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hapi
提交 db5f3697 编写于 作者: L LielinJiang
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refine callback, add unittest for callback

上级 2c6d4097
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Showing with 347 addition and 125 deletion
hapi/callbacks.py
浏览文件 @ db5f3697
......@@ -33,7 +33,7 @@ def config_callbacks(callbacks=None,
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 = cbks + [ProgBarLogger(log_freq, verbose=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)]
......@@ -110,6 +110,9 @@ class CallbackList(object):
class Callback(object):
"""Base class used to build new callbacks.
"""
def __init__(self):
self.model = None
self.params = {}
......@@ -121,63 +124,115 @@ class Callback(object):
self.model = model
def on_train_begin(self, logs=None):
"""
"""Called at the start of train.
"""
def on_train_end(self, logs=None):
"""
"""Called at the end of train.
"""
def on_eval_begin(self, logs=None):
"""
"""Called at the start of eval.
"""
def on_eval_end(self, logs=None):
"""
"""Called at the end of train.
"""
def on_test_begin(self, logs=None):
"""
"""Called at the test of begin.
"""
def on_test_end(self, logs=None):
"""
"""Called at the test of end.
"""
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 `train` mode.
"""
def on_train_batch_end(self, step, logs=None):
"""
"""Called at the end of each batch in `train` mode.
"""
def on_eval_batch_begin(self, step, logs=None):
"""
"""Called at the beginning of each batch in `eval` mode.
"""
def on_eval_batch_end(self, step, logs=None):
"""
"""Called at the end of each batch in `eval` mode.
"""
def on_eval_batch_begin(self, step, logs=None):
"""
def on_test_batch_begin(self, step, logs=None):
"""Called at the beginning of each batch in `test` mode.
"""
def on_eval_batch_end(self, step, logs=None):
"""
def on_test_batch_end(self, step, logs=None):
"""Called at the end of each batch in `test` mode.
"""
class ProgBarLogger(Callback):
"""Logger callback function
Args:
log_freq (int): The frequency, in number of steps, the training logs
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
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)
inputs = [Input([-1, 1, 28, 28], 'float32', name='image')]
labels = [Input([None, 1], 'int64', name='label')]
train_dataset = MnistDataset(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
......@@ -204,9 +259,11 @@ class ProgBarLogger(Callback):
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):
......@@ -220,7 +277,9 @@ class ProgBarLogger(Callback):
def on_epoch_end(self, epoch, logs=None):
logs = logs or {}
if self.train_step % self.log_freq != 0 and self.verbose and ParallelEnv(
if self.verbose == 1 and ParallelEnv().local_rank == 0:
self._updates(logs, 'train')
elif self.train_step % self.log_freq != 0 and self.verbose and ParallelEnv(
).local_rank == 0:
self._updates(logs, 'train')
......@@ -229,6 +288,7 @@ class ProgBarLogger(Callback):
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 ParallelEnv().local_rank == 0:
......@@ -245,15 +305,95 @@ class ProgBarLogger(Callback):
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 ParallelEnv().local_rank == 0:
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.verbose and ParallelEnv(
).local_rank == 0:
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.verbose and ParallelEnv().local_rank == 0:
if self.eval_step % self.log_freq != 0:
if self.eval_step % self.log_freq != 0 or self.verbose == 1:
self._updates(logs, 'eval')
print('Eval samples: %d' % (self.evaled_samples))
def on_test_end(self, logs=None):
logs = logs or {}
if self.verbose and ParallelEnv().local_rank == 0:
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
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)
inputs = [Input([-1, 1, 28, 28], 'float32', name='image')]
labels = [Input([None, 1], 'int64', name='label')]
train_dataset = MnistDataset(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
......
hapi/model.py
浏览文件 @ db5f3697
......@@ -20,7 +20,6 @@ import pickle
import numpy as np
import six
import warnings
import tqdm
from collections import Iterable
from paddle import fluid
......@@ -859,7 +858,6 @@ class Model(fluid.dygraph.Layer):
num_workers=0,
callbacks=None, ):
"""
FIXME: add more comments and usage
Args:
train_data (Dataset|DataLoader): An iterable data loader is used for
train. An instance of paddle paddle.io.Dataset or
......@@ -897,11 +895,6 @@ class Model(fluid.dygraph.Layer):
assert train_data is not None, \
"train_data must be given!"
if fluid.in_dygraph_mode():
feed_list = None
else:
feed_list = [x.forward() for x in self._inputs + self._labels]
if isinstance(train_data, Dataset):
train_sampler = DistributedBatchSampler(
train_data,
......@@ -912,7 +905,6 @@ class Model(fluid.dygraph.Layer):
train_data,
batch_sampler=train_sampler,
places=self._place,
feed_list=feed_list,
num_workers=num_workers,
return_list=True)
else:
......@@ -925,7 +917,6 @@ class Model(fluid.dygraph.Layer):
eval_data,
batch_sampler=eval_sampler,
places=self._place,
feed_list=feed_list,
num_workers=num_workers,
return_list=True)
elif eval_data is not None:
......@@ -935,7 +926,7 @@ class Model(fluid.dygraph.Layer):
do_eval = eval_loader is not None
self._test_dataloader = eval_loader
metrics_name = self._metrics_name()
steps = self._len_data_loader(train_loader)
cbks = config_callbacks(
callbacks,
......@@ -951,26 +942,22 @@ class Model(fluid.dygraph.Layer):
cbks.on_begin('train')
for epoch in range(epochs):
# FIXME: adapt to DataLoader
loader = train_loader
if not isinstance(train_loader, Iterable):
loader = train_loader()
logs = self._run_one_epoch(
loader, cbks, 'train', metrics_name, epoch=epoch)
cbks.on_epoch_begin(epoch)
logs = self._run_one_epoch(loader, cbks, 'train')
cbks.on_epoch_end(epoch, logs)
if do_eval and epoch % eval_freq == 0:
# FIXME: adapt to DataLoader
loader = eval_loader
if not isinstance(eval_loader, Iterable):
loader = eval_loader()
eval_steps = self._len_data_loader(loader)
cbks.on_begin('eval', {
'steps': eval_steps,
'metrics_name': metrics_name
'metrics_name': self._metrics_name()
})
logs = self._run_one_epoch(loader, cbks, 'eval', metrics_name)
logs = self._run_one_epoch(loader, cbks, 'eval')
cbks.on_end('eval', logs)
......@@ -986,7 +973,6 @@ class Model(fluid.dygraph.Layer):
num_workers=0,
callbacks=None, ):
"""
FIXME: add more comments and usage
Args:
eval_data (Dataset|DataLoader): An iterable data loader is used for
evaluation. An instance of paddle.io.Dataset or
......@@ -1006,12 +992,55 @@ class Model(fluid.dygraph.Layer):
are automatically inserted. Default: None.
Returns:
dict: Result of metric.
"""
if fluid.in_dygraph_mode():
feed_list = None
else:
feed_list = [x.forward() for x in self._inputs + self._labels]
Examples:
.. code-block:: python
# declarative mode
import numpy as np
from hapi.metrics import Accuracy
from hapi.datasets import MNIST
from hapi.vision.transforms import Compose,Resize
from hapi.vision.models import LeNet
from hapi.model import Input, set_device
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)
inputs = [Input([-1, 1, 28, 28], 'float32', name='image')]
labels = [Input([None, 1], 'int64', name='label')]
val_dataset = MnistDataset(mode='test')
model = LeNet()
model.prepare(metrics=Accuracy(), inputs=inputs, labels=labels)
result = model.evaluate(val_dataset, batch_size=64)
print(result)
# imperative mode
import paddle.fluid.dygraph as dg
place = set_device('cpu')
with dg.guard(place) as g:
model = LeNet()
model.prepare(metrics=Accuracy(), inputs=inputs, labels=labels)
result = model.evaluate(val_dataset, batch_size=64)
print(result)
"""
if eval_data is not None and isinstance(eval_data, Dataset):
eval_sampler = DistributedBatchSampler(
......@@ -1020,14 +1049,12 @@ class Model(fluid.dygraph.Layer):
eval_data,
batch_sampler=eval_sampler,
places=self._place,
feed_list=feed_list,
num_workers=num_workers,
return_list=True)
else:
eval_loader = eval_data
self._test_dataloader = eval_loader
metrics_name = self._metrics_name()
cbks = config_callbacks(
callbacks,
......@@ -1037,15 +1064,14 @@ class Model(fluid.dygraph.Layer):
metrics=self._metrics_name(), )
loader = eval_loader
if not isinstance(eval_loader, Iterable):
loader = eval_loader()
eval_steps = self._len_data_loader(loader)
cbks.on_begin('eval',
{'steps': eval_steps,
'metrics_name': metrics_name})
cbks.on_begin('eval', {
'steps': eval_steps,
'metrics_name': self._metrics_name()
})
logs = self._run_one_epoch(loader, cbks, 'eval', metrics_name)
logs = self._run_one_epoch(loader, cbks, 'eval')
cbks.on_end('eval', logs)
......@@ -1061,9 +1087,9 @@ class Model(fluid.dygraph.Layer):
test_data,
batch_size=1,
num_workers=0,
stack_outputs=False):
stack_outputs=False,
callbacks=None):
"""
FIXME: add more comments and usage
Args:
test_data (Dataset|DataLoader): An iterable data loader is used for
predict. An instance of paddle.io.Dataset or paddle.io.Dataloader
......@@ -1082,12 +1108,53 @@ class Model(fluid.dygraph.Layer):
it is recommended set as True if outputs contains no LoDTensor. Default: False.
Returns:
list: output of models.
"""
if fluid.in_dygraph_mode():
feed_list = None
else:
feed_list = [x.forward() for x in self._inputs]
Examples:
.. code-block:: python
# declarative mode
import numpy as np
from hapi.metrics import Accuracy
from hapi.datasets import MNIST
from hapi.vision.transforms import Compose,Resize
from hapi.vision.models import LeNet
from hapi.model import Input, set_device
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)
inputs = [Input([-1, 1, 28, 28], 'float32', name='image')]
labels = [Input([None, 1], 'int64', name='label')]
test_dataset = MnistDataset(mode='test', return_label=False)
model = LeNet()
model.prepare(metrics=Accuracy(), inputs=inputs, labels=labels)
result = model.predict(test_dataset, batch_size=64)
print(result)
# imperative mode
import paddle.fluid.dygraph as dg
place = set_device('cpu')
with dg.guard(place) as g:
model = LeNet()
model.prepare(metrics=Accuracy(), inputs=inputs, labels=labels)
result = model.predict(test_dataset, batch_size=64)
print(result)
"""
if test_data is not None and isinstance(test_data, Dataset):
test_sampler = DistributedBatchSampler(
......@@ -1096,7 +1163,6 @@ class Model(fluid.dygraph.Layer):
test_data,
batch_sampler=test_sampler,
places=self._place,
feed_list=feed_list,
num_workers=num_workers,
return_list=True)
else:
......@@ -1105,33 +1171,28 @@ class Model(fluid.dygraph.Layer):
self._test_dataloader = test_loader
loader = test_loader
if not isinstance(test_loader, Iterable):
loader = test_loader()
outputs = []
count = 0
for data in tqdm.tqdm(loader):
data = flatten(data)
out = to_list(self.test_batch(data[:len(self._inputs)]))
outputs.append(out)
count += out[0].shape[0]
cbks = config_callbacks(callbacks, model=self, verbose=1)
if test_loader is not None and self._adapter._nranks > 1 \
and isinstance(test_loader, DataLoader) \
and count > len(test_loader.dataset):
size = outputs[-1][0].shape[0] - (count - len(test_loader.dataset))
outputs[-1] = [o[:size] for o in outputs[-1]]
test_steps = self._len_data_loader(loader)
logs = {'steps': test_steps}
# NOTE: for lod tensor output, we should not stack outputs
# for stacking may loss its detail info
cbks.on_begin('test', logs)
outputs = []
logs, outputs = self._run_one_epoch(loader, cbks, 'test')
outputs = list(zip(*outputs))
# NOTE: for lod tensor output, we should not stack outputs
# for stacking may loss its detail info
if stack_outputs:
outputs = [np.vstack(outs) for outs in outputs]
self._test_dataloader = None
cbks.on_end('test', logs)
return outputs
def save_inference_model(self,
......@@ -1179,22 +1240,8 @@ class Model(fluid.dygraph.Layer):
params_filename=params_filename,
program_only=program_only)
def _run_one_epoch(self,
data_loader,
callbacks,
mode,
metrics_name,
epoch=None):
size = self._len_data_loader(data_loader)
logs = {
'steps': size,
'metrics_name': metrics_name,
}
if mode == 'train':
assert epoch is not None, 'when mode is train, epoch must be given'
callbacks.on_epoch_begin(epoch)
def _run_one_epoch(self, data_loader, callbacks, mode, logs={}):
outputs = []
for step, data in enumerate(data_loader):
# data might come from different types of data_loader and have
# different format, as following:
......@@ -1214,25 +1261,25 @@ class Model(fluid.dygraph.Layer):
0].shape) else data[0].shape[0]
callbacks.on_batch_begin(mode, step, logs)
if mode == 'train':
outs = self.train_batch(data[:len(self._inputs)],
data[len(self._inputs):])
else:
outs = self.eval_batch(data[:len(self._inputs)],
data[len(self._inputs):])
# losses
loss = outs[0] if self._metrics else outs
metrics = [[l[0] for l in loss]]
# metrics
for metric in self._metrics:
res = metric.accumulate()
metrics.extend(to_list(res))
assert len(metrics_name) == len(metrics)
for k, v in zip(metrics_name, metrics):
logs[k] = v
if mode != 'test':
outs = getattr(self, mode + '_batch')(data[:len(self._inputs)],
data[len(self._inputs):])
# losses
loss = outs[0] if self._metrics else outs
metrics = [[l[0] for l in loss]]
# metrics
for metric in self._metrics:
res = metric.accumulate()
metrics.extend(to_list(res))
assert len(self._metrics_name()) == len(metrics)
for k, v in zip(self._metrics_name(), metrics):
logs[k] = v
else:
outs = getattr(self, mode + '_batch')(data)
outputs.append(outs)
logs['step'] = step
if mode == 'train' or self._adapter._merge_count.get(
......@@ -1245,10 +1292,8 @@ class Model(fluid.dygraph.Layer):
callbacks.on_batch_end(mode, step, logs)
self._reset_metrics()
if mode == 'train':
assert epoch is not None, 'when mode is train, epoch must be given'
callbacks.on_epoch_end(epoch, logs)
if mode == 'test':
return logs, outputs
return logs
def _reset_metrics(self):
......
hapi/tests/test_callbacks.py
浏览文件 @ db5f3697
......@@ -12,27 +12,43 @@
# 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 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 test_callback(self):
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 = 1
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,
verbose=2,
metrics=['loss', 'acc'], )
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}
......@@ -48,13 +64,12 @@ class TestCallbacks(unittest.TestCase):
eval_logs = {'eval_loss': 20.341673, 'eval_acc': 0.256}
params = {
'eval_steps': eval_steps,
'eval_metrics': ['eval_loss', 'eval_acc'],
'log_freq': 10,
'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, logs)
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
......@@ -62,8 +77,30 @@ class TestCallbacks(unittest.TestCase):
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()
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