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How to use assert_message method in localstack

Best Python code snippet using localstack_python

problem_unittests.py

Source:problem_unittests.py

1from unittest.mock import MagicMock, patch2import numpy as np3import torch4def _print_success_message():5    print('Tests Passed')6class AssertTest(object):7    def __init__(self, params):8        self.assert_param_message = '\n'.join([str(k) + ': ' + str(v) + '' for k, v in params.items()])9    def test(self, assert_condition, assert_message):10        assert assert_condition, assert_message + '\n\nUnit Test Function Parameters\n' + self.assert_param_message11        12def test_discriminator(Discriminator):13    batch_size = 5014    conv_dim=1015    D = Discriminator(conv_dim)16    # create random image input17    x = torch.from_numpy(np.random.randint(1, size=(batch_size, 3, 32, 32))*2 -1).float()18    19    train_on_gpu = torch.cuda.is_available()20    if train_on_gpu:21        x.cuda()22    output = D(x)23    assert_test = AssertTest({24        'Conv Dim': conv_dim,25        'Batch Size': batch_size,26        'Input': x})27    correct_output_size = (batch_size, 1)28    assert_condition = output.size() == correct_output_size29    assert_message = 'Wrong output size. Expected type {}. Got type {}'.format(correct_output_size, output.size())30    assert_test.test(assert_condition, assert_message)31    _print_success_message()32    33def test_generator(Generator):34    batch_size = 5035    z_size = 2536    conv_dim=1037    G = Generator(z_size, conv_dim)38    # create random input39    z = np.random.uniform(-1, 1, size=(batch_size, z_size))40    z = torch.from_numpy(z).float()41    42    train_on_gpu = torch.cuda.is_available()43    if train_on_gpu:44        z.cuda()45    #b = torch.LongTensor(a)46    #nn_input = torch.autograd.Variable(b)47    output = G(z)48    assert_test = AssertTest({49        'Z size': z_size,50        'Conv Dim': conv_dim,51        'Batch Size': batch_size,52        'Input': z})53    correct_output_size = (batch_size, 3, 32, 32)54    assert_condition = output.size() == correct_output_size55    assert_message = 'Wrong output size. Expected type {}. Got type {}'.format(correct_output_size, output.size())56    assert_test.test(assert_condition, assert_message)...

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