simplify pre-resnet model code

examples/ResNet/imagenet_resnet_utils.py

@@ -112,25 +112,21 @@ def get_imagenet_dataflow(
     return ds
 
 
-def resnet_shortcut(l, n_out, stride):
+def resnet_shortcut(l, n_out, stride, nl=tf.identity):
     data_format = get_arg_scope()['Conv2D']['data_format']
     n_in = l.get_shape().as_list()[1 if data_format == 'NCHW' else 3]
     if n_in != n_out:   # change dimension when channel is not the same
-        return Conv2D('convshortcut', l, n_out, 1, stride=stride)
+        return Conv2D('convshortcut', l, n_out, 1, stride=stride, nl=nl)
     else:
         return l
 
 
 def apply_preactivation(l, preact):
     """
-    'no_preact' for the first resblock only, because the input is activated already.
-    'both_preact' for the first block in each group, due to the projection shotcut.
+    'no_preact' for the first resblock in each group only, because the input is activated already.
     'default' for all the non-first blocks, where identity mapping is preserved on shortcut path.
     """
-    if preact == 'both_preact':
-        l = BNReLU('preact', l)
-        shortcut = l
-    elif preact == 'default':
+    if preact == 'default':
         shortcut = l
         l = BNReLU('preact', l)
     else:
@@ -153,15 +149,17 @@ def resnet_bottleneck(l, ch_out, stride, preact):
     return l + resnet_shortcut(shortcut, ch_out * 4, stride)
 
 
-def resnet_group(l, name, block_func, features, count, stride, first=False):
+def preresnet_group(l, name, block_func, features, count, stride):
     with tf.variable_scope(name):
-        with tf.variable_scope('block0'):
-            l = block_func(l, features, stride,
-                           'no_preact' if first else 'both_preact')
-        for i in range(1, count):
+        for i in range(0, count):
             with tf.variable_scope('block{}'.format(i)):
-                l = block_func(l, features, 1, 'default')
-        return l
+                # first block doesn't need activation
+                l = block_func(l, features,
+                               stride if i == 0 else 1,
+                               'no_preact' if i == 0 else 'default')
+        # end of each group need an extra activation
+        l = BNReLU('bnlast', l)
+    return l
 
 
 def resnet_backbone(image, num_blocks, block_func):
@@ -170,11 +168,10 @@ def resnet_backbone(image, num_blocks, block_func):
         logits = (LinearWrap(image)
                   .Conv2D('conv0', 64, 7, stride=2, nl=BNReLU)
                   .MaxPooling('pool0', shape=3, stride=2, padding='SAME')
-                  .apply(resnet_group, 'group0', block_func, 64, num_blocks[0], 1, first=True)
-                  .apply(resnet_group, 'group1', block_func, 128, num_blocks[1], 2)
-                  .apply(resnet_group, 'group2', block_func, 256, num_blocks[2], 2)
-                  .apply(resnet_group, 'group3', block_func, 512, num_blocks[3], 2)
-                  .BNReLU('bnlast')
+                  .apply(preresnet_group, 'group0', block_func, 64, num_blocks[0], 1)
+                  .apply(preresnet_group, 'group1', block_func, 128, num_blocks[1], 2)
+                  .apply(preresnet_group, 'group2', block_func, 256, num_blocks[2], 2)
+                  .apply(preresnet_group, 'group3', block_func, 512, num_blocks[3], 2)
                   .GlobalAvgPooling('gap')
                   .FullyConnected('linear', 1000, nl=tf.identity)())
     return logits

examples/Saliency/CAM-resnet.py

@@ -20,7 +20,7 @@ from tensorpack.utils.gpu import get_nr_gpu
 from tensorpack.utils import viz
 
 from imagenet_resnet_utils import (
-    fbresnet_augmentor, resnet_basicblock, resnet_bottleneck, resnet_group,
+    fbresnet_augmentor, resnet_basicblock, preresnet_group,
     image_preprocess, compute_loss_and_error)
 
 
@@ -42,8 +42,6 @@ class Model(ModelDesc):
         cfg = {
             18: ([2, 2, 2, 2], resnet_basicblock),
             34: ([3, 4, 6, 3], resnet_basicblock),
-            50: ([3, 4, 6, 3], resnet_bottleneck),
-            101: ([3, 4, 23, 3], resnet_bottleneck)
         }
         defs, block_func = cfg[DEPTH]
 
@@ -53,11 +51,10 @@ class Model(ModelDesc):
             convmaps = (LinearWrap(image)
                         .Conv2D('conv0', 64, 7, stride=2, nl=BNReLU)
                         .MaxPooling('pool0', shape=3, stride=2, padding='SAME')
-                        .apply(resnet_group, 'group0', block_func, 64, defs[0], 1, first=True)
-                        .apply(resnet_group, 'group1', block_func, 128, defs[1], 2)
-                        .apply(resnet_group, 'group2', block_func, 256, defs[2], 2)
-                        .apply(resnet_group, 'group3new', block_func, 512, defs[3], 1)
-                        .BNReLU('bnlast')())
+                        .apply(preresnet_group, 'group0', block_func, 64, defs[0], 1)
+                        .apply(preresnet_group, 'group1', block_func, 128, defs[1], 2)
+                        .apply(preresnet_group, 'group2', block_func, 256, defs[2], 2)
+                        .apply(preresnet_group, 'group3new', block_func, 512, defs[3], 1)())
             print(convmaps)
             logits = (LinearWrap(convmaps)
                       .GlobalAvgPooling('gap')
@@ -125,7 +122,7 @@ def viz_cam(model_file, data_dir):
         model=Model(),
         session_init=get_model_loader(model_file),
         input_names=['input', 'label'],
-        output_names=['wrong-top1', 'bnlast/Relu', 'linearnew/W'],
+        output_names=['wrong-top1', 'group3new/bnlast/Relu', 'linearnew/W'],
         return_input=True
     )
     meta = dataset.ILSVRCMeta().get_synset_words_1000()

tensorpack/dataflow/prefetch.py

@@ -317,6 +317,7 @@ class ThreadedMapData(ProxyDataFlow):
         self.buffer_size = buffer_size
         self.map_func = map_func
         self._threads = []
+        self._evt = None
 
     def reset_state(self):
         super(ThreadedMapData, self).reset_state()
@@ -372,6 +373,7 @@ class ThreadedMapData(ProxyDataFlow):
                     yield self._out_queue.get()
 
     def __del__(self):
-        self._evt.set()
+        if self._evt is not None:
+            self._evt.set()
         for p in self._threads:
             p.join()