74 lines
2.6 KiB
Python
74 lines
2.6 KiB
Python
# Copyright 2022 Google LLC
|
|
#
|
|
# 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 that film_efficientnet can detect an image of a cat."""
|
|
|
|
from absl.testing import parameterized
|
|
import numpy as np
|
|
from robotics_transformer.film_efficientnet import film_efficientnet_encoder
|
|
from skimage import data
|
|
import tensorflow as tf
|
|
|
|
|
|
class FilmEfficientnetTest(tf.test.TestCase, parameterized.TestCase):
|
|
|
|
def _helper(self, include_film, model_variant):
|
|
if model_variant == 'b0':
|
|
size = 224
|
|
fe = film_efficientnet_encoder.EfficientNetB0
|
|
elif model_variant == 'b1':
|
|
size = 240
|
|
fe = film_efficientnet_encoder.EfficientNetB1
|
|
elif model_variant == 'b2':
|
|
size = 260
|
|
fe = film_efficientnet_encoder.EfficientNetB2
|
|
elif model_variant == 'b3':
|
|
size = 300
|
|
fe = film_efficientnet_encoder.EfficientNetB3
|
|
elif model_variant == 'b4':
|
|
size = 380
|
|
fe = film_efficientnet_encoder.EfficientNetB4
|
|
elif model_variant == 'b5':
|
|
size = 456
|
|
fe = film_efficientnet_encoder.EfficientNetB5
|
|
elif model_variant == 'b6':
|
|
size = 528
|
|
fe = film_efficientnet_encoder.EfficientNetB6
|
|
elif model_variant == 'b7':
|
|
size = 600
|
|
fe = film_efficientnet_encoder.EfficientNetB7
|
|
else:
|
|
raise ValueError(f'Unknown variant: {model_variant}')
|
|
fe = fe(include_top=True, weights='imagenet', include_film=include_film)
|
|
image = np.expand_dims(data.chelsea(), axis=0)
|
|
image = tf.image.resize(image, (size, size))
|
|
context = np.random.randn(1, 512)
|
|
if include_film:
|
|
eff_output = fe(
|
|
(film_efficientnet_encoder.preprocess_input(image), context),
|
|
training=False)
|
|
else:
|
|
eff_output = fe(
|
|
film_efficientnet_encoder.preprocess_input(image), training=False)
|
|
film_preds = film_efficientnet_encoder.decode_predictions(
|
|
eff_output.numpy(), top=10)
|
|
self.assertIn('tabby', [f[1] for f in film_preds[0]])
|
|
|
|
@parameterized.parameters([True, False])
|
|
def test_keras_equivalence_b3(self, include_film):
|
|
self._helper(include_film, 'b3')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
tf.test.main()
|