text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
torch.LongTensor: Output coarse acoustics tokens.
If `return_output_lengths=True`:
`Tuple(torch.Tensor, torch.Tensor): The output coarse acoustics tokens, and the length of each sample
of the batch.
""" | 2,896 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
if semantic_generation_config is None:
raise ValueError("`semantic_generation_config` has to be provided")
if coarse_generation_config is None:
raise ValueError("`coarse_generation_config` has to be provided")
max_coarse_input_length = coarse_generation_config.max_coarse_input_... | 2,896 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
semantic_to_coarse_ratio = (
coarse_generation_config.coarse_rate_hz
/ semantic_generation_config.semantic_rate_hz
* coarse_generation_config.n_coarse_codebooks
)
max_semantic_history = int(np.floor(max_coarse_history / semantic_to_coarse_ratio))
output_lengt... | 2,896 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
x_semantic_history, x_coarse = self.preprocess_histories(
history_prompt=history_prompt,
max_coarse_history=max_coarse_history,
semantic_to_coarse_ratio=semantic_to_coarse_ratio,
batch_size=batch_size,
semantic_generation_config=semantic_generation_config,
... | 2,896 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
# pad from right side
input_coarse = semantic_output[:, np.max([0, semantic_idx - max_semantic_history]) :]
input_coarse = input_coarse[:, :max_coarse_input_length]
input_coarse = F.pad(
input_coarse,
(0, max_coarse_input_length - input_coarse.shape[-1... | 2,896 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
output_coarse = super().generate(
input_coarse,
logits_processor=[alternatingLogitsProcessor],
max_new_tokens=min(sliding_window_len, max_generated_len - total_generated_len),
generation_config=coarse_generation_config,
**kwargs,
... | 2,896 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
class BarkFineModel(BarkPreTrainedModel):
base_model_prefix = "fine_acoustics"
config_class = BarkFineConfig
main_input_name = "codebook_idx"
def __init__(self, config):
# non-causal gpt-like model with one embedding layer and one lm_head for each codebook of Encodec
super().__init__(co... | 2,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
self.layernorm_final = nn.LayerNorm(config.hidden_size)
self.lm_heads = nn.ModuleList(
[
nn.Linear(config.hidden_size, config.output_vocab_size, bias=False)
for _ in range(config.n_codes_given, config.n_codes_total)
]
)
self.gradient_check... | 2,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
def _resize_token_embeddings(self, new_num_tokens, pad_to_multiple_of=None):
old_embeddings_list = self.get_input_embeddings()
new_embeddings_list = nn.ModuleList(
[
self._get_resized_embeddings(old_embeddings, new_num_tokens, pad_to_multiple_of)
for old_embed... | 2,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
def resize_token_embeddings(
self, new_num_tokens: Optional[int] = None, pad_to_multiple_of: Optional[int] = None
) -> nn.Embedding:
"""
Resizes input token embeddings matrix of the model if `new_num_tokens != config.vocab_size`.
Takes care of tying weights embeddings afterwards if ... | 2,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
`>= 7.5` (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128. For more
details about this, or help on choosing the correct value for resizing, refer to t... | 2,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
# Update base model and current model config
self.config.output_vocab_size = model_embeds[0].weight.shape[0]
self.config.vocab_size = model_embeds[0].weight.shape[0]
self.output_vocab_size = model_embeds[0].weight.shape[0]
self.vocab_size = model_embeds[0].weight.shape[0]
# Tie ... | 2,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
def tie_weights(self):
"""
Tie the weights between the input embeddings list and the output embeddings list.
If the `torchscript` flag is set in the configuration, can't handle parameter sharing so we are cloning the
weights instead.
"""
if getattr(self.config, "tie_word... | 2,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
@add_start_docstrings_to_model_forward(BARK_FINE_INPUTS_DOCSTRING)
def forward(
self,
codebook_idx: int, # an additionnal idx corresponding to the id of the codebook that will be predicted
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
... | 2,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 2,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
loss = None
if labels is not None:
raise NotImplementedError("Training is not implemented yet")
if codebook_idx == 0:
raise ValueError("Cannot predict 0th codebook - 0th codebook should be predicted by the coarse model")
if input_ids is not None and input_embeds is not ... | 2,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
# forward the GPT model itself
input_embeds = [
input_embeds_layer(input_ids[:, :, i]).unsqueeze(-1)
for i, input_embeds_layer in enumerate(self.input_embeds_layers)
] # token embeddings of shape (b, t, n_embd)
input_embeds = torch.cat(input_embeds, d... | 2,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
# Attention mask.
if attention_mask is not None:
if batch_size <= 0:
raise ValueError("batch_size has to be defined and > 0")
if self._use_flash_attention_2:
attention_mask = attention_mask if 0 in attention_mask else None
else:
... | 2,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
for i, block in enumerate(self.layers):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
outputs = block(
hidden_states,
attention_mask=attention_mask,
head_mask=head_mask[i],
output... | 2,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
return MaskedLMOutput(
loss=loss,
logits=logits,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
def generate(
self,
coarse_output: torch.Tensor,
semantic_generation_config: BarkSemanticGenerationConfig = None,
... | 2,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
Args:
coarse_output (`torch.Tensor` of shape (batch_size, seq_len)):
Input coarse acoustics ids, i.e the output of `BarkCoarseModel.generate`.
semantic_generation_config (`BarkSemanticGenerationConfig`):
Generation config indicating how to generate the semantic to... | 2,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
if semantic_generation_config is None:
raise ValueError("`semantic_generation_config` has to be provided") | 2,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
if coarse_generation_config is None:
raise ValueError("`coarse_generation_config` has to be provided")
if fine_generation_config is None:
raise ValueError("`fine_generation_config` has to be provided")
# since we don't really use GenerationConfig through the fine model (autoenc... | 2,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
# brings ids into the range [0, codebook_size -1]
coarse_output = torch.remainder(coarse_output - semantic_generation_config.semantic_vocab_size, codebook_size)
batch_size = coarse_output.shape[0]
if history_prompt is not None:
x_fine_history = torch.repeat_interleave(history_prompt... | 2,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
# len of the fine_history that has been added to fine_input
n_history = x_fine_history[:, -max_fine_history_length:, :].shape[1]
else:
n_history = 0
n_remove_from_end = 0
# need to pad if too short (since non-causal model)
if fine_input.shape[1] < max_fine_input_... | 2,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
n_loops = (coarse_output.shape[1] - (max_fine_input_length - n_history)) / max_fine_history_length
n_loops = int(np.ceil(n_loops))
n_loops = max(0, n_loops) + 1
for n_outer in range(n_loops):
start_idx = min([n_outer * max_fine_history_length, fine_input.shape[1] - max_fine_input_le... | 2,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
start_fill_idx = min(
[n_history + n_outer * max_fine_history_length, fine_input.shape[1] - max_fine_history_length]
)
rel_start_fill_idx = start_fill_idx - start_idx
input_buffer = fine_input[:, start_idx : start_idx + max_fine_input_length, :]
for n_inne... | 2,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
# reshape to 2D: (batch_size, seq_len, codebook_size) -> (batch_size*seq_len, codebook_size)
probs = probs.reshape((-1, codebook_size))
# multinomial then reshape : (batch_size*seq_len)-> (batch_size,seq_len)
codebook_preds = torch.multinomial(probs, num_sampl... | 2,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
# transfer into fine_input
for n_inner in range(n_coarse, fine_generation_config.n_fine_codebooks):
fine_input[
:, start_fill_idx : start_fill_idx + (max_fine_input_length - rel_start_fill_idx), n_inner
] = input_buffer[:, rel_start_fill_idx:, n_inner]
... | 2,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
class BarkModel(BarkPreTrainedModel):
config_class = BarkConfig
def __init__(self, config):
super().__init__(config)
self.semantic = BarkSemanticModel(config.semantic_config)
self.coarse_acoustics = BarkCoarseModel(config.coarse_acoustics_config)
self.fine_acoustics = BarkFineM... | 2,898 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
@property
def device(self) -> torch.device:
"""
`torch.device`: The device on which the module is (assuming that all the module parameters are on the same
device).
"""
# for bark_model, device must be verified on its sub-models
# if has _hf_hook, has been offloaded so... | 2,898 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
def enable_cpu_offload(self, gpu_id: Optional[int] = 0):
r"""
Offloads all sub-models to CPU using accelerate, reducing memory usage with a low impact on performance. This
method moves one whole sub-model at a time to the GPU when it is used, and the sub-model remains in GPU until
the ne... | 2,898 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
# this layer is used outside the first foward pass of semantic so need to be loaded before semantic
self.semantic.input_embeds_layer, _ = cpu_offload_with_hook(self.semantic.input_embeds_layer, device)
hook = None
for cpu_offloaded_model in [
self.semantic,
self.coarse_a... | 2,898 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
if output_lengths is not None:
# encodec uses LSTMs which behaves differently with appended padding
# decoding with encodec takes around 0.1% of the total generation time
# to keep generation quality, we break batching
out = [sample[:, :l].unsqueeze(0) for (sample, l) in ... | 2,898 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
Args:
input_ids (`Optional[torch.Tensor]` of shape (batch_size, seq_len), *optional*):
Input ids. Will be truncated up to 256 tokens. Note that the output audios will be as long as the
longest generation among the batch.
history_prompt (`Optional[Dict[str,torch.Te... | 2,898 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
This means you can, for example, specify a generation strategy for all sub-models except one.
return_output_lengths (`bool`, *optional*):
Whether or not to return the waveform lengths. Useful when batching.
Returns:
By default:
- **audio_waveform** (`torch... | 2,898 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
>>> # To add a voice preset, you can pass `voice_preset` to `BarkProcessor.__call__(...)`
>>> voice_preset = "v2/en_speaker_6"
>>> inputs = processor("Hello, my dog is cute, I need him in my life", voice_preset=voice_preset)
>>> audio_array = model.generate(**inputs, semantic_max_new_tokens=10... | 2,898 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
kwargs_semantic = {
# if "attention_mask" is set, it should not be passed to CoarseModel and FineModel
"attention_mask": kwargs.pop("attention_mask", None),
"min_eos_p": kwargs.pop("min_eos_p", None),
}
kwargs_coarse = {}
kwargs_fine = {}
for key, valu... | 2,898 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
if key not in kwargs_coarse:
kwargs_coarse[key] = value
if key not in kwargs_fine:
kwargs_fine[key] = value | 2,898 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
# 1. Generate from the semantic model
if "generation_config" in kwargs_semantic:
kwargs_semantic.pop("generation_config")
semantic_output = self.semantic.generate(
input_ids,
history_prompt=history_prompt,
semantic_generation_config=semantic_generation_con... | 2,898 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
output_lengths = None
if return_output_lengths:
coarse_output, output_lengths = coarse_output
# (batch_size, seq_len*coarse_codebooks) -> (batch_size, seq_len)
output_lengths = output_lengths // coarse_generation_config.n_coarse_codebooks
# 3. "generate" from the fin... | 2,898 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
if getattr(self, "fine_acoustics_hook", None) is not None:
# Manually offload fine_acoustics to CPU
# and load codec_model to GPU
# since bark doesn't use codec_model forward pass
self.fine_acoustics_hook.offload()
self.codec_model = self.codec_model.to(self.d... | 2,898 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
@classmethod
def _check_and_enable_flash_attn_2(
cls,
config,
torch_dtype: Optional[torch.dtype] = None,
device_map: Optional[Union[str, Dict[str, int]]] = None,
hard_check_only: bool = False,
check_device_map: bool = False,
):
"""
`_check_and_enab... | 2,898 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
The method checks if the current setup is compatible with Flash Attention as it requires the model to be in
half precision and not ran on CPU.
If all checks pass and `hard_check_only` is False, the method will set the config attribute `_attn_implementation` to "flash_attention_2" so that the model
... | 2,898 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
class TFConvNextV2DropPath(keras.layers.Layer):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
References:
(1) github.com:rwightman/pytorch-image-models
"""
def __init__(self, drop_path: float, **kwargs):
super().__init__(**kwargs)
se... | 2,899 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
class TFConvNextV2GRN(keras.layers.Layer):
"""GRN (Global Response Normalization) layer"""
def __init__(self, config: ConvNextV2Config, dim: int, **kwargs):
super().__init__(**kwargs)
self.dim = dim
def build(self, input_shape: tf.TensorShape = None):
# PT's `nn.Parameters` must be... | 2,900 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
def call(self, hidden_states: tf.Tensor):
global_features = tf.norm(hidden_states, ord="euclidean", axis=(1, 2), keepdims=True)
norm_features = global_features / (tf.reduce_mean(global_features, axis=-1, keepdims=True) + 1e-6)
hidden_states = self.weight * (hidden_states * norm_features) + self.... | 2,900 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
class TFConvNextV2Embeddings(keras.layers.Layer):
"""This class is comparable to (and inspired by) the SwinEmbeddings class
found in src/transformers/models/swin/modeling_swin.py.
"""
def __init__(self, config: ConvNextV2Config, **kwargs):
super().__init__(**kwargs)
self.patch_embedding... | 2,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
tf.debugging.assert_equal(
shape_list(pixel_values)[1],
self.num_channels,
message="Make sure that the channel dimension of the pixel values match with the one set in the configuration.",
)
# When running on CPU, `keras.layers.Conv2D` doesn't support `NCHW` format.
... | 2,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "patch_embeddings", None) is not None:
with tf.name_scope(self.patch_embeddings.name):
self.patch_embeddings.build([None, None, None, self.config.num_channels])
... | 2,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
class TFConvNextV2Layer(keras.layers.Layer):
"""This corresponds to the `Block` class in the original implementation.
There are two equivalent implementations: [DwConv, LayerNorm (channels_first), Conv, GELU,1x1 Conv]; all in (N, C,
H, W) (2) [DwConv, Permute to (N, H, W, C), LayerNorm (channels_last), Lin... | 2,902 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
def __init__(self, config: ConvNextV2Config, dim: int, drop_path: float = 0.0, **kwargs):
super().__init__(**kwargs)
self.dim = dim
self.config = config
self.dwconv = keras.layers.Conv2D(
filters=dim,
kernel_size=7,
padding="same",
groups=d... | 2,902 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
self.grn = TFConvNextV2GRN(config, 4 * dim, dtype=tf.float32, name="grn")
self.pwconv2 = keras.layers.Dense(
units=dim,
kernel_initializer=get_initializer(config.initializer_range),
bias_initializer=keras.initializers.Zeros(),
name="pwconv2",
)
# U... | 2,902 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
def call(self, hidden_states, training=False):
input = hidden_states
x = self.dwconv(hidden_states)
x = self.layernorm(x)
x = self.pwconv1(x)
x = self.act(x)
x = self.grn(x)
x = self.pwconv2(x)
x = self.drop_path(x, training=training)
x = input + x... | 2,902 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dwconv", None) is not None:
with tf.name_scope(self.dwconv.name):
self.dwconv.build([None, None, None, self.dim])
if getattr(self, "layernorm", None) is no... | 2,902 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
with tf.name_scope(self.drop_path.name):
self.drop_path.build(None) | 2,902 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
class TFConvNextV2Stage(keras.layers.Layer):
"""ConvNextV2 stage, consisting of an optional downsampling layer + multiple residual blocks.
Args:
config (`ConvNextV2V2Config`):
Model configuration class.
in_channels (`int`):
Number of input channels.
out_channels ... | 2,903 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
def __init__(
self,
config: ConvNextV2Config,
in_channels: int,
out_channels: int,
kernel_size: int = 2,
stride: int = 2,
depth: int = 2,
drop_path_rates: Optional[List[float]] = None,
**kwargs,
):
super().__init__(**kwargs)
if ... | 2,903 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
kernel_size=kernel_size,
strides=stride,
kernel_initializer=get_initializer(config.initializer_range),
bias_initializer=keras.initializers.Zeros(),
name="downsampling_layer.1",
),
]
else:
self... | 2,903 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
drop_path_rates = drop_path_rates or [0.0] * depth
self.layers = [
TFConvNextV2Layer(
config,
dim=out_channels,
drop_path=drop_path_rates[j],
name=f"layers.{j}",
)
for j in range(depth)
]
self.in_... | 2,903 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "layers", None) is not None:
for layer in self.layers:
with tf.name_scope(layer.name):
layer.build(None)
if self.in_channels != self.out... | 2,903 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
class TFConvNextV2Encoder(keras.layers.Layer):
def __init__(self, config: ConvNextV2Config, **kwargs):
super().__init__(**kwargs)
self.stages = []
drop_path_rates = tf.linspace(0.0, config.drop_path_rate, sum(config.depths))
drop_path_rates = tf.split(drop_path_rates, config.depths)
... | 2,904 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
def call(
self,
hidden_states: tf.Tensor,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> Union[Tuple, TFBaseModelOutputWithNoAttention]:
all_hidden_states = () if output_hidden_states else None
for i, layer_module in enumerate(... | 2,904 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
class TFConvNextV2MainLayer(keras.layers.Layer):
config_class = ConvNextV2Config
def __init__(self, config: ConvNextV2Config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embeddings = TFConvNextV2Embeddings(config, name="embeddings")
self.encoder = TFConvNext... | 2,905 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
@unpack_inputs
def call(
self,
pixel_values: TFModelInputType | None = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
output_hidden_states =... | 2,905 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
# Change to NCHW output format have uniformity in the modules
pooled_output = self.pooler(last_hidden_state)
last_hidden_state = tf.transpose(last_hidden_state, perm=(0, 3, 1, 2))
pooled_output = self.layernorm(pooled_output)
# Change the other hidden state outputs to NCHW as well
... | 2,905 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.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:
... | 2,905 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
class TFConvNextV2PreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ConvNextV2Config
base_model_prefix = "convnextv2"
main_input_name = "pixel_values" | 2,906 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
class TFConvNextV2Model(TFConvNextV2PreTrainedModel):
def __init__(self, config: ConvNextV2Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.convnextv2 = TFConvNextV2MainLayer(config, name="convnextv2") | 2,907 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(CONVNEXTV2_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFBaseModelOutputWithPoolingAndNoAttention,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECT... | 2,907 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
outputs = self.convnextv2(
pixel_values=pixel_values,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
if not return_dict:
return outputs[:]
return TFBaseModelOutputWithPoolingAndNoAttention(
... | 2,907 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
class TFConvNextV2ForImageClassification(TFConvNextV2PreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config: ConvNextV2Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.convnextv2 = TFConvNextV2MainLayer(confi... | 2,908 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(CONVNEXTV2_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=TFImageClassifierOutputWithNoAttention,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
... | 2,908 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
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 else self.config.use_r... | 2,908 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
outputs = self.convnextv2(
pixel_values,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
pooled_output = outputs.po... | 2,908 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "convnextv2", None) is not None:
with tf.name_scope(self.convnextv2.name):
self.convnextv2.build(None)
if getattr(self, "classifier", None) is not None:
... | 2,908 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py |
class ConvNextV2DropPath(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) ->... | 2,909 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_convnextv2.py |
class ConvNextV2GRN(nn.Module):
"""GRN (Global Response Normalization) layer"""
def __init__(self, dim: int):
super().__init__()
self.weight = nn.Parameter(torch.zeros(1, 1, 1, dim))
self.bias = nn.Parameter(torch.zeros(1, 1, 1, dim))
def forward(self, hidden_states: torch.FloatTen... | 2,910 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_convnextv2.py |
class ConvNextV2LayerNorm(nn.Module):
r"""LayerNorm that supports two data formats: channels_last (default) or channels_first.
The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height,
width, channels) while channels_first corresponds to inputs with sh... | 2,911 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_convnextv2.py |
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self.data_format == "channels_last":
x = torch.nn.functional.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
elif self.data_format == "channels_first":
input_dtype = x.dtype
x = x.float()
... | 2,911 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_convnextv2.py |
class ConvNextV2Embeddings(nn.Module):
"""This class is comparable to (and inspired by) the SwinEmbeddings class
found in src/transformers/models/swin/modeling_swin.py.
"""
def __init__(self, config):
super().__init__()
self.patch_embeddings = nn.Conv2d(
config.num_channels,... | 2,912 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_convnextv2.py |
def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
num_channels = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
... | 2,912 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_convnextv2.py |
class ConvNextV2Layer(nn.Module):
"""This corresponds to the `Block` class in the original implementation.
There are two equivalent implementations: [DwConv, LayerNorm (channels_first), Conv, GELU,1x1 Conv]; all in (N, C,
H, W) (2) [DwConv, Permute to (N, H, W, C), LayerNorm (channels_last), Linear, GELU, ... | 2,913 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_convnextv2.py |
def __init__(self, config, dim, drop_path=0):
super().__init__()
# depthwise conv
self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim)
self.layernorm = ConvNextV2LayerNorm(dim, eps=1e-6)
# pointwise/1x1 convs, implemented with linear layers
self.pwconv1... | 2,913 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_convnextv2.py |
def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
input = hidden_states
x = self.dwconv(hidden_states)
# (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
x = x.permute(0, 2, 3, 1)
x = self.layernorm(x)
x = self.pwco... | 2,913 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_convnextv2.py |
class ConvNextV2Stage(nn.Module):
"""ConvNeXTV2 stage, consisting of an optional downsampling layer + multiple residual blocks.
Args:
config ([`ConvNextV2Config`]): Model configuration class.
in_channels (`int`): Number of input channels.
out_channels (`int`): Number of output channels.... | 2,914 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_convnextv2.py |
if in_channels != out_channels or stride > 1:
self.downsampling_layer = nn.Sequential(
ConvNextV2LayerNorm(in_channels, eps=1e-6, data_format="channels_first"),
nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride),
)
else:
... | 2,914 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_convnextv2.py |
class ConvNextV2Encoder(nn.Module):
def __init__(self, config):
super().__init__()
self.stages = nn.ModuleList()
drop_path_rates = [
x.tolist() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths)).split(config.depths)
]
prev_chs = config.hidden_si... | 2,915 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_convnextv2.py |
def forward(
self,
hidden_states: torch.FloatTensor,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> Union[Tuple, BaseModelOutputWithNoAttention]:
all_hidden_states = () if output_hidden_states else None
for i, layer_module in e... | 2,915 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_convnextv2.py |
class ConvNextV2PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ConvNextV2Config
base_model_prefix = "convnextv2"
main_input_name = "pixel_values"
_no_split_... | 2,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_convnextv2.py |
class ConvNextV2Model(ConvNextV2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embeddings = ConvNextV2Embeddings(config)
self.encoder = ConvNextV2Encoder(config)
# final layernorm layer
self.layernorm = nn.LayerNorm(... | 2,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_convnextv2.py |
@add_start_docstrings_to_model_forward(CONVNEXTV2_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndNoAttention,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
... | 2,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_convnextv2.py |
encoder_outputs = self.encoder(
embedding_output,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
# global average pooling, (N, C, H, W) -> (N, C)
pooled_output = self.layernorm(last_hidden... | 2,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_convnextv2.py |
class ConvNextV2ForImageClassification(ConvNextV2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.convnextv2 = ConvNextV2Model(config)
# Classifier head
self.classifier = (
nn.Linear(config.hidden_si... | 2,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_convnextv2.py |
@add_start_docstrings_to_model_forward(CONVNEXTV2_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=ImageClassifierOutputWithNoAttention,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
... | 2,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_convnextv2.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 2,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_convnextv2.py |
outputs = self.convnextv2(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
pooled_output = outputs.pooler_output if return_dict else outputs[1]
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.proble... | 2,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_convnextv2.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... | 2,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_convnextv2.py |
class ConvNextV2Backbone(ConvNextV2PreTrainedModel, BackboneMixin):
def __init__(self, config):
super().__init__(config)
super()._init_backbone(config)
self.embeddings = ConvNextV2Embeddings(config)
self.encoder = ConvNextV2Encoder(config)
self.num_features = [config.hidden_... | 2,919 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_convnextv2.py |
@add_start_docstrings_to_model_forward(CONVNEXTV2_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BackboneOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: torch.Tensor,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,... | 2,919 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_convnextv2.py |
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