text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
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... | 9,826 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
class Dinov2Backbone(Dinov2PreTrainedModel, BackboneMixin):
def __init__(self, config):
super().__init__(config)
super()._init_backbone(config)
self.num_features = [config.hidden_size for _ in range(config.num_hidden_layers + 1)]
self.embeddings = Dinov2Embeddings(config)
se... | 9,827 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
@add_start_docstrings_to_model_forward(DINOV2_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BackboneOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: torch.Tensor,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = Non... | 9,827 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
>>> inputs = processor(image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> feature_maps = outputs.feature_maps
>>> list(feature_maps[-1].shape)
[1, 768, 16, 16]
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
... | 9,827 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
feature_maps = ()
for stage, hidden_state in zip(self.stage_names, hidden_states):
if stage in self.out_features:
if self.config.apply_layernorm:
hidden_state = self.layernorm(hidden_state)
if self.config.reshape_hidden_states:
... | 9,827 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
if not return_dict:
if output_hidden_states:
output = (feature_maps,) + outputs[1:]
else:
output = (feature_maps,) + outputs[2:]
return output
return BackboneOutput(
feature_maps=feature_maps,
hidden_states=outputs.hidd... | 9,827 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
class Dinov2Config(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Dinov2Model`]. It is used to instantiate an
Dinov2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the default... | 9,828 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/configuration_dinov2.py |
Args:
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults t... | 9,828 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/configuration_dinov2.py |
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
laye... | 9,828 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/configuration_dinov2.py |
drop_path_rate (`float`, *optional*, defaults to 0.0):
Stochastic depth rate per sample (when applied in the main path of residual layers).
use_swiglu_ffn (`bool`, *optional*, defaults to `False`):
Whether to use the SwiGLU feedforward neural network.
out_features (`List[str]`, *... | 9,828 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/configuration_dinov2.py |
many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
If unset and `out_features` is unset, will default to the last stage. Must be in the
same order as defined in the `stage_names` attribute.
apply_layernorm (`bool`, *optional*, defaul... | 9,828 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/configuration_dinov2.py |
Example:
```python
>>> from transformers import Dinov2Config, Dinov2Model
>>> # Initializing a Dinov2 dinov2-base-patch16-224 style configuration
>>> configuration = Dinov2Config()
>>> # Initializing a model (with random weights) from the dinov2-base-patch16-224 style configuration
>>> model ... | 9,828 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/configuration_dinov2.py |
def __init__(
self,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
mlp_ratio=4,
hidden_act="gelu",
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
initializer_range=0.02,
layer_norm_eps=1e-6,
image_size=22... | 9,828 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/configuration_dinov2.py |
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.mlp_ratio = mlp_ratio
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_pr... | 9,828 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/configuration_dinov2.py |
self.apply_layernorm = apply_layernorm
self.reshape_hidden_states = reshape_hidden_states | 9,828 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/configuration_dinov2.py |
class Dinov2OnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
]
)... | 9,829 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/configuration_dinov2.py |
class FlaxDinov2PatchEmbeddings(nn.Module):
config: Dinov2Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
image_size = self.config.image_size
patch_size = self.config.patch_size
image_size = image_size if isinstance(image_size, collections.abc.... | 9,830 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
# Copied from transformers.models.vit.modeling_flax_vit.FlaxViTPatchEmbeddings.__call__
def __call__(self, pixel_values):
num_channels = pixel_values.shape[-1]
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel value... | 9,830 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
class FlaxDinov2Embeddings(nn.Module):
"""Construct the CLS token, position and patch embeddings."""
config: Dinov2Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation | 9,831 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
def setup(self):
self.cls_token = self.param(
"cls_token",
jax.nn.initializers.variance_scaling(self.config.initializer_range**2, "fan_in", "truncated_normal"),
(1, 1, self.config.hidden_size),
)
self.mask_token = self.param(
"mask_token",
... | 9,831 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
def interpolate_pos_encoding(self, config, hidden_states, height, width, position_embeddings):
num_patches = hidden_states.shape[1] - 1
num_positions = position_embeddings.shape[1] - 1
if num_patches == num_positions and height == width:
return position_embeddings
class_pos_e... | 9,831 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
scale = jnp.array([new_height_ratio, new_width_ratio], dtype=jnp.float32)
translation = jnp.array([0.0, 0.0], dtype=jnp.float32)
patch_pos_embed = jax.image.scale_and_translate(
patch_pos_embed.astype(jnp.float32),
shape=(patch_pos_embed.shape[0], patch_pos_embed.shape[1], h, w)... | 9,831 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
embeddings = self.patch_embeddings(pixel_values.astype(target_dtype))
cls_tokens = jnp.broadcast_to(self.cls_token, (batch_size, 1, self.config.hidden_size))
embeddings = jnp.concatenate((cls_tokens, embeddings), axis=1)
embeddings = embeddings + self.interpolate_pos_encoding(
self... | 9,831 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
class FlaxDinov2SelfAttention(nn.Module):
config: Dinov2Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
if self.config.hidden_size % self.config.num_attention_heads != 0:
raise ValueError(
"`config.hidden_size`: {self.config.hidden_... | 9,832 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
self.query = nn.Dense(
self.config.hidden_size,
dtype=self.dtype,
kernel_init=jax.nn.initializers.variance_scaling(
self.config.initializer_range**2, mode="fan_in", distribution="truncated_normal"
),
use_bias=self.config.qkv_bias,
)
... | 9,832 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
def __call__(self, hidden_states, deterministic: bool = True, output_attentions: bool = False):
head_dim = self.config.hidden_size // self.config.num_attention_heads
query_states = self.query(hidden_states).reshape(
hidden_states.shape[:2] + (self.config.num_attention_heads, head_dim)
... | 9,832 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
attn_weights = dot_product_attention_weights(
query_states,
key_states,
dropout_rng=dropout_rng,
dropout_rate=self.config.attention_probs_dropout_prob,
broadcast_dropout=True,
deterministic=deterministic,
dtype=self.dtype,
p... | 9,832 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
class FlaxDinov2SelfOutput(nn.Module):
config: Dinov2Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.variance_scaling(
self.config.initializ... | 9,833 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
class FlaxDinov2Attention(nn.Module):
config: Dinov2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.attention = FlaxDinov2SelfAttention(self.config, dtype=self.dtype)
self.output = FlaxDinov2SelfOutput(self.config, dtype=self.dtype)
def __call__(self, hidden_states, determi... | 9,834 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
class FlaxDinov2LayerScale(nn.Module):
config: Dinov2Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.lambda1 = self.config.layerscale_value * self.param(
"lambda1",
jax.nn.initializers.ones,
(self.config.hidden_size,),
... | 9,835 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
class FlaxDinov2DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
rate: float
@nn.module.compact
def __call__(self, inputs, deterministic: Optional[bool] = True):
if self.rate == 0.0:
return inputs
keep_prob ... | 9,836 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
class FlaxDinov2MLP(nn.Module):
config: Dinov2Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.fc1 = nn.Dense(
self.config.hidden_size * self.config.mlp_ratio,
kernel_init=jax.nn.initializers.variance_scaling(
self.c... | 9,837 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
def __call__(self, hidden_states):
hidden_states = self.fc1(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states | 9,837 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
class FlaxDinov2SwiGLUFFN(nn.Module):
config: Dinov2Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
hidden_features = int(self.config.hidden_size * self.config.mlp_ratio)
hidden_features = (int(self.hidden_features * 2 / 3) + 7) // 8 * 8
self.... | 9,838 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
def __call__(self, hidden_states):
hidden_states = self.weights_in(hidden_states)
x1, x2 = jnp.split(hidden_states, 2, axis=-1)
hidden = nn.silu(x1) * x2
return self.weights_out(hidden) | 9,838 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
class FlaxDinov2Layer(nn.Module):
config: Dinov2Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.norm1 = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
self.attention = FlaxDinov2Attention(self.config, dtype=self.dtype)
... | 9,839 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
def __call__(self, hidden_states, deterministic: bool = True, output_attentions: bool = False):
self_attention_outputs = self.attention(
self.norm1(hidden_states), # in Dinov2, layernorm is applied before self-attention
deterministic=deterministic,
output_attentions=output_a... | 9,839 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
class FlaxDinov2LayerCollection(nn.Module):
config: Dinov2Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxDinov2Layer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers)
]
def _... | 9,840 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
if output_attentions:
all_attentions += (layer_outputs[1],)
if output_hidden_states:
all_hidden_states += (hidden_states,)
outputs = (hidden_states,)
if not return_dict:
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutpu... | 9,840 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
class FlaxDinov2Encoder(nn.Module):
config: Dinov2Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layer = FlaxDinov2LayerCollection(self.config, dtype=self.dtype)
def __call__(
self,
hidden_states,
deterministic: bool = True,
... | 9,841 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
class FlaxDinov2PreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Dinov2Config
base_model_prefix = "dinov2"
main_input_name = "pixel_values"
module_class: ... | 9,842 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensors
pixel_values = jnp.zeros(input_shape, dtype=self.dtype)
params_rng, dropout_rng = jax.random.split(rng)
dropout_rng, droppath_rng = jax.random.split(dropout... | 9,842 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
@add_start_docstrings_to_model_forward(DINOV2_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
def __call__(
self,
pixel_values,
params: dict = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,
... | 9,842 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1))
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
dropout_rng, droppath_rng = jax.random.split(dropout_rng)
rngs["dropout"] = dropout_rng
rngs["droppath"] = droppath_rng
return s... | 9,842 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
class FlaxDinov2Module(nn.Module):
config: Dinov2Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.embeddings = FlaxDinov2Embeddings(self.config, dtype=self.dtype)
self.encoder = FlaxDinov2Encoder(self.config, dtype=self.dtype)
self.layernor... | 9,843 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
sequence_output = encoder_outputs[0]
sequence_output = self.layernorm(sequence_output)
pooled_output = sequence_output[:, 0, :]
if not return_dict:
head_outputs = (sequence_output, pooled_output)
return head_outputs + encoder_outputs[1:]
return FlaxBaseModelOutp... | 9,843 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
class FlaxDinov2Model(FlaxDinov2PreTrainedModel):
module_class = FlaxDinov2Module | 9,844 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
class FlaxDinov2ForImageClassificationModule(nn.Module):
config: Dinov2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.dinov2 = FlaxDinov2Module(config=self.config, dtype=self.dtype)
self.classifier = nn.Dense(
self.config.num_labels,
dtype=self.dtype,
... | 9,845 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
hidden_states = outputs[0]
cls_token = hidden_states[:, 0]
patch_tokens = hidden_states[:, 1:]
linear_input = jnp.concatenate([cls_token, patch_tokens.mean(axis=1)], axis=-1)
logits = self.classifier(linear_input)
if not return_dict:
output = (logits,) + outputs[2:... | 9,845 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
class FlaxDinov2ForImageClassification(FlaxDinov2PreTrainedModel):
module_class = FlaxDinov2ForImageClassificationModule | 9,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py |
class LiltConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LiltModel`]. It is used to instantiate a LiLT
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar confi... | 9,847 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/configuration_lilt.py |
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the LiLT model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`LiltModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensio... | 9,847 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/configuration_lilt.py |
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings,... | 9,847 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/configuration_lilt.py |
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Typ... | 9,847 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/configuration_lilt.py |
channel_shrink_ratio (`int`, *optional*, defaults to 4):
The shrink ratio compared to the `hidden_size` for the channel dimension of the layout embeddings.
max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum value that the 2D position embedding might ever be use... | 9,847 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/configuration_lilt.py |
Examples:
```python
>>> from transformers import LiltConfig, LiltModel
>>> # Initializing a LiLT SCUT-DLVCLab/lilt-roberta-en-base style configuration
>>> configuration = LiltConfig()
>>> # Randomly initializing a model from the SCUT-DLVCLab/lilt-roberta-en-base style configuration
>>> model =... | 9,847 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/configuration_lilt.py |
def __init__(
self,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
... | 9,847 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/configuration_lilt.py |
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
... | 9,847 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/configuration_lilt.py |
class LiltTextEmbeddings(nn.Module):
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
... | 9,848 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
self.position_embedding_type = getattr(config, "position_embedding_type", "... | 9,848 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
def forward(
self,
input_ids=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
):
if position_ids is None:
if input_ids is not None:
# Create the position ids from the input token ids. Any padded tokens remain padded.
... | 9,848 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
if self.position_embedding_type == "absolute":
position_embeddings = self.posit... | 9,848 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
def create_position_ids_from_input_ids(self, input_ids, padding_idx):
"""
Args:
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding
symbols are ignored. This is modified from fairseq's `utils.make_positions`.
x: torch.Tens... | 9,848 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
def create_position_ids_from_inputs_embeds(self, inputs_embeds):
"""
Args:
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.:
inputs_embeds: torch.Tensor
Returns: torch.Tensor
"""
input_shape = inpu... | 9,848 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
class LiltLayoutEmbeddings(nn.Module):
def __init__(self, config):
super().__init__()
# we divide the hidden_size by 6 here as there are 6 different layout embeddings,
# namely left_position, upper_position, right_position, lower_position, height, width
self.x_position_embeddings = n... | 9,849 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
self.padding_idx = config.pad_token_id
self.box_position_embeddings = nn.Embedding(
config.max_position_embeddings,
config.hidden_size // config.channel_shrink_ratio,
padding_idx=self.padding_idx,
)
self.box_linear_embeddings = nn.Linear(
in_featur... | 9,849 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
def forward(self, bbox=None, position_ids=None):
try:
left_position_embeddings = self.x_position_embeddings(bbox[:, :, 0])
upper_position_embeddings = self.y_position_embeddings(bbox[:, :, 1])
right_position_embeddings = self.x_position_embeddings(bbox[:, :, 2])
l... | 9,849 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
spatial_position_embeddings = torch.cat(
[
left_position_embeddings,
upper_position_embeddings,
right_position_embeddings,
lower_position_embeddings,
h_position_embeddings,
w_position_embeddings,
],
... | 9,849 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
class LiltSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=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}) i... | 9,850 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
self.layout_query = nn.Linear(
config.hidden_size // config.channel_shrink_ratio, self.all_head_size // config.channel_shrink_ratio
)
self.layout_key = nn.Linear(
config.hidden_size // config.channel_shrink_ratio, self.all_head_size // config.channel_shrink_ratio
)
... | 9,850 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
self.channel_shrink_ratio = config.channel_shrink_ratio
def transpose_for_scores(self, x, r=1):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size // r)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states... | 9,850 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
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)
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
layout_attention_scores ... | 9,850 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
seq_length = hidden_states.size()[1]
position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
position_ids_r = torch.arange(seq_lengt... | 9,850 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores
elif self.position_embedding_type == "relative_key_query":
... | 9,850 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
tmp_attention_scores = attention_scores / math.sqrt(self.attention_head_size)
tmp_layout_attention_scores = layout_attention_scores / math.sqrt(
self.attention_head_size // self.channel_shrink_ratio
)
attention_scores = tmp_attention_scores + tmp_layout_attention_scores
layou... | 9,850 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
# Mask heads if we want to
if head_mask is not None:
layout_attention_probs = layout_attention_probs * head_mask
layout_context_layer = torch.matmul(layout_attention_probs, layout_value_layer)
layout_context_layer = layout_context_layer.permute(0, 2, 1, 3).contiguous()
new_... | 9,850 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
# 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.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_pr... | 9,850 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
class LiltSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def f... | 9,851 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
class LiltAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = LiltSelfAttention(config, position_embedding_type=position_embedding_type)
self.output = LiltSelfOutput(config)
self.pruned_heads = set()
ori_hidden_size ... | 9,852 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Upda... | 9,852 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
def forward(
self,
hidden_states: torch.Tensor,
layout_inputs: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
self_outputs = ... | 9,852 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
class LiltIntermediate(nn.Module):
def __init__(self, config):
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:
self.interm... | 9,853 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
class LiltOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def... | 9,854 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
class LiltLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = LiltAttention(config)
self.intermediate = LiltIntermediate(config)
self.output = LiltOutput(c... | 9,855 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
def forward(
self,
hidden_states: torch.Tensor,
layout_inputs: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
self_attention_... | 9,855 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
)
layout_layer_output = apply_chunking_to_forward(
self.layout_feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, layout_attention_... | 9,855 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
class LiltEncoder(nn.Module):
# Copied from transformers.models.bert.modeling_bert.BertEncoder.__init__ with Bert->Lilt
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([LiltLayer(config) for _ in range(config.num_hidden_layers)])
sel... | 9,856 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
if self.gradient_checkpointing and self.training:
layer_... | 9,856 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
... | 9,856 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
class LiltPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hidde... | 9,857 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
class LiltPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = LiltConfig
base_model_prefix = "lilt"
supports_gradient_checkpointing = True
_no_split_modules = [] | 9,858 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
# Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf ... | 9,858 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
class LiltModel(LiltPreTrainedModel):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = LiltTextEmbeddings(config)
self.layout_embeddings = LiltLayoutEmbeddings(config)
self.encoder = LiltEncoder(config)
... | 9,859 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
@add_start_docstrings_to_model_forward(LILT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
bbox: Optional[torch.Tensor] =... | 9,859 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
>>> tokenizer = AutoTokenizer.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
>>> model = AutoModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train", trust_remote_code=True)
>>> example = dataset[0]
>>> words =... | 9,859 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
... | 9,859 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
if token_type_ids is None:
if hasattr(self.embeddings, "token_type_ids"):
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
token_type_ids = buffer... | 9,859 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.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... | 9,859 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
encoder_outputs = self.encoder(
embedding_output,
layout_embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_d... | 9,859 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
class LiltForSequenceClassification(LiltPreTrainedModel):
# Copied from transformers.models.roberta.modeling_roberta.RobertaForSequenceClassification.__init__ with Roberta->Lilt, roberta->lilt
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.c... | 9,860 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
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