linter

Author: eaidova

tools/accuracy_checker/.pylintrc

@@ -21,7 +21,7 @@ disable = C0103,
 max-line-length = 120
 ignore-docstrings = yes
 extension-pkg-whitelist=inference_engine,cv2,numpy,mxnet,tensorflow,pycocotools,onnxruntime
-ignored-modules = numpy,cv2,openvino.inference_engine,caffe,mxnet,tensorflow,pycocotools,onnxruntime
+ignored-modules = numpy,cv2,openvino.inference_engine,caffe,mxnet,tensorflow,pycocotools,onnxruntime,torch
 load-plugins = pylint_checkers
 ignored-classes = pathlib.PurePath
 jobs=0

tools/accuracy_checker/accuracy_checker/launcher/pytorch_launcher.py

@@ -64,7 +64,7 @@ def _generate_inputs(self):
 
     @property
     def inputs(self):
-       return self._inputs
+        return self._inputs
 
     @property
     def batch(self):
@@ -99,7 +99,7 @@ def fit_to_input(self, data, layer_name, layout):
         with torch.no_grad():
             return Variable(tensor)
 
-    def predict(self,inputs, metadata=None, **kwargs):
+    def predict(self, inputs, metadata=None, **kwargs):
         results = []
         for batch_input in inputs:
             outputs = list(self.module(*batch_input.values()))
@@ -111,6 +111,9 @@ def predict(self,inputs, metadata=None, **kwargs):
 
         return results
 
+    def predict_async(self, *args, **kwargs):
+        raise ValueError('MxNet Launcher does not support async mode yet')
+
     def release(self):
         del self.module
 

tools/accuracy_checker/dataset_definitions.yml

@@ -60,9 +60,9 @@ datasets:
   - name: imagenet_1000_classes
     annotation_conversion:
       converter: imagenet
-      annotation_file: val.txt
+      annotation_file: /home/automation/datasets/imagenet/val.txt
     annotation: imagenet1000.pickle
-    data_source: ILSVRC2012_img_val
+    data_source: /home/automation/datasets/imagenet/images
     metrics:
     - name: accuracy@top1
       type: accuracy

tools/accuracy_checker/setup.cfg

@@ -5,4 +5,4 @@ ignore = F401
 [isort]
 line_length = 120
 use_parentheses = True
-known_third_party = openvino.inference_engine,caffe,cv2,mxnet,tensorflow
+known_third_party = openvino.inference_engine,caffe,cv2,mxnet,tensorflow,torch