1478 lines
63 KiB
Python
1478 lines
63 KiB
Python
# coding=utf-8
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# Copyright 2022 The OpenAI 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 CLIPSeg model."""
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import copy
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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_attn_mask_utils import _create_4d_causal_attention_mask, _prepare_4d_attention_mask
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from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
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from ...modeling_utils import PreTrainedModel
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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_clipseg import CLIPSegConfig, CLIPSegTextConfig, CLIPSegVisionConfig
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logger = logging.get_logger(__name__)
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_CHECKPOINT_FOR_DOC = "CIDAS/clipseg-rd64-refined"
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from ..deprecated._archive_maps import CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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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))
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# Copied from transformers.models.clip.modeling_clip.clip_loss with clip->clipseg
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def clipseg_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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@dataclass
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# Copied from transformers.models.clip.modeling_clip.CLIPOutput with CLIP->CLIPSeg
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class CLIPSegOutput(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 [`CLIPSegTextModel`].
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image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
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The image embeddings obtained by applying the projection layer to the pooled output of [`CLIPSegVisionModel`].
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text_model_output(`BaseModelOutputWithPooling`):
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The output of the [`CLIPSegTextModel`].
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vision_model_output(`BaseModelOutputWithPooling`):
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The output of the [`CLIPSegVisionModel`].
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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: BaseModelOutputWithPooling = None
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vision_model_output: BaseModelOutputWithPooling = 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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@dataclass
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class CLIPSegDecoderOutput(ModelOutput):
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"""
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Args:
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logits (`torch.FloatTensor` of shape `(batch_size, height, width)`):
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Classification scores for each pixel.
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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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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)`. Attentions weights after the attention softmax, used to compute the weighted average in
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the self-attention heads.
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"""
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logits: 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 CLIPSegImageSegmentationOutput(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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...
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vision_model_output (`BaseModelOutputWithPooling`):
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The output of the [`CLIPSegVisionModel`].
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"""
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loss: Optional[torch.FloatTensor] = None
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logits: torch.FloatTensor = None
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conditional_embeddings: torch.FloatTensor = None
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pooled_output: torch.FloatTensor = None
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vision_model_output: BaseModelOutputWithPooling = None
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decoder_output: CLIPSegDecoderOutput = 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 ["vision_model_output", "decoder_output"] else getattr(self, k).to_tuple()
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for k in self.keys()
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)
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class CLIPSegVisionEmbeddings(nn.Module):
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# Copied from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings.__init__ with CLIP->CLIPSeg
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def __init__(self, config: CLIPSegVisionConfig):
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super().__init__()
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self.config = config
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self.embed_dim = config.hidden_size
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self.image_size = config.image_size
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self.patch_size = config.patch_size
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self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))
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self.patch_embedding = nn.Conv2d(
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in_channels=config.num_channels,
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out_channels=self.embed_dim,
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kernel_size=self.patch_size,
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stride=self.patch_size,
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bias=False,
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)
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self.num_patches = (self.image_size // self.patch_size) ** 2
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self.num_positions = self.num_patches + 1
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self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
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self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False)
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def interpolate_position_embeddings(self, new_size):
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if len(new_size) != 2:
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raise ValueError("new_size should consist of 2 values")
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num_patches_one_direction = int(self.num_patches**0.5)
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# we interpolate the position embeddings in 2D
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a = self.position_embedding.weight[1:].T.view(
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1, self.config.hidden_size, num_patches_one_direction, num_patches_one_direction
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)
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b = (
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nn.functional.interpolate(a, new_size, mode="bicubic", align_corners=False)
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.squeeze(0)
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.view(self.config.hidden_size, new_size[0] * new_size[1])
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.T
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)
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result = torch.cat([self.position_embedding.weight[:1], b])
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return result
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def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
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batch_size = pixel_values.shape[0]
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patch_embeds = self.patch_embedding(pixel_values) # shape = [*, width, grid, grid]
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patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
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class_embeds = self.class_embedding.expand(batch_size, 1, -1)
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embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
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if embeddings.shape[1] != self.num_positions:
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new_shape = int(math.sqrt(embeddings.shape[1] - 1))
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embeddings = embeddings + self.interpolate_position_embeddings((new_shape, new_shape))
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embeddings = embeddings.to(embeddings.dtype)
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else:
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embeddings = embeddings + self.position_embedding(self.position_ids)
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return embeddings
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# Copied from transformers.models.clip.modeling_clip.CLIPTextEmbeddings with CLIP->CLIPSeg
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class CLIPSegTextEmbeddings(nn.Module):
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def __init__(self, config: CLIPSegTextConfig):
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super().__init__()
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embed_dim = config.hidden_size
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self.token_embedding = nn.Embedding(config.vocab_size, embed_dim)
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self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_dim)
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# position_ids (1, len position emb) is contiguous in memory and exported when serialized
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self.register_buffer(
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"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
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)
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def forward(
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self,
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input_ids: Optional[torch.LongTensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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) -> torch.Tensor:
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seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
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if position_ids is None:
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position_ids = self.position_ids[:, :seq_length]
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if inputs_embeds is None:
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inputs_embeds = self.token_embedding(input_ids)
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position_embeddings = self.position_embedding(position_ids)
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embeddings = inputs_embeds + position_embeddings
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return embeddings
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# Copied from transformers.models.clip.modeling_clip.CLIPAttention with CLIP->CLIPSeg
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class CLIPSegAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.embed_dim = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.head_dim = self.embed_dim // self.num_heads
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if self.head_dim * self.num_heads != self.embed_dim:
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raise ValueError(
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f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
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f" {self.num_heads})."
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)
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self.scale = self.head_dim**-0.5
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self.dropout = config.attention_dropout
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self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
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self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
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self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
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self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
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def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
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return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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causal_attention_mask: Optional[torch.Tensor] = None,
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output_attentions: Optional[bool] = False,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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"""Input shape: Batch x Time x Channel"""
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bsz, tgt_len, embed_dim = hidden_states.size()
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# get query proj
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query_states = self.q_proj(hidden_states) * self.scale
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key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
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value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
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proj_shape = (bsz * self.num_heads, -1, self.head_dim)
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query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
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key_states = key_states.view(*proj_shape)
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value_states = value_states.view(*proj_shape)
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src_len = key_states.size(1)
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attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
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if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
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raise ValueError(
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f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
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f" {attn_weights.size()}"
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)
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# apply the causal_attention_mask first
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if causal_attention_mask is not None:
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if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
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raise ValueError(
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f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
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f" {causal_attention_mask.size()}"
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)
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attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + causal_attention_mask
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attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
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if attention_mask is not None:
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if attention_mask.size() != (bsz, 1, tgt_len, src_len):
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raise ValueError(
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f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
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)
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attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
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attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
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attn_weights = nn.functional.softmax(attn_weights, dim=-1)
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if output_attentions:
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# this operation is a bit akward, but it's required to
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# make sure that attn_weights keeps its gradient.
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# In order to do so, attn_weights have to reshaped
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# twice and have to be reused in the following
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attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
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attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
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else:
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attn_weights_reshaped = None
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attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
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attn_output = torch.bmm(attn_probs, value_states)
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if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
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raise ValueError(
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f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
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f" {attn_output.size()}"
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)
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attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
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attn_output = attn_output.transpose(1, 2)
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attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
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attn_output = self.out_proj(attn_output)
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return attn_output, attn_weights_reshaped
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# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->CLIPSeg
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class CLIPSegMLP(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.activation_fn = ACT2FN[config.hidden_act]
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self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
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self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states = self.fc1(hidden_states)
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hidden_states = self.activation_fn(hidden_states)
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hidden_states = self.fc2(hidden_states)
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return hidden_states
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# Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->CLIPSeg
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class CLIPSegEncoderLayer(nn.Module):
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def __init__(self, config: CLIPSegConfig):
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super().__init__()
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self.embed_dim = config.hidden_size
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self.self_attn = CLIPSegAttention(config)
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self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
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self.mlp = CLIPSegMLP(config)
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self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: torch.Tensor,
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causal_attention_mask: torch.Tensor,
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output_attentions: Optional[bool] = False,
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) -> Tuple[torch.FloatTensor]:
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"""
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Args:
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hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
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attention_mask (`torch.FloatTensor`): attention mask of size
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`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
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`(config.encoder_attention_heads,)`.
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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
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returned tensors for more detail.
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"""
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residual = hidden_states
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hidden_states = self.layer_norm1(hidden_states)
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hidden_states, attn_weights = self.self_attn(
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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causal_attention_mask=causal_attention_mask,
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output_attentions=output_attentions,
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)
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hidden_states = residual + hidden_states
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residual = hidden_states
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hidden_states = self.layer_norm2(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + hidden_states
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outputs = (hidden_states,)
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if output_attentions:
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outputs += (attn_weights,)
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return outputs
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class CLIPSegPreTrainedModel(PreTrainedModel):
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"""
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An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
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models.
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"""
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config_class = CLIPSegConfig
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base_model_prefix = "clip"
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supports_gradient_checkpointing = True
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def _init_weights(self, module):
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"""Initialize the weights"""
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factor = self.config.initializer_factor
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if isinstance(module, CLIPSegTextEmbeddings):
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module.token_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02)
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module.position_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02)
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elif isinstance(module, CLIPSegVisionEmbeddings):
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factor = self.config.initializer_factor
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nn.init.normal_(module.class_embedding, mean=0.0, std=module.embed_dim**-0.5 * factor)
|
|
nn.init.normal_(module.patch_embedding.weight, std=module.config.initializer_range * factor)
|
|
nn.init.normal_(module.position_embedding.weight, std=module.config.initializer_range * factor)
|
|
elif isinstance(module, CLIPSegAttention):
|
|
factor = self.config.initializer_factor
|
|
in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
|
|
out_proj_std = (module.embed_dim**-0.5) * factor
|
|
nn.init.normal_(module.q_proj.weight, std=in_proj_std)
|
|
nn.init.normal_(module.k_proj.weight, std=in_proj_std)
|
|
nn.init.normal_(module.v_proj.weight, std=in_proj_std)
|
|
nn.init.normal_(module.out_proj.weight, std=out_proj_std)
|
|
elif isinstance(module, CLIPSegMLP):
|
|
factor = self.config.initializer_factor
|
|
in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
|
|
fc_std = (2 * module.config.hidden_size) ** -0.5 * factor
|
|
nn.init.normal_(module.fc1.weight, std=fc_std)
|
|
nn.init.normal_(module.fc2.weight, std=in_proj_std)
|
|
elif isinstance(module, CLIPSegModel):
|
|
nn.init.normal_(
|
|
module.text_projection.weight,
|
|
std=module.text_embed_dim**-0.5 * self.config.initializer_factor,
|
|
)
|
|
nn.init.normal_(
|
|
module.visual_projection.weight,
|
|
std=module.vision_embed_dim**-0.5 * self.config.initializer_factor,
|
|
)
|
|
|
|
if isinstance(module, nn.LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
if isinstance(module, nn.Linear) and module.bias is not None:
|
|
module.bias.data.zero_()
|
|
|
|
|
|
CLIPSEG_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 ([`CLIPSegConfig`]): 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.
|
|
"""
|
|
|
|
CLIPSEG_TEXT_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
|
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
|
it.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
|
config.max_position_embeddings - 1]`.
|
|
|
|
[What are position IDs?](../glossary#position-ids)
|
|
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.
|
|
"""
|
|
|
|
CLIPSEG_VISION_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
|
|
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
|
|
[`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.
|
|
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.
|
|
"""
|
|
|
|
CLIPSEG_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
|
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
|
it.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
|
config.max_position_embeddings - 1]`.
|
|
|
|
[What are position IDs?](../glossary#position-ids)
|
|
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
|
|
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
|
|
[`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.
|
|
return_loss (`bool`, *optional*):
|
|
Whether or not to return the contrastive loss.
|
|
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.
|
|
"""
|
|
|
|
|
|
# Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->CLIPSeg
|
|
class CLIPSegEncoder(nn.Module):
|
|
"""
|
|
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
|
|
[`CLIPSegEncoderLayer`].
|
|
|
|
Args:
|
|
config: CLIPSegConfig
|
|
"""
|
|
|
|
def __init__(self, config: CLIPSegConfig):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layers = nn.ModuleList([CLIPSegEncoderLayer(config) for _ in range(config.num_hidden_layers)])
|
|
self.gradient_checkpointing = False
|
|
|
|
def forward(
|
|
self,
|
|
inputs_embeds,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
causal_attention_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutput]:
|
|
r"""
|
|
Args:
|
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
|
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
|
|
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
|
|
than the model's internal embedding lookup matrix.
|
|
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Causal mask for the text model. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
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.
|
|
"""
|
|
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
|
|
|
|
encoder_states = () if output_hidden_states else None
|
|
all_attentions = () if output_attentions else None
|
|
|
|
hidden_states = inputs_embeds
|
|
for idx, encoder_layer in enumerate(self.layers):
|
|
if output_hidden_states:
|
|
encoder_states = encoder_states + (hidden_states,)
|
|
if self.gradient_checkpointing and self.training:
|
|
layer_outputs = self._gradient_checkpointing_func(
|
|
encoder_layer.__call__,
|
|
hidden_states,
|
|
attention_mask,
|
|
causal_attention_mask,
|
|
output_attentions,
|
|
)
|
|
else:
|
|
layer_outputs = encoder_layer(
|
|
hidden_states,
|
|
attention_mask,
|
|
causal_attention_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if output_attentions:
|
|
all_attentions = all_attentions + (layer_outputs[1],)
|
|
|
|
if output_hidden_states:
|
|
encoder_states = encoder_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
|
|
return BaseModelOutput(
|
|
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
|
|
)
|
|
|
|
|
|
class CLIPSegTextTransformer(nn.Module):
|
|
# Copied from transformers.models.clip.modeling_clip.CLIPTextTransformer.__init__ with CLIP->CLIPSeg
|
|
def __init__(self, config: CLIPSegTextConfig):
|
|
super().__init__()
|
|
self.config = config
|
|
embed_dim = config.hidden_size
|
|
self.embeddings = CLIPSegTextEmbeddings(config)
|
|
self.encoder = CLIPSegEncoder(config)
|
|
self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
|
|
|
|
# For `pooled_output` computation
|
|
self.eos_token_id = config.eos_token_id
|
|
|
|
@add_start_docstrings_to_model_forward(CLIPSEG_TEXT_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPSegTextConfig)
|
|
# Copied from transformers.models.clip.modeling_clip.CLIPTextTransformer.forward with clip->clipseg, CLIP->CLIPSeg
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutputWithPooling]:
|
|
r"""
|
|
Returns:
|
|
|
|
"""
|
|
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 None:
|
|
raise ValueError("You have to specify input_ids")
|
|
|
|
input_shape = input_ids.size()
|
|
input_ids = input_ids.view(-1, input_shape[-1])
|
|
|
|
hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids)
|
|
|
|
# CLIPSeg's text model uses causal mask, prepare it here.
|
|
# https://github.com/openai/CLIPSeg/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clipseg/model.py#L324
|
|
causal_attention_mask = _create_4d_causal_attention_mask(
|
|
input_shape, hidden_states.dtype, device=hidden_states.device
|
|
)
|
|
# expand attention_mask
|
|
if attention_mask is not None:
|
|
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
|
attention_mask = _prepare_4d_attention_mask(attention_mask, hidden_states.dtype)
|
|
|
|
encoder_outputs = self.encoder(
|
|
inputs_embeds=hidden_states,
|
|
attention_mask=attention_mask,
|
|
causal_attention_mask=causal_attention_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
last_hidden_state = encoder_outputs[0]
|
|
last_hidden_state = self.final_layer_norm(last_hidden_state)
|
|
|
|
if self.eos_token_id == 2:
|
|
# The `eos_token_id` was incorrect before PR #24773: Let's keep what have been done here.
|
|
# A CLIPSeg model with such `eos_token_id` in the config can't work correctly with extra new tokens added
|
|
# ------------------------------------------------------------
|
|
# text_embeds.shape = [batch_size, sequence_length, transformer.width]
|
|
# take features from the eot embedding (eot_token is the highest number in each sequence)
|
|
# casting to torch.int for onnx compatibility: argmax doesn't support int64 inputs with opset 14
|
|
pooled_output = last_hidden_state[
|
|
torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device),
|
|
input_ids.to(dtype=torch.int, device=last_hidden_state.device).argmax(dim=-1),
|
|
]
|
|
else:
|
|
# The config gets updated `eos_token_id` from PR #24773 (so the use of exta new tokens is possible)
|
|
pooled_output = last_hidden_state[
|
|
torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device),
|
|
# We need to get the first position of `eos_token_id` value (`pad_token_ids` might equal to `eos_token_id`)
|
|
(input_ids.to(dtype=torch.int, device=last_hidden_state.device) == self.eos_token_id)
|
|
.int()
|
|
.argmax(dim=-1),
|
|
]
|
|
|
|
if not return_dict:
|
|
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
|
|
|
|
return BaseModelOutputWithPooling(
|
|
last_hidden_state=last_hidden_state,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
class CLIPSegTextModel(CLIPSegPreTrainedModel):
|
|
config_class = CLIPSegTextConfig
|
|
|
|
_no_split_modules = ["CLIPSegTextEmbeddings", "CLIPSegEncoderLayer"]
|
|
|
|
def __init__(self, config: CLIPSegTextConfig):
|
|
super().__init__(config)
|
|
self.text_model = CLIPSegTextTransformer(config)
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self) -> nn.Module:
|
|
return self.text_model.embeddings.token_embedding
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.text_model.embeddings.token_embedding = value
|
|
|
|
@add_start_docstrings_to_model_forward(CLIPSEG_TEXT_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPSegTextConfig)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutputWithPooling]:
|
|
r"""
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, CLIPSegTextModel
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("CIDAS/clipseg-rd64-refined")
|
|
>>> model = CLIPSegTextModel.from_pretrained("CIDAS/clipseg-rd64-refined")
|
|
|
|
>>> 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
|
|
```"""
|
|
return self.text_model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
|
|
class CLIPSegVisionTransformer(nn.Module):
|
|
# Copied from transformers.models.clip.modeling_clip.CLIPVisionTransformer.__init__ with CLIP->CLIPSeg
|
|
def __init__(self, config: CLIPSegVisionConfig):
|
|
super().__init__()
|
|
self.config = config
|
|
embed_dim = config.hidden_size
|
|
|
|
self.embeddings = CLIPSegVisionEmbeddings(config)
|
|
self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
|
|
self.encoder = CLIPSegEncoder(config)
|
|
self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
|
|
|
|
@add_start_docstrings_to_model_forward(CLIPSEG_VISION_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPSegVisionConfig)
|
|
# Copied from transformers.models.clip.modeling_clip.CLIPVisionTransformer.forward
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutputWithPooling]:
|
|
r"""
|
|
Returns:
|
|
|
|
"""
|
|
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 pixel_values is None:
|
|
raise ValueError("You have to specify pixel_values")
|
|
|
|
hidden_states = self.embeddings(pixel_values)
|
|
hidden_states = self.pre_layrnorm(hidden_states)
|
|
|
|
encoder_outputs = self.encoder(
|
|
inputs_embeds=hidden_states,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
last_hidden_state = encoder_outputs[0]
|
|
pooled_output = last_hidden_state[:, 0, :]
|
|
pooled_output = self.post_layernorm(pooled_output)
|
|
|
|
if not return_dict:
|
|
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
|
|
|
|
return BaseModelOutputWithPooling(
|
|
last_hidden_state=last_hidden_state,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
class CLIPSegVisionModel(CLIPSegPreTrainedModel):
|
|
config_class = CLIPSegVisionConfig
|
|
main_input_name = "pixel_values"
|
|
|
|
def __init__(self, config: CLIPSegVisionConfig):
|
|
super().__init__(config)
|
|
self.vision_model = CLIPSegVisionTransformer(config)
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self) -> nn.Module:
|
|
return self.vision_model.embeddings.patch_embedding
|
|
|
|
@add_start_docstrings_to_model_forward(CLIPSEG_VISION_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPSegVisionConfig)
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutputWithPooling]:
|
|
r"""
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from PIL import Image
|
|
>>> import requests
|
|
>>> from transformers import AutoProcessor, CLIPSegVisionModel
|
|
|
|
>>> processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
|
|
>>> model = CLIPSegVisionModel.from_pretrained("CIDAS/clipseg-rd64-refined")
|
|
|
|
>>> 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
|
|
```"""
|
|
return self.vision_model(
|
|
pixel_values=pixel_values,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(CLIPSEG_START_DOCSTRING)
|
|
class CLIPSegModel(CLIPSegPreTrainedModel):
|
|
config_class = CLIPSegConfig
|
|
|
|
def __init__(self, config: CLIPSegConfig):
|
|
super().__init__(config)
|
|
|
|
if not isinstance(config.text_config, CLIPSegTextConfig):
|
|
raise ValueError(
|
|
"config.text_config is expected to be of type CLIPSegTextConfig but is of type"
|
|
f" {type(config.text_config)}."
|
|
)
|
|
|
|
if not isinstance(config.vision_config, CLIPSegVisionConfig):
|
|
raise ValueError(
|
|
"config.vision_config is expected to be of type CLIPSegVisionConfig 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.vision_embed_dim = vision_config.hidden_size
|
|
|
|
self.text_model = CLIPSegTextTransformer(text_config)
|
|
self.vision_model = CLIPSegVisionTransformer(vision_config)
|
|
|
|
self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False)
|
|
self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim, bias=False)
|
|
self.logit_scale = nn.Parameter(torch.tensor(self.config.logit_scale_init_value))
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(CLIPSEG_TEXT_INPUTS_DOCSTRING)
|
|
def get_text_features(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: 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 [`CLIPSegTextModel`].
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, CLIPSegModel
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("CIDAS/clipseg-rd64-refined")
|
|
>>> model = CLIPSegModel.from_pretrained("CIDAS/clipseg-rd64-refined")
|
|
|
|
>>> 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 CLIPSEG 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,
|
|
position_ids=position_ids,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
pooled_output = text_outputs[1]
|
|
text_features = self.text_projection(pooled_output)
|
|
|
|
return text_features
|
|
|
|
@add_start_docstrings_to_model_forward(CLIPSEG_VISION_INPUTS_DOCSTRING)
|
|
def get_image_features(
|
|
self,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = 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 [`CLIPSegVisionModel`].
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from PIL import Image
|
|
>>> import requests
|
|
>>> from transformers import AutoProcessor, CLIPSegModel
|
|
|
|
>>> processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
|
|
>>> model = CLIPSegModel.from_pretrained("CIDAS/clipseg-rd64-refined")
|
|
|
|
>>> 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 CLIPSEG 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_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
pooled_output = vision_outputs[1] # pooled_output
|
|
image_features = self.visual_projection(pooled_output)
|
|
|
|
return image_features
|
|
|
|
@add_start_docstrings_to_model_forward(CLIPSEG_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=CLIPSegOutput, config_class=CLIPSegConfig)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
return_loss: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, CLIPSegOutput]:
|
|
r"""
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from PIL import Image
|
|
>>> import requests
|
|
>>> from transformers import AutoProcessor, CLIPSegModel
|
|
|
|
>>> processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
|
|
>>> model = CLIPSegModel.from_pretrained("CIDAS/clipseg-rd64-refined")
|
|
|
|
>>> 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 CLIPSEG 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_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
text_outputs = self.text_model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
image_embeds = vision_outputs[1]
|
|
image_embeds = self.visual_projection(image_embeds)
|
|
|
|
text_embeds = text_outputs[1]
|
|
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
|
|
logit_scale = self.logit_scale.exp()
|
|
logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * logit_scale
|
|
logits_per_image = logits_per_text.t()
|
|
|
|
loss = None
|
|
if return_loss:
|
|
loss = clipseg_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 CLIPSegOutput(
|
|
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,
|
|
)
|
|
|
|
|
|
class CLIPSegDecoderLayer(nn.Module):
|
|
"""
|
|
CLIPSeg decoder layer, which is identical to `CLIPSegEncoderLayer`, except that normalization is applied after
|
|
self-attention/MLP, rather than before.
|
|
"""
|
|
|
|
# Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer.__init__ with CLIP->CLIPSeg
|
|
def __init__(self, config: CLIPSegConfig):
|
|
super().__init__()
|
|
self.embed_dim = config.hidden_size
|
|
self.self_attn = CLIPSegAttention(config)
|
|
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
|
|
self.mlp = CLIPSegMLP(config)
|
|
self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: torch.Tensor,
|
|
causal_attention_mask: torch.Tensor,
|
|
output_attentions: Optional[bool] = False,
|
|
) -> Tuple[torch.FloatTensor]:
|
|
"""
|
|
Args:
|
|
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
|
attention_mask (`torch.FloatTensor`): attention mask of size
|
|
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
|
|
`(config.encoder_attention_heads,)`.
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
|
returned tensors for more detail.
|
|
"""
|
|
residual = hidden_states
|
|
|
|
hidden_states, attn_weights = self.self_attn(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
causal_attention_mask=causal_attention_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
|
|
hidden_states = residual + hidden_states
|
|
hidden_states = self.layer_norm1(hidden_states)
|
|
|
|
residual = hidden_states
|
|
hidden_states = self.mlp(hidden_states)
|
|
hidden_states = residual + hidden_states
|
|
hidden_states = self.layer_norm2(hidden_states)
|
|
|
|
outputs = (hidden_states,)
|
|
|
|
if output_attentions:
|
|
outputs += (attn_weights,)
|
|
|
|
return outputs
|
|
|
|
|
|
class CLIPSegDecoder(CLIPSegPreTrainedModel):
|
|
def __init__(self, config: CLIPSegConfig):
|
|
super().__init__(config)
|
|
|
|
self.conditional_layer = config.conditional_layer
|
|
|
|
self.film_mul = nn.Linear(config.projection_dim, config.reduce_dim)
|
|
self.film_add = nn.Linear(config.projection_dim, config.reduce_dim)
|
|
|
|
if config.use_complex_transposed_convolution:
|
|
transposed_kernels = (config.vision_config.patch_size // 4, config.vision_config.patch_size // 4)
|
|
|
|
self.transposed_convolution = nn.Sequential(
|
|
nn.Conv2d(config.reduce_dim, config.reduce_dim, kernel_size=3, padding=1),
|
|
nn.ReLU(),
|
|
nn.ConvTranspose2d(
|
|
config.reduce_dim,
|
|
config.reduce_dim // 2,
|
|
kernel_size=transposed_kernels[0],
|
|
stride=transposed_kernels[0],
|
|
),
|
|
nn.ReLU(),
|
|
nn.ConvTranspose2d(
|
|
config.reduce_dim // 2, 1, kernel_size=transposed_kernels[1], stride=transposed_kernels[1]
|
|
),
|
|
)
|
|
else:
|
|
self.transposed_convolution = nn.ConvTranspose2d(
|
|
config.reduce_dim, 1, config.vision_config.patch_size, stride=config.vision_config.patch_size
|
|
)
|
|
|
|
depth = len(config.extract_layers)
|
|
self.reduces = nn.ModuleList(
|
|
[nn.Linear(config.vision_config.hidden_size, config.reduce_dim) for _ in range(depth)]
|
|
)
|
|
|
|
decoder_config = copy.deepcopy(config.vision_config)
|
|
decoder_config.hidden_size = config.reduce_dim
|
|
decoder_config.num_attention_heads = config.decoder_num_attention_heads
|
|
decoder_config.intermediate_size = config.decoder_intermediate_size
|
|
decoder_config.hidden_act = "relu"
|
|
self.layers = nn.ModuleList([CLIPSegDecoderLayer(decoder_config) for _ in range(len(config.extract_layers))])
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: Tuple[torch.Tensor],
|
|
conditional_embeddings: torch.Tensor,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = True,
|
|
):
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_attentions = () if output_attentions else None
|
|
|
|
activations = hidden_states[::-1]
|
|
|
|
output = None
|
|
for i, (activation, layer, reduce) in enumerate(zip(activations, self.layers, self.reduces)):
|
|
if output is not None:
|
|
output = reduce(activation) + output
|
|
else:
|
|
output = reduce(activation)
|
|
|
|
if i == self.conditional_layer:
|
|
output = self.film_mul(conditional_embeddings) * output.permute(1, 0, 2) + self.film_add(
|
|
conditional_embeddings
|
|
)
|
|
output = output.permute(1, 0, 2)
|
|
|
|
layer_outputs = layer(
|
|
output, attention_mask=None, causal_attention_mask=None, output_attentions=output_attentions
|
|
)
|
|
|
|
output = layer_outputs[0]
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states += (output,)
|
|
|
|
if output_attentions:
|
|
all_attentions += (layer_outputs[1],)
|
|
|
|
output = output[:, 1:, :].permute(0, 2, 1) # remove cls token and reshape to [batch_size, reduce_dim, seq_len]
|
|
|
|
size = int(math.sqrt(output.shape[2]))
|
|
|
|
batch_size = conditional_embeddings.shape[0]
|
|
output = output.view(batch_size, output.shape[1], size, size)
|
|
|
|
logits = self.transposed_convolution(output).squeeze(1)
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [logits, all_hidden_states, all_attentions] if v is not None)
|
|
|
|
return CLIPSegDecoderOutput(
|
|
logits=logits,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
CLIPSeg model with a Transformer-based decoder on top for zero-shot and one-shot image segmentation.
|
|
""",
|
|
CLIPSEG_START_DOCSTRING,
|
|
)
|
|
class CLIPSegForImageSegmentation(CLIPSegPreTrainedModel):
|
|
config_class = CLIPSegConfig
|
|
|
|
def __init__(self, config: CLIPSegConfig):
|
|
super().__init__(config)
|
|
|
|
self.config = config
|
|
|
|
self.clip = CLIPSegModel(config)
|
|
self.extract_layers = config.extract_layers
|
|
|
|
self.decoder = CLIPSegDecoder(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_conditional_embeddings(
|
|
self,
|
|
batch_size: int = None,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
conditional_pixel_values: Optional[torch.Tensor] = None,
|
|
):
|
|
if input_ids is not None:
|
|
# compute conditional embeddings from texts
|
|
if len(input_ids) != batch_size:
|
|
raise ValueError("Make sure to pass as many prompt texts as there are query images")
|
|
with torch.no_grad():
|
|
conditional_embeddings = self.clip.get_text_features(
|
|
input_ids, attention_mask=attention_mask, position_ids=position_ids
|
|
)
|
|
elif conditional_pixel_values is not None:
|
|
# compute conditional embeddings from images
|
|
if len(conditional_pixel_values) != batch_size:
|
|
raise ValueError("Make sure to pass as many prompt images as there are query images")
|
|
with torch.no_grad():
|
|
conditional_embeddings = self.clip.get_image_features(conditional_pixel_values)
|
|
else:
|
|
raise ValueError(
|
|
"Invalid conditional, should be either provided as `input_ids` or `conditional_pixel_values`"
|
|
)
|
|
|
|
return conditional_embeddings
|
|
|
|
@add_start_docstrings_to_model_forward(CLIPSEG_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=CLIPSegImageSegmentationOutput, config_class=CLIPSegTextConfig)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.FloatTensor] = None,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
conditional_pixel_values: Optional[torch.FloatTensor] = None,
|
|
conditional_embeddings: Optional[torch.FloatTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, CLIPSegOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the sequence 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).
|
|
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoProcessor, CLIPSegForImageSegmentation
|
|
>>> from PIL import Image
|
|
>>> import requests
|
|
|
|
>>> processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
|
|
>>> model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")
|
|
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
>>> texts = ["a cat", "a remote", "a blanket"]
|
|
>>> inputs = processor(text=texts, images=[image] * len(texts), padding=True, return_tensors="pt")
|
|
|
|
>>> outputs = model(**inputs)
|
|
|
|
>>> logits = outputs.logits
|
|
>>> print(logits.shape)
|
|
torch.Size([3, 352, 352])
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
# step 1: forward the query images through the frozen CLIP vision encoder
|
|
with torch.no_grad():
|
|
vision_outputs = self.clip.vision_model(
|
|
pixel_values=pixel_values,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=True, # we need the intermediate hidden states
|
|
return_dict=return_dict,
|
|
)
|
|
pooled_output = self.clip.visual_projection(vision_outputs[1])
|
|
|
|
hidden_states = vision_outputs.hidden_states if return_dict else vision_outputs[2]
|
|
# we add +1 here as the hidden states also include the initial embeddings
|
|
activations = [hidden_states[i + 1] for i in self.extract_layers]
|
|
|
|
# update vision_outputs
|
|
if return_dict:
|
|
vision_outputs = BaseModelOutputWithPooling(
|
|
last_hidden_state=vision_outputs.last_hidden_state,
|
|
pooler_output=vision_outputs.pooler_output,
|
|
hidden_states=vision_outputs.hidden_states if output_hidden_states else None,
|
|
attentions=vision_outputs.attentions,
|
|
)
|
|
else:
|
|
vision_outputs = (
|
|
vision_outputs[:2] + vision_outputs[3:] if not output_hidden_states else vision_outputs
|
|
)
|
|
|
|
# step 2: compute conditional embeddings, either from text, images or an own provided embedding
|
|
if conditional_embeddings is None:
|
|
conditional_embeddings = self.get_conditional_embeddings(
|
|
batch_size=pixel_values.shape[0],
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
conditional_pixel_values=conditional_pixel_values,
|
|
)
|
|
else:
|
|
if conditional_embeddings.shape[0] != pixel_values.shape[0]:
|
|
raise ValueError(
|
|
"Make sure to pass as many conditional embeddings as there are query images in the batch"
|
|
)
|
|
if conditional_embeddings.shape[1] != self.config.projection_dim:
|
|
raise ValueError(
|
|
"Make sure that the feature dimension of the conditional embeddings matches"
|
|
" `config.projection_dim`."
|
|
)
|
|
|
|
# step 3: forward both the pooled output and the activations through the lightweight decoder to predict masks
|
|
decoder_outputs = self.decoder(
|
|
activations,
|
|
conditional_embeddings,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
logits = decoder_outputs.logits if return_dict else decoder_outputs[0]
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# move labels to the correct device to enable PP
|
|
labels = labels.to(logits.device)
|
|
loss_fn = nn.BCEWithLogitsLoss()
|
|
loss = loss_fn(logits, labels)
|
|
|
|
if not return_dict:
|
|
output = (logits, conditional_embeddings, pooled_output, vision_outputs, decoder_outputs)
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return CLIPSegImageSegmentationOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
conditional_embeddings=conditional_embeddings,
|
|
pooled_output=pooled_output,
|
|
vision_model_output=vision_outputs,
|
|
decoder_output=decoder_outputs,
|
|
)
|