class Model(ModelBase):
def __init__(self):
# load config file
config = json.load(open("model/config.json"))
# get the image processor
self._imageProcessor = ImageProcessor(config)
# load the DL model
self._model = Xception()
self._model.load_weights('model/model.h5')
self._model._make_predict_function()
def infer(self, input):
# load preprocessed input
inputAsNpArr = self._imageProcessor.loadAndPreprocess(input)
# Run inference with caffe2
results = self._model.predict(inputAsNpArr)
# postprocess results into output
output = self._imageProcessor.computeOutput(results)
return output
service_principal_id=config['service_principal_id'],
service_principal_password=config['service_principal_password'])
except KeyError as e:
print("Getting Service Principal Authentication from Azure Devops")
svr_pr = None
pass
ws = Workspace.from_config(path=config_json, auth=svc_pr)
try:
model_root = Model.get_model_path('trained_xception', _workspace=ws)
except ModelNotFoundException as e:
print("Didn't find model, cannot perform knowledge distillation.")
model = Xception()
model.load_weights(os.path.join(model_root, "xception_weights.hdf5"))
# Remove softmax
model.layers.pop()
# Now model outputs logits
model = KerasModel(model.input, model.layers[-1].output)
# # Save logits as a dict: image name -> logit (256 dimensional vector)
train_logits = {}
batches = 0
for x_batch, _, name_batch in tqdm(train_generator):
batch_logits = model.predict_on_batch(x_batch)