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|---|---|---|
# Mesh TensorFlow initialization to avoid scaling before softmax
self.query = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
self.key = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
self.value = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
self.output ... | 4,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
@staticmethod
# Copied from transformers.models.t5.modeling_t5.T5Attention._relative_position_bucket
def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
"""
Adapted from Mesh Tensorflow:
https://github.com/tensorflow/mesh/blob/0cb87fe07... | 4,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
Translate relative position to a bucket number for relative attention. The relative position is defined as
memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
position. If bidirectional=False, then positive relative positions are invalid. We use s... | 4,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
Returns:
a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
"""
relative_buckets = 0
if bidirectional:
num_buckets //= 2
relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
... | 4,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
# The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
relative_position_if_large = max_exact + (
torch.log(relative_position.float() / max_exact)
/ math.log(max_distance / max_exact)
* (num_buckets - max_exact)
).to(torch.... | 4,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
# Adapted from transformers.models.t5.modeling_t5.T5Attention.compute_bias
def compute_bias(self, query_length, key_length, device=None, cache_position=None):
"""Compute binned relative position bias"""
if device is None:
device = self.relative_attention_bias.weight.device
if cac... | 4,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
values = self.relative_attention_bias(relative_position_bucket) # shape (query_length, key_length, num_heads)
values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length)
return values | 4,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
# Adapted from transformers.models.t5.modeling_t5.T5Attention.forward
def forward(
self,
hidden_states,
mask=None,
key_value_states=None,
position_bias=None,
past_key_value=None,
layer_head_mask=None,
query_length=None,
use_cache=False,
... | 4,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
query_states = self.query(hidden_states)
query_states = query_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
if past_key_value is not None:
is_updated = past_key_value.is_updated.get(self.layer_idx)
if is_cross_attention:
# aft... | 4,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
current_states = key_value_states if is_cross_attention else hidden_states
if is_cross_attention and past_key_value and is_updated:
# reuse k,v, cross_attentions
key_states = curr_past_key_value.key_cache[self.layer_idx]
value_states = curr_past_key_value.value_cache[self.lay... | 4,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
if past_key_value is not None:
# save all key/value_states to cache to be re-used for fast auto-regressive generation
cache_position = cache_position if not is_cross_attention else None
key_states, value_states = curr_past_key_value.update(
key_states,... | 4,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
if position_bias is None:
key_length = key_states.shape[-2]
# cache position is 0-indexed so we add 1 to get the real length of queries (aka with past)
real_seq_length = query_length if query_length is not None else cache_position[-1] + 1
if not self.has_relative_attentio... | 4,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
if mask is not None:
causal_mask = mask[:, :, :, : key_states.shape[-2]]
position_bias = position_bias + causal_mask
if self.pruned_heads:
mask = torch.ones(position_bias.shape[1])
mask[list(self.pruned_heads)] = 0
position_bias_masked = posit... | 4,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.view(batch_size, -1, self.inner_dim)
attn_output = self.output(attn_output)
outputs = (attn_output, past_key_value, position_bias)
if output_attentions:
outputs = outputs + (attn_weights,)
... | 4,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
class Pix2StructTextLayerSelfAttention(nn.Module):
def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None):
super().__init__()
self.attention = Pix2StructTextAttention(
config, has_relative_attention_bias=has_relative_attention_bias, layer_idx=layer... | 4,047 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
def forward(
self,
hidden_states,
attention_mask=None,
position_bias=None,
layer_head_mask=None,
past_key_value=None,
use_cache=False,
output_attentions=False,
cache_position=None,
):
normed_hidden_states = self.layer_norm(hidden_states... | 4,047 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
class Pix2StructTextLayerCrossAttention(nn.Module):
def __init__(self, config, layer_idx: Optional[int] = None):
super().__init__()
self.attention = Pix2StructTextAttention(config, has_relative_attention_bias=False, layer_idx=layer_idx)
self.layer_norm = Pix2StructLayerNorm(config.hidden_siz... | 4,048 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
def forward(
self,
hidden_states,
key_value_states,
attention_mask=None,
position_bias=None,
layer_head_mask=None,
past_key_value=None,
use_cache=False,
query_length=None,
output_attentions=False,
cache_position=None,
):
... | 4,048 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
return outputs | 4,048 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
class Pix2StructTextBlock(nn.Module):
def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None):
super().__init__()
self.self_attention = Pix2StructTextLayerSelfAttention(
config,
has_relative_attention_bias=has_relative_attention_bias,
... | 4,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
def forward(
self,
hidden_states,
attention_mask=None,
position_bias=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
encoder_decoder_position_bias=None,
layer_head_mask=None,
cross_attn_layer_head_mask=None,
past_key_value=No... | 4,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
# clamp inf values to enable fp16 training
if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
do_cross_attention = e... | 4,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
# clamp inf values to enable fp16 training
if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
# Keep... | 4,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
if use_cache:
outputs = outputs + (past_key_value,) + attention_outputs
else:
outputs = outputs + attention_outputs
return outputs | 4,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
class Pix2StructTextModel(Pix2StructPreTrainedModel):
config_class = Pix2StructTextConfig
_no_split_modules = ["Pix2StructTextBlock"]
_tied_weights_keys = ["lm_head.weight"]
supports_gradient_checkpointing = True
def __init__(self, config):
super().__init__(config)
self.embed_tokens... | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
# Copied from transformers.models.t5.modeling_t5.T5PreTrainedModel._reorder_cache
def _reorder_cache(self, past_key_values, beam_idx):
# if decoder past is not included in output
# speedy decoding is disabled and no need to reorder
if past_key_values is None:
logger.warning("You ... | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
if reordered_layer_past_states[0].shape != layer_past_states[0].shape:
raise ValueError(
f"reordered_layer_past_states[0] shape {reordered_layer_past_states[0].shape} and layer_past_states[0] shape {layer_past_states[0].shape} mismatched"
)
if len(reordere... | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
@add_start_docstrings_to_model_forward(PIX2STRUCT_TEXT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = ... | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
**kwargs,
) -> Union[Tuple[torch.FloatTensor, ...], CausalLMOutputWithCrossAttentions]:
r"""
Returns: | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
Example:
```python
>>> from transformers import AutoProcessor, Pix2StructTextModel
>>> processor = AutoProcessor.from_pretrained("google/pix2struct-textcaps-base")
>>> model = Pix2StructTextModel.from_pretrained("google/pix2struct-textcaps-base")
>>> inputs = processor(text="H... | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
... | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
# initialize past_key_values
return_legacy_cache = False
return_self_attention_cache = False
if use_cache or past_key_values is not None:
if isinstance(past_key_values, Cache) and not isinstance(past_key_values, EncoderDecoderCache):
return_self_attention_cache = True... | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
past_key_values = EncoderDecoderCache(DynamicCache(), DynamicCache()) | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
past_key_values_length = 0
if cache_position is not None:
past_key_values_length = cache_position[0]
elif past_key_values is not None:
past_key_values_length = past_key_values.get_seq_length()
if cache_position is None:
cache_position = torch.arange(
... | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
if self.config.is_decoder:
causal_mask = self._update_causal_mask(
attention_mask,
inputs_embeds,
cache_position,
past_key_values.self_attention_cache if past_key_values is not None else None,
output_attentions,
)
... | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if encoder_hidden_states is not None:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
encoder_hidden... | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
# Prepare head mask if needed
head_mask = self.get_head_mask(head_mask, self.config.num_layers)
cross_attn_head_mask = self.get_head_mask(cross_attn_head_mask, self.config.num_layers)
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else N... | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
layer_outpu... | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
layer_outputs = layer_module(
hidden_states,
attention_mask=causal_mask,
position_bias=position_bias,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
... | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
# layer_outputs is a tuple with:
# hidden-states, key-value-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights)
if use_cache is False:
layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:]
... | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
if output_attentions:
all_attentions = all_attentions + (layer_outputs[3],)
if encoder_hidden_states is not None:
all_cross_attentions = all_cross_attentions + (layer_outputs[5],)
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = se... | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
next_cache = next_decoder_cache if use_cache else None
if return_self_attention_cache:
next_cache = past_key_values.self_attention_cache
if return_legacy_cache:
next_cache = past_key_values.to_legacy_cache()
if not return_dict:
return tuple(
v... | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.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,
):
... | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.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,
... | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.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... | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.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... | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
@staticmethod
# Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel._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... | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.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.
... | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.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(... | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.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
) | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
return causal_mask | 4,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
class Pix2StructForConditionalGeneration(Pix2StructPreTrainedModel, GenerationMixin):
config_class = Pix2StructConfig
main_input_name = "flattened_patches"
_tied_weights_keys = ["decoder.lm_head.weight"]
def __init__(self, config: Pix2StructConfig):
super().__init__(config)
self.encode... | 4,051 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
def get_encoder(self):
return self.encoder | 4,051 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
@add_start_docstrings_to_model_forward(PIX2STRUCT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
flattened_patches: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
... | 4,051 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[Tuple[torch.FloatTensor], Seq2SeqModelOutput]:
r"""
Returns: | 4,051 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
Example:
Inference:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, Pix2StructForConditionalGeneration
>>> processor = AutoProcessor.from_pretrained("google/pix2struct-textcaps-base")
>>> model = Pix2StructFor... | 4,051 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
>>> # conditional generation
>>> text = "A picture of"
>>> inputs = processor(text=text, images=image, return_tensors="pt", add_special_tokens=False)
>>> generated_ids = model.generate(**inputs, max_new_tokens=50)
>>> generated_text = processor.batch_decode(generated_ids, skip_special_t... | 4,051 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
>>> inputs = processor(images=image, return_tensors="pt")
>>> labels = processor(text=text, return_tensors="pt").input_ids
>>> # forward pass
>>> outputs = model(**inputs, labels=labels)
>>> loss = outputs.loss
>>> print(f"{loss.item():.5f}")
5.94282
```"""
... | 4,051 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
# Encode if needed (training, first prediction pass)
if encoder_outputs is None:
encoder_outputs = self.encoder(
flattened_patches=flattened_patches,
attention_mask=attention_mask,
head_mask=head_mask,
output_attentions=output_attention... | 4,051 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
# get decoder inputs from shifting lm labels to the right
decoder_input_ids = self._shift_right(labels)
decoder_attention_mask = (
decoder_attention_mask
if decoder_... | 4,051 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
# Decode
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
inputs_embeds=decoder_inputs_embeds,
past_key_values=past_key_values,
encoder_hidden_states=hidden_states,
encoder_attention_mask=a... | 4,051 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
return Seq2SeqLMOutput(
loss=decoder_outputs.loss,
logits=decoder_outputs.logits,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=... | 4,051 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/modeling_pix2struct.py |
class Pix2StructImageProcessor(BaseImageProcessor):
r"""
Constructs a Pix2Struct image processor. | 4,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/image_processing_pix2struct.py |
Args:
do_convert_rgb (`bool`, *optional*, defaults to `True`):
Whether to convert the image to RGB.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
method. Acc... | 4,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/image_processing_pix2struct.py |
Whether or not the image processor is for the VQA task. If `True` and `header_text` is passed in, text is
rendered onto the input images.
""" | 4,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/image_processing_pix2struct.py |
model_input_names = ["flattened_patches"]
def __init__(
self,
do_convert_rgb: bool = True,
do_normalize: bool = True,
patch_size: Dict[str, int] = None,
max_patches: int = 2048,
is_vqa: bool = False,
**kwargs,
) -> None:
super().__init__(**kwargs)... | 4,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/image_processing_pix2struct.py |
Args:
image (`np.ndarray`):
Image to extract flattened patches from.
max_patches (`int`):
Maximum number of patches to extract.
patch_size (`dict`):
Dictionary containing the patch height and width.
Returns:
result ... | 4,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/image_processing_pix2struct.py |
# maximize scale s.t.
scale = math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width))
num_feasible_rows = max(min(math.floor(scale * image_height / patch_height), max_patches), 1)
num_feasible_cols = max(min(math.floor(scale * image_width / patch_width), max_patches)... | 4,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/image_processing_pix2struct.py |
# [rows * columns, patch_height * patch_width * image_channels]
patches = patches.reshape([rows * columns, depth])
# [rows * columns, 1]
row_ids = torch.arange(rows).reshape([rows, 1]).repeat(1, columns).reshape([rows * columns, 1])
col_ids = torch.arange(columns).reshape([1, columns]).... | 4,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/image_processing_pix2struct.py |
return result
def normalize(
self,
image: np.ndarray,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> np.ndarray:
"""
Normalize an image. image = (image - image_m... | 4,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/image_processing_pix2struct.py |
Args:
image (`np.ndarray`):
Image to normalize.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format for the output image. If unset, the channel dimension format of the input
image is used.
input_data_form... | 4,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/image_processing_pix2struct.py |
def preprocess(
self,
images: ImageInput,
header_text: Optional[str] = None,
do_convert_rgb: bool = None,
do_normalize: Optional[bool] = None,
max_patches: Optional[int] = None,
patch_size: Optional[Dict[str, int]] = None,
return_tensors: Optional[Union[st... | 4,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/image_processing_pix2struct.py |
(https://www.tensorflow.org/api_docs/python/tf/image/per_image_standardization). | 4,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/image_processing_pix2struct.py |
Args:
images (`ImageInput`):
Image to preprocess. Expects a single or batch of images.
header_text (`Union[List[str], str]`, *optional*):
Text to render as a header. Only has an effect if `image_processor.is_vqa` is `True`.
do_convert_rgb (`bool`, *opt... | 4,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/image_processing_pix2struct.py |
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- `TensorType.JAX` or `'jax'`: Return a batch... | 4,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/image_processing_pix2struct.py |
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimens... | 4,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/image_processing_pix2struct.py |
if kwargs.get("data_format", None) is not None:
raise ValueError("data_format is not an accepted input as the outputs are ")
images = make_list_of_images(images)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image,... | 4,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/image_processing_pix2struct.py |
if is_vqa:
if header_text is None:
raise ValueError("A header text must be provided for VQA models.")
font_bytes = kwargs.pop("font_bytes", None)
font_path = kwargs.pop("font_path", None)
if isinstance(header_text, str):
header_text = [hea... | 4,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/image_processing_pix2struct.py |
# create attention mask in numpy
attention_masks = [(image.sum(axis=-1) != 0).astype(np.float32) for image in images]
encoded_outputs = BatchFeature(
data={"flattened_patches": images, "attention_mask": attention_masks}, tensor_type=return_tensors
)
return encoded_outputs | 4,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pix2struct/image_processing_pix2struct.py |
class ConditionalDetrFeatureExtractor(ConditionalDetrImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class ConditionalDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use ConditionalDetrImageProcessor ins... | 4,053 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py |
class ConditionalDetrDecoderOutput(BaseModelOutputWithCrossAttentions):
"""
Base class for outputs of the Conditional DETR decoder. This class adds one attribute to
BaseModelOutputWithCrossAttentions, namely an optional stack of intermediate decoder activations, i.e. the output
of each decoder layer, ea... | 4,054 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `con... | 4,054 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output... | 4,054 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
intermediate_hidden_states: Optional[torch.FloatTensor] = None
reference_points: Optional[Tuple[torch.FloatTensor]] = None | 4,054 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
class ConditionalDetrModelOutput(Seq2SeqModelOutput):
"""
Base class for outputs of the Conditional DETR encoder-decoder model. This class adds one attribute to
Seq2SeqModelOutput, namely an optional stack of intermediate decoder activations, i.e. the output of each decoder
layer, each of them gone thro... | 4,055 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the decoder of the model.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True`... | 4,055 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
... | 4,055 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_l... | 4,055 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
intermediate_hidden_states (`torch.FloatTensor` of shape `(config.decoder_layers, batch_size, sequence_length, hidden_size)`, *optional*, returned when `config.auxiliary_loss=True`):
Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a
layernorm.
... | 4,055 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
intermediate_hidden_states: Optional[torch.FloatTensor] = None
reference_points: Optional[Tuple[torch.FloatTensor]] = None | 4,055 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
class ConditionalDetrObjectDetectionOutput(ModelOutput):
"""
Output type of [`ConditionalDetrForObjectDetection`]. | 4,056 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a
bounding box loss. The latter is defined as a linear combination of the L1 loss and... | 4,056 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
possible padding). You can use [`~ConditionalDetrImageProcessor.post_process_object_detection`] to retrieve the
unnormalized bounding boxes.
auxiliary_outputs (`list[Dict]`, *optional*):
Optional, only returned when auxilary losses are activated (i.e. `config.auxiliary_loss` is set to `T... | 4,056 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each
layer plus the initial embedding outputs.
decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
... | 4,056 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
used to compute the weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (... | 4,056 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.
""" | 4,056 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
loss: Optional[torch.FloatTensor] = None
loss_dict: Optional[Dict] = None
logits: torch.FloatTensor = None
pred_boxes: torch.FloatTensor = None
auxiliary_outputs: Optional[List[Dict]] = None
last_hidden_state: Optional[torch.FloatTensor] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTe... | 4,056 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
class ConditionalDetrSegmentationOutput(ModelOutput):
"""
Output type of [`ConditionalDetrForSegmentation`]. | 4,057 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a
bounding box loss. The latter is defined as a linear combination of the L1 loss and... | 4,057 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
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