change pytorch version

loader/data_loader_dynamic.py

@@ -0,0 +1,233 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import logging
+from torch.utils.data import Dataset
+import os
+import scipy.io as sio
+#from skimage.transform import resize
+#from skimage.transform import rotate
+from PIL import Image
+import numpy as np
+from scipy import optimize
+from IPython import embed
+np.set_printoptions(threshold=np.inf, suppress=True)
+import random
+import copy
+import math
+
+def transpose_traj(traj):
+    for i in range(traj.shape[0]):
+        temp = traj[i,0] 
+        traj[i,0]= traj[i,1]
+        traj[i,1] = temp 
+    return traj
+
+def check_neighbor(first, second):
+    actions = np.array([[0,0], [1,0], [-1,0], [0,1], [0,-1]])
+    possible_neighbors = list(first) + actions
+    for neighbor in possible_neighbors:
+        if (second==neighbor).all():
+            return True
+    return False
+
+def is_valid_traj(traj):
+    i = 0
+    while i < len(traj)-1:
+        if not check_neighbor(traj[i], traj[i+1]):
+            return False
+        i = i+1
+    return True
+
+class OffroadLoader(Dataset):
+    def __init__(self, grid_size, train=True, demo=None, datadir='/root/medirl/data/irl_driving_inter3_junwoo', pre_train=False, tangent=False,
+                 more_kinematic=None, human = False):
+        assert grid_size % 2 == 0, "grid size must be even number"
+        self.grid_size = grid_size
+        if train:
+            self.data_dir = datadir + '/train'
+        else:
+            self.data_dir = datadir + '/test'
+        print(self.data_dir)
+        # if not train:
+        #     self.data_dir = '/root/medirl/data/irl_driving_full_future'
+        # if demo is not None:
+        #     self.data_dir = datadir + '/irl_data/' + demo
+        items = os.listdir(self.data_dir)
+        self.data_list = []
+        for item in items:
+            past_traj = self.data_dir + '/' + item+"/past_traj.npy"
+            if not (os.path.exists(past_traj)):
+                continue
+            if (self.check_isnone(self.data_dir + '/' + item)):
+                continue
+            future_traj = self.data_dir + '/' + item+"/future_traj.npy"
+            if not (os.path.exists(future_traj)):
+                continue
+            if (self.check_isnone(self.data_dir + '/' + item)):
+                continue
+            self.data_list.append(self.data_dir + '/' + item)
+
+
+        #self.data_normalization = sio.loadmat(datadir + '/irl_data/train-data-mean-std.mat')
+        self.pre_train = pre_train
+
+        # kinematic related feature
+        self.center_idx = self.grid_size / 2
+        self.is_human = human
+
+    def check_isnone(self,image_fol):
+        self.image_fol = image_fol
+        goal_sink_feat = np.array(Image.open(self.image_fol+"/goal_sink.png")).T
+        goal_sink_feat = goal_sink_feat/255*np.ones(goal_sink_feat.shape)
+        temp_sink_feat = np.zeros([1,goal_sink_feat.shape[1], goal_sink_feat.shape[2]])
+        for i in range(goal_sink_feat.shape[1]):
+            for j in range(goal_sink_feat.shape[2]):
+                if (goal_sink_feat[1,i,j] == 1):
+                    temp_sink_feat[0,i,j] = 5
+                if (goal_sink_feat[2,i,j] == 1):
+                    temp_sink_feat[0,i,j] = 10
+        goal_sink_feat = temp_sink_feat
+        semantic_img_feat = np.array(Image.open(self.image_fol+"/semantic_img.png"))[:,:,0:3].T
+        with open(self.image_fol+"/traj_fixed.npy", 'rb') as f:
+            traj = np.load(f)
+            if (len(traj) == 0):
+                return True
+
+        for i in range(traj.shape[0]):
+            temp = traj[i,0] 
+            traj[i,0]= traj[i,1]
+            traj[i,1] = temp 
+        # visualize rgb
+        # with open(self.image_fol+"/sdf.npy", 'rb') as f:
+        #     sdf_feat = np.load(f)
+        feat = np.concatenate((goal_sink_feat, semantic_img_feat), axis = 0)
+        # normalize features locally
+
+        for i in range(feat.shape[0]):
+            # print(feat[i].shape)
+            # print("Before nomalize", np.max(feat[i]))  
+            feat[i] = feat[i] - np.mean(feat[i])*np.ones(feat[i].shape)
+            feat[i] = feat[i] / np.std(feat[i])*np.ones(feat[i].shape)
+            if (np.isnan(feat[i]).any()):
+                return True
+        return False
+
+
+
+
+
+    def __getitem__(self, index):
+        self.image_fol = self.data_list[index]
+        goal_sink_feat = np.array(Image.open(self.image_fol+"/goal_sink.png")).T
+        goal_sink_feat = goal_sink_feat/255*np.ones(goal_sink_feat.shape)
+        temp_sink_feat = np.zeros([1,goal_sink_feat.shape[1], goal_sink_feat.shape[2]])
+        for i in range(goal_sink_feat.shape[1]):
+            for j in range(goal_sink_feat.shape[2]):
+                if (goal_sink_feat[1,i,j] == 1):
+                    temp_sink_feat[0,i,j] = 5
+                if (goal_sink_feat[2,i,j] == 1):
+                    temp_sink_feat[0,i,j] = 10
+        goal_sink_feat = temp_sink_feat
+        semantic_img_feat = np.array(Image.open(self.image_fol+"/semantic_img.png"))[:,:,0:3].T
+        with open(self.image_fol+"/past_traj.npy", 'rb') as f:
+            full_traj = np.load(f)
+            past_full_traj = full_traj
+        past_other_traj = []
+        if self.is_human:
+            past_traj = full_traj[:,:,1]
+            past_other_traj = full_traj[:,:,0]
+        else:
+            past_traj = full_traj[:,:,0]
+            past_other_traj = full_traj[:,:,1]
+        past_traj = transpose_traj(past_traj)
+        past_other_traj = transpose_traj(past_other_traj)
+        if not is_valid_traj(past_traj) or not is_valid_traj(past_other_traj):
+            print("Bad past traj in ", self.image_fol)
+            # embed()
+        with open(self.image_fol+"/future_traj.npy", 'rb') as f:
+            full_traj = np.load(f)
+        # if len(full_traj) == 0:
+        #     with open(self.image_fol+"/traj_fixed.npy", 'rb') as f:
+        #         full_traj = np.load(f)
+        future_other_traj = []
+        if self.is_human:
+            future_traj = full_traj[:,:,1]
+            future_other_traj = full_traj[:,:,0]
+        else:
+            future_traj = full_traj[:,:,0]
+            future_other_traj = full_traj[:,:,1]
+        future_traj = transpose_traj(future_traj)
+        future_other_traj = transpose_traj(future_other_traj)
+        if not is_valid_traj(future_traj) or not is_valid_traj(future_other_traj):
+            print("Bad future traj in ", self.image_fol)
+            # embed()
+
+        # visualize rgb
+        # with open(self.image_fol+"/sdf.npy", 'rb') as f:
+        #     sdf_feat = np.load(f)
+        feat = np.concatenate((goal_sink_feat, semantic_img_feat), axis = 0)
+        ### Add the traj features 
+        self_traj_feature = np.zeros(goal_sink_feat.shape)
+        self_traj_feature[0,list(np.array(past_traj[:,0], np.int)), list(np.array(past_traj[:,1], np.int))] = 100
+        other_traj_feature = np.zeros(goal_sink_feat.shape)
+
+        other_traj_feature[0,list(np.array(past_other_traj[:,0], np.int)), list(np.array(past_other_traj[:,1], np.int))] = 100
+        kin_feats = np.concatenate((self_traj_feature, other_traj_feature), axis = 0)
+        feat = np.concatenate((feat, kin_feats), axis = 0)
+        # normalize features locally
+
+        for i in range(feat.shape[0]):
+            # print(feat[i].shape)
+            # print("Before nomalize", np.max(feat[i]))  
+            feat[i] = feat[i] - np.mean(feat[i])*np.ones(feat[i].shape)
+            if np.isclose(np.std(feat[i]), 0.0, atol = 0.0001):
+                continue
+            feat[i] = feat[i] / np.std(feat[i])*np.ones(feat[i].shape)
+            if (np.isnan(feat[i]).any()):
+                print("Still Nan somehow for feature ", i)
+                feat[i] = feat[i] / np.mean(feat[i])*np.ones(feat[i].shape)
+            # print("for i mean is std is ", i, np.mean(feat[i]), np.std(feat[i]))
+            # print("After normalize min max", np.min(feat[i]), np.max(feat[i]))       
+
+
+        past_traj = self.auto_pad_past(past_traj[:, :2])
+        future_traj = self.auto_pad_future(future_traj[:, :2])
+        past_other_traj = self.auto_pad_past(past_other_traj[:, :2])
+        future_other_traj = self.auto_pad_future(future_other_traj[:, :2])
+        return feat, past_traj, future_traj, past_other_traj, future_other_traj
+
+    def __len__(self):
+        return len(self.data_list)
+
+    def auto_pad_past(self, traj):
+        """
+        add padding (NAN) to traj to keep traj length fixed.
+        traj shape needs to be fixed in order to use batch sampling
+        :param traj: numpy array. (traj_len, 2)
+        :return:
+        """
+        fixed_len = self.grid_size
+        if traj.shape[0] >= self.grid_size:
+            traj = traj[traj.shape[0]-self.grid_size:, :]
+            #raise ValueError('traj length {} must be less than grid_size {}'.format(traj.shape[0], self.grid_size))
+        pad_len = self.grid_size - traj.shape[0]
+        pad_array = np.full((pad_len, 2), np.nan)
+        output = np.vstack((traj, pad_array))
+        return output
+
+    def auto_pad_future(self, traj):
+        """
+        add padding (NAN) to traj to keep traj length fixed.
+        traj shape needs to be fixed in order to use batch sampling
+        :param traj: numpy array. (traj_len, 2)
+        :return:
+        """
+        fixed_len = self.grid_size
+        if traj.shape[0] >= self.grid_size:
+            traj = traj[:self.grid_size, :]
+            #raise ValueError('traj length {} must be less than grid_size {}'.format(traj.shape[0], self.grid_size))
+        pad_len = self.grid_size - traj.shape[0]
+        pad_array = np.full((pad_len, 2), np.nan)
+        output = np.vstack((traj, pad_array))
+        return output

loader/data_loader_tribhi.py

@@ -19,7 +19,7 @@
 
 class OffroadLoader(Dataset):
     def __init__(self, grid_size, train=True, demo=None, datadir='/root/medirl/data/irl_data_dec_11_rl_agent', pre_train=False, tangent=False,
-                 more_kinematic=None):
+                 more_kinematic=None, human = False):
         assert grid_size % 2 == 0, "grid size must be even number"
         self.grid_size = grid_size
         # if train:

loader/single_data_loader.py

@@ -19,18 +19,21 @@
 
 class OffroadLoader(Dataset):
     def __init__(self, grid_size, train=True, demo=None, datadir='/root/medirl/data/', pre_train=False, tangent=False,
-                 more_kinematic=None):
+                 more_kinematic=None, human = False):
         assert grid_size % 2 == 0, "grid size must be even number"
         self.grid_size = grid_size
-        self.image_fol = datadir + "demo_0"
+        # self.image_fol = datadir + "demo_8"
+        self.data_list = [datadir + "demo_8"]
         #self.data_normalization = sio.loadmat(datadir + '/irl_data/train-data-mean-std.mat')
         self.pre_train = pre_train
 
         # kinematic related feature
         self.center_idx = self.grid_size / 2
+        self.is_human = human
 
 
-    def __getitem__(self, index):
+    def check_isnone(self,image_fol):
+        self.image_fol = image_fol
         goal_sink_feat = np.array(Image.open(self.image_fol+"/goal_sink.png")).T
         goal_sink_feat = goal_sink_feat/255*np.ones(goal_sink_feat.shape)
         temp_sink_feat = np.zeros([1,goal_sink_feat.shape[1], goal_sink_feat.shape[2]])
@@ -42,34 +45,84 @@ def __getitem__(self, index):
                     temp_sink_feat[0,i,j] = 10
         goal_sink_feat = temp_sink_feat
         semantic_img_feat = np.array(Image.open(self.image_fol+"/semantic_img.png"))[:,:,0:3].T
-        with open(self.image_fol+"/trajectory.npy", 'rb') as f:
+        with open(self.image_fol+"/traj_fixed.npy", 'rb') as f:
             traj = np.load(f)
 
         for i in range(traj.shape[0]):
             temp = traj[i,0] 
             traj[i,0]= traj[i,1]
             traj[i,1] = temp 
         # visualize rgb
-        with open(self.image_fol+"/sdf.npy", 'rb') as f:
-            sdf_feat = np.load(f)
-        feat = np.concatenate((goal_sink_feat, sdf_feat.T), axis = 0)
+        # with open(self.image_fol+"/sdf.npy", 'rb') as f:
+        #     sdf_feat = np.load(f)
+        feat = np.concatenate((goal_sink_feat, semantic_img_feat), axis = 0)
         # normalize features locally
 
         for i in range(feat.shape[0]):
             # print(feat[i].shape)
             # print("Before nomalize", np.max(feat[i]))  
             feat[i] = feat[i] - np.mean(feat[i])*np.ones(feat[i].shape)
             feat[i] = feat[i] / np.std(feat[i])*np.ones(feat[i].shape)
+            if (np.isnan(feat[i]).any()):
+                return True
+        return False
+
+    def __getitem__(self, index):
+        self.image_fol = self.data_list[index]
+        goal_sink_feat = np.array(Image.open(self.image_fol+"/goal_sink.png")).T
+        goal_sink_feat = goal_sink_feat/255*np.ones(goal_sink_feat.shape)
+        temp_sink_feat = np.zeros([1,goal_sink_feat.shape[1], goal_sink_feat.shape[2]])
+        for i in range(goal_sink_feat.shape[1]):
+            for j in range(goal_sink_feat.shape[2]):
+                if (goal_sink_feat[1,i,j] == 1):
+                    temp_sink_feat[0,i,j] = 5
+                if (goal_sink_feat[2,i,j] == 1):
+                    temp_sink_feat[0,i,j] = 10
+        goal_sink_feat = temp_sink_feat
+        semantic_img_feat = np.array(Image.open(self.image_fol+"/semantic_img.png"))[:,:,0:3].T
+        with open(self.image_fol+"/traj_fixed.npy", 'rb') as f:
+            full_traj = np.load(f)
+        other_traj = []
+        if self.is_human:
+            traj = full_traj[:,:,1]
+            other_traj = full_traj[:,:,0]
+        else:
+            traj = full_traj[:,:,0]
+            other_traj = full_traj[:,:,1]
+        for i in range(traj.shape[0]):
+            temp = traj[i,0] 
+            traj[i,0]= traj[i,1]
+            traj[i,1] = temp 
+        # visualize rgb
+        # with open(self.image_fol+"/sdf.npy", 'rb') as f:
+        #     sdf_feat = np.load(f)
+        feat = np.concatenate((goal_sink_feat, semantic_img_feat), axis = 0)
+        ### Add the traj features 
+        self_traj_feature = np.zeros(goal_sink_feat.shape)
+        self_traj_feature[0,int(traj[0,0]), int(traj[0,1])] = 100
+        other_traj_feature = np.zeros(goal_sink_feat.shape)
+        other_traj_feature[0,int(other_traj[0,0]), int(other_traj[0,1])] = 100
+        kin_feats = np.concatenate((self_traj_feature, other_traj_feature), axis = 0)
+        feat = np.concatenate((feat, kin_feats), axis = 0)
+
+        # normalize features locally
+
+        for i in range(feat.shape[0]):
+            # print(feat[i].shape)
+            # print("Before nomalize", np.max(feat[i]))  
+            feat[i] = feat[i] - np.mean(feat[i])*np.ones(feat[i].shape)
+            feat[i] = feat[i] / np.std(feat[i])*np.ones(feat[i].shape)
+            if (np.isnan(feat[i]).any()):
+                return None, None
             # print("for i mean is std is ", i, np.mean(feat[i]), np.std(feat[i]))
             # print("After normalize min max", np.min(feat[i]), np.max(feat[i]))       
-
-
 
+
+        traj = self.auto_pad_future(traj[:, :2])
         return feat, traj
 
     def __len__(self):
-        return 1
-
+        return len(self.data_list)
     def auto_pad_past(self, traj):
         """
         add padding (NAN) to traj to keep traj length fixed.