text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
# flatten to a single sequence
patch_embeds = torch.cat([p.flatten(1).T for p in patch_embeds_list], dim=0).unsqueeze(0)
patch_embeds = self.ln_pre(patch_embeds)
# positional embeddings
position_ids = position_ids_in_meshgrid(
patch_embeds_list, max_width=self.config.image_si... | 2,940 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/modeling_pixtral.py |
class BatchMixFeature(BatchFeature):
def to(self, *args, **kwargs) -> "BatchMixFeature":
"""
Send all values to device by calling `v.to(*args, **kwargs)` (PyTorch only). This should support casting in
different `dtypes` and sending the `BatchFeature` to a different `device`.
Args:
... | 2,941 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral.py |
def _recursive_to(obj, device, *args, **kwargs):
# Lists can be nested, so keep digging until we hit tensors
if isinstance(obj, list):
return [_recursive_to(o, device, *args, **kwargs) for o in obj]
# We cast only floating point tensors to avoid issues with tokenizers... | 2,941 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral.py |
device = kwargs.get("device")
# Check if the args are a device or a dtype
if device is None and len(args) > 0:
# device should be always the first argument
arg = args[0]
if is_torch_dtype(arg):
# The first argument is a dtype
pass
... | 2,941 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral.py |
class PixtralImageProcessor(BaseImageProcessor):
r"""
Constructs a Pixtral image processor. | 2,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral.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
`do_resize` in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{"longest_edge": 1024}`):
... | 2,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral.py |
Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by `do_rescale` in
the `preprocess` met... | 2,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral.py |
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):
Standard deviation to use if normalizing the image. This is a float or list of floats the length of... | 2,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral.py |
model_input_names = ["pixel_values"]
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
patch_size: Dict[str, int] = None,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_rescale: bool = True,
rescale_factor: Union[int, flo... | 2,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral.py |
self.do_resize = do_resize
self.size = size
self.patch_size = patch_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 [0.4814... | 2,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral.py |
def resize(
self,
image: np.ndarray,
size: Dict[str, int],
patch_size: Dict[str, int],
resample: PILImageResampling = PILImageResampling.BICUBIC,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] ... | 2,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral.py |
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Dict containing the longest possible edge of the image.
patch_size (`Dict[str, int]`):
Patch size used to calculate the size of the output image.
resample... | 2,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral.py |
size = (size["height"], size["width"])
else:
raise ValueError("size must contain either 'longest_edge' or 'height' and 'width'.") | 2,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral.py |
if "height" in patch_size and "width" in patch_size:
patch_size = (patch_size["height"], patch_size["width"])
else:
raise ValueError("patch_size must contain either 'shortest_edge' or 'height' and 'width'.")
output_size = get_resize_output_image_size(
image,
... | 2,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral.py |
def preprocess(
self,
images: ImageInput,
do_resize: bool = None,
size: Dict[str, int] = None,
patch_size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_rescale: bool = None,
rescale_factor: float = None,
do_normalize: bool = None,... | 2,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral.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`.
do_resize (`bool`, *optional*, defaults to `self.do_resiz... | 2,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral.py |
Whether to rescale the image.
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
Whether to normalize... | 2,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral.py |
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `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 ... | 2,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral.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,... | 2,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral.py |
do_resize = do_resize if do_resize is not None else self.do_resize
size = size if size is not None else self.size
resample = resample if resample is not None else self.resample
do_rescale = do_rescale if do_rescale is not None else self.do_rescale
rescale_factor = rescale_factor if resca... | 2,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral.py |
if not valid_images(images_list[0]):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
validate_preprocess_arguments(
do_rescale=do_rescale,
rescale... | 2,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral.py |
if do_rescale and is_scaled_image(images_list[0][0]):
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, set `do_rescale=False` to avoid rescaling them again."
)
... | 2,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral.py |
batch_images = []
batch_image_sizes = []
for sample_images in images_list:
images = []
image_sizes = []
for image in sample_images:
if do_resize:
image = self.resize(
image=image,
size... | 2,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral.py |
images.append(image)
image_sizes.append(get_image_size(image, input_data_format))
batch_images.append(images)
batch_image_sizes.append(image_sizes)
images_list = [
[to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image... | 2,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral.py |
class PixtralImageProcessorFast(BaseImageProcessorFast):
r"""
Constructs a fast Pixtral image processor that leverages torchvision. | 2,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral_fast.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
`do_resize` in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{"longest_edge": 1024}`):
... | 2,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral_fast.py |
Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by `do_rescale` in
the `preprocess` met... | 2,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral_fast.py |
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):
Standard deviation to use if normalizing the image. This is a float or list of floats the length of... | 2,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral_fast.py |
model_input_names = ["pixel_values"]
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
patch_size: Dict[str, int] = None,
resample: Union[PILImageResampling, "F.InterpolationMode"] = PILImageResampling.BICUBIC,
do_rescale: bool = True,
... | 2,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral_fast.py |
self.do_resize = do_resize
self.size = size
self.patch_size = patch_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 [0.4814... | 2,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral_fast.py |
def resize(
self,
image: torch.Tensor,
size: Dict[str, int],
patch_size: Dict[str, int],
interpolation: "F.InterpolationMode" = None,
**kwargs,
) -> torch.Tensor:
"""
Resize an image. The shortest edge of the image is resized to size["shortest_edge"], ... | 2,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral_fast.py |
Args:
image (`torch.Tensor`):
Image to resize.
size (`Dict[str, int]`):
Dict containing the longest possible edge of the image.
patch_size (`Dict[str, int]`):
Patch size used to calculate the size of the output image.
interp... | 2,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral_fast.py |
if "height" in patch_size and "width" in patch_size:
patch_size = (patch_size["height"], patch_size["width"])
else:
raise ValueError("patch_size must contain either 'shortest_edge' or 'height' and 'width'.")
output_size = get_resize_output_image_size(
image,
... | 2,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral_fast.py |
def preprocess(
self,
images: ImageInput,
do_resize: bool = None,
size: Dict[str, int] = None,
patch_size: Dict[str, int] = None,
resample: Optional[Union[PILImageResampling, "F.InterpolationMode"]] = None,
do_rescale: bool = None,
rescale_factor: float = ... | 2,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral_fast.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`.
do_resize (`bool`, *optional*, defaults to `self.do_resiz... | 2,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral_fast.py |
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to rescale the image.
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
do_normalize (`bool`, *o... | 2,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral_fast.py |
return_tensors (`str` or `TensorType`, *optional*):
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a ba... | 2,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral_fast.py |
- Unset: Use the channel dimension format of the input image.
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:
... | 2,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral_fast.py |
do_resize = do_resize if do_resize is not None else self.do_resize
size = size if size is not None else self.size
resample = resample if resample is not None else self.resample
do_rescale = do_rescale if do_rescale is not None else self.do_rescale
rescale_factor = rescale_factor if resca... | 2,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral_fast.py |
if image_type not in [ImageType.PIL, ImageType.TORCH, ImageType.NUMPY]:
raise ValueError(f"Unsupported input image type {image_type}")
validate_fast_preprocess_arguments(
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
... | 2,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral_fast.py |
if image_type == ImageType.PIL:
images_list = [[F.pil_to_tensor(image) for image in images] for images in images_list]
elif image_type == ImageType.NUMPY:
# not using F.to_tensor as it doesn't handle (C, H, W) numpy arrays
images_list = [[torch.from_numpy(image).contiguous() ... | 2,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral_fast.py |
if do_rescale and do_normalize:
# fused rescale and normalize
new_mean = torch.tensor(image_mean, device=images_list[0][0].device) * (1.0 / rescale_factor)
new_std = torch.tensor(image_std, device=images_list[0][0].device) * (1.0 / rescale_factor)
batch_images = []
b... | 2,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral_fast.py |
if do_rescale and do_normalize:
# fused rescale and normalize
image = F.normalize(image.to(dtype=torch.float32), new_mean, new_std)
elif do_rescale:
image = image * rescale_factor
elif do_normalize:
image = F... | 2,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pixtral/image_processing_pixtral_fast.py |
class GLPNConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GLPNModel`]. It is used to instantiate an GLPN
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar conf... | 2,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/configuration_glpn.py |
Args:
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
num_encoder_blocks (`int`, *optional*, defaults to 4):
The number of encoder blocks (i.e. stages in the Mix Transformer encoder).
depths (`List[int]`, *optional*, defaults to `[2, 2, 2, 2... | 2,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/configuration_glpn.py |
Number of attention heads for each attention layer in each block of the Transformer encoder.
mlp_ratios (`List[int]`, *optional*, defaults to `[4, 4, 4, 4]`):
Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the
encoder blocks.
hidd... | 2,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/configuration_glpn.py |
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
drop_path_rate (`float`, *optional*, defaults to 0.1):
The dropout probability for stochastic depth, used in the blocks of the Transformer encoder.
layer_norm_eps (`float`, *optional*, defaults t... | 2,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/configuration_glpn.py |
Example:
```python
>>> from transformers import GLPNModel, GLPNConfig
>>> # Initializing a GLPN vinvino02/glpn-kitti style configuration
>>> configuration = GLPNConfig()
>>> # Initializing a model from the vinvino02/glpn-kitti style configuration
>>> model = GLPNModel(configuration)
>>> ... | 2,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/configuration_glpn.py |
def __init__(
self,
num_channels=3,
num_encoder_blocks=4,
depths=[2, 2, 2, 2],
sr_ratios=[8, 4, 2, 1],
hidden_sizes=[32, 64, 160, 256],
patch_sizes=[7, 3, 3, 3],
strides=[4, 2, 2, 2],
num_attention_heads=[1, 2, 5, 8],
mlp_ratios=[4, 4, 4, 4... | 2,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/configuration_glpn.py |
self.num_channels = num_channels
self.num_encoder_blocks = num_encoder_blocks
self.depths = depths
self.sr_ratios = sr_ratios
self.hidden_sizes = hidden_sizes
self.patch_sizes = patch_sizes
self.strides = strides
self.mlp_ratios = mlp_ratios
self.num_atten... | 2,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/configuration_glpn.py |
class GLPNImageProcessor(BaseImageProcessor):
r"""
Constructs a GLPN image processor. | 2,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/image_processing_glpn.py |
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions, rounding them down to the closest multiple of
`size_divisor`. Can be overridden by `do_resize` in `preprocess`.
size_divisor (`int`, *optional*, defaults to 32):
... | 2,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/image_processing_glpn.py |
def __init__(
self,
do_resize: bool = True,
size_divisor: int = 32,
resample=PILImageResampling.BILINEAR,
do_rescale: bool = True,
**kwargs,
) -> None:
self.do_resize = do_resize
self.do_rescale = do_rescale
self.size_divisor = size_divisor
... | 2,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/image_processing_glpn.py |
Args:
image (`np.ndarray`):
The image to resize.
size_divisor (`int`):
The image is resized so its height and width are rounded down to the closest multiple of
`size_divisor`.
resample:
`PIL.Image` resampling filter to u... | 2,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/image_processing_glpn.py |
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. | 2,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/image_processing_glpn.py |
Returns:
`np.ndarray`: The resized image.
"""
height, width = get_image_size(image, channel_dim=input_data_format)
# Rounds the height and width down to the closest multiple of size_divisor
new_h = height // size_divisor * size_divisor
new_w = width // size_divisor * ... | 2,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/image_processing_glpn.py |
@filter_out_non_signature_kwargs()
def preprocess(
self,
images: Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]],
do_resize: Optional[bool] = None,
size_divisor: Optional[int] = None,
resample=None,
do_rescale: Optional[bool] = None,
... | 2,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/image_processing_glpn.py |
Args:
images (`PIL.Image.Image` or `TensorType` or `List[np.ndarray]` or `List[TensorType]`):
Images 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_normalize=False`.
... | 2,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/image_processing_glpn.py |
an effect if `do_resize` is set to `True`.
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether or not to apply the scaling factor (to make pixel values floats between 0. and 1.).
return_tensors (`str` or `TensorType`, *optional*):
The type of t... | 2,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/image_processing_glpn.py |
- `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `ChannelDimension.LAST`: image in (height, width, num_channels) format.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, th... | 2,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/image_processing_glpn.py |
resample = resample if resample is not None else self.resample | 2,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/image_processing_glpn.py |
images = make_list_of_images(images)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
# Here, the rescale() method uses a constant re... | 2,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/image_processing_glpn.py |
if do_rescale and is_scaled_image(images[0]):
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, set `do_rescale=False` to avoid rescaling them again."
)
if inpu... | 2,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/image_processing_glpn.py |
data = {"pixel_values": images}
return BatchFeature(data=data, tensor_type=return_tensors)
def post_process_depth_estimation(
self,
outputs: "DepthEstimatorOutput",
target_sizes: Optional[Union[TensorType, List[Tuple[int, int]], None]] = None,
) -> List[Dict[str, TensorType]]:
... | 2,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/image_processing_glpn.py |
Returns:
`List[Dict[str, TensorType]]`: A list of dictionaries of tensors representing the processed depth
predictions.
"""
requires_backends(self, "torch")
predicted_depth = outputs.predicted_depth
if (target_sizes is not None) and (len(predicted_depth) != len(... | 2,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/image_processing_glpn.py |
class GLPNDropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: Optional[float] = None) -> None:
super().__init__()
self.drop_prob = drop_prob
def forward(self, hidden_states: torch.Tensor) -> torch... | 2,946 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
class GLPNOverlapPatchEmbeddings(nn.Module):
"""Construct the overlapping patch embeddings."""
def __init__(self, patch_size, stride, num_channels, hidden_size):
super().__init__()
self.proj = nn.Conv2d(
num_channels,
hidden_size,
kernel_size=patch_size,
... | 2,947 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
class GLPNEfficientSelfAttention(nn.Module):
"""SegFormer's efficient self-attention mechanism. Employs the sequence reduction process introduced in the [PvT
paper](https://arxiv.org/abs/2102.12122)."""
def __init__(self, config, hidden_size, num_attention_heads, sequence_reduction_ratio):
super().... | 2,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
self.query = nn.Linear(self.hidden_size, self.all_head_size)
self.key = nn.Linear(self.hidden_size, self.all_head_size)
self.value = nn.Linear(self.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.sr_ratio = sequence_reduction_ratio
... | 2,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
def forward(
self,
hidden_states,
height,
width,
output_attentions=False,
):
query_layer = self.transpose_for_scores(self.query(hidden_states))
if self.sr_ratio > 1:
batch_size, seq_len, num_channels = hidden_states.shape
# Reshape to ... | 2,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
# Normalize the attention scores to probabilities.
atten... | 2,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
class GLPNSelfOutput(nn.Module):
def __init__(self, config, hidden_size):
super().__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_s... | 2,949 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
class GLPNAttention(nn.Module):
def __init__(self, config, hidden_size, num_attention_heads, sequence_reduction_ratio):
super().__init__()
self.self = GLPNEfficientSelfAttention(
config=config,
hidden_size=hidden_size,
num_attention_heads=num_attention_heads,
... | 2,950 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Upda... | 2,950 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
class GLPNDWConv(nn.Module):
def __init__(self, dim=768):
super().__init__()
self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)
def forward(self, hidden_states, height, width):
batch_size, seq_len, num_channels = hidden_states.shape
hidden_states = hidden_states.t... | 2,951 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
class GLPNMixFFN(nn.Module):
def __init__(self, config, in_features, hidden_features=None, out_features=None):
super().__init__()
out_features = out_features or in_features
self.dense1 = nn.Linear(in_features, hidden_features)
self.dwconv = GLPNDWConv(hidden_features)
if isin... | 2,952 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
def forward(self, hidden_states, height, width):
hidden_states = self.dense1(hidden_states)
hidden_states = self.dwconv(hidden_states, height, width)
hidden_states = self.intermediate_act_fn(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.dense2(hi... | 2,952 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
class GLPNLayer(nn.Module):
"""This corresponds to the Block class in the original implementation."""
def __init__(self, config, hidden_size, num_attention_heads, drop_path, sequence_reduction_ratio, mlp_ratio):
super().__init__()
self.layer_norm_1 = nn.LayerNorm(hidden_size)
self.atten... | 2,953 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
def forward(self, hidden_states, height, width, output_attentions=False):
self_attention_outputs = self.attention(
self.layer_norm_1(hidden_states), # in GLPN, layernorm is applied before self-attention
height,
width,
output_attentions=output_attentions,
... | 2,953 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
class GLPNEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
# stochastic depth decay rule
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]
# patch embeddings
embeddings = []
for i in... | 2,954 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
# Transformer blocks
blocks = []
cur = 0
for i in range(config.num_encoder_blocks):
# each block consists of layers
layers = []
if i != 0:
cur += config.depths[i - 1]
for j in range(config.depths[i]):
layers.append(
... | 2,954 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
def forward(
self,
pixel_values,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
):
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
batch_size = pixel_values.shape... | 2,954 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
hidden_states = pixel_values
for idx, x in enumerate(zip(self.patch_embeddings, self.block, self.layer_norm)):
embedding_layer, block_layer, norm_layer = x
# first, obtain patch embeddings
hidden_states, height, width = embedding_layer(hidden_states)
# second, sen... | 2,954 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
all_hidden_states = all_hidden_states + (hidden_states,) | 2,954 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
) | 2,954 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
class GLPNPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GLPNConfig
base_model_prefix = "glpn"
main_input_name = "pixel_values"
_no_split_modules = [] | 2,955 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
# Copied from transformers.models.segformer.modeling_segformer.SegformerPreTrainedModel._init_weights
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Conv2d)):
# Slightly different from the TF version which uses truncated_normal for init... | 2,955 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
class GLPNModel(GLPNPreTrainedModel):
# Copied from transformers.models.segformer.modeling_segformer.SegformerModel.__init__ with Segformer->GLPN
def __init__(self, config):
super().__init__(config)
self.config = config
# hierarchical Transformer encoder
self.encoder = GLPNEncod... | 2,956 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
@add_start_docstrings_to_model_forward(GLPN_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SH... | 2,956 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 2,956 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
encoder_outputs = self.encoder(
pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
if not return_dict:
return (sequence_output,)... | 2,956 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
class GLPNSelectiveFeatureFusion(nn.Module):
"""
Selective Feature Fusion module, as explained in the [paper](https://arxiv.org/abs/2201.07436) (section 3.4). This
module adaptively selects and integrates local and global features by attaining an attention map for each feature.
"""
def __init__(sel... | 2,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
self.sigmoid = nn.Sigmoid()
def forward(self, local_features, global_features):
# concatenate features along the channel dimension
features = torch.cat((local_features, global_features), dim=1)
# pass through convolutional layers
features = self.convolutional_layer1(features)
... | 2,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
class GLPNDecoderStage(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
should_skip = in_channels == out_channels
self.convolution = nn.Conv2d(in_channels, out_channels, kernel_size=1) if not should_skip else nn.Identity()
self.fusion = GLPNSelectiveFeatu... | 2,958 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
class GLPNDecoder(nn.Module):
def __init__(self, config):
super().__init__()
# we use features from end -> start
reserved_hidden_sizes = config.hidden_sizes[::-1]
out_channels = config.decoder_hidden_size
self.stages = nn.ModuleList(
[GLPNDecoderStage(hidden_size... | 2,959 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
class SiLogLoss(nn.Module):
r"""
Implements the Scale-invariant log scale loss [Eigen et al., 2014](https://arxiv.org/abs/1406.2283).
$$L=\frac{1}{n} \sum_{i} d_{i}^{2}-\frac{1}{2 n^{2}}\left(\sum_{i} d_{i}^{2}\right)$$ where $d_{i}=\log y_{i}-\log
y_{i}^{*}$.
"""
def __init__(self, lambd=0.5... | 2,960 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
class GLPNDepthEstimationHead(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
channels = config.decoder_hidden_size
self.head = nn.Sequential(
nn.Conv2d(channels, channels, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=... | 2,961 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
class GLPNForDepthEstimation(GLPNPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.glpn = GLPNModel(config)
self.decoder = GLPNDecoder(config)
self.head = GLPNDepthEstimationHead(config)
# Initialize weights and apply final processing
self.... | 2,962 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
@add_start_docstrings_to_model_forward(GLPN_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=DepthEstimatorOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: torch.FloatTensor,
labels: Optional[torch.FloatTensor] = None,
... | 2,962 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("vinvino02/glpn-kitti")
>>> model = GLPNForDepthEstimation.from_pretrained("vinvino02/glpn-kitti")
>>> ... | 2,962 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/glpn/modeling_glpn.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.