Merge branch 'bob-devel-pytorch' into 'master'

[bob-devel] More tests for pytorch

See merge request bob/bob.conda!428

conda/beat-devel/meta.yaml

@@ -2,7 +2,7 @@
 {% set nopin = ['ca-certificates', 'certifi', 'openssl', 'libstdcxx-ng', 'libgcc-ng', 'libcxx', 'libcxxabi'] %}
 package:
   name: beat-devel
-  version: 2019.08.14
+  version: 2019.08.26
 
 build:
   # the build number must be zero all the time.
@@ -15,7 +15,7 @@ build:
 requirements:
   host:
     - python {{ python }}
-    - bob-devel 2019.08.14
+    - bob-devel 2019.08.26
     - docker-py 4.0.2
     - flask 1.0.2
     - flask-cors 3.0.7

conda/bob-devel/meta.yaml

@@ -4,7 +4,7 @@ package:
   name: bob-devel
   # please update the version of beat-devel and update bob-devel's version
   # there as well in the same merge request.
-  version: 2019.08.14
+  version: 2019.08.26
 
 build:
   # the build number must be zero all the time.
@@ -36,6 +36,7 @@ requirements:
     - ffmpeg 4.0 h2047f9e_1  # [osx]
     - freetype 2.9.1
     - giflib 5.1.4
+    - h5py 2.8.0
     - hdf5 1.10.2
     - jinja2 2.10.1
     - jpeg 9b
@@ -59,7 +60,7 @@ requirements:
     - pkg-config 0.29.2
     - pyedflib 0.1.14
     - cudatoolkit 9.0  # [linux]
-    - pytorch 1.1.0  # [linux]
+    - pytorch 1.0.1  # [linux]
     - pyyaml 5.1.1
     - requests 2.22.0
     - schema 0.7.0
@@ -74,7 +75,7 @@ requirements:
     - sqlalchemy 1.3.5
     - tabulate 0.8.3
     - tensorflow 1.14.0
-    - torchvision 0.3.0  # [linux]
+    - torchvision 0.2.1  # [linux]
     - vlfeat 0.9.21
     - zc.buildout 2.13.2
     - zc.recipe.egg 2.0.7
@@ -93,11 +94,11 @@ requirements:
   {% endfor %}
 
 test:
-  imports:
-    - torch  # [linux]
   requires:
+    - numpy
     - ffmpeg
     - pytorch  # [linux]
+    - torchvision  # [linux]
   commands:
     # we expect these features from ffmpeg:
     - ffmpeg -codecs | grep "DEVI.S zlib"  # [unix]

conda/bob-devel/run_test.py

+import sys
+
+# couple of imports to see if packages are working
+import numpy
+
+
+def test_pytorch():
+    import torch
+    from torchvision.models import DenseNet
+
+    model = DenseNet()
+    t = torch.randn(1, 3, 224, 224)
+    out = model(t)
+    assert out.shape[1] == 1000
+
+
+if sys.platform.startswith("linux"):
+    # test if pytorch installation is sane
+    test_pytorch()