text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
nn.init.normal_(module.gate_ffn.data, std=std) | 3,373 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
# Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool,
):
... | 3,373 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
if AttentionMaskConverter._ignore_causal_mask_sdpa(
attention_mask,
... | 3,373 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
attention_mask,
sequence_length=sequence_length,
target_length=target_length,
dtype=dtype,
dev... | 3,373 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type == "cuda"
and not output_attentions
):
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows whe... | 3,373 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
@staticmethod
# Copied from transformers.models.llama.modeling_llama.LlamaModel._prepare_4d_causal_attention_mask_with_cache_position
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dt... | 3,373 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
`(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
... | 3,373 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
if attention_mask is not None and attention_mask.dim() == 4:
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
causal_mask = attention_mask
else:
min_dtype = torch.finfo(dtype).min
causal_mask = torch.full(... | 3,373 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
) | 3,373 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
return causal_mask | 3,373 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
class MllamaVisionModel(MllamaPreTrainedModel):
config_class = MllamaVisionConfig
base_model_prefix = "vision_model"
def __init__(self, config: MllamaVisionConfig):
super().__init__(config)
self.image_size = config.image_size
self.patch_size = config.patch_size
self.max_num_... | 3,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
self.class_embedding = nn.Parameter(self.scale * torch.randn(self.hidden_size))
self.gated_positional_embedding = MllamaPrecomputedPositionEmbedding(config)
self.pre_tile_positional_embedding = MllamaPrecomputedAspectRatioEmbedding(config, is_gated=True)
self.post_tile_positional_embedding = Ml... | 3,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
def apply_class_embedding(self, hidden_state: torch.Tensor) -> torch.Tensor:
batch_size, _, hidden_size = hidden_state.shape
class_embedding = self.class_embedding.expand(batch_size, 1, hidden_size)
hidden_state = torch.cat([class_embedding, hidden_state], dim=1)
return hidden_state
... | 3,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, MllamaVisionModel
>>> checkpoint = "meta-llama/Llama-3.2-11B-Vision"
>>> model = MllamaVisionModel.from_pretrained(checkpoint)
>>> processor = AutoProcessor.from_pretraine... | 3,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
>>> print(output.last_hidden_state.shape)
torch.Size([1, 1, 4, 1025, 7680])
```
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not N... | 3,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
# Tile embeddings
_, num_patches, dim = hidden_state.shape
hidden_state = hidden_state.reshape(batch_size * num_concurrent_media, num_tiles, -1, dim)
hidden_state = self.pre_tile_positional_embedding(hidden_state, aspect_ratio_ids)
# Add cls token
hidden_state = hidden_state.res... | 3,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
# Compute the number of tokens to pad
num_padding_patches = (8 - (hidden_state.shape[-2] % 8)) % 8
# Compute padding tuple for pad function
padding = (0, 0, 0, num_padding_patches) # (pad_left, pad_right, pad_left for dim -2, pad_right for dim -2)
# Pad the tensor
hidden_state =... | 3,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
# Apply encoder
hidden_state = hidden_state.view(batch_size * num_concurrent_media, -1, dim)
output = self.transformer(
hidden_state,
attention_mask=attention_mask,
output_hidden_states=True,
output_attentions=output_attentions,
)
hidden_st... | 3,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
# Apply global encoder
hidden_state = hidden_state.reshape(
batch_size * num_concurrent_media, num_tiles, num_patches + num_padding_patches, dim
)
hidden_state = self.post_tile_positional_embedding(hidden_state, aspect_ratio_ids)
hidden_state = hidden_state.reshape(
... | 3,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
# Remove padding form hidden state
hidden_state = hidden_state.reshape(
batch_size * num_concurrent_media, num_tiles, num_patches + num_padding_patches, dim
)
hidden_state = hidden_state[:, :, :slice_index]
hidden_state = hidden_state.reshape(batch_size, num_concurrent_media,... | 3,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
# Remove padding from intermediate hidden states
intermediate_hidden_states = intermediate_hidden_states.reshape(
batch_size * num_concurrent_media, num_tiles, num_patches + num_padding_patches, -1
)
intermediate_hidden_states = intermediate_hidden_states[:, :, :slice_index]
... | 3,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
if output_attentions:
# global transformer in contrast to `self.transformer` doesn't always return hidden states so we might go index out-of-range
global_attn = tuple(global_output[2]) if output_hidden_states else tuple(global_output[1])
attentions = tuple(output[2]) + global_attn
... | 3,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
class MllamaTextModel(MllamaPreTrainedModel):
config_class = MllamaTextConfig
base_model_prefix = "language_model.model"
def __init__(self, config: MllamaTextConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.emb... | 3,375 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
self.layers = nn.ModuleList(layers)
self.norm = MllamaTextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.rotary_emb = MllamaRotaryEmbedding(config=config)
self.gradient_checkpointing = False
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens... | 3,375 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
@add_start_docstrings_to_model_forward(MLLAMA_TEXT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPast, config_class="MllamaTextConfig")
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
po... | 3,375 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
) -> Union[Tuple, BaseModelOutputWithPast]:
""" | 3,375 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
Returns:
Example:
```python
>>> from transformers import AutoProcessor, MllamaTextModel
>>> checkpoint = "meta-llama/Llama-3.2-11B-Vision"
>>> model = MllamaTextModel.from_pretrained(checkpoint)
>>> processor = AutoProcessor.from_pretrained(checkpoint)
>>> tex... | 3,375 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
>>> print(output.last_hidden_state.shape)
torch.Size([1, 13, 4096])
```
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else... | 3,375 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
hidden_states = inputs_embeds
if use_cache and past_key_values is None:
past_key_values = DynamicCache()
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if... | 3,375 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = None
for idx, decoder_layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_stat... | 3,375 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
cross_attention_states,
cross_attention_mask,
causal_mask,
... | 3,375 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
past_key_value=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
) | 3,375 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the l... | 3,375 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
class MllamaForCausalLM(MllamaPreTrainedModel, GenerationMixin):
config_class = MllamaTextConfig
_supports_static_cache = True # only the LLM without cross attn can do compile
base_model_prefix = "language_model"
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super()._... | 3,376 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
def set_decoder(self, decoder):
self.model = decoder
def get_decoder(self):
return self.model | 3,376 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
@add_start_docstrings_to_model_forward(MLLAMA_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class="MllamaTextConfig")
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Opti... | 3,376 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
num_logits_to_keep: int = 0,
**loss_kwargs,
) -> Union[Tuple, CausalLMOutputWithPast]:
r"""
Args:
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in ... | 3,376 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
num_logits_to_keep (`int`, *optional*):
Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
token can save memory, whic... | 3,376 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=40, do_sample=True, temperature=0.6)
>>> result = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
>>> print(result)
If I had to write a haiku, it would be: ... | 3,376 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
cross_attention_states=cross_attention_states,
attention_mask=attention_mask,
position_ids=position_ids,
cross_attention_mask=cro... | 3,376 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_sta... | 3,376 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
class MllamaForConditionalGeneration(MllamaPreTrainedModel, GenerationMixin):
_supports_quantized_cache = False # quant cache not supported in encoder-decoder setting
def __init__(self, config: MllamaConfig):
super().__init__(config)
self.vocab_size = config.text_config.vocab_size
self... | 3,377 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
self.multi_modal_projector = nn.Linear(
config.vision_config.vision_output_dim,
config.text_config.hidden_size,
bias=True,
)
self.post_init()
def get_input_embeddings(self):
return self.language_model.get_input_embeddings()
def set_input_embeddings(s... | 3,377 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
@add_start_docstrings_to_model_forward(MLLAMA_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class="MllamaConfig")
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
aspect_ra... | 3,377 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
num_logits_to_keep: int = 0,
) -> Union[Tuple, CausalLMOutputWithPast]:
r"""
Args:
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labe... | 3,377 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
num_logits_to_keep (`int`, *optional*):
Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
token can save memory, whic... | 3,377 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
>>> inputs = processor(text=prompt, images=image, return_tensors="pt")
>>> # Generate
>>> output = model.generate(**inputs, max_new_tokens=15)
>>> prompt_len = inputs.input_ids.shape[-1]
>>> generated_ids = output[:, prompt_len:]
>>> generated_text = processor.batch_decode(gene... | 3,377 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if pixel_values is not None and inputs_embeds is not None:
raise ValueError(
"You cannot specify both pixel_values and inputs_embeds at th... | 3,377 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
if pixel_values is not None:
if aspect_ratio_ids is None:
raise ValueError("`aspect_ratio_ids` must be provided if `pixel_values` is provided")
# get vision tokens from vision model
vision_outputs = self.vision_model(
pixel_values=pixel_values,
... | 3,377 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
if cross_attention_mask is not None:
cross_attention_mask, full_text_row_masked_out_mask = _prepare_cross_attention_mask(
cross_attention_mask,
num_vision_tokens=self.vision_model.num_patches,
dtype=self.dtype,
)
else:
full_text... | 3,377 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
outputs = self.language_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
cross_attention_states=cross_attention_states,
cross_attention_mask=cross_attention_mask,
full_text_row_masked_out_mask=full_text_row_mask... | 3,377 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
def prepare_inputs_for_generation(
self,
input_ids=None,
inputs_embeds=None,
attention_mask=None,
position_ids=None,
pixel_values=None,
aspect_ratio_ids=None,
aspect_ratio_mask=None,
cross_attention_mask=None,
past_key_values=None,
... | 3,377 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
# If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
# Exception 1: when passing input_embeds, input_ids may be missing entries
# Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
if past_key_... | 3,377 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
# TODO: we have no attention_mask so this won't work, check if we really won't need attention mask and find another way
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
... | 3,377 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and cache_position[0] == 0:
model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
else:
# The clone here is for the same reason as for `position_ids`.
... | 3,377 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
# If we're in pre-fill or cacheless decoding step, then we need pixel_values and aspect ratios
# to compute image hidden states, otherwise they are cached within each cross attn layer
if cache_position[0] == 0:
model_inputs["pixel_values"] = pixel_values
model_inputs["aspect_rati... | 3,377 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
# add cross-attn mask for new token
if cross_attention_mask_prev is not None:
model_kwargs["cross_attention_mask"] = torch.cat(
[cross_attention_mask_prev, cross_attention_mask_prev[:, -1:, ...]], dim=1
)
return model_kwargs | 3,377 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py |
class OmDetTurboTextKwargs(TextKwargs, total=False):
task: Optional[Union[str, List[str], TextInput, PreTokenizedInput]] | 3,378 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/processing_omdet_turbo.py |
class OmDetTurboProcessorKwargs(ProcessingKwargs, total=False):
text_kwargs: OmDetTurboTextKwargs
_defaults = {
"text_kwargs": {
"add_special_tokens": True,
"padding": "max_length",
"truncation": True,
"max_length": 77,
"stride": 0,
... | 3,379 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/processing_omdet_turbo.py |
class DictWithDeprecationWarning(dict):
message = (
"The `classes` key is deprecated for `OmDetTurboProcessor.post_process_grounded_object_detection` "
"output dict and will be removed in a 4.51.0 version. Please use `text_labels` instead."
)
def __getitem__(self, key):
if key == "c... | 3,380 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/processing_omdet_turbo.py |
class OmDetTurboProcessor(ProcessorMixin):
r"""
Constructs a OmDet-Turbo processor which wraps a Deformable DETR image processor and an AutoTokenizer into a
single processor.
[`OmDetTurboProcessor`] offers all the functionalities of [`DetrImageProcessor`] and
[`AutoTokenizer`]. See the docstring of... | 3,381 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/processing_omdet_turbo.py |
def __call__(
self,
images: ImageInput = None,
text: Union[List[str], List[List[str]]] = None,
audio=None,
videos=None,
**kwargs: Unpack[OmDetTurboProcessorKwargs],
) -> BatchFeature:
"""
This method uses [*DetrImageProcessor.__call__] method to prepar... | 3,381 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/processing_omdet_turbo.py |
Args:
images (`ImageInput`):
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255.
text (`Union[str, List[str], List[List[str]]]`):
The classes used to limit the scope of the open vocabulary detection. Expects a list of s... | 3,381 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/processing_omdet_turbo.py |
raise ValueError("You have to specify both `images` and `text`") | 3,381 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/processing_omdet_turbo.py |
output_kwargs = self._merge_kwargs(
OmDetTurboProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
)
if isinstance(text, str):
text = text.strip(" ").split(",")
if not (len(text) and isinstance(text[0], (list, tuple))):
... | 3,381 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/processing_omdet_turbo.py |
classes_structure = torch.tensor([len(class_single) for class_single in classes], dtype=torch.long)
classes_flattened = [class_single for class_batch in classes for class_single in class_batch]
classes_encoding = self.tokenizer(text=classes_flattened, **output_kwargs["text_kwargs"])
encoding = ... | 3,381 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/processing_omdet_turbo.py |
# Copied from transformers.models.blip.processing_blip.BlipProcessor.batch_decode with BertTokenizerFast->PreTrainedTokenizer
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to th... | 3,381 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/processing_omdet_turbo.py |
def _get_default_image_size(self) -> Tuple[int, int]:
height = (
self.image_processor.size["height"]
if "height" in self.image_processor.size
else self.image_processor.size["shortest_edge"]
)
width = (
self.image_processor.size["width"]
... | 3,381 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/processing_omdet_turbo.py |
@deprecate_kwarg("score_threshold", new_name="threshold", version="4.51.0")
@deprecate_kwarg("classes", new_name="text_labels", version="4.51.0")
def post_process_grounded_object_detection(
self,
outputs: "OmDetTurboObjectDetectionOutput",
text_labels: Optional[Union[List[str], List[List... | 3,381 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/processing_omdet_turbo.py |
Args:
outputs ([`OmDetTurboObjectDetectionOutput`]):
Raw outputs of the model.
text_labels (Union[List[str], List[List[str]]], *optional*):
The input classes names. If not provided, `text_labels` will be set to `None` in `outputs`.
threshold (float, de... | 3,381 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/processing_omdet_turbo.py |
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, classes and boxes for an image
in the batch as predicted by the model.
""" | 3,381 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/processing_omdet_turbo.py |
batch_size = len(outputs.decoder_coord_logits)
# Inputs consistency check for target sizes
if target_sizes is None:
height, width = self._get_default_image_size()
target_sizes = [(height, width)] * batch_size
if any(len(image_size) != 2 for image_size in target_sizes):
... | 3,381 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/processing_omdet_turbo.py |
# Convert target_sizes to list for easier handling
if isinstance(target_sizes, torch.Tensor):
target_sizes = target_sizes.tolist()
batch_boxes = outputs.decoder_coord_logits
batch_logits = outputs.decoder_class_logits
batch_num_classes = outputs.classes_structure
ba... | 3,381 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/processing_omdet_turbo.py |
results = []
for boxes, scores, image_size, image_num_classes in zip(
batch_boxes, batch_scores, target_sizes, batch_num_classes
):
boxes, scores, labels = _post_process_boxes_for_image(
boxes=boxes,
scores=scores,
labels=batch_labe... | 3,381 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/processing_omdet_turbo.py |
class OmDetTurboEncoderOutput(ModelOutput):
"""
Base class for outputs of the OmDetTurboHybridEncoder.
Args:
last_hidden_state (`torch.FloatTensor`):
Last hidden states of the encoder.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=Tru... | 3,382 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
extracted_states (`Tuple[torch.FloatTensor]`):
The extracted states from the Feature Pyramid Network (FPN) and Path Aggregation Network (PAN) of the encoder.
"""
las... | 3,382 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
class OmDetTurboDecoderOutput(ModelOutput):
"""
Base class for outputs of the OmDetTurboDecoder. | 3,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.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.
decoder_coords (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
The predicted coordinates o... | 3,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
intermediate_reference_points (`Tuple[Tuple[torch.FloatTensor]]`):
The intermediate reference points.
hidden_states (`Optional[Tuple[torch.FloatTensor]]`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTen... | 3,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
weighted average in the self-attention, cross-attention and multi-scale deformable attention heads.
""" | 3,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
last_hidden_state: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
decoder_coords: torch.FloatTensor = None
decoder_classes: torch.FloatTensor = None
encoder_coord_logits: torch.FloatTensor = None
enco... | 3,383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
class OmDetTurboObjectDetectionOutput(ModelOutput):
"""
Output type of [`OmDetTurboObjectDetectionOutput`]. | 3,384 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
Args:
loss (`torch.FloatTensor`):
The loss value.
decoder_coord_logits (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
The predicted coordinates logits of the objects.
decoder_class_logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes... | 3,384 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
The extracted states from the Feature Pyramid Network (FPN) and Path Aggregation Network (PAN) of the encoder.
decoder_hidden_states (`Tuple[torch.FloatTensor]`, *optional*):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape
`... | 3,384 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
plus the initial embedding outputs.
encoder_attentions (`Tuple[Tuple[torch.... | 3,384 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
loss: torch.FloatTensor = None
decoder_coord_logits: torch.FloatTensor = None
decoder_class_logits: torch.FloatTensor = None
init_reference_points: torch.FloatTensor = None
intermediate_reference_points: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
encoder_coord_logits: torch.FloatTensor = None
... | 3,384 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
class OmDetTurboLRUCache:
def __init__(self, capacity: int):
self.cache = OrderedDict()
self.capacity = capacity
self.current_load = 0
def has(self, key) -> bool:
return key in self.cache
def get(self, key):
"""
Get the value of the key if the key exists in ... | 3,385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
def put(self, key, value) -> None:
"""
Add the key-value pair to the cache.
Move the key to the end of the cache to show that it was recently used.
If the cache is full, remove the first key (least recently used).
"""
if key not in self.cache:
self.current_loa... | 3,385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
class OmDetTurboLanguageBackbone(nn.Module):
def __init__(self, config: OmDetTurboConfig):
super().__init__()
self.model = AutoModel.from_config(config.text_config)
self.text_projection = nn.Parameter(torch.zeros(config.text_projection_in_dim, config.text_projection_out_dim)) | 3,386 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
def forward(self, hidden_states, mask=None, encode_type="task"):
text_outputs = self.model(hidden_states)
pooled_output = text_outputs[0]
if encode_type == "task":
if mask is None:
raise ValueError("mask is required for task encoding")
max_len = (mask != 0... | 3,386 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
class OmDetTurboVisionBackbone(nn.Module):
def __init__(self, config: OmDetTurboConfig):
super().__init__()
self.apply_layernorm_after_vision_backbone = config.apply_layernorm_after_vision_backbone
self.vision_backbone = load_backbone(config)
self.layer_norms = nn.ModuleList(
... | 3,387 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
class MultiScaleDeformableAttentionFunction(Function):
@staticmethod
def forward(
context,
value,
value_spatial_shapes,
value_level_start_index,
sampling_locations,
attention_weights,
im2col_step,
):
context.im2col_step = im2col_step
ou... | 3,388 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
@staticmethod
@once_differentiable
def backward(context, grad_output):
(
value,
value_spatial_shapes,
value_level_start_index,
sampling_locations,
attention_weights,
) = context.saved_tensors
grad_value, grad_sampling_loc, grad_... | 3,388 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
class OmDetTurboMultiscaleDeformableAttention(nn.Module):
"""
Multiscale deformable attention as proposed in Deformable DETR.
"""
def __init__(self, config: OmDetTurboConfig, num_heads: int, n_points: int):
super().__init__()
kernel_loaded = MultiScaleDeformableAttention is not None
... | 3,389 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
if config.d_model % num_heads != 0:
raise ValueError(
f"embed_dim (d_model) must be divisible by num_heads, but got {config.d_model} and {num_heads}"
)
dim_per_head = config.d_model // num_heads
# check if dim_per_head is power of 2
if not ((dim_per_head &... | 3,389 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
self.sampling_offsets = nn.Linear(config.d_model, num_heads * self.n_levels * n_points * 2)
self.attention_weights = nn.Linear(config.d_model, num_heads * self.n_levels * n_points)
self.value_proj = nn.Linear(config.d_model, config.d_model)
self.output_proj = nn.Linear(config.d_model, config.d_m... | 3,389 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states=None,
encoder_attention_mask=None,
position_embeddings: Optional[torch.Tensor] = None,
reference_points=None,
spatial_shapes=None,
... | 3,389 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
batch_size, num_queries, _ = hidden_states.shape
batch_size, sequence_length, _ = encoder_hidden_states.shape
# Ignore copy
total_elements = sum([shape[0] * shape[1] for shape in spatial_shapes_list])
if total_elements != sequence_length:
raise ValueError(
"Ma... | 3,389 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
value = self.value_proj(encoder_hidden_states)
if attention_mask is not None:
# we invert the attention_mask
value = value.masked_fill(~attention_mask[..., None], float(0))
value = value.view(batch_size, sequence_length, self.n_heads, self.d_model // self.n_heads)
samplin... | 3,389 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1)
sampling_locations = (
reference_points[:, :, None, :, None, :]
+ sampling_offsets / offset_normalizer[None, None, None, :, None, :]
)
elif num_coordinates == 4:
... | 3,389 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
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