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Enable dynamic resolution input for Swin Transformer and variants (#30656) 

* add interpolation of positional encoding support to swin

* add style changes

* use default image processor and make size a dictionary

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* remove logits testing

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Refactor image size validation logic when interpolation is disabled

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* remove asserts in modeling

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* add dynamic resolution input support to swinv2

* change size to ensure interpolation encoding path is triggered

* set interpolate_pos_encoding default value to False

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* set interpolate_pos_encoding default value to False

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* set interpolate_pos_encoding default value to False

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* set interpolate_pos_encoding default value to False

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* set interpolate_pos_encoding default value to False

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* set interpolate_pos_encoding default value to False

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* set interpolate_pos_encoding default value to False

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* set interpolate_pos_encoding default value to False

* add dynamic resolution input to donut swin

* add dynamic resolution input to maskformer swin

---------

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
This commit is contained in:
Abhiroop Tejomay 2024-05-17 13:38:46 -04:00 committed by GitHub
parent b6eb708bf1
commit 481a957814
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6 changed files with 291 additions and 20 deletions
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63
src/transformers/models/donut/modeling_donut_swin.py
View File

@ -166,10 +166,48 @@ class DonutSwinEmbeddings(nn.Module):
self.norm = nn.LayerNorm(config.embed_dim)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
resolution images.
Source:
https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
"""
num_patches = embeddings.shape[1] - 1
num_positions = self.position_embeddings.shape[1] - 1
if num_patches == num_positions and height == width:
return self.position_embeddings
class_pos_embed = self.position_embeddings[:, 0]
patch_pos_embed = self.position_embeddings[:, 1:]
dim = embeddings.shape[-1]
h0 = height // self.config.patch_size
w0 = width // self.config.patch_size
# we add a small number to avoid floating point error in the interpolation
# see discussion at https://github.com/facebookresearch/dino/issues/8
h0, w0 = h0 + 0.1, w0 + 0.1
patch_pos_embed = patch_pos_embed.reshape(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim)
patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
patch_pos_embed = nn.functional.interpolate(
patch_pos_embed,
scale_factor=(h0 / math.sqrt(num_positions), w0 / math.sqrt(num_positions)),
mode="bicubic",
align_corners=False,
)
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
def forward(
self, pixel_values: Optional[torch.FloatTensor], bool_masked_pos: Optional[torch.BoolTensor] = None
self,
pixel_values: Optional[torch.FloatTensor],
bool_masked_pos: Optional[torch.BoolTensor] = None,
interpolate_pos_encoding: bool = False,
) -> Tuple[torch.Tensor]:
embeddings, output_dimensions = self.patch_embeddings(pixel_values)
_, num_channels, height, width = pixel_values.shape
embeddings, output_dimensions = self.patch_embeddings(
pixel_values, interpolate_pos_encoding=interpolate_pos_encoding
)
embeddings = self.norm(embeddings)
batch_size, seq_len, _ = embeddings.size()
@ -180,7 +218,10 @@ class DonutSwinEmbeddings(nn.Module):
embeddings = embeddings * (1.0 - mask) + mask_tokens * mask
if self.position_embeddings is not None:
embeddings = embeddings + self.position_embeddings
if interpolate_pos_encoding:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
embeddings = embeddings + self.position_embeddings
embeddings = self.dropout(embeddings)
@ -219,7 +260,9 @@ class DonutSwinPatchEmbeddings(nn.Module):
pixel_values = nn.functional.pad(pixel_values, pad_values)
return pixel_values
def forward(self, pixel_values: Optional[torch.FloatTensor]) -> Tuple[torch.Tensor, Tuple[int]]:
def forward(
self, pixel_values: Optional[torch.FloatTensor], interpolate_pos_encoding: bool = False
) -> Tuple[torch.Tensor, Tuple[int]]:
_, num_channels, height, width = pixel_values.shape
if num_channels != self.num_channels:
raise ValueError(
@ -227,6 +270,11 @@ class DonutSwinPatchEmbeddings(nn.Module):
)
# pad the input to be divisible by self.patch_size, if needed
pixel_values = self.maybe_pad(pixel_values, height, width)
if not interpolate_pos_encoding and (height != self.image_size[0] or width != self.image_size[1]):
raise ValueError(
f"Input image size ({height}*{width}) doesn't match model"
f" ({self.image_size[0]}*{self.image_size[1]})."
)
embeddings = self.projection(pixel_values)
_, _, height, width = embeddings.shape
output_dimensions = (height, width)
@ -849,6 +897,8 @@ SWIN_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`):
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.
"""
@ -899,6 +949,7 @@ class DonutSwinModel(DonutSwinPreTrainedModel):
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
return_dict: Optional[bool] = None,
) -> Union[Tuple, DonutSwinModelOutput]:
r"""
@ -921,7 +972,9 @@ class DonutSwinModel(DonutSwinPreTrainedModel):
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, len(self.config.depths))
embedding_output, input_dimensions = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos)
embedding_output, input_dimensions = self.embeddings(
pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding
)
encoder_outputs = self.encoder(
embedding_output,

58
src/transformers/models/maskformer/modeling_maskformer_swin.py
View File

@ -163,12 +163,50 @@ class MaskFormerSwinEmbeddings(nn.Module):
self.norm = nn.LayerNorm(config.embed_dim)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, pixel_values):
embeddings, output_dimensions = self.patch_embeddings(pixel_values)
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
resolution images.
Source:
https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
"""
num_patches = embeddings.shape[1] - 1
num_positions = self.position_embeddings.shape[1] - 1
if num_patches == num_positions and height == width:
return self.position_embeddings
class_pos_embed = self.position_embeddings[:, 0]
patch_pos_embed = self.position_embeddings[:, 1:]
dim = embeddings.shape[-1]
h0 = height // self.config.patch_size
w0 = width // self.config.patch_size
# we add a small number to avoid floating point error in the interpolation
# see discussion at https://github.com/facebookresearch/dino/issues/8
h0, w0 = h0 + 0.1, w0 + 0.1
patch_pos_embed = patch_pos_embed.reshape(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim)
patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
patch_pos_embed = nn.functional.interpolate(
patch_pos_embed,
scale_factor=(h0 / math.sqrt(num_positions), w0 / math.sqrt(num_positions)),
mode="bicubic",
align_corners=False,
)
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
def forward(self, pixel_values, interpolate_pos_encoding):
_, num_channels, height, width = pixel_values.shape
embeddings, output_dimensions = self.patch_embeddings(
pixel_values, interpolate_pos_encoding=interpolate_pos_encoding
)
embeddings = self.norm(embeddings)
if self.position_embeddings is not None:
embeddings = embeddings + self.position_embeddings
if interpolate_pos_encoding:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
embeddings = embeddings + self.position_embeddings
embeddings = self.dropout(embeddings)
@ -207,7 +245,9 @@ class MaskFormerSwinPatchEmbeddings(nn.Module):
pixel_values = nn.functional.pad(pixel_values, pad_values)
return pixel_values
def forward(self, pixel_values: Optional[torch.FloatTensor]) -> Tuple[torch.Tensor, Tuple[int]]:
def forward(
self, pixel_values: Optional[torch.FloatTensor], interpolate_pos_encoding: bool = False
) -> Tuple[torch.Tensor, Tuple[int]]:
_, num_channels, height, width = pixel_values.shape
if num_channels != self.num_channels:
raise ValueError(
@ -215,6 +255,11 @@ class MaskFormerSwinPatchEmbeddings(nn.Module):
)
# pad the input to be divisible by self.patch_size, if needed
pixel_values = self.maybe_pad(pixel_values, height, width)
if not interpolate_pos_encoding and (height != self.image_size[0] or width != self.image_size[1]):
raise ValueError(
f"Input image size ({height}*{width}) doesn't match model"
f" ({self.image_size[0]}*{self.image_size[1]})."
)
embeddings = self.projection(pixel_values)
_, _, height, width = embeddings.shape
output_dimensions = (height, width)
@ -780,6 +825,7 @@ class MaskFormerSwinModel(MaskFormerSwinPreTrainedModel):
head_mask=None,
output_attentions=None,
output_hidden_states=None,
interpolate_pos_encoding=False,
return_dict=None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
@ -798,7 +844,9 @@ class MaskFormerSwinModel(MaskFormerSwinPreTrainedModel):
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, len(self.config.depths))
embedding_output, input_dimensions = self.embeddings(pixel_values)
embedding_output, input_dimensions = self.embeddings(
pixel_values, interpolate_pos_encoding=interpolate_pos_encoding
)
encoder_outputs = self.encoder(
embedding_output,

75
src/transformers/models/swin/modeling_swin.py
View File

@ -252,10 +252,48 @@ class SwinEmbeddings(nn.Module):
self.norm = nn.LayerNorm(config.embed_dim)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
resolution images.
Source:
https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
"""
num_patches = embeddings.shape[1] - 1
num_positions = self.position_embeddings.shape[1] - 1
if num_patches == num_positions and height == width:
return self.position_embeddings
class_pos_embed = self.position_embeddings[:, 0]
patch_pos_embed = self.position_embeddings[:, 1:]
dim = embeddings.shape[-1]
h0 = height // self.config.patch_size
w0 = width // self.config.patch_size
# we add a small number to avoid floating point error in the interpolation
# see discussion at https://github.com/facebookresearch/dino/issues/8
h0, w0 = h0 + 0.1, w0 + 0.1
patch_pos_embed = patch_pos_embed.reshape(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim)
patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
patch_pos_embed = nn.functional.interpolate(
patch_pos_embed,
scale_factor=(h0 / math.sqrt(num_positions), w0 / math.sqrt(num_positions)),
mode="bicubic",
align_corners=False,
)
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
def forward(
self, pixel_values: Optional[torch.FloatTensor], bool_masked_pos: Optional[torch.BoolTensor] = None
self,
pixel_values: Optional[torch.FloatTensor],
bool_masked_pos: Optional[torch.BoolTensor] = None,
interpolate_pos_encoding: bool = False,
) -> Tuple[torch.Tensor]:
embeddings, output_dimensions = self.patch_embeddings(pixel_values)
_, num_channels, height, width = pixel_values.shape
embeddings, output_dimensions = self.patch_embeddings(
pixel_values, interpolate_pos_encoding=interpolate_pos_encoding
)
embeddings = self.norm(embeddings)
batch_size, seq_len, _ = embeddings.size()
@ -266,7 +304,10 @@ class SwinEmbeddings(nn.Module):
embeddings = embeddings * (1.0 - mask) + mask_tokens * mask
if self.position_embeddings is not None:
embeddings = embeddings + self.position_embeddings
if interpolate_pos_encoding:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
embeddings = embeddings + self.position_embeddings
embeddings = self.dropout(embeddings)
@ -304,7 +345,9 @@ class SwinPatchEmbeddings(nn.Module):
pixel_values = nn.functional.pad(pixel_values, pad_values)
return pixel_values
def forward(self, pixel_values: Optional[torch.FloatTensor]) -> Tuple[torch.Tensor, Tuple[int]]:
def forward(
self, pixel_values: Optional[torch.FloatTensor], interpolate_pos_encoding: bool = False
) -> Tuple[torch.Tensor, Tuple[int]]:
_, num_channels, height, width = pixel_values.shape
if num_channels != self.num_channels:
raise ValueError(
@ -312,6 +355,11 @@ class SwinPatchEmbeddings(nn.Module):
)
# pad the input to be divisible by self.patch_size, if needed
pixel_values = self.maybe_pad(pixel_values, height, width)
if not interpolate_pos_encoding and (height != self.image_size[0] or width != self.image_size[1]):
raise ValueError(
f"Input image size ({height}*{width}) doesn't match model"
f" ({self.image_size[0]}*{self.image_size[1]})."
)
embeddings = self.projection(pixel_values)
_, _, height, width = embeddings.shape
output_dimensions = (height, width)
@ -924,6 +972,8 @@ SWIN_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`):
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.
"""
@ -981,6 +1031,7 @@ class SwinModel(SwinPreTrainedModel):
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
return_dict: Optional[bool] = None,
) -> Union[Tuple, SwinModelOutput]:
r"""
@ -1003,7 +1054,9 @@ class SwinModel(SwinPreTrainedModel):
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, len(self.config.depths))
embedding_output, input_dimensions = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos)
embedding_output, input_dimensions = self.embeddings(
pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding
)
encoder_outputs = self.encoder(
embedding_output,
@ -1074,6 +1127,7 @@ class SwinForMaskedImageModeling(SwinPreTrainedModel):
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
return_dict: Optional[bool] = None,
) -> Union[Tuple, SwinMaskedImageModelingOutput]:
r"""
@ -1113,6 +1167,7 @@ class SwinForMaskedImageModeling(SwinPreTrainedModel):
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
)
@ -1156,6 +1211,14 @@ class SwinForMaskedImageModeling(SwinPreTrainedModel):
"""
Swin Model transformer with an image classification head on top (a linear layer on top of the final hidden state of
the [CLS] token) e.g. for ImageNet.
<Tip>
Note that it's possible to fine-tune Swin on higher resolution images than the ones it has been trained on, by
setting `interpolate_pos_encoding` to `True` in the forward of the model. This will interpolate the pre-trained
position embeddings to the higher resolution.
</Tip>
""",
SWIN_START_DOCSTRING,
)
@ -1188,6 +1251,7 @@ class SwinForImageClassification(SwinPreTrainedModel):
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
return_dict: Optional[bool] = None,
) -> Union[Tuple, SwinImageClassifierOutput]:
r"""
@ -1203,6 +1267,7 @@ class SwinForImageClassification(SwinPreTrainedModel):
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
)

75
src/transformers/models/swinv2/modeling_swinv2.py
View File

@ -295,10 +295,48 @@ class Swinv2Embeddings(nn.Module):
self.norm = nn.LayerNorm(config.embed_dim)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
resolution images.
Source:
https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
"""
num_patches = embeddings.shape[1] - 1
num_positions = self.position_embeddings.shape[1] - 1
if num_patches == num_positions and height == width:
return self.position_embeddings
class_pos_embed = self.position_embeddings[:, 0]
patch_pos_embed = self.position_embeddings[:, 1:]
dim = embeddings.shape[-1]
h0 = height // self.config.patch_size
w0 = width // self.config.patch_size
# we add a small number to avoid floating point error in the interpolation
# see discussion at https://github.com/facebookresearch/dino/issues/8
h0, w0 = h0 + 0.1, w0 + 0.1
patch_pos_embed = patch_pos_embed.reshape(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim)
patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
patch_pos_embed = nn.functional.interpolate(
patch_pos_embed,
scale_factor=(h0 / math.sqrt(num_positions), w0 / math.sqrt(num_positions)),
mode="bicubic",
align_corners=False,
)
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
def forward(
self, pixel_values: Optional[torch.FloatTensor], bool_masked_pos: Optional[torch.BoolTensor] = None
self,
pixel_values: Optional[torch.FloatTensor],
bool_masked_pos: Optional[torch.BoolTensor] = None,
interpolate_pos_encoding: bool = False,
) -> Tuple[torch.Tensor]:
embeddings, output_dimensions = self.patch_embeddings(pixel_values)
_, num_channels, height, width = pixel_values.shape
embeddings, output_dimensions = self.patch_embeddings(
pixel_values, interpolate_pos_encoding=interpolate_pos_encoding
)
embeddings = self.norm(embeddings)
batch_size, seq_len, _ = embeddings.size()
@ -309,7 +347,10 @@ class Swinv2Embeddings(nn.Module):
embeddings = embeddings * (1.0 - mask) + mask_tokens * mask
if self.position_embeddings is not None:
embeddings = embeddings + self.position_embeddings
if interpolate_pos_encoding:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
embeddings = embeddings + self.position_embeddings
embeddings = self.dropout(embeddings)
@ -348,7 +389,9 @@ class Swinv2PatchEmbeddings(nn.Module):
pixel_values = nn.functional.pad(pixel_values, pad_values)
return pixel_values
def forward(self, pixel_values: Optional[torch.FloatTensor]) -> Tuple[torch.Tensor, Tuple[int]]:
def forward(
self, pixel_values: Optional[torch.FloatTensor], interpolate_pos_encoding: bool = False
) -> Tuple[torch.Tensor, Tuple[int]]:
_, num_channels, height, width = pixel_values.shape
if num_channels != self.num_channels:
raise ValueError(
@ -356,6 +399,11 @@ class Swinv2PatchEmbeddings(nn.Module):
)
# pad the input to be divisible by self.patch_size, if needed
pixel_values = self.maybe_pad(pixel_values, height, width)
if not interpolate_pos_encoding and (height != self.image_size[0] or width != self.image_size[1]):
raise ValueError(
f"Input image size ({height}*{width}) doesn't match model"
f" ({self.image_size[0]}*{self.image_size[1]})."
)
embeddings = self.projection(pixel_values)
_, _, height, width = embeddings.shape
output_dimensions = (height, width)
@ -979,6 +1027,8 @@ SWINV2_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*, default `False`):
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.
"""
@ -1031,6 +1081,7 @@ class Swinv2Model(Swinv2PreTrainedModel):
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Swinv2ModelOutput]:
r"""
@ -1053,7 +1104,9 @@ class Swinv2Model(Swinv2PreTrainedModel):
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, len(self.config.depths))
embedding_output, input_dimensions = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos)
embedding_output, input_dimensions = self.embeddings(
pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding
)
encoder_outputs = self.encoder(
embedding_output,
@ -1126,6 +1179,7 @@ class Swinv2ForMaskedImageModeling(Swinv2PreTrainedModel):
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Swinv2MaskedImageModelingOutput]:
r"""
@ -1165,6 +1219,7 @@ class Swinv2ForMaskedImageModeling(Swinv2PreTrainedModel):
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
)
@ -1208,6 +1263,14 @@ class Swinv2ForMaskedImageModeling(Swinv2PreTrainedModel):
"""
Swinv2 Model transformer with an image classification head on top (a linear layer on top of the final hidden state
of the [CLS] token) e.g. for ImageNet.
<Tip>
Note that it's possible to fine-tune SwinV2 on higher resolution images than the ones it has been trained on, by
setting `interpolate_pos_encoding` to `True` in the forward of the model. This will interpolate the pre-trained
position embeddings to the higher resolution.
</Tip>
""",
SWINV2_START_DOCSTRING,
)
@ -1241,6 +1304,7 @@ class Swinv2ForImageClassification(Swinv2PreTrainedModel):
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Swinv2ImageClassifierOutput]:
r"""
@ -1256,6 +1320,7 @@ class Swinv2ForImageClassification(Swinv2PreTrainedModel):
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
)

20
tests/models/swin/test_modeling_swin.py
View File

@ -493,6 +493,26 @@ class SwinModelIntegrationTest(unittest.TestCase):
expected_slice = torch.tensor([-0.0948, -0.6454, -0.0921]).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
@slow
def test_inference_interpolate_pos_encoding(self):
# Swin models have an `interpolate_pos_encoding` argument in their forward method,
# allowing to interpolate the pre-trained position embeddings in order to use
# the model on higher resolutions.
model = SwinModel.from_pretrained("microsoft/swin-tiny-patch4-window7-224").to(torch_device)
image_processor = self.default_image_processor
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
inputs = image_processor(images=image, size={"height": 481, "width": 481}, return_tensors="pt")
pixel_values = inputs.pixel_values.to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(pixel_values, interpolate_pos_encoding=True)
# verify the logits
expected_shape = torch.Size((1, 256, 768))
self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
@require_torch
class SwinBackboneTest(unittest.TestCase, BackboneTesterMixin):

20
tests/models/swinv2/test_modeling_swinv2.py
View File

@ -485,6 +485,26 @@ class Swinv2ModelIntegrationTest(unittest.TestCase):
expected_slice = torch.tensor([-0.3947, -0.4306, 0.0026]).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
@slow
def test_inference_interpolate_pos_encoding(self):
# Swinv2 models have an `interpolate_pos_encoding` argument in their forward method,
# allowing to interpolate the pre-trained position embeddings in order to use
# the model on higher resolutions.
model = Swinv2Model.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256").to(torch_device)
image_processor = self.default_image_processor
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
inputs = image_processor(images=image, size={"height": 481, "width": 481}, return_tensors="pt")
pixel_values = inputs.pixel_values.to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(pixel_values, interpolate_pos_encoding=True)
# verify the logits
expected_shape = torch.Size((1, 256, 768))
self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
@require_torch
class Swinv2BackboneTest(unittest.TestCase, BackboneTesterMixin):