from typing import List, Sequence
import numpy as np
import onnxruntime as rt
from src.app.configurations import _ModelConfigurations
from src.app.constants import MODEL_RUNTIME
from src.app.ml.base_predictor import BaseData, BaseDataInterface, BaseDataConverter, BasePredictor
from src.app.ml.save_helper import load_labels
import logging
logger = logging.getLogger(__name__)
LABELS = load_labels(_ModelConfigurations().options["label_filepath"])
class _Data(BaseData):
test_data: List[List[int]] = [[5.1, 3.5, 1.4, 0.2]]
labels: List[str] = LABELS
class _DataInterface(BaseDataInterface):
pass
class _DataConverter(BaseDataConverter):
pass
class _Classifier(BasePredictor):
def __init__(self, model_runners):
self.model_runners = model_runners
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,
)
from src.app.ml.save_helper import save_interface, load_labels, dump_sklearn
from src.app.ml.transformers import TFImagePreprocessTransformer, SoftmaxTransformer
from src.app.ml.extract_from_tfhub import get_model
WORK_DIR = './src/app/ml/inceptionv3/'
MODEL_DIR = os.path.join(WORK_DIR, 'model')
SAVEDMODEL_DIR = os.path.join(MODEL_DIR, 'savedmodel/inceptionv3/4')
PB_FILE = os.path.join(SAVEDMODEL_DIR, 'saved_model.pb')
HUB_URL = 'https://tfhub.dev/google/imagenet/inception_v3/classification/4'
DATA_DIR = os.path.join(WORK_DIR, 'data')
SAMPLE_IMAGE = os.path.join(DATA_DIR, 'good_cat.jpg')
LABEL_FILEPATH = os.path.join(DATA_DIR, 'imagenet_labels_1001.json')
LABELS = load_labels(LABEL_FILEPATH)
def validate(image, preprocess, predictor, postprocess):
np_image = preprocess.transform(image)
result = predictor.predict(np_image)
result_proba = postprocess.transform(result)
print(result_proba)
top1_index = np.argmax(result_proba[0], axis=-1)
print(top1_index)
print(LABELS[top1_index])
def main():
os.makedirs(SAVEDMODEL_DIR, exist_ok=True)
import os
from onnx import numpy_helper
from google.protobuf.json_format import MessageToJson
from src.app.configurations import ModelConfigurations
from src.app.constants import MODEL_RUNTIME
from src.app.ml.base_predictor import BaseData, BaseDataInterface, BaseDataConverter, BasePredictor
from src.app.ml.transformers import PytorchImagePreprocessTransformer, SoftmaxTransformer
from src.app.ml.save_helper import load_labels
from src.app.ml.resnet50_onnx_server.proto import predict_pb2, onnx_ml_pb2
import logging
logger = logging.getLogger(__name__)
LABELS = load_labels(ModelConfigurations.options['label_filepath'])
ONNX_RUNTIME_SERVER_HTTP = os.getenv('ONNX_RUNTIME_SERVER_HTTP',
'prep_pred_onnx_resnet50:8001')
MODEL_NAME = os.getenv('MODEL_NAME', 'default')
VERSION = int(os.getenv('VERSION', 1))
TIMEOUT_SECOND = int(os.getenv('TIMEOUT_SECOND', 5.0))
class _Data(BaseData):
image_data: Any = None
test_data: str = os.path.join('./src/app/ml/resnet50_onnx/data',
'good_cat.jpg')
labels: List[str] = LABELS
class _DataInterface(BaseDataInterface):
