[test] fixed the download of pretrained FASNet (saves CI time)

.gitignore

@@ -15,5 +15,3 @@ log*
 results*
 build/
 record.txt
-*lightcnn*
-bob/learn/pytorch/preprocessor/audio/data

bob/learn/pytorch/architectures/TwoLayerMLP.py

-#!/usr/bin/env python2
-# -*- coding: utf-8 -*-
-"""
-@author: Olegs Nikisins
-"""
-#==============================================================================
-# Import here:
-
-from torch import nn
-
-import torch.nn.functional as F
-
-
-#==============================================================================
-# Define the network:
-
-class TwoLayerMLP(nn.Module):
-    """
-    A simple two-layer MLP for binary classification. The output activation
-    function is sigmoid.
-
-    Attributes
-    ----------
-    in_features : int
-        Dimensionality of the input feature vectors.
-
-    n_hidden_relu : int
-        Number of ReLU units in the hidden layer of the MLP.
-
-    apply_sigmoid : bool
-        If set to ``True`` the sigmoid will be applied to the output of the
-        hidden FC layer. If ``False`` the sigmoid is not applied.
-    """
-
-    def __init__(self, in_features, n_hidden_relu, apply_sigmoid = True):
-        super(TwoLayerMLP, self).__init__()
-        """
-        Init method.
-
-        Parameters
-        ----------
-        in_features : int
-            Dimensionality of the input feature vectors.
-
-        n_hidden_relu : int
-            Number of ReLU units in the hidden layer of the MLP.
-
-        apply_sigmoid : bool
-            If set to ``True`` the sigmoid will be applied to the output of the
-            hidden FC layer. If ``False`` the sigmoid is not applied.
-            Default: ``True``.
-        """
-
-        self.in_features = in_features
-
-        self.n_hidden_relu = n_hidden_relu
-
-        self.apply_sigmoid = apply_sigmoid
-
-        self.fc1 = nn.Linear(in_features = self.in_features, out_features = self.n_hidden_relu, bias=True)
-
-        self.fc2 = nn.Linear(in_features = self.n_hidden_relu, out_features = 1, bias=True)
-
-
-    def forward(self, x):
-        """
-        The forward method.
-
-        Parameters
-        ----------
-        x : :py:class:`torch.Tensor`
-            The batch to forward through the network. Size of the input batch
-            is [batch_size, 1, self.in_features].
-
-        Returns
-        -------
-        x : :py:class:`torch.Tensor`
-            Output of the MLP, class probability.
-        """
-
-        # input is a batch of the size: [batch_size, 1, self.in_features],
-        # convert it to the size [batch_size, self.in_features] as expected by FC layer:
-        x = x.squeeze()
-
-        # first fully connected activated by ReLu:
-        x = self.fc1(x)
-        x = F.relu(x)
-
-        # second fully connected activated by sigmoid:
-        x = self.fc2(x)
-
-        if not self.apply_sigmoid:
-
-            return x
-
-        x = F.sigmoid(x)
-
-        return x
-
-

bob/learn/pytorch/architectures/__init__.py

@@ -16,7 +16,6 @@ from .ConditionalGAN import ConditionalGAN_generator
 from .ConditionalGAN import ConditionalGAN_discriminator
 
 from .ConvAutoencoder import ConvAutoencoder
-from .TwoLayerMLP import TwoLayerMLP
 
 from .utils import weights_init
 

bob/learn/pytorch/test/test.py

@@ -96,7 +96,7 @@ def test_architectures():
   a = numpy.random.rand(1, 3, 224, 224).astype("float32")
   t = torch.from_numpy(a)
   from ..architectures import DeepPixBiS
-  net = DeepPixBiS(pretrained=True)
+  net = DeepPixBiS(pretrained=False)
   output = net.forward(t)
   assert output[0].shape == torch.Size([1, 1, 14, 14])
   assert output[1].shape == torch.Size([1, 1])
@@ -291,7 +291,7 @@ class DummyDataSetFASNet(Dataset):
 def test_FASNettrainer():
 
   from ..architectures import FASNet
-  net = FASNet()
+  net = FASNet(pretrained=False)
 
   dataloader={}
   dataloader['train'] = torch.utils.data.DataLoader(DummyDataSetFASNet(), batch_size=32, shuffle=True)
@@ -309,7 +309,7 @@ def test_FASNettrainer():
 def test_FASNettrainer_cv():
 
   from ..architectures import FASNet
-  net = FASNet()
+  net = FASNet(pretrained=False)
 
   dataloader={}
   dataloader['train'] = torch.utils.data.DataLoader(DummyDataSetFASNet(), batch_size=32, shuffle=True)
@@ -455,24 +455,3 @@ def test_extractors():
   data = numpy.random.rand(3, 224, 224).astype("uint8")
   output = extractor(data)
   assert output.shape[0] == 1
-  
-def test_two_layer_mlp():
-    """
-    Test the TwoLayerMLP class.
-    """
-
-    from bob.learn.pytorch.architectures import TwoLayerMLP
-
-    batch = torch.randn(10, 1, 100)
-
-    model = TwoLayerMLP(in_features = 100,
-                        n_hidden_relu = 10,
-                        apply_sigmoid = True)
-    output = model(batch)
-    assert list(output.shape) == [10, 1]
-
-    model.apply_sigmoid = False
-    output = model(batch)
-    assert list(output.shape) == [10, 1]
- 
-