1634 lines
70 KiB
Python
1634 lines
70 KiB
Python
# coding=utf-8
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# Copyright 2023 The Google Research Team Authors and The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" PyTorch ALIGN model."""
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import math
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from dataclasses import dataclass
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from typing import Any, Optional, Tuple, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from ...activations import ACT2FN
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from ...modeling_outputs import (
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BaseModelOutputWithNoAttention,
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BaseModelOutputWithPastAndCrossAttentions,
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BaseModelOutputWithPoolingAndCrossAttentions,
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BaseModelOutputWithPoolingAndNoAttention,
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)
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from ...modeling_utils import PreTrainedModel
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from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
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from ...utils import (
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ModelOutput,
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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logging,
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replace_return_docstrings,
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)
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from .configuration_align import AlignConfig, AlignTextConfig, AlignVisionConfig
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logger = logging.get_logger(__name__)
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_CHECKPOINT_FOR_DOC = "kakaobrain/align-base"
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_CONFIG_FOR_DOC = "AlignConfig"
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from ..deprecated._archive_maps import ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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ALIGN_START_DOCSTRING = r"""
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This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
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library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
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etc.)
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This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
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Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
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and behavior.
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Parameters:
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config ([`AlignConfig`]): Model configuration class with all the parameters of the model.
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Initializing with a config file does not load the weights associated with the model, only the
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configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
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"""
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ALIGN_TEXT_INPUTS_DOCSTRING = r"""
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Args:
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input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
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Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
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it.
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Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
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[`PreTrainedTokenizer.__call__`] for details.
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[What are input IDs?](../glossary#input-ids)
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attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
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Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
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- 1 for tokens that are **not masked**,
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- 0 for tokens that are **masked**.
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[What are attention masks?](../glossary#attention-mask)
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position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
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config.max_position_embeddings - 1]`.
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[What are position IDs?](../glossary#position-ids)
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token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
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Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
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1]`:
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- 0 corresponds to a *sentence A* token,
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- 1 corresponds to a *sentence B* token.
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[What are token type IDs?](../glossary#token-type-ids)
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head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
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Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
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- 1 indicates the head is **not masked**,
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- 0 indicates the head is **masked**.
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inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
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Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
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is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
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model's internal embedding lookup matrix.
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output_attentions (`bool`, *optional*):
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Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
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tensors for more detail.
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output_hidden_states (`bool`, *optional*):
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Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
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more detail.
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return_dict (`bool`, *optional*):
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Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
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"""
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ALIGN_VISION_INPUTS_DOCSTRING = r"""
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Args:
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pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
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Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
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[`AutoImageProcessor`]. See [`EfficientNetImageProcessor.__call__`] for details.
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output_hidden_states (`bool`, *optional*):
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Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
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more detail.
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return_dict (`bool`, *optional*):
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Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
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"""
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ALIGN_INPUTS_DOCSTRING = r"""
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Args:
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input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
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Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
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it.
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Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
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[`PreTrainedTokenizer.__call__`] for details.
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[What are input IDs?](../glossary#input-ids)
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attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
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Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
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- 1 for tokens that are **not masked**,
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- 0 for tokens that are **masked**.
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[What are attention masks?](../glossary#attention-mask)
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position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
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config.max_position_embeddings - 1]`.
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[What are position IDs?](../glossary#position-ids)
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token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
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Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
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1]`:
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- 0 corresponds to a *sentence A* token,
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- 1 corresponds to a *sentence B* token.
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[What are token type IDs?](../glossary#token-type-ids)
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head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
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Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
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- 1 indicates the head is **not masked**,
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- 0 indicates the head is **masked**.
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inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
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Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
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is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
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model's internal embedding lookup matrix.
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pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
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Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
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[`AutoImageProcessor`]. See [`EfficientNetImageProcessor.__call__`] for details.
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return_loss (`bool`, *optional*):
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Whether or not to return the contrastive loss.
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output_attentions (`bool`, *optional*):
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Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
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tensors for more detail.
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output_hidden_states (`bool`, *optional*):
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Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
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more detail.
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return_dict (`bool`, *optional*):
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Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
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"""
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@dataclass
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class AlignVisionModelOutput(ModelOutput):
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"""
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Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
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Args:
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image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
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The image embeddings obtained by applying the projection layer to the pooler_output.
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last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
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Sequence of hidden-states at the output of the last layer of the model.
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
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"""
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image_embeds: Optional[torch.FloatTensor] = None
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last_hidden_state: torch.FloatTensor = None
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hidden_states: Optional[Tuple[torch.FloatTensor]] = None
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@dataclass
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class AlignTextModelOutput(ModelOutput):
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"""
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Base class for text model's outputs that also contains a pooling of the last hidden states.
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Args:
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text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
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The text embeddings obtained by applying the projection layer to the pooler_output.
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last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
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Sequence of hidden-states at the output of the last layer of the model.
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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heads.
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"""
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text_embeds: Optional[torch.FloatTensor] = None
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last_hidden_state: torch.FloatTensor = None
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hidden_states: Optional[Tuple[torch.FloatTensor]] = None
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attentions: Optional[Tuple[torch.FloatTensor]] = None
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@dataclass
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class AlignOutput(ModelOutput):
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"""
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Args:
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loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
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Contrastive loss for image-text similarity.
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logits_per_image:(`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`):
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The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
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similarity scores.
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logits_per_text:(`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`):
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The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
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similarity scores.
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text_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
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The text embeddings obtained by applying the projection layer to the pooled output of [`AlignTextModel`].
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image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
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The output of [`AlignVisionModel`].
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text_model_output(`BaseModelOutputWithPoolingAndCrossAttentions`):
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The output of the [`AlignTextModel`].
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vision_model_output(`BaseModelOutputWithPoolingAndNoAttention`):
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The output of the [`AlignVisionModel`].
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"""
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loss: Optional[torch.FloatTensor] = None
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logits_per_image: torch.FloatTensor = None
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logits_per_text: torch.FloatTensor = None
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text_embeds: torch.FloatTensor = None
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image_embeds: torch.FloatTensor = None
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text_model_output: BaseModelOutputWithPoolingAndCrossAttentions = None
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vision_model_output: BaseModelOutputWithPoolingAndNoAttention = None
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def to_tuple(self) -> Tuple[Any]:
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return tuple(
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self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
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for k in self.keys()
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)
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# contrastive loss function, adapted from
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# https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html
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def contrastive_loss(logits: torch.Tensor) -> torch.Tensor:
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return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device), label_smoothing=0.1)
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def align_loss(similarity: torch.Tensor) -> torch.Tensor:
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caption_loss = contrastive_loss(similarity)
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image_loss = contrastive_loss(similarity.t())
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return (caption_loss + image_loss) / 2.0
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# Copied from transformers.models.efficientnet.modeling_efficientnet.round_filters with EfficientNet->AlignVision
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def round_filters(config: AlignVisionConfig, num_channels: int):
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r"""
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Round number of filters based on depth multiplier.
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"""
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divisor = config.depth_divisor
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num_channels *= config.width_coefficient
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new_dim = max(divisor, int(num_channels + divisor / 2) // divisor * divisor)
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# Make sure that round down does not go down by more than 10%.
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if new_dim < 0.9 * num_channels:
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new_dim += divisor
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return int(new_dim)
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# Copied from transformers.models.efficientnet.modeling_efficientnet.correct_pad
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def correct_pad(kernel_size: Union[int, Tuple], adjust: bool = True):
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r"""
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Utility function to get the tuple padding value for the depthwise convolution.
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Args:
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kernel_size (`int` or `tuple`):
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Kernel size of the convolution layers.
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adjust (`bool`, *optional*, defaults to `True`):
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Adjusts padding value to apply to right and bottom sides of the input.
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"""
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if isinstance(kernel_size, int):
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kernel_size = (kernel_size, kernel_size)
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correct = (kernel_size[0] // 2, kernel_size[1] // 2)
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if adjust:
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return (correct[1] - 1, correct[1], correct[0] - 1, correct[0])
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else:
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return (correct[1], correct[1], correct[0], correct[0])
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# Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetEmbeddings with EfficientNet->AlignVision
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class AlignVisionEmbeddings(nn.Module):
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r"""
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A module that corresponds to the stem module of the original work.
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"""
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def __init__(self, config: AlignVisionConfig):
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super().__init__()
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self.out_dim = round_filters(config, 32)
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self.padding = nn.ZeroPad2d(padding=(0, 1, 0, 1))
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self.convolution = nn.Conv2d(
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config.num_channels, self.out_dim, kernel_size=3, stride=2, padding="valid", bias=False
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)
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self.batchnorm = nn.BatchNorm2d(self.out_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum)
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self.activation = ACT2FN[config.hidden_act]
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def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
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features = self.padding(pixel_values)
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features = self.convolution(features)
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features = self.batchnorm(features)
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features = self.activation(features)
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return features
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# Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetDepthwiseConv2d with EfficientNet->AlignVision
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class AlignVisionDepthwiseConv2d(nn.Conv2d):
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def __init__(
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self,
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in_channels,
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depth_multiplier=1,
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kernel_size=3,
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stride=1,
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padding=0,
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dilation=1,
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bias=True,
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padding_mode="zeros",
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):
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out_channels = in_channels * depth_multiplier
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super().__init__(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=in_channels,
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bias=bias,
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padding_mode=padding_mode,
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)
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# Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetExpansionLayer with EfficientNet->AlignVision
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class AlignVisionExpansionLayer(nn.Module):
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r"""
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This corresponds to the expansion phase of each block in the original implementation.
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"""
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def __init__(self, config: AlignVisionConfig, in_dim: int, out_dim: int, stride: int):
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super().__init__()
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self.expand_conv = nn.Conv2d(
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in_channels=in_dim,
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out_channels=out_dim,
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kernel_size=1,
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padding="same",
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bias=False,
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)
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self.expand_bn = nn.BatchNorm2d(num_features=out_dim, eps=config.batch_norm_eps)
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self.expand_act = ACT2FN[config.hidden_act]
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def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
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# Expand phase
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hidden_states = self.expand_conv(hidden_states)
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hidden_states = self.expand_bn(hidden_states)
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hidden_states = self.expand_act(hidden_states)
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return hidden_states
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# Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetDepthwiseLayer with EfficientNet->AlignVision
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class AlignVisionDepthwiseLayer(nn.Module):
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r"""
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This corresponds to the depthwise convolution phase of each block in the original implementation.
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"""
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def __init__(
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self,
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config: AlignVisionConfig,
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in_dim: int,
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stride: int,
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kernel_size: int,
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adjust_padding: bool,
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):
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super().__init__()
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self.stride = stride
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conv_pad = "valid" if self.stride == 2 else "same"
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padding = correct_pad(kernel_size, adjust=adjust_padding)
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self.depthwise_conv_pad = nn.ZeroPad2d(padding=padding)
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self.depthwise_conv = AlignVisionDepthwiseConv2d(
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in_dim, kernel_size=kernel_size, stride=stride, padding=conv_pad, bias=False
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)
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self.depthwise_norm = nn.BatchNorm2d(
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num_features=in_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum
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)
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self.depthwise_act = ACT2FN[config.hidden_act]
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def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
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# Depthwise convolution
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if self.stride == 2:
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hidden_states = self.depthwise_conv_pad(hidden_states)
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hidden_states = self.depthwise_conv(hidden_states)
|
|
hidden_states = self.depthwise_norm(hidden_states)
|
|
hidden_states = self.depthwise_act(hidden_states)
|
|
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetSqueezeExciteLayer with EfficientNet->AlignVision
|
|
class AlignVisionSqueezeExciteLayer(nn.Module):
|
|
r"""
|
|
This corresponds to the Squeeze and Excitement phase of each block in the original implementation.
|
|
"""
|
|
|
|
def __init__(self, config: AlignVisionConfig, in_dim: int, expand_dim: int, expand: bool = False):
|
|
super().__init__()
|
|
self.dim = expand_dim if expand else in_dim
|
|
self.dim_se = max(1, int(in_dim * config.squeeze_expansion_ratio))
|
|
|
|
self.squeeze = nn.AdaptiveAvgPool2d(output_size=1)
|
|
self.reduce = nn.Conv2d(
|
|
in_channels=self.dim,
|
|
out_channels=self.dim_se,
|
|
kernel_size=1,
|
|
padding="same",
|
|
)
|
|
self.expand = nn.Conv2d(
|
|
in_channels=self.dim_se,
|
|
out_channels=self.dim,
|
|
kernel_size=1,
|
|
padding="same",
|
|
)
|
|
self.act_reduce = ACT2FN[config.hidden_act]
|
|
self.act_expand = nn.Sigmoid()
|
|
|
|
def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
|
|
inputs = hidden_states
|
|
hidden_states = self.squeeze(hidden_states)
|
|
hidden_states = self.reduce(hidden_states)
|
|
hidden_states = self.act_reduce(hidden_states)
|
|
|
|
hidden_states = self.expand(hidden_states)
|
|
hidden_states = self.act_expand(hidden_states)
|
|
hidden_states = torch.mul(inputs, hidden_states)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class AlignVisionFinalBlockLayer(nn.Module):
|
|
r"""
|
|
This corresponds to the final phase of each block in the original implementation.
|
|
"""
|
|
|
|
def __init__(
|
|
self, config: AlignVisionConfig, in_dim: int, out_dim: int, stride: int, drop_rate: float, id_skip: bool
|
|
):
|
|
super().__init__()
|
|
self.apply_dropout = stride == 1 and not id_skip
|
|
self.project_conv = nn.Conv2d(
|
|
in_channels=in_dim,
|
|
out_channels=out_dim,
|
|
kernel_size=1,
|
|
padding="same",
|
|
bias=False,
|
|
)
|
|
self.project_bn = nn.BatchNorm2d(
|
|
num_features=out_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum
|
|
)
|
|
self.dropout = nn.Dropout(p=drop_rate)
|
|
|
|
def forward(self, embeddings: torch.FloatTensor, hidden_states: torch.FloatTensor) -> torch.Tensor:
|
|
hidden_states = self.project_conv(hidden_states)
|
|
hidden_states = self.project_bn(hidden_states)
|
|
|
|
if self.apply_dropout:
|
|
hidden_states = self.dropout(hidden_states)
|
|
hidden_states = hidden_states + embeddings
|
|
|
|
return hidden_states
|
|
|
|
|
|
class AlignVisionBlock(nn.Module):
|
|
r"""
|
|
This corresponds to the block module of original the EfficientNet vision encoder implementation.
|
|
|
|
Args:
|
|
config ([`AlignVisionConfig`]):
|
|
Model configuration class.
|
|
in_dim (`int`):
|
|
Number of input channels.
|
|
out_dim (`int`):
|
|
Number of output channels.
|
|
stride (`int`):
|
|
Stride size to be used in convolution layers.
|
|
expand_ratio (`int`):
|
|
Expand ratio to set the output dimensions for the expansion and squeeze-excite layers.
|
|
kernel_size (`int`):
|
|
Kernel size for the depthwise convolution layer.
|
|
drop_rate (`float`):
|
|
Dropout rate to be used in the final phase of each block.
|
|
id_skip (`bool`):
|
|
Whether to apply dropout and sum the final hidden states with the input embeddings during the final phase
|
|
of each block. Set to `True` for the first block of each stage.
|
|
adjust_padding (`bool`):
|
|
Whether to apply padding to only right and bottom side of the input kernel before the depthwise convolution
|
|
operation, set to `True` for inputs with odd input sizes.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
config: AlignVisionConfig,
|
|
in_dim: int,
|
|
out_dim: int,
|
|
stride: int,
|
|
expand_ratio: int,
|
|
kernel_size: int,
|
|
drop_rate: float,
|
|
id_skip: bool,
|
|
adjust_padding: bool,
|
|
):
|
|
super().__init__()
|
|
self.expand_ratio = expand_ratio
|
|
self.expand = True if self.expand_ratio != 1 else False
|
|
expand_in_dim = in_dim * expand_ratio
|
|
|
|
if self.expand:
|
|
self.expansion = AlignVisionExpansionLayer(
|
|
config=config, in_dim=in_dim, out_dim=expand_in_dim, stride=stride
|
|
)
|
|
|
|
self.depthwise_conv = AlignVisionDepthwiseLayer(
|
|
config=config,
|
|
in_dim=expand_in_dim if self.expand else in_dim,
|
|
stride=stride,
|
|
kernel_size=kernel_size,
|
|
adjust_padding=adjust_padding,
|
|
)
|
|
self.squeeze_excite = AlignVisionSqueezeExciteLayer(
|
|
config=config, in_dim=in_dim, expand_dim=expand_in_dim, expand=self.expand
|
|
)
|
|
self.projection = AlignVisionFinalBlockLayer(
|
|
config=config,
|
|
in_dim=expand_in_dim if self.expand else in_dim,
|
|
out_dim=out_dim,
|
|
stride=stride,
|
|
drop_rate=drop_rate,
|
|
id_skip=id_skip,
|
|
)
|
|
|
|
def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
|
|
embeddings = hidden_states
|
|
# Expansion and depthwise convolution phase
|
|
if self.expand_ratio != 1:
|
|
hidden_states = self.expansion(hidden_states)
|
|
hidden_states = self.depthwise_conv(hidden_states)
|
|
|
|
# Squeeze and excite phase
|
|
hidden_states = self.squeeze_excite(hidden_states)
|
|
hidden_states = self.projection(embeddings, hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class AlignVisionEncoder(nn.Module):
|
|
r"""
|
|
Forward propogates the embeddings through each vision encoder (EfficientNet) block.
|
|
|
|
Args:
|
|
config ([`AlignVisionConfig`]):
|
|
Model configuration class.
|
|
"""
|
|
|
|
def __init__(self, config: AlignVisionConfig):
|
|
super().__init__()
|
|
self.depth_coefficient = config.depth_coefficient
|
|
|
|
def round_repeats(repeats):
|
|
# Round number of block repeats based on depth multiplier.
|
|
return int(math.ceil(self.depth_coefficient * repeats))
|
|
|
|
num_base_blocks = len(config.in_channels)
|
|
num_blocks = sum(round_repeats(n) for n in config.num_block_repeats)
|
|
|
|
curr_block_num = 0
|
|
blocks = []
|
|
for i in range(num_base_blocks):
|
|
in_dim = round_filters(config, config.in_channels[i])
|
|
out_dim = round_filters(config, config.out_channels[i])
|
|
stride = config.strides[i]
|
|
kernel_size = config.kernel_sizes[i]
|
|
expand_ratio = config.expand_ratios[i]
|
|
|
|
for j in range(round_repeats(config.num_block_repeats[i])):
|
|
id_skip = True if j == 0 else False
|
|
stride = 1 if j > 0 else stride
|
|
in_dim = out_dim if j > 0 else in_dim
|
|
adjust_padding = False if curr_block_num in config.depthwise_padding else True
|
|
drop_rate = config.drop_connect_rate * curr_block_num / num_blocks
|
|
|
|
block = AlignVisionBlock(
|
|
config=config,
|
|
in_dim=in_dim,
|
|
out_dim=out_dim,
|
|
stride=stride,
|
|
kernel_size=kernel_size,
|
|
expand_ratio=expand_ratio,
|
|
drop_rate=drop_rate,
|
|
id_skip=id_skip,
|
|
adjust_padding=adjust_padding,
|
|
)
|
|
blocks.append(block)
|
|
curr_block_num += 1
|
|
|
|
self.blocks = nn.ModuleList(blocks)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.FloatTensor,
|
|
output_hidden_states: Optional[bool] = False,
|
|
return_dict: Optional[bool] = True,
|
|
) -> BaseModelOutputWithPoolingAndNoAttention:
|
|
all_hidden_states = (hidden_states,) if output_hidden_states else None
|
|
|
|
for block in self.blocks:
|
|
hidden_states = block(hidden_states)
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
|
|
|
|
return BaseModelOutputWithNoAttention(
|
|
last_hidden_state=hidden_states,
|
|
hidden_states=all_hidden_states,
|
|
)
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertEmbeddings with Bert->AlignText
|
|
class AlignTextEmbeddings(nn.Module):
|
|
"""Construct the embeddings from word, position and token_type embeddings."""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
|
|
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
|
|
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
|
|
|
|
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
|
|
# any TensorFlow checkpoint file
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
|
|
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
|
|
self.register_buffer(
|
|
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
|
|
)
|
|
self.register_buffer(
|
|
"token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
past_key_values_length: int = 0,
|
|
) -> torch.Tensor:
|
|
if input_ids is not None:
|
|
input_shape = input_ids.size()
|
|
else:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
|
|
seq_length = input_shape[1]
|
|
|
|
if position_ids is None:
|
|
position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
|
|
|
|
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
|
|
# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
|
|
# issue #5664
|
|
if token_type_ids is None:
|
|
if hasattr(self, "token_type_ids"):
|
|
buffered_token_type_ids = self.token_type_ids[:, :seq_length]
|
|
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
|
|
token_type_ids = buffered_token_type_ids_expanded
|
|
else:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.word_embeddings(input_ids)
|
|
token_type_embeddings = self.token_type_embeddings(token_type_ids)
|
|
|
|
embeddings = inputs_embeds + token_type_embeddings
|
|
if self.position_embedding_type == "absolute":
|
|
position_embeddings = self.position_embeddings(position_ids)
|
|
embeddings += position_embeddings
|
|
embeddings = self.LayerNorm(embeddings)
|
|
embeddings = self.dropout(embeddings)
|
|
return embeddings
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->AlignText
|
|
class AlignTextSelfAttention(nn.Module):
|
|
def __init__(self, config, position_embedding_type=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)
|
|
self.key = nn.Linear(config.hidden_size, self.all_head_size)
|
|
self.value = nn.Linear(config.hidden_size, self.all_head_size)
|
|
|
|
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
|
|
self.position_embedding_type = position_embedding_type or getattr(
|
|
config, "position_embedding_type", "absolute"
|
|
)
|
|
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
|
|
self.max_position_embeddings = config.max_position_embeddings
|
|
self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
|
|
|
|
self.is_decoder = config.is_decoder
|
|
|
|
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: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
) -> Tuple[torch.Tensor]:
|
|
mixed_query_layer = self.query(hidden_states)
|
|
|
|
# If this is instantiated as a cross-attention module, the keys
|
|
# and values come from an encoder; the attention mask needs to be
|
|
# such that the encoder's padding tokens are not attended to.
|
|
is_cross_attention = encoder_hidden_states is not None
|
|
|
|
if is_cross_attention and past_key_value is not None:
|
|
# reuse k,v, cross_attentions
|
|
key_layer = past_key_value[0]
|
|
value_layer = past_key_value[1]
|
|
attention_mask = encoder_attention_mask
|
|
elif is_cross_attention:
|
|
key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
|
|
value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
|
|
attention_mask = encoder_attention_mask
|
|
elif past_key_value is not None:
|
|
key_layer = self.transpose_for_scores(self.key(hidden_states))
|
|
value_layer = self.transpose_for_scores(self.value(hidden_states))
|
|
key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
|
|
value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
|
|
else:
|
|
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)
|
|
|
|
use_cache = past_key_value is not None
|
|
if self.is_decoder:
|
|
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
|
|
# Further calls to cross_attention layer can then reuse all cross-attention
|
|
# key/value_states (first "if" case)
|
|
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
|
|
# all previous decoder key/value_states. Further calls to uni-directional self-attention
|
|
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
|
|
# if encoder bi-directional self-attention `past_key_value` is always `None`
|
|
past_key_value = (key_layer, value_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))
|
|
|
|
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
|
|
query_length, key_length = query_layer.shape[2], key_layer.shape[2]
|
|
if use_cache:
|
|
position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(
|
|
-1, 1
|
|
)
|
|
else:
|
|
position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
|
|
position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
|
|
distance = position_ids_l - position_ids_r
|
|
|
|
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
|
|
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
|
|
|
|
if self.position_embedding_type == "relative_key":
|
|
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
|
|
attention_scores = attention_scores + relative_position_scores
|
|
elif self.position_embedding_type == "relative_key_query":
|
|
relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
|
|
relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
|
|
attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
|
|
|
|
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
|
|
if attention_mask is not None:
|
|
# Apply the attention mask is (precomputed for all layers in AlignTextModel forward() function)
|
|
attention_scores = attention_scores + attention_mask
|
|
|
|
# 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,)
|
|
|
|
if self.is_decoder:
|
|
outputs = outputs + (past_key_value,)
|
|
return outputs
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->AlignText
|
|
class AlignTextSelfOutput(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
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 = self.LayerNorm(hidden_states + input_tensor)
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->AlignText
|
|
class AlignTextAttention(nn.Module):
|
|
def __init__(self, config, position_embedding_type=None):
|
|
super().__init__()
|
|
self.self = AlignTextSelfAttention(config, position_embedding_type=position_embedding_type)
|
|
self.output = AlignTextSelfOutput(config)
|
|
self.pruned_heads = set()
|
|
|
|
def prune_heads(self, heads):
|
|
if len(heads) == 0:
|
|
return
|
|
heads, index = find_pruneable_heads_and_indices(
|
|
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
|
|
)
|
|
|
|
# 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)
|
|
|
|
# Update hyper params and store pruned heads
|
|
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
|
|
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
|
|
self.pruned_heads = self.pruned_heads.union(heads)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
) -> Tuple[torch.Tensor]:
|
|
self_outputs = self.self(
|
|
hidden_states,
|
|
attention_mask,
|
|
head_mask,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
past_key_value,
|
|
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.bert.modeling_bert.BertIntermediate with Bert->AlignText
|
|
class AlignTextIntermediate(nn.Module):
|
|
def __init__(self, config):
|
|
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.bert.modeling_bert.BertOutput with Bert->AlignText
|
|
class AlignTextOutput(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
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 = self.LayerNorm(hidden_states + input_tensor)
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->AlignText
|
|
class AlignTextLayer(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.chunk_size_feed_forward = config.chunk_size_feed_forward
|
|
self.seq_len_dim = 1
|
|
self.attention = AlignTextAttention(config)
|
|
self.is_decoder = config.is_decoder
|
|
self.add_cross_attention = config.add_cross_attention
|
|
if self.add_cross_attention:
|
|
if not self.is_decoder:
|
|
raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
|
|
self.crossattention = AlignTextAttention(config, position_embedding_type="absolute")
|
|
self.intermediate = AlignTextIntermediate(config)
|
|
self.output = AlignTextOutput(config)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
) -> Tuple[torch.Tensor]:
|
|
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
|
|
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
|
|
self_attention_outputs = self.attention(
|
|
hidden_states,
|
|
attention_mask,
|
|
head_mask,
|
|
output_attentions=output_attentions,
|
|
past_key_value=self_attn_past_key_value,
|
|
)
|
|
attention_output = self_attention_outputs[0]
|
|
|
|
# if decoder, the last output is tuple of self-attn cache
|
|
if self.is_decoder:
|
|
outputs = self_attention_outputs[1:-1]
|
|
present_key_value = self_attention_outputs[-1]
|
|
else:
|
|
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
|
|
|
|
cross_attn_present_key_value = None
|
|
if self.is_decoder and encoder_hidden_states is not None:
|
|
if not hasattr(self, "crossattention"):
|
|
raise ValueError(
|
|
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
|
|
" by setting `config.add_cross_attention=True`"
|
|
)
|
|
|
|
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
|
|
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
|
|
cross_attention_outputs = self.crossattention(
|
|
attention_output,
|
|
attention_mask,
|
|
head_mask,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
cross_attn_past_key_value,
|
|
output_attentions,
|
|
)
|
|
attention_output = cross_attention_outputs[0]
|
|
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
|
|
|
|
# add cross-attn cache to positions 3,4 of present_key_value tuple
|
|
cross_attn_present_key_value = cross_attention_outputs[-1]
|
|
present_key_value = present_key_value + cross_attn_present_key_value
|
|
|
|
layer_output = apply_chunking_to_forward(
|
|
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
|
|
)
|
|
outputs = (layer_output,) + outputs
|
|
|
|
# if decoder, return the attn key/values as the last output
|
|
if self.is_decoder:
|
|
outputs = outputs + (present_key_value,)
|
|
|
|
return outputs
|
|
|
|
def feed_forward_chunk(self, attention_output):
|
|
intermediate_output = self.intermediate(attention_output)
|
|
layer_output = self.output(intermediate_output, attention_output)
|
|
return layer_output
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->AlignText
|
|
class AlignTextEncoder(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer = nn.ModuleList([AlignTextLayer(config) for _ in range(config.num_hidden_layers)])
|
|
self.gradient_checkpointing = False
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
output_hidden_states: Optional[bool] = False,
|
|
return_dict: Optional[bool] = True,
|
|
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attentions = () if output_attentions else None
|
|
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
if use_cache:
|
|
logger.warning_once(
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
|
)
|
|
use_cache = False
|
|
|
|
next_decoder_cache = () if use_cache 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
|
|
past_key_value = past_key_values[i] if past_key_values is not None else None
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
layer_outputs = self._gradient_checkpointing_func(
|
|
layer_module.__call__,
|
|
hidden_states,
|
|
attention_mask,
|
|
layer_head_mask,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
past_key_value,
|
|
output_attentions,
|
|
)
|
|
else:
|
|
layer_outputs = layer_module(
|
|
hidden_states,
|
|
attention_mask,
|
|
layer_head_mask,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
past_key_value,
|
|
output_attentions,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
if use_cache:
|
|
next_decoder_cache += (layer_outputs[-1],)
|
|
if output_attentions:
|
|
all_self_attentions = all_self_attentions + (layer_outputs[1],)
|
|
if self.config.add_cross_attention:
|
|
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [
|
|
hidden_states,
|
|
next_decoder_cache,
|
|
all_hidden_states,
|
|
all_self_attentions,
|
|
all_cross_attentions,
|
|
]
|
|
if v is not None
|
|
)
|
|
return BaseModelOutputWithPastAndCrossAttentions(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=next_decoder_cache,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
cross_attentions=all_cross_attentions,
|
|
)
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert -> AlignText
|
|
class AlignTextPooler(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.activation = nn.Tanh()
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
# We "pool" the model by simply taking the hidden state corresponding
|
|
# to the first token.
|
|
first_token_tensor = hidden_states[:, 0]
|
|
pooled_output = self.dense(first_token_tensor)
|
|
pooled_output = self.activation(pooled_output)
|
|
return pooled_output
|
|
|
|
|
|
class AlignPreTrainedModel(PreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = AlignConfig
|
|
base_model_prefix = "align"
|
|
supports_gradient_checkpointing = True
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
if isinstance(module, (nn.Linear, nn.Conv2d)):
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, AlignModel):
|
|
nn.init.xavier_uniform_(module.text_projection.weight)
|
|
module.text_projection.bias.data.zero_()
|
|
module.text_projection._is_hf_initialized = True
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
if isinstance(module, nn.LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""The text model from ALIGN without any head or projection on top.""",
|
|
ALIGN_START_DOCSTRING,
|
|
)
|
|
class AlignTextModel(AlignPreTrainedModel):
|
|
config_class = AlignTextConfig
|
|
|
|
def __init__(self, config: AlignTextConfig, add_pooling_layer: bool = True):
|
|
super().__init__(config)
|
|
self.config = config
|
|
|
|
self.embeddings = AlignTextEmbeddings(config)
|
|
self.encoder = AlignTextEncoder(config)
|
|
|
|
self.pooler = AlignTextPooler(config) if add_pooling_layer else None
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embeddings.word_embeddings = value
|
|
|
|
@add_start_docstrings_to_model_forward(ALIGN_TEXT_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=BaseModelOutputWithPoolingAndCrossAttentions, config_class=AlignTextConfig)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutputWithPoolingAndCrossAttentions]:
|
|
r"""
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, AlignTextModel
|
|
|
|
>>> model = AlignTextModel.from_pretrained("kakaobrain/align-base")
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("kakaobrain/align-base")
|
|
|
|
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
|
|
|
|
>>> outputs = model(**inputs)
|
|
>>> last_hidden_state = outputs.last_hidden_state
|
|
>>> pooled_output = outputs.pooler_output # pooled (EOS token) states
|
|
```"""
|
|
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_ids is not None and inputs_embeds is not None:
|
|
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
|
|
input_shape = input_ids.size()
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
|
|
|
batch_size, seq_length = input_shape
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(((batch_size, seq_length)), device=device)
|
|
|
|
if token_type_ids is None:
|
|
if hasattr(self.embeddings, "token_type_ids"):
|
|
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
|
|
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
|
|
token_type_ids = buffered_token_type_ids_expanded
|
|
else:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
|
|
|
|
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
|
|
# ourselves in which case we just need to make it broadcastable to all heads.
|
|
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
|
|
|
|
# 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_ids=input_ids,
|
|
position_ids=position_ids,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
)
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
attention_mask=extended_attention_mask,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output = encoder_outputs[0]
|
|
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
|
|
|
|
if not return_dict:
|
|
return (sequence_output, pooled_output) + encoder_outputs[1:]
|
|
|
|
return BaseModelOutputWithPoolingAndCrossAttentions(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
cross_attentions=encoder_outputs.cross_attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""The vision model from ALIGN without any head or projection on top.""",
|
|
ALIGN_START_DOCSTRING,
|
|
)
|
|
class AlignVisionModel(AlignPreTrainedModel):
|
|
config_class = AlignVisionConfig
|
|
main_input_name = "pixel_values"
|
|
supports_gradient_checkpointing = False
|
|
|
|
def __init__(self, config: AlignVisionConfig):
|
|
super().__init__(config)
|
|
self.config = config
|
|
self.embeddings = AlignVisionEmbeddings(config)
|
|
self.encoder = AlignVisionEncoder(config)
|
|
|
|
# Final pooling layer
|
|
if config.pooling_type == "mean":
|
|
self.pooler = nn.AvgPool2d(config.hidden_dim, ceil_mode=True)
|
|
elif config.pooling_type == "max":
|
|
self.pooler = nn.MaxPool2d(config.hidden_dim, ceil_mode=True)
|
|
else:
|
|
raise ValueError(f"config.pooling must be one of ['mean', 'max'] got {config.pooling}")
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self) -> nn.Module:
|
|
return self.vision_model.embeddings.convolution
|
|
|
|
@add_start_docstrings_to_model_forward(ALIGN_VISION_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=BaseModelOutputWithPoolingAndNoAttention, config_class=AlignVisionConfig)
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutputWithPoolingAndNoAttention]:
|
|
r"""
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from PIL import Image
|
|
>>> import requests
|
|
>>> from transformers import AutoProcessor, AlignVisionModel
|
|
|
|
>>> model = AlignVisionModel.from_pretrained("kakaobrain/align-base")
|
|
>>> processor = AutoProcessor.from_pretrained("kakaobrain/align-base")
|
|
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
|
|
>>> inputs = processor(images=image, return_tensors="pt")
|
|
|
|
>>> outputs = model(**inputs)
|
|
>>> last_hidden_state = outputs.last_hidden_state
|
|
>>> pooled_output = outputs.pooler_output # pooled CLS states
|
|
```"""
|
|
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 pixel_values is None:
|
|
raise ValueError("You have to specify pixel_values")
|
|
|
|
embedding_output = self.embeddings(pixel_values)
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
# Apply pooling
|
|
last_hidden_state = encoder_outputs[0]
|
|
pooled_output = self.pooler(last_hidden_state)
|
|
# Reshape (batch_size, projection_dim, 1 , 1) -> (batch_size, projection_dim)
|
|
pooled_output = pooled_output.reshape(pooled_output.shape[:2])
|
|
|
|
if not return_dict:
|
|
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
|
|
|
|
return BaseModelOutputWithPoolingAndNoAttention(
|
|
last_hidden_state=last_hidden_state,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(ALIGN_START_DOCSTRING)
|
|
class AlignModel(AlignPreTrainedModel):
|
|
config_class = AlignConfig
|
|
|
|
def __init__(self, config: AlignConfig):
|
|
super().__init__(config)
|
|
|
|
if not isinstance(config.text_config, AlignTextConfig):
|
|
raise ValueError(
|
|
"config.text_config is expected to be of type AlignTextConfig but is of type"
|
|
f" {type(config.text_config)}."
|
|
)
|
|
|
|
if not isinstance(config.vision_config, AlignVisionConfig):
|
|
raise ValueError(
|
|
"config.vision_config is expected to be of type AlignVisionConfig but is of type"
|
|
f" {type(config.vision_config)}."
|
|
)
|
|
|
|
text_config = config.text_config
|
|
vision_config = config.vision_config
|
|
|
|
self.projection_dim = config.projection_dim
|
|
self.text_embed_dim = text_config.hidden_size
|
|
|
|
self.text_model = AlignTextModel(text_config)
|
|
self.vision_model = AlignVisionModel(vision_config)
|
|
|
|
self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim)
|
|
self.temperature = nn.Parameter(torch.tensor(self.config.temperature_init_value))
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(ALIGN_TEXT_INPUTS_DOCSTRING)
|
|
def get_text_features(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> torch.FloatTensor:
|
|
r"""
|
|
Returns:
|
|
text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by
|
|
applying the projection layer to the pooled output of [`AlignTextModel`].
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, AlignModel
|
|
|
|
>>> model = AlignModel.from_pretrained("kakaobrain/align-base")
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("kakaobrain/align-base")
|
|
|
|
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
|
|
>>> text_features = model.get_text_features(**inputs)
|
|
```"""
|
|
# Use ALIGN model's config for some fields (if specified) instead of those of vision & text components.
|
|
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
|
|
|
|
text_outputs = self.text_model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
last_hidden_state = text_outputs[0][:, 0, :]
|
|
text_features = self.text_projection(last_hidden_state)
|
|
|
|
return text_features
|
|
|
|
@add_start_docstrings_to_model_forward(ALIGN_VISION_INPUTS_DOCSTRING)
|
|
def get_image_features(
|
|
self,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> torch.FloatTensor:
|
|
r"""
|
|
Returns:
|
|
image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by
|
|
applying the projection layer to the pooled output of [`AlignVisionModel`].
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from PIL import Image
|
|
>>> import requests
|
|
>>> from transformers import AutoProcessor, AlignModel
|
|
|
|
>>> model = AlignModel.from_pretrained("kakaobrain/align-base")
|
|
>>> processor = AutoProcessor.from_pretrained("kakaobrain/align-base")
|
|
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
|
|
>>> inputs = processor(images=image, return_tensors="pt")
|
|
|
|
>>> image_features = model.get_image_features(**inputs)
|
|
```"""
|
|
# Use ALIGN model's config for some fields (if specified) instead of those of vision & text components.
|
|
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
|
|
|
|
vision_outputs = self.vision_model(
|
|
pixel_values=pixel_values,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
image_features = vision_outputs[1] # pooled_output
|
|
|
|
return image_features
|
|
|
|
@add_start_docstrings_to_model_forward(ALIGN_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=AlignOutput, config_class=AlignConfig)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
return_loss: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, AlignOutput]:
|
|
r"""
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from PIL import Image
|
|
>>> import requests
|
|
>>> from transformers import AutoProcessor, AlignModel
|
|
|
|
>>> model = AlignModel.from_pretrained("kakaobrain/align-base")
|
|
>>> processor = AutoProcessor.from_pretrained("kakaobrain/align-base")
|
|
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
|
|
>>> inputs = processor(
|
|
... text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True
|
|
... )
|
|
|
|
>>> outputs = model(**inputs)
|
|
>>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score
|
|
>>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
|
|
```"""
|
|
# Use ALIGN model's config for some fields (if specified) instead of those of vision & text components.
|
|
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
|
|
|
|
vision_outputs = self.vision_model(
|
|
pixel_values=pixel_values,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
text_outputs = self.text_model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
image_embeds = vision_outputs[1]
|
|
text_embeds = text_outputs[0][:, 0, :]
|
|
text_embeds = self.text_projection(text_embeds)
|
|
|
|
# normalized features
|
|
image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True)
|
|
text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True)
|
|
|
|
# cosine similarity as logits
|
|
logits_per_text = torch.matmul(text_embeds, image_embeds.t()) / self.temperature
|
|
logits_per_image = logits_per_text.t()
|
|
|
|
loss = None
|
|
if return_loss:
|
|
loss = align_loss(logits_per_text)
|
|
|
|
if not return_dict:
|
|
output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return AlignOutput(
|
|
loss=loss,
|
|
logits_per_image=logits_per_image,
|
|
logits_per_text=logits_per_text,
|
|
text_embeds=text_embeds,
|
|
image_embeds=image_embeds,
|
|
text_model_output=text_outputs,
|
|
vision_model_output=vision_outputs,
|
|
)
|