Remove tf logger

deep_rl/utils/logger.py

@@ -41,9 +41,7 @@ class Logger(object):
         self.all_steps = {}
 
     def to_numpy(self, v):
-        if isinstance(v, torch.autograd.Variable):
-            v = v.data
-        if isinstance(v, torch.FloatTensor):
+        if isinstance(v, torch.Tensor):
             v = v.cpu().detach().numpy()
         return v
 

deep_rl/utils/tf_logger.py

@@ -1,59 +0,0 @@
-#######################################################################
-# Copyright (C) 2017 Shangtong Zhang(zhangshangtong.cpp@gmail.com)    #
-# Permission given to modify the code as long as you keep this        #
-# declaration at the top                                              #
-#######################################################################
-
-from tensorboardX import SummaryWriter
-import os
-import numpy as np
-import torch
-
-class Logger(object):
-    def __init__(self, log_dir, vanilla_logger, skip=False):
-        try:
-            for f in os.listdir(log_dir):
-                if not f.startswith('events'):
-                    continue
-                os.remove('%s/%s' % (log_dir, f))
-        except IOError:
-            os.mkdir(log_dir)
-        if not skip:
-            self.writer = SummaryWriter(log_dir)
-        self.info = vanilla_logger.info
-        self.debug = vanilla_logger.debug
-        self.warning = vanilla_logger.warning
-        self.skip = skip
-        self.all_steps = {}
-
-    def to_numpy(self, v):
-        if isinstance(v, torch.autograd.Variable):
-            v = v.data
-        if isinstance(v, torch.FloatTensor):
-            v = v.cpu().detach().numpy()
-        return v
-
-    def get_step(self, tag):
-        if tag not in self.all_steps:
-            self.all_steps[tag] = 0
-        step = self.all_steps[tag]
-        self.all_steps[tag] += 1
-        return step
-
-    def scalar_summary(self, tag, value, step=None):
-        if self.skip:
-            return
-        self.to_numpy(value)
-        if step is None:
-            step = self.get_step(tag)
-        if np.isscalar(value):
-            value = np.asarray([value])
-        self.writer.add_scalar(tag, value, step)
-
-    def histo_summary(self, tag, values, step=None):
-        if self.skip:
-            return
-        self.to_numpy(values)
-        if step is None:
-            step = self.get_step(tag)
-        self.writer.add_histogram(tag, values, step, bins=1000)