Allow app execution in Jupyter notebook

[gigony]

• Refactor AppContext to be updatable
• Set up logging level in Application.run()
• Accept arguments in Application.run()
• Do not run Application if no arguments are specified in
  Application.run() method
• Create an output folder if not exists, before executing the app.

With this PR, the below code is possible with Jupyter Notebook.

# Copyright 2021 MONAI Consortium # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from monai.deploy.core import ( Application, DataPath, ExecutionContext, Image, InputContext, IOType, Operator, OutputContext, env, input, output, resource, ) from monai.transforms import AddChannel, Compose, EnsureType, ScaleIntensity MEDNIST_CLASSES = ["AbdomenCT", "BreastMRI", "CXR", "ChestCT", "Hand", "HeadCT"] @input("image", DataPath, IOType.DISK) @output("image", Image, IOType.IN_MEMORY) @env(pip_packages=["pillow"]) class LoadPILOperator(Operator): """Load image from the given input (DataPath) and set numpy array to the output (Image).""" def compute(self, input: InputContext, output: OutputContext, context: ExecutionContext): import numpy as np from PIL import Image as PILImage input_path = input.get().path image = PILImage.open(input_path) image = image.convert("L") # convert to greyscale image image_arr = np.asarray(image) output_image = Image(image_arr) # create Image domain object with a numpy array output.set(output_image) @input("image", Image, IOType.IN_MEMORY) @output("output", DataPath, IOType.DISK) @env(pip_packages=["monai"]) class MedNISTClassifierOperator(Operator): """Classifies the given image and returns the class name.""" @property def transform(self): return Compose([AddChannel(), ScaleIntensity(), EnsureType()]) def compute(self, input: InputContext, output: OutputContext, context: ExecutionContext): import json import torch img = input.get().asnumpy() # (64, 64), uint8 image_tensor = self.transform(img) # (1, 64, 64), torch.float64 image_tensor = image_tensor[None].float() # (1, 1, 64, 64), torch.float32 # Comment below line if you want to do CPU inference image_tensor = image_tensor.cuda() model = context.models.get() # get a TorchScriptModel object # Uncomment the following line if you want to do CPU inference # model.predictor = torch.jit.load(model.path, map_location="cpu").eval() with torch.no_grad(): outputs = model(image_tensor) _, output_classes = outputs.max(dim=1) result = MEDNIST_CLASSES[output_classes[0]] # get the class name print(result) # Get output (folder) path and create the folder if not exists output_folder = output.get().path output_folder.mkdir(parents=True, exist_ok=True) # Write result to "output.json" output_path = output_folder / "output.json" with open(output_path, "w") as fp: json.dump(result, fp) @resource(cpu=1, memory="1Gi") class App(Application): """Application class for the MedNIST classifier.""" def compose(self): load_pil_op = LoadPILOperator() classifier_op = MedNISTClassifierOperator() self.add_flow(load_pil_op, classifier_op) if __name__ == "__main__": App(do_run=True)

app = App() app.context

AppContext(graph=nx_digraph, input_path=input, output_path=output, model_path=models, workdir=, datastore=memory, executor=single_process_executor, resource=Resource(cpu=1, memory=1073741824, gpu=0)) 

app.run(input="input/AbdomenCT_007000.jpeg", model="classifier.zip")

�[34mGoing to initiate execution of operator LoadPILOperator�[39m �[32mExecuting operator LoadPILOperator �[33m(Process ID: 24042, Operator ID: f68514e8-36bd-4d69-a668-36f96be9713b)�[39m �[34mDone performing execution of operator LoadPILOperator �[39m �[34mGoing to initiate execution of operator MedNISTClassifierOperator�[39m �[32mExecuting operator MedNISTClassifierOperator �[33m(Process ID: 24042, Operator ID: f5341505-f058-4531-b2ef-06f18c09a411)�[39m AbdomenCT �[34mDone performing execution of operator MedNISTClassifierOperator �[39m 

app.context

AppContext(graph=nx_digraph, input_path=/home/gbae/repo/monai-deploy-app-sdk/input/AbdomenCT_007000.jpeg, output_path=output, model_path=classifier.zip, workdir=, datastore=memory, executor=single_process_executor, resource=Resource(cpu=0, memory=0, gpu=0)) 

Closes #82

[MMelQin]

Look good