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Improve DETR models (#19644)

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* Improve DETR models

* Fix Deformable DETR loss and matcher

* Fixup

* Fix integration tests

* Improve variable names

* Apply suggestion

* Fix copies

* Fix DeformableDetrLoss

* Make Conditional DETR copy from Deformable DETR

* Copy from deformable detr's hungarian matcher

* Fix bug
This commit is contained in:
NielsRogge
2022-10-18 10:29:14 +02:00
committed by GitHub
parent 072dfdaee4
commit 90071fe42b
12 changed files with 253 additions and 257 deletions
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51
tests/models/conditional_detr/test_modeling_conditional_detr.py
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@@ -12,7 +12,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch CONDITIONAL_DETR model. """
""" Testing suite for the PyTorch Conditional DETR model. """
import inspect
@@ -213,19 +213,19 @@ class ConditionalDetrModelTest(ModelTesterMixin, GenerationTesterMixin, unittest
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_conditional_detr_object_detection_head_model(*config_and_inputs)
@unittest.skip(reason="CONDITIONAL_DETR does not use inputs_embeds")
@unittest.skip(reason="Conditional DETR does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="CONDITIONAL_DETR does not have a get_input_embeddings method")
@unittest.skip(reason="Conditional DETR does not have a get_input_embeddings method")
def test_model_common_attributes(self):
pass
@unittest.skip(reason="CONDITIONAL_DETR is not a generative model")
@unittest.skip(reason="Conditional DETR is not a generative model")
def test_generate_without_input_ids(self):
pass
@unittest.skip(reason="CONDITIONAL_DETR does not use token embeddings")
@unittest.skip(reason="Conditional DETR does not use token embeddings")
def test_resize_tokens_embeddings(self):
pass
@@ -474,7 +474,7 @@ class ConditionalDetrModelIntegrationTests(unittest.TestCase):
expected_shape = torch.Size((1, 300, 256))
self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
expected_slice = torch.tensor(
[[0.0616, -0.5146, -0.4032], [-0.7629, -0.4934, -1.7153], [-0.4768, -0.6403, -0.7826]]
[[0.4222, 0.7471, 0.8760], [0.6395, -0.2729, 0.7127], [-0.3090, 0.7642, 0.9529]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))
@@ -495,48 +495,13 @@ class ConditionalDetrModelIntegrationTests(unittest.TestCase):
expected_shape_logits = torch.Size((1, model.config.num_queries, model.config.num_labels))
self.assertEqual(outputs.logits.shape, expected_shape_logits)
expected_slice_logits = torch.tensor(
[[-19.1194, -0.0893, -11.0154], [-17.3640, -1.8035, -14.0219], [-20.0461, -0.5837, -11.1060]]
[[-10.4372, -5.7558, -8.6764], [-10.5410, -5.8704, -8.0590], [-10.6827, -6.3469, -8.3923]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_slice_logits, atol=1e-4))
expected_shape_boxes = torch.Size((1, model.config.num_queries, 4))
self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes)
expected_slice_boxes = torch.tensor(
[[0.4433, 0.5302, 0.8853], [0.5494, 0.2517, 0.0529], [0.4998, 0.5360, 0.9956]]
[[0.7733, 0.6576, 0.4496], [0.5171, 0.1184, 0.9094], [0.8846, 0.5647, 0.2486]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4))
def test_inference_panoptic_segmentation_head(self):
model = ConditionalDetrForSegmentation.from_pretrained("microsoft/conditional-detr-resnet-50-panoptic").to(
torch_device
)
feature_extractor = self.default_feature_extractor
image = prepare_img()
encoding = feature_extractor(images=image, return_tensors="pt").to(torch_device)
pixel_values = encoding["pixel_values"].to(torch_device)
pixel_mask = encoding["pixel_mask"].to(torch_device)
with torch.no_grad():
outputs = model(pixel_values, pixel_mask)
expected_shape_logits = torch.Size((1, model.config.num_queries, model.config.num_labels))
self.assertEqual(outputs.logits.shape, expected_shape_logits)
expected_slice_logits = torch.tensor(
[[-18.1565, -1.7568, -13.5029], [-16.8888, -1.4138, -14.1028], [-17.5709, -2.5080, -11.8654]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_slice_logits, atol=1e-4))
expected_shape_boxes = torch.Size((1, model.config.num_queries, 4))
self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes)
expected_slice_boxes = torch.tensor(
[[0.5344, 0.1789, 0.9285], [0.4420, 0.0572, 0.0875], [0.6630, 0.6887, 0.1017]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4))
expected_shape_masks = torch.Size((1, model.config.num_queries, 200, 267))
self.assertEqual(outputs.pred_masks.shape, expected_shape_masks)
expected_slice_masks = torch.tensor(
[[-7.7558, -10.8788, -11.9797], [-11.8881, -16.4329, -17.7451], [-14.7316, -19.7383, -20.3004]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.pred_masks[0, 0, :3, :3], expected_slice_masks, atol=1e-3))