Enable HF pretrained backbones (#31145)
* Enable load HF or tim backbone checkpoints * Fix up * Fix test - pass in proper out_indices * Update docs * Fix tvp tests * Fix doc examples * Fix doc examples * Try to resolve DPT backbone param init * Don't conditionally set to None * Add condition based on whether backbone is defined * Address review comments
This commit is contained in:
@@ -501,6 +501,34 @@ class GroundingDinoModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.Tes
|
||||
|
||||
self.assertTrue(outputs)
|
||||
|
||||
@require_timm
|
||||
def test_hf_backbone(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
# Load a pretrained HF checkpoint as backbone
|
||||
config.backbone = "microsoft/resnet-18"
|
||||
config.backbone_config = None
|
||||
config.use_timm_backbone = False
|
||||
config.use_pretrained_backbone = True
|
||||
config.backbone_kwargs = {"out_indices": [2, 3, 4]}
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
model = model_class(config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
with torch.no_grad():
|
||||
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
||||
|
||||
if model_class.__name__ == "GroundingDinoForObjectDetection":
|
||||
expected_shape = (
|
||||
self.model_tester.batch_size,
|
||||
self.model_tester.num_queries,
|
||||
config.max_text_len,
|
||||
)
|
||||
self.assertEqual(outputs.logits.shape, expected_shape)
|
||||
|
||||
self.assertTrue(outputs)
|
||||
|
||||
def test_initialization(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
|
||||
Reference in New Issue
Block a user