fix pylint

src/features/preprocess_ki.py

@@ -79,8 +79,6 @@ class PreprocessKitti:
 
         for name in self.names_gt:
             # Extract ground truth
-            if name == '004223.txt':
-                aa = 5
             path_gt = os.path.join(self.dir_gt, name)
             basename, _ = os.path.splitext(name)
             boxes_gt = []
@@ -117,7 +115,7 @@ class PreprocessKitti:
                 with open(os.path.join(self.dir_ann, basename + '.png.pifpaf.json'), 'r') as f:
                     annotations = json.load(f)
                 boxes, keypoints = self.preprocess_pif(annotations)
-                (inputs, xy_kps), (uv_kps, uv_boxes, _, _) = self.get_input_data(boxes, keypoints, kk)
+                (inputs, _), (uv_kps, uv_boxes, _, _) = self.get_input_data(boxes, keypoints, kk)
 
             except FileNotFoundError:
                 uv_boxes = []
@@ -138,10 +136,10 @@ class PreprocessKitti:
                     boxes_gt.pop(idx_max)
                     dds.pop(idx_max)
 
-        with open(self.path_joints, 'w') as f:
-            json.dump(self.dic_jo, f)
-        with open(os.path.join(self.path_names), 'w') as f:
-            json.dump(self.dic_names, f)
+        with open(self.path_joints, 'w') as file:
+            json.dump(self.dic_jo, file)
+        with open(os.path.join(self.path_names), 'w') as file:
+            json.dump(self.dic_names, file)
         for phase in ['train', 'val', 'test']:
             print("Saved {} annotations for phase {}"
                   .format(self.dic_cnt[phase], phase))

src/features/preprocess_nu.py

@@ -1,4 +1,6 @@
-import numpy as np
+"""Extract joints annotations and match with nuScenes ground truths
+"""
+
 import os
 import sys
 import time
@@ -7,6 +9,8 @@ import logging
 from collections import defaultdict
 import datetime
 
+import numpy as np
+
 
 class PreprocessNuscenes:
     """
@@ -48,7 +52,7 @@ class PreprocessNuscenes:
         # Initialize dicts to save joints for training
         self.dic_jo = {'train': dict(X=[], Y=[], names=[], kps=[], boxes_3d=[], K=[],
                                      clst=defaultdict(lambda: defaultdict(list))),
-                       'val': dict(X=[], Y=[], names=[], kps=[], boxes_3d=[],  K=[],
+                       'val': dict(X=[], Y=[], names=[], kps=[], boxes_3d=[], K=[],
                                    clst=defaultdict(lambda: defaultdict(list))),
                        'test': dict(X=[], Y=[], names=[], kps=[], boxes_3d=[], K=[],
                                     clst=defaultdict(lambda: defaultdict(list)))
@@ -73,12 +77,12 @@ class PreprocessNuscenes:
 
         elif dataset == 'nuscenes_teaser':
             self.nusc = NuScenes(version='v1.0-trainval', dataroot=dir_nuscenes, verbose=True)
-            with open("splits/nuscenes_teaser_scenes.txt", "r") as ff:
-                teaser_scenes = ff.read().splitlines()
+            with open("splits/nuscenes_teaser_scenes.txt", "r") as file:
+                teaser_scenes = file.read().splitlines()
             self.scenes = self.nusc.scene
             self.scenes = [scene for scene in self.scenes if scene['token'] in teaser_scenes]
-            with open("splits/split_nuscenes_teaser.json", "r") as ff:
-                dic_split = json.load(ff)
+            with open("splits/split_nuscenes_teaser.json", "r") as file:
+                dic_split = json.load(file)
             self.split_train = [scene['name'] for scene in self.scenes if scene['token'] in dic_split['train']]
             self.split_val = [scene['name'] for scene in self.scenes if scene['token'] in dic_split['val']]
 
@@ -147,11 +151,11 @@ class PreprocessNuscenes:
                     exists = os.path.isfile(path_pif)
 
                     if exists:
-                        with open(path_pif, 'r') as f:
-                            annotations = json.load(f)
+                        with open(path_pif, 'r') as file:
+                            annotations = json.load(file)
 
                         boxes, keypoints = self.preprocess_pif(annotations, im_size=None)
-                        (inputs, xy_kps), (uv_kps, uv_boxes, _, _) = self.get_input_data(boxes, keypoints, kk)
+                        (inputs, _), (uv_kps, uv_boxes, _, _) = self.get_input_data(boxes, keypoints, kk)
 
                         for ii, box in enumerate(uv_boxes):
                             idx_max, iou_max = self.get_idx_max(box, boxes_gt)

src/main.py

@@ -70,7 +70,7 @@ def cli():
 
     # Training
     training_parser.add_argument('--joints', help='Json file with input joints',
-                                 default='data/arrays/joints-nuscenes-190507-0852.json')
+                                 default='data/arrays/joints-nuscenes_teaser-190513-1846.json')
     training_parser.add_argument('--save', help='whether to not save model and log file', action='store_false')
     training_parser.add_argument('-e', '--epochs', type=int, help='number of epochs to train for', default=150)
     training_parser.add_argument('--bs', type=int, default=256, help='input batch size')

src/models/trainer.py

@@ -1,16 +1,16 @@
-import torch
-import torch.nn as nn
+
 import copy
-import numpy as np
-import matplotlib.pyplot as plt
 import os
 import datetime
 import logging
 from collections import defaultdict
-import json
 import sys
 import time
 
+import matplotlib.pyplot as plt
+
+import torch
+import torch.nn as nn
 from torch.utils.data import DataLoader
 from torch.optim import lr_scheduler
 

src/visuals/results.py

@@ -1,6 +1,5 @@
 
 import os
-import matplotlib
 import numpy as np
 import matplotlib.pyplot as plt
 from matplotlib.patches import Ellipse