text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
Args:
vocab_size (`int`, *optional*, defaults to 50267):
Vocabulary size of the MVP model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`MvpModel`].
d_model (`int`, *optional*, defaults to 1024):
Dimensionalit... | 4,102 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/configuration_mvp.py |
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
activation_function (`str` or `function`, *optional*, defaults to `"gelu"... | 4,102 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/configuration_mvp.py |
The dropout ratio for classifier.
max_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
init_std (`float`, *optional*, def... | 4,102 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/configuration_mvp.py |
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
forced_eos_token_id (`int`, *optional*, defaults to 2):
The id of the token to force as the last generated token when `max_length` is reache... | 4,102 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/configuration_mvp.py |
```python
>>> from transformers import MvpConfig, MvpModel
>>> # Initializing a MVP RUCAIBox/mvp style configuration
>>> configuration = MvpConfig()
>>> # Initializing a model (with random weights) from the RUCAIBox/mvp style configuration
>>> model = MvpModel(configuration)
>>> # Accessing t... | 4,102 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/configuration_mvp.py |
def __init__(
self,
vocab_size=50267,
max_position_embeddings=1024,
encoder_layers=12,
encoder_ffn_dim=4096,
encoder_attention_heads=16,
decoder_layers=12,
decoder_ffn_dim=4096,
decoder_attention_heads=16,
encoder_layerdrop=0.0,
dec... | 4,102 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/configuration_mvp.py |
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_layers = encoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.dropo... | 4,102 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/configuration_mvp.py |
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
is_encoder_decoder=is_encoder_decoder,
decoder_start_token_id=decoder_start_token_id,
forced_eos_token_id=forced_eos_token_id,
**kwargs,
... | 4,102 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/configuration_mvp.py |
class MvpTokenizer(PreTrainedTokenizer):
"""
Constructs a MVP tokenizer, which is smilar to the RoBERTa tokenizer, using byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is... | 4,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp.py |
</Tip>
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
Path to the vocabulary file.
merges_file (`str`):
Path ... | 4,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp.py |
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip> | 4,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp.py |
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequenc... | 4,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp.py |
The token used for padding, for example when batching sequences of different lengths.
mask_token (`str`, *optional*, defaults to `"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the mode... | 4,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp.py |
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"] | 4,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp.py |
def __init__(
self,
vocab_file,
merges_file,
errors="replace",
bos_token="<s>",
eos_token="</s>",
sep_token="</s>",
cls_token="<s>",
unk_token="<unk>",
pad_token="<pad>",
mask_token="<mask>",
add_prefix_space=False,
... | 4,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp.py |
# Mask token behave like a normal word, i.e. include the space before it
mask_token = AddedToken(mask_token, lstrip=True, special=True) if isinstance(mask_token, str) else mask_token
with open(vocab_file, encoding="utf-8") as vocab_handle:
self.encoder = json.load(vocab_handle)
self.... | 4,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp.py |
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
super().__init__(
errors=errors,
bos_token=bos_token,
eos_toke... | 4,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp.py |
while True:
bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
if bigram not in self.bpe_ranks:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
... | 4,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp.py |
if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
new_word.append(first + second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
... | 4,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp.py |
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.encoder.get(token, self.encoder.get(self.unk_token))
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.decod... | 4,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp.py |
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
vocab_file = os.path.join(
save_directory, (file... | 4,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp.py |
index = 0
with open(merge_file, "w", encoding="utf-8") as writer:
writer.write("#version: 0.2\n")
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
f"Sa... | 4,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp.py |
- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s></s> B </s>`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pa... | 4,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp.py |
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens ... | 4,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp.py |
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=Tru... | 4,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp.py |
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_tok... | 4,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp.py |
class PvtV2Config(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`PvtV2Model`]. It is used to instantiate a Pvt V2
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults w... | 4,104 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/configuration_pvt_v2.py |
Args:
image_size (`Union[int, Tuple[int, int]]`, *optional*, defaults to 224):
The input image size. Pass int value for square image, or tuple of (height, width).
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
num_encoder_blocks (`[int]`, *... | 4,104 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/configuration_pvt_v2.py |
strides (`List[int]`, *optional*, defaults to `[4, 2, 2, 2]`):
Stride for overlapping patch embedding before each encoder block.
num_attention_heads (`List[int]`, *optional*, defaults to `[1, 2, 5, 8]`):
Number of attention heads for each attention layer in each block of the Transformer ... | 4,104 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/configuration_pvt_v2.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.
drop... | 4,104 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/configuration_pvt_v2.py |
dimensionality reduction in the attention layers rather than strided convolution.
out_features (`List[str]`, *optional*):
If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
(depending on how many stages the model has). If unset and `out_... | 4,104 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/configuration_pvt_v2.py |
```python
>>> from transformers import PvtV2Model, PvtV2Config
>>> # Initializing a pvt_v2_b0 style configuration
>>> configuration = PvtV2Config()
>>> # Initializing a model from the OpenGVLab/pvt_v2_b0 style configuration
>>> model = PvtV2Model(configuration)
>>> # Accessing the model confi... | 4,104 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/configuration_pvt_v2.py |
def __init__(
self,
image_size: Union[int, Tuple[int, int]] = 224,
num_channels: int = 3,
num_encoder_blocks: int = 4,
depths: List[int] = [2, 2, 2, 2],
sr_ratios: List[int] = [8, 4, 2, 1],
hidden_sizes: List[int] = [32, 64, 160, 256],
patch_sizes: List[in... | 4,104 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/configuration_pvt_v2.py |
image_size = (image_size, image_size) if isinstance(image_size, int) else image_size | 4,104 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/configuration_pvt_v2.py |
self.image_size = image_size
self.num_channels = num_channels
self.num_encoder_blocks = num_encoder_blocks
self.depths = depths
self.sr_ratios = sr_ratios
self.hidden_sizes = hidden_sizes
self.patch_sizes = patch_sizes
self.strides = strides
self.mlp_ratio... | 4,104 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/configuration_pvt_v2.py |
out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
) | 4,104 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/configuration_pvt_v2.py |
class PvtV2DropPath(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) -> torc... | 4,105 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
class PvtV2OverlapPatchEmbeddings(nn.Module):
"""Image to Patch Embedding"""
def __init__(self, config: PvtV2Config, layer_idx: int):
super().__init__()
patch_size = config.patch_sizes[layer_idx]
patch_size = (patch_size, patch_size) if isinstance(patch_size, int) else patch_size
... | 4,106 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
def forward(self, pixel_values):
embeddings = self.proj(pixel_values)
_, _, height, width = embeddings.shape
embeddings = embeddings.flatten(2).transpose(1, 2)
embeddings = self.layer_norm(embeddings)
return embeddings, height, width | 4,106 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
class PvtV2DepthWiseConv(nn.Module):
"""
Depth-wise (DW) convolution to infuse positional information using zero-padding. Depth-wise convolutions
have an equal number of groups to the number of input channels, meaning one filter per input channel. This
reduces the overall parameters and compute costs si... | 4,107 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
class PvtV2SelfAttention(nn.Module):
"""Efficient self-attention mechanism."""
def __init__(self, config: PvtV2Config, hidden_size: int, num_attention_heads: int, spatial_reduction_ratio: int):
super().__init__()
self.linear_attention = config.linear_attention
self.pruned_heads = set()
... | 4,108 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
self.query = nn.Linear(self.hidden_size, self.all_head_size, bias=config.qkv_bias)
self.key = nn.Linear(self.hidden_size, self.all_head_size, bias=config.qkv_bias)
self.value = nn.Linear(self.hidden_size, self.all_head_size, bias=config.qkv_bias)
self.attn_drop = nn.Dropout(config.attention_prob... | 4,108 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
self.spatial_reduction_ratio = spatial_reduction_ratio
if self.linear_attention:
self.pool = nn.AdaptiveAvgPool2d(7)
self.spatial_reduction = nn.Conv2d(self.hidden_size, self.hidden_size, kernel_size=1, stride=1)
self.layer_norm = nn.LayerNorm(self.hidden_size, eps=config.lay... | 4,108 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
def forward(
self,
hidden_states: torch.Tensor,
height: int,
width: int,
output_attentions: bool = False,
) -> Tuple[torch.Tensor]:
batch_size, seq_len, num_channels = hidden_states.shape
query_layer = self.transpose_for_scores(self.query(hidden_states)) | 4,108 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
if self.linear_attention:
hidden_states = hidden_states.permute(0, 2, 1).reshape(batch_size, num_channels, height, width)
hidden_states = (
self.spatial_reduction(self.pool(hidden_states)).reshape(batch_size, num_channels, -1).permute(0, 2, 1)
)
hidden_sta... | 4,108 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
# Normalize the attention scores to probabilities.
atten... | 4,108 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.num_attention_heads, self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.query = prune_linear_layer(self.query, inde... | 4,108 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
class PvtV2ConvFeedForwardNetwork(nn.Module):
def __init__(
self,
config: PvtV2Config,
in_features: int,
hidden_features: Optional[int] = None,
out_features: Optional[int] = None,
):
super().__init__()
out_features = out_features if out_features is not Non... | 4,109 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
def forward(self, hidden_states: torch.Tensor, height, width) -> torch.Tensor:
hidden_states = self.dense1(hidden_states)
hidden_states = self.relu(hidden_states)
hidden_states = self.dwconv(hidden_states, height, width)
hidden_states = self.intermediate_act_fn(hidden_states)
hid... | 4,109 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
class PvtV2BlockLayer(nn.Module):
def __init__(self, config: PvtV2Config, layer_idx: int, drop_path: float = 0.0):
super().__init__()
hidden_size: int = config.hidden_sizes[layer_idx]
num_attention_heads: int = config.num_attention_heads[layer_idx]
spatial_reduction_ratio: int = conf... | 4,110 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
self.mlp = PvtV2ConvFeedForwardNetwork(config=config, in_features=hidden_size, hidden_features=mlp_hidden_size) | 4,110 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
def forward(self, hidden_states: torch.Tensor, height: int, width: int, output_attentions: bool = False):
self_attention_outputs = self.attention(
hidden_states=self.layer_norm_1(hidden_states),
height=height,
width=width,
output_attentions=output_attentions,
... | 4,110 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
class PvtV2EncoderLayer(nn.Module):
def __init__(self, config: PvtV2Config, layer_idx: int):
super().__init__()
self.patch_embedding = PvtV2OverlapPatchEmbeddings(
config=config,
layer_idx=layer_idx,
)
# Transformer block
# stochastic depth decay rule
... | 4,111 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
def forward(self, hidden_states, output_attentions):
all_self_attentions = () if output_attentions else None
# first, obtain patch embeddings
hidden_states, height, width = self.patch_embedding(hidden_states)
# second, send embeddings through blocks
for block in self.blocks:
... | 4,111 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
class PvtV2Encoder(nn.Module):
def __init__(self, config: PvtV2Config):
super().__init__()
self.config = config
self.gradient_checkpointing = False
# encoder layers
self.layers = nn.ModuleList([PvtV2EncoderLayer(config, i) for i in range(config.num_encoder_blocks)])
def... | 4,112 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
batch_size = pixel_values.shape[0]
hidden_states = pixel_values
for idx, layer in enumerate(self.layers):
if self.gradient_checkpointing and self.training:
layer_output = self._gradient_checkpointing_func(layer.__call__, hidden_states, output_attentions)
else:
... | 4,112 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
) | 4,112 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
class PvtV2PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = PvtV2Config
base_model_prefix = "pvt_v2"
main_input_name = "pixel_values"
supports_gradient_checkp... | 4,113 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Upcast the input in `fp32` and cast it back to desired `dtype` to avoid
# `trunc_normal_cpu` not implemented in `half` issues
... | 4,113 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
class PvtV2Model(PvtV2PreTrainedModel):
def __init__(self, config: PvtV2Config):
super().__init__(config)
self.config = config
# hierarchical Transformer encoder
self.encoder = PvtV2Encoder(config)
# Initialize weights and apply final processing
self.post_init()
... | 4,114 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
@add_start_docstrings_to_model_forward(PVT_V2_INPUTS_DOCSTRING.format("(batch_size, channels, height, width)"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED... | 4,114 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
encoder_outputs = self.encoder(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
if not return_dict:
return (sequ... | 4,114 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
class PvtV2ForImageClassification(PvtV2PreTrainedModel):
def __init__(self, config: PvtV2Config) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.pvt_v2 = PvtV2Model(config)
# Classifier head
self.classifier = (
nn.Linear(config.hidden_... | 4,115 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
@add_start_docstrings_to_model_forward(PVT_V2_INPUTS_DOCSTRING.format("(batch_size, channels, height, width)"))
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=ImageClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTP... | 4,115 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 4,115 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
outputs = self.pvt_v2(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
# convert last hidden states to (batch_size, height*width, h... | 4,115 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
sel... | 4,115 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "singl... | 4,115 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
class PvtV2Backbone(PvtV2Model, BackboneMixin):
def __init__(self, config: PvtV2Config):
super().__init__(config)
super()._init_backbone(config)
self.num_features = config.hidden_sizes
@add_start_docstrings_to_model_forward(PVT_V2_INPUTS_DOCSTRING)
@replace_return_docstrings(output_... | 4,116 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
>>> processor = AutoImageProcessor.from_pretrained("OpenGVLab/pvt_v2_b0")
>>> model = AutoBackbone.from_pretrained(
... "OpenGVLab/pvt_v2_b0", out_features=["stage1", "stage2", "stage3", "stage4"]
... )
>>> inputs = processor(image, return_tensors="pt")
>>> outputs = model(... | 4,116 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
feature_maps = ()
for idx, stage in enumerate(self.stage_names):
if stage in self.out_features:
feature_maps += (hidden_states[idx],)
if not return_dict:
output = (feature_maps,)
if output_hidden_states:
output += (outputs.hidden_state... | 4,116 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt_v2/modeling_pvt_v2.py |
class DonutSwinConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`DonutSwinModel`]. It is used to instantiate a
Donut model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a si... | 4,117 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/configuration_donut_swin.py |
Args:
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 4):
The size (resolution) of each patch.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
... | 4,117 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/configuration_donut_swin.py |
Whether or not a learnable bias should be added to the queries, keys and values.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings and encoder.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.... | 4,117 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/configuration_donut_swin.py |
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the layer normalization layers. | 4,117 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/configuration_donut_swin.py |
Example:
```python
>>> from transformers import DonutSwinConfig, DonutSwinModel
>>> # Initializing a Donut naver-clova-ix/donut-base style configuration
>>> configuration = DonutSwinConfig()
>>> # Randomly initializing a model from the naver-clova-ix/donut-base style configuration
>>> model =... | 4,117 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/configuration_donut_swin.py |
def __init__(
self,
image_size=224,
patch_size=4,
num_channels=3,
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4.0,
qkv_bias=True,
hidden_dropout_prob=0.0,
attention_probs_dropout_pro... | 4,117 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/configuration_donut_swin.py |
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.embed_dim = embed_dim
self.depths = depths
self.num_layers = len(depths)
self.num_heads = num_heads
self.window_size = window_size
self.mlp_ratio = mlp_ratio
... | 4,117 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/configuration_donut_swin.py |
class DonutSwinEncoderOutput(ModelOutput):
"""
DonutSwin encoder's outputs, with potential hidden states and attentions.
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 ... | 4,118 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/modeling_donut_swin.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each stage) of shape `(batch... | 4,118 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/modeling_donut_swin.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
"""
last_hidden_state: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, .... | 4,118 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/modeling_donut_swin.py |
class DonutSwinModelOutput(ModelOutput):
"""
DonutSwin model's outputs that also contains a pooling of the last hidden states.
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 o... | 4,119 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/modeling_donut_swin.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each stage) of shape `(batch... | 4,119 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/modeling_donut_swin.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
"""
last_hidden_state: torch.FloatTensor = None
pooler_output: Optional[torch.FloatTensor] = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = No... | 4,119 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/modeling_donut_swin.py |
class DonutSwinEmbeddings(nn.Module):
"""
Construct the patch and position embeddings. Optionally, also the mask token.
"""
def __init__(self, config, use_mask_token=False):
super().__init__()
self.patch_embeddings = DonutSwinPatchEmbeddings(config)
num_patches = self.patch_emb... | 4,120 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/modeling_donut_swin.py |
# Copied from transformers.models.vit.modeling_vit.ViTEmbeddings.interpolate_pos_encoding
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on hi... | 4,120 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/modeling_donut_swin.py |
# always interpolate when tracing to ensure the exported model works for dynamic input shapes
if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
return self.position_embeddings
class_pos_embed = self.position_embeddings[:, :1]
patch_pos_embed = self.... | 4,120 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/modeling_donut_swin.py |
return torch.cat((class_pos_embed, patch_pos_embed), dim=1)
def forward(
self,
pixel_values: Optional[torch.FloatTensor],
bool_masked_pos: Optional[torch.BoolTensor] = None,
interpolate_pos_encoding: bool = False,
) -> Tuple[torch.Tensor]:
_, num_channels, height, width ... | 4,120 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/modeling_donut_swin.py |
if self.position_embeddings is not None:
if interpolate_pos_encoding:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
embeddings = embeddings + self.position_embeddings
embeddings = self.dropout(embeddings)
... | 4,120 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/modeling_donut_swin.py |
class DonutSwinPatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
""" | 4,121 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/modeling_donut_swin.py |
def __init__(self, config):
super().__init__()
image_size, patch_size = config.image_size, config.patch_size
num_channels, hidden_size = config.num_channels, config.embed_dim
image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
... | 4,121 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/modeling_donut_swin.py |
def maybe_pad(self, pixel_values, height, width):
if width % self.patch_size[1] != 0:
pad_values = (0, self.patch_size[1] - width % self.patch_size[1])
pixel_values = nn.functional.pad(pixel_values, pad_values)
if height % self.patch_size[0] != 0:
pad_values = (0, 0, ... | 4,121 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/modeling_donut_swin.py |
class DonutSwinPatchMerging(nn.Module):
"""
Patch Merging Layer.
Args:
input_resolution (`Tuple[int]`):
Resolution of input feature.
dim (`int`):
Number of input channels.
norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`):
Normaliza... | 4,122 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/modeling_donut_swin.py |
def forward(self, input_feature: torch.Tensor, input_dimensions: Tuple[int, int]) -> torch.Tensor:
height, width = input_dimensions
# `dim` is height * width
batch_size, dim, num_channels = input_feature.shape | 4,122 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/modeling_donut_swin.py |
input_feature = input_feature.view(batch_size, height, width, num_channels)
# pad input to be disible by width and height, if needed
input_feature = self.maybe_pad(input_feature, height, width)
# [batch_size, height/2, width/2, num_channels]
input_feature_0 = input_feature[:, 0::2, 0::2,... | 4,122 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/modeling_donut_swin.py |
input_feature = self.norm(input_feature)
input_feature = self.reduction(input_feature)
return input_feature | 4,122 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/modeling_donut_swin.py |
class DonutSwinDropPath(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) -> ... | 4,123 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/modeling_donut_swin.py |
class DonutSwinSelfAttention(nn.Module):
def __init__(self, config, dim, num_heads, window_size):
super().__init__()
if dim % num_heads != 0:
raise ValueError(
f"The hidden size ({dim}) is not a multiple of the number of attention heads ({num_heads})"
)
... | 4,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/modeling_donut_swin.py |
# get pair-wise relative position index for each token inside the window
coords_h = torch.arange(self.window_size[0])
coords_w = torch.arange(self.window_size[1])
coords = torch.stack(meshgrid([coords_h, coords_w], indexing="ij"))
coords_flatten = torch.flatten(coords, 1)
relativ... | 4,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/modeling_donut_swin.py |
self.query = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
self.key = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
self.value = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
self.dropout = nn.Dropout(config.attention... | 4,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/donut/modeling_donut_swin.py |
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