tidy

Makefile

@@ -1,11 +1,12 @@
+SHELL=/bin/bash
 python=/home/wassname/anaconda/envs/diygym3/bin/python
-date=2021-01-03_13-30-07
 LOGURU_LEVEL=INFO
-# ulimit -S -m 35000000
-# ulimit -S -v 35000000
 
 run:
-	LOGURU_LEVEL=INFO ${python} -m pdb main.py  --cuda --automatic_entropy_tuning true --replay_size 15000 --load auto
+	ulimit -S -m 65000000
+	ulimit -S -v 65000000
+	LOGURU_LEVEL=INFO ${python} main.py  --cuda --automatic_entropy_tuning true --replay_size 50000 --load auto
+	# ${python} -m pdb main.py  --cuda --automatic_entropy_tuning true --replay_size 10000 --load auto --start_steps 200
 	# LOGURU_LEVEL=INFO ${python} main.py  --demonstrations data/demonstrations --cuda --automatic_entropy_tuning true --replay_size 20000 --load auto
 	# LOGURU_LEVEL=INFO ${python} main.py --demonstrations data/demonstrations --cuda --updates_per_step 2 --load auto --alpha 0.1 --tau 1 --target_update_interval 1000
 	# LOGURU_LEVEL=INFO ${python} main.py --demonstrations data/demonstrations --cuda --updates_per_step 2 --load auto --tau 1 --target_update_interval 1000 --policy Deterministic

main.py

@@ -13,26 +13,12 @@ import apple_gym.env
 import pickle
 from process_obs import ProcessObservation
 # from torchinfo import summary
-from tqdm.auto import tqdm
 from torch.utils.tensorboard import SummaryWriter
 
-
+from progress import RichTQDM
 from loguru import logger
 from rich import print
 from rich.logging import RichHandler
-from rich.progress import (
-    ProgressColumn,
-    BarColumn,
-    DownloadColumn,
-    TextColumn,
-    TransferSpeedColumn,
-    TimeRemainingColumn,
-    Progress,
-    TaskID,
-    TimeElapsedColumn,
-    SpinnerColumn,
-    Text
-)
 logging.basicConfig(level=logging.INFO, handlers=[RichHandler(rich_tracebacks=True, markup=True)])
 logger.configure(handlers=[{"sink": RichHandler(markup=True),
                          "format": "{message}"}])
@@ -156,32 +142,11 @@ if args.demonstrations:
 total_numsteps = 0
 updates = 0
 
-class SpeedColumn(ProgressColumn):
-    """Renders human readable transfer speed."""
 
-    def render(self, task: "Task") -> Text:
-        """Show data transfer speed."""
-        speed = task.speed
-        if speed is None:
-            return Text("?", style="progress.data.speed")
-        return Text(f"{speed:2.2f} it/s", style="progress.data.speed")
-
-with Progress(
-    SpinnerColumn(),
-    "[progress.description]{task.description}",
-    BarColumn(),
-    TextColumn("{task.completed}/{task.total}"),
-    "[",
-    TimeElapsedColumn(),
-    "<",
-    TimeRemainingColumn(),
-    ',',
-    SpeedColumn(),
-    ']',
-    refresh_per_second=1, speed_estimate_period=360
-    ) as prog:
+with RichTQDM() as prog:
     task1 = prog.add_task("[red]steps", total=args.num_steps)
     task2 = prog.add_task("[red]updates", total=args.num_steps)
+    task3 = prog.add_task("[red]test", total=args.num_steps)
     for i_episode in itertools.count(0):
         print('1')
         episode_reward = 0
@@ -234,7 +199,7 @@ with Progress(
         logger.info("\nEpisode: {}, total numsteps: {}, episode steps: {}, reward: {}, updates: {}".format(i_episode, total_numsteps, episode_steps, round(episode_reward, 2), updates))
         prog.desc = "e: {}, r: {}, u: {}, m: {}".format(i_episode, round(episode_reward, 2), updates, len(memory))
 
-        if i_episode % 10 == 0 and args.eval is True:
+        if (i_episode % 100 == 0) and (args.eval is True) and i_episode>0:
             avg_reward = 0.
             episodes = 10
             for _  in range(episodes):
@@ -243,6 +208,7 @@ with Progress(
                 done = False
                 while not done:
                     action = agent.select_action(state, evaluate=True)
+                    prog.update(task3, advance=1)
 
                     next_state, reward, done, _ = env.step(action)
                     episode_reward += reward

process_obs.py

@@ -122,7 +122,7 @@ class ProcessObservation(nn.Module):
             os.path.dirname(os.path.abspath(__file__)),
             'data/nets/cornell-randsplit-rgbd-grconvnet3-drop1-ch16/epoch_30_iou_0.97.pt'
         )
-        self.feature_extractor = GenerativeResnet3Headless()#.half()
+        self.feature_extractor = GenerativeResnet3Headless().half()
         self.feature_extractor.load_state_dict(state_dict=torch.load(grconvnet3_path), strict=False)
 
         old_img_size = (res[0], res[1], 8)
@@ -146,6 +146,7 @@ class ProcessObservation(nn.Module):
         # make a batch
         x = torch.cat([base_rgbd, arm_rgbd], 0)
         x = x.permute((0, 3, 1, 2))  # to ((-1, 4, x, y))
+        x = x.half()
         h = self.feature_extractor(x)
 
         # undo fake batch

progress.py

@@ -0,0 +1,39 @@
+from rich.progress import (
+    ProgressColumn,
+    BarColumn,
+    DownloadColumn,
+    TextColumn,
+    TransferSpeedColumn,
+    TimeRemainingColumn,
+    Progress,
+    TaskID,
+    TimeElapsedColumn,
+    SpinnerColumn,
+    Text
+)
+
+class SpeedColumn(ProgressColumn):
+    """Renders human readable transfer speed."""
+
+    def render(self, task: "Task") -> Text:
+        """Show data transfer speed."""
+        speed = task.speed
+        if speed is None:
+            return Text("?", style="progress.data.speed")
+        return Text(f"{speed:2.2f} it/s", style="progress.data.speed")
+
+def RichTQDM():
+    return Progress(
+    SpinnerColumn(),
+    "[progress.description]{task.description}",
+    BarColumn(),
+    TextColumn("{task.completed}/{task.total}"),
+    "[",
+    TimeElapsedColumn(),
+    "<",
+    TimeRemainingColumn(),
+    ',',
+    SpeedColumn(),
+    ']',
+    refresh_per_second=.1, speed_estimate_period=30
+    )

replay_memory.py

@@ -21,8 +21,8 @@ def unpack(data):
     return data
 
 
-class ReplayMemory2:
-    def __init__(self, capacity, seed):
+class ReplayMemory:
+    def __init__(self, capacity, seed, *args, **kwargs):
         random.seed(seed)
         self.capacity = capacity
         self.buffer = []
@@ -56,7 +56,7 @@ class ReplayMemory2:
             self.position = len(self.buffer)
 
 
-class ReplayMemory:
+class ReplayMemory2:
     def __init__(self, capacity, seed, observation_dim, action_dim):
         random.seed(seed)
         self.capacity = capacity