Add debug mode and logger

Add debug flag to run in debug mode (useful for gdb) and use logger
instead of print.

Author: scientist1642

main.py

@@ -3,6 +3,7 @@
 import argparse
 import os
 import sys
+import math
 
 import torch
 import torch.optim as optim
@@ -13,8 +14,11 @@
 from model import ActorCritic
 from train import train
 from test import test
+from utils import logger
 import my_optim
 
+logger = logger.getLogger('main')
+
 # Based on
 # https://github.com/pytorch/examples/tree/master/mnist_hogwild
 # Training settings
@@ -37,13 +41,16 @@
                     help='environment to train on (default: PongDeterministic-v3)')
 parser.add_argument('--no-shared', default=False, metavar='O',
                     help='use an optimizer without shared momentum.')
+parser.add_argument('--max-iters', type=int, default=math.inf,
+                    help='maximum iterations per process.')
 
+parser.add_argument('--debug', action='store_true', default=False,
+                    help='run in a way its easier to debug')
 
 if __name__ == '__main__':
     args = parser.parse_args()
 
     torch.manual_seed(args.seed)
-
     env = create_atari_env(args.env_name)
     shared_model = ActorCritic(
         env.observation_space.shape[0], env.action_space)
@@ -55,15 +62,19 @@
         optimizer = my_optim.SharedAdam(shared_model.parameters(), lr=args.lr)
         optimizer.share_memory()
 
-    processes = []
-
-    p = mp.Process(target=test, args=(args.num_processes, args, shared_model))
-    p.start()
-    processes.append(p)
+    
+    if not args.debug:
+        processes = []
 
-    for rank in range(0, args.num_processes):
-        p = mp.Process(target=train, args=(rank, args, shared_model, optimizer))
+        p = mp.Process(target=test, args=(args.num_processes, args, shared_model))
         p.start()
         processes.append(p)
-    for p in processes:
-        p.join()
+        for rank in range(0, args.num_processes):
+            p = mp.Process(target=train, args=(rank, args, shared_model, optimizer))
+            p.start()
+            processes.append(p)
+        for p in processes:
+            p.join()
+    else: ## debug is enabled
+        # run only one process in a main, easier to debug
+        train(0, args, shared_model, optimizer)

model.py

@@ -44,8 +44,11 @@ def __init__(self, num_inputs, action_space):
         self.lstm = nn.LSTMCell(32 * 3 * 3, 256)
 
         num_outputs = action_space.n
+
         self.critic_linear = nn.Linear(256, 1)
         self.actor_linear = nn.Linear(256, num_outputs)
+        #self.critic_linear = nn.Linear(288, 1)
+        #self.actor_linear = nn.Linear(288, num_outputs)
 
         self.apply(weights_init)
         self.actor_linear.weight.data = normalized_columns_initializer(
@@ -66,7 +69,6 @@ def forward(self, inputs):
         x = F.elu(self.conv2(x))
         x = F.elu(self.conv3(x))
         x = F.elu(self.conv4(x))
-
         x = x.view(-1, 32 * 3 * 3)
         hx, cx = self.lstm(x, (hx, cx))
         x = hx

test.py

@@ -11,7 +11,9 @@
 from torchvision import datasets, transforms
 import time
 from collections import deque
+from utils import logger
 
+logger = logger.getLogger('test')
 
 def test(rank, args, shared_model):
     torch.manual_seed(args.seed + rank)
@@ -59,7 +61,7 @@ def test(rank, args, shared_model):
             done = True
 
         if done:
-            print("Time {}, episode reward {}, episode length {}".format(
+            logger.info("Time {}, episode reward {}, episode length {}".format(
                 time.strftime("%Hh %Mm %Ss",
                               time.gmtime(time.time() - start_time)),
                 reward_sum, episode_length))

train.py

@@ -1,6 +1,8 @@
 import math
 import os
 import sys
+import resource 
+import gc
 
 import torch
 import torch.nn.functional as F
@@ -9,15 +11,16 @@
 from model import ActorCritic
 from torch.autograd import Variable
 from torchvision import datasets, transforms
+from utils import logger
 
+logger = logger.getLogger('main')
 
 def ensure_shared_grads(model, shared_model):
     for param, shared_param in zip(model.parameters(), shared_model.parameters()):
         if shared_param.grad is not None:
             return
         shared_param._grad = param.grad
 
-
 def train(rank, args, shared_model, optimizer=None):
     torch.manual_seed(args.seed + rank)
 
@@ -36,8 +39,29 @@ def train(rank, args, shared_model, optimizer=None):
     done = True
 
     episode_length = 0
+
+    iteration = 0 
+    
     while True:
+
+        values = []
+        log_probs = []
+        rewards = []
+        entropies = []
+        
+        if iteration == args.max_iters:
+            logger.info('Max iteration {} reached..'.format(args.max_iters))
+            break
+
+        if iteration % 200 == 0 and rank == 0:
+            mem_used = int(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) 
+            mem_used_mb = mem_used / 1024 
+            logger.info('Memory usage of one proc: {} (mb)'.format(mem_used_mb))
+
+
+        iteration += 1
         episode_length += 1
+
         # Sync with the shared model
         model.load_state_dict(shared_model.state_dict())
         if done:
@@ -47,10 +71,6 @@ def train(rank, args, shared_model, optimizer=None):
             cx = Variable(cx.data)
             hx = Variable(hx.data)
 
-        values = []
-        log_probs = []
-        rewards = []
-        entropies = []
 
         for step in range(args.num_steps):
             value, logit, (hx, cx) = model(

utils/logger.py

@@ -0,0 +1,49 @@
+# -*- coding: utf-8 -*-
+import os
+import logging
+import logging.config
+
+
+LOG_LEVEL = os.getenv('LOG_LEVEL', 'INFO')
+LOGGING = {
+    'version': 1,
+    'disable_existing_loggers': True,
+    'formatters': {
+        'verbose': {
+            'format': "[%(asctime)s] %(levelname)s " \
+                "[%(threadName)s:%(lineno)s] %(message)s",
+            'datefmt': "%Y-%m-%d %H:%M:%S"
+        },
+        'simple': {
+            'format': '%(levelname)s %(message)s'
+        },
+    },
+    'handlers': {
+        'console': {
+            'level': LOG_LEVEL,
+            'class': 'logging.StreamHandler',
+            'formatter': 'verbose'
+        },
+        'file': {
+            'level': LOG_LEVEL,
+            'class': 'logging.handlers.RotatingFileHandler',
+            'formatter': 'verbose',
+            'filename': 'rl.log',
+            'maxBytes': 10*10**6,
+            'backupCount': 3
+            }
+    },
+    'loggers': {
+        '': {
+            'handlers': ['console', 'file'],
+            'level': LOG_LEVEL,
+        },
+    }
+}
+
+
+logging.config.dictConfig(LOGGING)
+
+def getLogger(name):
+
+    return logging.getLogger(name)