps is None or self.eval_step < self.eval_steps:
self._updates(logs, 'eval')
def on_test_begin(self, logs=None):
self.test_steps = logs.get('steps', None)
self.test_metrics = logs.get('metrics_name', [])
self.test_step = 0
self.tested_samples = 0
self.test_progbar = ProgressBar(
num=self.test_steps, verbose=self.verbose)
if self._is_print():
print('Predict begin...')
def on_test_batch_end(self, step, logs=None):
logs = logs or {}
self.test_step += 1
samples = logs.get('batch_size', 1)
self.tested_samples += samples
if self.test_step % self.log_freq == 0 and self._is_print():
if self.test_steps is None or self.test_step < self.test_steps:
self._updates(logs, 'test')
def on_eval_end(self, logs=None):
logs = logs or {}
if self._is_print() and (self.eval_steps is not None):
self._updates(logs, 'eval')
print('Eval samples: %d' % (self.evaled_samples))
def on_test_end(self, logs=None):
logs = logs or {}
if self._is_print():
if self.test_step % self.log_freq != 0 or self.verbose == 1:
self._updates(logs, 'test')
print('Predict samples: %d' % (self.tested_samples))
class ModelCheckpoint(Callback):
"""Model checkpoint callback function
Args:
save_freq(int): The frequency, in number of epochs, the model checkpoint
are saved. Default: 1.
save_dir(str|None): The directory to save checkpoint during training.
If None, will not save checkpoint. Default: None.
Examples:
.. code-block:: python
import numpy as np
from paddle import fluid
from hapi.metrics import Accuracy
from hapi.loss import CrossEntropy
from hapi.datasets import MNIST
from hapi.vision.transforms import Compose, Resize
from hapi.vision.models import LeNet
from hapi.callbacks import ModelCheckpoint
from hapi.model import Input, set_device
inputs = [Input([-1, 1, 28, 28], 'float32', name='image')]
labels = [Input([None, 1], 'int64', name='label')]
train_dataset = MNIST(mode='train')
model = LeNet()
optim = fluid.optimizer.Adam(0.001)
model.prepare(optimizer=optim,
loss_function=CrossEntropy(),
metrics=Accuracy(),
inputs=inputs,
labels=labels)
callback = ModelCheckpoint(save_dir='./temp')
model.fit(train_dataset, batch_size=64, callbacks=callback)
"""
def __init__(self, save_freq=1, save_dir=None):
self.save_freq = save_freq
self.save_dir = save_dir
def on_epoch_begin(self, epoch=None, logs=None):
self.epoch = epoch
def _is_save(self):
return self.model and self.save_dir and ParallelEnv().local_rank == 0
def on_epoch_end(self, epoch, logs=None):
if self._is_save() and self.epoch % self.save_freq == 0:
path = '{}/{}'.format(self.save_dir, epoch)
print('save checkpoint at {}'.format(path))
self.model.save(path)
def on_train_end(self, logs=None):
if self._is_save():
path = '{}/final'.format(self.save_dir)
print('save checkpoint at {}'.format(path))
self.model.save(path)
hapi/configure.py 已删除 100644 → 0
浏览文件 @ 224515a6
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import argparse
import json
import yaml
import six
import logging
logging_only_message = "%(message)s"
logging_details = "%(asctime)s.%(msecs)03d %(levelname)s %(module)s - %(funcName)s: %(message)s"
class JsonConfig(object):
"""
A high-level api for handling json configure file.
"""
def __init__(self, config_path):
self._config_dict = self._parse(config_path)
def _parse(self, config_path):
try:
with open(config_path) as json_file:
config_dict = json.load(json_file)
except:
raise IOError("Error in parsing bert model config file '%s'" %
config_path)
else:
return config_dict
def __getitem__(self, key):
return self._config_dict[key]
def print_config(self):
for arg, value in sorted(six.iteritems(self._config_dict)):
print('%s: %s' % (arg, value))
print('------------------------------------------------')
class ArgumentGroup(object):
def __init__(self, parser, title, des):
self._group = parser.add_argument_group(title=title, description=des)
def add_arg(self, name, type, default, help, **kwargs):
type = str2bool if type == bool else type
self._group.add_argument(
"--" + name,
default=default,
type=type,
help=help + ' Default: %(default)s.',
**kwargs)
class ArgConfig(object):
"""
A high-level api for handling argument configs.
"""
def __init__(self):
parser = argparse.ArgumentParser()
custom_g = ArgumentGroup(parser, "customize", "customized options.")
self.custom_g = custom_g
self.parser = parser
def add_arg(self, name, dtype, default, descrip):
self.custom_g.add_arg(name, dtype, default, descrip)
def build_conf(self):
return self.parser.parse_args()
def str2bool(v):
# because argparse does not support to parse "true, False" as python
# boolean directly
return v.lower() in ("true", "t", "1")
def print_arguments(args, log=None):
if not log:
print('----------- Configuration Arguments -----------')
for arg, value in sorted(six.iteritems(vars(args))):
print('%s: %s' % (arg, value))
print('------------------------------------------------')
else:
log.info('----------- Configuration Arguments -----------')
for arg, value in sorted(six.iteritems(vars(args))):
log.info('%s: %s' % (arg, value))
log.info('------------------------------------------------')
class Config(object):
"""
A high-level API for managing configuration files in PaddlePaddle.
Can jointly work with command-line-arugment, json files and yaml files.
"""
def __init__(self, json_file="", yaml_file="", fuse_args=True):
"""
Init funciton for PDConfig.
json_file: the path to the json configure file.
yaml_file: the path to the yaml configure file.
fuse_args: if fuse the json/yaml configs with argparse.
"""
assert isinstance(json_file, str)
assert isinstance(yaml_file, str)
if json_file != "" and yaml_file != "":
raise Warning(
"json_file and yaml_file can not co-exist for now. please only use one configure file type."
)
return
self.args = None
self.arg_config = {}
self.json_config = {}
self.yaml_config = {}
parser = argparse.ArgumentParser()
self.default_g = ArgumentGroup(parser, "default", "default options.")
self.yaml_g = ArgumentGroup(parser, "yaml", "options from yaml.")
self.json_g = ArgumentGroup(parser, "json", "options from json.")
self.com_g = ArgumentGroup(parser, "custom", "customized options.")
self.parser = parser
if json_file != "":
self.load_json(json_file, fuse_args=fuse_args)
if yaml_file:
self.load_yaml(yaml_file, fuse_args=fuse_args)
def load_json(self, file_path, fuse_args=True):
if not os.path.exists(file_path):
raise Warning("the json file %s does not exist." % file_path)
return
with open(file_path, "r") as fin:
self.json_config = json.loads(fin.read())
fin.close()
if fuse_args:
for name in self.json_config:
if isinstance(self.json_config[name], list):
self.json_g.add_arg(
name,
type(self.json_config[name][0]),
self.json_config[name],
"This is from %s" % file_path,
nargs=len(self.json_config[name]))
continue
if not isinstance(self.json_config[name], int) \
and not isinstance(self.json_config[name], float) \
and not isinstance(self.json_config[name], str) \
and not isinstance(self.json_config[name], bool):
continue
self.json_g.add_arg(name,
type(self.json_config[name]),
self.json_config[name],
"This is from %s" % file_path)
def load_yaml(self, file_path, fuse_args=True):
if not os.path.exists(file_path):
raise Warning("the yaml file %s does not exist." % file_path)
return
with open(file_path, "r") as fin:
self.yaml_config = yaml.load(fin, Loader=yaml.SafeLoader)
fin.close()
if fuse_args:
for name in self.yaml_config:
if isinstance(self.yaml_config[name], list):
self.yaml_g.add_arg(
name,
type(self.yaml_config[name][0]),
self.yaml_config[name],
"This is from %s" % file_path,
nargs=len(self.yaml_config[name]))
continue
if not isinstance(self.yaml_config[name], int) \
and not isinstance(self.yaml_config[name], float) \
and not isinstance(self.yaml_config[name], str) \
and not isinstance(self.yaml_config[name], bool):
continue
self.yaml_g.add_arg(name,
type(self.yaml_config[name]),
self.yaml_config[name],
"This is from %s" % file_path)
def build(self):
self.args = self.parser.parse_args()
self.arg_config = vars(self.args)
def __add__(self, new_arg):
assert isinstance(new_arg, list) or isinstance(new_arg, tuple)
assert len(new_arg) >= 3
assert self.args is None
name = new_arg[0]
dtype = new_arg[1]
dvalue = new_arg[2]
desc = new_arg[3] if len(
new_arg) == 4 else "Description is not provided."
self.com_g.add_arg(name, dtype, dvalue, desc)
return self
def __getattr__(self, name):
if name in self.arg_config:
return self.arg_config[name]
if name in self.json_config:
return self.json_config[name]
if name in self.yaml_config:
return self.yaml_config[name]
raise Warning("The argument %s is not defined." % name)
def Print(self):
print("-" * 70)
for name in self.arg_config:
print("%s:\t\t\t\t%s" % (str(name), str(self.arg_config[name])))
for name in self.json_config:
if name not in self.arg_config:
print("%s:\t\t\t\t%s" %
(str(name), str(self.json_config[name])))
for name in self.yaml_config:
if name not in self.arg_config:
print("%s:\t\t\t\t%s" %
(str(name), str(self.yaml_config[name])))
print("-" * 70)
if __name__ == "__main__":
"""
pd_config = PDConfig(json_file = "./test/bert_config.json")
pd_config.build()
print(pd_config.do_train)
print(pd_config.hidden_size)
pd_config = PDConfig(yaml_file = "./test/bert_config.yaml")
pd_config.build()
print(pd_config.do_train)
print(pd_config.hidden_size)
"""
config = Config(yaml_file="./bert.yaml")
config += ("my_age", int, 18, "I am forever 18.")
config.build()
print(config.data_dir)
print(config.my_age)
hapi/datasets/__init__.py 已删除 100644 → 0
浏览文件 @ 224515a6
# 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.
from . import folder
from . import mnist
from . import flowers
from .folder import *
from .mnist import *
from .flowers import *
__all__ = folder.__all__ \
+ mnist.__all__ \
+ flowers.__all__
hapi/datasets/flowers.py 已删除 100644 → 0
浏览文件 @ 224515a6
# 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.
from __future__ import print_function
import os
import io
import tarfile
import numpy as np
import scipy.io as scio
from PIL import Image
from paddle.io import Dataset
from .utils import _check_exists_and_download
__all__ = ["Flowers"]
DATA_URL = 'http://paddlemodels.bj.bcebos.com/flowers/102flowers.tgz'
LABEL_URL = 'http://paddlemodels.bj.bcebos.com/flowers/imagelabels.mat'
SETID_URL = 'http://paddlemodels.bj.bcebos.com/flowers/setid.mat'
DATA_MD5 = '52808999861908f626f3c1f4e79d11fa'
LABEL_MD5 = 'e0620be6f572b9609742df49c70aed4d'
SETID_MD5 = 'a5357ecc9cb78c4bef273ce3793fc85c'
# In official 'readme', tstid is the flag of test data
# and trnid is the flag of train data. But test data is more than train data.
# So we exchange the train data and test data.
MODE_FLAG_MAP = {'train': 'tstid', 'test': 'trnid', 'valid': "valid"}
class Flowers(Dataset):
"""
Implement of flowers dataset
Args:
data_file(str): path to data file, can be set None if
:attr:`download` is True. Default None
label_file(str): path to label file, can be set None if
:attr:`download` is True. Default None
setid_file(str): path to subset index file, can be set
None if :attr:`download` is True. Default None
mode(str): 'train', 'valid' or 'test' mode. Default 'train'.
download(bool): whether auto download mnist dataset if
:attr:`image_path`/:attr:`label_path` unset. Default
True
Examples:
.. code-block:: python
from hapi.vision.datasets import Flowers
flowers = Flowers(mode='test')
for i in range(len(flowers)):
sample = flowers[i]
print(sample[0].shape, sample[1])
"""
def __init__(self,
data_file=None,
label_file=None,
setid_file=None,
mode='train',
transform=None,
download=True):
assert mode.lower() in ['train', 'valid', 'test'], \
"mode should be 'train', 'valid' or 'test', but got {}".format(mode)
self.flag = MODE_FLAG_MAP[mode.lower()]
self.data_file = data_file
if self.data_file is None:
assert download, "data_file not set and auto download disabled"
self.data_file = _check_exists_and_download(
data_file, DATA_URL, DATA_MD5, 'flowers', download)
self.label_file = label_file
if self.label_file is None:
assert download, "label_file not set and auto download disabled"
self.label_file = _check_exists_and_download(
label_file, LABEL_URL, LABEL_MD5, 'flowers', download)
self.setid_file = setid_file
if self.setid_file is None:
assert download, "setid_file not set and auto download disabled"
self.setid_file = _check_exists_and_download(
setid_file, SETID_URL, SETID_MD5, 'flowers', download)
self.transform = transform
# read dataset into memory
self._load_anno()
def _load_anno(self):
self.name2mem = {}
self.data_tar = tarfile.open(self.data_file)
for ele in self.data_tar.getmembers():
self.name2mem[ele.name] = ele
self.labels = scio.loadmat(self.label_file)['labels'][0]
self.indexes = scio.loadmat(self.setid_file)[self.flag][0]
def __getitem__(self, idx):
index = self.indexes[idx]
label = np.array([self.labels[index - 1]])
img_name = "jpg/image_%05d.jpg" % index
img_ele = self.name2mem[img_name]
image = self.data_tar.extractfile(img_ele).read()
image = np.array(Image.open(io.BytesIO(image)))
if self.transform is not None:
image = self.transform(image)
return image, label.astype('int64')
def __len__(self):
return len(self.indexes)
hapi/datasets/folder.py 已删除 100644 → 0
浏览文件 @ 224515a6
# 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 os
import sys
import cv2
from paddle.io import Dataset
__all__ = ["DatasetFolder", "ImageFolder"]
def has_valid_extension(filename, extensions):
"""Checks if a file is a vilid extension.
Args:
filename (str): path to a file
extensions (tuple of str): extensions to consider (lowercase)
Returns:
bool: True if the filename ends with one of given extensions
"""
return filename.lower().endswith(extensions)
def make_dataset(dir, class_to_idx, extensions, is_valid_file=None):
images = []
dir = os.path.expanduser(dir)
if extensions is not None:
def is_valid_file(x):
return has_valid_extension(x, extensions)
for target in sorted(class_to_idx.keys()):
d = os.path.join(dir, target)
if not os.path.isdir(d):
continue
for root, _, fnames in sorted(os.walk(d, followlinks=True)):
for fname in sorted(fnames):
path = os.path.join(root, fname)
if is_valid_file(path):
item = (path, class_to_idx[target])
images.append(item)
return images
class DatasetFolder(Dataset):
"""A generic data loader where the samples are arranged in this way:
root/class_a/1.ext
root/class_a/2.ext
root/class_a/3.ext
root/class_b/123.ext
root/class_b/456.ext
root/class_b/789.ext
Args:
root (string): Root directory path.
loader (callable|optional): A function to load a sample given its path.
extensions (tuple[str]|optional): A list of allowed extensions.
both extensions and is_valid_file should not be passed.
transform (callable|optional): A function/transform that takes in
a sample and returns a transformed version.
is_valid_file (callable|optional): A function that takes path of a file
and check if the file is a valid file (used to check of corrupt files)
both extensions and is_valid_file should not be passed.
Attributes:
classes (list): List of the class names.
class_to_idx (dict): Dict with items (class_name, class_index).
samples (list): List of (sample path, class_index) tuples
targets (list): The class_index value for each image in the dataset
"""
def __init__(self,
root,
loader=None,
extensions=None,
transform=None,
is_valid_file=None):
self.root = root
self.transform = transform
if extensions is None:
extensions = IMG_EXTENSIONS
classes, class_to_idx = self._find_classes(self.root)
samples = make_dataset(self.root, class_to_idx, extensions,
is_valid_file)
if len(samples) == 0:
raise (RuntimeError(
"Found 0 files in subfolders of: " + self.root + "\n"
"Supported extensions are: " + ",".join(extensions)))
self.loader = cv2_loader if loader is None else loader
self.extensions = extensions
self.classes = classes
self.class_to_idx = class_to_idx
self.samples = samples
self.targets = [s[1] for s in samples]
def _find_classes(self, dir):
"""
Finds the class folders in a dataset.
Args:
dir (string): Root directory path.
Returns:
tuple: (classes, class_to_idx) where classes are relative to (dir),
and class_to_idx is a dictionary.
"""
if sys.version_info >= (3, 5):
# Faster and available in Python 3.5 and above
classes = [d.name for d in os.scandir(dir) if d.is_dir()]
else:
classes = [
d for d in os.listdir(dir)
if os.path.isdir(os.path.join(dir, d))
]
classes.sort()
class_to_idx = {classes[i]: i for i in range(len(classes))}
return classes, class_to_idx
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (sample, target) where target is class_index of the target class.
"""
path, target = self.samples[index]
sample = self.loader(path)
if self.transform is not None:
sample = self.transform(sample)
return sample, target
def __len__(self):
return len(self.samples)
IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif',
'.tiff', '.webp')
def cv2_loader(path):
return cv2.imread(path)
class ImageFolder(Dataset):
"""A generic data loader where the samples are arranged in this way:
root/1.ext
root/2.ext
root/sub_dir/3.ext
Args:
root (string): Root directory path.
loader (callable, optional): A function to load a sample given its path.
extensions (tuple[string], optional): A list of allowed extensions.
both extensions and is_valid_file should not be passed.
transform (callable, optional): A function/transform that takes in
a sample and returns a transformed version.
is_valid_file (callable, optional): A function that takes path of a file
and check if the file is a valid file (used to check of corrupt files)
both extensions and is_valid_file should not be passed.
Attributes:
samples (list): List of sample path
"""
def __init__(self,
root,
loader=None,
extensions=None,
transform=None,
is_valid_file=None):
self.root = root
if extensions is None:
extensions = IMG_EXTENSIONS
samples = []
path = os.path.expanduser(root)
if extensions is not None:
def is_valid_file(x):
return has_valid_extension(x, extensions)
for root, _, fnames in sorted(os.walk(path, followlinks=True)):
for fname in sorted(fnames):
f = os.path.join(root, fname)
if is_valid_file(f):
samples.append(f)
if len(samples) == 0:
raise (RuntimeError(
"Found 0 files in subfolders of: " + self.root + "\n"
"Supported extensions are: " + ",".join(extensions)))
self.loader = cv2_loader if loader is None else loader
self.extensions = extensions
self.samples = samples
self.transform = transform
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (sample, target) where target is class_index of the target class.
"""
path = self.samples[index]
sample = self.loader(path)
if self.transform is not None:
sample = self.transform(sample)
return [sample]
def __len__(self):
return len(self.samples)
hapi/datasets/mnist.py 已删除 100644 → 0
浏览文件 @ 224515a6
# 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.
from __future__ import print_function
import os
import gzip
import struct
import numpy as np
import paddle.dataset.common
from paddle.io import Dataset
from .utils import _check_exists_and_download
__all__ = ["MNIST"]
URL_PREFIX = 'https://dataset.bj.bcebos.com/mnist/'
TEST_IMAGE_URL = URL_PREFIX + 't10k-images-idx3-ubyte.gz'
TEST_IMAGE_MD5 = '9fb629c4189551a2d022fa330f9573f3'
TEST_LABEL_URL = URL_PREFIX + 't10k-labels-idx1-ubyte.gz'
TEST_LABEL_MD5 = 'ec29112dd5afa0611ce80d1b7f02629c'
TRAIN_IMAGE_URL = URL_PREFIX + 'train-images-idx3-ubyte.gz'
TRAIN_IMAGE_MD5 = 'f68b3c2dcbeaaa9fbdd348bbdeb94873'
TRAIN_LABEL_URL = URL_PREFIX + 'train-labels-idx1-ubyte.gz'
TRAIN_LABEL_MD5 = 'd53e105ee54ea40749a09fcbcd1e9432'
class MNIST(Dataset):
"""
Implement of MNIST dataset
Args:
image_path(str): path to image file, can be set None if
:attr:`download` is True. Default None
label_path(str): path to label file, can be set None if
:attr:`download` is True. Default None
chw_format(bool): If set True, the output shape is [1, 28, 28],
otherwise, output shape is [1, 784]. Default True.
mode(str): 'train' or 'test' mode. Default 'train'.
download(bool): whether auto download mnist dataset if
:attr:`image_path`/:attr:`label_path` unset. Default
True
Returns:
Dataset: MNIST Dataset.
Examples:
.. code-block:: python
from hapi.vision.datasets import MNIST
mnist = MNIST(mode='test')
for i in range(len(mnist)):
sample = mnist[i]
print(sample[0].shape, sample[1])
"""
def __init__(self,
image_path=None,
label_path=None,
chw_format=True,
mode='train',
transform=None,
download=True):
assert mode.lower() in ['train', 'test'], \
"mode should be 'train' or 'test', but got {}".format(mode)
self.mode = mode.lower()
self.chw_format = chw_format
self.image_path = image_path
if self.image_path is None:
assert download, "image_path not set and auto download disabled"
image_url = TRAIN_IMAGE_URL if mode == 'train' else TEST_IMAGE_URL
image_md5 = TRAIN_IMAGE_MD5 if mode == 'train' else TEST_IMAGE_MD5
self.image_path = _check_exists_and_download(
image_path, image_url, image_md5, 'mnist', download)
self.label_path = label_path
if self.label_path is None:
assert download, "label_path not set and auto download disabled"
label_url = TRAIN_LABEL_URL if mode == 'train' else TEST_LABEL_URL
label_md5 = TRAIN_LABEL_MD5 if mode == 'train' else TEST_LABEL_MD5
self.label_path = _check_exists_and_download(
label_path, label_url, label_md5, 'mnist', download)
self.transform = transform
# read dataset into memory
self._parse_dataset()
def _parse_dataset(self, buffer_size=100):
self.images = []
self.labels = []
with gzip.GzipFile(self.image_path, 'rb') as image_file:
img_buf = image_file.read()
with gzip.GzipFile(self.label_path, 'rb') as label_file:
lab_buf = label_file.read()
step_label = 0
offset_img = 0
# read from Big-endian
# get file info from magic byte
# image file : 16B
magic_byte_img = '>IIII'
magic_img, image_num, rows, cols = struct.unpack_from(
magic_byte_img, img_buf, offset_img)
offset_img += struct.calcsize(magic_byte_img)
offset_lab = 0
# label file : 8B
magic_byte_lab = '>II'
magic_lab, label_num = struct.unpack_from(magic_byte_lab,
lab_buf, offset_lab)
offset_lab += struct.calcsize(magic_byte_lab)
while True:
if step_label >= label_num:
break
fmt_label = '>' + str(buffer_size) + 'B'
labels = struct.unpack_from(fmt_label, lab_buf, offset_lab)
offset_lab += struct.calcsize(fmt_label)
step_label += buffer_size
fmt_images = '>' + str(buffer_size * rows * cols) + 'B'
images_temp = struct.unpack_from(fmt_images, img_buf,
offset_img)
images = np.reshape(images_temp, (buffer_size, rows *
cols)).astype('float32')
offset_img += struct.calcsize(fmt_images)
images = images / 255.0
images = images * 2.0
images = images - 1.0
for i in range(buffer_size):
self.images.append(images[i, :])
self.labels.append(
np.array([labels[i]]).astype('int64'))
def __getitem__(self, idx):
image, label = self.images[idx], self.labels[idx]
if self.chw_format:
image = np.reshape(image, [1, 28, 28])
if self.transform is not None:
image = self.transform(image)
return image, label
def __len__(self):
return len(self.labels)
hapi/datasets/utils.py 已删除 100644 → 0
浏览文件 @ 224515a6
# 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.
from __future__ import print_function
import os
import paddle.dataset.common
def _check_exists_and_download(path, url, md5, module_name, download=True):
if path and os.path.exists(path):
return path
if download:
return paddle.dataset.common.download(url, module_name, md5)
else:
raise ValueError('{} not exists and auto download disabled'.format(
path))
hapi/distributed.py 已删除 100644 → 0
浏览文件 @ 224515a6
# 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 os
import sys
import six
import time
import math
import socket
import contextlib
import numpy as np
from paddle import fluid
from paddle.fluid.layers import collective
from paddle.fluid.dygraph.parallel import ParallelEnv, ParallelStrategy
from paddle.io import BatchSampler
_parallel_context_initialized = False
class DistributedBatchSampler(BatchSampler):
"""Sampler that restricts data loading to a subset of the dataset.
In such case, each process can pass a DistributedBatchSampler instance
as a DataLoader sampler, and load a subset of the original dataset that
is exclusive to it.
.. note::
Dataset is assumed to be of constant size.
Args:
data_source: this could be a `paddle.io.Dataset` implement
or other python object which implemented
`__len__` for BatchSampler to get sample
number of data source.
batch_size(int): sample indice number in a mini-batch indices.
shuffle(bool): whther to shuffle indices order before genrating
batch indices. Default False.
drop_last(bool): whether drop the last incomplete batch dataset size
is not divisible by the batch size. Default False
Examples:
.. code-block:: python
import numpy as np
from hapi.datasets import MNIST
from hapi.distributed import DistributedBatchSampler
class MnistDataset(MNIST):
def __init__(self, mode, return_label=True):
super(MnistDataset, self).__init__(mode=mode)
self.return_label = return_label
def __getitem__(self, idx):
img = np.reshape(self.images[idx], [1, 28, 28])
if self.return_label:
return img, np.array(self.labels[idx]).astype('int64')
return img,
def __len__(self):
return len(self.images)
train_dataset = MnistDataset(mode='train')
dist_train_dataloader = DistributedBatchSampler(train_dataset, batch_size=64)
for data in dist_train_dataloader:
# do something
break
"""
def __init__(self, dataset, batch_size, shuffle=False, drop_last=False):
self.dataset = dataset
assert isinstance(batch_size, int) and batch_size > 0, \
"batch_size should be a positive integer"
self.batch_size = batch_size
assert isinstance(shuffle, bool), \
"shuffle should be a boolean value"
self.shuffle = shuffle
assert isinstance(drop_last, bool), \
"drop_last should be a boolean number"
self.drop_last = drop_last
self.nranks = ParallelEnv().nranks
self.local_rank = ParallelEnv().local_rank
self.epoch = 0
self.num_samples = int(
math.ceil(len(self.dataset) * 1.0 / self.nranks))
self.total_size = self.num_samples * self.nranks
def __iter__(self):
num_samples = len(self.dataset)
indices = np.arange(num_samples).tolist()
indices += indices[:(self.total_size - len(indices))]
assert len(indices) == self.total_size
if self.shuffle:
np.random.RandomState(self.epoch).shuffle(indices)
self.epoch += 1
# subsample
def _get_indices_by_batch_size(indices):
subsampled_indices = []
last_batch_size = self.total_size % (self.batch_size * self.nranks)
assert last_batch_size % self.nranks == 0
last_local_batch_size = last_batch_size // self.nranks
for i in range(self.local_rank * self.batch_size,
len(indices) - last_batch_size,
self.batch_size * self.nranks):
subsampled_indices.extend(indices[i:i + self.batch_size])
indices = indices[len(indices) - last_batch_size:]
subsampled_indices.extend(indices[
self.local_rank * last_local_batch_size:(
self.local_rank + 1) * last_local_batch_size])
return subsampled_indices
if self.nranks > 1:
indices = _get_indices_by_batch_size(indices)
assert len(indices) == self.num_samples
_sample_iter = iter(indices)
batch_indices = []
for idx in _sample_iter:
batch_indices.append(idx)
if len(batch_indices) == self.batch_size:
yield batch_indices
batch_indices = []
if not self.drop_last and len(batch_indices) > 0:
yield batch_indices
def __len__(self):
num_samples = self.num_samples
num_samples += int(not self.drop_last) * (self.batch_size - 1)
return num_samples // self.batch_size
def set_epoch(self, epoch):
self.epoch = epoch
def _all_gather(x, nranks, ring_id=0, use_calc_stream=True):
return collective._c_allgather(
x, nranks, ring_id=ring_id, use_calc_stream=use_calc_stream)
def wait_server_ready(endpoints):
assert not isinstance(endpoints, six.string_types)
while True:
all_ok = True
not_ready_endpoints = []
for ep in endpoints:
ip_port = ep.split(":")
with contextlib.closing(
socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
sock.settimeout(2)
result = sock.connect_ex((ip_port[0], int(ip_port[1])))
if result != 0:
all_ok = False
not_ready_endpoints.append(ep)
if not all_ok:
time.sleep(3)
else:
break
def init_communicator(program, rank, nranks, wait_port, current_endpoint,
endpoints):
if nranks < 2:
return
other_endpoints = endpoints[:]
other_endpoints.remove(current_endpoint)
if rank == 0 and wait_port:
wait_server_ready(other_endpoints)
block = program.global_block()
nccl_id_var = block.create_var(
name=fluid.unique_name.generate('nccl_id'),
persistable=True,
type=fluid.core.VarDesc.VarType.RAW)
block.append_op(
type='c_gen_nccl_id',
inputs={},
outputs={'Out': nccl_id_var},
attrs={
'rank': rank,
'endpoint': current_endpoint,
'other_endpoints': other_endpoints
})
block.append_op(
type='c_comm_init',
inputs={'X': nccl_id_var},
outputs={},
attrs={
'nranks': nranks,
'rank': rank,
'ring_id': 0,
})
def prepare_distributed_context(place=None):
if place is None:
place = fluid.CUDAPlace(ParallelEnv().dev_id) if ParallelEnv().nranks > 1 \
else fluid.CUDAPlace(0)
strategy = ParallelStrategy()
strategy.nranks = ParallelEnv().nranks
strategy.local_rank = ParallelEnv().local_rank
strategy.trainer_endpoints = ParallelEnv().trainer_endpoints
strategy.current_endpoint = ParallelEnv().current_endpoint
if strategy.nranks < 2:
return
global _parallel_context_initialized
if not _parallel_context_initialized and isinstance(place,
fluid.CUDAPlace):
def _init_context():
communicator_prog = fluid.Program()
init_communicator(communicator_prog, strategy.local_rank,
strategy.nranks, True, strategy.current_endpoint,
strategy.trainer_endpoints)
exe = fluid.Executor(place)
exe.run(communicator_prog)
if fluid.in_dygraph_mode():
fluid.disable_dygraph()
_init_context()
fluid.enable_dygraph(place)
else:
_init_context()
else:
assert ("Only support CUDAPlace for now.")
_parallel_context_initialized = True
return strategy
hapi/download.py 已删除 100644 → 0
浏览文件 @ 224515a6
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import os.path as osp
import shutil
import requests
import hashlib
import time
from collections import OrderedDict
from paddle.fluid.dygraph.parallel import ParallelEnv
try:
from tqdm import tqdm
except:
class tqdm(object):
def __init__(self, total=None):
self.total = total
self.n = 0
def update(self, n):
self.n += n
if self.total is None:
sys.stderr.write("\r{0:.1f} bytes".format(self.n))
else:
sys.stderr.write("\r{0:.1f}%".format(100 * self.n / float(
self.total)))
sys.stderr.flush()
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
sys.stderr.write('\n')
import logging
logger = logging.getLogger(__name__)
__all__ = ['get_weights_path_from_url', 'is_url']
WEIGHTS_HOME = osp.expanduser("~/.cache/paddle/hapi/weights")
DOWNLOAD_RETRY_LIMIT = 3
nlp_models = OrderedDict((
('RoBERTa-zh-base',
'https://bert-models.bj.bcebos.com/chinese_roberta_wwm_ext_L-12_H-768_A-12.tar.gz'
),
('RoBERTa-zh-large',
'https://bert-models.bj.bcebos.com/chinese_roberta_wwm_large_ext_L-24_H-1024_A-16.tar.gz'
),
('ERNIE-v2-en-base',
'https://ernie.bj.bcebos.com/ERNIE_Base_en_stable-2.0.0.tar.gz'),
('ERNIE-v2-en-large',
'https://ernie.bj.bcebos.com/ERNIE_Large_en_stable-2.0.0.tar.gz'),
('XLNet-cased-base',
'https://xlnet.bj.bcebos.com/xlnet_cased_L-12_H-768_A-12.tgz'),
('XLNet-cased-large',
'https://xlnet.bj.bcebos.com/xlnet_cased_L-24_H-1024_A-16.tgz'),
('ERNIE-v1-zh-base',
'https://baidu-nlp.bj.bcebos.com/ERNIE_stable-1.0.1.tar.gz'),
('ERNIE-v1-zh-base-max-len-512',
'https://ernie.bj.bcebos.com/ERNIE_1.0_max-len-512.tar.gz'),
('BERT-en-uncased-large-whole-word-masking',
'https://bert-models.bj.bcebos.com/wwm_uncased_L-24_H-1024_A-16.tar.gz'),
('BERT-en-cased-large-whole-word-masking',
'https://bert-models.bj.bcebos.com/wwm_cased_L-24_H-1024_A-16.tar.gz'),
('BERT-en-uncased-base',
'https://bert-models.bj.bcebos.com/uncased_L-12_H-768_A-12.tar.gz'),
('BERT-en-uncased-large',
'https://bert-models.bj.bcebos.com/uncased_L-24_H-1024_A-16.tar.gz'),
('BERT-en-cased-base',
'https://bert-models.bj.bcebos.com/cased_L-12_H-768_A-12.tar.gz'),
('BERT-en-cased-large',
'https://bert-models.bj.bcebos.com/cased_L-24_H-1024_A-16.tar.gz'),
('BERT-multilingual-uncased-base',
'https://bert-models.bj.bcebos.com/multilingual_L-12_H-768_A-12.tar.gz'),
('BERT-multilingual-cased-base',
'https://bert-models.bj.bcebos.com/multi_cased_L-12_H-768_A-12.tar.gz'),
('BERT-zh-base',
'https://bert-models.bj.bcebos.com/chinese_L-12_H-768_A-12.tar.gz'), ))
def is_url(path):
"""
Whether path is URL.
Args:
path (string): URL string or not.
"""
return path.startswith('http://') or path.startswith('https://')
def get_weights_path_from_url(url, md5sum=None):
"""Get weights path from WEIGHT_HOME, if not exists,
download it from url.
Args:
url (str): download url
md5sum (str): md5 sum of download package
Returns:
str: a local path to save downloaded weights.
Examples:
.. code-block:: python
from hapi.download import get_weights_path_from_url
resnet18_pretrained_weight_url = 'https://paddle-hapi.bj.bcebos.com/models/resnet18.pdparams'
local_weight_path = get_weights_path_from_url(resnet18_pretrained_weight_url)
"""
path = get_path_from_url(url, WEIGHTS_HOME, md5sum)
return path
def _map_path(url, root_dir):
# parse path after download under root_dir
fname = osp.split(url)[-1]
fpath = fname
return osp.join(root_dir, fpath)
def get_path_from_url(url, root_dir, md5sum=None, check_exist=True):
""" Download from given url to root_dir.
if file or directory specified by url is exists under
root_dir, return the path directly, otherwise download
from url and decompress it, return the path.
Args:
url (str): download url
root_dir (str): root dir for downloading, it should be
WEIGHTS_HOME or DATASET_HOME
md5sum (str): md5 sum of download package
Returns:
str: a local path to save downloaded models & weights & datasets.
"""
assert is_url(url), "downloading from {} not a url".format(url)
# parse path after download to decompress under root_dir
fullpath = _map_path(url, root_dir)
if osp.exists(fullpath) and check_exist and _md5check(fullpath, md5sum):
logger.info("Found {}".format(fullpath))
else:
if ParallelEnv().local_rank == 0:
fullpath = _download(url, root_dir, md5sum)
else:
while not os.path.exists(fullpath):
time.sleep(1)
return fullpath
def _download(url, path, md5sum=None):
"""
Download from url, save to path.
url (str): download url
path (str): download to given path
"""
if not osp.exists(path):
os.makedirs(path)
fname = osp.split(url)[-1]
fullname = osp.join(path, fname)
retry_cnt = 0
while not (osp.exists(fullname) and _md5check(fullname, md5sum)):
if retry_cnt < DOWNLOAD_RETRY_LIMIT:
retry_cnt += 1
else:
raise RuntimeError("Download from {} failed. "
"Retry limit reached".format(url))
logger.info("Downloading {} from {}".format(fname, url))
req = requests.get(url, stream=True)
if req.status_code != 200:
raise RuntimeError("Downloading from {} failed with code "
"{}!".format(url, req.status_code))
# For protecting download interupted, download to
# tmp_fullname firstly, move tmp_fullname to fullname
# after download finished
tmp_fullname = fullname + "_tmp"
total_size = req.headers.get('content-length')
with open(tmp_fullname, 'wb') as f:
if total_size:
with tqdm(total=(int(total_size) + 1023) // 1024) as pbar:
for chunk in req.iter_content(chunk_size=1024):
f.write(chunk)
pbar.update(1)
else:
for chunk in req.iter_content(chunk_size=1024):
if chunk:
f.write(chunk)
shutil.move(tmp_fullname, fullname)
return fullname
def _md5check(fullname, md5sum=None):
if md5sum is None:
return True
logger.info("File {} md5 checking...".format(fullname))
md5 = hashlib.md5()
with open(fullname, 'rb') as f:
for chunk in iter(lambda: f.read(4096), b""):
md5.update(chunk)
calc_md5sum = md5.hexdigest()
if calc_md5sum != md5sum:
logger.info("File {} md5 check failed, {}(calc) != "
"{}(base)".format(fullname, calc_md5sum, md5sum))
return False
return True
hapi/logger.py 已删除 100644 → 0
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# 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 os
import sys
import logging
from paddle.fluid.dygraph.parallel import ParallelEnv
def setup_logger(output=None, name="hapi", log_level=logging.INFO):
"""
Initialize logger of hapi and set its verbosity level to "INFO".
Args:
output (str): a file name or a directory to save log. If None, will not save log file.
If ends with ".txt" or ".log", assumed to be a file name.
Otherwise, logs will be saved to `output/log.txt`.
name (str): the root module name of this logger. Default: 'hapi'.
log_level (enum): log level. eg.'INFO', 'DEBUG', 'ERROR'. Default: logging.INFO.
Returns:
logging.Logger: a logger
"""
logger = logging.getLogger(name)
logger.propagate = False
logger.setLevel(log_level)
format_str = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
# stdout logging: only local rank==0
local_rank = ParallelEnv().local_rank
if local_rank == 0 and len(logger.handlers) == 0:
ch = logging.StreamHandler(stream=sys.stdout)
ch.setLevel(log_level)
ch.setFormatter(logging.Formatter(format_str))
logger.addHandler(ch)
# file logging if output is not None: all workers
if output is not None:
if output.endswith(".txt") or output.endswith(".log"):
filename = output
else:
filename = os.path.join(output, "log.txt")
if local_rank > 0:
filename = filename + ".rank{}".format(local_rank)
if not os.path.exists(os.path.dirname(filename)):
os.makedirs(os.path.dirname(filename))
fh = logging.StreamHandler(filename)
fh.setLevel(log_level)
fh.setFormatter(logging.Formatter(format_str))
logger.addHandler(fh)
return logger
hapi/loss.py 已删除 100644 → 0
浏览文件 @ 224515a6
# 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.
from __future__ import absolute_import
import os
from paddle import fluid
from paddle.fluid.framework import in_dygraph_mode, Variable
from paddle.fluid.dygraph.base import to_variable
from hapi.utils import to_list
__all__ = ['Loss', 'CrossEntropy', 'SoftmaxWithCrossEntropy']
class Loss(object):
"""
Base class for loss, encapsulates loss logic and APIs
Usage:
custom_loss = CustomLoss()
loss = custom_loss(inputs, labels)
"""
def __init__(self, average=True):
super(Loss, self).__init__()
self.average = average
def forward(self, outputs, labels):
raise NotImplementedError()
def __call__(self, outputs, labels=None):
labels = to_list(labels)
if in_dygraph_mode() and labels:
labels = [to_variable(l) for l in labels]
losses = to_list(self.forward(to_list(outputs), labels))
if self.average:
losses = [fluid.layers.reduce_mean(l) for l in losses]
else:
losses = [fluid.layers.reduce_sum(l) for l in losses]
return losses
class CrossEntropy(Loss):
"""
Args:
input (list[Variable]): Input tensor, the data type is float32,
float64, int32, int64.
label (list[Variable]): Label tensor, the data type is float32,
float64, int32, int64.
average (bool, optional): Indicate whether to average the loss, Default: True.
Returns:
list[Variable]: The tensor variable storing the cross_entropy_loss of inputs and labels.
Examples:
.. code-block:: python
from hapi.model import Input
from hapi.vision.models import LeNet
from hapi.loss import CrossEntropy
inputs = [Input([-1, 1, 28, 28], 'float32', name='image')]
labels = [Input([None, 1], 'int64', name='label')]
model = LeNet()
loss = CrossEntropy()
model.prepare(loss_function=loss, inputs=inputs, labels=labels)
"""
def __init__(self, average=True):
super(CrossEntropy, self).__init__(average)
def forward(self, outputs, labels):
return [
fluid.layers.cross_entropy(o, l) for o, l in zip(outputs, labels)
]
class SoftmaxWithCrossEntropy(Loss):
"""
this op combined softmax and cross entropy.
Args:
input (list[Variable]): Input tensor, the data type is float32,
float64, int32, int64.
label (list[Variable]): Label tensor, the data type is float32,
float64, int32, int64.
average (bool, optional): Indicate whether to average the loss, Default: True.
Returns:
list[Variable]: The tensor variable storing the cross_entropy_loss of inputs and labels.
Examples:
.. code-block:: python
from hapi.model import Input
from hapi.vision.models import LeNet
from hapi.loss import SoftmaxWithCrossEntropy
inputs = [Input([-1, 1, 28, 28], 'float32', name='image')]
labels = [Input([None, 1], 'int64', name='label')]
model = LeNet(classifier_activation=None)
loss = SoftmaxWithCrossEntropy()
model.prepare(loss_function=loss, inputs=inputs, labels=labels)
"""
def __init__(self, average=True):
super(SoftmaxWithCrossEntropy, self).__init__(average)
def forward(self, outputs, labels):
return [
fluid.layers.softmax_with_cross_entropy(
o, l, return_softmax=False) for o, l in zip(outputs, labels)
]
hapi/metrics.py 已删除 100644 → 0
浏览文件 @ 224515a6
# 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.
from __future__ import absolute_import
import six
import abc
import numpy as np
import paddle.fluid as fluid
import logging
FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
logging.basicConfig(level=logging.INFO, format=FORMAT)
logger = logging.getLogger(__name__)
__all__ = ['Metric', 'Accuracy']
@six.add_metaclass(abc.ABCMeta)
class Metric(object):
"""
Base class for metric, encapsulates metric logic and APIs
Usage:
m = SomeMetric()
for prediction, label in ...:
m.update(prediction, label)
m.accumulate()
"""
@abc.abstractmethod
def reset(self):
"""
Reset states and result
"""
raise NotImplementedError("function 'reset' not implemented in {}.".
format(self.__class__.__name__))
@abc.abstractmethod
def update(self, *args):
"""
Update states for metric
Inputs of :code:`update` is the outputs of :code:`Metric.add_metric_op`,
if :code:`add_metric_op` is not defined, the inputs of :code:`update`
will be flatten arguments of **output** of mode and **label** from data:
:code:`update(output1, output2, ..., label1, label2,...)`
see :code:`Metric.add_metric_op`
"""
raise NotImplementedError("function 'update' not implemented in {}.".
format(self.__class__.__name__))
@abc.abstractmethod
def accumulate(self):
"""
Accumulates statistics, computes and returns the metric value
"""
raise NotImplementedError(
"function 'accumulate' not implemented in {}.".format(
self.__class__.__name__))
@abc.abstractmethod
def name(self):
"""
Returns metric name
"""
raise NotImplementedError("function 'name' not implemented in {}.".
format(self.__class__.__name__))
def add_metric_op(self, *args):
"""
This API is advanced usage to accelerate metric calculating, calulations
from outputs of model to the states which should be updated by Metric can
be defined here, where Paddle OPs is also supported. Outputs of this API
will be the inputs of "Metric.update".
If :code:`add_metric_op` is defined, it will be called with **outputs**
of model and **labels** from data as arguments, all outputs and labels
will be concatenated and flatten and each filed as a separate argument
as follows:
:code:`add_metric_op(output1, output2, ..., label1, label2,...)`
If :code:`add_metric_op` is not defined, default behaviour is to pass
input to output, so output format will be:
:code:`return output1, output2, ..., label1, label2,...`
see :code:`Metric.update`
"""
return args
class Accuracy(Metric):
"""
Encapsulates accuracy metric logic
"""
def __init__(self, topk=(1, ), name=None, *args, **kwargs):
super(Accuracy, self).__init__(*args, **kwargs)
self.topk = topk
self.maxk = max(topk)
self._init_name(name)
self.reset()
def add_metric_op(self, pred, label, *args):
pred = fluid.layers.argsort(pred, descending=True)[1][:, :self.maxk]
correct = pred == label
return fluid.layers.cast(correct, dtype='float32')
def update(self, correct, *args):
accs = []
for i, k in enumerate(self.topk):
num_corrects = correct[:, :k].sum()
num_samples = len(correct)
accs.append(float(num_corrects) / num_samples)
self.total[i] += num_corrects
self.count[i] += num_samples
return accs
def reset(self):
self.total = [0.] * len(self.topk)
self.count = [0] * len(self.topk)
def accumulate(self):
res = []
for t, c in zip(self.total, self.count):
res.append(float(t) / c)
return res
def _init_name(self, name):
name = name or 'acc'
if self.maxk != 1:
self._name = ['{}_top{}'.format(name, k) for k in self.topk]
else:
self._name = [name]
def name(self):
return self._name
hapi/model.py 已删除 100644 → 0
浏览文件 @ 224515a6
此差异已折叠。
hapi/progressbar.py 已删除 100644 → 0
浏览文件 @ 224515a6
import sys
import time
import numpy as np
class ProgressBar(object):
"""progress bar """
def __init__(self,
num=None,
width=30,
verbose=1,
start=True,
file=sys.stdout):
self._num = num
if isinstance(num, int) and num <= 0:
raise TypeError('num should be None or integer (> 0)')
max_width = self._get_max_width()
self._width = width if width <= max_width else max_width
self._total_width = 0
self._verbose = verbose
self.file = file
self._values = {}
self._values_order = []
if start:
self._start = time.time()
self._last_update = 0
self._dynamic_display = (
(hasattr(self.file, 'isatty') and
self.file.isatty()) or 'ipykernel' in sys.modules or
'posix' in sys.modules or 'PYCHARM_HOSTED' in os.environ)
def _get_max_width(self):
if sys.version_info > (3, 3):
from shutil import get_terminal_size
else:
from backports.shutil_get_terminal_size import get_terminal_size
terminal_width, _ = get_terminal_size()
max_width = min(int(terminal_width * 0.6), terminal_width - 50)
return max_width
def start(self):
self.file.flush()
self._start = time.time()
def update(self, current_num, values=None):
now = time.time()
if current_num:
time_per_unit = (now - self._start) / current_num
else:
time_per_unit = 0
if time_per_unit >= 1 or time_per_unit == 0:
fps = ' - %.0fs/%s' % (time_per_unit, 'step')
elif time_per_unit >= 1e-3:
fps = ' - %.0fms/%s' % (time_per_unit * 1e3, 'step')
else:
fps = ' - %.0fus/%s' % (time_per_unit * 1e6, 'step')
info = ''
if self._verbose == 1:
prev_total_width = self._total_width
if self._dynamic_display:
sys.stdout.write('\b' * prev_total_width)
sys.stdout.write('\r')
else:
sys.stdout.write('\n')
if self._num is not None:
numdigits = int(np.log10(self._num)) + 1
bar_chars = ('step %' + str(numdigits) + 'd/%d [') % (
current_num, self._num)
prog = float(current_num) / self._num
prog_width = int(self._width * prog)
if prog_width > 0:
bar_chars += ('=' * (prog_width - 1))
if current_num < self._num:
bar_chars += '>'
else:
bar_chars += '='
bar_chars += ('.' * (self._width - prog_width))
bar_chars += ']'
else:
bar_chars = 'step %3d' % current_num
self._total_width = len(bar_chars)
sys.stdout.write(bar_chars)
for k, val in values:
info += ' - %s:' % k
val = val if isinstance(val, list) else [val]
for i, v in enumerate(val):
if isinstance(v, (float, np.float32, np.float64)):
if abs(v) > 1e-3:
info += ' %.4f' % v
else:
info += ' %.4e' % v
else:
info += ' %s' % v
if self._num is not None and current_num < self._num:
eta = time_per_unit * (self._num - current_num)
if eta > 3600:
eta_format = '%d:%02d:%02d' % (eta // 3600, (eta % 3600) //
60, eta % 60)
elif eta > 60:
eta_format = '%d:%02d' % (eta // 60, eta % 60)
else:
eta_format = '%ds' % eta
info += ' - ETA: %s' % eta_format
info += fps
self._total_width += len(info)
if prev_total_width > self._total_width:
info += (' ' * (prev_total_width - self._total_width))
# newline for another epoch
if self._num is not None and current_num >= self._num:
info += '\n'
if self._num is None:
info += '\n'
sys.stdout.write(info)
sys.stdout.flush()
self._last_update = now
elif self._verbose == 2:
if self._num:
numdigits = int(np.log10(self._num)) + 1
count = ('step %' + str(numdigits) + 'd/%d') % (current_num,
self._num)
else:
count = 'step %3d' % current_num
info = count + info
for k, val in values:
info += ' - %s:' % k
val = val if isinstance(val, list) else [val]
for v in val:
if isinstance(v, (float, np.float32, np.float64)):
if abs(v) > 1e-3:
info += ' %.4f' % v
else:
info += ' %.4e' % v
elif isinstance(v, np.ndarray) and \
v.size == 1 and \
isinstance(v.dtype, (np.float32, np.float64)):
if abs(v[0]) > 1e-3:
info += ' %.4f' % v[0]
else:
info += ' %.4e' % v[0]
else:
info += ' %s' % v
info += fps
info += '\n'
sys.stdout.write(info)
sys.stdout.flush()
hapi/tests/dist_mnist.py 已删除 100644 → 0
浏览文件 @ 224515a6
# 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.
from __future__ import division
from __future__ import print_function
import unittest
import os
import numpy as np
import contextlib
import paddle
from paddle import fluid
from hapi.model import Model, Input, set_device
from hapi.loss import CrossEntropy
from hapi.vision.models import LeNet
from hapi.metrics import Accuracy
from hapi.callbacks import ProgBarLogger
from hapi.datasets import MNIST
class MnistDataset(MNIST):
def __init__(self, mode, return_label=True):
super(MnistDataset, self).__init__(mode=mode)
self.return_label = return_label
def __getitem__(self, idx):
img = np.reshape(self.images[idx], [1, 28, 28])
if self.return_label:
return img, np.array(self.labels[idx]).astype('int64')
return img,
def __len__(self):
return len(self.images)
def compute_accuracy(pred, gt):
pred = np.argmax(pred, -1)
gt = np.array(gt)
correct = pred[:, np.newaxis] == gt
return np.sum(correct) / correct.shape[0]
class TestModel(unittest.TestCase):
def run(self, dynamic):
device = set_device('gpu')
fluid.enable_dygraph(device) if dynamic else None
im_shape = (-1, 784)
batch_size = 128
inputs = [Input(im_shape, 'float32', name='image')]
labels = [Input([None, 1], 'int64', name='label')]
train_dataset = MnistDataset(mode='train')
val_dataset = MnistDataset(mode='test')
test_dataset = MnistDataset(mode='test', return_label=False)
model = LeNet()
optim = fluid.optimizer.Momentum(
learning_rate=0.001,
momentum=.9,
parameter_list=model.parameters())
loss = CrossEntropy()
model.prepare(optim, loss, Accuracy(), inputs, labels, device=device)
cbk = ProgBarLogger(50)
model.fit(train_dataset,
val_dataset,
epochs=2,
batch_size=batch_size,
callbacks=cbk)
eval_result = model.evaluate(val_dataset, batch_size=batch_size)
output = model.predict(
test_dataset, batch_size=batch_size, stack_outputs=True)
np.testing.assert_equal(output[0].shape[0], len(test_dataset))
acc = compute_accuracy(output[0], val_dataset.labels)
np.testing.assert_allclose(acc, eval_result['acc'])
def test_multiple_gpus_static(self):
self.run(False)
def test_multiple_gpus_dygraph(self):
self.run(True)
if __name__ == '__main__':
unittest.main()
hapi/tests/test_bert_dataloader.py 已删除 100644 → 0
浏览文件 @ 224515a6
import paddle
from hapi.model import set_device
from hapi.text.bert.dataloader import SingleSentenceDataLoader
import hapi.text.tokenizer.tokenization as tokenization
device = set_device("cpu")
paddle.fluid.enable_dygraph(device)
tokenizer = tokenization.FullTokenizer(
vocab_file="./tmp/hapi/data/pretrained_models/uncased_L-12_H-768_A-12/vocab.txt",
do_lower_case=True)
bert_dataloader = SingleSentenceDataLoader(
"./tmp/hapi/aaa.txt",
tokenizer, ["1", "2"],
max_seq_length=32,
batch_size=1)
for data in bert_dataloader.dataloader():
print(data)
hapi/tests/test_callbacks.py 已删除 100644 → 0
浏览文件 @ 224515a6
# 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 unittest
import time
import random
import tempfile
import shutil
from hapi.model import Input
from hapi.vision.models import LeNet
from hapi.callbacks import config_callbacks
class TestCallbacks(unittest.TestCase):
def setUp(self):
self.save_dir = tempfile.mkdtemp()
def tearDown(self):
shutil.rmtree(self.save_dir)
def run_callback(self):
epochs = 2
steps = 50
freq = 2
eval_steps = 20
lenet = LeNet()
inputs = [Input([None, 1, 28, 28], 'float32', name='image')]
lenet.prepare(inputs=inputs)
cbks = config_callbacks(
model=lenet,
batch_size=128,
epochs=epochs,
steps=steps,
log_freq=freq,
verbose=self.verbose,
metrics=['loss', 'acc'],
save_dir=self.save_dir)
cbks.on_begin('train')
logs = {'loss': 50.341673, 'acc': 0.00256}
for epoch in range(epochs):
cbks.on_epoch_begin(epoch)
for step in range(steps):
cbks.on_batch_begin('train', step, logs)
logs['loss'] -= random.random() * 0.1
logs['acc'] += random.random() * 0.1
time.sleep(0.005)
cbks.on_batch_end('train', step, logs)
cbks.on_epoch_end(epoch, logs)
eval_logs = {'eval_loss': 20.341673, 'eval_acc': 0.256}
params = {
'steps': eval_steps,
'metrics_name': ['eval_loss', 'eval_acc'],
}
cbks.on_begin('eval', params)
for step in range(eval_steps):
cbks.on_batch_begin('eval', step, eval_logs)
eval_logs['eval_loss'] -= random.random() * 0.1
eval_logs['eval_acc'] += random.random() * 0.1
eval_logs['batch_size'] = 2
time.sleep(0.005)
cbks.on_batch_end('eval', step, eval_logs)
cbks.on_end('eval', eval_logs)
test_logs = {}
params = {'steps': eval_steps}
cbks.on_begin('test', params)
for step in range(eval_steps):
cbks.on_batch_begin('test', step, test_logs)
test_logs['batch_size'] = 2
time.sleep(0.005)
cbks.on_batch_end('test', step, test_logs)
cbks.on_end('test', test_logs)
cbks.on_end('train')
def test_callback_verbose_0(self):
self.verbose = 0
self.run_callback()
def test_callback_verbose_1(self):
self.verbose = 1
self.run_callback()
def test_callback_verbose_2(self):
self.verbose = 2
self.run_callback()
if __name__ == '__main__':
unittest.main()
hapi/tests/test_datasets.py 已删除 100644 → 0
浏览文件 @ 224515a6
# 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 unittest
import os
import numpy as np
import tempfile
import shutil
import cv2
from hapi.datasets import *
from hapi.datasets.utils import _check_exists_and_download
from hapi.vision.transforms import Compose
class TestFolderDatasets(unittest.TestCase):
def setUp(self):
self.data_dir = tempfile.mkdtemp()
self.empty_dir = tempfile.mkdtemp()
for i in range(2):
sub_dir = os.path.join(self.data_dir, 'class_' + str(i))
if not os.path.exists(sub_dir):
os.makedirs(sub_dir)
for j in range(2):
fake_img = (np.random.random(
(32, 32, 3)) * 255).astype('uint8')
cv2.imwrite(os.path.join(sub_dir, str(j) + '.jpg'), fake_img)
def tearDown(self):
shutil.rmtree(self.data_dir)
def test_dataset(self):
dataset_folder = DatasetFolder(self.data_dir)
for _ in dataset_folder:
pass
assert len(dataset_folder) == 4
assert len(dataset_folder.classes) == 2
transform = Compose([])
dataset_folder = DatasetFolder(self.data_dir, transform=transform)
for _ in dataset_folder:
pass
def test_folder(self):
loader = ImageFolder(self.data_dir)
for _ in loader:
pass
transform = Compose([])
loader = ImageFolder(self.data_dir, transform=transform)
for _ in loader:
pass
def test_errors(self):
with self.assertRaises(RuntimeError):
ImageFolder(self.empty_dir)
with self.assertRaises(RuntimeError):
DatasetFolder(self.empty_dir)
with self.assertRaises(ValueError):
_check_exists_and_download('temp_paddle', None, None, None, False)
class TestMNISTTest(unittest.TestCase):
def test_main(self):
mnist = MNIST(mode='test')
self.assertTrue(len(mnist) == 10000)
for i in range(len(mnist)):
image, label = mnist[i]
self.assertTrue(image.shape[0] == 784)
self.assertTrue(label.shape[0] == 1)
self.assertTrue(0 <= int(label) <= 9)
class TestMNISTTrain(unittest.TestCase):
def test_main(self):
mnist = MNIST(mode='train')
self.assertTrue(len(mnist) == 60000)
for i in range(len(mnist)):
image, label = mnist[i]
self.assertTrue(image.shape[0] == 784)
self.assertTrue(label.shape[0] == 1)
self.assertTrue(0 <= int(label) <= 9)
class TestFlowersTrain(unittest.TestCase):
def test_main(self):
flowers = Flowers(mode='train')
self.assertTrue(len(flowers) == 6149)
# traversal whole dataset may cost a
# long time, randomly check 1 sample
idx = np.random.randint(0, 6149)
image, label = flowers[idx]
self.assertTrue(len(image.shape) == 3)
self.assertTrue(image.shape[2] == 3)
self.assertTrue(label.shape[0] == 1)
class TestFlowersValid(unittest.TestCase):
def test_main(self):
flowers = Flowers(mode='valid')
self.assertTrue(len(flowers) == 1020)
# traversal whole dataset may cost a
# long time, randomly check 1 sample
idx = np.random.randint(0, 1020)
image, label = flowers[idx]
self.assertTrue(len(image.shape) == 3)
self.assertTrue(image.shape[2] == 3)
self.assertTrue(label.shape[0] == 1)
class TestFlowersTest(unittest.TestCase):
def test_main(self):
flowers = Flowers(mode='test')
self.assertTrue(len(flowers) == 1020)
# traversal whole dataset may cost a
# long time, randomly check 1 sample
idx = np.random.randint(0, 1020)
image, label = flowers[idx]
self.assertTrue(len(image.shape) == 3)
self.assertTrue(image.shape[2] == 3)
self.assertTrue(label.shape[0] == 1)
if __name__ == '__main__':
unittest.main()
hapi/tests/test_distributed.py 已删除 100644 → 0
浏览文件 @ 224515a6
# 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.
from __future__ import print_function
import unittest
import os
import time
import six
import copy
from argparse import ArgumentParser, REMAINDER
import paddle
import paddle.fluid as fluid
from paddle.distributed.utils import *
import paddle.distributed.cloud_utils as cloud_utils
def get_cluster_from_args(selected_gpus):
cluster_node_ips = '127.0.0.1'
node_ip = '127.0.0.1'
node_ips = [x.strip() for x in cluster_node_ips.split(',')]
node_ips.index(node_ip)
free_ports = None
free_ports = find_free_ports(len(selected_gpus))
if free_ports is not None:
free_ports = list(free_ports)
return get_cluster(node_ips, node_ip, free_ports, selected_gpus)
def get_gpus(selected_gpus):
selected_gpus = [x.strip() for x in selected_gpus.split(',')]
return selected_gpus
def start_local_trainers(cluster,
pod,
training_script,
training_script_args,
log_dir=None):
current_env = copy.copy(os.environ.copy())
#paddle broadcast ncclUniqueId use socket, and
#proxy maybe make trainers unreachable, so delete them.
#if we set them to "", grpc will log error message "bad uri"
#so just delete them.
current_env.pop("http_proxy", None)
current_env.pop("https_proxy", None)
procs = []
for idx, t in enumerate(pod.trainers):
proc_env = {
"FLAGS_selected_gpus": "%s" % ",".join([str(g) for g in t.gpus]),
"PADDLE_TRAINER_ID": "%d" % t.rank,
"PADDLE_CURRENT_ENDPOINT": "%s" % t.endpoint,
"PADDLE_TRAINERS_NUM": "%d" % cluster.trainers_nranks(),
"PADDLE_TRAINER_ENDPOINTS": ",".join(cluster.trainers_endpoints())
}
current_env.update(proc_env)
print("trainer proc env:{}".format(current_env))
cmd = "python -u " + training_script
print("start trainer proc:{} env:{}".format(cmd, proc_env))
fn = None
proc = subprocess.Popen(cmd.split(" "), env=current_env)
tp = TrainerProc()
tp.proc = proc
tp.rank = t.rank
tp.log_fn = fn
tp.cmd = cmd
procs.append(tp)
return procs
class TestMultipleGpus(unittest.TestCase):
def test_mnist_2gpu(self):
if fluid.core.get_cuda_device_count() == 0:
return
selected_gpus = get_gpus('0,1')
cluster = None
pod = None
cluster, pod = get_cluster_from_args(selected_gpus)
procs = start_local_trainers(
cluster,
pod,
training_script='dist_mnist.py',
training_script_args=[])
while True:
alive = watch_local_trainers(procs, cluster.trainers_nranks())
if not alive:
print("Local procs complete, POD info:{}".format(pod))
break
time.sleep(3)
if __name__ == "__main__":
unittest.main()
hapi/tests/test_logger.py 已删除 100644 → 0
浏览文件 @ 224515a6
# 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.
from __future__ import division
from __future__ import print_function
import unittest
import os
import numpy as np
import shutil
import tempfile
from hapi.logger import setup_logger
class TestSetupLogger(unittest.TestCase):
def setUp(self):
self.save_dir = tempfile.mkdtemp()
self.save_file = os.path.join(self.save_dir, 'logger.txt')
def tearDown(self):
shutil.rmtree(self.save_dir)
def logger(self, output=None):
setup_logger(output=output)
def test_logger_no_output(self):
self.logger()
def test_logger_dir(self):
self.logger(self.save_dir)
def test_logger_file(self):
self.logger(self.save_file)
if __name__ == '__main__':
unittest.main()
hapi/tests/test_loss.py 已删除 100644 → 0
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# 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.
from __future__ import division
from __future__ import print_function
import unittest
import os
import six
import numpy as np
import shutil
import copy
import paddle
from paddle import fluid
from hapi.model import Model, Input
from hapi.vision.models import resnet18
from hapi.loss import CrossEntropy, SoftmaxWithCrossEntropy
def stable_softmax(x):
"""Compute the softmax of vector x in a numerically stable way."""
# clip to shiftx, otherwise, when calc loss with
# log(exp(shiftx)), may get log(0)=INF
shiftx = (x - np.max(x)).clip(-64.)
exps = np.exp(shiftx)
return exps / np.sum(exps)
def randomize_probability(batch_size, class_num, dtype='float32'):
prob = np.random.uniform(
0.1, 1.0, size=(batch_size, class_num)).astype(dtype)
prob_sum = prob.sum(axis=1)
for i in six.moves.xrange(len(prob)):
prob[i] /= prob_sum[i]
return prob
def numpy_ce(x, label):
return np.asmatrix(
[[-np.log(x[i][label[i][0]])] for i in range(x.shape[0])],
dtype="float32").mean()
class TestLoss(unittest.TestCase):
def test_cross_entropy(self):
class_num = 100
batch_size = 128
inputs = [randomize_probability(128, class_num) for _ in range(2)]
labels = [
np.random.randint(
0, class_num, (batch_size, 1), dtype="int64") for _ in range(2)
]
gt_out = [numpy_ce(inputs[i], labels[i]) for i in range(2)]
fluid.enable_dygraph()
cross_entropy = CrossEntropy()
out = cross_entropy(
[fluid.dygraph.to_variable(x) for x in inputs],
[fluid.dygraph.to_variable(label) for label in labels])
out = [o.numpy() for o in out]
for o, g in zip(out, gt_out):
np.testing.assert_allclose(o, g, atol=1e-5)
def test_soft_cross_entronpy(self):
class_num = 100
batch_size = 128
inputs = [randomize_probability(128, class_num) for _ in range(2)]
labels = [
np.random.randint(
0, class_num, (batch_size, 1), dtype="int64") for _ in range(2)
]
fluid.enable_dygraph()
softmax_cross_entropy = SoftmaxWithCrossEntropy()
softmax_cross_entropy(
[fluid.dygraph.to_variable(x) for x in inputs],
[fluid.dygraph.to_variable(label) for label in labels])
softmax_cross_entropy = SoftmaxWithCrossEntropy(average=False)
inputs = [randomize_probability(128, class_num)]
labels = [
np.random.randint(
0, class_num, (batch_size, 1), dtype="int64")
]
softmax_cross_entropy([fluid.dygraph.to_variable(x) for x in inputs],
fluid.dygraph.to_variable(labels[0]))
if __name__ == '__main__':
unittest.main()
hapi/tests/test_metrics.py 已删除 100644 → 0
浏览文件 @ 224515a6
# 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.
from __future__ import division
from __future__ import print_function
import os
import unittest
import numpy as np
import paddle.fluid as fluid
from paddle.fluid.dygraph.base import to_variable
from hapi.metrics import *
from hapi.utils import to_list
def accuracy(pred, label, topk=(1, )):
maxk = max(topk)
pred = np.argsort(pred)[:, ::-1][:, :maxk]
correct = (pred == np.repeat(label, maxk, 1))
batch_size = label.shape[0]
res = []
for k in topk:
correct_k = correct[:, :k].sum()
res.append(correct_k / batch_size)
return res
def convert_to_one_hot(y, C):
oh = np.random.random((y.shape[0], C)).astype('float32') * .5
for i in range(y.shape[0]):
oh[i, int(y[i])] = 1.
return oh
class TestAccuracyDynamic(unittest.TestCase):
def setUp(self):
self.topk = (1, )
self.class_num = 5
self.sample_num = 1000
self.name = None
def random_pred_label(self):
label = np.random.randint(0, self.class_num, (self.sample_num, 1)).astype('int64')
pred = np.random.randint(0, self.class_num, (self.sample_num, 1)).astype('int32')
pred_one_hot = convert_to_one_hot(pred, self.class_num)
pred_one_hot = pred_one_hot.astype('float32')
return label, pred_one_hot
def test_main(self):
with fluid.dygraph.guard(fluid.CPUPlace()):
acc = Accuracy(topk=self.topk, name=self.name)
for i in range(10):
label, pred = self.random_pred_label()
label_var = to_variable(label)
pred_var = to_variable(pred)
state = to_list(acc.add_metric_op(pred_var, label_var))
acc.update(*[s.numpy() for s in state])
res_m = acc.accumulate()
res_f = accuracy(pred, label, self.topk)
assert np.all(np.isclose(np.array(res_m), np.array(res_f), rtol=1e-3)), \
"Accuracy precision error: {} != {}".format(res_m, res_f)
acc.reset()
assert np.sum(acc.total) == 0
assert np.sum(acc.count) == 0
class TestAccuracyDynamicMultiTopk(TestAccuracyDynamic):
def setUp(self):
self.topk = (1, 5)
self.class_num = 10
self.sample_num = 1000
self.name = "accuracy"
class TestAccuracyStatic(TestAccuracyDynamic):
def test_main(self):
main_prog = fluid.Program()
startup_prog = fluid.Program()
with fluid.program_guard(main_prog, startup_prog):
pred = fluid.data(name='pred', shape=[None, self.class_num], dtype='float32')
label = fluid.data(name='label', shape=[None, 1], dtype='int64')
acc = Accuracy(topk=self.topk, name=self.name)
state = acc.add_metric_op(pred, label)
exe = fluid.Executor(fluid.CPUPlace())
compiled_main_prog = fluid.CompiledProgram(main_prog)
for i in range(10):
label, pred = self.random_pred_label()
state_ret = exe.run(compiled_main_prog,
feed={'pred': pred, 'label': label},
fetch_list=[s.name for s in to_list(state)],
return_numpy=True)
acc.update(*state_ret)
res_m = acc.accumulate()
res_f = accuracy(pred, label, self.topk)
assert np.all(np.isclose(np.array(res_m), np.array(res_f), rtol=1e-3)), \
"Accuracy precision error: {} != {}".format(res_m, res_f)
acc.reset()
assert np.sum(acc.total) == 0
assert np.sum(acc.count) == 0
class TestAccuracyStaticMultiTopk(TestAccuracyStatic):
def setUp(self):
self.topk = (1, 5)
self.class_num = 10
self.sample_num = 1000
self.name = "accuracy"
if __name__ == '__main__':
unittest.main()
hapi/tests/test_model.py 已删除 100644 → 0
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此差异已折叠。
hapi/tests/test_progressbar.py 已删除 100644 → 0
浏览文件 @ 224515a6
# 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.
# when test, you should add hapi root path to the PYTHONPATH,
# export PYTHONPATH=PATH_TO_HAPI:$PYTHONPATH
import unittest
import random
import time
from hapi.progressbar import ProgressBar
class TestProgressBar(unittest.TestCase):
def prog_bar(self, num, epoch, width, verbose=1):
for epoch in range(epoch):
progbar = ProgressBar(num, verbose=verbose)
values = [
['loss', 50.341673],
['acc', 0.00256],
]
for step in range(1, num + 1):
values[0][1] -= random.random() * 0.1
values[1][1] += random.random() * 0.1
if step % 10 == 0:
progbar.update(step, values)
time.sleep(0.002)
progbar.update(step, values)
def test1(self):
self.prog_bar(50, 1, 30)
def test2(self):
self.prog_bar(50, 2, 30)
def test4(self):
self.prog_bar(50, 2, 30, verbose=2)
if __name__ == '__main__':
unittest.main()
hapi/tests/test_save_inference_model.py 已删除 100644 → 0
浏览文件 @ 224515a6
# 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.
from __future__ import division
from __future__ import print_function
import unittest
import os
import numpy as np
import shutil
import tempfile
import paddle
from paddle import fluid
from hapi.model import Model, Input
from hapi.vision.models import resnet18
class TestSaveInferenceModel(unittest.TestCase):
def tearDown(self):
shutil.rmtree(self.save_dir)
def export_deploy_model(self):
model = resnet18()
inputs = [Input([None, 3, 224, 224], 'float32', name='image')]
model.prepare(inputs=inputs)
self.save_dir = tempfile.mkdtemp()
if not os.path.exists(self.save_dir):
os.makedirs(self.save_dir)
model.save_inference_model(self.save_dir)
place = fluid.CPUPlace() if not fluid.is_compiled_with_cuda(
) else fluid.CUDAPlace(0)
exe = fluid.Executor(place)
[inference_program, feed_target_names, fetch_targets] = (
fluid.io.load_inference_model(
dirname=self.save_dir, executor=exe))
tensor_img = np.array(
np.random.random((1, 3, 224, 224)), dtype=np.float32)
ori_results = model.test_batch(tensor_img)
results = exe.run(inference_program,
feed={feed_target_names[0]: tensor_img},
fetch_list=fetch_targets)
np.testing.assert_allclose(results, ori_results)
def test_save_inference_model(self):
self.export_deploy_model()
if __name__ == '__main__':
unittest.main()
hapi/tests/test_transforms.py 已删除 100644 → 0
浏览文件 @ 224515a6
# 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.
# when test, you should add hapi root path to the PYTHONPATH,
# export PYTHONPATH=PATH_TO_HAPI:$PYTHONPATH
import unittest
import os
import tempfile
import cv2
import shutil
import numpy as np
from hapi.datasets import DatasetFolder
import hapi.vision.transforms as transforms
class TestTransforms(unittest.TestCase):
def setUp(self):
self.data_dir = tempfile.mkdtemp()
for i in range(2):
sub_dir = os.path.join(self.data_dir, 'class_' + str(i))
if not os.path.exists(sub_dir):
os.makedirs(sub_dir)
for j in range(2):
if j == 0:
fake_img = (np.random.random(
(280, 350, 3)) * 255).astype('uint8')
else:
fake_img = (np.random.random(
(400, 300, 3)) * 255).astype('uint8')
cv2.imwrite(os.path.join(sub_dir, str(j) + '.jpg'), fake_img)
def tearDown(self):
shutil.rmtree(self.data_dir)
def do_transform(self, trans):
dataset_folder = DatasetFolder(self.data_dir, transform=trans)
for _ in dataset_folder:
pass
def test_trans_all(self):
normalize = transforms.Normalize(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.120, 57.375])
trans = transforms.Compose([
transforms.RandomResizedCrop(224), transforms.GaussianNoise(),
transforms.ColorJitter(
brightness=0.4, contrast=0.4, saturation=0.4,
hue=0.4), transforms.RandomHorizontalFlip(),
transforms.Permute(mode='CHW'), normalize
])
self.do_transform(trans)
def test_trans_resize(self):
trans = transforms.Compose([
transforms.Resize(300, [0, 1]),
transforms.RandomResizedCrop((280, 280)),
transforms.Resize(280, [0, 1]),
transforms.Resize((256, 200)),
transforms.Resize((180, 160)),
transforms.CenterCrop(128),
transforms.CenterCrop((128, 128)),
])
self.do_transform(trans)
def test_trans_centerCrop(self):
trans = transforms.Compose([
transforms.CenterCropResize(224),
transforms.CenterCropResize(128, 160),
])
self.do_transform(trans)
def test_flip(self):
trans = transforms.Compose([
transforms.RandomHorizontalFlip(1.0),
transforms.RandomHorizontalFlip(0.0),
transforms.RandomVerticalFlip(0.0),
transforms.RandomVerticalFlip(1.0),
])
self.do_transform(trans)
def test_color_jitter(self):
trans = transforms.BatchCompose([
transforms.BrightnessTransform(0.0),
transforms.HueTransform(0.0),
transforms.SaturationTransform(0.0),
transforms.ContrastTransform(0.0),
])
self.do_transform(trans)
def test_exception(self):
trans = transforms.Compose([transforms.Resize(-1)])
trans_batch = transforms.BatchCompose([transforms.Resize(-1)])
with self.assertRaises(Exception):
self.do_transform(trans)
with self.assertRaises(Exception):
self.do_transform(trans_batch)
with self.assertRaises(ValueError):
transforms.ContrastTransform(-1.0)
with self.assertRaises(ValueError):
transforms.SaturationTransform(-1.0),
with self.assertRaises(ValueError):
transforms.HueTransform(-1.0)
with self.assertRaises(ValueError):
transforms.BrightnessTransform(-1.0)
def test_info(self):
str(transforms.Compose([transforms.Resize((224, 224))]))
str(transforms.BatchCompose([transforms.Resize((224, 224))]))
if __name__ == '__main__':
unittest.main()
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