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未验证 提交 af74675b 编写于 作者: Q qingqing01 提交者： GitHub
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Remove paddle.incubate.hapi.loss and reuse paddle.nn.layer.loss in high level API (#25590) 

* Remove paddle.incubate.hapi.loss and reuse the paddle.nn.layer.loss in high level API
上级 2a191d8f
隐藏空白更改
内联 并排
Showing with 111 addition and 335 deletion
python/paddle/incubate/hapi/__init__.py
浏览文件 @ af74675b
......@@ -21,7 +21,6 @@ from . import model
from .model import *
from . import metrics
from . import loss
from . import datasets
from . import distributed
from . import vision
......@@ -40,7 +39,6 @@ __all__ = [
'distributed',
'download',
'metrics',
'loss',
'vision',
'text',
] + model.__all__ + device.__all__
......
python/paddle/incubate/hapi/callbacks.py
浏览文件 @ af74675b
......@@ -291,6 +291,7 @@ class ProgBarLogger(Callback):
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import paddle.incubate.hapi as hapi
......@@ -299,11 +300,12 @@ class ProgBarLogger(Callback):
train_dataset = hapi.datasets.MNIST(mode='train')
model = hapi.Model(hapi.vision.LeNet(), inputs, labels)
model = hapi.Model(hapi.vision.LeNet(classifier_activation=None),
inputs, labels)
optim = fluid.optimizer.Adam(0.001)
model.prepare(optimizer=optim,
loss_function=hapi.loss.CrossEntropy(),
loss_function=paddle.nn.CrossEntropyLoss(),
metrics=hapi.metrics.Accuracy())
callback = hapi.callbacks.ProgBarLogger(log_freq=10)
......@@ -425,6 +427,7 @@ class ModelCheckpoint(Callback):
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import paddle.incubate.hapi as hapi
......@@ -433,11 +436,12 @@ class ModelCheckpoint(Callback):
train_dataset = hapi.datasets.MNIST(mode='train')
model = hapi.Model(hapi.vision.LeNet(), inputs, labels)
model = hapi.Model(hapi.vision.LeNet(classifier_activation=None),
inputs, labels)
optim = fluid.optimizer.Adam(0.001)
model.prepare(optimizer=optim,
loss_function=hapi.loss.CrossEntropy(),
loss_function=paddle.nn.CrossEntropyLoss(),
metrics=hapi.metrics.Accuracy())
callback = hapi.callbacks.ModelCheckpoint(save_dir='./temp')
......
python/paddle/incubate/hapi/loss.py 已删除 100644 → 0
浏览文件 @ 2a191d8f
# 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
from paddle import fluid
from paddle.fluid.framework import in_dygraph_mode, Variable
from paddle.fluid.dygraph.base import to_variable
from .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)
Examples:
.. code-block:: python
from paddle.incubate.hapi.loss import Loss
from paddle import fluid
class SoftmaxWithCrossEntropy(Loss):
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)
]
"""
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
import paddle.fluid as fluid
import paddle.incubate.hapi as hapi
fluid.enable_dygraph()
model = hapi.Model(hapi.vision.LeNet())
model.prepare(loss_function=hapi.loss.CrossEntropy())
"""
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
import paddle.fluid as fluid
import paddle.incubate.hapi as hapi
fluid.enable_dygraph()
model = hapi.Model(hapi.vision.LeNet(classifier_activation=None))
loss = hapi.loss.SoftmaxWithCrossEntropy()
model.prepare(loss_function=loss)
"""
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)
]
python/paddle/incubate/hapi/metrics.py
浏览文件 @ af74675b
......@@ -170,6 +170,7 @@ class Accuracy(Metric):
.. code-block:: python
import paddle
import paddle.fluid as fluid
import paddle.incubate.hapi as hapi
......@@ -177,12 +178,12 @@ class Accuracy(Metric):
train_dataset = hapi.datasets.MNIST(mode='train')
model = hapi.Model(hapi.vision.LeNet())
model = hapi.Model(hapi.vision.LeNet(classifier_activation=None))
optim = fluid.optimizer.Adam(
learning_rate=0.001, parameter_list=model.parameters())
model.prepare(
optim,
loss_function=hapi.loss.CrossEntropy(average=False),
loss_function=paddle.nn.CrossEntropyLoss(),
metrics=hapi.metrics.Accuracy())
model.fit(train_dataset, batch_size=64)
......
python/paddle/incubate/hapi/model.py
浏览文件 @ af74675b
......@@ -35,7 +35,6 @@ from paddle.fluid.incubate.fleet.collective import fleet, DistributedStrategy
from paddle.fluid.incubate.fleet.base import role_maker
from paddle.io import DataLoader, Dataset
from .loss import Loss
from .distributed import DistributedBatchSampler, _all_gather, prepare_distributed_context, _parallel_context_initialized
from .metrics import Metric
from .callbacks import config_callbacks
......@@ -327,7 +326,7 @@ class StaticGraphAdapter(object):
rets = [np.array(v) for v in rets]
if self.mode == 'test':
return rets[:]
losses = rets[:num_loss]
metric_states = restore_flatten_list(rets[num_loss:], metric_splits)
metrics = []
for metric, state in zip(self.model._metrics, metric_states):
......@@ -351,7 +350,11 @@ class StaticGraphAdapter(object):
self._merge_count[self.mode + '_batch'] = samples
metrics.append(metric.update(*state))
return (losses, metrics) if len(metrics) > 0 else losses
if num_loss and len(metrics):
return rets[:num_loss], metrics
else:
return rets[:num_loss] if num_loss else metrics
def prepare(self):
modes = ['train', 'eval', 'test']
......@@ -383,15 +386,15 @@ class StaticGraphAdapter(object):
losses = []
metrics = []
with fluid.program_guard(prog, self._startup_prog):
ins = self.model._inputs
lbls = self.model._labels if self.model._labels else []
inputs = [k.forward() for k in to_list(ins)]
labels = [k.forward() for k in to_list(lbls)]
inputs = self.model._inputs
labels = self.model._labels if self.model._labels else []
inputs = [k.forward() for k in to_list(inputs)]
labels = [k.forward() for k in to_list(labels)]
self._label_vars[mode] = labels
outputs = to_list(self.model.network.forward(*inputs))
if mode != 'test' and self.model._loss_function:
losses = self.model._loss_function(outputs, labels)
losses = self.model._loss_function(*(outputs + labels))
if self._nranks > 1 and mode != 'train':
outputs = [_all_gather(o, self._nranks) for o in outputs]
......@@ -424,7 +427,7 @@ class StaticGraphAdapter(object):
self._progs[mode] = prog
self._endpoints[mode] = {
"output": outputs,
"loss": losses,
"loss": to_list(losses),
"metric": metrics
}
......@@ -501,11 +504,13 @@ class DynamicGraphAdapter(object):
self.model.network.train()
self.mode = 'train'
inputs = to_list(inputs)
if labels is not None:
labels = [to_variable(l) for l in to_list(labels)]
labels = labels or []
labels = [to_variable(l) for l in to_list(labels)]
if self._nranks > 1:
outputs = self.ddp_model.forward(* [to_variable(x) for x in inputs])
losses = self.model._loss_function(outputs, labels)
losses = self.model._loss_function(*(to_list(outputs) + labels))
losses = to_list(losses)
final_loss = fluid.layers.sum(losses)
final_loss = self.ddp_model.scale_loss(final_loss)
final_loss.backward()
......@@ -513,7 +518,8 @@ class DynamicGraphAdapter(object):
else:
outputs = self.model.network.forward(
* [to_variable(x) for x in inputs])
losses = self.model._loss_function(outputs, labels)
losses = self.model._loss_function(*(to_list(outputs) + labels))
losses = to_list(losses)
final_loss = fluid.layers.sum(losses)
final_loss.backward()
......@@ -521,8 +527,7 @@ class DynamicGraphAdapter(object):
self.model.network.clear_gradients()
metrics = []
for metric in self.model._metrics:
metric_outs = metric.add_metric_op(*(to_list(outputs) + to_list(
labels)))
metric_outs = metric.add_metric_op(*(to_list(outputs) + labels))
m = metric.update(* [to_numpy(m) for m in to_list(metric_outs)])
metrics.append(m)
......@@ -533,13 +538,14 @@ class DynamicGraphAdapter(object):
self.model.network.eval()
self.mode = 'eval'
inputs = to_list(inputs)
if labels is not None:
labels = [to_variable(l) for l in to_list(labels)]
labels = labels or []
labels = [to_variable(l) for l in to_list(labels)]
outputs = self.model.network.forward(* [to_variable(x) for x in inputs])
if self.model._loss_function:
losses = self.model._loss_function(outputs, labels)
else:
losses = []
losses = self.model._loss_function(*(to_list(outputs) + labels))
losses = to_list(losses)
if self._nranks > 1:
outputs = [_all_gather(o, self._nranks) for o in to_list(outputs)]
labels = [_all_gather(l, self._nranks) for l in labels]
......@@ -565,15 +571,16 @@ class DynamicGraphAdapter(object):
self._merge_count[self.mode + '_total'] += samples
self._merge_count[self.mode + '_batch'] = samples
metric_outs = metric.add_metric_op(*(to_list(outputs) + to_list(
labels)))
metric_outs = metric.add_metric_op(*(to_list(outputs) + labels))
m = metric.update(* [to_numpy(m) for m in to_list(metric_outs)])
metrics.append(m)
# To be consistent with static graph
# return empty loss if loss_function is None
return ([to_numpy(l) for l in losses], metrics) \
if len(metrics) > 0 else [to_numpy(l) for l in losses]
if self.model._loss_function and len(metrics):
return [to_numpy(l) for l in losses], metrics
elif self.model._loss_function:
return [to_numpy(l) for l in losses]
else:
return metrics
def test_batch(self, inputs):
self.model.network.eval()
......@@ -679,13 +686,15 @@ class Model(object):
Usage:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import paddle.incubate.hapi as hapi
class MyNet(fluid.dygraph.Layer):
def __init__(self):
def __init__(self, classifier_act=None):
super(MyNet, self).__init__()
self._fc1 = fluid.dygraph.Linear(784, 200, act='softmax')
self._fc1 = fluid.dygraph.Linear(784, 200, act=classifier_act)
def forward(self, x):
y = self._fc1(x)
return y
......@@ -702,7 +711,7 @@ class Model(object):
optim = fluid.optimizer.SGD(learning_rate=1e-3,
parameter_list=model.parameters())
model.prepare(optim,
hapi.loss.CrossEntropy(average=True),
paddle.nn.CrossEntropyLoss(),
hapi.metrics.Accuracy())
mnist_data = hapi.datasets.MNIST(mode='train', chw_format=False)
......@@ -762,13 +771,15 @@ class Model(object):
.. code-block:: python
import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.incubate.hapi as hapi
class MyNet(fluid.dygraph.Layer):
def __init__(self):
def __init__(self, classifier_act=None):
super(MyNet, self).__init__()
self._fc = fluid.dygraph.Linear(784, 10, act='softmax')
self._fc = fluid.dygraph.Linear(784, 10, act=classifier_act)
def forward(self, x):
y = self._fc(x)
return y
......@@ -781,7 +792,7 @@ class Model(object):
model = hapi.Model(MyNet(), input, label)
optim = fluid.optimizer.SGD(learning_rate=1e-3,
parameter_list=model.parameters())
model.prepare(optim, hapi.loss.CrossEntropy(average=True))
model.prepare(optim, paddle.nn.CrossEntropyLoss())
data = np.random.random(size=(4,784)).astype(np.float32)
label = np.random.randint(0, 10, size=(4, 1)).astype(np.int64)
loss = model.train_batch([data], [label])
......@@ -809,13 +820,15 @@ class Model(object):
.. code-block:: python
import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.incubate.hapi as hapi
class MyNet(fluid.dygraph.Layer):
def __init__(self):
def __init__(self, classifier_act=None):
super(MyNet, self).__init__()
self._fc = fluid.dygraph.Linear(784, 10, act='softmax')
self._fc = fluid.dygraph.Linear(784, 10, act=classifier_act)
def forward(self, x):
y = self._fc(x)
return y
......@@ -829,7 +842,7 @@ class Model(object):
optim = fluid.optimizer.SGD(learning_rate=1e-3,
parameter_list=model.parameters())
model.prepare(optim,
hapi.loss.CrossEntropy(average=True))
paddle.nn.CrossEntropyLoss())
data = np.random.random(size=(4,784)).astype(np.float32)
label = np.random.randint(0, 10, size=(4, 1)).astype(np.int64)
loss = model.eval_batch([data], [label])
......@@ -1054,9 +1067,10 @@ class Model(object):
optimizer (Optimizer|None): Optimizer must be set in training
and should be a Optimizer instance. It can be None in eval
and test mode.
loss_function (Loss|None): Loss function must be set in training
and should be a Loss instance. It can be None when there is
no loss.
loss_function (Loss|callable function|None): Loss function can
be a `fluid.dygraph.Layer` instance or any callable function
taken the predicted values and ground truth values as input.
It can be None when there is no loss.
metrics (Metric|list of Metric|None): If metrics is set, all
metrics will be calculated and output in train/eval mode.
......@@ -1086,8 +1100,10 @@ class Model(object):
self._optimizer = optimizer
if loss_function:
if not isinstance(loss_function, Loss):
raise TypeError("'loss_function' must be sub classes of 'Loss'")
if not isinstance(loss_function, fluid.dygraph.Layer) or \
not callable(loss_function):
raise TypeError("'loss_function' must be sub classes of \
`fluid.dygraph.Layer` or any callable function.")
self._loss_function = loss_function
metrics = metrics or []
......@@ -1167,6 +1183,7 @@ class Model(object):
.. code-block:: python
import paddle
import paddle.fluid as fluid
import paddle.incubate.hapi as hapi
......@@ -1180,12 +1197,13 @@ class Model(object):
input = hapi.Input('image', [None, 1, 28, 28], 'float32')
label = hapi.Input('label', [None, 1], 'int64')
model = hapi.Model(hapi.vision.LeNet(), input, label)
model = hapi.Model(hapi.vision.LeNet(classifier_activation=None),
input, label)
optim = fluid.optimizer.Adam(
learning_rate=0.001, parameter_list=model.parameters())
model.prepare(
optim,
hapi.loss.CrossEntropy(),
paddle.nn.CrossEntropyLoss(),
hapi.metrics.Accuracy(topk=(1, 2)))
model.fit(train_dataset,
val_dataset,
......@@ -1198,6 +1216,7 @@ class Model(object):
.. code-block:: python
import paddle
import paddle.fluid as fluid
import paddle.incubate.hapi as hapi
......@@ -1215,12 +1234,13 @@ class Model(object):
input = hapi.Input('image', [None, 1, 28, 28], 'float32')
label = hapi.Input('label', [None, 1], 'int64')
model = hapi.Model(hapi.vision.LeNet(), input, label)
model = hapi.Model(hapi.vision.LeNet(classifier_activation=None),
input, label)
optim = fluid.optimizer.Adam(
learning_rate=0.001, parameter_list=model.parameters())
model.prepare(
optim,
hapi.loss.CrossEntropy(),
paddle.nn.CrossEntropyLoss(),
hapi.metrics.Accuracy(topk=(1, 2)))
model.fit(train_loader,
val_loader,
......@@ -1581,9 +1601,12 @@ class Model(object):
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]]
if self._metrics and self._loss_function:
metrics = [[l[0] for l in outs[0]]]
elif self._loss_function:
metrics = [[l[0] for l in outs]]
else:
metrics = []
# metrics
for metric in self._metrics:
......@@ -1621,7 +1644,7 @@ class Model(object):
metric.reset()
def _metrics_name(self):
metrics_name = ['loss']
metrics_name = ['loss'] if self._loss_function else []
for m in self._metrics:
metrics_name.extend(to_list(m.name()))
return metrics_name
......
python/paddle/incubate/hapi/tests/dist_hapi_mnist_dynamic.py
浏览文件 @ af74675b
......@@ -23,7 +23,7 @@ import contextlib
from paddle import fluid
from paddle.incubate.hapi import Model, Input, set_device
from paddle.incubate.hapi.loss import CrossEntropy
from paddle.nn.layer.loss import CrossEntropyLoss
from paddle.incubate.hapi.vision.models import LeNet
from paddle.incubate.hapi.metrics import Accuracy
from paddle.incubate.hapi.callbacks import ProgBarLogger
......@@ -67,10 +67,10 @@ class TestDistTraning(unittest.TestCase):
inputs = [Input('image', im_shape, 'float32')]
labels = [Input('label', [None, 1], 'int64')]
model = Model(LeNet(), inputs, labels)
model = Model(LeNet(classifier_activation=None), inputs, labels)
optim = fluid.optimizer.Momentum(
learning_rate=0.001, momentum=.9, parameter_list=model.parameters())
model.prepare(optim, CrossEntropy(), Accuracy())
model.prepare(optim, CrossEntropyLoss(), Accuracy())
train_dataset = MnistDataset(mode='train')
val_dataset = MnistDataset(mode='test')
......
python/paddle/incubate/hapi/tests/dist_hapi_mnist_static.py
浏览文件 @ af74675b
......@@ -23,7 +23,7 @@ import contextlib
from paddle import fluid
from paddle.incubate.hapi import Model, Input, set_device
from paddle.incubate.hapi.loss import CrossEntropy
from paddle.nn.layer.loss import CrossEntropyLoss
from paddle.incubate.hapi.vision.models import LeNet
from paddle.incubate.hapi.metrics import Accuracy
from paddle.incubate.hapi.callbacks import ProgBarLogger
......@@ -66,10 +66,10 @@ class TestDistTraning(unittest.TestCase):
inputs = [Input('image', im_shape, 'float32')]
labels = [Input('label', [None, 1], 'int64')]
model = Model(LeNet(), inputs, labels)
model = Model(LeNet(classifier_activation=None), inputs, labels)
optim = fluid.optimizer.Momentum(
learning_rate=0.001, momentum=.9, parameter_list=model.parameters())
model.prepare(optim, CrossEntropy(), Accuracy())
model.prepare(optim, CrossEntropyLoss(), Accuracy())
train_dataset = MnistDataset(mode='train')
val_dataset = MnistDataset(mode='test')
......
python/paddle/incubate/hapi/tests/test_loss.py 已删除 100644 → 0
浏览文件 @ 2a191d8f
# 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 paddle.incubate.hapi.model import Model, Input
from paddle.incubate.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()
python/paddle/incubate/hapi/tests/test_model.py
浏览文件 @ af74675b
......@@ -28,7 +28,7 @@ from paddle.fluid.dygraph.base import to_variable
import paddle.incubate.hapi as hapi
from paddle.incubate.hapi import Model, Input
from paddle.incubate.hapi.loss import CrossEntropy
from paddle.nn.layer.loss import CrossEntropyLoss
from paddle.incubate.hapi.metrics import Accuracy
from paddle.incubate.hapi.datasets import MNIST
from paddle.incubate.hapi.vision.models import LeNet
......@@ -36,7 +36,7 @@ from paddle.incubate.hapi.distributed import DistributedBatchSampler, prepare_di
class LeNetDygraph(fluid.dygraph.Layer):
def __init__(self, num_classes=10, classifier_activation='softmax'):
def __init__(self, num_classes=10, classifier_activation=None):
super(LeNetDygraph, self).__init__()
self.num_classes = num_classes
self.features = Sequential(
......@@ -97,7 +97,7 @@ def dynamic_train(model, dataloader):
model.train()
for inputs, labels in dataloader:
outputs = model(inputs)
loss = fluid.layers.cross_entropy(outputs, labels)
loss = CrossEntropyLoss(reduction="sum")(outputs, labels)
avg_loss = fluid.layers.reduce_sum(loss)
avg_loss.backward()
optim.minimize(avg_loss)
......@@ -190,13 +190,13 @@ class TestModel(unittest.TestCase):
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
net = LeNet()
net = LeNet(classifier_activation=None)
optim_new = fluid.optimizer.Adam(
learning_rate=0.001, parameter_list=net.parameters())
model = Model(net, inputs=self.inputs, labels=self.labels)
model.prepare(
optim_new,
loss_function=CrossEntropy(average=False),
loss_function=CrossEntropyLoss(reduction="sum"),
metrics=Accuracy())
model.fit(self.train_dataset, batch_size=64, shuffle=False)
......@@ -271,9 +271,9 @@ class TestModel(unittest.TestCase):
class MyModel(fluid.dygraph.Layer):
def __init__(self):
def __init__(self, classifier_activation='softmax'):
super(MyModel, self).__init__()
self._fc = Linear(20, 10, act='softmax')
self._fc = Linear(20, 10, act=classifier_activation)
def forward(self, x):
y = self._fc(x)
......@@ -293,13 +293,12 @@ class TestModelFunction(unittest.TestCase):
def get_expect():
fluid.enable_dygraph(fluid.CPUPlace())
self.set_seed()
m = MyModel()
m = MyModel(classifier_activation=None)
optim = fluid.optimizer.SGD(learning_rate=0.001,
parameter_list=m.parameters())
m.train()
output = m(to_variable(data))
l = to_variable(label)
loss = fluid.layers.cross_entropy(output, l)
loss = CrossEntropyLoss(reduction='sum')(output, to_variable(label))
avg_loss = fluid.layers.reduce_sum(loss)
avg_loss.backward()
optim.minimize(avg_loss)
......@@ -313,14 +312,15 @@ class TestModelFunction(unittest.TestCase):
fluid.enable_dygraph(device) if dynamic else None
self.set_seed()
net = MyModel()
net = MyModel(classifier_activation=None)
optim2 = fluid.optimizer.SGD(learning_rate=0.001,
parameter_list=net.parameters())
inputs = [Input('x', [None, dim], 'float32')]
labels = [Input('label', [None, 1], 'int64')]
model = Model(net, inputs, labels)
model.prepare(optim2, loss_function=CrossEntropy(average=False))
model.prepare(
optim2, loss_function=CrossEntropyLoss(reduction="sum"))
loss, = model.train_batch([data], [label])
np.testing.assert_allclose(loss.flatten(), ref.flatten())
......@@ -358,14 +358,15 @@ class TestModelFunction(unittest.TestCase):
for dynamic in [True, False]:
device = hapi.set_device('cpu')
fluid.enable_dygraph(device) if dynamic else None
net = MyModel()
net = MyModel(classifier_activation=None)
inputs = [Input('x', [None, 20], 'float32')]
labels = [Input('label', [None, 1], 'int64')]
optim = fluid.optimizer.SGD(learning_rate=0.001,
parameter_list=net.parameters())
model = Model(net, inputs, labels)
model.prepare(
optimizer=optim, loss_function=CrossEntropy(average=False))
optimizer=optim,
loss_function=CrossEntropyLoss(reduction="sum"))
model.save(path + '/test')
model.load(path + '/test')
shutil.rmtree(path)
......@@ -376,48 +377,48 @@ class TestModelFunction(unittest.TestCase):
# dynamic saving
device = hapi.set_device('cpu')
fluid.enable_dygraph(device)
model = Model(MyModel())
model = Model(MyModel(classifier_activation=None))
optim = fluid.optimizer.SGD(learning_rate=0.001,
parameter_list=model.parameters())
model.prepare(
optimizer=optim, loss_function=CrossEntropy(average=False))
optimizer=optim, loss_function=CrossEntropyLoss(reduction="sum"))
model.save(path + '/test')
fluid.disable_dygraph()
inputs = [Input('x', [None, 20], 'float32')]
labels = [Input('label', [None, 1], 'int64')]
model = Model(MyModel(), inputs, labels)
model = Model(MyModel(classifier_activation=None), inputs, labels)
optim = fluid.optimizer.SGD(learning_rate=0.001,
parameter_list=model.parameters())
model.prepare(
optimizer=optim, loss_function=CrossEntropy(average=False))
optimizer=optim, loss_function=CrossEntropyLoss(reduction="sum"))
model.load(path + '/test')
shutil.rmtree(path)
def test_static_save_dynamic_load(self):
path = tempfile.mkdtemp()
net = MyModel()
net = MyModel(classifier_activation=None)
inputs = [Input('x', [None, 20], 'float32')]
labels = [Input('label', [None, 1], 'int64')]
optim = fluid.optimizer.SGD(learning_rate=0.001,
parameter_list=net.parameters())
model = Model(net, inputs, labels)
model.prepare(
optimizer=optim, loss_function=CrossEntropy(average=False))
optimizer=optim, loss_function=CrossEntropyLoss(reduction="sum"))
model.save(path + '/test')
device = hapi.set_device('cpu')
fluid.enable_dygraph(device) #if dynamic else None
net = MyModel()
net = MyModel(classifier_activation=None)
inputs = [Input('x', [None, 20], 'float32')]
labels = [Input('label', [None, 1], 'int64')]
optim = fluid.optimizer.SGD(learning_rate=0.001,
parameter_list=net.parameters())
model = Model(net, inputs, labels)
model.prepare(
optimizer=optim, loss_function=CrossEntropy(average=False))
optimizer=optim, loss_function=CrossEntropyLoss(reduction="sum"))
model.load(path + '/test')
shutil.rmtree(path)
fluid.disable_dygraph()
......
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