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Posted to commits@mxnet.apache.org by pt...@apache.org on 2019/06/03 15:03:09 UTC
[incubator-mxnet] branch master updated: remove warning in
tutorial: (#15135)
This is an automated email from the ASF dual-hosted git repository.
ptrendx pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/master by this push:
new 9125f6a remove warning in tutorial: (#15135)
9125f6a is described below
commit 9125f6ae8cf0fb11338f18cd27223b5ce367afbc
Author: Lai Wei <ro...@gmail.com>
AuthorDate: Mon Jun 3 08:02:17 2019 -0700
remove warning in tutorial: (#15135)
---
docs/tutorials/amp/amp_tutorial.md | 13 ++-----------
1 file changed, 2 insertions(+), 11 deletions(-)
diff --git a/docs/tutorials/amp/amp_tutorial.md b/docs/tutorials/amp/amp_tutorial.md
index 02bf82a..be18929 100644
--- a/docs/tutorials/amp/amp_tutorial.md
+++ b/docs/tutorials/amp/amp_tutorial.md
@@ -92,10 +92,9 @@ train_data = SyntheticDataLoader(data_shape, batch_size)
def get_network():
# SSD with RN50 backbone
net_name = 'ssd_512_resnet50_v1_coco'
- net = get_model(net_name, pretrained_base=True, norm_layer=gluon.nn.BatchNorm)
- async_net = net
with warnings.catch_warnings(record=True) as w:
- warnings.simplefilter("always")
+ warnings.simplefilter("ignore")
+ net = get_model(net_name, pretrained_base=True, norm_layer=gluon.nn.BatchNorm)
net.initialize()
net.collect_params().reset_ctx(ctx)
@@ -112,9 +111,6 @@ net = get_network()
net.hybridize(static_alloc=True, static_shape=True)
```
- /mxnet/code/python/mxnet/gluon/block.py:1138: UserWarning: Cannot decide type for the following arguments. Consider providing them as input:
- data: None
- input_sym_arg_type = in_param.infer_type()[0]
Next, we need to create a Gluon Trainer.
@@ -192,11 +188,6 @@ net = get_network()
net.hybridize(static_alloc=True, static_shape=True)
```
- /mxnet/code/python/mxnet/gluon/block.py:1138: UserWarning: Cannot decide type for the following arguments. Consider providing them as input:
- data: None
- input_sym_arg_type = in_param.infer_type()[0]
-
-
For some models that may be enough to start training in mixed precision, but the full FP16 recipe recommends using dynamic loss scaling to guard against over- and underflows of FP16 values. Therefore, as a next step, we create a trainer and initialize it with support for AMP's dynamic loss scaling. Currently, support for dynamic loss scaling is limited to trainers created with `update_on_kvstore=False` option, and so we add it to our trainer initialization.