text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings,... | 3,568 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/configuration_squeezebert.py |
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12): | 3,568 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/configuration_squeezebert.py |
pad_token_id (`int`, *optional*, defaults to 0):
The ID of the token in the word embedding to use as padding.
embedding_size (`int`, *optional*, defaults to 768):
The dimension of the word embedding vectors.
q_groups (`int`, *optional*, defaults to 4):
The number of ... | 3,568 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/configuration_squeezebert.py |
```python
>>> from transformers import SqueezeBertConfig, SqueezeBertModel
>>> # Initializing a SqueezeBERT configuration
>>> configuration = SqueezeBertConfig()
>>> # Initializing a model (with random weights) from the configuration above
>>> model = SqueezeBertModel(configuration)
>>> # Acc... | 3,568 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/configuration_squeezebert.py |
def __init__(
self,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
... | 3,568 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/configuration_squeezebert.py |
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
... | 3,568 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/configuration_squeezebert.py |
class SqueezeBertOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
else:
dynamic_axis = {0: "batch", 1: "sequence"}
return OrderedDict(
... | 3,569 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/configuration_squeezebert.py |
class SqueezeBertTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" SqueezeBERT tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this supercl... | 3,570 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert_fast.py |
Args:
vocab_file (`str`):
File containing the vocabulary.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
unk_token (`str`, *optional*, defaults to `"[UNK]"`):
The unknown token. A token that is no... | 3,570 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert_fast.py |
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The token used f... | 3,570 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert_fast.py |
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lowercase` (as in the original SqueezeBERT).
wordpieces_prefix (`str`, *optional*, defaults to `"##"`):
The prefix for subwor... | 3,570 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert_fast.py |
vocab_files_names = VOCAB_FILES_NAMES
slow_tokenizer_class = SqueezeBertTokenizer
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
do_lower_case=True,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mas... | 3,570 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert_fast.py |
normalizer_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
if (
normalizer_state.get("lowercase", do_lower_case) != do_lower_case
or normalizer_state.get("strip_accents", strip_accents) != strip_accents
or normalizer_state.get("handle_chinese_chars", toke... | 3,570 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert_fast.py |
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A SqueezeBERT sequence has the following format:
- single sequence: `[CLS... | 3,570 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert_fast.py |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A SqueezeBERT sequence
pair mask has the following format... | 3,570 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert_fast.py |
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_... | 3,570 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert_fast.py |
class SwinEncoderOutput(ModelOutput):
"""
Swin encoder's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
... | 3,571 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each stage) of shape `(batch... | 3,571 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
"""
last_hidden_state: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, .... | 3,571 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
class SwinModelOutput(ModelOutput):
"""
Swin model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the mode... | 3,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each stage) of shape `(batch... | 3,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
"""
last_hidden_state: torch.FloatTensor = None
pooler_output: Optional[torch.FloatTensor] = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = No... | 3,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
class SwinMaskedImageModelingOutput(ModelOutput):
"""
Swin masked image model outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `bool_masked_pos` is provided):
Masked image modeling (MLM) loss.
reconstruction (`torch.FloatTensor` of shape `(batc... | 3,573 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each stage) of shape `(batch... | 3,573 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
"""
loss: Optional[torch.FloatTensor] = None
reconstruction: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
atten... | 3,573 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
class SwinImageClassifierOutput(ModelOutput):
"""
Swin outputs for image classification.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of ... | 3,574 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each stage) of shape `(batch... | 3,574 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: O... | 3,574 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
class SwinEmbeddings(nn.Module):
"""
Construct the patch and position embeddings. Optionally, also the mask token.
"""
def __init__(self, config, use_mask_token=False):
super().__init__()
self.patch_embeddings = SwinPatchEmbeddings(config)
num_patches = self.patch_embeddings.nu... | 3,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
# Copied from transformers.models.vit.modeling_vit.ViTEmbeddings.interpolate_pos_encoding
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on hi... | 3,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
# always interpolate when tracing to ensure the exported model works for dynamic input shapes
if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
return self.position_embeddings
class_pos_embed = self.position_embeddings[:, :1]
patch_pos_embed = self.... | 3,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
return torch.cat((class_pos_embed, patch_pos_embed), dim=1)
def forward(
self,
pixel_values: Optional[torch.FloatTensor],
bool_masked_pos: Optional[torch.BoolTensor] = None,
interpolate_pos_encoding: bool = False,
) -> Tuple[torch.Tensor]:
_, num_channels, height, width ... | 3,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
if self.position_embeddings is not None:
if interpolate_pos_encoding:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
embeddings = embeddings + self.position_embeddings
embeddings = self.dropout(embeddings)
... | 3,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
class SwinPatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
""" | 3,576 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
def __init__(self, config):
super().__init__()
image_size, patch_size = config.image_size, config.patch_size
num_channels, hidden_size = config.num_channels, config.embed_dim
image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
... | 3,576 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
def maybe_pad(self, pixel_values, height, width):
if width % self.patch_size[1] != 0:
pad_values = (0, self.patch_size[1] - width % self.patch_size[1])
pixel_values = nn.functional.pad(pixel_values, pad_values)
if height % self.patch_size[0] != 0:
pad_values = (0, 0, ... | 3,576 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
class SwinPatchMerging(nn.Module):
"""
Patch Merging Layer.
Args:
input_resolution (`Tuple[int]`):
Resolution of input feature.
dim (`int`):
Number of input channels.
norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`):
Normalization ... | 3,577 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
def forward(self, input_feature: torch.Tensor, input_dimensions: Tuple[int, int]) -> torch.Tensor:
height, width = input_dimensions
# `dim` is height * width
batch_size, dim, num_channels = input_feature.shape | 3,577 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
input_feature = input_feature.view(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::2, 0::2,... | 3,577 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
input_feature = self.norm(input_feature)
input_feature = self.reduction(input_feature)
return input_feature | 3,577 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
class SwinDropPath(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... | 3,578 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
class SwinSelfAttention(nn.Module):
def __init__(self, config, dim, num_heads, window_size):
super().__init__()
if dim % num_heads != 0:
raise ValueError(
f"The hidden size ({dim}) is not a multiple of the number of attention heads ({num_heads})"
)
se... | 3,579 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
# get pair-wise relative position index for each token inside the window
coords_h = torch.arange(self.window_size[0])
coords_w = torch.arange(self.window_size[1])
coords = torch.stack(meshgrid([coords_h, coords_w], indexing="ij"))
coords_flatten = torch.flatten(coords, 1)
relativ... | 3,579 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
self.query = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
self.key = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
self.value = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
self.dropout = nn.Dropout(config.attention... | 3,579 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores... | 3,579 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in SwinModel forward() function)
mask_shape = attention_mask.shape[0]
attention_scores = attention_scores.view(
batch_size // mask_shape, mask_shape, self.num_attention_heads, dim... | 3,579 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(new_context_layer_shape)
outputs = (context_layer, att... | 3,579 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
class SwinSelfOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, dim)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden... | 3,580 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
class SwinAttention(nn.Module):
def __init__(self, config, dim, num_heads, window_size):
super().__init__()
self.self = SwinSelfAttention(config, dim, num_heads, window_size)
self.output = SwinSelfOutput(config, dim)
self.pruned_heads = set()
def prune_heads(self, heads):
... | 3,581 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
... | 3,581 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
class SwinIntermediate(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, int(config.mlp_ratio * dim))
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_... | 3,582 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
class SwinOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(int(config.mlp_ratio * dim), dim)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.de... | 3,583 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
class SwinLayer(nn.Module):
def __init__(self, config, dim, input_resolution, num_heads, drop_path_rate=0.0, shift_size=0):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.shift_size = shift_size
self.window_size = config.window_size
self... | 3,584 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
def set_shift_and_window_size(self, input_resolution):
if min(input_resolution) <= self.window_size:
# if window size is larger than input resolution, we don't partition windows
self.shift_size = torch_int(0)
self.window_size = (
torch.min(torch.tensor(input_r... | 3,584 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
def get_attn_mask(self, height, width, dtype, device):
if self.shift_size > 0:
# calculate attention mask for SW-MSA
img_mask = torch.zeros((1, height, width, 1), dtype=dtype, device=device)
height_slices = (
slice(0, -self.window_size),
slice(... | 3,584 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
mask_windows = window_partition(img_mask, self.window_size)
mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_ma... | 3,584 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
def forward(
self,
hidden_states: torch.Tensor,
input_dimensions: Tuple[int, int],
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
always_partition: Optional[bool] = False,
) -> Tuple[torch.Tensor, torch.Tensor]:
if no... | 3,584 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
_, height_pad, width_pad, _ = hidden_states.shape
# cyclic shift
if self.shift_size > 0:
shifted_hidden_states = torch.roll(hidden_states, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
else:
shifted_hidden_states = hidden_states
# partition windows
... | 3,584 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
attention_windows = attention_output.view(-1, self.window_size, self.window_size, channels)
shifted_windows = window_reverse(attention_windows, self.window_size, height_pad, width_pad)
# reverse cyclic shift
if self.shift_size > 0:
attention_windows = torch.roll(shifted_windows, shi... | 3,584 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
layer_outputs = (layer_output, attention_outputs[1]) if output_attentions else (layer_output,)
return layer_outputs | 3,584 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
class SwinStage(nn.Module):
def __init__(self, config, dim, input_resolution, depth, num_heads, drop_path, downsample):
super().__init__()
self.config = config
self.dim = dim
self.blocks = nn.ModuleList(
[
SwinLayer(
config=config,
... | 3,585 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
def forward(
self,
hidden_states: torch.Tensor,
input_dimensions: Tuple[int, int],
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
always_partition: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
height, width = inp... | 3,585 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
hidden_states_before_downsampling = hidden_states
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(hidden_stat... | 3,585 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
class SwinEncoder(nn.Module):
def __init__(self, config, grid_size):
super().__init__()
self.num_layers = len(config.depths)
self.config = config
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]
self.layers = nn.ModuleList(
[
... | 3,586 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
def forward(
self,
hidden_states: torch.Tensor,
input_dimensions: Tuple[int, int],
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
output_hidden_states_before_downsampling: Opt... | 3,586 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
if output_hidden_states:
batch_size, _, hidden_size = hidden_states.shape
# rearrange b (h w) c -> b c h w
reshaped_hidden_state = hidden_states.view(batch_size, *input_dimensions, hidden_size)
reshaped_hidden_state = reshaped_hidden_state.permute(0, 3, 1, 2)
... | 3,586 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
input_dimensions,
layer_head_mask,
output_attentions,
... | 3,586 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
if output_hidden_states and output_hidden_states_before_downsampling:
batch_size, _, hidden_size = hidden_states_before_downsampling.shape
# rearrange b (h w) c -> b c h w
# here we use the original (not downsampled) height and width
reshaped_hidden_state ... | 3,586 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
reshaped_hidden_state = reshaped_hidden_state.permute(0, 3, 1, 2)
all_hidden_states += (hidden_states,)
all_reshaped_hidden_states += (reshaped_hidden_state,) | 3,586 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
if output_attentions:
all_self_attentions += layer_outputs[3:]
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return SwinEncoderOutput(
last_hidden_state=hidden_states,
hidden_sta... | 3,586 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
class SwinPreTrainedModel(PreTrainedModel):
"""
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"
supports_gradient_checkpoint... | 3,587 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
class SwinModel(SwinPreTrainedModel):
def __init__(self, config, add_pooling_layer=True, use_mask_token=False):
super().__init__(config)
self.config = config
self.num_layers = len(config.depths)
self.num_features = int(config.embed_dim * 2 ** (self.num_layers - 1))
self.embe... | 3,588 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
def _prune_heads(self, heads_to_prune):
"""
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].attention.prune_he... | 3,588 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
@add_start_docstrings_to_model_forward(SWIN_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SwinModelOutput,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self... | 3,588 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_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... | 3,588 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
# 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_head... | 3,588 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
sequence_output = encoder_outputs[0]
sequence_output = self.layernorm(sequence_output)
pooled_output = None
if self.pooler is not None:
pooled_output = self.pooler(sequence_output.transpose(1, 2))
pooled_output = torch.flatten(pooled_output, 1)
if not return_dic... | 3,588 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
class SwinForMaskedImageModeling(SwinPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.swin = SwinModel(config, add_pooling_layer=False, use_mask_token=True)
num_features = int(config.embed_dim * 2 ** (config.num_layers - 1))
self.decoder = nn.Sequential(
... | 3,589 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
@add_start_docstrings_to_model_forward(SWIN_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=SwinMaskedImageModelingOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
bool_masked_pos: Optional[torch.BoolTensor] = None,
... | 3,589 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
Examples:
```python
>>> from transformers import AutoImageProcessor, SwinForMaskedImageModeling
>>> import torch
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(u... | 3,589 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_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, 192, 192]
```"""
return_dict = return_dict if return_dict is not None else self.config.... | 3,589 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
sequence_output = outputs[0]
# Reshape to (batch_size, num_channels, height, width)
sequence_output = sequence_output.transpose(1, 2)
batch_size, num_channels, sequence_length = sequence_output.shape
height = width = math.floor(sequence_length**0.5)
sequence_output = sequence_out... | 3,589 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
masked_im_loss = None
if bool_masked_pos is not None:
size = self.config.image_size // self.config.patch_size
bool_masked_pos = bool_masked_pos.reshape(-1, size, size)
mask = (
bool_masked_pos.repeat_interleave(self.config.patch_size, 1)
.repea... | 3,589 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
return SwinMaskedImageModelingOutput(
loss=masked_im_loss,
reconstruction=reconstructed_pixel_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
reshaped_hidden_states=outputs.reshaped_hidden_states,
) | 3,589 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
class SwinForImageClassification(SwinPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.swin = SwinModel(config)
# Classifier head
self.classifier = (
nn.Linear(self.swin.num_features, config.num_label... | 3,590 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
@add_start_docstrings_to_model_forward(SWIN_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=SwinImageClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
self,
... | 3,590 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`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 | 3,590 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
outputs = self.swin(
pixel_values,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
)
pooled_output = o... | 3,590 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.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... | 3,590 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
return SwinImageClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
reshaped_hidden_states=outputs.reshaped_hidden_states,
) | 3,590 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
class SwinBackbone(SwinPreTrainedModel, BackboneMixin):
def __init__(self, config: SwinConfig):
super().__init__(config)
super()._init_backbone(config)
self.num_features = [config.embed_dim] + [int(config.embed_dim * 2**i) for i in range(len(config.depths))]
self.embeddings = SwinEm... | 3,591 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
def forward(
self,
pixel_values: torch.Tensor,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> BackboneOutput:
"""
Returns:
Examples:
```python
>>> from t... | 3,591 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
>>> inputs = processor(image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> feature_maps = outputs.feature_maps
>>> list(feature_maps[-1].shape)
[1, 768, 7, 7]
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
ou... | 3,591 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
hidden_states = outputs.reshaped_hidden_states
feature_maps = ()
for stage, hidden_state in zip(self.stage_names, hidden_states):
if stage in self.out_features:
batch_size, num_channels, height, width = hidden_state.shape
hidden_state = hidden_state.permute(0... | 3,591 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
return BackboneOutput(
feature_maps=feature_maps,
hidden_states=outputs.hidden_states if output_hidden_states else None,
attentions=outputs.attentions,
) | 3,591 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py |
class SwinConfig(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`SwinModel`]. It is used to instantiate a Swin
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will ... | 3,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/configuration_swin.py |
Args:
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 4):
The size (resolution) of each patch.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
... | 3,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/configuration_swin.py |
Whether or not a learnable bias should be added to the queries, keys and values.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings and encoder.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.... | 3,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/configuration_swin.py |
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the layer normalization layers.
encoder_stride (`int`, *optional*, defaults to 32):
Factor to increase the... | 3,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/configuration_swin.py |
If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
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... | 3,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/configuration_swin.py |
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