text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
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output_tokens = output_tokens.repeat(batch_size, point_batch_size, 1, 1) | 4,148 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
if sparse_prompt_embeddings.sum().item() != 0:
tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=2)
else:
tokens = output_tokens
point_embeddings = tokens.to(self.iou_token.weight.dtype)
# Expand per-image data in batch direction to be per-point
i... | 4,148 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
# Run the transformer, image_positional_embedding are consumed
point_embedding, image_embeddings, attentions = self.transformer(
point_embeddings=point_embeddings,
image_embeddings=image_embeddings,
image_positional_embeddings=image_positional_embeddings,
attentio... | 4,148 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
upscaled_embedding = self.upscale_conv1(image_embeddings)
upscaled_embedding = self.activation(self.upscale_layer_norm(upscaled_embedding))
upscaled_embedding = self.activation(self.upscale_conv2(upscaled_embedding))
hyper_in_list = []
for i in range(self.num_mask_tokens):
c... | 4,148 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
# Select the correct mask or masks for output
if multimask_output:
mask_slice = slice(1, None)
else:
mask_slice = slice(0, 1)
masks = masks[:, :, mask_slice, :, :]
iou_pred = iou_pred[:, :, mask_slice]
outputs = (masks, iou_pred)
if output_attent... | 4,148 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
class SamPositionalEmbedding(nn.Module):
def __init__(self, config):
super().__init__()
self.scale = config.hidden_size // 2
self.register_buffer("positional_embedding", self.scale * torch.randn((2, config.num_pos_feats)))
def forward(self, input_coords, input_shape=None):
"""Po... | 4,149 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
# assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
coordinates = 2 * coordinates - 1
coordinates = coordinates.to(self.positional_embedding.dtype)
coordinates = coordinates @ self.positional_embedding
coordinates = 2 * np.pi * coordinates
# outputs d_1 x ... | 4,149 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
class SamMaskEmbedding(nn.Module):
def __init__(self, config: SamPromptEncoderConfig):
super().__init__()
self.mask_input_channels = config.mask_input_channels // 4
self.activation = ACT2FN[config.hidden_act]
self.conv1 = nn.Conv2d(1, self.mask_input_channels, kernel_size=2, stride=2... | 4,150 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
hidden_states = self.conv2(hidden_states)
hidden_states = self.layer_norm2(hidden_states)
hidden_states = self.activation(hidden_states)
dense_embeddings = self.conv3(hidden_states)
return dense_embeddings | 4,150 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
class SamPromptEncoder(nn.Module):
def __init__(self, config: SamPromptEncoderConfig, shared_patch_embedding):
super().__init__()
self.shared_embedding = shared_patch_embedding
self.mask_embed = SamMaskEmbedding(config)
self.no_mask_embed = nn.Embedding(1, config.hidden_size)
... | 4,151 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
def _embed_points(self, points: torch.Tensor, labels: torch.Tensor, pad: bool) -> torch.Tensor:
"""Embeds point prompts."""
points = points + 0.5 # Shift to center of pixel
if pad:
target_point_shape = (points.shape[0], points.shape[1], 1, points.shape[-1])
target_labels... | 4,151 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
# This is required for the ONNX export. The dtype, device need to be explicitely
# specificed as otherwise torch.onnx.export interprets as double
point_embedding = torch.where(
labels[..., None] != -10,
point_embedding,
torch.tensor(0.0, dtype=point_embedding.dtype, d... | 4,151 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
"""Embeds box prompts."""
boxes = boxes + 0.5 # Shift to center of pixel
batch_size, nb_boxes = boxes.shape[:2]
coords = boxes.reshape(batch_size, nb_boxes, 2, 2)
input_shape = (self.input_image_size, self.input_image_... | 4,151 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
Args:
points (`torch.Tensor`, *optional*):
point coordinates and labels to embed.
boxes (`torch.Tensor`, *optional*):
boxes to embed
masks (`torch.Tensor`, *optional*):
masks to embed
"""
sparse_embeddings = None
... | 4,151 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
sparse_embeddings = box_embeddings
else:
sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=2)
if input_masks is not None:
dense_embeddings = self.mask_embed(input_masks)
else:
dense_embeddings = self.no_mask_embed.weight.reshape(1,... | 4,151 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
if sparse_embeddings is None:
sparse_embeddings = torch.zeros((batch_size, 1, 1, self.hidden_size), device=target_device)
return sparse_embeddings, dense_embeddings | 4,151 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
class SamVisionAttention(nn.Module):
"""Multi-head Attention block with relative position embeddings."""
def __init__(self, config, window_size):
super().__init__()
input_size = (
(config.image_size // config.patch_size, config.image_size // config.patch_size)
if window_... | 4,152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
# initialize relative positional embeddings
self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
def get_rel_pos(self, q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
... | 4,152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
Returns:
Extracted positional embeddings according to relative positions.
"""
max_rel_dist = int(2 * max(q_size, k_size) - 1)
# Interpolate rel pos.
rel_pos_resized = F.interpolate(
rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
size=max_re... | 4,152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
def add_decomposed_rel_pos(
self,
attn: torch.Tensor,
query: torch.Tensor,
rel_pos_h: torch.Tensor,
rel_pos_w: torch.Tensor,
q_size: Tuple[int, int],
k_size: Tuple[int, int],
) -> torch.Tensor:
"""
Calculate decomposed Relative Positional Embed... | 4,152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
Args:
attn (`torch.Tensor`):
attention map.
query (`torch.Tensor`):
query q in the attention layer with shape (batch_size, query_height * query_width, channel).
rel_pos_h (`torch.Tensor`):
relative position embeddings (Lh, channel) for ... | 4,152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
Returns:
attn (`torch.Tensor`):
attention map with added relative positional embeddings.
"""
query_height, query_width = q_size
key_height, key_width = k_size
relative_position_height = self.get_rel_pos(query_height, key_height, rel_pos_h)
relative_pos... | 4,152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
def forward(self, hidden_states: torch.Tensor, output_attentions=False) -> torch.Tensor:
batch_size, height, width, _ = hidden_states.shape
# qkv with shape (3, batch_size, nHead, height * width, channel)
qkv = (
self.qkv(hidden_states)
.reshape(batch_size, height * width... | 4,152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = (attn_probs @ value).reshape(batch_size, self.num_attention_heads, height, width, -1)
attn_output = attn_output.permute(0, 2, 3, 1, 4).reshape(batch_size, height, width, -1)
attn_output = sel... | 4,152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
class SamVisionSdpaAttention(SamVisionAttention):
"""
Multi-head Attention block with relative position embeddings.
Using SDPA instead of the default attention.
"""
def __init__(self, config, window_size):
super().__init__(config, window_size) | 4,153 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
def add_decomposed_rel_pos(
self,
query: torch.Tensor,
rel_pos_h: torch.Tensor,
rel_pos_w: torch.Tensor,
q_size: Tuple[int, int],
k_size: Tuple[int, int],
) -> torch.Tensor:
"""
Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
... | 4,153 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
k_size (Tuple): spatial sequence size of key k with (k_h, k_w). | 4,153 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
Returns:
attn (Tensor): attention map with added relative positional embeddings.
"""
query_height, query_width = q_size
key_height, key_width = k_size
relative_position_height = self.get_rel_pos(query_height, key_height, rel_pos_h)
relative_position_width = self.get_r... | 4,153 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
def forward(self, hidden_states: torch.Tensor, output_attentions=False) -> torch.Tensor:
batch_size, height, width, _ = hidden_states.shape
# qkv with shape (3, B, nHead, H * W, C)
qkv = (
self.qkv(hidden_states)
.reshape(batch_size, height * width, 3, self.num_attention_... | 4,153 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
if self.use_rel_pos:
rel_h = rel_h.view(batch_size, self.num_attention_heads, rel_h.size(1), rel_h.size(2), rel_h.size(3))
rel_w = rel_w.view(batch_size, self.num_attention_heads, rel_w.size(1), rel_w.size(2), rel_w.size(3))
attn_bias = (rel_h + rel_w).view(
batch_siz... | 4,153 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
if output_attentions:
# For output_attentions, calculate the attention weights
attn_weights = (query @ key.transpose(-2, -1)) * self.scale
if attn_bias is not None:
attn_weights = attn_weights + attn_bias
attn_weights = F.softmax(attn_weights, dim=-1)
... | 4,153 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
class SamVisionLayer(nn.Module):
def __init__(self, config, window_size):
super().__init__()
self.layer_norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.attn = SAM_VISION_ATTENTION_CLASSES[config._attn_implementation](config, window_size)
self.layer_norm2 = nn... | 4,154 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
Returns:
windows: windows after partition with [batch_size * num_windows, window_size, window_size, channel].
(pad_height, pad_width): padded height and width before partition
"""
batch_size, height, width, channel = hidden_states.shape
pad_h = (window_size - height % wi... | 4,154 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
def window_unpartition(
self, windows: torch.Tensor, window_size: int, padding_shape: Tuple[int, int], original_shape: Tuple[int, int]
) -> torch.Tensor:
"""
Args:
Window unpartition into original sequences and removing padding.
hidden_states (tensor):
inp... | 4,154 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
Returns:
hidden_states: unpartitioned sequences with [batch_size, height, width, channel].
"""
pad_height, pad_width = padding_shape
height, width = original_shape
batch_size = windows.shape[0] // (pad_height * pad_width // window_size // window_size)
hidden_states = ... | 4,154 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
hidden_states = self.layer_norm1(hidden_states)
# Window partition
if self.window_size > 0:
height, width = hidden_states.shape[1], hidden_states.shape[2]
hidden_states, padding_shape = self.window_partition(hidden_states, self.window_size)
hidden_states, attn_weights = ... | 4,154 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
class SamVisionNeck(nn.Module):
def __init__(self, config: SamVisionConfig):
super().__init__()
self.config = config
self.conv1 = nn.Conv2d(config.hidden_size, config.output_channels, kernel_size=1, bias=False)
self.layer_norm1 = SamLayerNorm(config.output_channels, data_format="cha... | 4,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
class SamVisionEncoder(nn.Module):
def __init__(self, config: SamVisionConfig):
super().__init__()
self.config = config
self.image_size = config.image_size
self.patch_embed = SamPatchEmbeddings(config)
self.pos_embed = None
if config.use_abs_pos:
# Initi... | 4,156 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
self.gradient_checkpointing = False
def get_input_embeddings(self):
return self.patch_embed
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: O... | 4,156 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
for i, layer_module in enumerate(self.layers):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if self.gradient_che... | 4,156 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
if not return_dict:
outputs = (hidden_states,)
if output_hidden_states:
outputs = outputs + (all_hidden_states,)
if output_attentions:
outputs = outputs + (all_self_attentions,)
return outputs
return SamVisionEncoderOutput(
... | 4,156 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
class SamPreTrainedModel(PreTrainedModel):
config_class = SamConfig
base_model_prefix = "sam"
main_input_name = "pixel_values"
_no_split_modules = ["SamVisionAttention"]
supports_gradient_checkpointing = True
_supports_sdpa = True
def _init_weights(self, module):
std = self.config.i... | 4,157 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
class SamModel(SamPreTrainedModel):
_tied_weights_keys = ["prompt_encoder.shared_embedding.positional_embedding"]
def __init__(self, config):
super().__init__(config)
self.shared_image_embedding = SamPositionalEmbedding(config.vision_config)
self.vision_encoder = SamVisionEncoder(confi... | 4,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
def get_image_wide_positional_embeddings(self):
size = self.config.prompt_encoder_config.image_embedding_size
target_device = self.shared_image_embedding.positional_embedding.device
target_dtype = self.shared_image_embedding.positional_embedding.dtype
grid = torch.ones((size, size), devi... | 4,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
@torch.no_grad()
def get_image_embeddings(
self,
pixel_values,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
Returns the image embeddings by passing the pixel values throu... | 4,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
"""
vision_output = self.vision_encoder(
pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
image_embeddings = vision_output[0]
return image_embeddings
@torch.no_gra... | 4,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
Args:
input_points (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_points_per_image, 2)`):
Optional input points for the prompt encoder. The padding of the point is automatically done by the
processor. `point_batch_size` refers to the number of masks that we... | 4,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
input_masks (`torch.LongTensor` of shape `(batch_size, image_size, image_size)`):
Optional input masks for the prompt encoder.
"""
prompt_output = self.prompt_encoder(
input_points=input_points,
input_labels=input_labels,
input_boxes=input_boxes,
... | 4,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
@add_start_docstrings_to_model_forward(SAM_INPUTS_DOCSTRING)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
input_points: Optional[torch.FloatTensor] = None,
input_labels: Optional[torch.LongTensor] = None,
input_boxes: Optional[torch.FloatTensor] = None... | 4,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
>>> model = AutoModel.from_pretrained("facebook/sam-vit-base")
>>> processor = AutoProcessor.from_pretrained("facebook/sam-vit-base")
>>> img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-car.png"
>>> raw_image = Image.open(reque... | 4,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
>>> # Postprocess masks
>>> masks = processor.post_process_masks(
... outputs.pred_masks, inputs["original_sizes"], inputs["reshaped_input_sizes"]
... )
```
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
... | 4,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
if input_points is not None and len(input_points.shape) != 4:
raise ValueError(
"The input_points must be a 4D tensor. Of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.",
" got {}.".format(input_points.shape),
)
if input_boxes is not N... | 4,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
image_positional_embeddings = self.get_image_wide_positional_embeddings()
# repeat with batch size
batch_size = pixel_values.shape[0] if pixel_values is not None else image_embeddings.shape[0]
image_positional_embeddings = image_positional_embeddings.repeat(batch_size, 1, 1, 1)
vision_a... | 4,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
if input_points is not None and input_labels is None:
input_labels = torch.ones_like(input_points[:, :, :, 0], dtype=torch.int, device=input_points.device)
if input_points is not None and image_embeddings.shape[0] != input_points.shape[0]:
raise ValueError(
"The batch si... | 4,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
sparse_embeddings, dense_embeddings = self.prompt_encoder(
input_points=input_points,
input_labels=input_labels,
input_boxes=input_boxes,
input_masks=input_masks,
)
low_res_masks, iou_predictions, mask_decoder_attentions = self.mask_decoder(
i... | 4,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
if output_attentions:
output = output + (vision_attentions, mask_decoder_attentions)
return output
return SamImageSegmentationOutput(
iou_scores=iou_predictions,
pred_masks=low_res_masks,
vision_hidden_states=vision_hidden_states,
visi... | 4,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/modeling_sam.py |
class SamImagesKwargs(ImagesKwargs):
segmentation_maps: Optional[ImageInput]
input_points: Optional[List[List[float]]]
input_labels: Optional[List[List[int]]]
input_boxes: Optional[List[List[List[float]]]]
point_pad_value: Optional[int] | 4,159 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/processing_sam.py |
class SamProcessorKwargs(ProcessingKwargs, total=False):
images_kwargs: SamImagesKwargs
_defaults = {
"images_kwargs": {
"point_pad_value": -10,
}
} | 4,160 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/processing_sam.py |
class SamProcessor(ProcessorMixin):
r"""
Constructs a SAM processor which wraps a SAM image processor and an 2D points & Bounding boxes processor into a
single processor.
[`SamProcessor`] offers all the functionalities of [`SamImageProcessor`]. See the docstring of
[`~SamImageProcessor.__call__`] f... | 4,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/processing_sam.py |
def __call__(
self,
images: Optional[ImageInput] = None,
# The following is to capture `segmentation_maps`, `input_points`, `input_labels` and `input_boxes`
# arguments that may be passed as a positional argument.
# See transformers.processing_utils.ProcessorMixin.prepare_and_val... | 4,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/processing_sam.py |
points and bounding boxes for the model if they are provided.
"""
output_kwargs = self._merge_kwargs(
SamProcessorKwargs,
tokenizer_init_kwargs={},
**kwargs,
**self.prepare_and_validate_optional_call_args(*args),
)
input_points = output_kwa... | 4,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/processing_sam.py |
encoding_image_processor = self.image_processor(
images,
**output_kwargs["images_kwargs"],
)
# pop arguments that are not used in the foward but used nevertheless
original_sizes = encoding_image_processor["original_sizes"]
if hasattr(original_sizes, "numpy"): #... | 4,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/processing_sam.py |
encoding_image_processor = self._normalize_and_convert(
encoding_image_processor,
original_sizes,
input_points=input_points,
input_labels=input_labels,
input_boxes=input_boxes,
return_tensors=output_kwargs["common_kwargs"].get("return_tensors"),
... | 4,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/processing_sam.py |
def _normalize_and_convert(
self,
encoding_image_processor,
original_sizes,
input_points=None,
input_labels=None,
input_boxes=None,
return_tensors="pt",
point_pad_value=-10,
):
if input_points is not None:
if len(original_sizes) != ... | 4,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/processing_sam.py |
input_points, input_labels, point_pad_value
) | 4,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/processing_sam.py |
input_points = np.array(input_points)
if input_labels is not None:
input_labels = np.array(input_labels)
if input_boxes is not None:
if len(original_sizes) != len(input_boxes):
input_boxes = [
self._normalize_coordinates(self.target_size, box... | 4,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/processing_sam.py |
if input_boxes is not None:
if return_tensors == "pt":
input_boxes = torch.from_numpy(input_boxes)
# boxes batch size of 1 by default
input_boxes = input_boxes.unsqueeze(1) if len(input_boxes.shape) != 3 else input_boxes
elif return_tensors == "tf"... | 4,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/processing_sam.py |
input_points = tf.convert_to_tensor(input_points)
# point batch size of 1 by default
input_points = tf.expand_dims(input_points, 1) if len(input_points.shape) != 4 else input_points
encoding_image_processor.update({"input_points": input_points})
if input_labels is not... | 4,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/processing_sam.py |
return encoding_image_processor
def _pad_points_and_labels(self, input_points, input_labels, point_pad_value):
r"""
The method pads the 2D points and labels to the maximum number of points in the batch.
"""
expected_nb_points = max([point.shape[0] for point in input_points])
... | 4,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/processing_sam.py |
def _normalize_coordinates(
self, target_size: int, coords: np.ndarray, original_size, is_bounding_box=False
) -> np.ndarray:
"""
Expects a numpy array of length 2 in the final dimension. Requires the original image size in (H, W) format.
"""
old_h, old_w = original_size
... | 4,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/processing_sam.py |
def _check_and_preprocess_points(
self,
input_points=None,
input_labels=None,
input_boxes=None,
):
r"""
Check and preprocesses the 2D points, labels and bounding boxes. It checks if the input is valid and if they
are, it converts the coordinates of the points ... | 4,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/processing_sam.py |
if input_labels is not None:
if hasattr(input_labels, "numpy"):
input_labels = input_labels.numpy().tolist()
if not isinstance(input_labels, list) or not isinstance(input_labels[0], list):
raise ValueError("Input labels must be a list of list integers.")
... | 4,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/processing_sam.py |
return input_points, input_labels, input_boxes
@property
def model_input_names(self):
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(image_processor_input_names))
def post_process_masks(self, *args, **kwargs):
return self.image_proces... | 4,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/processing_sam.py |
class SamImageProcessor(BaseImageProcessor):
r"""
Constructs a SAM image processor. | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.py |
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
`do_resize` parameter in the `preprocess` method.
size (`dict`, *optional*, defaults to `{"longest_edge": 1024}`):
... | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.py |
Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
`preprocess` method.
do_rescale (`bool`, *optional*, defaults to `True`):
Wwhether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the
`do_r... | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.py |
image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_MEAN`):
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
... | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.py |
`preprocess` method.
pad_size (`dict`, *optional*, defaults to `{"height": 1024, "width": 1024}`):
Size of the output image after padding. Can be overridden by the `pad_size` parameter in the `preprocess`
method.
mask_pad_size (`dict`, *optional*, defaults to `{"height": 256, "wi... | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.py |
model_input_names = ["pixel_values"]
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
mask_size: Dict[str, int] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_rescale: bool = True,
rescale_factor: Union[int, flo... | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.py |
pad_size = pad_size if pad_size is not None else {"height": 1024, "width": 1024}
pad_size = get_size_dict(pad_size, default_to_square=True)
mask_size = mask_size if mask_size is not None else {"longest_edge": 256}
mask_size = (
get_size_dict(max_size=mask_size, default_to_square=Fal... | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.py |
self.do_resize = do_resize
self.size = size
self.mask_size = mask_size
self.resample = resample
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_normalize = do_normalize
self.image_mean = image_mean if image_mean is not None else IMAGENET_... | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.py |
Args:
image (`np.ndarray`):
Image to pad.
pad_size (`Dict[str, int]`):
Size of the output image after padding.
data_format (`str` or `ChannelDimension`, *optional*):
The data format of the image. Can be either "channels_first" or "chann... | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.py |
padded_image = pad(
image,
((0, pad_height), (0, pad_width)),
data_format=data_format,
input_data_format=input_data_format,
**kwargs,
)
return padded_image
def _get_preprocess_shape(self, old_shape: Tuple[int, int], longest_edge: int):
... | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.py |
def resize(
self,
image: np.ndarray,
size: Dict[str, int],
resample: PILImageResampling = PILImageResampling.BICUBIC,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> n... | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.py |
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Dictionary in the format `{"longest_edge": int}` specifying the size of the output image. The longest
edge of the image will be resized to the specified size, while the other edg... | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.py |
input_data_format (`ChannelDimension` or `str`, *optional*):
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,... | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.py |
Returns:
`np.ndarray`: The resized image.
"""
size = get_size_dict(size)
if "longest_edge" not in size:
raise ValueError(f"The `size` dictionary must contain the key `longest_edge`. Got {size.keys()}")
input_size = get_image_size(image, channel_dim=input_data_form... | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.py |
def _preprocess(
self,
image: ImageInput,
do_resize: bool,
do_rescale: bool,
do_normalize: bool,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
rescale_factor: Optional[float] = None,
image_mean: Optional[Union[float, L... | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.py |
if do_normalize:
image = self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
if do_pad:
image = self.pad_image(image=image, pad_size=pad_size, input_data_format=input_data_format)
return image, reshaped_input_size | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.py |
def _preprocess_image(
self,
image: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_rescale: bool = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] = None,
... | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.py |
# All transformations expect numpy arrays.
image = to_numpy_array(image)
if do_rescale and is_scaled_image(image):
logger.warning_once(
"It looks like you are trying to rescale already rescaled images. If the input"
" images have pixel values between 0 and 1,... | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.py |
image, reshaped_input_size = self._preprocess(
image=image,
do_resize=do_resize,
size=size,
resample=resample,
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
... | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.py |
def _preprocess_mask(
self,
segmentation_map: ImageInput,
do_resize: Optional[bool] = None,
mask_size: Dict[str, int] = None,
do_pad: Optional[bool] = None,
mask_pad_size: Optional[Dict[str, int]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] =... | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.py |
segmentation_map, _ = self._preprocess(
image=segmentation_map,
do_resize=do_resize,
size=mask_size,
resample=PILImageResampling.NEAREST,
do_rescale=False,
do_normalize=False,
do_pad=do_pad,
pad_size=mask_pad_size,
... | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.py |
@filter_out_non_signature_kwargs()
def preprocess(
self,
images: ImageInput,
segmentation_maps: Optional[ImageInput] = None,
do_resize: Optional[bool] = None,
size: Optional[Dict[str, int]] = None,
mask_size: Optional[Dict[str, int]] = None,
resample: Optional... | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.py |
input_data_format: Optional[Union[str, ChannelDimension]] = None,
):
"""
Preprocess an image or batch of images. | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.py |
Args:
images (`ImageInput`):
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
segmentation_maps (`ImageInput`, *optional*):
... | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.py |
resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
`PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to rescale the image pixel values b... | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.py |
do_pad (`bool`, *optional*, defaults to `self.do_pad`):
Whether to pad the image.
pad_size (`Dict[str, int]`, *optional*, defaults to `self.pad_size`):
Controls the size of the padding applied to the image. The image is padded to `pad_size["height"]` and
`pad_... | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.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... | 4,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sam/image_processing_sam.py |
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