Fixed more flake8 warnings

tasks/facial_landmark_detection/search_space.py

@@ -188,7 +188,7 @@ class DiscreteSearchSpaceMobileNetV2(DiscreteSearchSpace):
             arch = self._create_uniq_arch(cfg)
         else:
             arch = None
-            while arch == None:
+            while arch is None:
                 cfg = self._rand_modify_config(
                     self.cfgs_orig[0],
                     len(self.e_range),
@@ -198,7 +198,7 @@ class DiscreteSearchSpaceMobileNetV2(DiscreteSearchSpace):
                     len(self.depth_mult_range),
                 )
                 arch = self._create_uniq_arch(cfg)
-            assert arch != None
+            assert arch is not None
 
         logger.info(f"{sys._getframe(0).f_code.co_name} return archid = {arch.archid} with config = {arch.metadata}")
 
@@ -232,7 +232,7 @@ class DiscreteSearchSpaceMobileNetV2(DiscreteSearchSpace):
         block_cfg = cfg["arch_def"][block][0]
 
         res = re.search(rf"_{type}(\d)_", block_cfg)
-        if res != None:
+        if res is not None:
             curr = res.group(1)
             curr_idx = curr_range.index(int(curr))
             mod_range = curr_range[max(0, curr_idx - delta) : min(len(curr_range), curr_idx + delta + 1)]
@@ -304,7 +304,7 @@ class ConfigSearchSpaceExt(DiscreteSearchSpaceMobileNetV2, EvolutionarySearchSpa
         cfg_1 = json.loads(model_1.metadata["config"])
 
         arch = None
-        while arch == None:
+        while arch is None:
             cfg = self._rand_modify_config(
                 cfg_1,
                 len(self.e_range),
@@ -314,7 +314,7 @@ class ConfigSearchSpaceExt(DiscreteSearchSpaceMobileNetV2, EvolutionarySearchSpa
                 len(self.depth_mult_range),
             )
             arch = self._create_uniq_arch(cfg)
-        assert arch != None
+        assert arch is not None
         logger.info(f"{sys._getframe(0).f_code.co_name} return archid = {arch.archid} with config = {arch.metadata}")
 
         return arch
@@ -329,7 +329,7 @@ class ConfigSearchSpaceExt(DiscreteSearchSpaceMobileNetV2, EvolutionarySearchSpa
         cfg = copy.deepcopy(cfg_1)
 
         arch = None
-        while arch == None:
+        while arch is None:
             for block in range(2, len(cfg["arch_def"])):
                 cfg["arch_def"][block] = random.choice((cfg_1["arch_def"][block], cfg_2["arch_def"][block]))
 
@@ -337,7 +337,7 @@ class ConfigSearchSpaceExt(DiscreteSearchSpaceMobileNetV2, EvolutionarySearchSpa
             cfg["depth_multiplier"] = random.choice((cfg_1["depth_multiplier"], cfg_2["depth_multiplier"]))
 
             arch = self._create_uniq_arch(cfg)
-        assert arch != None
+        assert arch is not None
         logger.info(f"{sys._getframe(0).f_code.co_name} return archid = {arch.archid} with config = {arch.metadata}")
 
         return arch
@@ -360,12 +360,14 @@ if __name__ == "__main__":
 
         model.to("cpu").eval()
         pred = model(torch.randn(1, 3, img_size, img_size))
+        assert pred is not None
         model_summary = summary(
             model,
             input_size=(1, 3, img_size, img_size),
             col_names=["input_size", "output_size", "num_params", "kernel_size", "mult_adds"],
             device="cpu",
         )
+        print(model_summary)
 
         return arch
 

tasks/facial_landmark_detection/train.py

@@ -120,10 +120,10 @@ def evaluate(model, criterion, data_loader, epoch, device, print_freq=100, log_s
     return float(metric_logger.error.global_avg)
 
 
-def load_data(traindir, valdir, args):
+def load_data(traindir, args):
     # Data loading code
     print("Loading data")
-    val_resize_size, val_crop_size, train_crop_size = args.val_resize_size, args.val_crop_size, args.train_crop_size
+    _, val_crop_size, train_crop_size = args.val_resize_size, args.val_crop_size, args.train_crop_size
 
     print("Loading training data")
     st = time.time()
@@ -163,11 +163,8 @@ def train(args, model: nn.Module = None):
     else:
         torch.backends.cudnn.benchmark = True
 
-    train_dir = os.path.join(args.data_path, "train")
+    dataset, dataset_test, train_sampler, test_sampler = load_data(args.data_path, args)
-    val_dir = os.path.join(args.data_path, "val")
-    dataset, dataset_test, train_sampler, test_sampler = load_data(args.data_path, val_dir, args)
 
-    collate_fn = None
     num_classes = dataset.dataset.num_landmarks
     data_loader = torch.utils.data.DataLoader(
         dataset,