def test_get_model(mocker, hub_url, input_shape, kwargs):
mock_keras_layer = mocker.Mock(tensorflow_hub.keras_layer.KerasLayer)
mock_sequential = mocker.Mock(tensorflow.python.keras.engine.sequential.Sequential)
mocker.patch('tensorflow_hub.KerasLayer', return_value=mock_keras_layer)
mocker.patch('tensorflow.keras.Sequential', return_value=mock_sequential)
model = extract_from_tfhub.get_model(hub_url, input_shape, **kwargs)
assert model == mock_sequential
def main():
os.makedirs(SAVEDMODEL_DIR, exist_ok=True)
if os.path.exists(PB_FILE):
print(f"saved model {SAVEDMODEL_DIR} found")
model = tf.keras.models.load_model(SAVEDMODEL_DIR)
else:
print(f"saved model {SAVEDMODEL_DIR} not found")
model = get_model(HUB_URL, (224, 224, 3))
preprocess = TFImagePreprocessTransformer(image_size=(224, 224),
prediction_shape=(1, 224, 224,
3))
postprocess = SoftmaxTransformer()
image = Image.open(SAMPLE_IMAGE)
validate(image, preprocess, model, postprocess)
tf.saved_model.save(model, SAVEDMODEL_DIR)
modelname = "mobilenetv2_plant"
interface_filename = f"{modelname}.yaml"
preprocess_filename = f"{modelname}_preprocess_transformer.pkl"
postprocess_filename = f"{modelname}_softmax_transformer.pkl"
preprocess_filepath = os.path.join(MODEL_DIR, preprocess_filename)
postprocess_filepath = os.path.join(MODEL_DIR, postprocess_filename)
dump_sklearn(preprocess, preprocess_filepath)
dump_sklearn(postprocess, postprocess_filepath)
save_interface(
modelname,
os.path.join(MODEL_DIR, interface_filename),
[1, 224, 224, 3],
"float32",
[1, 2102],
"float32",
DATA_TYPE.IMAGE,
[{
preprocess_filepath: MODEL_RUNTIME.SKLEARN
}, {
SAVEDMODEL_DIR: MODEL_RUNTIME.TF_SERVING
}, {
postprocess_filepath: MODEL_RUNTIME.SKLEARN
}],
PREDICTION_TYPE.CLASSIFICATION,
"src.app.ml.mobilenetv2_plant.mobilenetv2_predictor",
label_filepath=LABEL_FILEPATH,
model_spec_name="mobilenetv2_plant",
model_spec_signature_name="serving_default",
input_name="input_1",
output_name="keras_layer",
)
def main():
os.makedirs(SAVEDMODEL_DIR, exist_ok=True)
if os.path.exists(PB_FILE):
print(f'saved model {SAVEDMODEL_DIR} found')
model = tf.keras.models.load_model(SAVEDMODEL_DIR)
else:
print(f'saved model {SAVEDMODEL_DIR} not found')
model = get_model(HUB_URL, (299, 299, 3))
preprocess = TFImagePreprocessTransformer()
postprocess = SoftmaxTransformer()
image = Image.open(SAMPLE_IMAGE)
validate(image, preprocess, model, postprocess)
tf.saved_model.save(model, SAVEDMODEL_DIR)
modelname = 'inceptionv3'
interface_filename = f'{modelname}.yaml'
preprocess_filename = f'{modelname}_preprocess_transformer.pkl'
postprocess_filename = f'{modelname}_softmax_transformer.pkl'
preprocess_filepath = os.path.join(MODEL_DIR, preprocess_filename)
postprocess_filepath = os.path.join(MODEL_DIR, postprocess_filename)
dump_sklearn(preprocess, preprocess_filepath)
dump_sklearn(postprocess, postprocess_filepath)
save_interface(modelname,
os.path.join(MODEL_DIR,
interface_filename), [1, 299, 299, 3],
'float32', [1, 1001],
'float32',
DATA_TYPE.IMAGE,
[{
preprocess_filepath: MODEL_RUNTIME.SKLEARN
}, {
SAVEDMODEL_DIR: MODEL_RUNTIME.TF_SERVING
}, {
postprocess_filepath: MODEL_RUNTIME.SKLEARN
}],
PREDICTION_TYPE.CLASSIFICATION,
'src.app.ml.inceptionv3.inceptionv3_predictor',
label_filepath=LABEL_FILEPATH,
model_spec_name='inceptionv3',
model_spec_signature_name='serving_default',
input_name='input_1',
output_name='keras_layer')