Remove old implementation of Human3.6m from the dataset module. May b…

…ecome too cluttered.

src/skelcast/data/dataset.py

@@ -299,62 +299,6 @@ def store_to_cache(self, cache_file: str) -> None:
         with open(cache_file, 'wb') as f:
             pickle.dump(self.skeleton_files_clean, f)
         logging.info(f"Stored {len(self.skeleton_files_clean)} files to cache file {cache_file}.")
-
-
-@DATASETS.register_module()
-class Human36mDataset(Dataset):
-    def __init__(self, data_path, use_hourglass_detections=True, train=True) -> None:
-        self.data_path = data_path
-        self.use_hourglass_detections = use_hourglass_detections
-        self.train = train
-
-        self.train_inputs, self.test_inputs = [], []
-        self.act = []
-
-        if self.use_hourglass_detections:
-            train_2d_file = 'train_2d_ft.pth.tar'
-            test_2d_file = 'test_2d_ft.pth.tar'
-        else:
-            train_2d_file = 'train_2d.pth.tar'
-            train_2d_file = 'test_2d.pth.tar'
-
-        if self.train:
-            self.train_3d = torch.load(os.path.join(data_path, 'train_3d.pth.tar'))
-            self.train_2d = torch.load(os.path.join(data_path, train_2d_file))
-
-            for k2d in self.train_2d.keys():
-                (sub, act, fname) = k2d
-                k3d = k2d
-                k3d = (sub, act, fname[:-3]) if fname.endswith('-sh') else k3d
-                assert self.train_3d[k3d].shape[0] == self.train_2d[k2d].shape[0], f'(training) 3d and 2d shapes not matching'
-                self.train_inputs.append(self.train_3d[k3d])
-                self.act.append(act)
-
-        else:
-            self.test_3d = torch.load(os.path.join(data_path, 'test_3d.pth.tar'))
-            self.test_2d = torch.load(os.path.join(data_path, test_2d_file))
-            for k2d in self.test_2d.keys():
-                (sub, act, fname) = k2d
-                k3d = k2d
-                k3d = (sub, act, fname[:-3]) if fname.endswith('-sh') else k3d
-                assert self.test_2d[k2d].shape[0] == self.test_3d[k3d].shape[0], '(test) 3d and 2d shapes not matching'
-                self.test_inputs.append(self.test_3d[k3d])
-                self.act.append(act)
-
-    def __getitem__(self, index) -> Any:
-        if self.train:
-            # We want the sampeles to be returned as sequences
-            # i.e.: [seq_len, n_joints, 3]
-            x = torch.from_numpy(self.train_inputs[index]).float()
-        else:
-            x = torch.from_numpy(self.test_inputs[index]).float()
-        return x.view(-1, 16, 3), self.act[index]
-
-    def __len__(self):
-        if self.train:
-            return len(self.train_inputs)
-        else:
-            return len(self.test_inputs)
 
 
 @DATASETS.register_module()
@@ -421,4 +365,5 @@ def hparam(self) -> dict:
         return {
             'DATA_history_length': self.history_length,
             'DATA_prediction_horizon': self.prediction_horizon,
-        }
+        }
+