test.py 6.02 KB
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#!/usr/bin/env python
# encoding: utf-8


""" Unit tests

"""

import numpy
import torch

def test_architectures():

  a = numpy.random.rand(1, 3, 128, 128).astype("float32")
  t = torch.from_numpy(a)
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  number_of_classes = 20
  output_dimension = number_of_classes
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  # 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])
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  # 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])
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  # DCGAN
  d = numpy.random.rand(1, 3, 64, 64).astype("float32")
  t = torch.from_numpy(d)
  from ..architectures import DCGAN_discriminator
  discriminator = DCGAN_discriminator(1)
  output = discriminator.forward(t)
  assert output.shape == torch.Size([1])

  g = numpy.random.rand(1, 100, 1, 1).astype("float32")
  t = torch.from_numpy(g)
  from ..architectures import DCGAN_generator
  generator = DCGAN_generator(1)
  output = generator.forward(t)
  assert output.shape == torch.Size([1, 3, 64, 64])
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  # Conditional GAN
  d = numpy.random.rand(1, 3, 64, 64).astype("float32")
  t = torch.from_numpy(d)
  cfm = numpy.zeros((1, 13, 64, 64), dtype="float32")
  cfm[:, 0, :, :] = 1
  cfmt = torch.from_numpy(cfm)
  from ..architectures import ConditionalGAN_discriminator
  discriminator = ConditionalGAN_discriminator(13)
  output = discriminator.forward(t, cfmt)
  assert output.shape == torch.Size([1])

  g = numpy.random.rand(1, 100, 1, 1).astype("float32")
  t = torch.from_numpy(g)
  oh = numpy.zeros((1, 13, 1, 1), dtype="float32")
  oh[0] = 1
  oht = torch.from_numpy(oh)
  from ..architectures import ConditionalGAN_generator
  discriminator = ConditionalGAN_generator(100, 13)
  output = discriminator.forward(t, oht)
  assert output.shape == torch.Size([1, 3, 64, 64])

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def test_transforms():

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  image = numpy.random.rand(3, 128, 128).astype("uint8")
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  from ..datasets import RollChannels
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  sample = {'image': image}
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  rc = RollChannels()
  rc(sample)
  assert sample['image'].shape == (128, 128, 3)

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  from ..datasets import ToTensor
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  tt = ToTensor()
  tt(sample)
  assert isinstance(sample['image'], torch.Tensor)

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  from ..datasets import Normalize
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  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']
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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}
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    return sample
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def test_CNNtrainer():
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  from ..architectures import CNN8
  net = CNN8(20)

  dataloader = torch.utils.data.DataLoader(DummyDataSet(), batch_size=32, shuffle=True)
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  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')
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class DummyDataSetGAN(Dataset):
  def __init__(self):
    pass
  def __len__(self):
    return 100
  def __getitem__(self, idx):
    data =  numpy.random.rand(3, 64, 64).astype("float32")
    sample = {'image': torch.from_numpy(data)}
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    return sample
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def test_DCGANtrainer():

  from ..architectures import DCGAN_generator
  from ..architectures import DCGAN_discriminator
  g = DCGAN_generator(1)
  d = DCGAN_discriminator(1)

  dataloader = torch.utils.data.DataLoader(DummyDataSetGAN(), batch_size=32, shuffle=True)
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  from ..trainers import DCGANTrainer
  trainer = DCGANTrainer(g, d, batch_size=32, noise_dim=100, use_gpu=False, verbosity_level=2)
  trainer.train(dataloader, n_epochs=1, output_dir='.')

  import os
  assert os.path.isfile('fake_samples_epoch_000.png')
  assert os.path.isfile('netD_epoch_0.pth')
  assert os.path.isfile('netG_epoch_0.pth')

  os.remove('fake_samples_epoch_000.png')
  os.remove('netD_epoch_0.pth')
  os.remove('netG_epoch_0.pth')

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class DummyDataSetConditionalGAN(Dataset):
  def __init__(self):
    pass
  def __len__(self):
    return 100
  def __getitem__(self, idx):
    data =  numpy.random.rand(3, 64, 64).astype("float32")
    sample = {'image': torch.from_numpy(data), 'pose': numpy.random.randint(0, 13)}
    return sample

def test_ConditionalGANTrainer():

  from ..architectures import ConditionalGAN_generator
  from ..architectures import ConditionalGAN_discriminator
  g = ConditionalGAN_generator(100, 13)
  d = ConditionalGAN_discriminator(13)

  dataloader = torch.utils.data.DataLoader(DummyDataSetConditionalGAN(), batch_size=32, shuffle=True)
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  from ..trainers import ConditionalGANTrainer
  trainer = ConditionalGANTrainer(g, d, [3, 64, 64], batch_size=32, noise_dim=100, conditional_dim=13)
  trainer.train(dataloader, n_epochs=1, output_dir='.')
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  import os
  assert os.path.isfile('fake_samples_epoch_000.png')
  assert os.path.isfile('netD_epoch_0.pth')
  assert os.path.isfile('netG_epoch_0.pth')
  os.remove('fake_samples_epoch_000.png')
  os.remove('netD_epoch_0.pth')
  os.remove('netG_epoch_0.pth')
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def test_conv_autoencoder():
    """
    Test the ConvAutoencoder class.
    """

    from bob.learn.pytorch.architectures import ConvAutoencoder

    batch = torch.randn(1, 3, 64, 64)

    model = ConvAutoencoder()

    output = model(batch)

    assert batch.shape == output.shape