added daml code

data_generator.py

@@ -107,11 +107,13 @@ def generate_batches(self, noisy=False):
                 offset = self.dataset_size - FLAGS.training_set_size - FLAGS.val_set_size
             else:
                 offset = 0
-            train_img_folders = {i: os.path.join(self.demo_gif_dir, self.gif_prefix + '_%d' % i) for i in self.train_idx}
-            val_img_folders = {i: os.path.join(self.demo_gif_dir, self.gif_prefix + '_%d' % (i+offset)) for i in self.val_idx}
+            img_folders = natsorted(glob.glob(self.demo_gif_dir + self.gif_prefix + '_*'))
+            train_img_folders = {i: img_folders[i] for i in self.train_idx}
+            val_img_folders = {i: img_folders[i+offset] for i in self.val_idx}
             if noisy:
-                noisy_train_img_folders = {i: os.path.join(self.noisy_demo_gif_dir, self.gif_prefix + '_%d' % i) for i in self.train_idx}
-                noisy_val_img_folders = {i: os.path.join(self.noisy_demo_gif_dir, self.gif_prefix + '_%d' % (i+offset)) for i in self.val_idx}
+                noisy_img_folders = natsorted(glob.glob(self.noisy_demo_gif_dir + self.gif_prefix + '_*'))
+                noisy_train_img_folders = {i: noisy_img_folders[i] for i in self.train_idx}
+                noisy_val_img_folders = {i: noisy_img_folders[i] for i in self.val_idx}
             TEST_PRINT_INTERVAL = 500
             TOTAL_ITERS = FLAGS.metatrain_iterations
             self.all_training_filenames = []

main.py

@@ -29,7 +29,7 @@
 flags.DEFINE_bool('no_action', False, 'do not include actions in the demonstrations for inner update')
 flags.DEFINE_bool('no_state', False, 'do not include states in the demonstrations during training')
 flags.DEFINE_bool('no_final_eept', False, 'do not include final ee pos in the demonstrations for inner update')
-flags.DEFINE_bool('zero_state', False, 'zero-out states (meta-learn state) in the demonstrations for inner update')
+flags.DEFINE_bool('zero_state', False, 'zero-out states (meta-learn state) in the demonstrations for inner update (used in the paper with video-only demos)')
 flags.DEFINE_bool('two_arms', False, 'use two-arm structure when state is zeroed-out')
 flags.DEFINE_integer('training_set_size', -1, 'size of the training set, 1500 for sim_reach, 693 for sim push, and \
                                                 -1 for all data except those in validation set')
@@ -55,6 +55,7 @@
 flags.DEFINE_bool('learn_final_eept', False, 'learn an auxiliary loss for predicting final end-effector pose')
 flags.DEFINE_bool('learn_final_eept_whole_traj', False, 'learn an auxiliary loss for predicting final end-effector pose \
                                                          by passing the whole trajectory of eepts (used for video-only models)')
+flags.DEFINE_bool('stopgrad_final_eept', True, 'stop the gradient when concatenate the predicted final eept with the feature points')
 flags.DEFINE_integer('final_eept_min', 6, 'first index of the final eept in the action array')
 flags.DEFINE_integer('final_eept_max', 8, 'last index of the final eept in the action array')
 flags.DEFINE_float('final_eept_loss_eps', 0.1, 'the coefficient of the auxiliary loss')
@@ -78,6 +79,14 @@
 flags.DEFINE_bool('conv', True, 'whether or not to use a convolutional network, only applicable in some cases')
 flags.DEFINE_integer('num_fc_layers', 3, 'number of fully-connected layers')
 flags.DEFINE_integer('layer_size', 100, 'hidden dimension of fully-connected layers')
+flags.DEFINE_bool('temporal_conv_2_head', False, 'whether or not to use temporal convolutions for the two-head architecture in video-only setting.')
+flags.DEFINE_bool('temporal_conv_2_head_ee', False, 'whether or not to use temporal convolutions for the two-head architecture in video-only setting \
+                for predicting the ee pose.')
+flags.DEFINE_integer('temporal_filter_size', 5, 'filter size for temporal convolution')
+flags.DEFINE_integer('temporal_num_filters', 64, 'number of filters for temporal convolution')
+flags.DEFINE_integer('temporal_num_filters_ee', 64, 'number of filters for temporal convolution for ee pose prediction')
+flags.DEFINE_integer('temporal_num_layers', 3, 'number of layers for temporal convolution for ee pose prediction')
+flags.DEFINE_integer('temporal_num_layers_ee', 3, 'number of layers for temporal convolution for ee pose prediction')
 flags.DEFINE_string('init', 'random', 'initializer for conv weights. Choose among random, xavier, and he')
 flags.DEFINE_bool('max_pool', False, 'Whether or not to use max pooling rather than strided convolutions')
 flags.DEFINE_bool('stop_grad', False, 'if True, do not use second derivatives in meta-optimization (for speed)')
@@ -88,7 +97,8 @@
 flags.DEFINE_bool('resume', False, 'resume training if there is a model available')
 flags.DEFINE_bool('train', True, 'True to train, False to test.')
 flags.DEFINE_integer('restore_iter', 0, 'iteration to load model (-1 for latest model)')
-flags.DEFINE_integer('train_update_batch_size', -1, 'number of examples used for gradient update during training (use if you want to test with a different number).')
+flags.DEFINE_integer('train_update_batch_size', -1, 'number of examples used for gradient update during training \
+                    (use if you want to test with a different number).')
 flags.DEFINE_integer('test_update_batch_size', 1, 'number of demos used during test time')
 flags.DEFINE_float('gpu_memory_fraction', 1.0, 'fraction of memory used in gpu')
 flags.DEFINE_bool('record_gifs', True, 'record gifs during evaluation')
@@ -241,6 +251,11 @@ def main():
         exp_string += '.two_heads'
     if FLAGS.two_arms:
         exp_string += '.two_arms'
+    if FLAGS.temporal_conv_2_head:
+        exp_string += '.1d_conv_act_' + str(FLAGS.temporal_num_layers) + '_' + str(FLAGS.temporal_num_filters)
+        if FLAGS.temporal_conv_2_head_ee:
+            exp_string += '_ee_' + str(FLAGS.temporal_num_layers_ee) + '_' + str(FLAGS.temporal_num_filters_ee)
+        exp_string += '_' + str(FLAGS.temporal_filter_size) + 'x1_filters'
     if FLAGS.training_set_size != -1:
         exp_string += '.' + str(FLAGS.training_set_size) + '_trials'