text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
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class Swin2SRPatchMerging(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`):
Normalizati... | 10,029 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.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 | 10,029 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.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,... | 10,029 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
input_feature = self.reduction(input_feature)
input_feature = self.norm(input_feature)
return input_feature | 10,029 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
class Swin2SRSelfAttention(nn.Module):
def __init__(self, config, dim, num_heads, window_size, pretrained_window_size=[0, 0]):
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_... | 10,030 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
self.num_attention_heads = num_heads
self.attention_head_size = int(dim / num_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.window_size = (
window_size if isinstance(window_size, collections.abc.Iterable) else (window_size, window_size)
... | 10,030 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
# get relative_coords_table
relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.int64).float()
relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.int64).float()
relative_coords_table = (
torch.stack(... | 10,030 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
torch.sign(relative_coords_table) * torch.log2(torch.abs(relative_coords_table) + 1.0) / math.log2(8)
)
# set to same dtype as mlp weight
relative_coords_table = relative_coords_table.to(next(self.continuous_position_bias_mlp.parameters()).dtype)
self.register_buffer("relative_coords_tab... | 10,030 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.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... | 10,030 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.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=False)
self.value = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
self.dropout = nn.Dropout(config.attention_probs_drop... | 10,030 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.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) | 10,030 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
# cosine attention
attention_scores = nn.functional.normalize(query_layer, dim=-1) @ nn.functional.normalize(
key_layer, dim=-1
).transpose(-2, -1)
logit_scale = torch.clamp(self.logit_scale, max=math.log(1.0 / 0.01)).exp()
attention_scores = attention_scores * logit_scale
... | 10,030 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
attention_scores = attention_scores + relative_position_bias.unsqueeze(0) | 10,030 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in Swin2SRModel forward() function)
mask_shape = attention_mask.shape[0]
attention_scores = attention_scores.view(
batch_size // mask_shape, mask_shape, self.num_attention_heads, ... | 10,030 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
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] + ... | 10,030 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
class Swin2SRSelfOutput(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:
hid... | 10,031 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
class Swin2SRAttention(nn.Module):
def __init__(self, config, dim, num_heads, window_size, pretrained_window_size=0):
super().__init__()
self.self = Swin2SRSelfAttention(
config=config,
dim=dim,
num_heads=num_heads,
window_size=window_size,
... | 10,032 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Upda... | 10,032 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
self_outputs = self.self(hidden_states, attention_ma... | 10,032 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
class Swin2SRIntermediate(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.intermedia... | 10,033 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
class Swin2SROutput(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... | 10,034 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
class Swin2SRLayer(nn.Module):
def __init__(
self, config, dim, input_resolution, num_heads, drop_path_rate=0.0, shift_size=0, pretrained_window_size=0
):
super().__init__()
self.input_resolution = input_resolution
window_size, shift_size = self._compute_window_shift(
... | 10,035 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
self.drop_path = Swin2SRDropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
self.intermediate = Swin2SRIntermediate(config, dim)
self.output = Swin2SROutput(config, dim)
self.layernorm_after = nn.LayerNorm(dim, eps=config.layer_norm_eps) | 10,035 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
def _compute_window_shift(self, target_window_size, target_shift_size) -> Tuple[Tuple[int, int], Tuple[int, int]]:
window_size = [r if r <= w else w for r, w in zip(self.input_resolution, target_window_size)]
shift_size = [0 if r <= w else s for r, w, s in zip(self.input_resolution, window_size, target_... | 10,035 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
def get_attn_mask(self, height, width, dtype):
if self.shift_size > 0:
# calculate attention mask for shifted window multihead self attention
img_mask = torch.zeros((1, height, width, 1), dtype=dtype)
height_slices = (
slice(0, -self.window_size),
... | 10,035 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.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... | 10,035 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
def forward(
self,
hidden_states: torch.Tensor,
input_dimensions: Tuple[int, int],
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, torch.Tensor]:
height, width = input_dimensions
batch_size, _,... | 10,035 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
# partition windows
hidden_states_windows = window_partition(shifted_hidden_states, self.window_size)
hidden_states_windows = hidden_states_windows.view(-1, self.window_size * self.window_size, channels)
attn_mask = self.get_attn_mask(height_pad, width_pad, dtype=hidden_states.dtype)
if ... | 10,035 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
# reverse cyclic shift
if self.shift_size > 0:
attention_windows = torch.roll(shifted_windows, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
else:
attention_windows = shifted_windows
was_padded = pad_values[3] > 0 or pad_values[5] > 0
if was_padded:
... | 10,035 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
class Swin2SRStage(nn.Module):
"""
This corresponds to the Residual Swin Transformer Block (RSTB) in the original implementation.
"""
def __init__(self, config, dim, input_resolution, depth, num_heads, drop_path, pretrained_window_size=0):
super().__init__()
self.config = config
... | 10,036 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
if config.resi_connection == "1conv":
self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
elif config.resi_connection == "3conv":
# to save parameters and memory
self.conv = nn.Sequential(
nn.Conv2d(dim, dim // 4, 3, 1, 1),
nn.LeakyReLU(negative_slope=0.2... | 10,036 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
height, width = input_dimensions
for i, layer_module in enumerate(self.layers):
layer_head_mask = head_mask[i] if head_mask is not None else None
layer_outputs = layer_module(hidden_states, input_dimensions, layer_head_mask, output_attentions)
hidden_states = layer_outputs[... | 10,036 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
class Swin2SREncoder(nn.Module):
def __init__(self, config, grid_size):
super().__init__()
self.num_stages = len(config.depths)
self.config = config
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]
self.stages = nn.ModuleList(
... | 10,037 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.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,
return_dict: Optional[bool] = True,
) -> ... | 10,037 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
stage_module.__call__, hidden_states, input_dimensions, layer_head_mask, output_attentions
)
else:
layer_outputs = stage_module(hidden_stat... | 10,037 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
return Swin2SREncoderOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
) | 10,037 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
class Swin2SRPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Swin2SRConfig
base_model_prefix = "swin2sr"
main_input_name = "pixel_values"
supports_gradient_c... | 10,038 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
class Swin2SRModel(Swin2SRPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
if config.num_channels == 3 and config.num_channels_out == 3:
rgb_mean = (0.4488, 0.4371, 0.4040)
self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)... | 10,039 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
def get_input_embeddings(self):
return self.embeddings.patch_embeddings
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, head... | 10,039 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
@add_start_docstrings_to_model_forward(SWIN2SR_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
s... | 10,039 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.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... | 10,039 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
sequence_output = encoder_outputs[0]
sequence_output = self.layernorm(sequence_output)
sequence_output = self.patch_unembed(sequence_output, (height, width))
sequence_output = self.conv_after_body(sequence_output) + embeddings
if not return_dict:
output = (sequence_output,)... | 10,039 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
class Upsample(nn.Module):
"""Upsample module.
Args:
scale (`int`):
Scale factor. Supported scales: 2^n and 3.
num_features (`int`):
Channel number of intermediate features.
"""
def __init__(self, scale, num_features):
super().__init__()
self.sc... | 10,040 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
def forward(self, hidden_state):
if (self.scale & (self.scale - 1)) == 0:
for i in range(int(math.log(self.scale, 2))):
hidden_state = self.__getattr__(f"convolution_{i}")(hidden_state)
hidden_state = self.__getattr__(f"pixelshuffle_{i}")(hidden_state)
elif s... | 10,040 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
class UpsampleOneStep(nn.Module):
"""UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
Used in lightweight SR to save parameters.
Args:
scale (int):
Scale factor. Supported scales: 2^n and 3.
in_channels (int):
Ch... | 10,041 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
class PixelShuffleUpsampler(nn.Module):
def __init__(self, config, num_features):
super().__init__()
self.conv_before_upsample = nn.Conv2d(config.embed_dim, num_features, 3, 1, 1)
self.activation = nn.LeakyReLU(inplace=True)
self.upsample = Upsample(config.upscale, num_features)
... | 10,042 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
class NearestConvUpsampler(nn.Module):
def __init__(self, config, num_features):
super().__init__()
if config.upscale != 4:
raise ValueError("The nearest+conv upsampler only supports an upscale factor of 4 at the moment.")
self.conv_before_upsample = nn.Conv2d(config.embed_dim, ... | 10,043 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
def forward(self, sequence_output):
sequence_output = self.conv_before_upsample(sequence_output)
sequence_output = self.activation(sequence_output)
sequence_output = self.lrelu(
self.conv_up1(torch.nn.functional.interpolate(sequence_output, scale_factor=2, mode="nearest"))
)
... | 10,043 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
class PixelShuffleAuxUpsampler(nn.Module):
def __init__(self, config, num_features):
super().__init__()
self.upscale = config.upscale
self.conv_bicubic = nn.Conv2d(config.num_channels, num_features, 3, 1, 1)
self.conv_before_upsample = nn.Conv2d(config.embed_dim, num_features, 3, 1,... | 10,044 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
def forward(self, sequence_output, bicubic, height, width):
bicubic = self.conv_bicubic(bicubic)
sequence_output = self.conv_before_upsample(sequence_output)
sequence_output = self.activation(sequence_output)
aux = self.conv_aux(sequence_output)
sequence_output = self.conv_after_... | 10,044 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
class Swin2SRForImageSuperResolution(Swin2SRPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.swin2sr = Swin2SRModel(config)
self.upsampler = config.upsampler
self.upscale = config.upscale | 10,045 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
# Upsampler
num_features = 64
if self.upsampler == "pixelshuffle":
self.upsample = PixelShuffleUpsampler(config, num_features)
elif self.upsampler == "pixelshuffle_aux":
self.upsample = PixelShuffleAuxUpsampler(config, num_features)
elif self.upsampler == "pixelsh... | 10,045 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
@add_start_docstrings_to_model_forward(SWIN2SR_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=ImageSuperResolutionOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
la... | 10,045 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
>>> processor = AutoImageProcessor.from_pretrained("caidas/swin2SR-classical-sr-x2-64")
>>> model = Swin2SRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64")
>>> url = "https://huggingface.co/spaces/jjourney1125/swin2sr/resolve/main/samples/butterfly.jpg"
>>> image ... | 10,045 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
loss = None
if labels is not None:
raise NotImplementedError("Training is not supported at the moment")
height, width = pixel_values.shape[2:]
if self.config.upsampler == "pixelshuffle_aux":
bicubic = nn.functional.interpolate(
pixel_values,
... | 10,045 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
if self.upsampler in ["pixelshuffle", "pixelshuffledirect", "nearest+conv"]:
reconstruction = self.upsample(sequence_output)
elif self.upsampler == "pixelshuffle_aux":
reconstruction, aux = self.upsample(sequence_output, bicubic, height, width)
aux = aux / self.swin2sr.img_ra... | 10,045 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py |
class Swin2SRImageProcessor(BaseImageProcessor):
r"""
Constructs a Swin2SR image processor.
Args:
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
parameter in the... | 10,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/image_processing_swin2sr.py |
def pad(
self,
image: np.ndarray,
size: int,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
):
"""
Pad an image to make the height and width divisible by `size`. | 10,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/image_processing_swin2sr.py |
Args:
image (`np.ndarray`):
Image to pad.
size (`int`):
The size to make the height and width divisible by.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format for the output image. If unset, the channel ... | 10,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/image_processing_swin2sr.py |
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. | 10,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/image_processing_swin2sr.py |
Returns:
`np.ndarray`: The padded image.
"""
old_height, old_width = get_image_size(image, input_data_format)
pad_height = (old_height // size + 1) * size - old_height
pad_width = (old_width // size + 1) * size - old_width
return pad(
image,
(... | 10,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/image_processing_swin2sr.py |
@filter_out_non_signature_kwargs()
def preprocess(
self,
images: ImageInput,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_pad: Optional[bool] = None,
pad_size: Optional[int] = None,
return_tensors: Optional[Union[str, TensorTyp... | 10,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/image_processing_swin2sr.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_rescale (`bool`, *optional*, defaults to `self.do_resc... | 10,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/image_processing_swin2sr.py |
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of typ, input_data_format=input_data_formate
`tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`:... | 10,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/image_processing_swin2sr.py |
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels,... | 10,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/image_processing_swin2sr.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... | 10,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/image_processing_swin2sr.py |
if input_data_format is None:
# We assume that all images have the same channel dimension format.
input_data_format = infer_channel_dimension_format(images[0])
if do_rescale:
images = [
self.rescale(image=image, scale=rescale_factor, input_data_format=input_d... | 10,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/image_processing_swin2sr.py |
class MBartTokenizer(PreTrainedTokenizer):
"""
Construct an MBART tokenizer.
Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
[SentencePiece](https://github.com/google/sentencepiece).
The tokenization method is `<tokens> <eos> <language code>` for source language documents, and `... | 10,047 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart.py |
prefix_tokens: List[int] = []
suffix_tokens: List[int] = []
def __init__(
self,
vocab_file,
bos_token="<s>",
eos_token="</s>",
sep_token="</s>",
cls_token="<s>",
unk_token="<unk>",
pad_token="<pad>",
mask_token="<mask>",
tokenizer_... | 10,047 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart.py |
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | ... | 10,047 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart.py |
self.sp_model_size = len(self.sp_model)
self.lang_code_to_id = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(FAIRSEQ_LANGUAGE_CODES)
}
self.id_to_lang_code = {v: k for k, v in self.lang_code_to_id.items()}
self.fairseq_tokens_to_ids["<mask>"] =... | 10,047 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart.py |
super().__init__(
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
sep_token=sep_token,
cls_token=cls_token,
pad_token=pad_token,
mask_token=mask_token,
tokenizer_file=None,
src_lang=src_lang,
... | 10,047 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart.py |
# for backward compatibility
if not hasattr(self, "sp_model_kwargs"):
self.sp_model_kwargs = {}
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
@property
def vocab_size(self):
return len(s... | 10,047 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart.py |
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens ... | 10,047 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart.py |
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
prefix_ones = [1] * len(self.prefix_tokens)
suffix_ones = [1] * len(self.suffix_tokens)
if token_ids... | 10,047 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart.py |
BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a
separator.
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optio... | 10,047 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart.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. mBART does not
make use of token type ids, therefore a li... | 10,047 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart.py |
def _build_translation_inputs(
self, raw_inputs, return_tensors: str, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs
):
"""Used by translation pipeline, to prepare inputs for the generate function"""
if src_lang is None or tgt_lang is None:
raise ValueError("Tra... | 10,047 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart.py |
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
spm_id = self.sp_model.PieceToId(token)
# Need to return unknown token if the SP model returned 0
... | 10,047 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart.py |
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_vocab_file = os.path.join(
save_directory, (... | 10,047 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart.py |
def prepare_seq2seq_batch(
self,
src_texts: List[str],
src_lang: str = "en_XX",
tgt_texts: Optional[List[str]] = None,
tgt_lang: str = "ro_RO",
**kwargs,
) -> BatchEncoding:
self.src_lang = src_lang
self.tgt_lang = tgt_lang
return super().prepa... | 10,047 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart.py |
def set_tgt_lang_special_tokens(self, lang: str) -> None:
"""Reset the special tokens to the target language setting. No prefix and suffix=[eos, tgt_lang_code]."""
self.cur_lang_code = self.lang_code_to_id[lang]
self.prefix_tokens = []
self.suffix_tokens = [self.eos_token_id, self.cur_la... | 10,047 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart.py |
class MBartConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MBartModel`]. It is used to instantiate an MBART
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar c... | 10,048 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/configuration_mbart.py |
Args:
vocab_size (`int`, *optional*, defaults to 50265):
Vocabulary size of the MBART model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`MBartModel`] or [`TFMBartModel`].
d_model (`int`, *optional*, defaults to 1024):
... | 10,048 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/configuration_mbart.py |
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
activation_function (`str` or `function`, *optional*, defaults to `"gelu"... | 10,048 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/configuration_mbart.py |
The dropout ratio for classifier.
max_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
init_std (`float`, *optional*, def... | 10,048 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/configuration_mbart.py |
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models)
forced_eos_token_id (`int`, *optional*, defaults to 2):
The id of the token to force as the last generated token when `max_length` is reached... | 10,048 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/configuration_mbart.py |
Example:
```python
>>> from transformers import MBartConfig, MBartModel
>>> # Initializing a MBART facebook/mbart-large-cc25 style configuration
>>> configuration = MBartConfig()
>>> # Initializing a model (with random weights) from the facebook/mbart-large-cc25 style configuration
>>> model ... | 10,048 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/configuration_mbart.py |
def __init__(
self,
vocab_size=50265,
max_position_embeddings=1024,
encoder_layers=12,
encoder_ffn_dim=4096,
encoder_attention_heads=16,
decoder_layers=12,
decoder_ffn_dim=4096,
decoder_attention_heads=16,
encoder_layerdrop=0.0,
dec... | 10,048 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/configuration_mbart.py |
self.encoder_attention_heads = encoder_attention_heads
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_drop... | 10,048 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/configuration_mbart.py |
**kwargs,
) | 10,048 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/configuration_mbart.py |
class MBartOnnxConfig(OnnxSeq2SeqConfigWithPast):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
common_inputs = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
... | 10,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/configuration_mbart.py |
if self.use_past:
self.fill_with_past_key_values_(common_inputs, direction="inputs")
elif self.task == "causal-lm":
# TODO: figure this case out.
common_inputs = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
... | 10,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/configuration_mbart.py |
("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
]
) | 10,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/configuration_mbart.py |
return common_inputs
@property
def outputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
common_outputs = super().outputs
else:
common_outputs = super(OnnxConfigWithPast, self).outputs
if self.use_past:
n... | 10,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/configuration_mbart.py |
def _generate_dummy_inputs_for_default_and_seq2seq_lm(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
encoder_inputs = self._generate_dumm... | 10,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/configuration_mbart.py |
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
else:
import torch
batch, encoder_seq_length = common_inputs["input_ids"].shape
decoder_seq_length = common... | 10,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/configuration_mbart.py |
common_inputs["decoder_attention_mask"] = torch.cat(
[common_inputs["decoder_attention_mask"], torch.ones(batch, decoder_past_length)], dim=1
)
common_inputs["past_key_values"] = []
# If the number of encoder and decoder layers are present in the model configuration,... | 10,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/configuration_mbart.py |
for _ in range(min_num_layers):
common_inputs["past_key_values"].append(
(
torch.zeros(decoder_shape),
torch.zeros(decoder_shape),
torch.zeros(encoder_shape),
torch.zeros(encoder_shape),
... | 10,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/configuration_mbart.py |
def _generate_dummy_inputs_for_causal_lm(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
common_inputs = self._generate_dummy_inputs_for_s... | 10,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/configuration_mbart.py |
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