text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
@add_start_docstrings_to_model_forward(ERNIE_M_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
i... | 10,234 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.co... | 10,234 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
inputs... | 10,234 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dic... | 10,234 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
class ErnieMForTokenClassification(ErnieMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.ernie_m = ErnieMModel(config, add_pooling_layer=False)
classifier_dropout = (
config.classifier_dropout if config.clas... | 10,235 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
@add_start_docstrings_to_model_forward(ERNIE_M_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forwa... | 10,235 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 10,235 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
outputs = self.ernie_m(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
past_key_values=past_key_values,
output_attentions=output_attentions,
output_hidd... | 10,235 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 10,235 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
class ErnieMForQuestionAnswering(ErnieMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.ernie_m = ErnieMModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
#... | 10,236 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
@add_start_docstrings_to_model_forward(ERNIE_M_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
de... | 10,236 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
... | 10,236 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
outputs = self.ernie_m(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
r... | 10,236 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
... | 10,236 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
... | 10,236 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
class ErnieMForInformationExtraction(ErnieMPreTrainedModel):
def __init__(self, config):
super(ErnieMForInformationExtraction, self).__init__(config)
self.ernie_m = ErnieMModel(config)
self.linear_start = nn.Linear(config.hidden_size, 1)
self.linear_end = nn.Linear(config.hidden_size... | 10,237 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
@add_start_docstrings_to_model_forward(ERNIE_M_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_ma... | 10,237 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) for computing the end_positions loss. Position outside of the sequence are not
taken into account for computing the loss.
""" | 10,237 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
result = self.ernie_m(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
re... | 10,237 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
... | 10,237 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
if not return_dict:
return tuple(
i
for i in [total_loss, start_logits, end_logits, result.hidden_states, result.attentions]
if i is not None
)
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=star... | 10,237 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py |
class ErnieMTokenizer(PreTrainedTokenizer):
r"""
Constructs a Ernie-M tokenizer. It uses the `sentencepiece` tools to cut the words to sub-words. | 10,238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/tokenization_ernie_m.py |
Args:
sentencepiece_model_file (`str`):
The file path of sentencepiece model.
vocab_file (`str`, *optional*):
The file path of the vocabulary.
do_lower_case (`str`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
... | 10,238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/tokenization_ernie_m.py |
A special token used for sequence classification. It is the last token of the sequence when built with
special tokens.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
A special token representing a masked token. This is the token used in the masked language modeling task
... | 10,238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/tokenization_ernie_m.py |
# Ernie-M model doesn't have token_type embedding.
model_input_names: List[str] = ["input_ids"]
vocab_files_names = VOCAB_FILES_NAMES
resource_files_names = RESOURCE_FILES_NAMES
def __init__(
self,
sentencepiece_model_ckpt,
vocab_file=None,
do_lower_case=False,
... | 10,238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/tokenization_ernie_m.py |
self.do_lower_case = do_lower_case
self.sentencepiece_model_ckpt = sentencepiece_model_ckpt
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(sentencepiece_model_ckpt)
# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
... | 10,238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/tokenization_ernie_m.py |
def get_offset_mapping(self, text):
if text is None:
return None
split_tokens = self.tokenize(text)
normalized_text, char_mapping = "", []
for i, ch in enumerate(text):
if ch in self.SP_CHAR_MAPPING:
ch = self.SP_CHAR_MAPPING.get(ch)
... | 10,238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/tokenization_ernie_m.py |
@property
def vocab_size(self):
return len(self.vocab)
def get_vocab(self):
return dict(self.vocab, **self.added_tokens_encoder)
def __getstate__(self):
state = self.__dict__.copy()
state["sp_model"] = None
return state
def __setstate__(self, d):
self._... | 10,238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/tokenization_ernie_m.py |
if self.sp_model_kwargs.get("enable_sampling") is True:
enable_sampling = True
if self.sp_model_kwargs.get("alpha") is not None:
alpha = self.sp_model_kwargs.get("alpha")
if self.sp_model_kwargs.get("nbest_size") is not None:
nbest_size = self.sp_model_kwargs.get("nbe... | 10,238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/tokenization_ernie_m.py |
if not enable_sampling:
pieces = self.sp_model.EncodeAsPieces(text)
else:
pieces = self.sp_model.SampleEncodeAsPieces(text, nbest_size, alpha)
new_pieces = []
for pi, piece in enumerate(pieces):
if piece == SPIECE_UNDERLINE:
if not pieces[pi + ... | 10,238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/tokenization_ernie_m.py |
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i])
lst_i = i
elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new... | 10,238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/tokenization_ernie_m.py |
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (strings for sub-words) in a single string."""
out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
return out_string
def convert_ids_to_string(self, ids):
"""
Converts a sequence of ... | 10,238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/tokenization_ernie_m.py |
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
r"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. An ErnieM sequence has the following format:
- single sequence: `[CLS] X... | 10,238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/tokenization_ernie_m.py |
def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None):
r"""
Build offset map from a pair of offset map by concatenating and adding offsets of special tokens. An Ernie-M
offset_mapping has the following format:
- single sequence: `(0,0) X (0,0)`
... | 10,238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/tokenization_ernie_m.py |
def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
r"""
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `encode` method.
Args:
tok... | 10,238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/tokenization_ernie_m.py |
if already_has_special_tokens:
if token_ids_1 is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model."
)
return [1 if... | 10,238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/tokenization_ernie_m.py |
Args:
token_ids_0 (`List[int]`):
The first tokenized sequence.
token_ids_1 (`List[int]`, *optional*):
The second tokenized sequence.
Returns:
`List[int]`: The token type ids.
"""
# called when `add_special_tokens` is True, so al... | 10,238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/tokenization_ernie_m.py |
def is_punct(self, char):
"""
is_punct
"""
if char in ",;:.?!~，；：。？！《》【】":
return True
return False
def is_whitespace(self, char):
"""
is whitespace
"""
if char == " " or char == "\t" or char == "\n" or char == "\r":
re... | 10,238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/tokenization_ernie_m.py |
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
index = 0
if os.path.isdir(save_directory):
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
... | 10,238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/tokenization_ernie_m.py |
tokenizer_model_file = os.path.join(save_directory, "sentencepiece.bpe.model")
with open(tokenizer_model_file, "wb") as fi:
content_spiece_model = self.sp_model.serialized_model_proto()
fi.write(content_spiece_model)
return (vocab_file,) | 10,238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/tokenization_ernie_m.py |
class ErnieMConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ErnieMModel`]. It is used to instantiate a
Ernie-M model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a simila... | 10,239 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/configuration_ernie_m.py |
Args:
vocab_size (`int`, *optional*, defaults to 250002):
Vocabulary size of `inputs_ids` in [`ErnieMModel`]. Also is the vocab size of token embedding matrix.
Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling
[`ErnieMModel... | 10,239 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/configuration_ernie_m.py |
firstly projected from hidden_size to intermediate_size, and then projected back to hidden_size. Typically
intermediate_size is larger than hidden_size.
hidden_act (`str`, *optional*, defaults to `"gelu"`):
The non-linear activation function in the feed-forward layer. `"gelu"`, `"relu"` ... | 10,239 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/configuration_ernie_m.py |
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the normal initializer for initializing all weight matrices. The index of padding
token in the token vocabulary.
pad_token_id (`int`, *optional*, defaults to 1):
Padding token id.
lay... | 10,239 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/configuration_ernie_m.py |
A normal_initializer initializes weight matrices as normal distributions. See
`ErnieMPretrainedModel._init_weights()` for how weights are initialized in `ErnieMModel`.
"""
model_type = "ernie_m"
attribute_map: Dict[str, str] = {"dropout": "classifier_dropout", "num_classes": "num_labels"} | 10,239 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/configuration_ernie_m.py |
def __init__(
self,
vocab_size: int = 250002,
hidden_size: int = 768,
num_hidden_layers: int = 12,
num_attention_heads: int = 12,
intermediate_size: int = 3072,
hidden_act: str = "gelu",
hidden_dropout_prob: float = 0.1,
attention_probs_dropout_pro... | 10,239 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/configuration_ernie_m.py |
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.classifier_dropout = classifier_dropout
self.act_dropout = act_dropout | 10,239 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/configuration_ernie_m.py |
class VanDropPath(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.Tensor) -> torch.... | 10,240 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py |
class VanOverlappingPatchEmbedder(nn.Module):
"""
Downsamples the input using a patchify operation with a `stride` of 4 by default making adjacent windows overlap by
half of the area. From [PVTv2: Improved Baselines with Pyramid Vision
Transformer](https://arxiv.org/abs/2106.13797).
"""
def __i... | 10,241 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py |
class VanMlpLayer(nn.Module):
"""
MLP with depth-wise convolution, from [PVTv2: Improved Baselines with Pyramid Vision
Transformer](https://arxiv.org/abs/2106.13797).
"""
def __init__(
self,
in_channels: int,
hidden_size: int,
out_channels: int,
hidden_act: s... | 10,242 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py |
def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
hidden_state = self.in_dense(hidden_state)
hidden_state = self.depth_wise(hidden_state)
hidden_state = self.activation(hidden_state)
hidden_state = self.dropout1(hidden_state)
hidden_state = self.out_dense(hidden_stat... | 10,242 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py |
class VanLargeKernelAttention(nn.Module):
"""
Basic Large Kernel Attention (LKA).
"""
def __init__(self, hidden_size: int):
super().__init__()
self.depth_wise = nn.Conv2d(hidden_size, hidden_size, kernel_size=5, padding=2, groups=hidden_size)
self.depth_wise_dilated = nn.Conv2d(... | 10,243 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py |
class VanLargeKernelAttentionLayer(nn.Module):
"""
Computes attention using Large Kernel Attention (LKA) and attends the input.
"""
def __init__(self, hidden_size: int):
super().__init__()
self.attention = VanLargeKernelAttention(hidden_size)
def forward(self, hidden_state: torch.T... | 10,244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py |
class VanSpatialAttentionLayer(nn.Module):
"""
Van spatial attention layer composed by projection (via conv) -> act -> Large Kernel Attention (LKA) attention ->
projection (via conv) + residual connection.
"""
def __init__(self, hidden_size: int, hidden_act: str = "gelu"):
super().__init__(... | 10,245 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py |
def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
residual = hidden_state
hidden_state = self.pre_projection(hidden_state)
hidden_state = self.attention_layer(hidden_state)
hidden_state = self.post_projection(hidden_state)
hidden_state = hidden_state + residual
... | 10,245 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py |
class VanLayerScaling(nn.Module):
"""
Scales the inputs by a learnable parameter initialized by `initial_value`.
"""
def __init__(self, hidden_size: int, initial_value: float = 1e-2):
super().__init__()
self.weight = nn.Parameter(initial_value * torch.ones((hidden_size)), requires_grad=... | 10,246 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py |
class VanLayer(nn.Module):
"""
Van layer composed by normalization layers, large kernel attention (LKA) and a multi layer perceptron (MLP).
"""
def __init__(
self,
config: VanConfig,
hidden_size: int,
mlp_ratio: int = 4,
drop_path_rate: float = 0.5,
):
... | 10,247 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py |
def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
residual = hidden_state
# attention
hidden_state = self.pre_normomalization(hidden_state)
hidden_state = self.attention(hidden_state)
hidden_state = self.attention_scaling(hidden_state)
hidden_state = self.dro... | 10,247 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py |
class VanStage(nn.Module):
"""
VanStage, consisting of multiple layers.
"""
def __init__(
self,
config: VanConfig,
in_channels: int,
hidden_size: int,
patch_size: int,
stride: int,
depth: int,
mlp_ratio: int = 4,
drop_path_rate: fl... | 10,248 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py |
def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
hidden_state = self.embeddings(hidden_state)
hidden_state = self.layers(hidden_state)
# rearrange b c h w -> b (h w) c
batch_size, hidden_size, height, width = hidden_state.shape
hidden_state = hidden_state.flatten(2)... | 10,248 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py |
class VanEncoder(nn.Module):
"""
VanEncoder, consisting of multiple stages.
"""
def __init__(self, config: VanConfig):
super().__init__()
self.stages = nn.ModuleList([])
patch_sizes = config.patch_sizes
strides = config.strides
hidden_sizes = config.hidden_sizes
... | 10,249 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py |
for num_stage, (patch_size, stride, hidden_size, depth, mlp_expantion, drop_path_rate) in enumerate(
zip(patch_sizes, strides, hidden_sizes, depths, mlp_ratios, drop_path_rates)
):
is_first_stage = num_stage == 0
in_channels = hidden_sizes[num_stage - 1]
if is_fir... | 10,249 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py |
def forward(
self,
hidden_state: torch.Tensor,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> Union[Tuple, BaseModelOutputWithNoAttention]:
all_hidden_states = () if output_hidden_states else None
for _, stage_module in enumera... | 10,249 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py |
class VanPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = VanConfig
base_model_prefix = "van"
main_input_name = "pixel_values"
supports_gradient_checkpointing... | 10,250 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py |
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
nn.init.trunc_normal_(module.weight, std=self.config.initializer_range)
if isinstance(module, nn.Linear) and module.bias is not None:
nn.init.constant_(module.bias, 0)
... | 10,250 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py |
class VanModel(VanPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.encoder = VanEncoder(config)
# final layernorm layer
self.layernorm = nn.LayerNorm(config.hidden_sizes[-1], eps=config.layer_norm_eps)
# Initialize weigh... | 10,251 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py |
@add_start_docstrings_to_model_forward(VAN_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndNoAttention,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
d... | 10,251 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py |
encoder_outputs = self.encoder(
pixel_values,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
# global average pooling, n c w h -> n c
pooled_output = last_hidden_state.mean(dim=[-2, -1])
... | 10,251 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py |
class VanForImageClassification(VanPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.van = VanModel(config)
# Classifier head
self.classifier = (
nn.Linear(config.hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity()
... | 10,252 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py |
@add_start_docstrings_to_model_forward(VAN_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=ImageClassifierOutputWithNoAttention,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
s... | 10,252 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 10,252 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py |
outputs = self.van(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
pooled_output = outputs.pooler_output if return_dict else outputs[1]
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type ... | 10,252 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py |
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.config.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type ==... | 10,252 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py |
class VanConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`VanModel`]. It is used to instantiate a VAN model
according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configur... | 10,253 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/configuration_van.py |
Args:
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
patch_sizes (`List[int]`, *optional*, defaults to `[7, 3, 3, 3]`):
Patch size to use in e... | 10,253 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/configuration_van.py |
The non-linear activation function (function or string) in each layer. If string, `"gelu"`, `"relu"`,
`"selu"` and `"gelu_new"` are supported.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight... | 10,253 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/configuration_van.py |
Example:
```python
>>> from transformers import VanModel, VanConfig
>>> # Initializing a VAN van-base style configuration
>>> configuration = VanConfig()
>>> # Initializing a model from the van-base style configuration
>>> model = VanModel(configuration)
>>> # Accessing the model configurat... | 10,253 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/configuration_van.py |
def __init__(
self,
image_size=224,
num_channels=3,
patch_sizes=[7, 3, 3, 3],
strides=[4, 2, 2, 2],
hidden_sizes=[64, 128, 320, 512],
depths=[3, 3, 12, 3],
mlp_ratios=[8, 8, 4, 4],
hidden_act="gelu",
initializer_range=0.02,
layer_no... | 10,253 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/configuration_van.py |
class Tracker:
module: nn.Module
traced: List[nn.Module] = field(default_factory=list)
handles: list = field(default_factory=list)
def _forward_hook(self, m, inputs: Tensor, outputs: Tensor):
has_not_submodules = len(list(m.modules())) == 1 or isinstance(m, nn.Conv2d) or isinstance(m, nn.BatchN... | 10,254 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/convert_van_to_pytorch.py |
class ModuleTransfer:
src: nn.Module
dest: nn.Module
verbose: int = 0
src_skip: List = field(default_factory=list)
dest_skip: List = field(default_factory=list)
def __call__(self, x: Tensor):
"""
Transfer the weights of `self.src` to `self.dest` by performing a forward pass usin... | 10,255 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/convert_van_to_pytorch.py |
for dest_m, src_m in zip(dest_traced, src_traced):
dest_m.load_state_dict(src_m.state_dict())
if self.verbose == 1:
print(f"Transfered from={src_m} to={dest_m}") | 10,255 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/convert_van_to_pytorch.py |
class TvltConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`TvltModel`]. It is used to instantiate a TVLT
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar confi... | 10,256 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/configuration_tvlt.py |
Args:
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
spectrogram_length (`int`, *optional*, defaults to 2048):
The time length of each audio spectrogram.
frequency_length (`int`, *optional*, defaults to 128):
The frequenc... | 10,256 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/configuration_tvlt.py |
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 to 12):
Number of attention heads for each attention layer in the... | 10,256 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/configuration_tvlt.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... | 10,256 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/configuration_tvlt.py |
decoder_num_hidden_layers (`int`, *optional*, defaults to 8):
Number of hidden layers in the decoder.
decoder_intermediate_size (`int`, *optional*, defaults to 2048):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the decoder.
pixel_mask_ratio (`float`, *optio... | 10,256 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/configuration_tvlt.py |
Loss types including regression and classification. | 10,256 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/configuration_tvlt.py |
Example:
```python
>>> from transformers import TvltConfig, TvltModel
>>> # # Initializing a TVLT ZinengTang/tvlt-base style configuration
>>> configuration = TvltConfig()
>>> # # Initializing a model (with random weights) from the ZinengTang/tvlt-base style configuration
>>> model = TvltMode... | 10,256 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/configuration_tvlt.py |
def __init__(
self,
image_size=224,
spectrogram_length=2048,
frequency_length=128,
image_patch_size=[16, 16],
audio_patch_size=[16, 16],
num_image_channels=3,
num_audio_channels=1,
num_frames=8,
hidden_size=768,
num_hidden_layers=12... | 10,256 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/configuration_tvlt.py |
if audio_mask_type not in ("frame-level", "patch_level"):
raise ValueError(
"audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') "
f"got {audio_mask_type}"
)
self.image_size = image_size
self.spectrogram_lengt... | 10,256 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/configuration_tvlt.py |
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_pro... | 10,256 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/configuration_tvlt.py |
self.task_matching = task_matching
self.task_mae = task_mae
self.loss_type = loss_type | 10,256 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/configuration_tvlt.py |
class TvltProcessor(ProcessorMixin):
r"""
Constructs a TVLT processor which wraps a TVLT image processor and TVLT feature extractor into a single processor.
[`TvltProcessor`] offers all the functionalities of [`TvltImageProcessor`] and [`TvltFeatureExtractor`]. See the
docstring of [`~TvltProcessor.__c... | 10,257 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/processing_tvlt.py |
self.image_processor = image_processor
self.feature_extractor = feature_extractor
def __call__(
self,
images=None,
audio=None,
images_mixed=None,
sampling_rate=None,
mask_audio=False,
mask_pixel=False,
*args,
**kwargs,
):
"... | 10,257 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/processing_tvlt.py |
images_mixed_dict = None
if images is not None:
images_dict = self.image_processor(images, mask_pixel=mask_pixel, *args, **kwargs)
if images_mixed is not None:
images_mixed_dict = self.image_processor(images_mixed, is_mixed=True, *args, **kwargs)
if audio is not None:
... | 10,257 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/processing_tvlt.py |
@property
def model_input_names(self):
image_processor_input_names = self.image_processor.model_input_names
feature_extractor_input_names = self.feature_extractor.model_input_names
return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names)) | 10,257 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/processing_tvlt.py |
class TvltImageProcessor(BaseImageProcessor):
r"""
Constructs a TVLT image processor.
This processor can be used to prepare either videos or images for the model by converting images to 1-frame videos. | 10,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py |
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
`do_resize` parameter in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge":... | 10,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py |
Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
`preprocess` method.
do_center_crop (`bool`, *optional*, defaults to `True`):
Whether to center crop the image to the specified `crop_size`. Can be overridden by the `do_center_crop`
... | 10,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py |
Defines the scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter
in the `preprocess` method.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preproc... | 10,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py |
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