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create LxmertModelIntegrationTest Pytorch (#9989)

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* create LxmertModelIntegrationTest

* implementation using numpy seeding to fix inputs params.

* fix code quality

* isort check
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sadakmed 2021-07-05 11:21:25 +02:00 committed by GitHub
parent 23ab0b6980
commit 0e1718afb6
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tests/test_modeling_lxmert.py
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@ -17,6 +17,8 @@
import copy
import unittest
import numpy as np
from transformers import is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
@ -727,3 +729,24 @@ class LxmertModelTest(ModelTesterMixin, unittest.TestCase):
self.assertIsNotNone(attentions_vision.grad)
self.assertIsNotNone(hidden_states_vision.grad)
self.assertIsNotNone(attentions_vision.grad)
@require_torch
class LxmertModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_no_head_absolute_embedding(self):
model = LxmertModel.from_pretrained(LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST[0])
input_ids = torch.tensor([[101, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 102]])
num_visual_features = 10
_, visual_feats = np.random.seed(0), np.random.rand(1, num_visual_features, LxmertModel.config.visual_feat_dim)
_, visual_pos = np.random.seed(0), np.random.rand(1, num_visual_features, 4)
visual_feats = torch.as_tensor(visual_feats, dtype=torch.float32)
visual_pos = torch.as_tensor(visual_pos, dtype=torch.float32)
output = model(input_ids, visual_feats=visual_feats, visual_pos=visual_pos)[0]
expected_shape = torch.Size([1, 11, 768])
self.assertEqual(expected_shape, output.shape)
expected_slice = torch.tensor(
[[[0.2417, -0.9807, 0.1480], [1.2541, -0.8320, 0.5112], [1.4070, -1.1052, 0.6990]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))