text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
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class EfficientFormerConvMlp(nn.Module):
def __init__(
self,
config: EfficientFormerConfig,
in_features: int,
hidden_features: Optional[int] = None,
out_features: Optional[int] = None,
drop: float = 0.0,
):
super().__init__()
out_features = out_fea... | 10,449 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
hidden_state = self.activation(hidden_state)
hidden_state = self.dropout(hidden_state)
hidden_state = self.convolution2(hidden_state)
hidden_state = self.batchnorm_after(hidden_state)
hidden_state = self.dropout(hidden_state)
return hidden_state | 10,449 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
class EfficientFormerDropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: Optional[float] = None) -> None:
super().__init__()
self.drop_prob = drop_prob
def forward(self, hidden_states: torch.Tenso... | 10,450 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
class EfficientFormerFlat(nn.Module):
def __init__(self):
super().__init__()
def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor]:
hidden_states = hidden_states.flatten(2).transpose(1, 2)
return hidden_states | 10,451 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
class EfficientFormerMeta3D(nn.Module):
def __init__(self, config: EfficientFormerConfig, dim: int, drop_path: float = 0.0):
super().__init__()
self.token_mixer = EfficientFormerSelfAttention(
dim=config.dim,
key_dim=config.key_dim,
num_heads=config.num_attention... | 10,452 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
self.drop_path = EfficientFormerDropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.use_layer_scale = config.use_layer_scale
if config.use_layer_scale:
self.layer_scale_1 = nn.Parameter(config.layer_scale_init_value * torch.ones((dim)), requires_grad=True)
self.layer_s... | 10,452 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
if self.use_layer_scale:
layer_output = hidden_states + self.drop_path(
self.layer_scale_1.unsqueeze(0).unsqueeze(0) * attention_output
)
layer_output = layer_output + self.drop_path(
self.layer_scale_2.unsqueeze(0).unsqueeze(0) * self.mlp(self.layerno... | 10,452 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
class EfficientFormerMeta3DLayers(nn.Module):
def __init__(self, config: EfficientFormerConfig):
super().__init__()
drop_paths = [
config.drop_path_rate * (block_idx + sum(config.depths[:-1]))
for block_idx in range(config.num_meta3d_blocks)
]
self.blocks = nn... | 10,453 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
if output_attentions:
outputs = (hidden_states[0],) + all_attention_outputs
return outputs
return hidden_states | 10,453 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
class EfficientFormerMeta4D(nn.Module):
def __init__(self, config: EfficientFormerConfig, dim: int, drop_path: float = 0.0):
super().__init__()
pool_size = config.pool_size if config.pool_size is not None else 3
self.token_mixer = EfficientFormerPooling(pool_size=pool_size)
mlp_hidde... | 10,454 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor]:
outputs = self.token_mixer(hidden_states)
if self.use_layer_scale:
layer_output = hidden_states + self.drop_path(self.layer_scale_1.unsqueeze(-1).unsqueeze(-1) * outputs)
layer_output = layer_output + self.d... | 10,454 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
class EfficientFormerMeta4DLayers(nn.Module):
def __init__(self, config: EfficientFormerConfig, stage_idx: int):
super().__init__()
num_layers = (
config.depths[stage_idx] if stage_idx != -1 else config.depths[stage_idx] - config.num_meta3d_blocks
)
drop_paths = [
... | 10,455 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
class EfficientFormerIntermediateStage(nn.Module):
def __init__(self, config: EfficientFormerConfig, index: int):
super().__init__()
self.meta4D_layers = EfficientFormerMeta4DLayers(config, index)
def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor]:
hidden_states = sel... | 10,456 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
class EfficientFormerLastStage(nn.Module):
def __init__(self, config: EfficientFormerConfig):
super().__init__()
self.meta4D_layers = EfficientFormerMeta4DLayers(config, -1)
self.flat = EfficientFormerFlat()
self.meta3D_layers = EfficientFormerMeta3DLayers(config)
def forward(se... | 10,457 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
class EfficientFormerEncoder(nn.Module):
def __init__(self, config: EfficientFormerConfig):
super().__init__()
self.config = config
num_intermediate_stages = len(config.depths) - 1
downsamples = [
config.downsamples[i] or config.hidden_sizes[i] != config.hidden_sizes[i + ... | 10,458 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
def forward(
self,
hidden_states: torch.Tensor,
output_hidden_states: bool = False,
output_attentions: bool = False,
return_dict: bool = True,
) -> BaseModelOutput:
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_att... | 10,458 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
if not return_dict:
return tuple(v for v in [layer_output[0], all_hidden_states, all_self_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=layer_output[0],
hidden_states=all_hidden_states,
attentions=all_self_attentions,
) | 10,458 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
class EfficientFormerPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = EfficientFormerConfig
base_model_prefix = "efficientformer"
main_input_name = "pixel_values"... | 10,459 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
class EfficientFormerModel(EfficientFormerPreTrainedModel):
def __init__(self, config: EfficientFormerConfig):
super().__init__(config)
self.config = config
_no_split_modules = ["EfficientFormerMeta4D"]
self.patch_embed = EfficientFormerConvStem(config, config.hidden_sizes[0])
... | 10,460 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
@add_start_docstrings_to_model_forward(EFFICIENTFORMER_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPooling,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def... | 10,460 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
embedding_output = self.patch_embed(pixel_values)
encoder_outputs = self.encoder(
embedding_output, output_attentions=output_attentions, output_hidden_states=output_hidden_states
)
seq... | 10,460 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
class EfficientFormerForImageClassification(EfficientFormerPreTrainedModel):
def __init__(self, config: EfficientFormerConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.efficientformer = EfficientFormerModel(config)
# Classifier head
self.classifier... | 10,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
@add_start_docstrings_to_model_forward(EFFICIENTFORMER_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=ImageClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
self... | 10,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 10,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
outputs = self.efficientformer(
pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.classifier(sequence_output.mean(-2))
loss... | 10,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "singl... | 10,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
class EfficientFormerForImageClassificationWithTeacherOutput(ModelOutput):
"""
Output type of [`EfficientFormerForImageClassificationWithTeacher`]. | 10,462 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
Args:
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Prediction scores as the average of the cls_logits and distillation logits.
cls_logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Prediction scores of the classification head (i.... | 10,462 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tu... | 10,462 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
logits: torch.FloatTensor = None
cls_logits: torch.FloatTensor = None
distillation_logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None | 10,462 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
class EfficientFormerForImageClassificationWithTeacher(EfficientFormerPreTrainedModel):
def __init__(self, config: EfficientFormerConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.efficientformer = EfficientFormerModel(config)
# Classifier head
self... | 10,463 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
@add_start_docstrings_to_model_forward(EFFICIENTFORMER_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=EfficientFormerForImageClassificationWithTeacherOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
... | 10,463 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
cls_logits = self.classifier(sequence_output.mean(-2))
distillation_logits = self.distillation_classifier(sequence_output.mean(-2))
# during inference, return the average of both classifier predictions
logits = (cls_logits + distillation_logits) / 2
if not return_dict:
outp... | 10,463 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
class EfficientFormerImageProcessor(BaseImageProcessor):
r"""
Constructs a EfficientFormer image processor. | 10,464 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/image_processing_efficientformer.py |
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `(size["height"],
size["width"])`. Can be overridden by the `do_resize` parameter in the `preprocess` method.
size (`dict`, *optional*, defaults to ... | 10,464 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/image_processing_efficientformer.py |
Size of the output image after applying `center_crop`. Can be overridden by `crop_size` in the `preprocess`
method.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
... | 10,464 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/image_processing_efficientformer.py |
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
... | 10,464 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/image_processing_efficientformer.py |
model_input_names = ["pixel_values"]
def __init__(
self,
do_resize: bool = True,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_center_crop: bool = True,
do_rescale: bool = True,
rescale_factor: Union[int,... | 10,464 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/image_processing_efficientformer.py |
self.do_resize = do_resize
self.do_rescale = do_rescale
self.do_normalize = do_normalize
self.do_center_crop = do_center_crop
self.crop_size = crop_size
self.size = size
self.resample = resample
self.rescale_factor = rescale_factor
self.image_mean = image_... | 10,464 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/image_processing_efficientformer.py |
def resize(
self,
image: np.ndarray,
size: Dict[str, int],
resample: PILImageResampling = PILImageResampling.BILINEAR,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> ... | 10,464 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/image_processing_efficientformer.py |
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
resample:
`PILImageResampling` filter to use when resizing the image... | 10,464 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/image_processing_efficientformer.py |
Returns:
`np.ndarray`: The resized image.
"""
size = get_size_dict(size)
if "shortest_edge" in size:
size = get_resize_output_image_size(
image, size=size["shortest_edge"], default_to_square=False, input_data_format=input_data_format
)
... | 10,464 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/image_processing_efficientformer.py |
def preprocess(
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_center_crop: bool = None,
crop_size: int = None,
do_rescale: Optional[bool] = None,
rescale_factor: Opt... | 10,464 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/image_processing_efficientformer.py |
Args:
images (`ImageInput`):
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
do_resize (`bool`, *optional*, defaults to `self.do_resiz... | 10,464 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/image_processing_efficientformer.py |
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to rescale the image values between [0 - 1].
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
c... | 10,464 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/image_processing_efficientformer.py |
return_tensors (`str` or `TensorType`, *optional*):
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a ba... | 10,464 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/image_processing_efficientformer.py |
- Unset: Use the channel dimension format of the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
... | 10,464 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/image_processing_efficientformer.py |
crop_size = crop_size if crop_size is not None else self.crop_size
crop_size = get_size_dict(crop_size, param_name="crop_size", default_to_square=True)
resample = resample if resample is not None else self.resample
rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_f... | 10,464 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/image_processing_efficientformer.py |
size = size if size is not None else self.size
size_dict = get_size_dict(size)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not is_batched(images):
images = [images]
if not valid_images(images):
raise ValueE... | 10,464 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/image_processing_efficientformer.py |
if do_rescale and is_scaled_image(images[0]):
logger.warning_once(
"It looks like you are trying to rescale already rescaled images. If the input"
" images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
)
if inpu... | 10,464 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/image_processing_efficientformer.py |
if do_rescale:
images = [
self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
for image in images
]
if do_normalize:
images = [
self.normalize(image=image, mean=image_mean, std=image_std, input_... | 10,464 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/image_processing_efficientformer.py |
class TFEfficientFormerPatchEmbeddings(keras.layers.Layer):
"""
This class performs downsampling between two stages. For the input tensor with the shape [batch_size, num_channels,
height, width] it produces output tensor with the shape [batch_size, num_channels, height/stride, width/stride]
"""
def... | 10,465 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
self.padding = keras.layers.ZeroPadding2D(padding=config.downsample_pad)
self.projection = keras.layers.Conv2D(
filters=embed_dim,
kernel_size=config.downsample_patch_size,
strides=config.downsample_stride,
padding="valid",
name="projection",
)... | 10,465 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
def call(self, pixel_values: tf.Tensor, training: bool = False) -> tf.Tensor:
tf.debugging.assert_shapes(
[(pixel_values, (..., None, None, self.num_channels))],
message="Make sure that the channel dimension of the pixel values match with the one set in the configuration.",
)
... | 10,465 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
class TFEfficientFormerSelfAttention(keras.layers.Layer):
def __init__(
self,
dim: int,
key_dim: int,
num_heads: int,
attention_ratio: int,
resolution: int,
config: EfficientFormerConfig,
**kwargs,
):
super().__init__(**kwargs)
sel... | 10,466 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
self.qkv = keras.layers.Dense(
units=hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="qkv"
)
self.projection = keras.layers.Dense(
units=dim, kernel_initializer=get_initializer(config.initializer_range), name="projection"
)
self.res... | 10,466 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
self.attention_biases = self.add_weight(
shape=(self.num_heads, len(attention_offsets)),
initializer=keras.initializers.zeros(),
trainable=True,
name="attention_biases",
)
self.attention_bias_idxs = self.add_weight(
shape=(num_points, num_point... | 10,466 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
def call(
self, hidden_states: tf.Tensor, output_attentions: bool = False, training: bool = False
) -> Tuple[tf.Tensor]:
batch_size, sequence_length, *_ = shape_list(hidden_states)
qkv = self.qkv(inputs=hidden_states)
query_layer, key_layer, value_layer = tf.split(
tf.re... | 10,466 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
attention_biases = tf.gather(params=self.attention_biases, indices=self.attention_bias_idxs, axis=1)
attention_probs = attention_probs + attention_biases
attention_probs = stable_softmax(logits=attention_probs, axis=-1)
context_layer = tf.matmul(attention_probs, value_layer)
context_lay... | 10,466 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
class TFEfficientFormerConvStem(keras.layers.Layer):
def __init__(self, config: EfficientFormerConfig, out_channels: int, **kwargs):
super().__init__(**kwargs)
self.padding = keras.layers.ZeroPadding2D(padding=1)
self.convolution1 = keras.layers.Conv2D(
filters=out_channels // 2... | 10,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
self.convolution2 = keras.layers.Conv2D(
filters=out_channels,
kernel_size=3,
strides=2,
padding="valid",
name="convolution2",
)
# Use same default momentum and epsilon as PyTorch equivalent for BatchNormalization
self.batchnorm_after =... | 10,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
def call(self, pixel_values: tf.Tensor, training: bool = False) -> tf.Tensor:
features = self.batchnorm_before(self.convolution1(self.padding(pixel_values)), training=training)
features = self.activation(features)
features = self.batchnorm_after(self.convolution2(self.padding(features)), trainin... | 10,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "convolution1", None) is not None:
with tf.name_scope(self.convolution1.name):
self.convolution1.build([None, None, None, self.config.num_channels])
if geta... | 10,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
with tf.name_scope(self.activation.name):
self.activation.build(None) | 10,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
class TFEfficientFormerPooling(keras.layers.Layer):
def __init__(self, pool_size: int, **kwargs):
super().__init__(**kwargs)
self.pool = keras.layers.AveragePooling2D(pool_size=pool_size, strides=1, padding="same")
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
output = self.poo... | 10,468 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
class TFEfficientFormerDenseMlp(keras.layers.Layer):
def __init__(
self,
config: EfficientFormerConfig,
in_features: int,
hidden_features: Optional[int] = None,
out_features: Optional[int] = None,
**kwargs,
):
super().__init__(**kwargs)
out_feature... | 10,469 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_states = self.linear_in(inputs=hidden_states)
hidden_states = self.activation(hidden_states)
hidden_states = self.dropout(inputs=hidden_states, training=training)
hidden_states = self.linear_out(inputs=... | 10,469 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
class TFEfficientFormerConvMlp(keras.layers.Layer):
def __init__(
self,
config: EfficientFormerConfig,
in_features: int,
hidden_features: Optional[int] = None,
out_features: Optional[int] = None,
drop: float = 0.0,
**kwargs,
):
super().__init__(**k... | 10,470 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
# Use same default momentum and epsilon as PyTorch equivalent for BatchNormalization
self.batchnorm_before = keras.layers.BatchNormalization(
axis=-1, epsilon=config.batch_norm_eps, momentum=0.9, name="batchnorm_before"
)
# Use same default momentum and epsilon as PyTorch equivalent ... | 10,470 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
def call(self, hidden_state: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_state = self.convolution1(hidden_state)
hidden_state = self.batchnorm_before(hidden_state, training=training)
hidden_state = self.activation(hidden_state)
hidden_state = self.dropout(hidden_state, traini... | 10,470 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "convolution1", None) is not None:
with tf.name_scope(self.convolution1.name):
self.convolution1.build([None, None, None, self.in_features])
if getattr(self... | 10,470 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
class TFEfficientFormerDropPath(keras.layers.Layer):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
References:
(1) github.com:rwightman/pytorch-image-models
"""
def __init__(self, drop_path: float, **kwargs):
super().__init__(**kwargs)
... | 10,471 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
class TFEfficientFormerFlat(keras.layers.Layer):
def __init__(self, **kwargs):
super().__init__(**kwargs)
def call(self, hidden_states: tf.Tensor) -> Tuple[tf.Tensor]:
batch_size, _, _, in_channels = shape_list(hidden_states)
hidden_states = tf.reshape(hidden_states, shape=[batch_size, ... | 10,472 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
class TFEfficientFormerMeta3D(keras.layers.Layer):
def __init__(self, config: EfficientFormerConfig, dim: int, drop_path: float = 0.0, **kwargs):
super().__init__(**kwargs)
self.token_mixer = TFEfficientFormerSelfAttention(
dim=config.dim,
key_dim=config.key_dim,
... | 10,473 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
# Using `layers.Activation` instead of `tf.identity` to better control `training' behavior.
self.drop_path = (
TFEfficientFormerDropPath(drop_path)
if drop_path > 0.0
else keras.layers.Activation("linear", name="drop_path")
)
self.config = config
def buil... | 10,473 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
if self.built:
return
self.built = True
if getattr(self, "token_mixer", None) is not None:
with tf.name_scope(self.token_mixer.name):
self.token_mixer.build(None)
if getattr(self, "layernorm1", None) is not None:
with tf.name_scope(self.layerno... | 10,473 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
def call(
self, hidden_states: tf.Tensor, output_attentions: bool = False, training: bool = False
) -> Tuple[tf.Tensor]:
self_attention_outputs = self.token_mixer(
hidden_states=self.layernorm1(hidden_states, training=training),
output_attentions=output_attentions,
... | 10,473 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
if self.config.use_layer_scale:
layer_output = hidden_states + self.drop_path(
tf.expand_dims(tf.expand_dims(self.layer_scale_1, 0), 0) * attention_output,
training=training,
)
layer_output = layer_output + self.drop_path(
tf.expand_dim... | 10,473 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
class TFEfficientFormerMeta3DLayers(keras.layers.Layer):
def __init__(self, config: EfficientFormerConfig, **kwargs):
super().__init__(**kwargs)
drop_paths = [
config.drop_path_rate * (block_idx + sum(config.depths[:-1]))
for block_idx in range(config.num_meta3d_blocks)
... | 10,474 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
hidden_states = layer_module(
hidden_states=hidden_states, output_attentions=output_attentions, training=training
)
if output_attentions:
all_attention_outputs = all_attention_outputs + (hidden_states[1],)
if output_attentions:
outputs = (hidd... | 10,474 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
class TFEfficientFormerMeta4D(keras.layers.Layer):
def __init__(self, config: EfficientFormerConfig, dim: int, drop_path: float = 0.0, **kwargs):
super().__init__(**kwargs)
pool_size = config.pool_size if config.pool_size is not None else 3
self.token_mixer = TFEfficientFormerPooling(pool_si... | 10,475 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
if self.config.use_layer_scale:
self.layer_scale_1 = self.add_weight(
shape=(self.dim),
initializer=keras.initializers.Constant(value=self.config.layer_scale_init_value),
trainable=True,
name="layer_scale_1",
)
self.laye... | 10,475 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
if self.built:
return
self.built = True
if getattr(self, "token_mixer", None) is not None:
with tf.name_scope(self.token_mixer.name):
self.token_mixer.build(None)
if getattr(self, "mlp", None) is not None:
with tf.name_scope(self.mlp.name):
... | 10,475 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
layer_output = layer_output + self.drop_path(
tf.expand_dims(tf.expand_dims(self.layer_scale_2, 0), 0)
* self.mlp(hidden_state=layer_output, training=training),
training=training,
)
else:
layer_output = hidden_states + self.drop_path(outpu... | 10,475 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
class TFEfficientFormerMeta4DLayers(keras.layers.Layer):
def __init__(self, config: EfficientFormerConfig, stage_idx: int, **kwargs):
super().__init__(**kwargs)
num_layers = (
config.depths[stage_idx] if stage_idx != -1 else config.depths[stage_idx] - config.num_meta3d_blocks
)
... | 10,476 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "blocks", None) is not None:
for layer in self.blocks:
with tf.name_scope(layer.name):
layer.build(None) | 10,476 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
class TFEfficientFormerIntermediateStage(keras.layers.Layer):
def __init__(self, config: EfficientFormerConfig, index: int, **kwargs):
super().__init__(**kwargs)
self.meta4D_layers = TFEfficientFormerMeta4DLayers(config=config, stage_idx=index, name="meta4D_layers")
def call(self, hidden_states... | 10,477 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
class TFEfficientFormerLastStage(keras.layers.Layer):
def __init__(self, config: EfficientFormerConfig, **kwargs):
super().__init__(**kwargs)
self.meta4D_layers = TFEfficientFormerMeta4DLayers(config=config, stage_idx=-1, name="meta4D_layers")
self.flat = TFEfficientFormerFlat(name="flat")
... | 10,478 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "meta4D_layers", None) is not None:
with tf.name_scope(self.meta4D_layers.name):
self.meta4D_layers.build(None)
if getattr(self, "flat", None) is not None:
... | 10,478 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
class TFEfficientFormerEncoder(keras.layers.Layer):
def __init__(self, config: EfficientFormerConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
num_intermediate_stages = len(config.depths) - 1
downsamples = [
config.downsamples[i] or config.hidden_sizes[... | 10,479 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
intermediate_stages = []
layer_count = -1
for i in range(num_intermediate_stages):
layer_count += 1
intermediate_stages.append(
TFEfficientFormerIntermediateStage(config, i, name=f"intermediate_stages.{layer_count}")
)
if downsamples[i]:
... | 10,479 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
def call(
self,
hidden_states: tf.Tensor,
output_hidden_states: bool,
output_attentions: bool,
return_dict: bool,
training: bool = False,
) -> TFBaseModelOutput:
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if outpu... | 10,479 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
if not return_dict:
return tuple(v for v in [layer_output[0], all_hidden_states, all_self_attentions] if v is not None)
return TFBaseModelOutput(
last_hidden_state=layer_output[0],
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
de... | 10,479 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
class TFEfficientFormerMainLayer(keras.layers.Layer):
config_class = EfficientFormerConfig
def __init__(self, config: EfficientFormerConfig, **kwargs) -> None:
super().__init__(**kwargs)
self.config = config
self.patch_embed = TFEfficientFormerConvStem(config, config.hidden_sizes[0], n... | 10,480 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
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 ... | 10,480 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
encoder_outputs = self.encoder(
hidden_states=embedding_output,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = encoder_outputs[0]
sequence... | 10,480 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
return TFBaseModelOutput(
last_hidden_state=sequence_output,
hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
... | 10,480 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
class TFEfficientFormerPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = EfficientFormerConfig
base_model_prefix = "efficientformer"
main_input_name = "pixel_val... | 10,481 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
class TFEfficientFormerModel(TFEfficientFormerPreTrainedModel):
def __init__(self, config: EfficientFormerConfig, **kwargs) -> None:
super().__init__(config, **kwargs)
self.efficientformer = TFEfficientFormerMainLayer(config, name="efficientformer") | 10,482 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(EFFICIENTFORMER_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFBaseModelOutputWithPooling,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT... | 10,482 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
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