def test_hash_ragged_string_input_farmhash(self):
layer = hashing.Hashing(num_bins=2)
inp_data = tf.ragged.constant([['omar', 'stringer', 'marlo', 'wire'],
['marlo', 'skywalker', 'wire']],
dtype=tf.string)
out_data = layer(inp_data)
# Same hashed output as test_hash_sparse_input_farmhash
expected_output = [[0, 0, 1, 0], [1, 0, 0]]
self.assertAllEqual(expected_output, out_data)
inp_t = input_layer.Input(shape=(None, ), ragged=True, dtype=tf.string)
out_t = layer(inp_t)
model = training.Model(inputs=inp_t, outputs=out_t)
self.assertAllClose(out_data, model.predict(inp_data))
def test_from_row_starts(self):
inp = layers.Input(shape=[None])
out = tf.RaggedTensor.from_row_starts(inp, row_starts=[0, 4, 4, 7, 8])
model = training.Model(inp, out)
x = tf.constant([3, 1, 4, 1, 5, 9, 2, 6])
expected = tf.RaggedTensor.from_row_starts(x,
row_starts=[0, 4, 4, 7, 8])
self.assertAllEqual(model(x), expected)
# Test that the model can serialize and deserialize as well
model_config = model.get_config()
model2 = training.Model.from_config(model_config)
self.assertAllEqual(model2(x), expected)
def testIter(self):
model = training.Model()
model.d = {1: 3}
model.d[1] = 3
self.assertEqual([1], list(model.d))
new_dict = {}
# This update() is super tricky. If the dict wrapper subclasses dict,
# CPython will access its storage directly instead of calling any
# methods/properties on the object. So the options are either not to
# subclass dict (in which case update will call normal iter methods, but the
# object won't pass isinstance checks) or to subclass dict and keep that
# storage updated (no shadowing all its methods like ListWrapper).
new_dict.update(model.d)
self.assertEqual({1: 3}, new_dict)
def test_from_uniform_row_length(self):
inp = layers.Input(shape=[None])
out = tf.RaggedTensor.from_uniform_row_length(inp, 2, 8)
model = training.Model(inp, out)
x = tf.constant(
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16])
expected = tf.RaggedTensor.from_uniform_row_length(x, 2, 8)
self.assertAllEqual(model(x), expected)
# Test that the model can serialize and deserialize as well
model_config = model.get_config()
model2 = training.Model.from_config(model_config)
self.assertAllEqual(model2(x), expected)
def test_sparse_instance_property(self, property_name):
inp = layers.Input(shape=[3], sparse=True)
out = getattr(inp, property_name)
model = training.Model(inp, out)
x = tf.SparseTensor([[0, 0], [0, 1], [1, 1], [1, 2]], [1, 2, 3, 4],
[2, 3])
expected_property = getattr(x, property_name)
self.assertAllEqual(model(x), expected_property)
# Test that it works with serialization and deserialization as well
model_config = model.get_config()
model2 = training.Model.from_config(model_config)
self.assertAllEqual(model2(x), expected_property)
def test_crossing_ragged_inputs(self):
inputs_0 = tf.ragged.constant(
[['omar', 'skywalker'], ['marlo']],
dtype=tf.string)
inputs_1 = tf.ragged.constant(
[['a'], ['b']],
dtype=tf.string)
inp_0_t = input_layer.Input(shape=(None,), ragged=True, dtype=tf.string)
inp_1_t = input_layer.Input(shape=(None,), ragged=True, dtype=tf.string)
non_hashed_layer = category_crossing.CategoryCrossing()
out_t = non_hashed_layer([inp_0_t, inp_1_t])
model = training.Model(inputs=[inp_0_t, inp_1_t], outputs=out_t)
expected_output = [[b'omar_X_a', b'skywalker_X_a'], [b'marlo_X_b']]
self.assertAllEqual(expected_output, model.predict([inputs_0, inputs_1]))
def test_invalid_custom_metric_class_error_msg(self):
x = layers.Input(shape=(2, ))
y = layers.Dense(3)(x)
model = training_module.Model(x, y)
class BadMetric(metrics.Metric):
def update_state(self, y_true, y_pred, sample_weight=None):
return
def result(self):
return
with self.assertRaisesRegex(RuntimeError, "can only be a single"):
model.compile("sgd", "mse", metrics=[BadMetric()])
model.fit(np.ones((10, 2)), np.ones((10, 3)))
def test_state_saving_and_loading(self):
with self.cached_session():
input_data = np.random.random((1, 2))
rff_layer = kernel_layers.RandomFourierFeatures(output_dim=10, scale=3.0)
inputs = input_layer.Input((2,))
outputs = rff_layer(inputs)
model = training.Model(inputs, outputs)
output_data = model.predict(input_data)
temp_dir = self.get_temp_dir()
self.addCleanup(shutil.rmtree, temp_dir)
saved_model_dir = os.path.join(temp_dir, 'rff_model')
model.save(saved_model_dir)
new_model = save.load_model(saved_model_dir)
new_output_data = new_model.predict(input_data)
self.assertAllClose(output_data, new_output_data, atol=1e-4)
def test_wide_deep_model_with_multi_outputs(self):
inp = input_layer.Input(shape=(1, ), name='linear')
l = linear.LinearModel(units=2, use_bias=False)(inp)
l1, l2 = tf.split(l, num_or_size_splits=2, axis=1)
linear_model = training.Model(inp, [l1, l2])
linear_model.set_weights([np.asarray([[0.5, 0.3]])])
h = core.Dense(units=2, use_bias=False)(inp)
h1, h2 = tf.split(h, num_or_size_splits=2, axis=1)
dnn_model = training.Model(inp, [h1, h2])
dnn_model.set_weights([np.asarray([[0.1, -0.5]])])
wide_deep_model = wide_deep.WideDeepModel(linear_model, dnn_model)
inp_np = np.asarray([[1.]])
out1, out2 = wide_deep_model(inp_np)
# output should be (0.5 + 0.1), and (0.3 - 0.5)
self.assertAllClose([[0.6]], out1)
self.assertAllClose([[-0.2]], out2)
wide_deep_model = wide_deep.WideDeepModel(linear_model,
dnn_model,
activation='relu')
out1, out2 = wide_deep_model(inp_np)
# output should be relu((0.5 + 0.1)), and relu((0.3 - 0.5))
self.assertAllClose([[0.6]], out1)
self.assertAllClose([[0.]], out2)
def test_crossing_ragged_inputs_depth_tuple(self):
layer = category_crossing.CategoryCrossing(depth=[2, 3])
inputs_0 = tf.ragged.constant([['a'], ['b'], ['c']])
inputs_1 = tf.ragged.constant([['d'], ['e'], ['f']])
inputs_2 = tf.ragged.constant([['g'], ['h'], ['i']])
inp_0_t = input_layer.Input(shape=(None,), ragged=True, dtype=tf.string)
inp_1_t = input_layer.Input(shape=(None,), ragged=True, dtype=tf.string)
inp_2_t = input_layer.Input(shape=(None,), ragged=True, dtype=tf.string)
out_t = layer([inp_0_t, inp_1_t, inp_2_t])
model = training.Model([inp_0_t, inp_1_t, inp_2_t], out_t)
expected_output = [[b'a_X_d', b'a_X_g', b'd_X_g', b'a_X_d_X_g'],
[b'b_X_e', b'b_X_h', b'e_X_h', b'b_X_e_X_h'],
[b'c_X_f', b'c_X_i', b'f_X_i', b'c_X_f_X_i']]
output = model.predict([inputs_0, inputs_1, inputs_2])
self.assertIsInstance(output, tf.RaggedTensor)
self.assertAllEqual(expected_output, output)
def test_crossing_dense_inputs_depth_tuple(self):
layer = category_crossing.CategoryCrossing(depth=[2, 3])
inputs_0 = tf.constant([['a'], ['b'], ['c']])
inputs_1 = tf.constant([['d'], ['e'], ['f']])
inputs_2 = tf.constant([['g'], ['h'], ['i']])
inp_0_t = input_layer.Input(shape=(1,), dtype=tf.string)
inp_1_t = input_layer.Input(shape=(1,), dtype=tf.string)
inp_2_t = input_layer.Input(shape=(1,), dtype=tf.string)
out_t = layer([inp_0_t, inp_1_t, inp_2_t])
model = training.Model([inp_0_t, inp_1_t, inp_2_t], out_t)
expected_outputs_0 = [[b'a_X_d', b'a_X_g', b'd_X_g', b'a_X_d_X_g']]
expected_outputs_1 = [[b'b_X_e', b'b_X_h', b'e_X_h', b'b_X_e_X_h']]
expected_outputs_2 = [[b'c_X_f', b'c_X_i', b'f_X_i', b'c_X_f_X_i']]
expected_output = tf.concat(
[expected_outputs_0, expected_outputs_1, expected_outputs_2], axis=0)
self.assertAllEqual(expected_output,
model.predict([inputs_0, inputs_1, inputs_2]))
def test_from_nested_row_splits(self):
nested_row_splits = [
tf.constant([0, 2, 3, 3, 5], tf.int64),
tf.constant([0, 2, 2, 5, 6, 7], tf.int64)
]
inp = layers.Input(shape=[None], dtype=tf.string)
out = tf.RaggedTensor.from_nested_row_splits(inp, nested_row_splits)
model = training.Model(inp, out)
x = tf.constant(['a', 'b', 'c', 'd', 'e', 'f', 'g'])
expected = tf.RaggedTensor.from_nested_row_splits(x, nested_row_splits)
self.assertAllEqual(model(x), expected)
# Test that the model can serialize and deserialize as well
model_config = model.get_config()
model2 = training.Model.from_config(model_config)
self.assertAllEqual(model2(x), expected)
def test_crossing_ragged_inputs_depth_int(self):
layer = category_crossing.CategoryCrossing(depth=1)
inputs_0 = tf.ragged.constant([['a'], ['b'], ['c']])
inputs_1 = tf.ragged.constant([['d'], ['e'], ['f']])
output = layer([inputs_0, inputs_1])
expected_output = [[b'a', b'd'], [b'b', b'e'], [b'c', b'f']]
self.assertIsInstance(output, tf.RaggedTensor)
self.assertAllEqual(expected_output, output)
layer = category_crossing.CategoryCrossing(depth=2)
inp_0_t = input_layer.Input(shape=(None,), ragged=True, dtype=tf.string)
inp_1_t = input_layer.Input(shape=(None,), ragged=True, dtype=tf.string)
out_t = layer([inp_0_t, inp_1_t])
model = training.Model([inp_0_t, inp_1_t], out_t)
expected_output = [[b'a', b'd', b'a_X_d'], [b'b', b'e', b'b_X_e'],
[b'c', b'f', b'c_X_f']]
self.assertAllEqual(expected_output, model.predict([inputs_0, inputs_1]))
def test_from_sparse(self):
inp = layers.Input(shape=[None], sparse=True, dtype=tf.string)
out = tf.RaggedTensor.from_sparse(inp)
model = training.Model(inp, out)
indices = [[0, 0], [1, 0], [1, 1], [2, 0]]
values = [b'a', b'b', b'c', b'd']
shape = [4, 5]
sp_value = tf.SparseTensor(indices, values, shape)
expected = tf.RaggedTensor.from_sparse(sp_value)
self.assertAllEqual(model(sp_value), expected)
# Test that the model can serialize and deserialize as well
model_config = model.get_config()
model2 = training.Model.from_config(model_config)
self.assertAllEqual(model2(sp_value), expected)
def test_crossing_dense_inputs_depth_int(self):
layer = category_crossing.CategoryCrossing(depth=1)
inputs_0 = tf.constant([['a'], ['b'], ['c']])
inputs_1 = tf.constant([['d'], ['e'], ['f']])
output = layer([inputs_0, inputs_1])
expected_output = [[b'a', b'd'], [b'b', b'e'], [b'c', b'f']]
self.assertAllEqual(expected_output, output)
layer = category_crossing.CategoryCrossing(depth=2)
inp_0_t = input_layer.Input(shape=(1, ), dtype=tf.string)
inp_1_t = input_layer.Input(shape=(1, ), dtype=tf.string)
out_t = layer([inp_0_t, inp_1_t])
model = training.Model([inp_0_t, inp_1_t], out_t)
crossed_output = [[b'a_X_d'], [b'b_X_e'], [b'c_X_f']]
expected_output = tf.concat([expected_output, crossed_output], axis=1)
self.assertAllEqual(expected_output,
model.predict([inputs_0, inputs_1]))
def test_from_row_limits(self):
row_limits = tf.constant([2, 2, 5, 6, 7], tf.int64)
inp = layers.Input(shape=[None], dtype=tf.string)
out = tf.RaggedTensor.from_row_limits(inp, row_limits, validate=False)
model = training.Model(inp, out)
x = tf.constant(['a', 'b', 'c', 'd', 'e', 'f', 'g'])
expected = tf.RaggedTensor.from_row_limits(x,
row_limits,
validate=False)
self.assertAllEqual(model(x), expected)
# Test that the model can serialize and deserialize as well
model_config = model.get_config()
model2 = training.Model.from_config(model_config)
self.assertAllEqual(model2(x), expected)
def test_getitem(self):
# Test slicing / getitem
inp = layers.Input(shape=(None, 2), ragged=True)
out = inp[:, :2]
model = training.Model(inp, out)
x = tf.RaggedTensor.from_row_lengths(
tf.cast(np.random.randn(6, 2), dtype=tf.float32), [3, 1, 2])
expected = x[:, :2]
self.assertAllEqual(model(x), expected)
# Test that models w/ slicing are correctly serialized/deserialized
config = model.get_config()
model = training.Model.from_config(config)
self.assertAllEqual(model(x), expected)
def test_from_nested_row_lengths(self):
nested_row_lengths = [
tf.constant([2, 1, 0, 2], tf.int64),
tf.constant([2, 0, 3, 1, 1], tf.int64),
]
inp = layers.Input(shape=[None], dtype=tf.string)
out = tf.RaggedTensor.from_nested_row_lengths(inp, nested_row_lengths)
model = training.Model(inp, out)
x = tf.constant(["a", "b", "c", "d", "e", "f", "g"])
expected = tf.RaggedTensor.from_nested_row_lengths(
x, nested_row_lengths)
self.assertAllEqual(model(x), expected)
# Test that the model can serialize and deserialize as well
model_config = model.get_config()
model2 = training.Model.from_config(model_config)
self.assertAllEqual(model2(x), expected)
def test_invalid_custom_metric_fn_error_msg(self):
x = layers.Input(shape=(2, ))
y = layers.Dense(3)(x)
model = training_module.Model(x, y)
def bad_metric(y_true, y_pred, sample_weight=None): # pylint: disable=unused-argument
return None
def dict_metric(y_true, y_pred, sample_weight=None): # pylint: disable=unused-argument
return {'value': 0.}
with self.assertRaisesRegex(
RuntimeError, 'The output of a metric function can only be'):
model.compile('sgd', 'mse', metrics=[bad_metric])
model.fit(np.ones((10, 2)), np.ones((10, 3)))
with self.assertRaisesRegex(RuntimeError,
'To return a dict of values, implement'):
model.compile('sgd', 'mse', metrics=[dict_metric])
model.fit(np.ones((10, 2)), np.ones((10, 3)))
def test_invalid_custom_metric_fn_error_msg(self):
x = layers.Input(shape=(2, ))
y = layers.Dense(3)(x)
model = training_module.Model(x, y)
def bad_metric(y_true, y_pred, sample_weight=None):
return None
def dict_metric(y_true, y_pred, sample_weight=None):
return {"value": 0.0}
with self.assertRaisesRegex(
RuntimeError, "The output of a metric function can only be"):
model.compile("sgd", "mse", metrics=[bad_metric])
model.fit(np.ones((10, 2)), np.ones((10, 3)))
with self.assertRaisesRegex(RuntimeError,
"To return a dict of values, implement"):
model.compile("sgd", "mse", metrics=[dict_metric])
model.fit(np.ones((10, 2)), np.ones((10, 3)))
def testNonAppendNotTrackable(self):
# Non-append mutations (deleting or overwriting values) are OK when the
# values aren't tracked.
a = tf.Module()
a.d = {}
a.d["a"] = [3]
a.d[1] = 3
a.d[1] = 2
self.assertEqual(2, a.d[1])
del a.d[1]
a.d[2] = data_structures.NoDependency(tf.Module())
second = tf.Module()
a.d[2] = data_structures.NoDependency(second)
self.assertIs(second, a.d[2])
self.assertEqual([a, a.d, a.d["a"]], util.list_objects(a))
model = training.Model()
model.sub = a
save_path = os.path.join(self.get_temp_dir(), "ckpt")
model.save_weights(save_path)
model.load_weights(save_path)
def test_wide_deep_model_as_layer(self):
linear_model = linear.LinearModel(units=1)
dnn_model = sequential.Sequential([core.Dense(units=1)])
linear_input = input_layer.Input(shape=(3,), name='linear')
dnn_input = input_layer.Input(shape=(5,), name='dnn')
wide_deep_model = wide_deep.WideDeepModel(linear_model, dnn_model)
wide_deep_output = wide_deep_model((linear_input, dnn_input))
input_b = input_layer.Input(shape=(1,), name='b')
output_b = core.Dense(units=1)(input_b)
model = training.Model(
inputs=[linear_input, dnn_input, input_b],
outputs=[wide_deep_output + output_b])
linear_input_np = np.random.uniform(low=-5, high=5, size=(64, 3))
dnn_input_np = np.random.uniform(low=-5, high=5, size=(64, 5))
input_b_np = np.random.uniform(low=-5, high=5, size=(64,))
output_np = linear_input_np[:, 0] + .2 * dnn_input_np[:, 1] + input_b_np
model.compile(
optimizer='sgd',
loss='mse',
metrics=[],
run_eagerly=testing_utils.should_run_eagerly())
model.fit([linear_input_np, dnn_input_np, input_b_np], output_np, epochs=5)
def test_sequence_on_epoch_end(self):
class MySequence(data_utils.Sequence):
def __init__(self):
self.epochs = 0
def __getitem__(self, idx):
return np.ones([10, 10], np.float32), np.ones([10, 1],
np.float32)
def __len__(self):
return 2
def on_epoch_end(self):
self.epochs += 1
inputs = input_layer.Input(10)
outputs = layers_module.Dense(1)(inputs)
model = training.Model(inputs, outputs)
model.compile('sgd', 'mse')
my_seq = MySequence()
model.fit(my_seq, epochs=2)
self.assertEqual(my_seq.epochs, 2)
def prepare_simple_model(input_tensor, loss_name, target):
axis = 1 if backend.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: backend.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: backend.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: backend.binary_crossentropy( # pylint: disable=g-long-lambda, unnecessary-lambda
y_true, y_pred)
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
def testOptimizerWithKerasModel(self):
a = input_layer.Input(shape=(3,), name='input_a')
b = input_layer.Input(shape=(3,), name='input_b')
dense = core.Dense(4, name='dense')
c = dense(a)
d = dense(b)
e = core.Dropout(0.5, name='dropout')(c)
model = training.Model([a, b], [d, e])
optimizer = gradient_descent.SGD(learning_rate=0.001)
loss = 'mse'
model.compile(optimizer, loss, metrics=['mae'])
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
output_d_np = np.random.random((10, 4))
output_e_np = np.random.random((10, 4))
model.fit([input_a_np, input_b_np], [output_d_np, output_e_np],
epochs=1,
batch_size=5)
def test_TensorBoard_multi_input_output(self):
np.random.seed(1337)
tmpdir = self.get_temp_dir()
self.addCleanup(shutil.rmtree, tmpdir, ignore_errors=True)
with tf.Graph().as_default(), self.cached_session():
filepath = os.path.join(tmpdir, "logs")
(x_train, y_train), (x_test, y_test) = test_utils.get_test_data(
train_samples=TRAIN_SAMPLES,
test_samples=TEST_SAMPLES,
input_shape=(INPUT_DIM, ),
num_classes=NUM_CLASSES,
)
y_test = np_utils.to_categorical(y_test)
y_train = np_utils.to_categorical(y_train)
def data_generator(train):
if train:
max_batch_index = len(x_train) // BATCH_SIZE
else:
max_batch_index = len(x_test) // BATCH_SIZE
i = 0
while 1:
if train:
# simulate multi-input/output models
yield (
[x_train[i * BATCH_SIZE:(i + 1) * BATCH_SIZE]] * 2,
[y_train[i * BATCH_SIZE:(i + 1) * BATCH_SIZE]] * 2,
)
else:
yield (
[x_test[i * BATCH_SIZE:(i + 1) * BATCH_SIZE]] * 2,
[y_test[i * BATCH_SIZE:(i + 1) * BATCH_SIZE]] * 2,
)
i += 1
i %= max_batch_index
inp1 = input_layer.Input((INPUT_DIM, ))
inp2 = input_layer.Input((INPUT_DIM, ))
inp = layers.add([inp1, inp2])
hidden = layers.Dense(2, activation="relu")(inp)
hidden = layers.Dropout(0.1)(hidden)
output1 = layers.Dense(NUM_CLASSES, activation="softmax")(hidden)
output2 = layers.Dense(NUM_CLASSES, activation="softmax")(hidden)
model = training.Model([inp1, inp2], [output1, output2])
model.compile(
loss="categorical_crossentropy",
optimizer="sgd",
metrics=["accuracy"],
)
# we must generate new callbacks for each test, as they aren't stateless
def callbacks_factory(histogram_freq):
return [
callbacks_v1.TensorBoard(
log_dir=filepath,
histogram_freq=histogram_freq,
write_images=True,
write_grads=True,
batch_size=5,
)
]
# fit without validation data
model.fit(
[x_train] * 2,
[y_train] * 2,
batch_size=BATCH_SIZE,
callbacks=callbacks_factory(histogram_freq=0),
epochs=3,
)
# fit with validation data and accuracy
model.fit(
[x_train] * 2,
[y_train] * 2,
batch_size=BATCH_SIZE,
validation_data=([x_test] * 2, [y_test] * 2),
callbacks=callbacks_factory(histogram_freq=1),
epochs=2,
)
# fit generator without validation data
model.fit_generator(
data_generator(True),
len(x_train),
epochs=2,
callbacks=callbacks_factory(histogram_freq=0),
)
# fit generator with validation data and accuracy
model.fit_generator(
data_generator(True),
len(x_train),
epochs=2,
validation_data=([x_test] * 2, [y_test] * 2),
callbacks=callbacks_factory(histogram_freq=1),
)
assert os.path.isdir(filepath)
def VGG19(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation='softmax'):
"""Instantiates the VGG19 architecture.
Reference:
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](
https://arxiv.org/abs/1409.1556) (ICLR 2015)
For image classification use cases, see
[this page for detailed examples](
https://keras.io/api/applications/#usage-examples-for-image-classification-models).
For transfer learning use cases, make sure to read the
[guide to transfer learning & fine-tuning](
https://keras.io/guides/transfer_learning/).
The default input size for this model is 224x224.
Note: each Keras Application expects a specific kind of input preprocessing.
For VGG19, call `tf.keras.applications.vgg19.preprocess_input` on your
inputs before passing them to the model.
`vgg19.preprocess_input` will convert the input images from RGB to BGR,
then will zero-center each color channel with respect to the ImageNet dataset,
without scaling.
Args:
include_top: whether to include the 3 fully-connected
layers at the top of the network.
weights: one of `None` (random initialization),
'imagenet' (pre-training on ImageNet),
or the path to the weights file to be loaded.
input_tensor: optional Keras tensor
(i.e. output of `layers.Input()`)
to use as image input for the model.
input_shape: optional shape tuple, only to be specified
if `include_top` is False (otherwise the input shape
has to be `(224, 224, 3)`
(with `channels_last` data format)
or `(3, 224, 224)` (with `channels_first` data format).
It should have exactly 3 inputs channels,
and width and height should be no smaller than 32.
E.g. `(200, 200, 3)` would be one valid value.
pooling: Optional pooling mode for feature extraction
when `include_top` is `False`.
- `None` means that the output of the model will be
the 4D tensor output of the
last convolutional block.
- `avg` means that global average pooling
will be applied to the output of the
last convolutional block, and thus
the output of the model will be a 2D tensor.
- `max` means that global max pooling will
be applied.
classes: optional number of classes to classify images
into, only to be specified if `include_top` is True, and
if no `weights` argument is specified.
classifier_activation: A `str` or callable. The activation function to use
on the "top" layer. Ignored unless `include_top=True`. Set
`classifier_activation=None` to return the logits of the "top" layer.
When loading pretrained weights, `classifier_activation` can only
be `None` or `"softmax"`.
Returns:
A `keras.Model` instance.
"""
if not (weights in {'imagenet', None} or tf.io.gfile.exists(weights)):
raise ValueError('The `weights` argument should be either '
'`None` (random initialization), `imagenet` '
'(pre-training on ImageNet), '
'or the path to the weights file to be loaded.')
if weights == 'imagenet' and include_top and classes != 1000:
raise ValueError(
'If using `weights` as `"imagenet"` with `include_top`'
' as true, `classes` should be 1000')
# Determine proper input shape
input_shape = imagenet_utils.obtain_input_shape(
input_shape,
default_size=224,
min_size=32,
data_format=backend.image_data_format(),
require_flatten=include_top,
weights=weights)
if input_tensor is None:
img_input = layers.Input(shape=input_shape)
else:
if not backend.is_keras_tensor(input_tensor):
img_input = layers.Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
# Block 1
x = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv1')(img_input)
x = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv2')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
# Block 2
x = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv1')(x)
x = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv2')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
# Block 3
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv1')(x)
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv2')(x)
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv3')(x)
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv4')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
# Block 4
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv1')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv2')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv3')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv4')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# Block 5
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv1')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv2')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv3')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv4')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
if include_top:
# Classification block
x = layers.Flatten(name='flatten')(x)
x = layers.Dense(4096, activation='relu', name='fc1')(x)
x = layers.Dense(4096, activation='relu', name='fc2')(x)
imagenet_utils.validate_activation(classifier_activation, weights)
x = layers.Dense(classes,
activation=classifier_activation,
name='predictions')(x)
else:
if pooling == 'avg':
x = layers.GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = layers.GlobalMaxPooling2D()(x)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = layer_utils.get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = training.Model(inputs, x, name='vgg19')
# Load weights.
if weights == 'imagenet':
if include_top:
weights_path = data_utils.get_file(
'vgg19_weights_tf_dim_ordering_tf_kernels.h5',
WEIGHTS_PATH,
cache_subdir='models',
file_hash='cbe5617147190e668d6c5d5026f83318')
else:
weights_path = data_utils.get_file(
'vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5',
WEIGHTS_PATH_NO_TOP,
cache_subdir='models',
file_hash='253f8cb515780f3b799900260a226db6')
model.load_weights(weights_path)
elif weights is not None:
model.load_weights(weights)
return model
def ResNetRS(
depth: int,
input_shape=None,
bn_momentum=0.0,
bn_epsilon=1e-5,
activation: str = "relu",
se_ratio=0.25,
dropout_rate=0.25,
drop_connect_rate=0.2,
include_top=True,
block_args: List[Dict[str, int]] = None,
model_name="resnet-rs",
pooling=None,
weights="imagenet",
input_tensor=None,
classes=1000,
classifier_activation: Union[str, Callable] = "softmax",
include_preprocessing=True,
):
"""Build Resnet-RS model, given provided parameters.
Args:
depth: Depth of ResNet network.
input_shape: optional shape tuple. It should have exactly 3 inputs
channels, and width and height should be no smaller than 32. E.g.
(200, 200, 3) would be one valid value.
bn_momentum: Momentum parameter for Batch Normalization layers.
bn_epsilon: Epsilon parameter for Batch Normalization layers.
activation: activation function.
se_ratio: Squeeze and Excitation layer ratio.
dropout_rate: dropout rate before final classifier layer.
drop_connect_rate: dropout rate at skip connections.
include_top: whether to include the fully-connected layer at the top of
the network.
block_args: list of dicts, parameters to construct block modules.
model_name: name of the model.
pooling: optional pooling mode for feature extraction when `include_top`
is `False`.
- `None` means that the output of the model will be the 4D tensor
output of the last convolutional layer.
- `avg` means that global average pooling will be applied to the
output of the last convolutional layer, and thus the output of the
model will be a 2D tensor.
- `max` means that global max pooling will be applied.
weights: one of `None` (random initialization), `'imagenet'`
(pre-training on ImageNet), or the path to the weights file to be
loaded. Note- one model can have multiple imagenet variants depending
on input shape it was trained with. For input_shape 224x224 pass
`imagenet-i224` as argument. By default, highest input shape weights
are downloaded.
input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to
use as image input for the model.
classes: optional number of classes to classify images into, only to be
specified if `include_top` is True, and if no `weights` argument is
specified.
classifier_activation: A `str` or callable. The activation function to
use on the "top" layer. Ignored unless `include_top=True`. Set
`classifier_activation=None` to return the logits of the "top" layer.
include_preprocessing: Boolean, whether to include the preprocessing
layer (`Rescaling`) at the bottom of the network. Defaults to `True`.
Note- Input image is normalized by ImageNet mean and standard
deviation.
Returns:
A `tf.keras.Model` instance.
Raises:
ValueError: in case of invalid argument for `weights`, or invalid input
shape.
ValueError: if `classifier_activation` is not `softmax` or `None` when
using a pretrained top layer.
"""
# Validate parameters
available_weight_variants = DEPTH_TO_WEIGHT_VARIANTS[depth]
if weights == "imagenet":
max_input_shape = max(available_weight_variants)
# `imagenet` argument without explicit weights input size.
# Picking weights trained with biggest available shape
weights = f"{weights}-i{max_input_shape}"
weights_allow_list = [f"imagenet-i{x}" for x in available_weight_variants]
if not (weights in {*weights_allow_list, None}
or tf.io.gfile.exists(weights)):
raise ValueError(
"The `weights` argument should be either "
"`None` (random initialization), `'imagenet'` "
"(pre-training on ImageNet, with highest available input shape),"
" or the path to the weights file to be loaded. "
f"For ResNetRS{depth} the following weight variants are "
f"available {weights_allow_list} (default=highest)."
f" Received weights={weights}")
if weights in weights_allow_list and include_top and classes != 1000:
raise ValueError(
f"If using `weights` as `'imagenet'` or any "
f"of {weights_allow_list} "
f"with `include_top` as true, `classes` should be 1000. "
f"Received classes={classes}")
input_shape = imagenet_utils.obtain_input_shape(
input_shape,
default_size=224,
min_size=32,
data_format=backend.image_data_format(),
require_flatten=include_top,
weights=weights,
)
# Define input tensor
if input_tensor is None:
img_input = layers.Input(shape=input_shape)
else:
if not backend.is_keras_tensor(input_tensor):
img_input = layers.Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
bn_axis = 3 if backend.image_data_format() == "channels_last" else 1
x = img_input
if include_preprocessing:
num_channels = input_shape[bn_axis - 1]
x = layers.Rescaling(scale=1.0 / 255)(x)
if num_channels == 3:
x = layers.Normalization(
mean=[0.485, 0.456, 0.406],
variance=[0.229**2, 0.224**2, 0.225**2],
axis=bn_axis,
)(x)
# Build stem
x = STEM(bn_momentum=bn_momentum,
bn_epsilon=bn_epsilon,
activation=activation)(x)
# Build blocks
if block_args is None:
block_args = BLOCK_ARGS[depth]
for i, args in enumerate(block_args):
survival_probability = get_survival_probability(
init_rate=drop_connect_rate,
block_num=i + 2,
total_blocks=len(block_args) + 1,
)
x = BlockGroup(
filters=args["input_filters"],
activation=activation,
strides=(1 if i == 0 else 2),
num_repeats=args["num_repeats"],
se_ratio=se_ratio,
bn_momentum=bn_momentum,
bn_epsilon=bn_epsilon,
survival_probability=survival_probability,
name=f"BlockGroup{i + 2}_",
)(x)
# Build head:
if include_top:
x = layers.GlobalAveragePooling2D(name="avg_pool")(x)
if dropout_rate > 0:
x = layers.Dropout(dropout_rate, name="top_dropout")(x)
imagenet_utils.validate_activation(classifier_activation, weights)
x = layers.Dense(classes,
activation=classifier_activation,
name="predictions")(x)
else:
if pooling == "avg":
x = layers.GlobalAveragePooling2D(name="avg_pool")(x)
elif pooling == "max":
x = layers.GlobalMaxPooling2D(name="max_pool")(x)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = layer_utils.get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = training.Model(inputs, x, name=model_name)
# Download weights
if weights in weights_allow_list:
weights_input_shape = weights.split("-")[-1] # e. g. "i160"
weights_name = f"{model_name}-{weights_input_shape}"
if not include_top:
weights_name += "_notop"
filename = f"{weights_name}.h5"
download_url = BASE_WEIGHTS_URL + filename
weights_path = data_utils.get_file(
fname=filename,
origin=download_url,
cache_subdir="models",
file_hash=WEIGHT_HASHES[filename],
)
model.load_weights(weights_path)
elif weights is not None:
model.load_weights(weights)
return model
def MobileNet(
input_shape=None,
alpha=1.0,
depth_multiplier=1,
dropout=1e-3,
include_top=True,
weights="imagenet",
input_tensor=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
**kwargs,
):
"""Instantiates the MobileNet architecture.
Reference:
- [MobileNets: Efficient Convolutional Neural Networks
for Mobile Vision Applications](
https://arxiv.org/abs/1704.04861)
This function returns a Keras image classification model,
optionally loaded with weights pre-trained on ImageNet.
For image classification use cases, see
[this page for detailed examples](
https://keras.io/api/applications/#usage-examples-for-image-classification-models).
For transfer learning use cases, make sure to read the
[guide to transfer learning & fine-tuning](
https://keras.io/guides/transfer_learning/).
Note: each Keras Application expects a specific kind of input preprocessing.
For MobileNet, call `tf.keras.applications.mobilenet.preprocess_input`
on your inputs before passing them to the model.
`mobilenet.preprocess_input` will scale input pixels between -1 and 1.
Args:
input_shape: Optional shape tuple, only to be specified if `include_top`
is False (otherwise the input shape has to be `(224, 224, 3)` (with
`channels_last` data format) or (3, 224, 224) (with `channels_first`
data format). It should have exactly 3 inputs channels, and width and
height should be no smaller than 32. E.g. `(200, 200, 3)` would be one
valid value. Default to `None`.
`input_shape` will be ignored if the `input_tensor` is provided.
alpha: Controls the width of the network. This is known as the width
multiplier in the MobileNet paper. - If `alpha` < 1.0, proportionally
decreases the number of filters in each layer. - If `alpha` > 1.0,
proportionally increases the number of filters in each layer. - If
`alpha` = 1, default number of filters from the paper are used at each
layer. Default to 1.0.
depth_multiplier: Depth multiplier for depthwise convolution. This is
called the resolution multiplier in the MobileNet paper. Default to 1.0.
dropout: Dropout rate. Default to 0.001.
include_top: Boolean, whether to include the fully-connected layer at the
top of the network. Default to `True`.
weights: One of `None` (random initialization), 'imagenet' (pre-training
on ImageNet), or the path to the weights file to be loaded. Default to
`imagenet`.
input_tensor: Optional Keras tensor (i.e. output of `layers.Input()`) to
use as image input for the model. `input_tensor` is useful for sharing
inputs between multiple different networks. Default to None.
pooling: Optional pooling mode for feature extraction when `include_top`
is `False`.
- `None` (default) means that the output of the model will be
the 4D tensor output of the last convolutional block.
- `avg` means that global average pooling
will be applied to the output of the
last convolutional block, and thus
the output of the model will be a 2D tensor.
- `max` means that global max pooling will be applied.
classes: Optional number of classes to classify images into, only to be
specified if `include_top` is True, and if no `weights` argument is
specified. Defaults to 1000.
classifier_activation: A `str` or callable. The activation function to use
on the "top" layer. Ignored unless `include_top=True`. Set
`classifier_activation=None` to return the logits of the "top" layer.
When loading pretrained weights, `classifier_activation` can only
be `None` or `"softmax"`.
**kwargs: For backwards compatibility only.
Returns:
A `keras.Model` instance.
"""
global layers
if "layers" in kwargs:
layers = kwargs.pop("layers")
else:
layers = VersionAwareLayers()
if kwargs:
raise ValueError(f"Unknown argument(s): {(kwargs,)}")
if not (weights in {"imagenet", None} or tf.io.gfile.exists(weights)):
raise ValueError(
"The `weights` argument should be either "
"`None` (random initialization), `imagenet` "
"(pre-training on ImageNet), "
"or the path to the weights file to be loaded. "
f"Received weights={weights}"
)
if weights == "imagenet" and include_top and classes != 1000:
raise ValueError(
'If using `weights` as `"imagenet"` with `include_top` '
"as true, `classes` should be 1000. "
f"Received classes={classes}"
)
# Determine proper input shape and default size.
if input_shape is None:
default_size = 224
else:
if backend.image_data_format() == "channels_first":
rows = input_shape[1]
cols = input_shape[2]
else:
rows = input_shape[0]
cols = input_shape[1]
if rows == cols and rows in [128, 160, 192, 224]:
default_size = rows
else:
default_size = 224
input_shape = imagenet_utils.obtain_input_shape(
input_shape,
default_size=default_size,
min_size=32,
data_format=backend.image_data_format(),
require_flatten=include_top,
weights=weights,
)
if backend.image_data_format() == "channels_last":
row_axis, col_axis = (0, 1)
else:
row_axis, col_axis = (1, 2)
rows = input_shape[row_axis]
cols = input_shape[col_axis]
if weights == "imagenet":
if depth_multiplier != 1:
raise ValueError(
"If imagenet weights are being loaded, "
"depth multiplier must be 1. "
f"Received depth_multiplier={depth_multiplier}"
)
if alpha not in [0.25, 0.50, 0.75, 1.0]:
raise ValueError(
"If imagenet weights are being loaded, "
"alpha can be one of"
"`0.25`, `0.50`, `0.75` or `1.0` only. "
f"Received alpha={alpha}"
)
if rows != cols or rows not in [128, 160, 192, 224]:
rows = 224
logging.warning(
"`input_shape` is undefined or non-square, "
"or `rows` is not in [128, 160, 192, 224]. "
"Weights for input shape (224, 224) will be "
"loaded as the default."
)
if input_tensor is None:
img_input = layers.Input(shape=input_shape)
else:
if not backend.is_keras_tensor(input_tensor):
img_input = layers.Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
x = _conv_block(img_input, 32, alpha, strides=(2, 2))
x = _depthwise_conv_block(x, 64, alpha, depth_multiplier, block_id=1)
x = _depthwise_conv_block(
x, 128, alpha, depth_multiplier, strides=(2, 2), block_id=2
)
x = _depthwise_conv_block(x, 128, alpha, depth_multiplier, block_id=3)
x = _depthwise_conv_block(
x, 256, alpha, depth_multiplier, strides=(2, 2), block_id=4
)
x = _depthwise_conv_block(x, 256, alpha, depth_multiplier, block_id=5)
x = _depthwise_conv_block(
x, 512, alpha, depth_multiplier, strides=(2, 2), block_id=6
)
x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=7)
x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=8)
x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=9)
x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=10)
x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=11)
x = _depthwise_conv_block(
x, 1024, alpha, depth_multiplier, strides=(2, 2), block_id=12
)
x = _depthwise_conv_block(x, 1024, alpha, depth_multiplier, block_id=13)
if include_top:
x = layers.GlobalAveragePooling2D(keepdims=True)(x)
x = layers.Dropout(dropout, name="dropout")(x)
x = layers.Conv2D(classes, (1, 1), padding="same", name="conv_preds")(x)
x = layers.Reshape((classes,), name="reshape_2")(x)
imagenet_utils.validate_activation(classifier_activation, weights)
x = layers.Activation(
activation=classifier_activation, name="predictions"
)(x)
else:
if pooling == "avg":
x = layers.GlobalAveragePooling2D()(x)
elif pooling == "max":
x = layers.GlobalMaxPooling2D()(x)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = layer_utils.get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = training.Model(inputs, x, name="mobilenet_%0.2f_%s" % (alpha, rows))
# Load weights.
if weights == "imagenet":
if alpha == 1.0:
alpha_text = "1_0"
elif alpha == 0.75:
alpha_text = "7_5"
elif alpha == 0.50:
alpha_text = "5_0"
else:
alpha_text = "2_5"
if include_top:
model_name = "mobilenet_%s_%d_tf.h5" % (alpha_text, rows)
weight_path = BASE_WEIGHT_PATH + model_name
weights_path = data_utils.get_file(
model_name, weight_path, cache_subdir="models"
)
else:
model_name = "mobilenet_%s_%d_tf_no_top.h5" % (alpha_text, rows)
weight_path = BASE_WEIGHT_PATH + model_name
weights_path = data_utils.get_file(
model_name, weight_path, cache_subdir="models"
)
model.load_weights(weights_path)
elif weights is not None:
model.load_weights(weights)
return model
def EfficientNetV2(
width_coefficient,
depth_coefficient,
default_size,
dropout_rate=0.2,
drop_connect_rate=0.2,
depth_divisor=8,
min_depth=8,
bn_momentum=0.9,
activation="swish",
blocks_args="default",
model_name="efficientnetv2",
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
include_preprocessing=True,
):
"""Instantiates the EfficientNetV2 architecture using given scaling coefficients.
Args:
width_coefficient: float, scaling coefficient for network width.
depth_coefficient: float, scaling coefficient for network depth.
default_size: integer, default input image size.
dropout_rate: float, dropout rate before final classifier layer.
drop_connect_rate: float, dropout rate at skip connections.
depth_divisor: integer, a unit of network width.
min_depth: integer, minimum number of filters.
bn_momentum: float. Momentum parameter for Batch Normalization layers.
activation: activation function.
blocks_args: list of dicts, parameters to construct block modules.
model_name: string, model name.
include_top: whether to include the fully-connected layer at the top of the
network.
weights: one of `None` (random initialization), `"imagenet"` (pre-training
on ImageNet), or the path to the weights file to be loaded.
input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) or
numpy array to use as image input for the model.
input_shape: optional shape tuple, only to be specified if `include_top` is
False. It should have exactly 3 inputs channels.
pooling: optional pooling mode for feature extraction when `include_top` is
`False`. - `None` means that the output of the model will be the 4D tensor
output of the last convolutional layer. - "avg" means that global average
pooling will be applied to the output of the last convolutional layer, and
thus the output of the model will be a 2D tensor. - `"max"` means that
global max pooling will be applied.
classes: optional number of classes to classify images into, only to be
specified if `include_top` is True, and if no `weights` argument is
specified.
classifier_activation: A string or callable. The activation function to use
on the `"top"` layer. Ignored unless `include_top=True`. Set
`classifier_activation=None` to return the logits of the `"top"` layer.
include_preprocessing: Boolean, whether to include the preprocessing layer
(`Rescaling`) at the bottom of the network. Defaults to `True`.
Returns:
A `keras.Model` instance.
Raises:
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
ValueError: if `classifier_activation` is not `"softmax"` or `None` when
using a pretrained top layer.
"""
if blocks_args == "default":
blocks_args = DEFAULT_BLOCKS_ARGS[model_name]
if not (weights in {"imagenet", None} or tf.io.gfile.exists(weights)):
raise ValueError("The `weights` argument should be either "
"`None` (random initialization), `imagenet` "
"(pre-training on ImageNet), "
"or the path to the weights file to be loaded."
f"Received: weights={weights}")
if weights == "imagenet" and include_top and classes != 1000:
raise ValueError(
"If using `weights` as `'imagenet'` with `include_top`"
" as true, `classes` should be 1000"
f"Received: classes={classes}")
# Determine proper input shape
input_shape = imagenet_utils.obtain_input_shape(
input_shape,
default_size=default_size,
min_size=32,
data_format=backend.image_data_format(),
require_flatten=include_top,
weights=weights)
if input_tensor is None:
img_input = layers.Input(shape=input_shape)
else:
if not backend.is_keras_tensor(input_tensor):
img_input = layers.Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
bn_axis = 3 if backend.image_data_format() == "channels_last" else 1
x = img_input
if include_preprocessing:
# Apply original V1 preprocessing for Bx variants
# if number of channels allows it
num_channels = input_shape[bn_axis - 1]
if model_name.split("-")[-1].startswith("b") and num_channels == 3:
x = layers.Rescaling(scale=1. / 255)(x)
x = layers.Normalization(
mean=[0.485, 0.456, 0.406],
variance=[0.229**2, 0.224**2, 0.225**2],
axis=bn_axis,
)(x)
else:
x = layers.Rescaling(scale=1. / 128.0, offset=-1)(x)
# Build stem
stem_filters = round_filters(
filters=blocks_args[0]["input_filters"],
width_coefficient=width_coefficient,
min_depth=min_depth,
depth_divisor=depth_divisor,
)
x = layers.Conv2D(
filters=stem_filters,
kernel_size=3,
strides=2,
kernel_initializer=CONV_KERNEL_INITIALIZER,
padding="same",
use_bias=False,
name="stem_conv",
)(x)
x = layers.BatchNormalization(
axis=bn_axis,
momentum=bn_momentum,
name="stem_bn",
)(x)
x = layers.Activation(activation, name="stem_activation")(x)
# Build blocks
blocks_args = copy.deepcopy(blocks_args)
b = 0
blocks = float(sum(args["num_repeat"] for args in blocks_args))
for (i, args) in enumerate(blocks_args):
assert args["num_repeat"] > 0
# Update block input and output filters based on depth multiplier.
args["input_filters"] = round_filters(
filters=args["input_filters"],
width_coefficient=width_coefficient,
min_depth=min_depth,
depth_divisor=depth_divisor)
args["output_filters"] = round_filters(
filters=args["output_filters"],
width_coefficient=width_coefficient,
min_depth=min_depth,
depth_divisor=depth_divisor)
# Determine which conv type to use:
block = {0: MBConvBlock, 1: FusedMBConvBlock}[args.pop("conv_type")]
repeats = round_repeats(repeats=args.pop("num_repeat"),
depth_coefficient=depth_coefficient)
for j in range(repeats):
# The first block needs to take care of stride and filter size increase.
if j > 0:
args["strides"] = 1
args["input_filters"] = args["output_filters"]
x = block(
activation=activation,
bn_momentum=bn_momentum,
survival_probability=drop_connect_rate * b / blocks,
name="block{}{}_".format(i + 1, chr(j + 97)),
**args,
)(x)
b += 1
# Build top
top_filters = round_filters(filters=1280,
width_coefficient=width_coefficient,
min_depth=min_depth,
depth_divisor=depth_divisor)
x = layers.Conv2D(
filters=top_filters,
kernel_size=1,
strides=1,
kernel_initializer=CONV_KERNEL_INITIALIZER,
padding="same",
data_format="channels_last",
use_bias=False,
name="top_conv",
)(x)
x = layers.BatchNormalization(
axis=bn_axis,
momentum=bn_momentum,
name="top_bn",
)(x)
x = layers.Activation(activation=activation, name="top_activation")(x)
if include_top:
x = layers.GlobalAveragePooling2D(name="avg_pool")(x)
if dropout_rate > 0:
x = layers.Dropout(dropout_rate, name="top_dropout")(x)
imagenet_utils.validate_activation(classifier_activation, weights)
x = layers.Dense(classes,
activation=classifier_activation,
kernel_initializer=DENSE_KERNEL_INITIALIZER,
bias_initializer=tf.constant_initializer(0),
name="predictions")(x)
else:
if pooling == "avg":
x = layers.GlobalAveragePooling2D(name="avg_pool")(x)
elif pooling == "max":
x = layers.GlobalMaxPooling2D(name="max_pool")(x)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = layer_utils.get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = training.Model(inputs, x, name=model_name)
# Load weights.
if weights == "imagenet":
if include_top:
file_suffix = ".h5"
file_hash = WEIGHTS_HASHES[model_name[-2:]][0]
else:
file_suffix = "_notop.h5"
file_hash = WEIGHTS_HASHES[model_name[-2:]][1]
file_name = model_name + file_suffix
weights_path = data_utils.get_file(file_name,
BASE_WEIGHTS_PATH + file_name,
cache_subdir="models",
file_hash=file_hash)
model.load_weights(weights_path)
elif weights is not None:
model.load_weights(weights)
return model
