Fixes after flake8 checking (#3673)

Author: mpushki

demos/smartlab_demo/python/evaluator.py

@@ -862,7 +862,7 @@ def evaluate_end_tidy(self):
 
             elif len(battery_coors) == 0:
                 self.evaluate_rider()
-                if self.rider_zero == True:
+                if self.rider_zero:
                     self.scoring["end_score_tidy"] = 1
                     self.keyframe["end_score_tidy"] = self.frame_counter
         else:

demos/speech_recognition_deepspeech_demo/python/asr_utils/audio_features.py

@@ -79,7 +79,7 @@ def samples_to_melspectrum(samples, sampling_rate, window_size, stride, n_mels,
     window_size, stride = round(window_size), round(stride)
 
     # window_size must be a power of 2 to match tf:
-    if not(window_size > 0  and  (window_size - 1) & window_size == 0):
+    if not (window_size > 0 and (window_size - 1) & window_size == 0):
         raise ValueError("window_size(ms)*sampling_rate(kHz) must be a power of two")
 
     spec = np.abs(librosa.core.spectrum.stft(

demos/tests/cases.py

@@ -629,7 +629,7 @@ def single_option_cases(key, *args):
                     ModelArg('pspnet-pytorch'),
                     ModelArg('drn-d-38'),
                     # ModelArg('erfnet'),  # TODO add after CI is up
-                    )),
+                )),
         ],
     )),
 
@@ -1279,7 +1279,7 @@ def single_option_cases(key, *args):
                     ModelArg('pspnet-pytorch'),
                     ModelArg('drn-d-38'),
                     # ModelArg('erfnet'),  # TODO add after CI is up
-                    )),
+                )),
             TestCase(options={
                 '-m': ModelArg('f3net'),
                 '-i': DataPatternArg('road-segmentation-adas'),

models/intel/handwritten-english-recognition-0001/preprocess_gnhk.py

@@ -53,7 +53,7 @@ def main():
         # open corresponding JSON annotation file
         with open(img_id + ".json") as f:
             data = json.load(f)
-            line_indices = set(map(lambda obj: obj["line_idx"], data))
+            line_indices = {obj["line_idx"] for obj in data}
             img = cv2.imread(image, cv2.IMREAD_GRAYSCALE)
             img = binarize(img)
             for idx in sorted(line_indices):
@@ -63,7 +63,7 @@ def main():
                 if not objects:
                     continue
                 objects = sorted(objects, key=lambda x: x['polygon']['x0'])
-                label = " ".join(map(lambda obj: obj["text"], objects))
+                label = " ".join((obj["text"] for obj in objects))
                 print(img_id, idx, label)
 
                 # create mask for the words
@@ -82,12 +82,12 @@ def main():
                 cv2.bitwise_not(bg, bg, mask = mask)
                 overlay = bg + masked
                 # crop bounding rectangle of the text region
-                polys = list(map(lambda obj: [
+                polys = [[
                     [obj["polygon"]["x0"], obj["polygon"]["y0"]],
                     [obj["polygon"]["x1"], obj["polygon"]["y1"]],
                     [obj["polygon"]["x2"], obj["polygon"]["y2"]],
                     [obj["polygon"]["x3"], obj["polygon"]["y3"]]
-                ], objects))
+                ] for obj in objects]
                 flat = [item for sublist in polys for item in sublist]
                 pts = np.array(flat)
                 rect = cv2.boundingRect(pts)

tools/accuracy_checker/openvino/tools/accuracy_checker/adapters/dna_seq_recognition.py

@@ -153,6 +153,7 @@ def backward_scores(self, scores):
         ms_t = scores[:, :, idx_t]
         idx_t = self._torch.div(idx_t, self.n_base + 1, rounding_mode='floor')
         return self.scan(ms_t.flip(0), idx_t.to(self._torch.int64), vt, self.log_semiring).flip(0)
+
     def select_output_blob(self, outputs):
         self.output_verified = True
         if self.output_blob:

tools/accuracy_checker/openvino/tools/accuracy_checker/annotation_converters/_nlp_common.py

@@ -447,7 +447,7 @@ def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_spe
             if token_ids_1 is not None:
                 raise ValueError("You should not supply a second sequence if the provided sequence of "
                                  "ids is already formatted with special tokens for the model.")
-            return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
+            return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_0]
 
         if token_ids_1 is not None:
             return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]

tools/accuracy_checker/openvino/tools/accuracy_checker/dataset.py

@@ -469,7 +469,7 @@ def __getitem__(self, item):
 
     @property
     def identifiers(self):
-        return list(map(lambda ann: ann.identifier, self._data_buffer.values()))
+        return [ann.identifier for ann in self._data_buffer.values()]
 
     def __len__(self):
         return len(self._data_buffer)

tools/accuracy_checker/openvino/tools/accuracy_checker/launcher/dlsdk_launcher.py

@@ -295,7 +295,7 @@ def _set_affinity_via_layers(self, custom_affinity, automatic_affinity):
             layers[layer_name].affinity = device
 
     def _is_vpu(self):
-        device_list = map(lambda device: device.split('.')[0], self._devices_list())
+        device_list = (device.split('.')[0] for device in self._devices_list())
         return contains_any(device_list, VPU_PLUGINS)
 
     @property
@@ -413,7 +413,7 @@ def _device_specific_configuration(self):
             if config:
                 self.ie_core.set_config(config, 'GPU')
         if self._is_vpu():
-            device_list = map(lambda device: device.split('.')[0], self._devices_list())
+            device_list = (device.split('.')[0] for device in self._devices_list())
             devices = [vpu_device for vpu_device in VPU_PLUGINS if vpu_device in device_list]
             log_level = self.config.get('_vpu_log_level')
             if log_level:

tools/accuracy_checker/openvino/tools/accuracy_checker/launcher/launcher.py

@@ -216,7 +216,7 @@ def validate_config(cls, config, delayed_model_loading=False, fetch_only=False,
                         validation_scheme=cls.validation_scheme())
                 )
                 return errors
-        uri = uri_prefix or'launcher.{}'.format(cls.__provider__)
+        uri = uri_prefix or 'launcher.{}'.format(cls.__provider__)
         return LauncherConfigValidator(
             uri, fields=cls.parameters(), delayed_model_loading=delayed_model_loading
         ).validate(

tools/accuracy_checker/openvino/tools/accuracy_checker/launcher/openvino_launcher.py

@@ -273,7 +273,7 @@ def _set_affinity(self, affinity_map_path):
             node.rt_info["affinity"] = device
 
     def _is_vpu(self):
-        device_list = map(lambda device: device.split('.')[0], self._devices_list())
+        device_list = (device.split('.')[0] for device in self._devices_list())
         return contains_any(device_list, VPU_PLUGINS)
 
     @property
@@ -377,7 +377,7 @@ def _device_specific_configuration(self):
             if config:
                 ov_set_config(self.ie_core, config, device='GPU')
         if self._is_vpu():
-            device_list = map(lambda device: device.split('.')[0], self._devices_list())
+            device_list = (device.split('.')[0] for device in self._devices_list())
             devices = [vpu_device for vpu_device in VPU_PLUGINS if vpu_device in device_list]
             log_level = self.config.get('_vpu_log_level')
             if log_level:
@@ -484,7 +484,7 @@ def _set_infer_precision_hint(self):
         if 'INFERENCE_PRECISION_HINT' not in supported_props:
             warning(f'inference precision hint is not supported for device {self._device}, option will be ingnored')
             return
-        if not precision_hint.upper() in PRECISION_STR_TO_TYPE and not precision_hint in format_map:
+        if precision_hint.upper() not in PRECISION_STR_TO_TYPE and precision_hint not in format_map:
             raise ConfigError(f'Unknown precision {precision_hint} for inference precision hint')
         precision_type = PRECISION_STR_TO_TYPE.get(precision_hint.upper(), precision_hint)
         self.ie_core.set_property(self._device, {'INFERENCE_PRECISION_HINT': precision_type})