text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
def __init__(
self,
do_resize: bool = True,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_center_crop: bool = True,
crop_size: Dict[str, int] = None,
do_rescale: bool = True,
rescale_factor: Union[int... | 3,619 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py |
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_STANDARD_MEAN
self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD | 3,619 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py |
# Copied from transformers.models.clip.image_processing_clip.CLIPImageProcessor.resize
def resize(
self,
image: np.ndarray,
size: Dict[str, int],
resample: PILImageResampling = PILImageResampling.BICUBIC,
data_format: Optional[Union[str, ChannelDimension]] = None,
inp... | 3,619 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py |
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Size of the output image.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
Resampling filter to use when resiizing the image.
... | 3,619 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py |
raise ValueError("Size must contain either 'shortest_edge' or 'height' and 'width'.") | 3,619 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py |
output_size = get_resize_output_image_size(
image,
size=size,
default_to_square=default_to_square,
input_data_format=input_data_format,
)
return resize(
image,
size=output_size,
resample=resample,
data_format... | 3,619 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py |
@filter_out_non_signature_kwargs()
def preprocess(
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_center_crop: bool = None,
crop_size: Dict[str, int] = None,
do_rescale: ... | 3,619 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.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... | 3,619 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py |
Whether to center crop the image.
crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`):
Size of the center crop. Only has an effect if `do_center_crop` is set to `True`.
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to r... | 3,619 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.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... | 3,619 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.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:
... | 3,619 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py |
crop_size = crop_size if crop_size is not None else self.crop_size
crop_size = get_size_dict(crop_size)
do_rescale = do_rescale if do_rescale is not None else self.do_rescale
rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
do_normalize = do_normaliz... | 3,619 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.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."
)
validate_preprocess_arguments(
do_re... | 3,619 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.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... | 3,619 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py |
if do_normalize:
image = self.normalize(
image=image, mean=image_mean, std=image_std, input_data_format=input_data_format
)
all_images.append(image)
images = [
to_channel_dimension_format(image, data_format, input_channel_dim=input_dat... | 3,619 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py |
class MobileNetV1ConvLayer(nn.Module):
def __init__(
self,
config: MobileNetV1Config,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: Optional[int] = 1,
groups: Optional[int] = 1,
bias: bool = False,
use_normalization: Optional[b... | 3,620 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/modeling_mobilenet_v1.py |
self.convolution = nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
groups=groups,
bias=bias,
padding_mode="zeros",
)
if use_normalizati... | 3,620 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/modeling_mobilenet_v1.py |
def forward(self, features: torch.Tensor) -> torch.Tensor:
if self.config.tf_padding:
features = apply_tf_padding(features, self.convolution)
features = self.convolution(features)
if self.normalization is not None:
features = self.normalization(features)
if self.a... | 3,620 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/modeling_mobilenet_v1.py |
class MobileNetV1PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MobileNetV1Config
load_tf_weights = load_tf_weights_in_mobilenet_v1
base_model_prefix = "mobilen... | 3,621 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/modeling_mobilenet_v1.py |
class MobileNetV1Model(MobileNetV1PreTrainedModel):
def __init__(self, config: MobileNetV1Config, add_pooling_layer: bool = True):
super().__init__(config)
self.config = config
depth = 32
out_channels = max(int(depth * config.depth_multiplier), config.min_depth)
self.conv_s... | 3,622 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/modeling_mobilenet_v1.py |
self.layer.append(
MobileNetV1ConvLayer(
config,
in_channels=in_channels,
out_channels=in_channels,
kernel_size=3,
stride=strides[i],
groups=in_channels,
)
... | 3,622 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/modeling_mobilenet_v1.py |
@add_start_docstrings_to_model_forward(MOBILENET_V1_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndNoAttention,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
... | 3,622 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/modeling_mobilenet_v1.py |
all_hidden_states = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer):
hidden_states = layer_module(hidden_states)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
last_hidden_state = hidden_state... | 3,622 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/modeling_mobilenet_v1.py |
class MobileNetV1ForImageClassification(MobileNetV1PreTrainedModel):
def __init__(self, config: MobileNetV1Config) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.mobilenet_v1 = MobileNetV1Model(config)
last_hidden_size = self.mobilenet_v1.layer[-1].convo... | 3,623 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/modeling_mobilenet_v1.py |
@add_start_docstrings_to_model_forward(MOBILENET_V1_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=ImageClassifierOutputWithNoAttention,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
... | 3,623 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/modeling_mobilenet_v1.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,623 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/modeling_mobilenet_v1.py |
outputs = self.mobilenet_v1(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
pooled_output = outputs.pooler_output if return_dict else outputs[1]
logits = self.classifier(self.dropout(pooled_output))
loss = None
if labels is not None:
if se... | 3,623 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/modeling_mobilenet_v1.py |
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "singl... | 3,623 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/modeling_mobilenet_v1.py |
class MobileNetV1Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MobileNetV1Model`]. It is used to instantiate a
MobileNetV1 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will ... | 3,624 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/configuration_mobilenet_v1.py |
Args:
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
depth_multiplier (`float`, *optional*, defaults to 1.0):
Shrinks or expands the number of... | 3,624 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/configuration_mobilenet_v1.py |
The dropout ratio for attached classifiers.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 0.001):
The epsilon used by the... | 3,624 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/configuration_mobilenet_v1.py |
Example:
```python
>>> from transformers import MobileNetV1Config, MobileNetV1Model
>>> # Initializing a "mobilenet_v1_1.0_224" style configuration
>>> configuration = MobileNetV1Config()
>>> # Initializing a model from the "mobilenet_v1_1.0_224" style configuration
>>> model = MobileNetV1Mod... | 3,624 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/configuration_mobilenet_v1.py |
self.num_channels = num_channels
self.image_size = image_size
self.depth_multiplier = depth_multiplier
self.min_depth = min_depth
self.hidden_act = hidden_act
self.tf_padding = tf_padding
self.classifier_dropout_prob = classifier_dropout_prob
self.initializer_rang... | 3,624 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/configuration_mobilenet_v1.py |
class MobileNetV1OnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict([("pixel_values", {0: "batch"})])
@property
def outputs(self) -> Mapping[str, Mapping[int, str]]:
if self.ta... | 3,625 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/configuration_mobilenet_v1.py |
class TFConvNextDropPath(keras.layers.Layer):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
References:
(1) github.com:rwightman/pytorch-image-models
"""
def __init__(self, drop_path: float, **kwargs):
super().__init__(**kwargs)
self... | 3,626 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
class TFConvNextEmbeddings(keras.layers.Layer):
"""This class is comparable to (and inspired by) the SwinEmbeddings class
found in src/transformers/models/swin/modeling_swin.py.
"""
def __init__(self, config: ConvNextConfig, **kwargs):
super().__init__(**kwargs)
self.patch_embeddings = ... | 3,627 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
tf.debugging.assert_equal(
shape_list(pixel_values)[1],
self.num_channels,
message="Make sure that the channel dimension of the pixel values match with the one set in the configuration.",
)
# When running on CPU, `keras.layers.Conv2D` doesn't support `NCHW` format.
... | 3,627 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "patch_embeddings", None) is not None:
with tf.name_scope(self.patch_embeddings.name):
self.patch_embeddings.build([None, None, None, self.config.num_channels])
... | 3,627 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
class TFConvNextLayer(keras.layers.Layer):
"""This corresponds to the `Block` class in the original implementation.
There are two equivalent implementations: [DwConv, LayerNorm (channels_first), Conv, GELU,1x1 Conv]; all in (N, C,
H, W) (2) [DwConv, Permute to (N, H, W, C), LayerNorm (channels_last), Linea... | 3,628 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
def __init__(self, config, dim, drop_path=0.0, **kwargs):
super().__init__(**kwargs)
self.dim = dim
self.config = config
self.dwconv = keras.layers.Conv2D(
filters=dim,
kernel_size=7,
padding="same",
groups=dim,
kernel_initializ... | 3,628 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
kernel_initializer=get_initializer(config.initializer_range),
bias_initializer="zeros",
name="pwconv2",
)
# Using `layers.Activation` instead of `tf.identity` to better control `training`
# behaviour.
self.drop_path = (
TFConvNextDropPath(drop_path, na... | 3,628 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
def build(self, input_shape: tf.TensorShape = None):
# PT's `nn.Parameters` must be mapped to a TF layer weight to inherit the same name hierarchy (and vice-versa)
self.layer_scale_parameter = (
self.add_weight(
shape=(self.dim,),
initializer=keras.initializer... | 3,628 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
if self.built:
return
self.built = True
if getattr(self, "dwconv", None) is not None:
with tf.name_scope(self.dwconv.name):
self.dwconv.build([None, None, None, self.dim])
if getattr(self, "layernorm", None) is not None:
with tf.name_scope(self... | 3,628 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
def call(self, hidden_states, training=False):
input = hidden_states
x = self.dwconv(hidden_states)
x = self.layernorm(x)
x = self.pwconv1(x)
x = self.act(x)
x = self.pwconv2(x)
if self.layer_scale_parameter is not None:
x = self.layer_scale_parameter... | 3,628 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
class TFConvNextStage(keras.layers.Layer):
"""ConvNext stage, consisting of an optional downsampling layer + multiple residual blocks.
Args:
config (`ConvNextV2Config`):
Model configuration class.
in_channels (`int`):
Number of input channels.
out_channels (`int`... | 3,629 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
def __init__(
self,
config: ConvNextConfig,
in_channels: int,
out_channels: int,
kernel_size: int = 2,
stride: int = 2,
depth: int = 2,
drop_path_rates: Optional[List[float]] = None,
**kwargs,
):
super().__init__(**kwargs)
if in... | 3,629 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
strides=stride,
kernel_initializer=get_initializer(config.initializer_range),
bias_initializer=keras.initializers.Zeros(),
name="downsampling_layer.1",
),
]
else:
self.downsampling_layer = [tf.identity] | 3,629 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
drop_path_rates = drop_path_rates or [0.0] * depth
self.layers = [
TFConvNextLayer(
config,
dim=out_channels,
drop_path=drop_path_rates[j],
name=f"layers.{j}",
)
for j in range(depth)
]
self.in_ch... | 3,629 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "layers", None) is not None:
for layer in self.layers:
with tf.name_scope(layer.name):
layer.build(None)
if self.in_channels != self.out... | 3,629 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
class TFConvNextEncoder(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.stages = []
drop_path_rates = tf.linspace(0.0, config.drop_path_rate, sum(config.depths))
drop_path_rates = tf.split(drop_path_rates, config.depths)
drop_path_ra... | 3,630 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
def call(self, hidden_states, output_hidden_states=False, return_dict=True):
all_hidden_states = () if output_hidden_states else None
for i, layer_module in enumerate(self.stages):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
... | 3,630 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
class TFConvNextMainLayer(keras.layers.Layer):
config_class = ConvNextConfig
def __init__(self, config: ConvNextConfig, add_pooling_layer: bool = True, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embeddings = TFConvNextEmbeddings(config, name="embeddings")
s... | 3,631 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
@unpack_inputs
def call(
self,
pixel_values: TFModelInputType | None = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
output_hidden_states =... | 3,631 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
last_hidden_state = encoder_outputs[0]
# Change to NCHW output format have uniformity in the modules
last_hidden_state = tf.transpose(last_hidden_state, perm=(0, 3, 1, 2))
pooled_output = self.layernorm(self.pooler(last_hidden_state))
# Change the other hidden state outputs to NCHW as w... | 3,631 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "embeddings", None) is not None:
with tf.name_scope(self.embeddings.name):
self.embeddings.build(None)
if getattr(self, "encoder", None) is not None:
... | 3,631 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
class TFConvNextPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ConvNextConfig
base_model_prefix = "convnext"
main_input_name = "pixel_values" | 3,632 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
class TFConvNextModel(TFConvNextPreTrainedModel):
def __init__(self, config, *inputs, add_pooling_layer=True, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.convnext = TFConvNextMainLayer(config, add_pooling_layer=add_pooling_layer, name="convnext")
@unpack_inputs
@add_start_do... | 3,633 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224")
>>> model = TFConvNextModel.from_pretrained("facebook/convnext-tiny-224")
... | 3,633 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
outputs = self.convnext(
pixel_values=pixel_values,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPooling(
... | 3,633 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
class TFConvNextForImageClassification(TFConvNextPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config: ConvNextConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.convnext = TFConvNextMainLayer(config, name="c... | 3,634 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(CONVNEXT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
pixel_values: TFModelInputType | None = None,
output_hidden_states: Optional[bool] = None,... | 3,634 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
```python
>>> from transformers import AutoImageProcessor, TFConvNextForImageClassification
>>> import tensorflow as tf
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(ur... | 3,634 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
>>> inputs = image_processor(images=image, return_tensors="tf")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_class_idx = tf.math.argmax(logits, axis=-1)[0]
>>> print("Predicted class:", model.config.... | 3,634 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
logits = self.classifier(pooled_output)
loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput... | 3,634 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py |
class ConvNextImageProcessor(BaseImageProcessor):
r"""
Constructs a ConvNeXT image processor. | 3,635 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/image_processing_convnext.py |
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Controls whether to resize the image's (height, width) dimensions to the specified `size`. Can be overriden
by `do_resize` in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 384}`... | 3,635 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/image_processing_convnext.py |
overriden by `crop_pct` in the `preprocess` method.
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
Resampling filter to use if resizing the image. Can be overriden by `resample` in the `preprocess` method.
do_rescale (`bool`, *optional*, defaults to `True`):
... | 3,635 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/image_processing_convnext.py |
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.
image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
St... | 3,635 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/image_processing_convnext.py |
model_input_names = ["pixel_values"]
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
crop_pct: float = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / ... | 3,635 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/image_processing_convnext.py |
self.do_resize = do_resize
self.size = size
# Default value set here for backwards compatibility where the value in config is None
self.crop_pct = crop_pct if crop_pct is not None else 224 / 256
self.resample = resample
self.do_rescale = do_rescale
self.rescale_factor = r... | 3,635 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/image_processing_convnext.py |
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Dictionary of the form `{"shortest_edge": int}`, specifying the size of the output image. If
`size["shortest_edge"]` >= 384 image is resized to `(size["shortest_edge"], size["sho... | 3,635 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/image_processing_convnext.py |
The channel dimension format of the image. If not provided, it will be the same as the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred from the input
image.
... | 3,635 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/image_processing_convnext.py |
if shortest_edge < 384:
# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
resize_shortest_edge = int(shortest_edge / crop_pct)
resize_size = get_resize_output_image_size(
image, size=resize_shortest_edge, default_to_square=False, input_data_forma... | 3,635 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/image_processing_convnext.py |
return resize(
image,
size=(shortest_edge, shortest_edge),
resample=resample,
data_format=data_format,
input_data_format=input_data_format,
**kwargs,
) | 3,635 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/image_processing_convnext.py |
@filter_out_non_signature_kwargs()
def preprocess(
self,
images: ImageInput,
do_resize: bool = None,
size: Dict[str, int] = None,
crop_pct: float = None,
resample: PILImageResampling = None,
do_rescale: bool = None,
rescale_factor: float = None,
... | 3,635 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/image_processing_convnext.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... | 3,635 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/image_processing_convnext.py |
Percentage of the image to crop if size < 384.
resample (`int`, *optional*, defaults to `self.resample`):
Resampling filter to use if resizing the image. This can be one of `PILImageResampling`, filters. Only
has an effect if `do_resize` is set to `True`.
do_resca... | 3,635 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/image_processing_convnext.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'`:... | 3,635 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/image_processing_convnext.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:
... | 3,635 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/image_processing_convnext.py |
rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
do_normalize = do_normalize if do_normalize is not None else self.do_normalize
image_mean = image_mean if image_mean is not None else self.image_mean
image_std = image_std if image_std is not None else self.im... | 3,635 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/image_processing_convnext.py |
size = size if size is not None else self.size
size = get_size_dict(size, default_to_square=False)
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, "
... | 3,635 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/image_processing_convnext.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... | 3,635 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/image_processing_convnext.py |
if do_normalize:
images = [
self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
for image in images
]
images = [
to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) ... | 3,635 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/image_processing_convnext.py |
class ConvNextFeatureExtractor(ConvNextImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use ConvNextImageProcessor instead.",
FutureWa... | 3,636 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/feature_extraction_convnext.py |
class ConvNextConfig(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ConvNextModel`]. It is used to instantiate an
ConvNeXT model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the d... | 3,637 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/configuration_convnext.py |
Args:
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
patch_size (`int`, *optional*, defaults to 4):
Patch size to use in the patch embedding layer.
num_stages (`int`, *optional*, defaults to 4):
The number of stages in the model... | 3,637 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/configuration_convnext.py |
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
layer_scale_init_value (`float`, *optional*, defaults to 1e-6):
The initial value for the layer scale.
drop_path_rate (`float`, *optional*, defaults to 0.0):
T... | 3,637 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/configuration_convnext.py |
many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
If unset and `out_features` is unset, will default to the last stage. Must be in the
same order as defined in the `stage_names` attribute. | 3,637 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/configuration_convnext.py |
Example:
```python
>>> from transformers import ConvNextConfig, ConvNextModel
>>> # Initializing a ConvNext convnext-tiny-224 style configuration
>>> configuration = ConvNextConfig()
>>> # Initializing a model (with random weights) from the convnext-tiny-224 style configuration
>>> model = Con... | 3,637 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/configuration_convnext.py |
self.num_channels = num_channels
self.patch_size = patch_size
self.num_stages = num_stages
self.hidden_sizes = [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
self.depths = [3, 3, 9, 3] if depths is None else depths
self.hidden_act = hidden_act
self.initiali... | 3,637 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/configuration_convnext.py |
class ConvNextOnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
]
... | 3,638 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/configuration_convnext.py |
class ConvNextDropPath(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) -> t... | 3,639 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py |
class ConvNextLayerNorm(nn.Module):
r"""LayerNorm that supports two data formats: channels_last (default) or channels_first.
The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height,
width, channels) while channels_first corresponds to inputs with shap... | 3,640 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py |
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self.data_format == "channels_last":
x = torch.nn.functional.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
elif self.data_format == "channels_first":
input_dtype = x.dtype
x = x.float()
... | 3,640 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py |
class ConvNextEmbeddings(nn.Module):
"""This class is comparable to (and inspired by) the SwinEmbeddings class
found in src/transformers/models/swin/modeling_swin.py.
"""
def __init__(self, config):
super().__init__()
self.patch_embeddings = nn.Conv2d(
config.num_channels, c... | 3,641 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py |
class ConvNextLayer(nn.Module):
"""This corresponds to the `Block` class in the original implementation.
There are two equivalent implementations: [DwConv, LayerNorm (channels_first), Conv, GELU,1x1 Conv]; all in (N, C,
H, W) (2) [DwConv, Permute to (N, H, W, C), LayerNorm (channels_last), Linear, GELU, Li... | 3,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py |
def __init__(self, config, dim, drop_path=0):
super().__init__()
self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv
self.layernorm = ConvNextLayerNorm(dim, eps=1e-6)
self.pwconv1 = nn.Linear(dim, 4 * dim) # pointwise/1x1 convs, implemented with lin... | 3,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py |
def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
input = hidden_states
x = self.dwconv(hidden_states)
x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)
x = self.layernorm(x)
x = self.pwconv1(x)
x = self.act(x)
x = self.pwconv2(x)
... | 3,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py |
class ConvNextStage(nn.Module):
"""ConvNeXT stage, consisting of an optional downsampling layer + multiple residual blocks.
Args:
config ([`ConvNextConfig`]): Model configuration class.
in_channels (`int`): Number of input channels.
out_channels (`int`): Number of output channels.
... | 3,643 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py |
if in_channels != out_channels or stride > 1:
self.downsampling_layer = nn.Sequential(
ConvNextLayerNorm(in_channels, eps=1e-6, data_format="channels_first"),
nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride),
)
else:
... | 3,643 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py |
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