75f75fc3bd
1. Change all test_util.run_all_in_graph_and_eager_modes to combination. 2. Replace import tensorflow.python.keras with explicit module import. 3. Update BUILD file to not rely on the overall Keras target. PiperOrigin-RevId: 300985437 Change-Id: Icb9e891619814704996649176360d8fee1d7d863
129 lines
6.0 KiB
Python
129 lines
6.0 KiB
Python
# Copyright 2016 The TensorFlow 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.
|
|
# ==============================================================================
|
|
"""Tests for training routines."""
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
from absl.testing import parameterized
|
|
import numpy as np
|
|
|
|
from tensorflow.python.framework import test_util
|
|
from tensorflow.python.keras import backend as K
|
|
from tensorflow.python.keras import combinations
|
|
from tensorflow.python.keras.engine import input_layer
|
|
from tensorflow.python.keras.engine import training
|
|
from tensorflow.python.keras.layers.convolutional import Conv2D
|
|
from tensorflow.python.platform import test
|
|
|
|
|
|
class TrainingGPUTest(test.TestCase, parameterized.TestCase):
|
|
|
|
@combinations.generate(combinations.combine(mode=['graph', 'eager']))
|
|
def test_model_with_crossentropy_losses_channels_first(self):
|
|
"""Tests use of all crossentropy losses with `channels_first`.
|
|
|
|
Tests `sparse_categorical_crossentropy`, `categorical_crossentropy`,
|
|
and `binary_crossentropy`.
|
|
Verifies that evaluate gives the same result with either `channels_first`
|
|
or `channels_last` image_data_format.
|
|
"""
|
|
def prepare_simple_model(input_tensor, loss_name, target):
|
|
axis = 1 if K.image_data_format() == 'channels_first' else -1
|
|
loss = None
|
|
num_channels = None
|
|
activation = None
|
|
if loss_name == 'sparse_categorical_crossentropy':
|
|
loss = lambda y_true, y_pred: K.sparse_categorical_crossentropy( # pylint: disable=g-long-lambda
|
|
y_true, y_pred, axis=axis)
|
|
num_channels = int(np.amax(target) + 1)
|
|
activation = 'softmax'
|
|
elif loss_name == 'categorical_crossentropy':
|
|
loss = lambda y_true, y_pred: K.categorical_crossentropy( # pylint: disable=g-long-lambda
|
|
y_true, y_pred, axis=axis)
|
|
num_channels = target.shape[axis]
|
|
activation = 'softmax'
|
|
elif loss_name == 'binary_crossentropy':
|
|
loss = lambda y_true, y_pred: K.binary_crossentropy(y_true, y_pred) # pylint: disable=unnecessary-lambda
|
|
num_channels = target.shape[axis]
|
|
activation = 'sigmoid'
|
|
|
|
predictions = Conv2D(num_channels,
|
|
1,
|
|
activation=activation,
|
|
kernel_initializer='ones',
|
|
bias_initializer='ones')(input_tensor)
|
|
simple_model = training.Model(inputs=input_tensor, outputs=predictions)
|
|
simple_model.compile(optimizer='rmsprop', loss=loss)
|
|
return simple_model
|
|
|
|
if test.is_gpu_available(cuda_only=True):
|
|
with test_util.use_gpu():
|
|
losses_to_test = ['sparse_categorical_crossentropy',
|
|
'categorical_crossentropy', 'binary_crossentropy']
|
|
|
|
data_channels_first = np.array([[[[8., 7.1, 0.], [4.5, 2.6, 0.55],
|
|
[0.9, 4.2, 11.2]]]], dtype=np.float32)
|
|
# Labels for testing 4-class sparse_categorical_crossentropy, 4-class
|
|
# categorical_crossentropy, and 2-class binary_crossentropy:
|
|
labels_channels_first = [np.array([[[[0, 1, 3], [2, 1, 0], [2, 2, 1]]]], dtype=np.float32), # pylint: disable=line-too-long
|
|
np.array([[[[0, 1, 0], [0, 1, 0], [0, 0, 0]],
|
|
[[1, 0, 0], [0, 0, 1], [0, 1, 0]],
|
|
[[0, 0, 0], [1, 0, 0], [0, 0, 1]],
|
|
[[0, 0, 1], [0, 0, 0], [1, 0, 0]]]], dtype=np.float32), # pylint: disable=line-too-long
|
|
np.array([[[[0, 1, 0], [0, 1, 0], [0, 0, 1]],
|
|
[[1, 0, 1], [1, 0, 1], [1, 1, 0]]]], dtype=np.float32)] # pylint: disable=line-too-long
|
|
# Compute one loss for each loss function in the list `losses_to_test`:
|
|
loss_channels_last = [0., 0., 0.]
|
|
loss_channels_first = [0., 0., 0.]
|
|
|
|
old_data_format = K.image_data_format()
|
|
|
|
# Evaluate a simple network with channels last, with all three loss
|
|
# functions:
|
|
K.set_image_data_format('channels_last')
|
|
data = np.moveaxis(data_channels_first, 1, -1)
|
|
for index, loss_function in enumerate(losses_to_test):
|
|
labels = np.moveaxis(labels_channels_first[index], 1, -1)
|
|
inputs = input_layer.Input(shape=(3, 3, 1))
|
|
model = prepare_simple_model(inputs, loss_function, labels)
|
|
loss_channels_last[index] = model.evaluate(x=data, y=labels,
|
|
batch_size=1, verbose=0)
|
|
|
|
# Evaluate the same network with channels first, with all three loss
|
|
# functions:
|
|
K.set_image_data_format('channels_first')
|
|
data = data_channels_first
|
|
for index, loss_function in enumerate(losses_to_test):
|
|
labels = labels_channels_first[index]
|
|
inputs = input_layer.Input(shape=(1, 3, 3))
|
|
model = prepare_simple_model(inputs, loss_function, labels)
|
|
loss_channels_first[index] = model.evaluate(x=data, y=labels,
|
|
batch_size=1, verbose=0)
|
|
|
|
K.set_image_data_format(old_data_format)
|
|
|
|
np.testing.assert_allclose(
|
|
loss_channels_first,
|
|
loss_channels_last,
|
|
rtol=1e-06,
|
|
err_msg='{}{}'.format('Computed different losses for ',
|
|
'channels_first and channels_last'))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test.main()
|