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Posted to commits@mxnet.apache.org by la...@apache.org on 2019/03/27 07:36:05 UTC
[incubator-mxnet] branch master updated: Adds context parameter to
check_rnn_layer_forward calls in test_lstmp (#14529)
This is an automated email from the ASF dual-hosted git repository.
lanking 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 67c10f9 Adds context parameter to check_rnn_layer_forward calls in test_lstmp (#14529)
67c10f9 is described below
commit 67c10f954d923cffcb9a932210d9ff0a2010e20b
Author: perdasilva <pe...@gmail.com>
AuthorDate: Wed Mar 27 08:35:44 2019 +0100
Adds context parameter to check_rnn_layer_forward calls in test_lstmp (#14529)
---
tests/python/gpu/test_gluon_gpu.py | 15 ++++++++-------
1 file changed, 8 insertions(+), 7 deletions(-)
diff --git a/tests/python/gpu/test_gluon_gpu.py b/tests/python/gpu/test_gluon_gpu.py
index 88b436a..9eeeec7 100644
--- a/tests/python/gpu/test_gluon_gpu.py
+++ b/tests/python/gpu/test_gluon_gpu.py
@@ -88,7 +88,8 @@ def test_lstmp():
rtol, atol = 1e-2, 1e-2
batch_size, seq_len = 7, 11
input_size = 5
- lstm_input = mx.nd.uniform(shape=(seq_len, batch_size, input_size), ctx=mx.gpu(0))
+ ctx = mx.gpu(0)
+ lstm_input = mx.nd.uniform(shape=(seq_len, batch_size, input_size), ctx=ctx)
shapes = {'i2h_weight': (hidden_size*4, input_size),
'h2h_weight': (hidden_size*4, projection_size),
'i2h_bias': (hidden_size*4,),
@@ -101,8 +102,8 @@ def test_lstmp():
projection_size=projection_size,
input_size=input_size,
prefix='lstm0_l0_')
- lstm_layer.initialize(ctx=mx.gpu(0))
- lstm_cell.initialize(ctx=mx.gpu(0))
+ lstm_layer.initialize(ctx=ctx)
+ lstm_cell.initialize(ctx=ctx)
layer_params = lstm_layer.collect_params()
cell_params = lstm_cell.collect_params()
for k, v in weights.items():
@@ -121,14 +122,14 @@ def test_lstmp():
print('checking gradient for {}'.format('lstm0_l0_'+k))
assert_almost_equal(layer_grad.asnumpy(), cell_grad.asnumpy(),
rtol=rtol, atol=atol)
- check_rnn_layer_forward(gluon.rnn.LSTM(10, 2, projection_size=5), mx.nd.ones((8, 3, 20)))
- check_rnn_layer_forward(gluon.rnn.LSTM(10, 2, projection_size=5, bidirectional=True), mx.nd.ones((8, 3, 20)), [mx.nd.ones((4, 3, 5)), mx.nd.ones((4, 3, 10))])
+ check_rnn_layer_forward(gluon.rnn.LSTM(10, 2, projection_size=5), mx.nd.ones((8, 3, 20)), ctx=ctx)
+ check_rnn_layer_forward(gluon.rnn.LSTM(10, 2, projection_size=5, bidirectional=True), mx.nd.ones((8, 3, 20)), [mx.nd.ones((4, 3, 5)), mx.nd.ones((4, 3, 10))], ctx=ctx)
check_rnn_layer_forward(gluon.rnn.LSTM(10, 2, dropout=0.5, projection_size=5), mx.nd.ones((8, 3, 20)),
- run_only=True)
+ run_only=True, ctx=ctx)
check_rnn_layer_forward(gluon.rnn.LSTM(10, 2, bidirectional=True, dropout=0.5, projection_size=5),
mx.nd.ones((8, 3, 20)),
- [mx.nd.ones((4, 3, 5)), mx.nd.ones((4, 3, 10))], run_only=True)
+ [mx.nd.ones((4, 3, 5)), mx.nd.ones((4, 3, 10))], run_only=True, ctx=ctx)
@with_seed()