deconv2d and leakyrelu

tensorpack/models/conv2d.py

@@ -91,13 +91,14 @@ def Deconv2D(x, out_shape, kernel_shape,
     stride2d = shape2d(stride)
     stride4d = shape4d(stride)
     padding = padding.upper()
-    filter_shape = kernel_shape + [in_channel, out_channel]
 
     if isinstance(out_shape, int):
-        out_shape = tf.pack([tf.shape(x)[0],
-            stride2d[0] * in_shape[0], stride2d[1] * in_shape[1], out_shape])
+        out_channel = out_shape
+        shape3 = [stride2d[0] * in_shape[0], stride2d[1] * in_shape[1], out_shape]
     else:
-        out_shape = tf.pack([tf.shape(x)[0]] + out_shape)
+        out_channel = out_shape[-1]
+        shape3 = out_shape
+    filter_shape = kernel_shape + [out_channel, in_channel]
 
     if W_init is None:
         W_init = tf.contrib.layers.xavier_initializer_conv2d()
@@ -107,5 +108,7 @@ def Deconv2D(x, out_shape, kernel_shape,
     if use_bias:
         b = tf.get_variable('b', [out_channel], initializer=b_init)
 
+    out_shape = tf.pack([tf.shape(x)[0]] + shape3)
     conv = tf.nn.conv2d_transpose(x, W, out_shape, stride4d, padding=padding)
+    conv.set_shape(tf.TensorShape([None] + shape3))
     return nl(tf.nn.bias_add(conv, b) if use_bias else conv, name='output')

tensorpack/models/model_desc.py

@@ -10,7 +10,7 @@ from collections import namedtuple
 import inspect
 
 from ..utils import logger, INPUT_VARS_KEY
-from ..tfutils.common import get_vars_by_names
+from ..tfutils.common import get_tensors_by_names
 from ..tfutils.gradproc import CheckGradient
 from ..tfutils.tower import get_current_tower_context
 
@@ -43,7 +43,7 @@ class ModelDesc(object):
     def reuse_input_vars(self):
         """ Find and return already-defined input_vars in default graph"""
         input_var_names = [k.name for k in self._get_input_vars()]
-        return get_vars_by_names(input_var_names)
+        return get_tensors_by_names(input_var_names)
 
     def get_input_vars_desc(self):
         """ return a list of `InputVar` instance"""

tensorpack/models/nonlin.py

@@ -57,11 +57,12 @@ def LeakyReLU(x, alpha, name=None):
     :param input: any tensor.
     :param alpha: the negative slope.
     """
-    alpha = float(alpha)
-    x = ((1 + alpha) * x + (1 - alpha) * tf.abs(x))
     if name is None:
         name = 'output'
-    return tf.mul(x, 0.5, name=name)
+    return tf.maximum(x, alpha * x, name=name)
+    #alpha = float(alpha)
+    #x = ((1 + alpha) * x + (1 - alpha) * tf.abs(x))
+    #return tf.mul(x, 0.5, name=name)
 
 def BNReLU(x, name=None):
     x = BatchNorm('bn', x, use_local_stat=None)

tensorpack/tfutils/varmanip.py

@@ -103,6 +103,7 @@ def is_training_name(name):
     :returns: guess whether this tensor is something only used in training.
     """
     # TODO: maybe simply check against TRAINABLE_VARIABLES and EXTRA_SAVE_VARS_KEY ?
+    # TODO or use get_slot_names()
     name = get_op_tensor_name(name)[0]
     if name.endswith('/Adam') or name.endswith('/Adam_1'):
         return True

tensorpack/train/base.py

@@ -143,6 +143,8 @@ class Trainer(object):
                         self.trigger_epoch()
             except StopTraining:
                 logger.info("Training was stopped.")
+            except:
+                raise
             finally:
                 callbacks.after_train()
                 self.coord.request_stop()

tensorpack/train/queue.py

@@ -52,15 +52,15 @@ class EnqueueThread(threading.Thread):
             except Exception:
                 logger.exception("Exception in EnqueueThread:")
             finally:
+                self.coord.request_stop()
                 try:
                     self.sess.run(self.close_op)
                 except RuntimeError:    # session already closed
                     pass
-                self.coord.request_stop()
                 logger.info("Enqueue Thread Exited.")
 
 class QueueInputTrainerBase(FeedlessTrainer):
-    def _build_enque_thread(self, input_queue):
+    def _build_enque_thread(self, input_queue=None):
         """ create a thread that keeps filling the queue """
         self.input_vars = self.model.get_input_vars()
         if input_queue is None:

tensorpack/train/trainer.py

@@ -11,7 +11,7 @@ from .base import Trainer
 from ..dataflow.common import RepeatedData
 
 from ..utils import logger, SUMMARY_BACKUP_KEYS
-from ..tfutils import (get_vars_by_names, freeze_collection,
+from ..tfutils import (get_tensors_by_names, freeze_collection,
         get_global_step_var, TowerContext)
 from ..tfutils.summary import summary_moving_average, add_moving_summary
 from ..predict import OnlinePredictor, build_multi_tower_prediction_graph
@@ -40,9 +40,9 @@ class PredictorFactory(object):
         if not self.tower_built:
             self._build_predict_tower()
         tower = self.towers[tower % len(self.towers)]
-        raw_input_vars = get_vars_by_names(input_names)
+        raw_input_vars = get_tensors_by_names(input_names)
         output_names = ['towerp{}/'.format(tower) + n for n in output_names]
-        output_vars = get_vars_by_names(output_names)
+        output_vars = get_tensors_by_names(output_names)
         return OnlinePredictor(self.sess, raw_input_vars, output_vars)
 
     def _build_predict_tower(self):