call model without scope & fix atari.py

examples/Atari2600/DQN.py

@@ -72,7 +72,8 @@ class Model(ModelDesc):
     def _get_DQN_prediction(self, image):
         """ image: [0,255]"""
         image = image / 255.0
-        with argscope(Conv2D, nl=PReLU.f, use_bias=True):
+        with argscope(Conv2D, nl=PReLU.f, use_bias=True), \
+                argscope(LeakyReLU, alpha=0.01):
             l = (LinearWrap(image)
                 .Conv2D('conv0', out_channel=32, kernel_shape=5)
                 .MaxPooling('pool0', 2)
@@ -87,7 +88,7 @@ class Model(ModelDesc):
                 #.Conv2D('conv1', out_channel=64, kernel_shape=4, stride=2)
                 #.Conv2D('conv2', out_channel=64, kernel_shape=3)
 
-                .FullyConnected('fc0', 512, nl=lambda x, name: LeakyReLU.f(x, 0.01, name))())
+                .FullyConnected('fc0', 512, nl=LeakyReLU)())
         if METHOD != 'Dueling':
             Q = FullyConnected('fct', l, NUM_ACTIONS, nl=tf.identity)
         else:

examples/Atari2600/atari.py

@@ -10,8 +10,7 @@ from collections import deque
 import threading
 import six
 from six.moves import range
-from tensorpack.utils import (get_rng, logger, memoized,
-        get_dataset_path, execute_only_once)
+from tensorpack.utils import (get_rng, logger, get_dataset_path, execute_only_once)
 from tensorpack.utils.stat import StatCounter
 
 from tensorpack.RL.envbase import RLEnvironment, DiscreteActionSpace

examples/GAN/DCGAN-CelebA.py

@@ -50,17 +50,13 @@ class Model(ModelDesc):
         with argscope(Conv2D, nl=tf.identity, kernel_shape=5, stride=2), \
                 argscope(LeakyReLU, alpha=0.2):
             l = (LinearWrap(imgs)
-                .Conv2D('conv0', 64)
-                .LeakyReLU('lr0')
+                .Conv2D('conv0', 64, nl=LeakyReLU)
                 .Conv2D('conv1', 64*2)
-                .BatchNorm('bn1')
-                .LeakyReLU('lr1')
+                .BatchNorm('bn1').LeakyReLU()
                 .Conv2D('conv2', 64*4)
-                .BatchNorm('bn2')
-                .LeakyReLU('lr2')
+                .BatchNorm('bn2').LeakyReLU()
                 .Conv2D('conv3', 64*8)
-                .BatchNorm('bn3')
-                .LeakyReLU('lr3')
+                .BatchNorm('bn3').LeakyReLU()
                 .FullyConnected('fct', 1, nl=tf.identity)())
         return l
 

tensorpack/RL/expreplay.py

@@ -56,7 +56,7 @@ class ExpReplay(DataFlow, Callback):
                 setattr(self, k, v)
         self.num_actions = player.get_action_space().num_actions()
         logger.info("Number of Legal actions: {}".format(self.num_actions))
-        self.mem = deque(maxlen=memory_size)
+        self.mem = deque(maxlen=int(memory_size))
         self.rng = get_rng(self)
         self._init_memory_flag = threading.Event()  # tell if memory has been initialized
         self._predictor_io_names = predictor_io_names

tensorpack/models/__init__.py

@@ -5,6 +5,7 @@
 from pkgutil import walk_packages
 from types import ModuleType
 import tensorflow as tf
+import six
 import os
 import os.path
 from ..utils import logger
@@ -49,13 +50,18 @@ class LinearWrap(object):
         layer = eval(layer_name)
         if hasattr(layer, 'f'):
             # this is a registered tensorpack layer
+            # parse arguments by tensorpack model convention
             if layer.use_scope:
                 def f(name, *args, **kwargs):
                     ret = layer(name, self._t, *args, **kwargs)
                     return LinearWrap(ret)
             else:
                 def f(*args, **kwargs):
-                    ret = layer(self._t, *args, **kwargs)
+                    if isinstance(args[0], six.string_types):
+                        name, args = args[0], args[1:]
+                        ret = layer(name, self._t, *args, **kwargs)
+                    else:
+                        ret = layer(self._t, *args, **kwargs)
                     return LinearWrap(ret)
             return f
         else:

tensorpack/models/_common.py

@@ -34,6 +34,7 @@ def layer_register(
         Can be overriden when creating the layer.
     :param log_shape: log input/output shape of this layer
     :param use_scope: whether to call this layer with an extra first argument as scope
+        if set to False, will try to figure out whether the first argument is scope name
     """
 
     def wrapper(func):
@@ -45,8 +46,16 @@ def layer_register(
                 assert isinstance(name, six.string_types), name
             else:
                 assert not log_shape and not summary_activation
-                inputs = args[0]
-                name = None
+                if isinstance(args[0], six.string_types):
+                    name, inputs = args[0], args[1]
+                    args = args[1:] # actual positional args used to call func
+                else:
+                    inputs = args[0]
+                    name = None
+            if not (isinstance(inputs, (tf.Tensor, tf.Variable)) or
+                    (isinstance(inputs, (list, tuple)) and
+                        isinstance(inputs[0], (tf.Tensor, tf.Variable)))):
+                raise ValueError("Invalid inputs to layer: " + str(inputs))
             do_summary = kwargs.pop(
                 'summary_activation', summary_activation)
 

tensorpack/models/nonlin.py

@@ -47,7 +47,7 @@ def PReLU(x, init=tf.constant_initializer(0.001), name=None):
         name = 'output'
     return tf.mul(x, 0.5, name=name)
 
-@layer_register(log_shape=False)
+@layer_register(use_scope=False, log_shape=False)
 def LeakyReLU(x, alpha, name=None):
     """
     Leaky relu as in `Rectifier Nonlinearities Improve Neural Network Acoustic
@@ -64,7 +64,7 @@ def LeakyReLU(x, alpha, name=None):
     #x = ((1 + alpha) * x + (1 - alpha) * tf.abs(x))
     #return tf.mul(x, 0.5, name=name)
 
-# TODO wrap it as a layer with use_scope=False?
+@layer_register(log_shape=False, use_scope=False)
 def BNReLU(x, name=None):
     x = BatchNorm('bn', x, use_local_stat=None)
     x = tf.nn.relu(x, name=name)

tensorpack/utils/viz.py

@@ -73,7 +73,8 @@ def interactive_imshow(img, lclick_cb=None, rclick_cb=None, **kwargs):
 def build_patch_list(patch_list,
         nr_row=None, nr_col=None, border=None,
         max_width=1000, max_height=1000,
-        shuffle=False, bgcolor=255, viz=False, lclick_cb=None):
+        shuffle=False, bgcolor=255,
+        viz=False, lclick_cb=None):
     """
     Generate patches.
     :param patch_list: bhw or bhwc