def test_activation_function(reset):
# creating a deep image embedder with relu
is_training = tf.Variable(False, trainable=False, collections=[tf.GraphKeys.LOCAL_VARIABLES])
embedder = ImageEmbedder(np.array([100, 100, 10]), name="relu", scheme=EmbedderScheme.Deep,
activation_function=tf.nn.relu, is_training=is_training)
# call the embedder
embedder()
# try feeding a batch of one example
input = np.random.rand(1, 100, 100, 10)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
output = sess.run(embedder.output, {embedder.input: input})
assert np.all(output >= 0) # should have flattened the input
# creating a deep image embedder with tanh
embedder_tanh = ImageEmbedder(np.array([100, 100, 10]), name="tanh", scheme=EmbedderScheme.Deep,
activation_function=tf.nn.tanh, is_training=is_training)
# call the embedder
embedder_tanh()
# try feeding a batch of one example
input = np.random.rand(1, 100, 100, 10)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
output = sess.run(embedder_tanh.output, {embedder_tanh.input: input})
assert np.all(output >= -1) and np.all(output <= 1)
def test_activation_function(reset):
# creating a deep image embedder with relu
embedder = ImageEmbedder(np.array([100, 100, 10]),
name="relu",
scheme=EmbedderScheme.Deep,
activation_function=tf.nn.relu)
# call the embedder
embedder()
# try feeding a batch of one example
input = np.random.rand(1, 100, 100, 10)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
output = sess.run(embedder.output, {embedder.input: input})
assert np.all(output >= 0) # should have flattened the input
# creating a deep image embedder with tanh
embedder_tanh = ImageEmbedder(np.array([100, 100, 10]),
name="tanh",
scheme=EmbedderScheme.Deep,
activation_function=tf.nn.tanh)
# call the embedder
embedder_tanh()
# try feeding a batch of one example
input = np.random.rand(1, 100, 100, 10)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
output = sess.run(embedder_tanh.output, {embedder_tanh.input: input})
assert np.all(output >= -1) and np.all(output <= 1)
def test_embedder(reset):
# creating an embedder with a non-image input
with pytest.raises(ValueError):
embedder = ImageEmbedder(np.array([100]), name="test")
with pytest.raises(ValueError):
embedder = ImageEmbedder(np.array([100, 100]), name="test")
with pytest.raises(ValueError):
embedder = ImageEmbedder(np.array([10, 100, 100, 100]), name="test")
# creating a simple image embedder
embedder = ImageEmbedder(np.array([100, 100, 10]), name="test")
# make sure the ops where not created yet
assert len(tf.get_default_graph().get_operations()) == 0
# call the embedder
input_ph, output_ph = embedder()
# make sure that now the ops were created
assert len(tf.get_default_graph().get_operations()) > 0
# try feeding a batch of one example
input = np.random.rand(1, 100, 100, 10)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
output = sess.run(embedder.output, {embedder.input: input})
assert output.shape == (1, 5184)
# now make sure the returned placeholders behave the same
output = sess.run(output_ph, {input_ph: input})
assert output.shape == (1, 5184)
# make sure the naming is correct
assert embedder.get_name() == "test"
def test_complex_embedder(reset):
# creating a deep vector embedder
is_training = tf.Variable(False, trainable=False, collections=[tf.GraphKeys.LOCAL_VARIABLES])
embedder = ImageEmbedder(np.array([100, 100, 10]), name="test", scheme=EmbedderScheme.Deep,
is_training=is_training)
# call the embedder
embedder()
# try feeding a batch of one example
input = np.random.rand(1, 100, 100, 10)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
output = sess.run(embedder.output, {embedder.input: input})
assert output.shape == (1, 256) # should have flattened the input
def test_embedder(reset):
# creating an embedder with a non-image input
with pytest.raises(ValueError):
embedder = ImageEmbedder(np.array([100]), name="test")
with pytest.raises(ValueError):
embedder = ImageEmbedder(np.array([100, 100]), name="test")
with pytest.raises(ValueError):
embedder = ImageEmbedder(np.array([10, 100, 100, 100]), name="test")
is_training = tf.Variable(
False,
trainable=False,
collections=[tf.compat.v1.GraphKeys.LOCAL_VARIABLES])
pre_ops = len(tf.get_default_graph().get_operations())
# creating a simple image embedder
embedder = ImageEmbedder(np.array([100, 100, 10]),
name="test",
is_training=is_training)
# make sure the only the is_training op is creates
assert len(tf.get_default_graph().get_operations()) == pre_ops
# call the embedder
input_ph, output_ph = embedder()
# make sure that now the ops were created
assert len(tf.get_default_graph().get_operations()) > pre_ops
# try feeding a batch of one example
input = np.random.rand(1, 100, 100, 10)
sess = tf.compat.v1.Session()
sess.run(tf.compat.v1.global_variables_initializer())
output = sess.run(embedder.output, {embedder.input: input})
assert output.shape == (1, 5184)
# now make sure the returned placeholders behave the same
output = sess.run(output_ph, {input_ph: input})
assert output.shape == (1, 5184)
# make sure the naming is correct
assert embedder.get_name() == "test"
def test_complex_embedder(reset):
# creating a deep vector embedder
embedder = ImageEmbedder(np.array([100, 100, 10]),
name="test",
scheme=EmbedderScheme.Deep)
# call the embedder
embedder()
# try feeding a batch of one example
input = np.random.rand(1, 100, 100, 10)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
output = sess.run(embedder.output, {embedder.input: input})
assert output.shape == (1, 256) # should have flattened the input