Fix bug in resnet; improve logs for #1100

examples/ResNet/imagenet-resnet.py

@@ -135,7 +135,7 @@ if __name__ == '__main__':
 
     model = Model(args.depth, args.mode)
     model.data_format = args.data_format
-    if model.weight_decay_norm:
+    if args.weight_decay_norm:
         model.weight_decay_pattern = ".*/W|.*/gamma|.*/beta"
 
     if args.eval:

tensorpack/tfutils/model_utils.py

@@ -40,10 +40,11 @@ def describe_trainable_vars():
         total += ele
         total_bytes += ele * v.dtype.size
         data.append([v.name, shape, ele, v.device, v.dtype.base_dtype.name])
-    headers = ['name', 'shape', 'dim', 'device', 'dtype']
+    headers = ['name', 'shape', '#elements', 'device', 'dtype']
 
-    dtypes = set([x[4] for x in data])
-    if len(dtypes) == 1:
+    dtypes = list(set([x[4] for x in data]))
+    if len(dtypes) == 1 and dtypes[0] == "float32":
+        # don't log the dtype if all vars are float32 (default dtype)
         for x in data:
             del x[4]
         del headers[4]
@@ -59,9 +60,11 @@ def describe_trainable_vars():
 
     size_mb = total_bytes / 1024.0**2
     summary_msg = colored(
-        "\nTotal #vars={}, #params={}, size={:.02f}MB".format(
-            len(data), total, size_mb), 'cyan')
-    logger.info(colored("Trainable Variables: \n", 'cyan') + table + summary_msg)
+        "\nNumber of trainable variables: {}".format(len(data)) +
+        "\nNumber of parameters (elements): {}".format(total) +
+        "\nStorage space needed for all trainable variables: {:.02f}MB".format(size_mb),
+        'cyan')
+    logger.info(colored("List of Trainable Variables: \n", 'cyan') + table + summary_msg)
 
 
 def get_shape_str(tensors):