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97faf90e
编写于
7月 08, 2021
作者:
S
shangliang Xu
提交者:
GitHub
7月 08, 2021
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差异文件
add num_iters in fit/evalate (#33986)
* add num_iters in fit/evalate, test=develop
上级
6a36977d
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
47 addition
and
13 deletion
+47
-13
python/paddle/hapi/model.py
python/paddle/hapi/model.py
+32
-12
python/paddle/tests/test_model.py
python/paddle/tests/test_model.py
+15
-1
未找到文件。
python/paddle/hapi/model.py
浏览文件 @
97faf90e
...
...
@@ -1520,8 +1520,7 @@ class Model(object):
if
not
in_dygraph_mode
():
self
.
_adapter
.
prepare
()
def
fit
(
self
,
def
fit
(
self
,
train_data
=
None
,
eval_data
=
None
,
batch_size
=
1
,
...
...
@@ -1535,7 +1534,8 @@ class Model(object):
shuffle
=
True
,
num_workers
=
0
,
callbacks
=
None
,
accumulate_grad_batches
=
1
,
):
accumulate_grad_batches
=
1
,
num_iters
=
None
):
"""
Trains the model for a fixed number of epochs. If `eval_data` is set,
evaluation will be done at the end of each epoch.
...
...
@@ -1581,6 +1581,9 @@ class Model(object):
accumulate_grad_batches (int): The number of batches to accumulate gradident
during training process before optimizer updates. It can mimic large batch
size. Default: 1.
num_iters (int|None): Integer number. The number of iterations to train
the model. If None, follow `epochs` to train the model, otherwise, train
the model `num_iters` times. Default: None.
Returns:
None
...
...
@@ -1705,6 +1708,11 @@ class Model(object):
self
.
_accumulate
=
accumulate_grad_batches
steps
=
self
.
_len_data_loader
(
train_loader
)
self
.
num_iters
=
num_iters
if
num_iters
is
not
None
and
isinstance
(
num_iters
,
int
):
assert
num_iters
>
0
,
"num_iters must be greater than 0!"
epochs
=
(
num_iters
//
steps
)
+
1
steps
=
min
(
num_iters
,
steps
)
cbks
=
config_callbacks
(
callbacks
,
model
=
self
,
...
...
@@ -1742,14 +1750,14 @@ class Model(object):
cbks
.
on_end
(
'train'
,
logs
)
self
.
_test_dataloader
=
None
def
evaluate
(
self
,
eval_data
,
batch_size
=
1
,
log_freq
=
10
,
verbose
=
2
,
num_workers
=
0
,
callbacks
=
None
,
):
def
evaluate
(
self
,
eval_data
,
batch_size
=
1
,
log_freq
=
10
,
verbose
=
2
,
num_workers
=
0
,
callbacks
=
None
,
num_iters
=
None
):
"""
Evaluate the loss and metrics of the model on input dataset.
...
...
@@ -1771,6 +1779,9 @@ class Model(object):
callbacks (Callback|None): A list of `Callback` instances to apply
during training. If None, `ProgBarLogger` and `ModelCheckpoint`
are automatically inserted. Default: None.
num_iters (int|None): Integer number. The number of iterations to
evaluate the model. If None, evaluate on whole input dataset,
otherwise, evaluate `num_iters` times. Default: None.
Returns:
dict: Result of metric. The key is the names of Metric,
value is a scalar or numpy.array.
...
...
@@ -1820,6 +1831,11 @@ class Model(object):
metrics
=
self
.
_metrics_name
(),
)
eval_steps
=
self
.
_len_data_loader
(
eval_loader
)
self
.
num_iters
=
num_iters
if
num_iters
is
not
None
and
isinstance
(
num_iters
,
int
):
assert
num_iters
>
0
,
"num_iters must be greater than 0!"
eval_steps
=
min
(
num_iters
,
eval_steps
)
self
.
num_iters
=
eval_steps
cbks
.
on_begin
(
'eval'
,
{
'steps'
:
eval_steps
,
'metrics'
:
self
.
_metrics_name
()})
...
...
@@ -2076,6 +2092,10 @@ class Model(object):
logs
[
'batch_size'
]
=
self
.
_adapter
.
_merge_count
[
mode
+
'_batch'
]
callbacks
.
on_batch_end
(
mode
,
step
,
logs
)
if
hasattr
(
self
,
'num_iters'
)
and
self
.
num_iters
is
not
None
:
self
.
num_iters
-=
1
if
self
.
num_iters
==
0
:
break
self
.
_reset_metrics
()
if
mode
==
'predict'
:
...
...
@@ -2091,7 +2111,7 @@ class Model(object):
one input, input_size can be tuple or InputSpec. if model have multiple
input, input_size must be a list which contain every input's shape.
Default: None.
dtype
s (str, optional): if dtypes
is None, 'float32' will be used, Default: None.
dtype
(str, optional): if dtype
is None, 'float32' will be used, Default: None.
Returns:
Dict: a summary of the network including total params and total trainable params.
...
...
python/paddle/tests/test_model.py
浏览文件 @
97faf90e
...
...
@@ -184,6 +184,12 @@ class TestModel(unittest.TestCase):
def
test_fit_static_with_rank
(
self
):
self
.
fit
(
False
,
2
,
0
)
def
test_fit_dynamic_with_num_iters
(
self
):
self
.
fit
(
True
,
num_iters
=
1
)
def
test_fit_static_with_num_iters
(
self
):
self
.
fit
(
False
,
num_iters
=
1
)
def
test_evaluate_dygraph
(
self
):
self
.
evaluate
(
True
)
...
...
@@ -199,7 +205,7 @@ class TestModel(unittest.TestCase):
def
test_prepare_context
(
self
):
prepare_distributed_context
()
def
fit
(
self
,
dynamic
,
num_replicas
=
None
,
rank
=
None
):
def
fit
(
self
,
dynamic
,
num_replicas
=
None
,
rank
=
None
,
num_iters
=
None
):
fluid
.
enable_dygraph
(
self
.
device
)
if
dynamic
else
None
seed
=
333
paddle
.
seed
(
seed
)
...
...
@@ -218,6 +224,14 @@ class TestModel(unittest.TestCase):
result
=
model
.
evaluate
(
self
.
val_dataset
,
batch_size
=
64
)
np
.
testing
.
assert_allclose
(
result
[
'acc'
],
self
.
acc1
)
model
.
fit
(
self
.
train_dataset
,
batch_size
=
64
,
shuffle
=
False
,
num_iters
=
num_iters
)
result
=
model
.
evaluate
(
self
.
val_dataset
,
batch_size
=
64
,
num_iters
=
num_iters
)
train_sampler
=
DistributedBatchSampler
(
self
.
train_dataset
,
batch_size
=
64
,
...
...
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