text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
def call(
self,
hidden_states: tf.Tensor,
head_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
attention_outputs = self.attention(
# in ViTMAE, layernorm is applied before self-attention
input_tensor=sel... | 9,270 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
# second residual connection is done here
layer_output = self.vit_output(
hidden_states=intermediate_output, input_tensor=hidden_states, training=training
)
outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them
return outputs | 9,270 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "attention", None) is not None:
with tf.name_scope(self.attention.name):
self.attention.build(None)
if getattr(self, "intermediate", None) is not None:
... | 9,270 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
class TFViTMAEEncoder(keras.layers.Layer):
def __init__(self, config: ViTMAEConfig, **kwargs):
super().__init__(**kwargs)
self.layer = [TFViTMAELayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
def call(
self,
hidden_states: tf.Tensor,
head_ma... | 9,271 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
layer_outputs = layer_module(
hidden_states=hidden_states,
head_mask=head_mask[i],
output_attentions=output_attentions,
training=training,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_att... | 9,271 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "layer", None) is not None:
for layer in self.layer:
with tf.name_scope(layer.name):
layer.build(None) | 9,271 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
class TFViTMAEMainLayer(keras.layers.Layer):
config_class = ViTMAEConfig
def __init__(self, config: ViTMAEConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embeddings = TFViTMAEEmbeddings(config, name="embeddings")
self.encoder = TFViTMAEEncoder(config, n... | 9,272 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
@unpack_inputs
def call(
self,
pixel_values: TFModelInputType | None = None,
noise: tf.Tensor = None,
head_mask: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[... | 9,272 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_lengt... | 9,272 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
return TFViTMAEModelOutput(
last_hidden_state=sequence_output,
mask=mask,
ids_restore=ids_restore,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
... | 9,272 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
class TFViTMAEPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ViTMAEConfig
base_model_prefix = "vit"
main_input_name = "pixel_values" | 9,273 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
class TFViTMAEModel(TFViTMAEPreTrainedModel):
def __init__(self, config: ViTMAEConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.vit = TFViTMAEMainLayer(config, name="vit")
def get_input_embeddings(self):
return self.vit.get_input_embeddings()
@unpack_in... | 9,274 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
```python
>>> from transformers import AutoImageProcessor, TFViTMAEModel
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = Aut... | 9,274 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "vit", None) is not None:
with tf.name_scope(self.vit.name):
self.vit.build(None) | 9,274 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
class TFViTMAEDecoder(keras.layers.Layer):
def __init__(self, config, num_patches, **kwargs):
super().__init__(**kwargs)
self.decoder_embed = keras.layers.Dense(config.decoder_hidden_size, name="decoder_embed")
decoder_config = deepcopy(config)
decoder_config.hidden_size = config.de... | 9,275 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
self.decoder_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="decoder_norm")
self.decoder_pred = keras.layers.Dense(
config.patch_size**2 * config.num_channels,
kernel_initializer=get_initializer(config.initializer_range),
name="decoder_pred",
... | 9,275 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
def build(self, input_shape=None):
self.mask_token = self.add_weight(
shape=(1, 1, self.config.decoder_hidden_size),
initializer=tf.random_normal_initializer(stddev=self.config.initializer_range),
trainable=True,
name="mask_token",
)
self.decoder_p... | 9,275 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
if self.built:
return
self.built = True
if getattr(self, "decoder_embed", None) is not None:
with tf.name_scope(self.decoder_embed.name):
self.decoder_embed.build([None, None, self.config.hidden_size])
if getattr(self, "decoder_norm", None) is not None:
... | 9,275 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
def interpolate_pos_encoding(self, embeddings) -> tf.Tensor:
"""
This method is a modified version of the interpolation function for ViT-mae model at the deocder, that
allows to interpolate the pre-trained decoder position encodings, to be able to use the model on higher
resolution image... | 9,275 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
# interpolate the position embeddings
patch_pos_embed = tf.image.resize(
images=tf.reshape(patch_pos_embed, shape=(1, 1, -1, dim)),
size=(1, seq_len),
method="bicubic",
)
# [1, seq_len, hidden_size]
patch_pos_embed = tf.reshape(tensor=patch_pos_embed,... | 9,275 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
def call(
self,
hidden_states,
ids_restore,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
interpolate_pos_encoding=False,
):
# embed tokens
x = self.decoder_embed(hidden_states)
# append mask tokens to sequence
... | 9,275 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
for i, layer_module in enumerate(self.decoder_layers):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,) | 9,275 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
layer_outputs = layer_module(
hidden_states,
head_mask=None,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)... | 9,275 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
class TFViTMAEForPreTraining(TFViTMAEPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.vit = TFViTMAEMainLayer(config, name="vit")
self.decoder = TFViTMAEDecoder(
config,
num_patches=self.vit.embeddings.num_patch... | 9,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
Returns:
`tf.Tensor` of shape `(batch_size, num_patches, patch_size**2 * num_channels)`:
Patchified pixel values.
"""
patch_size, num_channels = self.config.patch_size, self.config.num_channels
# make sure channels are last
if shape_list(pixel_values)[1] == nu... | 9,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
# sanity checks
if not interpolate_pos_encoding:
tf.debugging.assert_equal(
shape_list(pixel_values)[1],
shape_list(pixel_values)[2],
message="Make sure the pixel values have a squared size",
)
tf.debugging.assert_equal(
... | 9,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
# patchify
batch_size = shape_list(pixel_values)[0]
num_patches_h = shape_list(pixel_values)[1] // patch_size
num_patches_w = shape_list(pixel_values)[2] // patch_size
patchified_pixel_values = tf.reshape(
pixel_values,
(batch_size, num_patches_h, patch_size, num_... | 9,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
def unpatchify(self, patchified_pixel_values, original_image_size: Optional[Tuple[int, int]] = None):
"""
Args:
patchified_pixel_values (`tf.Tensor` of shape `(batch_size, num_patches, patch_size**2 * num_channels)`:
Patchified pixel values.
original_image_size (`... | 9,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
Returns:
`tf.Tensor` of shape `(batch_size, height, width, num_channels)`:
Pixel values.
"""
patch_size, num_channels = self.config.patch_size, self.config.num_channels
original_image_size = (
original_image_size
if original_image_size is not N... | 9,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
# unpatchify
batch_size = shape_list(patchified_pixel_values)[0]
patchified_pixel_values = tf.reshape(
patchified_pixel_values,
(batch_size, num_patches_h, num_patches_w, patch_size, patch_size, num_channels),
)
patchified_pixel_values = tf.einsum("nhwpqc->nhpwqc"... | 9,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
def forward_loss(self, pixel_values, pred, mask, interpolate_pos_encoding: bool = False):
"""
Args:
pixel_values (`tf.Tensor` of shape `(batch_size, height, width, num_channels)`):
Pixel values.
pred (`tf.Tensor` of shape `(batch_size, num_patches, patch_size**2 *... | 9,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
Returns:
`tf.Tensor`: Pixel reconstruction loss.
"""
target = self.patchify(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
if self.config.norm_pix_loss:
mean = tf.reduce_mean(target, axis=-1, keepdims=True)
var = tf.math.reduce_variance(targe... | 9,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(VIT_MAE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFViTMAEForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
pixel_values: TFModelInputType | None = None,
noise: tf.Tensor = None,
head_mask:... | 9,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/vit-mae-base")
>>> model = TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base")
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> loss = outputs.loss
>>>... | 9,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
# [batch_size, num_patches, patch_size**2*3]
decoder_outputs = self.decoder(latent, ids_restore, interpolate_pos_encoding=interpolate_pos_encoding)
logits = decoder_outputs.logits
loss = self.forward_loss(pixel_values, logits, mask, interpolate_pos_encoding=interpolate_pos_encoding)
if... | 9,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "vit", None) is not None:
with tf.name_scope(self.vit.name):
self.vit.build(None)
if getattr(self, "decoder", None) is not None:
with tf.name_sc... | 9,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
class ViTMAEModelOutput(ModelOutput):
"""
Class for ViTMAEModel's outputs, with potential hidden states and attentions. | 9,277 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
Tensor indicating which patches are ma... | 9,277 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the atte... | 9,277 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
last_hidden_state: torch.FloatTensor = None
mask: torch.LongTensor = None
ids_restore: torch.LongTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None | 9,277 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
class ViTMAEDecoderOutput(ModelOutput):
"""
Class for ViTMAEDecoder's outputs, with potential hidden states and attentions. | 9,278 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
Args:
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, patch_size ** 2 * num_channels)`):
Pixel reconstruction logits.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
... | 9,278 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
the self-attention heads.
""" | 9,278 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None | 9,278 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
class ViTMAEForPreTrainingOutput(ModelOutput):
"""
Class for ViTMAEForPreTraining's outputs, with potential hidden states and attentions. | 9,279 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`):
Pixel reconstruction loss.
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, patch_size ** 2 * num_channels)`):
Pixel reconstruction logits.
mask (`torch.FloatTensor` of shape `(batch_size, sequence_lengt... | 9,279 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the atte... | 9,279 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
mask: torch.LongTensor = None
ids_restore: torch.LongTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None | 9,279 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
class ViTMAEEmbeddings(nn.Module):
"""
Construct the CLS token, position and patch embeddings.
"""
def __init__(self, config):
super().__init__()
self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
self.patch_embeddings = ViTMAEPatchEmbeddings(config)
... | 9,280 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
def initialize_weights(self):
# initialize (and freeze) position embeddings by sin-cos embedding
pos_embed = get_2d_sincos_pos_embed(
self.position_embeddings.shape[-1], int(self.patch_embeddings.num_patches**0.5), add_cls_token=True
)
self.position_embeddings.data.copy_(torc... | 9,280 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
# Copied from transformers.models.vit.modeling_vit.ViTEmbeddings.interpolate_pos_encoding
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 hi... | 9,280 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
# always interpolate when tracing to ensure the exported model works for dynamic input shapes
if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
return self.position_embeddings
class_pos_embed = self.position_embeddings[:, :1]
patch_pos_embed = self.... | 9,280 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
return torch.cat((class_pos_embed, patch_pos_embed), dim=1)
def random_masking(self, sequence, noise=None):
"""
Perform per-sample random masking by per-sample shuffling. Per-sample shuffling is done by argsort random
noise.
Args:
sequence (`torch.LongTensor` of shape `... | 9,280 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
# sort noise for each sample
ids_shuffle = torch.argsort(noise, dim=1).to(sequence.device) # ascend: small is keep, large is remove
ids_restore = torch.argsort(ids_shuffle, dim=1).to(sequence.device)
# keep the first subset
ids_keep = ids_shuffle[:, :len_keep]
sequence_unmasked... | 9,280 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
def forward(self, pixel_values, noise=None, interpolate_pos_encoding: bool = False):
batch_size, num_channels, height, width = pixel_values.shape
embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
if interpolate_pos_encoding:
position_... | 9,280 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
class ViTMAEPatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config):
... | 9,281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)
def forward(self, pixel_values, interpolate_pos_encoding: bool = False):
batch_size, num_channels, height, width = pixel_values.shape
if num_channels != self.num_channels:
raise ValueError(... | 9,281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
class ViTMAESelfAttention(nn.Module):
def __init__(self, config: ViTMAEConfig) -> None:
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size {config.hidden_size,} is not a... | 9,282 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self, hidden_states, head_mask: Optional[torch.Tensor] = None, output... | 9,282 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dro... | 9,282 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
class ViTMAESdpaSelfAttention(ViTMAESelfAttention):
def __init__(self, config: ViTMAEConfig) -> None:
super().__init__(config)
self.attention_probs_dropout_prob = config.attention_probs_dropout_prob | 9,283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
def forward(
self,
hidden_states: torch.FloatTensor,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
if output_attentions or head_mask is not None:
logger.warning_once(
... | 9,283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
mixed_query_layer = self.query(hidden_states)
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
context_layer = torch.nn.functional.scaled_dot_prod... | 9,283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
class ViTMAESelfOutput(nn.Module):
"""
The residual connection is defined in ViTMAELayer instead of here (as is the case with other models), due to the
layernorm applied before each block.
"""
def __init__(self, config: ViTMAEConfig) -> None:
super().__init__()
self.dense = nn.Linea... | 9,284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
class ViTMAEAttention(nn.Module):
def __init__(self, config: ViTMAEConfig) -> None:
super().__init__()
self.attention = ViTMAESelfAttention(config)
self.output = ViTMAESelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads: Set[int]) -> None:
if len(hea... | 9,285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
# Update hyper params and store pruned heads
self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def for... | 9,285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
class ViTMAESdpaAttention(ViTMAEAttention):
def __init__(self, config: ViTMAEConfig) -> None:
super().__init__(config)
self.attention = ViTMAESdpaSelfAttention(config) | 9,286 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
class ViTMAEIntermediate(nn.Module):
def __init__(self, config: ViTMAEConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:... | 9,287 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
class ViTMAEOutput(nn.Module):
def __init__(self, config: ViTMAEConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: tor... | 9,288 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
class ViTMAELayer(nn.Module):
"""This corresponds to the Block class in the timm implementation."""
def __init__(self, config: ViTMAEConfig) -> None:
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = VITMAE_ATT... | 9,289 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
def forward(
self,
hidden_states: torch.Tensor,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
self_attention_outputs = self.attention(
self.layernorm_before(hidden_sta... | 9,289 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
outputs = (layer_output,) + outputs
return outputs | 9,289 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
class ViTMAEEncoder(nn.Module):
def __init__(self, config: ViTMAEConfig) -> None:
super().__init__()
self.config = config
self.layer = nn.ModuleList([ViTMAELayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
... | 9,290 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
layer_head_mask,
output_attentions,
)
else:
... | 9,290 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
class ViTMAEPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ViTMAEConfig
base_model_prefix = "vit"
main_input_name = "pixel_values"
supports_gradient_checkpo... | 9,291 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
class ViTMAEModel(ViTMAEPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embeddings = ViTMAEEmbeddings(config)
self.encoder = ViTMAEEncoder(config)
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
... | 9,292 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
@add_start_docstrings_to_model_forward(VIT_MAE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=ViTMAEModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
noise: Optional[torch.FloatTensor] = None,
head_mask: Opti... | 9,292 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/vit-mae-base")
>>> model = ViTMAEModel.from_pretrained("facebook/vit-mae-base")
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_s... | 9,292 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_lengt... | 9,292 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
if not return_dict:
return (sequence_output, mask, ids_restore) + encoder_outputs[1:]
return ViTMAEModelOutput(
last_hidden_state=sequence_output,
mask=mask,
ids_restore=ids_restore,
hidden_states=encoder_outputs.hidden_states,
attentions=... | 9,292 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
class ViTMAEDecoder(nn.Module):
def __init__(self, config, num_patches):
super().__init__()
self.decoder_embed = nn.Linear(config.hidden_size, config.decoder_hidden_size, bias=True)
self.mask_token = nn.Parameter(torch.zeros(1, 1, config.decoder_hidden_size))
self.decoder_pos_embed =... | 9,293 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
self.decoder_norm = nn.LayerNorm(config.decoder_hidden_size, eps=config.layer_norm_eps)
self.decoder_pred = nn.Linear(
config.decoder_hidden_size, config.patch_size**2 * config.num_channels, bias=True
) # encoder to decoder
self.gradient_checkpointing = False
self.config = c... | 9,293 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
# -1 removes the class dimension since we later append it without interpolation
embeddings_positions = embeddings.shape[1] - 1
# Separation of class token and patch tokens
class_pos_embed = self.decoder_pos_embed[:, :1]
patch_pos_embed = self.decoder_pos_embed[:, 1:]
# To retai... | 9,293 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
# Interpolating the decoder position embeddings shape wrt embeddings shape i.e (x).
# we keep the second last dimension constant
patch_pos_embed = nn.functional.interpolate(
patch_pos_embed,
size=(patch_pos_embed.shape[-2], embeddings_positions),
mode="bicubic",
... | 9,293 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
# timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.)
torch.nn.init.normal_(self.mask_token, std=self.config.initializer_range)
def forward(
self,
hidden_states,
ids_restore,
output_attentions=False,
output_hidden_states=False,
... | 9,293 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
# append mask tokens to sequence
mask_tokens = self.mask_token.repeat(x.shape[0], ids_restore.shape[1] + 1 - x.shape[1], 1)
x_ = torch.cat([x[:, 1:, :], mask_tokens], dim=1) # no cls token
# unshuffle
x_ = torch.gather(x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2]).... | 9,293 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
None,
output_attentions,
)
else:
layer_ou... | 9,293 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
if not return_dict:
return tuple(v for v in [logits, all_hidden_states, all_self_attentions] if v is not None)
return ViTMAEDecoderOutput(
logits=logits,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
) | 9,293 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
class ViTMAEForPreTraining(ViTMAEPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.vit = ViTMAEModel(config)
self.decoder = ViTMAEDecoder(config, num_patches=self.vit.embeddings.num_patches)
# Initialize weights and apply final... | 9,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
def patchify(self, pixel_values, interpolate_pos_encoding: bool = False):
"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values.
interpolate_pos_encoding (`bool`, *optional*, default `False`):
... | 9,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
Returns:
`torch.FloatTensor` of shape `(batch_size, num_patches, patch_size**2 * num_channels)`:
Patchified pixel values.
"""
patch_size, num_channels = self.config.patch_size, self.config.num_channels
# sanity checks
if not interpolate_pos_encoding and (
... | 9,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
# patchify
batch_size = pixel_values.shape[0]
num_patches_h = pixel_values.shape[2] // patch_size
num_patches_w = pixel_values.shape[3] // patch_size
patchified_pixel_values = pixel_values.reshape(
batch_size, num_channels, num_patches_h, patch_size, num_patches_w, patch_size... | 9,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
def unpatchify(self, patchified_pixel_values, original_image_size: Optional[Tuple[int, int]] = None):
"""
Args:
patchified_pixel_values (`torch.FloatTensor` of shape `(batch_size, num_patches, patch_size**2 * num_channels)`:
Patchified pixel values.
original_image... | 9,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
Returns:
`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`:
Pixel values.
"""
patch_size, num_channels = self.config.patch_size, self.config.num_channels
original_image_size = (
original_image_size
if original_image_size ... | 9,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
# unpatchify
batch_size = patchified_pixel_values.shape[0]
patchified_pixel_values = patchified_pixel_values.reshape(
batch_size,
num_patches_h,
num_patches_w,
patch_size,
patch_size,
num_channels,
)
patchified_pixel... | 9,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
def forward_loss(self, pixel_values, pred, mask, interpolate_pos_encoding: bool = False):
"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values.
pred (`torch.FloatTensor` of shape `(batch_size, num_patches,... | 9,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
Returns:
`torch.FloatTensor`: Pixel reconstruction loss.
"""
target = self.patchify(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
if self.config.norm_pix_loss:
mean = target.mean(dim=-1, keepdim=True)
var = target.var(dim=-1, keepdim=True)
... | 9,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
@add_start_docstrings_to_model_forward(VIT_MAE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=ViTMAEForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
noise: Optional[torch.FloatTensor] = None,
head_m... | 9,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/vit-mae-base")
>>> model = ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base")
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> loss = outputs.loss
>>> m... | 9,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
decoder_outputs = self.decoder(latent, ids_restore, interpolate_pos_encoding=interpolate_pos_encoding)
logits = decoder_outputs.logits # shape (batch_size, num_patches, patch_size*patch_size*num_channels)
loss = self.forward_loss(pixel_values, logits, mask, interpolate_pos_encoding=interpolate_pos_enc... | 9,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_vit_mae.py |
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