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zhenghuijin 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 dadb5b7c17 Fix CI on master branch (#21001)
dadb5b7c17 is described below
commit dadb5b7c1729418f98926438b69eedae5bc8dbbc
Author: bartekkuncer <[email protected]>
AuthorDate: Fri Apr 15 07:01:52 2022 +0200
Fix CI on master branch (#21001)
* Dissable test_conv2d_16c for gpu CI
* Add space
* Fix linkcheck
---
docs/python_docs/python/tutorials/packages/onnx/fine_tuning_gluon.md | 2 +-
.../python/tutorials/packages/onnx/inference_on_onnx_model.md | 2 +-
tests/python/unittest/test_gluon.py | 2 ++
3 files changed, 4 insertions(+), 2 deletions(-)
diff --git
a/docs/python_docs/python/tutorials/packages/onnx/fine_tuning_gluon.md
b/docs/python_docs/python/tutorials/packages/onnx/fine_tuning_gluon.md
index d7c9986240..312ed8f85a 100644
--- a/docs/python_docs/python/tutorials/packages/onnx/fine_tuning_gluon.md
+++ b/docs/python_docs/python/tutorials/packages/onnx/fine_tuning_gluon.md
@@ -106,7 +106,7 @@ if not os.path.isdir(os.path.join(model_folder,
current_model)):
## Downloading the Caltech101 dataset
-The [Caltech101
dataset](http://www.vision.caltech.edu/Image_Datasets/Caltech101/) is made of
pictures of objects belonging to 101 categories. About 40 to 800 images per
category. Most categories have about 50 images.
+The [Caltech101 dataset](https://data.caltech.edu/records/20086) is made of
pictures of objects belonging to 101 categories. About 40 to 800 images per
category. Most categories have about 50 images.
*L. Fei-Fei, R. Fergus and P. Perona. Learning generative visual models from
few training examples: an incremental Bayesian approach tested on 101 object
categories. IEEE. CVPR 2004, Workshop on Generative-Model
Based Vision. 2004*
diff --git
a/docs/python_docs/python/tutorials/packages/onnx/inference_on_onnx_model.md
b/docs/python_docs/python/tutorials/packages/onnx/inference_on_onnx_model.md
index f4465170e7..760f3d175a 100644
--- a/docs/python_docs/python/tutorials/packages/onnx/inference_on_onnx_model.md
+++ b/docs/python_docs/python/tutorials/packages/onnx/inference_on_onnx_model.md
@@ -243,7 +243,7 @@ plot_predictions(caltech101_images, result[3:7],
categories, TOP_P)
**Hmm, not so good...** Even though predictions are close, they are not
accurate, which is due to the fact that the ImageNet dataset does not contain
`wrench`, `dolphin`, or `lotus` categories and our network has been trained on
ImageNet.
-Lucky for us, the [Caltech101
dataset](http://www.vision.caltech.edu/Image_Datasets/Caltech101/) has them,
let's see how we can fine-tune our network to classify these categories
correctly.
+Lucky for us, the [Caltech101 dataset](https://data.caltech.edu/records/20086)
has them, let's see how we can fine-tune our network to classify these
categories correctly.
We show that in our next tutorial:
diff --git a/tests/python/unittest/test_gluon.py
b/tests/python/unittest/test_gluon.py
index 2a209face2..33fd48a256 100644
--- a/tests/python/unittest/test_gluon.py
+++ b/tests/python/unittest/test_gluon.py
@@ -1809,6 +1809,8 @@ def check_layer_forward_withinput(net, x):
mx.test_utils.assert_almost_equal(out1.asnumpy(), out2.asnumpy(),
rtol=1e-5, atol=1e-6)
@use_np
[email protected](mx.device.num_gpus(), reason="Temporairly disabled on gpu
due to failing centos-gpu CI " +
+ "tracked at
https://github.com/apache/incubator-mxnet/issues/20978";)
@pytest.mark.parametrize('chn_num', [16, 256])
@pytest.mark.parametrize('kernel', [1, 3, 224])
def test_conv2d_16c(chn_num, kernel):
