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[CLIPSeg] Make interpolate_pos_encoding default to True (#34419)

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* Remove interpolate_pos_encoding

* Make fixup

* Make interpolate_pos_encoding default to True

* Reuse existing interpolation

* Add integration test
This commit is contained in:
NielsRogge 2024-10-31 22:15:04 +01:00 committed by GitHub
parent 203e27059b
commit df8640cedb
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2 changed files with 11 additions and 15 deletions
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22
src/transformers/models/clipseg/modeling_clipseg.py
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@ -205,7 +205,7 @@ class CLIPSegVisionEmbeddings(nn.Module):
return torch.cat((class_pos_embed, patch_pos_embed), dim=1)
def forward(self, pixel_values: torch.FloatTensor, interpolate_pos_encoding=False) -> torch.Tensor:
def forward(self, pixel_values: torch.FloatTensor, interpolate_pos_encoding=True) -> torch.Tensor:
batch_size, _, height, width = pixel_values.shape
if not interpolate_pos_encoding and (height != self.image_size or width != self.image_size):
raise ValueError(
@ -535,7 +535,7 @@ CLIPSEG_VISION_INPUTS_DOCSTRING = r"""
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
interpolate_pos_encoding (`bool`, *optional*, defaults to `False`):
interpolate_pos_encoding (`bool`, *optional*, defaults to `True`):
Whether to interpolate the pre-trained position encodings.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
@ -574,7 +574,7 @@ CLIPSEG_INPUTS_DOCSTRING = r"""
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
interpolate_pos_encoding (`bool`, *optional*, defaults to `False`):
interpolate_pos_encoding (`bool`, *optional*, defaults to `True`):
Whether to interpolate the pre-trained position encodings.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
@ -845,14 +845,13 @@ class CLIPSegVisionTransformer(nn.Module):
@add_start_docstrings_to_model_forward(CLIPSEG_VISION_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPSegVisionConfig)
# Copied from transformers.models.clip.modeling_clip.CLIPVisionTransformer.forward
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
pixel_values: Optional[torch.FloatTensor],
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
interpolate_pos_encoding: Optional[bool] = False,
interpolate_pos_encoding: Optional[bool] = True,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
@ -864,9 +863,6 @@ class CLIPSegVisionTransformer(nn.Module):
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
hidden_states = self.pre_layrnorm(hidden_states)
@ -912,7 +908,7 @@ class CLIPSegVisionModel(CLIPSegPreTrainedModel):
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: Optional[bool] = False,
interpolate_pos_encoding: Optional[bool] = True,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
@ -1035,7 +1031,7 @@ class CLIPSegModel(CLIPSegPreTrainedModel):
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
interpolate_pos_encoding: bool = True,
return_dict: Optional[bool] = None,
) -> torch.FloatTensor:
r"""
@ -1091,7 +1087,7 @@ class CLIPSegModel(CLIPSegPreTrainedModel):
return_loss: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
interpolate_pos_encoding: bool = True,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CLIPSegOutput]:
r"""
@ -1397,7 +1393,7 @@ class CLIPSegForImageSegmentation(CLIPSegPreTrainedModel):
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
interpolate_pos_encoding: bool = True,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CLIPSegOutput]:
r"""

4
tests/models/clipseg/test_modeling_clipseg.py
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@ -796,7 +796,7 @@ class CLIPSegModelIntegrationTest(unittest.TestCase):
# forward pass
with torch.no_grad():
outputs = model(**inputs, interpolate_pos_encoding=True)
outputs = model(**inputs)
# verify the predicted masks
self.assertEqual(
@ -804,7 +804,7 @@ class CLIPSegModelIntegrationTest(unittest.TestCase):
torch.Size((3, 352, 352)),
)
expected_masks_slice = torch.tensor(
[[-7.4613, -7.4785, -7.3627], [-7.3268, -7.0898, -7.1333], [-6.9838, -6.7900, -6.8913]]
[[-7.4613, -7.4785, -7.3628], [-7.3268, -7.0899, -7.1333], [-6.9838, -6.7900, -6.8913]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_masks_slice, atol=1e-3))