text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_h... | 9,630 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
class Owlv2MLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
... | 9,631 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
class Owlv2EncoderLayer(nn.Module):
def __init__(self, config: Owlv2Config):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = Owlv2Attention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.mlp = Owlv2MLP(config)
... | 9,632 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
causal_attention_mask: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the... | 9,632 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
hidden_states = self.layer_norm1(hidden_states)
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
)
hidden_sta... | 9,632 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
class Owlv2PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Owlv2Config
base_model_prefix = "owlv2"
supports_gradient_checkpointing = True
_no_split_modules =... | 9,633 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
def _init_weights(self, module):
"""Initialize the weights"""
factor = self.config.initializer_factor
if isinstance(module, Owlv2TextEmbeddings):
module.token_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02)
module.position_embedding.weight.data.normal_(mean=0.0... | 9,633 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
out_proj_std = (module.embed_dim**-0.5) * factor
nn.init.normal_(module.q_proj.weight, std=in_proj_std)
nn.init.normal_(module.k_proj.weight, std=in_proj_std)
nn.init.normal_(module.v_proj.weight, std=in_proj_std)
nn.init.normal_(module.out_proj.weight, std=out_proj_std)
... | 9,633 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
module.visual_projection.weight,
std=module.vision_embed_dim**-0.5 * self.config.initializer_factor,
)
if isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
if isinstance(module, nn.Linear) and module.bias is not N... | 9,633 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
class Owlv2Encoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`Owlv2EncoderLayer`].
Args:
config: Owlv2Config
"""
def __init__(self, config: Owlv2Config):
super().__init__()
self.layers = nn.Modul... | 9,634 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
def forward(
self,
inputs_embeds,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) ... | 9,634 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
Causal mask for the text model. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
output_attentions (`bool`, *optional*):
Whethe... | 9,634 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 9,634 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
hidden_states = inputs_embeds
for encoder_layer in self.layers:
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if self.gr... | 9,634 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
... | 9,634 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
class Owlv2TextTransformer(nn.Module):
def __init__(self, config: Owlv2TextConfig):
super().__init__()
self.config = config
embed_dim = config.hidden_size
self.embeddings = Owlv2TextEmbeddings(config)
self.encoder = Owlv2Encoder(config)
self.final_layer_norm = nn.Laye... | 9,635 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
@add_start_docstrings_to_model_forward(OWLV2_TEXT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=Owlv2TextConfig)
def forward(
self,
input_ids: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[t... | 9,635 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids)
# num_samples, seq_len = input_shape where num_samples = batch_size * num_max_text_queries
# OWLV2's text model uses causal mas... | 9,635 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
encoder_outputs = self.encoder(
inputs_embeds=hidden_states,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
... | 9,635 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
) | 9,635 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
class Owlv2TextModel(Owlv2PreTrainedModel):
config_class = Owlv2TextConfig
def __init__(self, config: Owlv2TextConfig):
super().__init__(config)
self.text_model = Owlv2TextTransformer(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_emb... | 9,636 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
@add_start_docstrings_to_model_forward(OWLV2_TEXT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=Owlv2TextConfig)
def forward(
self,
input_ids: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optio... | 9,636 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
>>> model = Owlv2TextModel.from_pretrained("google/owlv2-base-patch16")
>>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16")
>>> inputs = processor(
... text=[["a photo of a cat", "a photo of a dog"], ["photo of a astranaut"]], return_tensors="pt"
... )
... | 9,636 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
class Owlv2VisionTransformer(nn.Module):
def __init__(self, config: Owlv2VisionConfig):
super().__init__()
self.config = config
self.embeddings = Owlv2VisionEmbeddings(config)
self.pre_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.encoder = Owl... | 9,637 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
@add_start_docstrings_to_model_forward(OWLV2_VISION_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=Owlv2VisionConfig)
def forward(
self,
pixel_values: torch.FloatTensor,
output_attentions: Optional[bool] = None,
output_hidden_sta... | 9,637 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
# Cast the input to the expected `dtype`
expected_input_dtype = self.embeddings.patch_embedding.weight.dtype
pixel_values = pixel_values.to(expected_input_dtype)
hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
hidden_states = self.pre_lay... | 9,637 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
) | 9,637 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
class Owlv2VisionModel(Owlv2PreTrainedModel):
config_class = Owlv2VisionConfig
main_input_name = "pixel_values"
def __init__(self, config: Owlv2VisionConfig):
super().__init__(config)
self.vision_model = Owlv2VisionTransformer(config)
# Initialize weights and apply final processing
... | 9,638 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, Owlv2VisionModel
>>> model = Owlv2VisionModel.from_pretrained("google/owlv2-base-patch16")
>>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch... | 9,638 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
class Owlv2Model(Owlv2PreTrainedModel):
config_class = Owlv2Config
def __init__(self, config: Owlv2Config):
super().__init__(config)
if not isinstance(config.text_config, Owlv2TextConfig):
raise TypeError(
"config.text_config is expected to be of type Owlv2TextConfi... | 9,639 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
self.text_model = Owlv2TextTransformer(text_config)
self.vision_model = Owlv2VisionTransformer(vision_config)
self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False)
self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim, bias=False)
... | 9,639 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
@add_start_docstrings_to_model_forward(OWLV2_TEXT_INPUTS_DOCSTRING)
def get_text_features(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
... | 9,639 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
>>> model = Owlv2Model.from_pretrained("google/owlv2-base-patch16-ensemble")
>>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16-ensemble")
>>> inputs = processor(
... text=[["a photo of a cat", "a photo of a dog"], ["photo of a astranaut"]], return_tensors="pt"
... | 9,639 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
@add_start_docstrings_to_model_forward(OWLV2_VISION_INPUTS_DOCSTRING)
def get_image_features(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: bool = False,... | 9,639 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
>>> model = Owlv2Model.from_pretrained("google/owlv2-base-patch16-ensemble")
>>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16-ensemble")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>... | 9,639 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
)
pooled_output = vision... | 9,639 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
@add_start_docstrings_to_model_forward(OWLV2_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Owlv2Output, config_class=Owlv2Config)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
attention_mask: Optional... | 9,639 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
>>> model = Owlv2Model.from_pretrained("google/owlv2-base-patch16-ensemble")
>>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16-ensemble")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>... | 9,639 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 9,639 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
)
# Get embeddings for a... | 9,639 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
# normalized features
image_embeds = image_embeds / torch.linalg.norm(image_embeds, ord=2, dim=-1, keepdim=True)
text_embeds_norm = text_embeds / torch.linalg.norm(text_embeds, ord=2, dim=-1, keepdim=True)
# cosine similarity as logits and set it on the correct device
logit_scale = self... | 9,639 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
return Owlv2Output(
loss=loss,
logits_per_image=logits_per_image,
logits_per_text=logits_per_text,
text_embeds=text_embeds,
image_embeds=image_embeds,
text_model_output=text_outputs,
vision_model_output=vision_outputs,
) | 9,639 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
class Owlv2BoxPredictionHead(nn.Module):
def __init__(self, config: Owlv2Config, out_dim: int = 4):
super().__init__()
width = config.vision_config.hidden_size
self.dense0 = nn.Linear(width, width)
self.dense1 = nn.Linear(width, width)
self.gelu = nn.GELU()
self.dens... | 9,640 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
class Owlv2ClassPredictionHead(nn.Module):
def __init__(self, config: Owlv2Config):
super().__init__()
out_dim = config.text_config.hidden_size
self.query_dim = config.vision_config.hidden_size
self.dense0 = nn.Linear(self.query_dim, out_dim)
self.logit_shift = nn.Linear(se... | 9,641 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
# Normalize image and text features
image_class_embeds = image_class_embeds / (torch.linalg.norm(image_class_embeds, dim=-1, keepdim=True) + 1e-6)
query_embeds = query_embeds / (torch.linalg.norm(query_embeds, dim=-1, keepdim=True) + 1e-6)
# Get class predictions
pred_logits = torch.ein... | 9,641 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
class Owlv2ForObjectDetection(Owlv2PreTrainedModel):
config_class = Owlv2Config
def __init__(self, config: Owlv2Config):
super().__init__(config)
self.owlv2 = Owlv2Model(config)
self.class_head = Owlv2ClassPredictionHead(config)
self.box_head = Owlv2BoxPredictionHead(config)
... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
@staticmethod
# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.normalize_grid_corner_coordinates
def normalize_grid_corner_coordinates(num_patches_height: int, num_patches_width: int) -> torch.Tensor:
# Create grid coordinates using torch
x_coordinates = torch.ar... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
def objectness_predictor(self, image_features: torch.FloatTensor) -> torch.FloatTensor:
"""Predicts the probability that each image feature token is an object.
Args:
image_features (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_dim)`)):
Features extracted fr... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
@lru_cache(maxsize=2)
# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.compute_box_bias
def compute_box_bias(
self, num_patches_height: int, num_patches_width: int, feature_map: Optional[torch.FloatTensor] = None
) -> torch.Tensor:
if feature_map is not None:... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
# The box size is biased to the patch size
box_size = torch.full_like(box_coord_bias, 1.0)
box_size[..., 0] /= num_patches_width
box_size[..., 1] /= num_patches_height
box_size_bias = torch.log(box_size + 1e-4) - torch.log1p(-box_size + 1e-4)
# Compute box bias
box_bias ... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.box_predictor
def box_predictor(
self,
image_feats: torch.FloatTensor,
feature_map: torch.FloatTensor,
interpolate_pos_encoding: bool = False,
) -> torch.FloatTensor:
"""
Args:
... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
# Compute the location of each token on the grid and use it to compute a bias for the bbox prediction
if interpolate_pos_encoding:
_, num_patches_height, num_patches_width, _ = feature_map.shape
box_bias = self.compute_box_bias(num_patches_height, num_patches_width)
else:
... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.class_predictor
def class_predictor(
self,
image_feats: torch.FloatTensor,
query_embeds: Optional[torch.FloatTensor] = None,
query_mask: Optional[torch.Tensor] = None,
) -> Tuple[torch.FloatTensor]:... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.image_text_embedder with owlvit->owlv2
def image_text_embedder(
self,
input_ids: torch.Tensor,
pixel_values: torch.FloatTensor,
attention_mask: torch.Tensor,
output_attentions: Optional[bool] = ... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
if interpolate_pos_encoding:
_, _, height, width = pixel_values.shape
num_patches_height = height // self.config.vision_config.patch_size
num_patches_width = width // self.config.vision_config.patch_size
else:
num_patches_height = self.num_patches_height
... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
# Resize to [batch_size, num_patches_height, num_patches_width, hidden_size]
new_size = (
image_embeds.shape[0],
num_patches_height,
num_patches_width,
image_embeds.shape[-1],
)
image_embeds = image_embeds.reshape(new_size)
text_embeds = ou... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.image_embedder with owlvit->owlv2, OwlViTModel->Owlv2Model
def image_embedder(
self,
pixel_values: torch.FloatTensor,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = Non... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
# Apply post_layernorm to last_hidden_state, return non-projected output
last_hidden_state = vision_outputs[0]
image_embeds = self.owlv2.vision_model.post_layernorm(last_hidden_state)
# Resize class token
class_token_out = torch.broadcast_to(image_embeds[:, :1, :], image_embeds[:, :-1].... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.embed_image_query
def embed_image_query(
self,
query_image_features: torch.FloatTensor,
query_feature_map: torch.FloatTensor,
interpolate_pos_encoding: bool = False,
) -> torch.FloatTensor:
... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
# If there are no overlapping boxes, fall back to generalized IoU
if torch.all(ious[0] == 0.0):
ious = generalized_box_iou(each_query_box, each_query_pred_boxes)
# Use an adaptive threshold to include all boxes within 80% of the best IoU
iou_threshold = torch.max(iou... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
if best_class_embeds:
query_embeds = torch.stack(best_class_embeds)
box_indices = torch.stack(best_box_indices)
else:
query_embeds, box_indices = None, None
return query_embeds, box_indices, pred_boxes
@add_start_docstrings_to_model_forward(OWLV2_IMAGE_GUIDED_OB... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
Examples:
```python
>>> import requests
>>> from PIL import Image
>>> import torch
>>> from transformers import AutoProcessor, Owlv2ForObjectDetection
>>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16-ensemble")
>>> model = Owlv2ForObjectDe... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
>>> # Convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax)
>>> results = processor.post_process_image_guided_detection(
... outputs=outputs, threshold=0.9, nms_threshold=0.3, target_sizes=target_sizes
... )
>>> i = 0 # Retrieve predictions ... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
Detected similar object with confidence 0.985 at location [176.98, -29.45, 672.69, 182.83]
Detected similar object with confidence 1.0 at location [6.53, 14.35, 624.87, 470.82]
Detected similar object with confidence 0.998 at location [579.98, 29.14, 615.49, 489.05]
Detected similar object with ... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
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 self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None els... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
# Compute feature maps for the input and query images
query_feature_map = self.image_embedder(
pixel_values=query_pixel_values, interpolate_pos_encoding=interpolate_pos_encoding
)[0]
feature_map, vision_outputs = self.image_embedder(
pixel_values=pixel_values,
... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
batch_size, num_patches_height, num_patches_width, hidden_dim = query_feature_map.shape
query_image_feats = torch.reshape(
query_feature_map, (batch_size, num_patches_height * num_patches_width, hidden_dim)
)
# Get top class embedding and best box index for each query image in batch
... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
if not return_dict:
output = (
feature_map,
query_feature_map,
target_pred_boxes,
query_pred_boxes,
pred_logits,
class_embeds,
vision_outputs.to_tuple(),
)
output = tuple(x... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
@add_start_docstrings_to_model_forward(OWLV2_OBJECT_DETECTION_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Owlv2ObjectDetectionOutput, config_class=Owlv2Config)
def forward(
self,
input_ids: torch.Tensor,
pixel_values: torch.FloatTensor,
attention_mask: Optional[torch... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> text_labels = [["a photo of a cat", "a photo of a dog"]]
>>> inputs = processor(text=text_labels, images=image, return_tensors="pt")
>>> outputs = model(**inp... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
>>> # Target image sizes (height, width) to rescale box predictions [batch_size, 2]
>>> target_sizes = torch.tensor([(image.height, image.width)])
>>> # Convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax)
>>> results = processor.post_process_grounded_o... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
Detected a photo of a cat with confidence 0.665 at location [6.75, 51.96, 326.62, 473.13]
```"""
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... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
# Embed images and text queries
query_embeds, feature_map, outputs = self.image_text_embedder(
input_ids=input_ids,
pixel_values=pixel_values,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
# Reshape from [batch_size * max_text_queries, hidden_dim] -> [batch_size, max_text_queries, hidden_dim]
max_text_queries = input_ids.shape[0] // batch_size
query_embeds = query_embeds.reshape(batch_size, max_text_queries, query_embeds.shape[-1])
# If first token is 0, then this is a padded que... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
if not return_dict:
output = (
pred_logits,
objectness_logits,
pred_boxes,
query_embeds,
feature_map,
class_embeds,
text_outputs.to_tuple(),
vision_outputs.to_tuple(),
... | 9,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlv2/modeling_owlv2.py |
class GitVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GitVisionModel`]. It is used to instantiate a GIT
vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yie... | 9,643 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/configuration_git.py |
Args:
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidd... | 9,643 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/configuration_git.py |
`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilitie... | 9,643 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/configuration_git.py |
Example:
```python
>>> from transformers import GitVisionConfig, GitVisionModel
>>> # Initializing a GitVisionConfig with microsoft/git-base style configuration
>>> configuration = GitVisionConfig()
>>> # Initializing a GitVisionModel (with random weights) from the microsoft/git-base style config... | 9,643 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/configuration_git.py |
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_channels = num_channels
self.patch_size = patch_size
self.image_size = image_size
self.init... | 9,643 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/configuration_git.py |
class GitConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GitModel`]. It is used to instantiate a GIT model
according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configur... | 9,644 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/configuration_git.py |
Args:
vision_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`GitVisionConfig`].
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the GIT model. Defines the number of different tokens that can be represented by the
... | 9,644 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/configuration_git.py |
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
... | 9,644 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/configuration_git.py |
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
... | 9,644 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/configuration_git.py |
The number of temporal embeddings to add, in case the model is used for video captioning/VQA. | 9,644 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/configuration_git.py |
Examples:
```python
>>> from transformers import GitConfig, GitModel
>>> # Initializing a GIT microsoft/git-base style configuration
>>> configuration = GitConfig()
>>> # Initializing a model (with random weights) from the microsoft/git-base style configuration
>>> model = GitModel(configurat... | 9,644 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/configuration_git.py |
def __init__(
self,
vision_config=None,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=6,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_positi... | 9,644 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/configuration_git.py |
self.vision_config = GitVisionConfig(**vision_config)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_s... | 9,644 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/configuration_git.py |
class GitProcessorKwargs(ProcessingKwargs, total=False):
_defaults = {} | 9,645 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/processing_git.py |
class GitProcessor(ProcessorMixin):
r"""
Constructs a GIT processor which wraps a CLIP image processor and a BERT tokenizer into a single processor.
[`GitProcessor`] offers all the functionalities of [`CLIPImageProcessor`] and [`BertTokenizerFast`]. See the
[`~GitProcessor.__call__`] and [`~GitProcesso... | 9,646 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/processing_git.py |
def __call__(
self,
images: Optional[ImageInput] = None,
text: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None,
audio=None,
videos=None,
**kwargs: Unpack[GitProcessorKwargs],
) -> BatchFeature:
"""
Main me... | 9,646 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/processing_git.py |
Args:
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. Both channels-first and channels-last ... | 9,646 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/processing_git.py |
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
Returns:
[`BatchFeature`]: A [`BatchFeature`] with the follow... | 9,646 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/processing_git.py |
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `t... | 9,646 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/processing_git.py |
if text is None and images is None:
raise ValueError("You have to specify either text or images. Both cannot be none.")
# check if images and text inputs are reversed for BC
images, text = _validate_images_text_input_order(images, text)
output_kwargs = self._merge_kwargs(
... | 9,646 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/processing_git.py |
return BatchFeature(data=data, tensor_type=output_kwargs["common_kwargs"].get("return_tensors"))
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to BertTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for... | 9,646 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/processing_git.py |
class GitVisionModelOutput(ModelOutput):
"""
Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
Args:
image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_proje... | 9,647 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
Hidden-states of the model at the output of each layer plus the optional 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 layer) of shap... | 9,647 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
class GitEmbeddings(nn.Module):
"""Construct the embeddings from word and position embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embe... | 9,648 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
past_key_values_length: int = 0,
) -> torch.Tensor:
if input_ids is not None:
input_shape =... | 9,648 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
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