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')
def test_dump_sklearn(mocker):
mocker.patch('joblib.dump', return_value=None)
save_helper.dump_sklearn("", "test.pkl")
def main():
modelname = "resnet50_onnx"
interface_filename = f"{modelname}.yaml"
model = resnet50(pretrained=True)
x_dummy = torch.rand((1, 3, 224, 224), device="cpu")
model.eval()
torch.onnx.export(
model,
x_dummy,
MODEL_FILEPATH,
export_params=True,
opset_version=10,
do_constant_folding=True,
input_names=["input"],
output_names=["output"],
verbose=False,
)
labels = load_labels(LABEL_FILEPATH)
preprocess = PytorchImagePreprocessTransformer()
image = Image.open(SAMPLE_IMAGE)
np_image = preprocess.transform(image)
print(np_image.shape)
preprocess_name = f"{modelname}_preprocess_transformer"
preprocess_filename = f"{preprocess_name}.pkl"
preprocess_filepath = os.path.join(MODEL_DIR, preprocess_filename)
dump_sklearn(preprocess, preprocess_filepath)
sess = rt.InferenceSession(MODEL_FILEPATH)
inp, out = sess.get_inputs()[0], sess.get_outputs()[0]
print(f"input name='{inp.name}' shape={inp.shape} type={inp.type}")
print(f"output name='{out.name}' shape={out.shape} type={out.type}")
pred_onx = sess.run([out.name], {inp.name: np_image})
postprocess = SoftmaxTransformer()
postprocess_name = f"{modelname}_softmax_transformer"
postprocess_filename = f"{postprocess_name}.pkl"
postprocess_filepath = os.path.join(MODEL_DIR, postprocess_filename)
dump_sklearn(postprocess, postprocess_filepath)
prediction = postprocess.transform(np.array(pred_onx))
print(prediction.shape)
print(labels[np.argmax(prediction[0])])
save_interface(
modelname,
os.path.join(MODEL_DIR, interface_filename),
[1, 3, 224, 224],
"float32",
[1, 1000],
"float32",
DATA_TYPE.IMAGE,
[{
preprocess_filepath: MODEL_RUNTIME.SKLEARN
}, {
MODEL_FILEPATH: MODEL_RUNTIME.ONNX_RUNTIME
}, {
postprocess_filepath: MODEL_RUNTIME.SKLEARN
}],
PREDICTION_TYPE.CLASSIFICATION,
"src.app.ml.resnet50_onnx.resnet50_predictor",
label_filepath=LABEL_FILEPATH,
)