def test_check_output(self):
ids = self.inputs['Ids']
flatten_idx = ids.flatten()
padding_idx = np.random.choice(flatten_idx, 1)[0]
self.outputs['Out'][np.squeeze(ids == padding_idx)] = np.zeros(31)
self.attrs = {'padding_idx': cpt.long_type(padding_idx)}
self.check_output()
embedding_dim,
padding_idx=int(padding_idx),
vocab_embeddings=table,
compare=False)
# print('outputs {}'.format(outputs))
#
ids = np.random.randint(low=0, high=vocab_size,
size=(2, 4, 5)).astype("int32")
embedding("embedding_tensorIds",
ids,
vocab_size,
embedding_dim,
vocab_embeddings=table,
compare=False)
#
ids = np.random.randint(low=0, high=vocab_size,
size=(2, 4, 5)).astype("int32")
flatten_idx = ids.flatten()
padding_idx = np.random.choice(flatten_idx, 1)[0]
# print('padding_idx {}'.format(padding_idx))
outputs = embedding("embedding_tensorIds_paddings",
ids,
vocab_size,
embedding_dim,
padding_idx=cpt.long_type(padding_idx),
vocab_embeddings=table,
compare=False)
# print('outputs {}'.format(outputs))
embedding("embedding_none_weight", ids, vocab_size, embedding_dim, compare=False)
#
ids = np.random.randint(0, vocab_size, 4).astype("int32")
ids = np.squeeze(ids)
padding_idx = np.random.choice(ids, 1)[0]
# print('padding_idx {}, ids {}'.format(padding_idx, ids))
outputs = embedding("embedding_paddings", ids, vocab_size, embedding_dim, padding_idx=int(padding_idx), vocab_embeddings=table, compare=False)
# print('outputs {}'.format(outputs))
# corner case
ids = np.random.randint(0, vocab_size, 4).astype("int32")
pick = np.random.choice(4, 1)[0] # pick randomly to be max vacab_size -1
ids[pick] = vocab_size-1
padding_idx = -1
# print('padding_idx {}, ids {}'.format(padding_idx, ids))
outputs = embedding("embedding_paddings_neg1", ids, vocab_size, embedding_dim, padding_idx=int(padding_idx), vocab_embeddings=table, compare=False)
# print('outputs {}'.format(outputs))
#
ids = np.random.randint(low=0, high=vocab_size, size=(2, 4, 5)).astype("int32")
embedding("embedding_tensorIds", ids, vocab_size, embedding_dim, vocab_embeddings=table, compare=False)
#
ids = np.random.randint(low=0, high=vocab_size, size=(2, 4, 5)).astype("int32")
flatten_idx = ids.flatten()
padding_idx = np.random.choice(flatten_idx, 1)[0]
# print('padding_idx {}'.format(padding_idx))
outputs = embedding("embedding_tensorIds_paddings", ids, vocab_size, embedding_dim, padding_idx=cpt.long_type(padding_idx), vocab_embeddings=table, compare=False)
# print('outputs {}'.format(outputs))
