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import pytest
import numpy as np
from numpy.testing import assert_allclose
# We don't use keras.applications.imagenet_utils here
# because we also test _obtain_input_shape which is not exposed.
from keras_applications import imagenet_utils as utils
from keras import backend
from keras import models
from keras import layers
from keras import utils as keras_utils
def decode_predictions(*args, **kwargs):
kwargs['backend'] = backend
kwargs['utils'] = keras_utils
return utils.decode_predictions(*args, **kwargs)
def preprocess_input(*args, **kwargs):
kwargs['backend'] = backend
return utils.preprocess_input(*args, **kwargs)
def test_preprocess_input():
# Test image batch with float and int image input
x = np.random.uniform(0, 255, (2, 10, 10, 3))
xint = x.astype('int32')
assert preprocess_input(x).shape == x.shape
assert preprocess_input(xint).shape == xint.shape
out1 = preprocess_input(x, 'channels_last')
out1int = preprocess_input(xint, 'channels_last')
out2 = preprocess_input(np.transpose(x, (0, 3, 1, 2)), 'channels_first')
out2int = preprocess_input(np.transpose(xint, (0, 3, 1, 2)), 'channels_first')
assert_allclose(out1, out2.transpose(0, 2, 3, 1))
assert_allclose(out1int, out2int.transpose(0, 2, 3, 1))
# Test single image
x = np.random.uniform(0, 255, (10, 10, 3))
xint = x.astype('int32')
assert preprocess_input(x).shape == x.shape
assert preprocess_input(xint).shape == xint.shape
out1 = preprocess_input(x, 'channels_last')
out1int = preprocess_input(xint, 'channels_last')
out2 = preprocess_input(np.transpose(x, (2, 0, 1)), 'channels_first')
out2int = preprocess_input(np.transpose(xint, (2, 0, 1)), 'channels_first')
assert_allclose(out1, out2.transpose(1, 2, 0))
assert_allclose(out1int, out2int.transpose(1, 2, 0))
# Test that writing over the input data works predictably
for mode in ['torch', 'tf']:
x = np.random.uniform(0, 255, (2, 10, 10, 3))
xint = x.astype('int')
x2 = preprocess_input(x, mode=mode)
xint2 = preprocess_input(xint)
assert_allclose(x, x2)
assert xint.astype('float').max() != xint2.max()
# Caffe mode works differently from the others
x = np.random.uniform(0, 255, (2, 10, 10, 3))
xint = x.astype('int')
x2 = preprocess_input(x, data_format='channels_last', mode='caffe')
xint2 = preprocess_input(xint)
assert_allclose(x, x2[..., ::-1])
assert xint.astype('float').max() != xint2.max()
def test_preprocess_input_symbolic():
# Test image batch
x = np.random.uniform(0, 255, (2, 10, 10, 3))
inputs = layers.Input(shape=x.shape[1:])
outputs = layers.Lambda(preprocess_input, output_shape=x.shape[1:])(inputs)
model = models.Model(inputs, outputs)
assert model.predict(x).shape == x.shape
outputs1 = layers.Lambda(
lambda x: preprocess_input(x, 'channels_last'),
output_shape=x.shape[1:])(inputs)
model1 = models.Model(inputs, outputs1)
out1 = model1.predict(x)
x2 = np.transpose(x, (0, 3, 1, 2))
inputs2 = layers.Input(shape=x2.shape[1:])
outputs2 = layers.Lambda(
lambda x: preprocess_input(x, 'channels_first'),
output_shape=x2.shape[1:])(inputs2)
model2 = models.Model(inputs2, outputs2)
out2 = model2.predict(x2)
assert_allclose(out1, out2.transpose(0, 2, 3, 1))
# Test single image
x = np.random.uniform(0, 255, (10, 10, 3))
inputs = layers.Input(shape=x.shape)
outputs = layers.Lambda(preprocess_input, output_shape=x.shape)(inputs)
model = models.Model(inputs, outputs)
assert model.predict(x[np.newaxis])[0].shape == x.shape
outputs1 = layers.Lambda(
lambda x: preprocess_input(x, 'channels_last'),
output_shape=x.shape)(inputs)
model1 = models.Model(inputs, outputs1)
out1 = model1.predict(x[np.newaxis])[0]
x2 = np.transpose(x, (2, 0, 1))
inputs2 = layers.Input(shape=x2.shape)
outputs2 = layers.Lambda(
lambda x: preprocess_input(x, 'channels_first'),
output_shape=x2.shape)(inputs2)
model2 = models.Model(inputs2, outputs2)
out2 = model2.predict(x2[np.newaxis])[0]
assert_allclose(out1, out2.transpose(1, 2, 0))
def test_decode_predictions():
x = np.zeros((2, 1000))
x[0, 372] = 1.0
x[1, 549] = 1.0
outs = decode_predictions(x, top=1)
scores = [out[0][2] for out in outs]
assert scores[0] == scores[1]
# the numbers of columns and ImageNet classes are not identical.
with pytest.raises(ValueError):
decode_predictions(np.ones((2, 100)))
def test_obtain_input_shape():
# input_shape and default_size are not identical.
with pytest.raises(ValueError):
utils._obtain_input_shape(
input_shape=(224, 224, 3),
default_size=299,
min_size=139,
data_format='channels_last',
require_flatten=True,
weights='imagenet')
# Test invalid use cases
for data_format in ['channels_last', 'channels_first']:
# test warning
shape = (139, 139)
if data_format == 'channels_last':
input_shape = shape + (99,)
else:
input_shape = (99,) + shape
with pytest.warns(UserWarning):
utils._obtain_input_shape(
input_shape=input_shape,
default_size=None,
min_size=139,
data_format=data_format,
require_flatten=False,
weights='fake_weights')
# input_shape is smaller than min_size.
shape = (100, 100)
if data_format == 'channels_last':
input_shape = shape + (3,)
else:
input_shape = (3,) + shape
with pytest.raises(ValueError):
utils._obtain_input_shape(
input_shape=input_shape,
default_size=None,
min_size=139,
data_format=data_format,
require_flatten=False)
# shape is 1D.
shape = (100,)
if data_format == 'channels_last':
input_shape = shape + (3,)
else:
input_shape = (3,) + shape
with pytest.raises(ValueError):
utils._obtain_input_shape(
input_shape=input_shape,
default_size=None,
min_size=139,
data_format=data_format,
require_flatten=False)
# the number of channels is 5 not 3.
shape = (100, 100)
if data_format == 'channels_last':
input_shape = shape + (5,)
else:
input_shape = (5,) + shape
with pytest.raises(ValueError):
utils._obtain_input_shape(
input_shape=input_shape,
default_size=None,
min_size=139,
data_format=data_format,
require_flatten=False)
# require_flatten=True with dynamic input shape.
with pytest.raises(ValueError):
utils._obtain_input_shape(
input_shape=None,
default_size=None,
min_size=139,
data_format='channels_first',
require_flatten=True)
# test include top
assert utils._obtain_input_shape(
input_shape=(3, 200, 200),
default_size=None,
min_size=139,
data_format='channels_first',
require_flatten=True) == (3, 200, 200)
assert utils._obtain_input_shape(
input_shape=None,
default_size=None,
min_size=139,
data_format='channels_last',
require_flatten=False) == (None, None, 3)
assert utils._obtain_input_shape(
input_shape=None,
default_size=None,
min_size=139,
data_format='channels_first',
require_flatten=False) == (3, None, None)
assert utils._obtain_input_shape(
input_shape=None,
default_size=None,
min_size=139,
data_format='channels_last',
require_flatten=False) == (None, None, 3)
assert utils._obtain_input_shape(
input_shape=(150, 150, 3),
default_size=None,
min_size=139,
data_format='channels_last',
require_flatten=False) == (150, 150, 3)
assert utils._obtain_input_shape(
input_shape=(3, None, None),
default_size=None,
min_size=139,
data_format='channels_first',
require_flatten=False) == (3, None, None)
if __name__ == '__main__':
pytest.main([__file__])
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