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Tensorflow 2 compatibility #26

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4 changes: 2 additions & 2 deletions classify_nsfw.py
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Original file line number Diff line number Diff line change
Expand Up @@ -38,7 +38,7 @@ def main(argv):

model = OpenNsfwModel()

with tf.Session() as sess:
with tf.compat.v1.Session() as sess:

input_type = InputType[args.input_type.upper()]
model.build(weights_path=args.model_weights, input_type=input_type)
Expand All @@ -54,7 +54,7 @@ def main(argv):
import base64
fn_load_image = lambda filename: np.array([base64.urlsafe_b64encode(open(filename, "rb").read())])

sess.run(tf.global_variables_initializer())
sess.run(tf.compat.v1.global_variables_initializer())

image = fn_load_image(args.input_file)

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32 changes: 16 additions & 16 deletions model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,18 +27,18 @@ def __init__(self):
def build(self, weights_path="open_nsfw-weights.npy",
input_type=InputType.TENSOR):

self.weights = np.load(weights_path, encoding="latin1").item()
self.weights = np.load(weights_path, encoding="latin1", allow_pickle=True).item()
self.input_tensor = None

if input_type == InputType.TENSOR:
self.input = tf.placeholder(tf.float32,
self.input = tf.compat.v1.placeholder(tf.float32,
shape=[None, 224, 224, 3],
name="input")
self.input_tensor = self.input
elif input_type == InputType.BASE64_JPEG:
from image_utils import load_base64_tensor

self.input = tf.placeholder(tf.string, shape=(None,), name="input")
self.input = tf.compat.v1.placeholder(tf.string, shape=(None,), name="input")
self.input_tensor = load_base64_tensor(self.input)
else:
raise ValueError("invalid input type '{}'".format(input_type))
Expand All @@ -52,7 +52,7 @@ def build(self, weights_path="open_nsfw-weights.npy",
x = self.__batch_norm("bn_1", x)
x = tf.nn.relu(x)

x = tf.layers.max_pooling2d(x, pool_size=3, strides=2, padding='same')
x = tf.compat.v1.layers.max_pooling2d(x, pool_size=3, strides=2, padding='same')

x = self.__conv_block(stage=0, block=0, inputs=x,
filter_depths=[32, 32, 128],
Expand Down Expand Up @@ -97,7 +97,7 @@ def build(self, weights_path="open_nsfw-weights.npy",
filter_depths=[256, 256, 1024],
kernel_size=3)

x = tf.layers.average_pooling2d(x, pool_size=7, strides=1,
x = tf.compat.v1.layers.average_pooling2d(x, pool_size=7, strides=1,
padding="valid", name="pool")

x = tf.reshape(x, shape=(-1, 1024))
Expand All @@ -123,11 +123,11 @@ def __get_weights(self, layer_name, field_name):
"""Layer creation and weight initialization
"""
def __fully_connected(self, name, inputs, num_outputs):
return tf.layers.dense(
return tf.compat.v1.layers.dense(
inputs=inputs, units=num_outputs, name=name,
kernel_initializer=tf.constant_initializer(
kernel_initializer=tf.compat.v1.constant_initializer(
self.__get_weights(name, "weights"), dtype=tf.float32),
bias_initializer=tf.constant_initializer(
bias_initializer=tf.compat.v1.constant_initializer(
self.__get_weights(name, "biases"), dtype=tf.float32))

def __conv2d(self, name, inputs, filter_depth, kernel_size, stride=1,
Expand All @@ -147,26 +147,26 @@ def __conv2d(self, name, inputs, filter_depth, kernel_size, stride=1,
raise Exception('unsupported kernel size for padding: "{}"'
.format(kernel_size))

return tf.layers.conv2d(
return tf.compat.v1.layers.conv2d(
inputs, filter_depth,
kernel_size=(kernel_size, kernel_size),
strides=(stride, stride), padding='valid',
activation=None, trainable=trainable, name=name,
kernel_initializer=tf.constant_initializer(
kernel_initializer=tf.compat.v1.constant_initializer(
self.__get_weights(name, "weights"), dtype=tf.float32),
bias_initializer=tf.constant_initializer(
bias_initializer=tf.compat.v1.constant_initializer(
self.__get_weights(name, "biases"), dtype=tf.float32))

def __batch_norm(self, name, inputs, training=False):
return tf.layers.batch_normalization(
return tf.compat.v1.layers.batch_normalization(
inputs, training=training, epsilon=self.bn_epsilon,
gamma_initializer=tf.constant_initializer(
gamma_initializer=tf.compat.v1.constant_initializer(
self.__get_weights(name, "scale"), dtype=tf.float32),
beta_initializer=tf.constant_initializer(
beta_initializer=tf.compat.v1.constant_initializer(
self.__get_weights(name, "offset"), dtype=tf.float32),
moving_mean_initializer=tf.constant_initializer(
moving_mean_initializer=tf.compat.v1.constant_initializer(
self.__get_weights(name, "mean"), dtype=tf.float32),
moving_variance_initializer=tf.constant_initializer(
moving_variance_initializer=tf.compat.v1.constant_initializer(
self.__get_weights(name, "variance"), dtype=tf.float32),
name=name)

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6 changes: 3 additions & 3 deletions tools/export_tflite.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,13 +36,13 @@
export_path = args.target
input_type = InputType[args.input_type.upper()]

with tf.Session() as sess:
with tf.compat.v1.Session() as sess:
model.build(weights_path=args.model_weights,
input_type=input_type)

sess.run(tf.global_variables_initializer())
sess.run(tf.compat.v1.global_variables_initializer())

converter = tf.contrib.lite.TFLiteConverter.from_session(sess, [model.input], [model.predictions])
converter = tf.compat.v1.lite.TFLiteConverter.from_session(sess, [model.input], [model.predictions])
tflite_model = converter.convert()

with open(export_path, "wb") as f:
Expand Down