adding data loader class

src/data_utils.py

@@ -0,0 +1,103 @@
+import torch
+from torch.utils.data import Dataset, DataLoader
+import numpy as np
+#from torchvision import transforms, utils
+from src.expert_data import load_expert_data
+import os
+opj = os.path.join
+
+class InteractionDatasetMultiAgent(Dataset):
+    """
+    Class to handle getting full multi-agent observations and actions
+    """
+    pass
+
+class InteractionDatasetSingleAgent(Dataset):
+    """Class to load states and actions for individual agents."""
+
+    def __init__(self, output_dir='expert_data', loc:int = 0, tracks:list = [0], transforms={}):
+        """
+        Args:
+            output_dir (string): Directory with all the images.
+            loc (int): location index
+            tracks (list[int]): track indices
+            transforms (dict): dictionary of transforms to apply to different variables
+        """
+        self.output_dir = output_dir
+        self.loc = loc
+        self.tracks = tracks
+        self.transforms = transforms
+        #self.action_transform = transforms.get('action', None)
+        #self.state_transform = transforms.get('state', None)
+        #self.relative_state_transform = transforms.get('relative_state', None)
+        #self.paths_x_transform = transforms.get('paths_x', None)
+        #self.paths_y_transform = transform.get('paths_y',None)
+
+        self._load_dataset()
+
+    def _load_dataset(self):
+        """
+        Load the full datasets ahead of time
+        """
+        self.raw_data = {'state':[], 'relative_state':[], 'action':[], 'path_x':[], 'path_y':[]}
+        max_nv = 0
+        for track in self.tracks:
+            try:
+                observations, actions = load_expert_data(path=self.output_dir, loc=self.loc, track=track)
+                print('Loaded location {} track {}'.format(self.loc,track))
+            except:
+                print('Failed to load location {} track {}'.format(self.loc,track))
+                continue
+            T = len(actions)
+            for t in range(T):
+                nni = ~torch.isnan(observations[t]['state'][:,0])
+                max_nv = max(max_nv,nni.count_nonzero())
+                self.raw_data['state'].append(observations[t]['state'][nni])
+                self.raw_data['relative_state'].append(observations[t]['relative_state'][nni.nonzero(),nni.nonzero()])
+                self.raw_data['action'].append(actions[t][nni])
+                self.raw_data['path_x'].append(observations[t]['paths'][0][nni])
+                self.raw_data['path_y'].append(observations[t]['paths'][1][nni])
+
+        # cat lists
+        self.raw_data['state'] = torch.cat(self.raw_data['state'])
+        self.raw_data['action'] = torch.cat(self.raw_data['action'])
+        self.raw_data['path_x'] = torch.cat(self.raw_data['path_x'])
+        self.raw_data['path_y'] = torch.cat(self.raw_data['path_y'])
+
+        # pad second dimension of relative state
+        for i in range(len(self.raw_data['relative_state'])):
+            nv1, nv2, d = self.raw_data['relative_state'][i].shape
+            pad = torch.zeros(nv1, max_nv-nv2, d) * np.nan
+            self.raw_data['relative_state'][i] = torch.cat((self.raw_data['relative_state'][i], pad), dim=1)
+        self.raw_data['relative_state'] = torch.cat(self.raw_data['relative_state'])
+
+        # mandate equal length
+        assert len(self.raw_data['state']) == len(self.raw_data['relative_state']) \
+            == len(self.raw_data['action']) \
+            == len(self.raw_data['path_x']) \
+            == len(self.raw_data['path_y']), 'dataset lengths unequal'
+        
+    def __len__(self):
+        return len(self.raw_data['state'])
+
+    def __getitem__(self, idx):
+        """ 
+        Sample from the dataset
+        Args:
+            idx: index or indices of B samples
+        Returns:
+            sample (dict): sample dictionary with the following entries:
+                state (torch.tensor): (B, 5) raw state
+                relative_state (torch.tensor): (B, max_nv, d) relative state (padded with nans)
+                path_x (torch.tensor): (B, P) tensor of P future path x positions
+                path_y (torch.tensor): (B, P) tensor of P future path y positions
+                action (torch.tensor): (B, 1) actions taken from each state
+        """
+        keys = ['state', 'relative_state', 'path_x', 'path_y', 'action']
+        sample = {key:self.raw_data[key][idx] for key in keys}
+
+        for key in keys: 
+            if key in self.transforms.keys():
+                sample[key] = self.transforms[key](sample[key])
+
+        return sample