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transformers_4_35_0
/models
/audio_spectrogram_transformer
/modeling_audio_spectrogram_transformer.py
| # coding=utf-8 | |
| # Copyright 2022 MIT and The HuggingFace Inc. team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ PyTorch Audio Spectrogram Transformer (AST) model.""" | |
| import math | |
| from typing import Dict, List, Optional, Set, Tuple, Union | |
| import torch | |
| import torch.utils.checkpoint | |
| from torch import nn | |
| from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss | |
| from ...activations import ACT2FN | |
| from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, SequenceClassifierOutput | |
| from ...modeling_utils import PreTrainedModel | |
| from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer | |
| from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging | |
| from .configuration_audio_spectrogram_transformer import ASTConfig | |
| logger = logging.get_logger(__name__) | |
| # General docstring | |
| _CONFIG_FOR_DOC = "ASTConfig" | |
| # Base docstring | |
| _CHECKPOINT_FOR_DOC = "MIT/ast-finetuned-audioset-10-10-0.4593" | |
| _EXPECTED_OUTPUT_SHAPE = [1, 1214, 768] | |
| # Audio classification docstring | |
| _SEQ_CLASS_CHECKPOINT = "MIT/ast-finetuned-audioset-10-10-0.4593" | |
| _SEQ_CLASS_EXPECTED_OUTPUT = "'Speech'" | |
| _SEQ_CLASS_EXPECTED_LOSS = 0.17 | |
| AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [ | |
| "MIT/ast-finetuned-audioset-10-10-0.4593", | |
| # See all Audio Spectrogram Transformer models at https://huggingface.co/models?filter=ast | |
| ] | |
| class ASTEmbeddings(nn.Module): | |
| """ | |
| Construct the CLS token, position and patch embeddings. | |
| """ | |
| def __init__(self, config: ASTConfig) -> None: | |
| super().__init__() | |
| self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) | |
| self.distillation_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) | |
| self.patch_embeddings = ASTPatchEmbeddings(config) | |
| frequency_out_dimension, time_out_dimension = self.get_shape(config) | |
| num_patches = frequency_out_dimension * time_out_dimension | |
| self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 2, config.hidden_size)) | |
| self.dropout = nn.Dropout(config.hidden_dropout_prob) | |
| self.config = config | |
| def get_shape(self, config): | |
| # see Karpathy's cs231n blog on how to calculate the output dimensions | |
| # https://cs231n.github.io/convolutional-networks/#conv | |
| frequency_out_dimension = (config.num_mel_bins - config.patch_size) // config.frequency_stride + 1 | |
| time_out_dimension = (config.max_length - config.patch_size) // config.time_stride + 1 | |
| return frequency_out_dimension, time_out_dimension | |
| def forward(self, input_values: torch.Tensor) -> torch.Tensor: | |
| batch_size = input_values.shape[0] | |
| embeddings = self.patch_embeddings(input_values) | |
| cls_tokens = self.cls_token.expand(batch_size, -1, -1) | |
| distillation_tokens = self.distillation_token.expand(batch_size, -1, -1) | |
| embeddings = torch.cat((cls_tokens, distillation_tokens, embeddings), dim=1) | |
| embeddings = embeddings + self.position_embeddings | |
| embeddings = self.dropout(embeddings) | |
| return embeddings | |
| class ASTPatchEmbeddings(nn.Module): | |
| """ | |
| This class turns `input_values` into the initial `hidden_states` (patch embeddings) of shape `(batch_size, | |
| seq_length, hidden_size)` to be consumed by a Transformer. | |
| """ | |
| def __init__(self, config): | |
| super().__init__() | |
| patch_size = config.patch_size | |
| frequency_stride = config.frequency_stride | |
| time_stride = config.time_stride | |
| self.projection = nn.Conv2d( | |
| 1, config.hidden_size, kernel_size=(patch_size, patch_size), stride=(frequency_stride, time_stride) | |
| ) | |
| def forward(self, input_values: torch.Tensor) -> torch.Tensor: | |
| input_values = input_values.unsqueeze(1) | |
| input_values = input_values.transpose(2, 3) | |
| embeddings = self.projection(input_values).flatten(2).transpose(1, 2) | |
| return embeddings | |
| # Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->AST | |
| class ASTSelfAttention(nn.Module): | |
| def __init__(self, config: ASTConfig) -> None: | |
| super().__init__() | |
| if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): | |
| raise ValueError( | |
| f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " | |
| f"heads {config.num_attention_heads}." | |
| ) | |
| self.num_attention_heads = config.num_attention_heads | |
| self.attention_head_size = int(config.hidden_size / config.num_attention_heads) | |
| self.all_head_size = self.num_attention_heads * self.attention_head_size | |
| self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) | |
| self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) | |
| self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) | |
| self.dropout = nn.Dropout(config.attention_probs_dropout_prob) | |
| def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: | |
| new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) | |
| x = x.view(new_x_shape) | |
| return x.permute(0, 2, 1, 3) | |
| def forward( | |
| self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False | |
| ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: | |
| mixed_query_layer = self.query(hidden_states) | |
| 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 = torch.matmul(query_layer, key_layer.transpose(-1, -2)) | |
| attention_scores = attention_scores / math.sqrt(self.attention_head_size) | |
| # Normalize the attention scores to probabilities. | |
| attention_probs = nn.functional.softmax(attention_scores, dim=-1) | |
| # This is actually dropping out entire tokens to attend to, which might | |
| # seem a bit unusual, but is taken from the original Transformer paper. | |
| attention_probs = self.dropout(attention_probs) | |
| # 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] + (self.all_head_size,) | |
| context_layer = context_layer.view(new_context_layer_shape) | |
| outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) | |
| return outputs | |
| # Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->AST | |
| class ASTSelfOutput(nn.Module): | |
| """ | |
| The residual connection is defined in ASTLayer instead of here (as is the case with other models), due to the | |
| layernorm applied before each block. | |
| """ | |
| def __init__(self, config: ASTConfig) -> None: | |
| super().__init__() | |
| self.dense = nn.Linear(config.hidden_size, config.hidden_size) | |
| self.dropout = nn.Dropout(config.hidden_dropout_prob) | |
| def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: | |
| hidden_states = self.dense(hidden_states) | |
| hidden_states = self.dropout(hidden_states) | |
| return hidden_states | |
| # Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->AST | |
| class ASTAttention(nn.Module): | |
| def __init__(self, config: ASTConfig) -> None: | |
| super().__init__() | |
| self.attention = ASTSelfAttention(config) | |
| self.output = ASTSelfOutput(config) | |
| self.pruned_heads = set() | |
| def prune_heads(self, heads: Set[int]) -> None: | |
| if len(heads) == 0: | |
| return | |
| heads, index = find_pruneable_heads_and_indices( | |
| heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads | |
| ) | |
| # Prune linear layers | |
| self.attention.query = prune_linear_layer(self.attention.query, index) | |
| self.attention.key = prune_linear_layer(self.attention.key, index) | |
| self.attention.value = prune_linear_layer(self.attention.value, index) | |
| self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) | |
| # Update hyper params and store pruned heads | |
| self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) | |
| self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads | |
| self.pruned_heads = self.pruned_heads.union(heads) | |
| def forward( | |
| self, | |
| hidden_states: torch.Tensor, | |
| head_mask: Optional[torch.Tensor] = None, | |
| output_attentions: bool = False, | |
| ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: | |
| self_outputs = self.attention(hidden_states, head_mask, output_attentions) | |
| attention_output = self.output(self_outputs[0], hidden_states) | |
| outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them | |
| return outputs | |
| # Copied from transformers.models.vit.modeling_vit.ViTIntermediate with ViT->AST | |
| class ASTIntermediate(nn.Module): | |
| def __init__(self, config: ASTConfig) -> None: | |
| super().__init__() | |
| self.dense = nn.Linear(config.hidden_size, config.intermediate_size) | |
| if isinstance(config.hidden_act, str): | |
| self.intermediate_act_fn = ACT2FN[config.hidden_act] | |
| else: | |
| self.intermediate_act_fn = config.hidden_act | |
| def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: | |
| hidden_states = self.dense(hidden_states) | |
| hidden_states = self.intermediate_act_fn(hidden_states) | |
| return hidden_states | |
| # Copied from transformers.models.vit.modeling_vit.ViTOutput with ViT->AST | |
| class ASTOutput(nn.Module): | |
| def __init__(self, config: ASTConfig) -> None: | |
| super().__init__() | |
| self.dense = nn.Linear(config.intermediate_size, config.hidden_size) | |
| self.dropout = nn.Dropout(config.hidden_dropout_prob) | |
| def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: | |
| hidden_states = self.dense(hidden_states) | |
| hidden_states = self.dropout(hidden_states) | |
| hidden_states = hidden_states + input_tensor | |
| return hidden_states | |
| # Copied from transformers.models.vit.modeling_vit.ViTLayer with ViT->AST | |
| class ASTLayer(nn.Module): | |
| """This corresponds to the Block class in the timm implementation.""" | |
| def __init__(self, config: ASTConfig) -> None: | |
| super().__init__() | |
| self.chunk_size_feed_forward = config.chunk_size_feed_forward | |
| self.seq_len_dim = 1 | |
| self.attention = ASTAttention(config) | |
| self.intermediate = ASTIntermediate(config) | |
| self.output = ASTOutput(config) | |
| self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) | |
| self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) | |
| def forward( | |
| self, | |
| hidden_states: torch.Tensor, | |
| head_mask: Optional[torch.Tensor] = None, | |
| output_attentions: bool = False, | |
| ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: | |
| self_attention_outputs = self.attention( | |
| self.layernorm_before(hidden_states), # in AST, layernorm is applied before self-attention | |
| head_mask, | |
| output_attentions=output_attentions, | |
| ) | |
| attention_output = self_attention_outputs[0] | |
| outputs = self_attention_outputs[1:] # add self attentions if we output attention weights | |
| # first residual connection | |
| hidden_states = attention_output + hidden_states | |
| # in AST, layernorm is also applied after self-attention | |
| layer_output = self.layernorm_after(hidden_states) | |
| layer_output = self.intermediate(layer_output) | |
| # second residual connection is done here | |
| layer_output = self.output(layer_output, hidden_states) | |
| outputs = (layer_output,) + outputs | |
| return outputs | |
| # Copied from transformers.models.vit.modeling_vit.ViTEncoder with ViT->AST | |
| class ASTEncoder(nn.Module): | |
| def __init__(self, config: ASTConfig) -> None: | |
| super().__init__() | |
| self.config = config | |
| self.layer = nn.ModuleList([ASTLayer(config) for _ in range(config.num_hidden_layers)]) | |
| self.gradient_checkpointing = False | |
| def forward( | |
| self, | |
| hidden_states: torch.Tensor, | |
| head_mask: Optional[torch.Tensor] = None, | |
| output_attentions: bool = False, | |
| output_hidden_states: bool = False, | |
| return_dict: bool = True, | |
| ) -> Union[tuple, BaseModelOutput]: | |
| all_hidden_states = () if output_hidden_states else None | |
| all_self_attentions = () if output_attentions else None | |
| for i, layer_module in enumerate(self.layer): | |
| if output_hidden_states: | |
| all_hidden_states = all_hidden_states + (hidden_states,) | |
| layer_head_mask = head_mask[i] if head_mask is not None else None | |
| if self.gradient_checkpointing and self.training: | |
| def create_custom_forward(module): | |
| def custom_forward(*inputs): | |
| return module(*inputs, output_attentions) | |
| return custom_forward | |
| layer_outputs = torch.utils.checkpoint.checkpoint( | |
| create_custom_forward(layer_module), | |
| hidden_states, | |
| layer_head_mask, | |
| ) | |
| else: | |
| layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions) | |
| hidden_states = layer_outputs[0] | |
| if output_attentions: | |
| all_self_attentions = all_self_attentions + (layer_outputs[1],) | |
| if output_hidden_states: | |
| all_hidden_states = all_hidden_states + (hidden_states,) | |
| if not return_dict: | |
| return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) | |
| return BaseModelOutput( | |
| last_hidden_state=hidden_states, | |
| hidden_states=all_hidden_states, | |
| attentions=all_self_attentions, | |
| ) | |
| class ASTPreTrainedModel(PreTrainedModel): | |
| """ | |
| An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained | |
| models. | |
| """ | |
| config_class = ASTConfig | |
| base_model_prefix = "audio_spectrogram_transformer" | |
| main_input_name = "input_values" | |
| supports_gradient_checkpointing = True | |
| # Copied from transformers.models.deit.modeling_deit.DeiTPreTrainedModel._init_weights | |
| def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None: | |
| """Initialize the weights""" | |
| if isinstance(module, (nn.Linear, nn.Conv2d)): | |
| # Upcast the input in `fp32` and cast it back to desired `dtype` to avoid | |
| # `trunc_normal_cpu` not implemented in `half` issues | |
| module.weight.data = nn.init.trunc_normal_( | |
| module.weight.data.to(torch.float32), mean=0.0, std=self.config.initializer_range | |
| ).to(module.weight.dtype) | |
| if module.bias is not None: | |
| module.bias.data.zero_() | |
| elif isinstance(module, nn.LayerNorm): | |
| module.bias.data.zero_() | |
| module.weight.data.fill_(1.0) | |
| # Copied from transformers.models.vit.modeling_vit.ViTPreTrainedModel._set_gradient_checkpointing with ViT->AST | |
| def _set_gradient_checkpointing(self, module: ASTEncoder, value: bool = False) -> None: | |
| if isinstance(module, ASTEncoder): | |
| module.gradient_checkpointing = value | |
| AUDIO_SPECTROGRAM_TRANSFORMER_START_DOCSTRING = r""" | |
| This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it | |
| as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and | |
| behavior. | |
| Parameters: | |
| config ([`ASTConfig`]): | |
| Model configuration class with all the parameters of the model. Initializing with a config file does not | |
| load the weights associated with the model, only the configuration. Check out the | |
| [`~PreTrainedModel.from_pretrained`] method to load the model weights. | |
| """ | |
| AUDIO_SPECTROGRAM_TRANSFORMER_INPUTS_DOCSTRING = r""" | |
| Args: | |
| input_values (`torch.FloatTensor` of shape `(batch_size, max_length, num_mel_bins)`): | |
| Float values mel features extracted from the raw audio waveform. Raw audio waveform can be obtained by | |
| loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via | |
| the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the | |
| [`AutoFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a | |
| tensor of type `torch.FloatTensor`. See [`~ASTFeatureExtractor.__call__`] | |
| head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): | |
| Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: | |
| - 1 indicates the head is **not masked**, | |
| - 0 indicates the head is **masked**. | |
| output_attentions (`bool`, *optional*): | |
| Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned | |
| tensors for more detail. | |
| output_hidden_states (`bool`, *optional*): | |
| Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for | |
| more detail. | |
| return_dict (`bool`, *optional*): | |
| Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. | |
| """ | |
| class ASTModel(ASTPreTrainedModel): | |
| def __init__(self, config: ASTConfig) -> None: | |
| super().__init__(config) | |
| self.config = config | |
| self.embeddings = ASTEmbeddings(config) | |
| self.encoder = ASTEncoder(config) | |
| self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) | |
| # Initialize weights and apply final processing | |
| self.post_init() | |
| def get_input_embeddings(self) -> ASTPatchEmbeddings: | |
| return self.embeddings.patch_embeddings | |
| def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None: | |
| """ | |
| 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_heads(heads) | |
| def forward( | |
| self, | |
| input_values: Optional[torch.Tensor] = None, | |
| head_mask: Optional[torch.Tensor] = None, | |
| output_attentions: Optional[bool] = None, | |
| output_hidden_states: Optional[bool] = None, | |
| return_dict: Optional[bool] = None, | |
| ) -> Union[Tuple, BaseModelOutputWithPooling]: | |
| 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 else self.config.use_return_dict | |
| if input_values is None: | |
| raise ValueError("You have to specify input_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_heads] | |
| # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] | |
| head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) | |
| embedding_output = self.embeddings(input_values) | |
| encoder_outputs = self.encoder( | |
| embedding_output, | |
| head_mask=head_mask, | |
| output_attentions=output_attentions, | |
| output_hidden_states=output_hidden_states, | |
| return_dict=return_dict, | |
| ) | |
| sequence_output = encoder_outputs[0] | |
| sequence_output = self.layernorm(sequence_output) | |
| pooled_output = (sequence_output[:, 0] + sequence_output[:, 1]) / 2 | |
| if not return_dict: | |
| return (sequence_output, pooled_output) + encoder_outputs[1:] | |
| return BaseModelOutputWithPooling( | |
| last_hidden_state=sequence_output, | |
| pooler_output=pooled_output, | |
| hidden_states=encoder_outputs.hidden_states, | |
| attentions=encoder_outputs.attentions, | |
| ) | |
| class ASTMLPHead(nn.Module): | |
| def __init__(self, config: ASTConfig): | |
| super().__init__() | |
| self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) | |
| self.dense = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity() | |
| def forward(self, hidden_state): | |
| hidden_state = self.layernorm(hidden_state) | |
| hidden_state = self.dense(hidden_state) | |
| return hidden_state | |
| class ASTForAudioClassification(ASTPreTrainedModel): | |
| def __init__(self, config: ASTConfig) -> None: | |
| super().__init__(config) | |
| self.num_labels = config.num_labels | |
| self.audio_spectrogram_transformer = ASTModel(config) | |
| # Classifier head | |
| self.classifier = ASTMLPHead(config) | |
| # Initialize weights and apply final processing | |
| self.post_init() | |
| def forward( | |
| self, | |
| input_values: Optional[torch.Tensor] = None, | |
| head_mask: Optional[torch.Tensor] = None, | |
| labels: Optional[torch.Tensor] = None, | |
| output_attentions: Optional[bool] = None, | |
| output_hidden_states: Optional[bool] = None, | |
| return_dict: Optional[bool] = None, | |
| ) -> Union[tuple, SequenceClassifierOutput]: | |
| r""" | |
| labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): | |
| Labels for computing the audio classification/regression loss. Indices should be in `[0, ..., | |
| 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 | |
| outputs = self.audio_spectrogram_transformer( | |
| input_values, | |
| head_mask=head_mask, | |
| output_attentions=output_attentions, | |
| output_hidden_states=output_hidden_states, | |
| return_dict=return_dict, | |
| ) | |
| pooled_output = outputs[1] | |
| logits = self.classifier(pooled_output) | |
| loss = None | |
| if labels is not None: | |
| if self.config.problem_type is None: | |
| if self.num_labels == 1: | |
| self.config.problem_type = "regression" | |
| elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): | |
| self.config.problem_type = "single_label_classification" | |
| else: | |
| self.config.problem_type = "multi_label_classification" | |
| 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 == "single_label_classification": | |
| loss_fct = CrossEntropyLoss() | |
| loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) | |
| elif self.config.problem_type == "multi_label_classification": | |
| loss_fct = BCEWithLogitsLoss() | |
| loss = loss_fct(logits, labels) | |
| if not return_dict: | |
| output = (logits,) + outputs[2:] | |
| return ((loss,) + output) if loss is not None else output | |
| return SequenceClassifierOutput( | |
| loss=loss, | |
| logits=logits, | |
| hidden_states=outputs.hidden_states, | |
| attentions=outputs.attentions, | |
| ) | |