Train rendecndec-cla

Author: pratikgujjar

code/autoencoder_model/scripts/config_sigc.py

@@ -22,16 +22,18 @@
 else:
     path_var = 'zhora/'
 
-DATA_DIR= '/local_home/JAAD_Dataset/iros/resized_imgs_208_sorted/train/'
+DATA_DIR= '/local_home/JAAD_Dataset/iros/resized_imgs_208_thesis/train/'
 # DATA_DIR= '/local_home/data/KITTI_data/'
 
-TEST_DATA_DIR= '/local_home/JAAD_Dataset/iros/resized_imgs_208_sorted/test/'
+TEST_DATA_DIR= '/local_home/JAAD_Dataset/iros/resized_imgs_208_thesis/test/'
 
-VAL_DATA_DIR= '/local_home/JAAD_Dataset/iros/resized_imgs_208_sorted/val/'
+VAL_DATA_DIR= '/local_home/JAAD_Dataset/iros/resized_imgs_208_thesis/val/'
 
 PRETRAINED_C3D= '/home/pratik/git_projects/c3d-keras/models/sports1M_weights_tf.json'
 PRETRAINED_C3D_WEIGHTS= '/home/pratik/git_projects/c3d-keras/models/sports1M_weights_tf.h5'
 
+RESULTS_DIR = '/local_home/JAAD_Dataset/thesis/results/baselineCla/'
+
 MODEL_DIR = './../' + path_var + 'models'
 if not os.path.exists(MODEL_DIR):
     os.mkdir(MODEL_DIR)
@@ -48,10 +50,6 @@
 if not os.path.exists(CLA_GEN_IMAGES_DIR):
     os.mkdir(CLA_GEN_IMAGES_DIR)
 
-# ATTN_WEIGHTS_DIR = './../' + path_var + 'attn_weights'
-# if not os.path.exists(ATTN_WEIGHTS_DIR):
-#     os.mkdir(ATTN_WEIGHTS_DIR)
-
 LOG_DIR = './../' + path_var + 'logs'
 if not os.path.exists(LOG_DIR):
     os.mkdir(LOG_DIR)
@@ -64,7 +62,7 @@
 if not os.path.exists(TF_LOG_CLA_DIR):
     os.mkdir(TF_LOG_CLA_DIR)
 
-TEST_RESULTS_DIR = './../' + path_var + 'test_results'
+TEST_RESULTS_DIR = RESULTS_DIR + 'test_results/'
 if not os.path.exists(TEST_RESULTS_DIR):
     os.mkdir(TEST_RESULTS_DIR)
 
@@ -90,8 +88,6 @@
 ZOOM_MAX = 0.2
 BRIGHT_RANGE_L = 0.5
 BRIGHT_RANGE_H = 1.5
-KL_COEFF = 0
-ATTN_COEFF = 10
 
 ped_actions = ['slow down', 'standing', 'walking', 'speed up', 'nod', 'unknown',
                'clear path', 'handwave', 'crossing', 'looking', 'no ped']
@@ -104,7 +100,8 @@
 print ("Config file: " + str(__name__))
 
 BATCH_SIZE = 15
-NB_EPOCHS_CLASS = 20
+TEST_BATCH_SIZE = 1
+NB_EPOCHS_CLASS = 30
 
 # OPTIM_C = Adam(lr=0.0000002, beta_1=0.5)
 # OPTIM_C = SGD(lr=0.0001, momentum=0.9, nesterov=True)
@@ -122,14 +119,15 @@
 #     return 0.000000001
 
 
-lr_schedule = [8, 15, 18]  # epoch_step
-
+lr_schedule = [7, 16, 30, 30]  # epoch_step
 
 def schedule(epoch_idx):
-    if (epoch_idx + 1) < lr_schedule[0]:
+    if (epoch_idx) <= lr_schedule[0]:
         return 0.00001
-    elif (epoch_idx + 1) < lr_schedule[1]:
+    elif (epoch_idx) <= lr_schedule[1]:
         return 0.000001  # lr_decay_ratio = 10
-    elif (epoch_idx + 1) < lr_schedule[2]:
+    elif (epoch_idx) <= lr_schedule[2]:
+        return 0.0000001  # lr_decay_ratio = 10
+    elif (epoch_idx) <= lr_schedule[3]:
         return 0.0000001
     return 0.0000001

code/autoencoder_model/scripts/gifimages.py

@@ -47,7 +47,7 @@ def strip(image, img_height, img_width, vid_len, vid_num=1, rev=False):
 
 
 def gifmax(folder, rev=False):
-    for i in range(880):
+    for i in range(1257):
         im_pred = cv2.imread(folder+'/pred/' + str(i) + '_pred.png', cv2.IMREAD_COLOR)
         strip(im_pred, 128, 208, 32, i, rev=rev)
 
@@ -78,7 +78,7 @@ def get_args():
 
     args = get_args()
     if args.mode == 'gifmax':
-        print ('going into gifmax')
+        print ('going into gifmax!!!!')
         gifmax(args.folder, args.rev)
     else:
         try: