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"""
Here come the tests for attention types and their compatibility
"""
import unittest
import torch
from torch.autograd import Variable
import onmt
class TestAttention(unittest.TestCase):
def test_masked_global_attention(self):
src_len = torch.IntTensor([7, 3, 5, 2])
# illegal_weights_mask = torch.ByteTensor([
# [0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 1, 1, 1, 1],
# [0, 0, 0, 0, 0, 1, 1],
# [0, 0, 1, 1, 1, 1, 1]])
batch_size = src_len.size(0)
dim = 20
enc_out = Variable(torch.randn(batch_size, src_len.max(), dim))
enc_final_hs = Variable(torch.randn(batch_size, dim))
attn = onmt.modules.GlobalAttention(dim)
_, alignments = attn(enc_final_hs, enc_out, src_len=src_len)
# TODO: fix for pytorch 0.3
# illegal_weights = alignments.masked_select(illegal_weights_mask)
# self.assertEqual(0.0, illegal_weights.data.sum())
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