text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
Example:
```python
>>> from transformers import SwinConfig, SwinModel
>>> # Initializing a Swin microsoft/swin-tiny-patch4-window7-224 style configuration
>>> configuration = SwinConfig()
>>> # Initializing a model (with random weights) from the microsoft/swin-tiny-patch4-window7-224 style config... | 3,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/configuration_swin.py |
def __init__(
self,
image_size=224,
patch_size=4,
num_channels=3,
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4.0,
qkv_bias=True,
hidden_dropout_prob=0.0,
attention_probs_dropout_pro... | 3,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/configuration_swin.py |
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.embed_dim = embed_dim
self.depths = depths
self.num_layers = len(depths)
self.num_heads = num_heads
self.window_size = window_size
self.mlp_ratio = mlp_ratio
... | 3,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/configuration_swin.py |
self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(depths) + 1)]
self._out_features, self._out_indices = get_aligned_output_features_output_indices(
out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
) | 3,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/configuration_swin.py |
class SwinOnnxConfig(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"}),
]
)
... | 3,593 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/configuration_swin.py |
class TFSwinEncoderOutput(ModelOutput):
"""
Swin encoder's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
h... | 3,594 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
reshaped_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` ... | 3,594 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
class TFSwinModelOutput(ModelOutput):
"""
Swin model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
... | 3,595 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each stage) of shape `(batch_size, num_heads... | 3,595 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
last_hidden_state: tf.Tensor = None
pooler_output: tf.Tensor | None = None
hidden_states: Tuple[tf.Tensor, ...] | None = None
attentions: Tuple[tf.Tensor, ...] | None = None
reshaped_hidden_states: Tuple[tf.Tensor, ...] | None = None | 3,595 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
class TFSwinMaskedImageModelingOutput(ModelOutput):
"""
Swin masked image model outputs.
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `bool_masked_pos` is provided):
Masked image modeling (MLM) loss.
reconstruction (`tf.Tensor` of shape `(batch_size, num_ch... | 3,596 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each stage) of shape `(batch_size, num_heads... | 3,596 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
loss: tf.Tensor | None = None
reconstruction: tf.Tensor = None
hidden_states: Tuple[tf.Tensor, ...] | None = None
attentions: Tuple[tf.Tensor, ...] | None = None
reshaped_hidden_states: Tuple[tf.Tensor, ...] | None = None
@property
def logits(self):
warnings.warn(
"logits at... | 3,596 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
class TFSwinImageClassifierOutput(ModelOutput):
"""
Swin outputs for image classification.
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`tf.Tensor` of shape `(batch_... | 3,597 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each stage) of shape `(batch_size, num_heads... | 3,597 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
loss: tf.Tensor | None = None
logits: tf.Tensor = None
hidden_states: Tuple[tf.Tensor, ...] | None = None
attentions: Tuple[tf.Tensor, ...] | None = None
reshaped_hidden_states: Tuple[tf.Tensor, ...] | None = None | 3,597 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
class TFSwinEmbeddings(keras.layers.Layer):
"""
Construct the patch and position embeddings. Optionally, also the mask token.
"""
def __init__(self, config: SwinConfig, use_mask_token: bool = False, **kwargs) -> None:
super().__init__(**kwargs)
self.patch_embeddings = TFSwinPatchEmbeddi... | 3,598 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
def build(self, input_shape: tf.TensorShape) -> None:
if self.use_mask_token:
self.mask_token = self.add_weight(shape=(1, 1, self.embed_dim), initializer="zeros", name="mask_token")
else:
self.mask_token = None
if self.use_absolute_embeddings:
self.position_e... | 3,598 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
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)
if getattr(self, "norm", None) is not None:
with tf.name_scope(sel... | 3,598 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
if bool_masked_pos is not None:
mask_tokens = tf.repeat(self.mask_token, batch_size, 0)
mask_tokens = tf.repeat(mask_tokens, seq_len, 1)
# replace the masked visual tokens by mask_tokens
mask = tf.expand_dims(bool_masked_pos, -1)
mask = tf.cast(mask, mask_toke... | 3,598 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
class TFSwinPatchEmbeddings(keras.layers.Layer):
"""
Image to Patch Embedding.
"""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
image_size, patch_size = config.image_size, config.patch_size
num_channels, hidden_size = config.num_channels, config.embed_dim
... | 3,599 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
self.projection = keras.layers.Conv2D(
filters=hidden_size,
kernel_size=self.patch_size,
strides=self.patch_size,
padding="valid",
name="projection",
)
def maybe_pad(self, pixel_values: tf.Tensor, height: int, width: int) -> tf.Tensor:
if ... | 3,599 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
def call(self, pixel_values: tf.Tensor, training: bool = False) -> Tuple[tf.Tensor, Tuple[int, int]]:
_, num_channels, height, width = shape_list(pixel_values)
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimen... | 3,599 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
embeddings = tf.reshape(embeddings, (batch_size, channels, -1))
embeddings = tf.transpose(embeddings, (0, 2, 1))
return embeddings, output_dimensions
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "projection", None) i... | 3,599 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
class TFSwinPatchMerging(keras.layers.Layer):
"""
Patch Merging Layer.
Args:
input_resolution (`Tuple[int]`):
Resolution of input feature.
dim (`int`):
Number of input channels.
norm_layer (`keras.layer.Layer`, *optional*, defaults to `keras.layers.LayerNorma... | 3,600 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
def maybe_pad(self, input_feature: tf.Tensor, height: int, width: int) -> tf.Tensor:
should_pad = (height % 2 == 1) or (width % 2 == 1)
if should_pad:
pad_values = ((0, 0), (0, height % 2), (0, width % 2), (0, 0))
input_feature = tf.pad(input_feature, pad_values)
return ... | 3,600 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
input_feature = tf.reshape(input_feature, (batch_size, height, width, num_channels))
# pad input to be disible by width and height, if needed
input_feature = self.maybe_pad(input_feature, height, width)
# [batch_size, height/2, width/2, num_channels]
input_feature_0 = input_feature[:, 0:... | 3,600 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
input_feature = self.norm(input_feature, training=training)
input_feature = self.reduction(input_feature, training=training)
return input_feature
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "reduction", None) is no... | 3,600 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
class TFSwinDropPath(keras.layers.Layer):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: float = None, scale_by_keep: bool = True, **kwargs) -> None:
super(TFSwinDropPath, self).__init__(**kwargs)
self.drop_prob = dro... | 3,601 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
class TFSwinSelfAttention(keras.layers.Layer):
def __init__(self, config: SwinConfig, dim: int, num_heads: int, **kwargs) -> None:
super().__init__(**kwargs)
if dim % num_heads != 0:
raise ValueError(
f"The hidden size ({dim}) is not a multiple of the number of attention ... | 3,602 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
self.query = keras.layers.Dense(
self.all_head_size,
kernel_initializer=get_initializer(config.initializer_range),
use_bias=config.qkv_bias,
name="query",
)
self.key = keras.layers.Dense(
self.all_head_size,
kernel_initializer=get_i... | 3,602 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
def build(self, input_shape: tf.TensorShape) -> None:
self.relative_position_bias_table = self.add_weight(
shape=(((2 * self.window_size[0] - 1) * (2 * self.window_size[1] - 1)), self.num_attention_heads),
initializer="zeros",
name="relative_position_bias_table",
)
... | 3,602 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
# get pair-wise relative position index for each token inside the window
coords_h = tf.range(self.window_size[0])
coords_w = tf.range(self.window_size[1])
coords = tf.stack(tf.meshgrid(coords_h, coords_w, indexing="ij"))
coords_flatten = tf.reshape(coords, (shape_list(coords)[0], -1))
... | 3,602 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
if self.built:
return
self.built = True
if getattr(self, "query", None) is not None:
with tf.name_scope(self.query.name):
self.query.build([None, None, self.all_head_size])
if getattr(self, "key", None) is not None:
with tf.name_scope(self.key.... | 3,602 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor | None = None,
head_mask: tf.Tensor | None = None,
output_attentions: bool = False,
training: bool = False,
) -> Tuple[tf.Tensor, ...]:
batch_size, dim, _ = shape_list(hidden_states)
m... | 3,602 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
relative_position_bias = tf.gather(
self.relative_position_bias_table, tf.reshape(self.relative_position_index, (-1,))
)
relative_position_bias = tf.reshape(
relative_position_bias,
(sel... | 3,602 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in SwinModel call() function)
mask_shape = shape_list(attention_mask)[0]
attention_scores = tf.reshape(
attention_scores, (batch_size // mask_shape, mask_shape, self.num_attention... | 3,602 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = tf.matmul(attention_probs, value_layer)
context_layer = tf.transpose(context_layer, (0, 2, 1, 3))
new_context_layer_shape = shape_list(context_layer)[:-2] + [
... | 3,602 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
class TFSwinSelfOutput(keras.layers.Layer):
def __init__(self, config: SwinConfig, dim: int, **kwargs) -> None:
super().__init__(**kwargs)
self.dense = keras.layers.Dense(dim, name="dense")
self.dropout = keras.layers.Dropout(config.attention_probs_dropout_prob, name="dropout")
self.... | 3,603 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
class TFSwinAttention(keras.layers.Layer):
def __init__(self, config: SwinConfig, dim: int, num_heads: int, **kwargs) -> None:
super().__init__(**kwargs)
self.self = TFSwinSelfAttention(config, dim, num_heads, name="self")
self.self_output = TFSwinSelfOutput(config, dim, name="output")
... | 3,604 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor | None = None,
head_mask: tf.Tensor | None = None,
output_attentions: bool = False,
training: bool = False,
) -> tf.Tensor:
self_outputs = self.self(hidden_states, attention_mask, head_mask, o... | 3,604 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
class TFSwinIntermediate(keras.layers.Layer):
def __init__(self, config: SwinConfig, dim: int, **kwargs) -> None:
super().__init__(**kwargs)
self.dense = keras.layers.Dense(int(config.mlp_ratio * dim), name="dense")
if isinstance(config.hidden_act, str):
self.intermediate_act_fn ... | 3,605 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
class TFSwinOutput(keras.layers.Layer):
def __init__(self, config: SwinConfig, dim: int, **kwargs) -> None:
super().__init__(**kwargs)
self.dense = keras.layers.Dense(dim, name="dense")
self.dropout = keras.layers.Dropout(config.hidden_dropout_prob, "dropout")
self.config = config
... | 3,606 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
class TFSwinLayer(keras.layers.Layer):
def __init__(
self,
config,
dim,
input_resolution: Tuple[int, int],
num_heads: int,
drop_path_rate: float = 0.0,
shift_size: int = 0,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.chunk_... | 3,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
self.layernorm_before = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm_before")
self.attention = TFSwinAttention(config, dim, num_heads, name="attention")
self.drop_path = (
TFSwinDropPath(drop_path_rate, name="drop_path")
if drop_path_rate > 0.0
... | 3,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
def get_attn_mask(self, height: int, width: int, window_size: int, shift_size: int) -> tf.Tensor | None:
img_mask = tf.zeros((height, width))
height_slices = ((0, -window_size), (-window_size, -shift_size), (-shift_size, -1))
width_slices = ((0, -window_size), (-window_size, -shift_size), (-shif... | 3,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
# calculate attention mask for SW-MSA
if shift_size > 0:
count = 0
for height_slice in height_slices:
for width_slice in width_slices:
height_inds = tf.range(height_slice[0] % height, height_slice[1] % height + 1)
width_inds = tf.ra... | 3,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
mask_windows = window_partition(img_mask, window_size)
mask_windows = tf.reshape(mask_windows, (-1, window_size * window_size))
attn_mask = tf.expand_dims(mask_windows, 1) - tf.expand_dims(mask_windows, 2)
attn_mask = tf.where(attn_mask != 0, float(-100.0), attn_mask)
attn_mask = tf.wher... | 3,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
def call(
self,
hidden_states: tf.Tensor,
input_dimensions: Tuple[int, int],
head_mask: tf.Tensor | None = None,
output_attentions: bool = False,
training: bool = False,
) -> tf.Tensor:
# if window size is larger than input resolution, we don't partition windo... | 3,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
_, height_pad, width_pad, _ = shape_list(hidden_states)
# cyclic shift
if shift_size > 0:
shifted_hidden_states = tf.roll(hidden_states, shift=(-shift_size, -shift_size), axis=(1, 2))
else:
shifted_hidden_states = hidden_states
# partition windows
hidden_... | 3,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
attention_windows = tf.reshape(attention_output, (-1, window_size, window_size, channels))
shifted_windows = window_reverse(attention_windows, window_size, height_pad, width_pad)
# reverse cyclic shift
if shift_size > 0:
attention_windows = tf.roll(shifted_windows, shift=(shift_size... | 3,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
layer_outputs = (layer_output, attention_outputs[1]) if output_attentions else (layer_output,)
return layer_outputs | 3,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "layernorm_before", None) is not None:
with tf.name_scope(self.layernorm_before.name):
self.layernorm_before.build([None, None, self.dim])
if getattr(self, ... | 3,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
with tf.name_scope(self.swin_output.name):
self.swin_output.build(None) | 3,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
class TFSwinStage(keras.layers.Layer):
def __init__(
self,
config: SwinConfig,
dim: int,
input_resolution: Tuple[int, int],
depth: int,
num_heads: int,
drop_path: List[float],
downsample: Optional[Callable],
**kwargs,
) -> None:
sup... | 3,608 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
# patch merging layer
if downsample is not None:
self.downsample = downsample(
input_resolution,
dim=dim,
norm_layer=partial(keras.layers.LayerNormalization, epsilon=1e-5),
name="downsample",
)
else:
self... | 3,608 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
if self.downsample is not None:
height_downsampled, width_downsampled = (height + 1) // 2, (width + 1) // 2
output_dimensions = (height, width, height_downsampled, width_downsampled)
hidden_states = self.downsample(layer_outputs[0], input_dimensions, training=training)
else:
... | 3,608 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
class TFSwinEncoder(keras.layers.Layer):
def __init__(self, config: SwinConfig, grid_size: Tuple[int, int], **kwargs):
super().__init__(**kwargs)
self.num_layers = len(config.depths)
self.config = config
dpr = list((tf.linspace(0, 1, sum(config.depths)) * config.drop_path_rate).numpy... | 3,609 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
def call(
self,
hidden_states: tf.Tensor,
input_dimensions: Tuple[int, int],
head_mask: tf.Tensor | None = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
training: bool = False,
) -> Union[Tuple[tf.... | 3,609 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
if output_hidden_states:
batch_size, _, hidden_size = shape_list(hidden_states)
# rearrange b (h w) c -> b c h w
reshaped_hidden_state = tf.reshape(hidden_states, (batch_size, *input_dimensions, hidden_size))
reshaped_hidden_state = tf.transpose(reshaped_hidden_state, (0,... | 3,609 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
if output_hidden_states:
batch_size, _, hidden_size = shape_list(hidden_states)
# rearrange b (h w) c -> b c h w
reshaped_hidden_state = tf.reshape(hidden_states, (batch_size, *input_dimensions, hidden_size))
reshaped_hidden_state = tf.transpose(reshaped_h... | 3,609 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.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) | 3,609 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
class TFSwinPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = SwinConfig
base_model_prefix = "swin"
main_input_name = "pixel_values" | 3,610 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
class AdaptiveAveragePooling1D(keras.layers.Layer):
"""
Args:
Average 1D Pooling with adaptive kernel size.
output_size: An integer or tuple/list of a single integer, specifying pooled_features.
The new size of output channels.
data_format: A string,
one of `channels_last` (defau... | 3,611 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
Adapted from [tensorflow-addon's adaptive pooling.py](
https://github.com/tensorflow/addons/blob/8cec33fcaaf1cf90aec7bdd55a0fcdbb251ce5c2/tensorflow_addons/layers/adaptive_pooling.py#L90-L120
)
"""
def __init__(
self,
output_size: Union[int, Iterable[int]],
reduce_function: ... | 3,611 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
def call(self, inputs: tf.Tensor, *args) -> None:
bins = self.output_size[0]
if self.data_format == "channels_last":
splits = tf.split(inputs, bins, axis=1)
splits = tf.stack(splits, axis=1)
out_vect = self.reduce_function(splits, axis=2)
else:
spl... | 3,611 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
def get_config(self) -> Dict[str, Any]:
config = {
"output_size": self.output_size,
"data_format": self.data_format,
}
base_config = super().get_config()
return {**base_config, **config} | 3,611 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
class TFSwinMainLayer(keras.layers.Layer):
config_class = SwinConfig
def __init__(
self, config: SwinConfig, add_pooling_layer: bool = True, use_mask_token: bool = False, **kwargs
) -> None:
super().__init__(**kwargs)
self.config = config
self.num_layers = len(config.depths)... | 3,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
def _prune_heads(self, heads_to_prune: Dict[int, List]):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].a... | 3,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
@unpack_inputs
def call(
self,
pixel_values: tf.Tensor | None = None,
bool_masked_pos: tf.Tensor | None = None,
head_mask: tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[boo... | 3,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_lengt... | 3,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
pooled_output = None
if self.pooler is not None:
batch_size, _, num_features = shape_list(sequence_output)
pooled_output = self.pooler(sequence_output)
pooled_output = tf.reshape(pooled_output, (batch_size, num_features))
if not return_dict:
output = (seq... | 3,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.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:
... | 3,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
class TFSwinModel(TFSwinPreTrainedModel):
def __init__(
self, config: SwinConfig, add_pooling_layer: bool = True, use_mask_token: bool = False, **kwargs
) -> None:
super().__init__(config, **kwargs)
self.config = config
self.swin = TFSwinMainLayer(config, name="swin") | 3,613 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
@add_start_docstrings_to_model_forward(SWIN_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFSwinModelOutput,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
@unpack_inputs
def ... | 3,613 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None els... | 3,613 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
swin_outputs = self.swin(
pixel_values=pixel_values,
bool_masked_pos=bool_masked_pos,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_sta... | 3,613 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
class TFSwinPixelShuffle(keras.layers.Layer):
"""TF layer implementation of torch.nn.PixelShuffle"""
def __init__(self, upscale_factor: int, **kwargs) -> None:
super().__init__(**kwargs)
if not isinstance(upscale_factor, int) or upscale_factor < 2:
raise ValueError(f"upscale_factor ... | 3,614 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
def call(self, x: tf.Tensor) -> tf.Tensor:
hidden_states = x
batch_size, _, _, num_input_channels = shape_list(hidden_states)
block_size_squared = self.upscale_factor**2
output_depth = int(num_input_channels / block_size_squared)
# When the number of output channels >= 2, PyTorch... | 3,614 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
return hidden_states | 3,614 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
class TFSwinDecoder(keras.layers.Layer):
def __init__(self, config: SwinConfig, **kwargs):
super().__init__(**kwargs)
self.conv2d = keras.layers.Conv2D(
filters=config.encoder_stride**2 * config.num_channels, kernel_size=1, strides=1, name="0"
)
self.pixel_shuffle = TFSwi... | 3,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "conv2d", None) is not None:
with tf.name_scope(self.conv2d.name):
self.conv2d.build([None, None, None, self.config.hidden_size])
if getattr(self, "pixel_sh... | 3,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
class TFSwinForMaskedImageModeling(TFSwinPreTrainedModel):
def __init__(self, config: SwinConfig):
super().__init__(config)
self.swin = TFSwinMainLayer(config, add_pooling_layer=False, use_mask_token=True, name="swin")
self.decoder = TFSwinDecoder(config, name="decoder") | 3,616 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
@add_start_docstrings_to_model_forward(SWIN_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFSwinMaskedImageModelingOutput, config_class=_CONFIG_FOR_DOC)
@unpack_inputs
def call(
self,
pixel_values: tf.Tensor | None = None,
bool_masked_pos: tf.Tensor | None = None,
... | 3,616 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
>>> model = TFSwinForMaskedImageModeling.from_pretrained("microsoft/sw... | 3,616 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
>>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
>>> loss, reconstructed_pixel_values = outputs.loss, outputs.reconstruction
>>> list(reconstructed_pixel_values.shape)
[1, 3, 224, 224]
```"""
return_dict = return_dict if return_dict is not None else self.config.... | 3,616 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
sequence_output = outputs[0]
# Reshape to (batch_size, num_channels, height, width)
sequence_output = tf.transpose(sequence_output, (0, 2, 1))
batch_size, num_channels, sequence_length = shape_list(sequence_output)
height = width = int(sequence_length**0.5)
sequence_output = tf.r... | 3,616 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
reconstruction_loss = keras.losses.mean_absolute_error(
# Swap axes as metric calculation reduces over the final dimension
tf.transpose(pixel_values, (1, 2, 3, 0)),
tf.transpose(reconstructed_pixel_values, (1, 2, 3, 0)),
)
reconstruction_loss = tf.... | 3,616 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
return TFSwinMaskedImageModelingOutput(
loss=masked_im_loss,
reconstruction=reconstructed_pixel_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
reshaped_hidden_states=outputs.reshaped_hidden_states,
)
def build(self, in... | 3,616 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
class TFSwinForImageClassification(TFSwinPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config: SwinConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.swin = TFSwinMainLayer(config, name="swin")
# Classifier head
self.classifier =... | 3,617 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
@add_start_docstrings_to_model_forward(SWIN_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=TFSwinImageClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
@unpack_inputs
def call(
... | 3,617 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,617 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
outputs = self.swin(
pixel_values,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
pooled_output = outputs[1]
logits = self... | 3,617 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "swin", None) is not None:
with tf.name_scope(self.swin.name):
self.swin.build(None)
if getattr(self, "classifier", None) is not None:
if hasatt... | 3,617 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py |
class MobileNetV1FeatureExtractor(MobileNetV1ImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use MobileNetV1ImageProcessor instead.",
... | 3,618 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/feature_extraction_mobilenet_v1.py |
class MobileNetV1ImageProcessor(BaseImageProcessor):
r"""
Constructs a MobileNetV1 image processor. | 3,619 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.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 `{"shortest_edge": 256}`):
... | 3,619 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py |
is padded with 0's and then center cropped. Can be overridden by the `do_center_crop` parameter in the
`preprocess` method.
crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`):
Desired output size when applying center-cropping. Only has an effect if `do_... | 3,619 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py |
Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
method.
image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
Mean to use if normalizing the image. This is a float or list of floats the length of the... | 3,619 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py |
model_input_names = ["pixel_values"] | 3,619 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py |
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