Adding Files

dynamic_bottleneck.py

@@ -68,7 +68,7 @@ class DynamicBottleneck(object):
             # contrastive projection
             z_a = self.projection_head(rec_vec)                                           # (None, 128)
             z_pos = tf.stop_gradient(self.projection_head_momentum(self.next_features))   # (None, 128)
-            assert z_a.get_shape().as_list()[-1] == 128 and len(z_a.get_shape().as_list()) == 2
+            assert z_a.get_shape().as_list()[-1] == 128 and len(z_a.get_shape().as_list()) == 2 
 
             # contrastive loss
             logits = self.contrastive_head([z_a, z_pos])                 # (batch_size, batch_size) 
@@ -129,12 +129,11 @@ class DynamicBottleneck(object):
 
     def get_features(self, x, momentum=False):     # x.shape=(None,None,84,84,4)
         x_has_timesteps = (x.get_shape().ndims == 5)      # True
-        if x_has_timesteps:
+        if x_has_timesteps: 
             sh = tf.shape(x)
             x = flatten_two_dims(x)                       # (None,84,84,4)
 
         if self.aug:
-            print(x.get_shape().as_list())
             x = tf.image.random_crop(x, size=[128*16, 80, 80, 4])            # (None,80,80,4)
             x = tf.pad(x, [[0, 0], [4, 4], [4, 4], [0, 0]], "SYMMETRIC")     # (None,88,88,4)
             x = tf.image.random_crop(x, size=[128*16, 84, 84, 4])            # (None,84,84,4)
@@ -158,6 +157,7 @@ class DynamicBottleneck(object):
         n_chunks = 8
         n = ob.shape[0]
         chunk_size = n // n_chunks
+
         assert n % n_chunks == 0
         sli = lambda i: slice(i * chunk_size, (i + 1) * chunk_size)
 

rollouts.py

@@ -10,7 +10,7 @@ class Rollout(object):
     def __init__(self, ob_space, ac_space, nenvs, nsteps_per_seg, nsegs_per_env, nlumps, envs, policy,
                  int_rew_coeff, ext_rew_coeff, record_rollouts, dynamic_bottleneck):  #, noisy_box, noisy_p):
         # int_rew_coeff=1.0, ext_rew_coeff=0.0, record_rollouts=True 
-        self.nenvs = nenvs                                          # 128
+        self.nenvs = nenvs                                          # 128/64
         self.nsteps_per_seg = nsteps_per_seg                        # 128
         self.nsegs_per_env = nsegs_per_env                          # 1
         self.nsteps = self.nsteps_per_seg * self.nsegs_per_env      # 128
@@ -102,6 +102,22 @@ class Rollout(object):
             if self.recorder is not None:
                 self.recorder.record(timestep=self.step_count, lump=l, acs=acs, infos=infos, int_rew=self.int_rew[sli],
                                      ext_rew=prevrews, news=news)
+                       
+            import matplotlib.pyplot as plt
+            from PIL import Image
+            x = self.buf_obs[sli, t][5][:,:,0]
+            img = Image.fromarray(x)
+            img.save('image1.png')
+            x = self.buf_obs[sli, t][5][:,:,1]
+            img = Image.fromarray(x)
+            img.save('image2.png')
+            x = self.buf_obs[sli, t][5][:,:,2]
+            img = Image.fromarray(x)
+            img.save('image3.png')
+            x = self.buf_obs[sli, t][5][:,:,3]
+            img = Image.fromarray(x)
+            img.save('image4.png')
+
         
         self.step_count += 1
         if s == self.nsteps_per_seg - 1:     # nsteps_per_seg=128

run.py

@@ -14,7 +14,8 @@ from baselines.bench import Monitor
 from baselines.common.atari_wrappers import NoopResetEnv, FrameStack
 from mpi4py import MPI
 
-from dynamic_bottleneck import DynamicBottleneck
+
+from dynamic_bottleneck import DynamicBottleneck    
 from cnn_policy import CnnPolicy
 from cppo_agent import PpoOptimizer
 from utils import random_agent_ob_mean_std
@@ -26,8 +27,8 @@ import json
 
 getsess = tf.get_default_session
 
-
 def start_experiment(**args):
+
     make_env = partial(make_env_all_params, add_monitor=True, args=args)
 
     trainer = Trainer(make_env=make_env,
@@ -39,7 +40,6 @@ def start_experiment(**args):
         print("results will be saved to ", logdir)
         trainer.train(saver, logger_dir)
 
-
 class Trainer(object):
     def __init__(self, make_env, hps, num_timesteps, envs_per_process):
         self.make_env = make_env
@@ -118,10 +118,10 @@ class Trainer(object):
                 previous_saved_tcount += 1
                 save_path = saver.save(tf.get_default_session(), os.path.join(logger_dir, "model_"+str(previous_saved_tcount)+".ckpt"))
                 print("Periodically model saved in path:", save_path)
-            if self.agent.rollout.stats['tcount'] > self.num_timesteps:
+            if self.agent.rollout.stats['tcount'] %10000: #self.agent.rollout.stats['tcount'] > self.num_timesteps:
                 save_path = saver.save(tf.get_default_session(), os.path.join(logger_dir, "model_last.ckpt"))
                 print("Model saved in path:", save_path)
-                break
+                #break
 
         self.agent.stop_interaction()