Merge branch 'update-CI' into 'master'

updated .gitlab-ci.yml with latest one

See merge request !5

.gitlab-ci.yml

@@ -48,10 +48,18 @@ stages:
     key: "linux-cache"
 
 
-build_linux_27:
-  <<: *linux_build_job
-  variables:
-    PYTHON_VERSION: "2.7"
+.build_macosx_template: &macosx_build_job
+  <<: *build_job
+  tags:
+    - macosx
+  artifacts:
+    expire_in: 1 week
+    paths:
+      - _ci/
+      - ${CONDA_ROOT}/conda-bld/osx-64/*.tar.bz2
+  cache:
+    <<: *build_caches
+    key: "macosx-cache"
 
 
 build_linux_36:
@@ -68,6 +76,12 @@ build_linux_36:
       - ${CONDA_ROOT}/conda-bld/linux-64/*.tar.bz2
 
 
+build_macosx_36:
+  <<: *macosx_build_job
+  variables:
+    PYTHON_VERSION: "3.6"
+
+
 # Deploy targets
 .deploy_template: &deploy_job
   stage: deploy
@@ -76,8 +90,8 @@ build_linux_36:
   script:
     - ./_ci/deploy.sh
   dependencies:
-    - build_linux_27
     - build_linux_36
+    - build_macosx_36
   tags:
     - deployer
 

bob/learn/pytorch/architectures/ConditionalGAN.py

@@ -80,10 +80,13 @@ class ConditionalGAN_generator(nn.Module):
     
     """
     generator_input = torch.cat((z, y), 1)
-    if isinstance(generator_input.data, torch.cuda.FloatTensor) and self.ngpu > 1:
-      output = nn.parallel.data_parallel(self.main, generator_input, range(self.ngpu))
-    else:
-      output = self.main(generator_input)
+    #if isinstance(generator_input.data, torch.cuda.FloatTensor) and self.ngpu > 1:
+    #  output = nn.parallel.data_parallel(self.main, generator_input, range(self.ngpu))
+    #else:
+    #  output = self.main(generator_input)
+    
+    # let's assume that we will never face the case where more than a GPU is used ...
+    output = self.main(generator_input)
     return output
 
 
@@ -159,8 +162,11 @@ class ConditionalGAN_discriminator(nn.Module):
       the output of the discriminator
     """
     input_discriminator = torch.cat((images, y), 1)
-    if isinstance(input_discriminator.data, torch.cuda.FloatTensor) and self.ngpu > 1:
-      output = nn.parallel.data_parallel(self.main, input_discriminator, range(self.ngpu))
-    else:
-      output = self.main(input_discriminator)
+    #if isinstance(input_discriminator.data, torch.cuda.FloatTensor) and self.ngpu > 1:
+    #  output = nn.parallel.data_parallel(self.main, input_discriminator, range(self.ngpu))
+    #else:
+    #  output = self.main(input_discriminator)
+    
+    # let's assume that we will never face the case where more than a GPU is used ...
+    output = self.main(input_discriminator)
     return output.view(-1, 1).squeeze(1)

bob/learn/pytorch/architectures/DCGAN.py

@@ -74,10 +74,13 @@ class DCGAN_generator(nn.Module):
       the output of the generator (i.e. an image)
 
     """
-    if isinstance(input.data, torch.cuda.FloatTensor) and self.ngpu > 1:
-      output = nn.parallel.data_parallel(self.main, input, range(self.ngpu))
-    else:
-      output = self.main(input)
+    #if isinstance(input.data, torch.cuda.FloatTensor) and self.ngpu > 1:
+    #  output = nn.parallel.data_parallel(self.main, input, range(self.ngpu))
+    #else:
+    #  output = self.main(input)
+    
+    # let's assume that we will never face the case where more than a GPU is used ...
+    output = self.main(input)
     return output
 
 
@@ -148,9 +151,12 @@ class DCGAN_discriminator(nn.Module):
       the output of the generator (i.e. an image)
 
     """
-    if isinstance(input.data, torch.cuda.FloatTensor) and self.ngpu > 1:
-      output = nn.parallel.data_parallel(self.main, input, range(self.ngpu))
-    else:
-      output = self.main(input)
+    #if isinstance(input.data, torch.cuda.FloatTensor) and self.ngpu > 1:
+    #  output = nn.parallel.data_parallel(self.main, input, range(self.ngpu))
+    #else:
+    #  output = self.main(input)
+    
+    # let's assume that we will never face the case where more than a GPU is used ...
+    output = self.main(input)
 
     return output.view(-1, 1).squeeze(1)

bob/learn/pytorch/datasets/casia_webface.py

@@ -23,9 +23,9 @@ class CasiaWebFaceDataset(Dataset):
     The path to the data
   transform : `torchvision.transforms`
     The transform(s) to apply to the face images
-  data_files : list of str
+  data_files : list of :obj:`str`
     The list of data files
-  id_labels : list of int
+  id_labels : list of :obj:`int`
     The list of identities, for each data file
 
   """
@@ -108,11 +108,11 @@ class CasiaDataset(Dataset):
     The path to the data
   transform : `torchvision.transforms`
     The transform(s) to apply to the face images
-  data_files: list of str
+  data_files: list of :obj:`str`
     The list of data files
-  id_labels : list of int
+  id_labels : list of :obj:`int`
     The list of identities, for each data file
-  pose_labels : list of int
+  pose_labels : list of :obj:`int`
     The list containing the pose labels 
 
   """

bob/learn/pytorch/trainers/CNNTrainer.py

@@ -24,7 +24,7 @@ class CNNTrainer(object):
     The network to train
   batch_size: int
     The size of your minibatch
-  use_gpu: boolean
+  use_gpu: bool
     If you would like to use the gpu
   verbosity_level: int
     The level of verbosity output to stdout
@@ -40,7 +40,7 @@ class CNNTrainer(object):
       The network to train
     batch_size: int
       The size of your minibatch
-    use_gpu: boolean
+    use_gpu: bool
       If you would like to use the gpu
     verbosity_level: int
       The level of verbosity output to stdout

bob/learn/pytorch/trainers/ConditionalGANTrainer.py

@@ -22,7 +22,7 @@ class ConditionalGANTrainer(object):
     The generator network
   discriminator : :py:class:`torch.nn.Module`
     The discriminator network
-  image_size: list of int
+  image_size: list of :obj:`int`
     The size of the images in this format: [channels,height, width]
   batch_size: int
     The size of your minibatch
@@ -49,7 +49,7 @@ class ConditionalGANTrainer(object):
       The generator network
     netD : :py:class:`torch.nn.Module`
       The discriminator network
-    image_size: list of int
+    image_size: list of :obj:`int`
       The size of the images in this format: [channels,height, width]
     batch_size: int
       The size of your minibatch

conda/meta.yaml

@@ -29,7 +29,7 @@ requirements:
     - bob.core
     - bob.io.base
     - bob.io.image
-    - pytorch =0.4
+    - pytorch >=0.4
     - torchvision >=0.2.0
   run:
     - python

requirements.txt

@@ -6,5 +6,5 @@ bob.extension
 bob.core
 bob.io.base
 bob.io.image
-torch == 0.4.0
+torch >= 0.4.0
 torchvision >= 0.2.0