def test_single_predictor_model_training(self):
model_proto = model_pb2.Model()
text_format.Merge(SINGLE_PREDICTOR_MODEL_TEXT_PROTO, model_proto)
model_object = model_builder.build(model_proto, True)
test_groundtruth_text_list = [
tf.constant(b'hello', dtype=tf.string),
tf.constant(b'world', dtype=tf.string)
]
model_object.provide_groundtruth(
{'groundtruth_text': test_groundtruth_text_list})
test_input_image = tf.random_uniform(shape=[2, 32, 100, 3],
minval=0,
maxval=255,
dtype=tf.float32,
seed=1)
prediction_dict = model_object.predict(
model_object.preprocess(test_input_image))
loss = model_object.loss(prediction_dict)
with self.test_session() as sess:
sess.run(
[tf.global_variables_initializer(),
tf.tables_initializer()])
outputs = sess.run({'loss': loss})
print(outputs['loss'])
def test_stn_multi_predictor_model_inference(self):
model_proto = model_pb2.Model()
text_format.Merge(STN_MULTIPLE_PREDICTOR_MODEL_TEXT_PROTO, model_proto)
model_object = model_builder.build(model_proto, False)
test_groundtruth_text_list = [
tf.constant(b'hello', dtype=tf.string),
tf.constant(b'world', dtype=tf.string)
]
model_object.provide_groundtruth(
{'groundtruth_text': test_groundtruth_text_list})
test_input_image = tf.random_uniform(shape=[2, 32, 100, 3],
minval=0,
maxval=255,
dtype=tf.float32,
seed=1)
prediction_dict = model_object.predict(
model_object.preprocess(test_input_image))
recognition_dict = model_object.postprocess(prediction_dict)
with self.test_session() as sess:
sess.run(
[tf.global_variables_initializer(),
tf.tables_initializer()])
outputs = sess.run(recognition_dict)
print(outputs)
def test_build_attention_model_single_branch(self):
model_text_proto = """
attention_recognition_model {
feature_extractor {
convnet {
crnn_net {
net_type: SINGLE_BRANCH
conv_hyperparams {
op: CONV
regularizer { l2_regularizer { weight: 1e-4 } }
initializer { variance_scaling_initializer { } }
batch_norm { }
}
summarize_activations: false
}
}
bidirectional_rnn {
fw_bw_rnn_cell {
lstm_cell {
num_units: 256
forget_bias: 1.0
initializer { orthogonal_initializer {} }
}
}
rnn_regularizer { l2_regularizer { weight: 1e-4 } }
num_output_units: 256
fc_hyperparams {
op: FC
activation: RELU
initializer { variance_scaling_initializer { } }
regularizer { l2_regularizer { weight: 1e-4 } }
}
}
summarize_activations: true
}
predictor {
name: "ForwardPredictor"
bahdanau_attention_predictor {
reverse: false
rnn_cell {
lstm_cell {
num_units: 256
forget_bias: 1.0
initializer { orthogonal_initializer { } }
}
}
rnn_regularizer { l2_regularizer { weight: 1e-4 } }
num_attention_units: 128
max_num_steps: 10
multi_attention: false
beam_width: 1
reverse: false
label_map {
character_set {
text_string: "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"
delimiter: ""
}
label_offset: 2
}
loss {
sequence_cross_entropy_loss {
sequence_normalize: false
sample_normalize: true
}
}
}
}
}
"""
model_proto = model_pb2.Model()
text_format.Merge(model_text_proto, model_proto)
model_object = model_builder.build(model_proto, True)
test_groundtruth_text_list = [
tf.constant(b'hello', dtype=tf.string),
tf.constant(b'world', dtype=tf.string)]
model_object.provide_groundtruth(test_groundtruth_text_list)
test_input_image = tf.random_uniform(
shape=[2, 32, 100, 3], minval=0, maxval=255,
dtype=tf.float32, seed=1)
prediction_dict = model_object.predict(model_object.preprocess(test_input_image))
loss = model_object.loss(prediction_dict)
with self.test_session() as sess:
sess.run([
tf.global_variables_initializer(),
tf.tables_initializer()])
outputs = sess.run({'loss': loss})
print(outputs['loss'])