text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
self.feature_layer_norm_eps = feature_layer_norm_eps
self.initializer_range = initializer_range
self.vocab_size = vocab_size | 2,858 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/configuration_sew_d.py |
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrec... | 2,858 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/configuration_sew_d.py |
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
self.apply_spec_augment = apply_spec_augment
self.mask_time_prob = mask_time_prob
self.mask_time_length = mask_time_length
self.mask_time_min_masks = mask_time_min_masks
self.mask_feature_prob = mas... | 2,858 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/configuration_sew_d.py |
def to_dict(self):
"""
Serializes this instance to a Python dictionary.
"""
output = super().to_dict()
output["hidden_dropout"] = output.pop("_hidden_dropout")
return output | 2,858 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/configuration_sew_d.py |
class TableTransformerDecoderOutput(BaseModelOutputWithCrossAttentions):
"""
Base class for outputs of the TABLE_TRANSFORMER decoder. This class adds one attribute to BaseModelOutputWithCrossAttentions,
namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each... | 2,859 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.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.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `con... | 2,859 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output... | 2,859 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
intermediate_hidden_states: Optional[torch.FloatTensor] = None | 2,859 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
class TableTransformerModelOutput(Seq2SeqModelOutput):
"""
Base class for outputs of the TABLE_TRANSFORMER encoder-decoder model. This class adds one attribute to Seq2SeqModelOutput,
namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each of them
gone th... | 2,860 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.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 decoder of the model.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True`... | 2,860 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
... | 2,860 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_l... | 2,860 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
intermediate_hidden_states (`torch.FloatTensor` of shape `(config.decoder_layers, batch_size, sequence_length, hidden_size)`, *optional*, returned when `config.auxiliary_loss=True`):
Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a
layernorm.
... | 2,860 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
intermediate_hidden_states: Optional[torch.FloatTensor] = None | 2,860 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
class TableTransformerObjectDetectionOutput(ModelOutput):
"""
Output type of [`TableTransformerForObjectDetection`]. | 2,861 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a
bounding box loss. The latter is defined as a linear combination of the L1 loss and... | 2,861 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
possible padding). You can use [`~TableTransformerImageProcessor.post_process_object_detection`] to retrieve the
unnormalized bounding boxes.
auxiliary_outputs (`list[Dict]`, *optional*):
Optional, only returned when auxilary losses are activated (i.e. `config.auxiliary_loss` is set to `... | 2,861 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each
layer plus the initial embedding outputs.
decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
... | 2,861 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
used to compute the weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (... | 2,861 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.
""" | 2,861 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
loss: Optional[torch.FloatTensor] = None
loss_dict: Optional[Dict] = None
logits: torch.FloatTensor = None
pred_boxes: torch.FloatTensor = None
auxiliary_outputs: Optional[List[Dict]] = None
last_hidden_state: Optional[torch.FloatTensor] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTe... | 2,861 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
class TableTransformerFrozenBatchNorm2d(nn.Module):
"""
BatchNorm2d where the batch statistics and the affine parameters are fixed.
Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than
torchvision.models.resnet[18,34,50,101] produce nans.
"""
de... | 2,862 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
super()._load_from_state_dict(
state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
)
def forward(self, x):
# move reshapes to the beginning
# to make it user-friendly
weight = self.weight.reshape(1, -1, 1, 1)
bias = self.bias.res... | 2,862 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
class TableTransformerConvEncoder(nn.Module):
"""
Convolutional backbone, using either the AutoBackbone API or one from the timm library.
nn.BatchNorm2d layers are replaced by TableTransformerFrozenBatchNorm2d as defined above.
"""
def __init__(self, config):
super().__init__()
s... | 2,863 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
# For backwards compatibility we have to use the timm library directly instead of the AutoBackbone API
if config.use_timm_backbone:
# We default to values which were previously hard-coded. This enables configurability from the config
# using backbone arguments, while keeping the default ... | 2,863 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
**kwargs,
)
else:
backbone = load_backbone(config) | 2,863 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
# replace batch norm by frozen batch norm
with torch.no_grad():
replace_batch_norm(backbone)
self.model = backbone
self.intermediate_channel_sizes = (
self.model.feature_info.channels() if config.use_timm_backbone else self.model.channels
)
backbone_model... | 2,863 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
if "resnet" in backbone_model_type:
for name, parameter in self.model.named_parameters():
if config.use_timm_backbone:
if "layer2" not in name and "layer3" not in name and "layer4" not in name:
parameter.requires_grad_(False)
else:
... | 2,863 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
out = []
for feature_map in features:
# downsample pixel_mask to match shape of corresponding feature_map
mask = nn.functional.interpolate(pixel_mask[None].float(), size=feature_map.shape[-2:]).to(torch.bool)[0]
out.append((feature_map, mask))
return out | 2,863 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
class TableTransformerConvModel(nn.Module):
"""
This module adds 2D position embeddings to all intermediate feature maps of the convolutional encoder.
"""
def __init__(self, conv_encoder, position_embedding):
super().__init__()
self.conv_encoder = conv_encoder
self.position_embe... | 2,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
class TableTransformerSinePositionEmbedding(nn.Module):
"""
This is a more standard version of the position embedding, very similar to the one used by the Attention is all you
need paper, generalized to work on images.
"""
def __init__(self, embedding_dim=64, temperature=10000, normalize=False, sca... | 2,865 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
def forward(self, pixel_values, pixel_mask):
if pixel_mask is None:
raise ValueError("No pixel mask provided")
y_embed = pixel_mask.cumsum(1, dtype=torch.float32)
x_embed = pixel_mask.cumsum(2, dtype=torch.float32)
if self.normalize:
y_embed = y_embed / (y_embed[:... | 2,865 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
pos_x = x_embed[:, :, :, None] / dim_t
pos_y = y_embed[:, :, :, None] / dim_t
pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
pos = torch.cat((p... | 2,865 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
class TableTransformerLearnedPositionEmbedding(nn.Module):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, embedding_dim=256):
super().__init__()
self.row_embeddings = nn.Embedding(50, embedding_dim)
self.column_embeddings = nn.Emb... | 2,866 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
class TableTransformerAttention(nn.Module):
"""
Multi-headed attention from 'Attention Is All You Need' paper.
Here, we add position embeddings to the queries and keys (as explained in the TABLE_TRANSFORMER paper).
"""
def __init__(
self,
embed_dim: int,
num_heads: int,
... | 2,867 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, batch_si... | 2,867 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
object_queries: Optional[torch.Tensor] = None,
key_value_states: Optional[torch.Tensor] = None,
spatial_position_embeddings: Optional[torch.Tensor] = None,
output_attent... | 2,867 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
# add key-value position embeddings to the key value states
if spatial_position_embeddings is not None:
key_value_states_original = key_value_states
key_value_states = self.with_pos_embed(key_value_states, spatial_position_embeddings)
# get query proj
query_states = self... | 2,867 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
proj_shape = (batch_size * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, target_len, batch_size).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
source_len = key_states.size(1)
attn_weights ... | 2,867 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
if attention_mask is not None:
if attention_mask.size() != (batch_size, 1, target_len, source_len):
raise ValueError(
f"Attention mask should be of size {(batch_size, 1, target_len, source_len)}, but is"
f" {attention_mask.size()}"
)
... | 2,867 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weigh... | 2,867 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
attn_output = attn_output.view(batch_size, self.num_heads, target_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(batch_size, target_len, embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped | 2,867 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
class TableTransformerEncoderLayer(nn.Module):
# Copied from transformers.models.detr.modeling_detr.DetrEncoderLayer.__init__ with Detr->TableTransformer
def __init__(self, config: TableTransformerConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = TableTransform... | 2,868 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
object_queries: torch.Tensor = None,
output_attentions: bool = False,
):
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embe... | 2,868 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
object_queries=object_queries,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=s... | 2,868 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
if self.training:
if torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any():
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
outputs = (hidden_states,)
if o... | 2,868 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
class TableTransformerDecoderLayer(nn.Module):
# Copied from transformers.models.detr.modeling_detr.DetrDecoderLayer.__init__ with Detr->TableTransformer
def __init__(self, config: TableTransformerConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = TableTransfor... | 2,869 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.encoder_attn = TableTransformerAttention(
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
)
self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self... | 2,869 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
object_queries: Optional[torch.Tensor] = None,
query_position_embeddings: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_at... | 2,869 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
object queries that are added to the queries and keys
in the self-attention layer.
encoder_hidden_states (`torch.FloatTensor`):
cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
encoder_attention_mask (`torch.FloatTensor`): encoder attention ma... | 2,869 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
# Self Attention
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
object_queries=query_position_embeddings,
attention_mask=attention_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(... | 2,869 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
# Cross-Attention Block
cross_attn_weights = None
if encoder_hidden_states is not None:
hidden_states, cross_attn_weights = self.encoder_attn(
hidden_states=hidden_states,
object_queries=query_position_embeddings,
key_value_states=encoder_hidde... | 2,869 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
# Fully Connected
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropou... | 2,869 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
class TableTransformerPreTrainedModel(PreTrainedModel):
config_class = TableTransformerConfig
base_model_prefix = "model"
main_input_name = "pixel_values"
_no_split_modules = [
r"TableTransformerConvEncoder",
r"TableTransformerEncoderLayer",
r"TableTransformerDecoderLayer",
]... | 2,870 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
if isinstance(module, TableTransformerLearnedPositionEmbedding):
nn.init.uniform_(module.row_embeddings.weight)
nn.init.uniform_(module.column_embeddings.weight)
if isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)):
# Slightly different from the TF version which uses... | 2,870 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
class TableTransformerEncoder(TableTransformerPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`TableTransformerEncoderLayer`].
The encoder updates the flattened feature map through multiple self-attention layers.
Small tweak f... | 2,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
def forward(
self,
inputs_embeds=None,
attention_mask=None,
object_queries=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_l... | 2,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
object_queries (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Position embeddings that are added to the queries and keys in each self-attention layer. | 2,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of a... | 2,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
hidden_states = inputs_embeds
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
# expand attention_mask
if attention_mask is not None:
# [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len]
attention_mask = _... | 2,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
if to_drop:
layer_outputs = (None, None)
else:
# we add object_queries as extra input to the encoder_layer
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
object_queries=object_queries,
... | 2,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
class TableTransformerDecoder(TableTransformerPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TableTransformerDecoderLayer`].
The decoder updates the query embeddings through multiple self-attention and cross-attention layers.
Some small tweaks... | 2,872 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
self.layers = nn.ModuleList([TableTransformerDecoderLayer(config) for _ in range(config.decoder_layers)])
# in TABLE_TRANSFORMER, the decoder uses layernorm after the last decoder layer output
self.layernorm = nn.LayerNorm(config.d_model)
self.gradient_checkpointing = False
# Initialize... | 2,872 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on certain queries. Mask values selected in `[0, 1]`:
- 1 for queries that are **not masked**,
- 0 for queries that are **masked**.
[... | 2,872 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
- 1 for pixels that are real (i.e. **not masked**),
- 0 for pixels that are padding (i.e. **masked**).
object_queries (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Object queries that are added to the queries and keys in each cross-... | 2,872 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of a... | 2,872 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
if inputs_embeds is not None:
hidden_states = inputs_embeds
input_shape = inputs_embeds.size()[:-1]
combined_attention_mask = None
if attention_mask is not None and combined_attention_mask is not None:
# [batch_size, seq_len] -> [batch_size, 1, target_seq_len, sourc... | 2,872 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
for idx, decoder_layer in enumerate(self.layers):
# a... | 2,872 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
combined_attention_mask,
encoder_hidden_states,
encoder_attentio... | 2,872 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
if self.config.auxiliary_loss:
hidden_states = self.layernorm(hidden_states)
intermediate += (hidden_states,)
if output_attentions:
all_self_attns += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_atte... | 2,872 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
if not return_dict:
return tuple(
v
for v in [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions, intermediate]
if v is not None
)
return TableTransformerDecoderOutput(
last_hidden_state=hidden_states,
... | 2,872 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
class TableTransformerModel(TableTransformerPreTrainedModel):
# Copied from transformers.models.detr.modeling_detr.DetrModel.__init__ with Detr->TableTransformer
def __init__(self, config: TableTransformerConfig):
super().__init__(config)
# Create backbone + positional encoding
backbone... | 2,873 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
def get_decoder(self):
return self.decoder
def freeze_backbone(self):
for name, param in self.backbone.conv_encoder.model.named_parameters():
param.requires_grad_(False)
def unfreeze_backbone(self):
for name, param in self.backbone.conv_encoder.model.named_parameters():
... | 2,873 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
@add_start_docstrings_to_model_forward(TABLE_TRANSFORMER_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TableTransformerModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: torch.FloatTensor,
pixel_mask: Optional[torch.FloatTensor] = None,
decoder... | 2,873 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
>>> file_path = hf_hub_download(repo_id="nielsr/example-pdf", repo_type="dataset", filename="example_pdf.png")
>>> image = Image.open(file_path).convert("RGB")
>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/table-transformer-detection")
>>> model = TableTransformerModel.fro... | 2,873 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
>>> # the last hidden states are the final query embeddings of the Transformer decoder
>>> # these are of shape (batch_size, num_queries, hidden_size)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 15, 256]
```"""
output_attentio... | 2,873 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
# First, sent pixel_values + pixel_mask through Backbone to obtain the features
# pixel_values should be of shape (batch_size, num_channels, height, width)
# pixel_mask should be of shape (batch_size, height, width)
features, position_embeddings_list = self.backbone(pixel_values, pixel_mask)
... | 2,873 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
# Third, flatten the feature map + object queries of shape NxCxHxW to NxCxHW, and permute it to NxHWxC
# In other words, turn their shape into (batch_size, sequence_length, hidden_size)
flattened_features = projected_feature_map.flatten(2).permute(0, 2, 1)
object_queries = position_embeddings_li... | 2,873 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
# Fourth, sent flattened_features + flattened_mask + object queries through encoder
# flattened_features is a Tensor of shape (batch_size, heigth*width, hidden_size)
# flattened_mask is a Tensor of shape (batch_size, heigth*width)
if encoder_outputs is None:
encoder_outputs = self.en... | 2,873 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
) | 2,873 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
# Fifth, sent query embeddings + object queries through the decoder (which is conditioned on the encoder output)
query_position_embeddings = self.query_position_embeddings.weight.unsqueeze(0).repeat(batch_size, 1, 1)
queries = torch.zeros_like(query_position_embeddings)
# decoder outputs consis... | 2,873 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
return TableTransformerModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_... | 2,873 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
class TableTransformerForObjectDetection(TableTransformerPreTrainedModel):
# Copied from transformers.models.detr.modeling_detr.DetrForObjectDetection.__init__ with Detr->TableTransformer
def __init__(self, config: TableTransformerConfig):
super().__init__(config)
# DETR encoder-decoder model
... | 2,874 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
@add_start_docstrings_to_model_forward(TABLE_TRANSFORMER_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TableTransformerObjectDetectionOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: torch.FloatTensor,
pixel_mask: Optional[torch.FloatTensor] = None,
... | 2,874 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the
following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch
respectively). The class labels themselves should be a `torch.LongTensor` of len `(n... | 2,874 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
Returns:
Examples:
```python
>>> from huggingface_hub import hf_hub_download
>>> from transformers import AutoImageProcessor, TableTransformerForObjectDetection
>>> import torch
>>> from PIL import Image
>>> file_path = hf_hub_download(repo_id="nielsr/example-p... | 2,874 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
>>> # convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax)
>>> target_sizes = torch.tensor([image.size[::-1]])
>>> results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[
... 0
... ]
... | 2,874 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
# First, sent images through TABLE_TRANSFORMER base model to obtain encoder + decoder outputs
outputs = self.model(
pixel_values,
pixel_mask=pixel_mask,
decoder_attention_mask=decoder_attention_mask,
encoder_outputs=encoder_outputs,
inputs_embeds=input... | 2,874 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
loss, loss_dict, auxiliary_outputs = None, None, None
if labels is not None:
outputs_class, outputs_coord = None, None
if self.config.auxiliary_loss:
intermediate = outputs.intermediate_hidden_states if return_dict else outputs[4]
outputs_class = self.clas... | 2,874 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
return TableTransformerObjectDetectionOutput(
loss=loss,
loss_dict=loss_dict,
logits=logits,
pred_boxes=pred_boxes,
auxiliary_outputs=auxiliary_outputs,
last_hidden_state=outputs.last_hidden_state,
decoder_hidden_states=outputs.decoder_... | 2,874 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
class TableTransformerMLPPredictionHead(nn.Module):
"""
Very simple multi-layer perceptron (MLP, also called FFN), used to predict the normalized center coordinates,
height and width of a bounding box w.r.t. an image.
Copied from https://github.com/facebookresearch/table_transformer/blob/master/models/... | 2,875 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py |
class TableTransformerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`TableTransformerModel`]. It is used to
instantiate a Table Transformer model according to the specified arguments, defining the model architecture.
Instantiating a configuration with th... | 2,876 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/configuration_table_transformer.py |
Args:
use_timm_backbone (`bool`, *optional*, defaults to `True`):
Whether or not to use the `timm` library for the backbone. If set to `False`, will use the [`AutoBackbone`]
API.
backbone_config (`PretrainedConfig` or `dict`, *optional*):
The configuration of the back... | 2,876 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/configuration_table_transformer.py |
decoder_layers (`int`, *optional*, defaults to 6):
Number of decoder layers.
encoder_attention_heads (`int`, *optional*, defaults to 8):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (`int`, *optional*, defaults to 8):
... | 2,876 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/configuration_table_transformer.py |
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_dropout (`float`,... | 2,876 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/configuration_table_transformer.py |
decoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
auxiliary_loss (`bool`, *optional*, defaults to `False`):
Whether auxiliary decoding losses (... | 2,876 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/configuration_table_transformer.py |
Whether to use pretrained weights for the backbone.
backbone_kwargs (`dict`, *optional*):
Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
dilation (`bool`, *option... | 2,876 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/configuration_table_transformer.py |
mask_loss_coefficient (`float`, *optional*, defaults to 1):
Relative weight of the Focal loss in the panoptic segmentation loss.
dice_loss_coefficient (`float`, *optional*, defaults to 1):
Relative weight of the DICE/F-1 loss in the panoptic segmentation loss.
bbox_loss_coefficie... | 2,876 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/configuration_table_transformer.py |
Examples:
```python
>>> from transformers import TableTransformerModel, TableTransformerConfig
>>> # Initializing a Table Transformer microsoft/table-transformer-detection style configuration
>>> configuration = TableTransformerConfig()
>>> # Initializing a model from the microsoft/table-transfor... | 2,876 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/configuration_table_transformer.py |
# Copied from transformers.models.detr.configuration_detr.DetrConfig.__init__
def __init__(
self,
use_timm_backbone=True,
backbone_config=None,
num_channels=3,
num_queries=100,
encoder_layers=6,
encoder_ffn_dim=2048,
encoder_attention_heads=8,
... | 2,876 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/configuration_table_transformer.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.