[unit test] added unit tests

bob/learn/pytorch/test/__init__.py

+# gets sphinx autodoc done right - don't remove it
+__all__ = [_ for _ in dir() if not _.startswith('_')]
+

bob/learn/pytorch/test/test.py

+#!/usr/bin/env python
+# encoding: utf-8
+
+
+""" Unit tests
+
+"""
+
+import numpy
+import torch
+
+from bob.io.base import load
+from bob.io.base.test_utils import datafile
+
+def test_architectures():
+
+  a = numpy.random.rand(1, 3, 128, 128).astype("float32")
+  t = torch.from_numpy(a)
+  
+  number_of_classes = 20
+  output_dimension = number_of_classes
+  
+  # CASIANet
+  from ..architectures import CASIANet
+  net = CASIANet(number_of_classes)
+  embedding_dimension = 320
+  output, emdedding = net.forward(t)
+  assert output.shape == torch.Size([1, 20])
+  assert emdedding.shape == torch.Size([1, 320])
+  
+  # CNN8
+  from ..architectures import CNN8
+  net = CNN8(number_of_classes)
+  embedding_dimension = 512
+  output, emdedding = net.forward(t)
+  assert output.shape == torch.Size([1, 20])
+  assert emdedding.shape == torch.Size([1, 512])
+ 
+
+def test_transforms():
+
+  sample = {}
+  image = load(datafile('001.jpg', 'bob.learn.pytorch.test'))
+
+  from ..datasets import RollChannels 
+  sample['image'] = image
+  rc = RollChannels()
+  rc(sample)
+  assert sample['image'].shape == (128, 128, 3)
+
+  from ..datasets import ToTensor 
+  tt = ToTensor()
+  tt(sample)
+  assert isinstance(sample['image'], torch.Tensor)
+
+  from ..datasets import Normalize 
+  image_copy = torch.Tensor(sample['image'])
+  norm = Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+  norm(sample)
+  for c in range(3):
+    for h in range(sample['image'].shape[0]):
+      for w in range(sample['image'].shape[1]):
+        assert (abs(sample['image'][c, h, w]) - abs((image_copy[c, h, w] - 0.5) / 0.5)) < 1e-10
+
+
+def test_map_labels():
+
+  labels = ['1', '4', '7']
+  from ..datasets import map_labels
+  new_labels = map_labels(labels)
+  new_labels = sorted(new_labels)
+  assert new_labels == ['0', '1', '2']
+
+  new_labels = map_labels(labels, start_index = 5)
+  new_labels = sorted(new_labels)
+  assert new_labels == ['5', '6', '7']
+ 
+
+from torch.utils.data import Dataset
+class DummyDataSet(Dataset):
+  def __init__(self):
+    pass
+  def __len__(self):
+    return 100
+  def __getitem__(self, idx):
+    data =  numpy.random.rand(3, 128, 128).astype("float32")
+    label = numpy.random.randint(20)
+    sample = {'image': torch.from_numpy(data), 'label': label}
+    return sample 
+
+
+def test_trainer():
+
+  from ..architectures import CNN8
+  net = CNN8(20)
+
+  dataloader = torch.utils.data.DataLoader(DummyDataSet(), batch_size=32, shuffle=True)
+  
+  from ..trainers import CNNTrainer
+  trainer = CNNTrainer(net, verbosity_level=3)
+  trainer.train(dataloader, n_epochs=1, output_dir='.')
+
+  import os
+  assert os.path.isfile('model_1_0.pth')
+
+  os.remove('model_1_0.pth')