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748 lines
30 KiB
748 lines
30 KiB
# Copyright 2021 Facebook AI Research (FAIR), Ross Wightman, The HuggingFace Inc. 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 DeiT model."""
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import collections.abc
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from collections.abc import Callable
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from dataclasses import dataclass
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import torch
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from torch import nn
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from ... import initialization as init
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from ...activations import ACT2FN
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import (
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BaseModelOutput,
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BaseModelOutputWithPooling,
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ImageClassifierOutput,
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MaskedImageModelingOutput,
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)
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from ...processing_utils import Unpack
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from ...utils import ModelOutput, TransformersKwargs, auto_docstring, logging, torch_int
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from ...utils.generic import can_return_tuple, check_model_inputs
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from .configuration_deit import DeiTConfig
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logger = logging.get_logger(__name__)
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class DeiTEmbeddings(nn.Module):
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"""
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Construct the CLS token, distillation token, position and patch embeddings. Optionally, also the mask token.
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"""
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def __init__(self, config: DeiTConfig, use_mask_token: bool = False) -> None:
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super().__init__()
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self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
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self.distillation_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
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self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) if use_mask_token else None
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self.patch_embeddings = DeiTPatchEmbeddings(config)
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num_patches = self.patch_embeddings.num_patches
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self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 2, config.hidden_size))
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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self.patch_size = config.patch_size
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def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
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"""
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This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
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images. This method is also adapted to support torch.jit tracing and 2 class embeddings.
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Adapted from:
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- https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
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- https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
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"""
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num_patches = embeddings.shape[1] - 2
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num_positions = self.position_embeddings.shape[1] - 2
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# always interpolate when tracing to ensure the exported model works for dynamic input shapes
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if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
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return self.position_embeddings
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class_and_dist_pos_embed = self.position_embeddings[:, :2]
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patch_pos_embed = self.position_embeddings[:, 2:]
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dim = embeddings.shape[-1]
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new_height = height // self.patch_size
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new_width = width // self.patch_size
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sqrt_num_positions = torch_int(num_positions**0.5)
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patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
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patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
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patch_pos_embed = nn.functional.interpolate(
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patch_pos_embed,
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size=(new_height, new_width),
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mode="bicubic",
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align_corners=False,
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)
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patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
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return torch.cat((class_and_dist_pos_embed, patch_pos_embed), dim=1)
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def forward(
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self,
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pixel_values: torch.Tensor,
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bool_masked_pos: torch.BoolTensor | None = None,
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interpolate_pos_encoding: bool = False,
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) -> torch.Tensor:
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_, _, height, width = pixel_values.shape
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embeddings = self.patch_embeddings(pixel_values)
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batch_size, seq_length, _ = embeddings.size()
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if bool_masked_pos is not None:
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mask_tokens = self.mask_token.expand(batch_size, seq_length, -1)
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# replace the masked visual tokens by mask_tokens
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mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
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embeddings = embeddings * (1.0 - mask) + mask_tokens * mask
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cls_tokens = self.cls_token.expand(batch_size, -1, -1)
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distillation_tokens = self.distillation_token.expand(batch_size, -1, -1)
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embeddings = torch.cat((cls_tokens, distillation_tokens, embeddings), dim=1)
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position_embedding = self.position_embeddings
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if interpolate_pos_encoding:
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position_embedding = self.interpolate_pos_encoding(embeddings, height, width)
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embeddings = embeddings + position_embedding
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embeddings = self.dropout(embeddings)
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return embeddings
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class DeiTPatchEmbeddings(nn.Module):
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"""
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This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
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`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
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Transformer.
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"""
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def __init__(self, config):
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super().__init__()
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image_size, patch_size = config.image_size, config.patch_size
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num_channels, hidden_size = config.num_channels, config.hidden_size
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image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
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patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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self.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.num_patches = num_patches
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self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)
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def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
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batch_size, num_channels, height, width = pixel_values.shape
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if num_channels != self.num_channels:
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raise ValueError(
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"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
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)
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x = self.projection(pixel_values).flatten(2).transpose(1, 2)
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return x
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# Copied from transformers.models.bert.modeling_bert.eager_attention_forward
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def eager_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: torch.Tensor | None,
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scaling: float | None = None,
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dropout: float = 0.0,
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**kwargs: Unpack[TransformersKwargs],
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):
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if scaling is None:
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scaling = query.size(-1) ** -0.5
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# Take the dot product between "query" and "key" to get the raw attention scores.
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attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
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if attention_mask is not None:
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attention_mask = attention_mask[:, :, :, : key.shape[-2]]
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attn_weights = attn_weights + attention_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1)
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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attn_output = torch.matmul(attn_weights, value)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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# Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->DeiT
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class DeiTSelfAttention(nn.Module):
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def __init__(self, config: DeiTConfig):
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super().__init__()
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if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
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raise ValueError(
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f"The hidden size {config.hidden_size} is not a multiple of the number of attention "
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f"heads {config.num_attention_heads}."
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)
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self.config = config
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.dropout_prob = config.attention_probs_dropout_prob
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self.scaling = self.attention_head_size**-0.5
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self.is_causal = False
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self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
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self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
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self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
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def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
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batch_size = hidden_states.shape[0]
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new_shape = batch_size, -1, self.num_attention_heads, self.attention_head_size
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key_layer = self.key(hidden_states).view(*new_shape).transpose(1, 2)
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value_layer = self.value(hidden_states).view(*new_shape).transpose(1, 2)
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query_layer = self.query(hidden_states).view(*new_shape).transpose(1, 2)
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attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
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self.config._attn_implementation, eager_attention_forward
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)
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context_layer, attention_probs = attention_interface(
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self,
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query_layer,
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key_layer,
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value_layer,
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None,
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is_causal=self.is_causal,
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scaling=self.scaling,
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dropout=0.0 if not self.training else self.dropout_prob,
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)
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new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
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context_layer = context_layer.reshape(new_context_layer_shape)
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return context_layer, attention_probs
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# Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->DeiT
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class DeiTSelfOutput(nn.Module):
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"""
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The residual connection is defined in DeiTLayer instead of here (as is the case with other models), due to the
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layernorm applied before each block.
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"""
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def __init__(self, config: DeiTConfig):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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return hidden_states
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# Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->DeiT
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class DeiTAttention(nn.Module):
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def __init__(self, config: DeiTConfig):
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super().__init__()
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self.attention = DeiTSelfAttention(config)
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self.output = DeiTSelfOutput(config)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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self_attn_output, _ = self.attention(hidden_states)
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output = self.output(self_attn_output, hidden_states)
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return output
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# Copied from transformers.models.vit.modeling_vit.ViTIntermediate with ViT->DeiT
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class DeiTIntermediate(nn.Module):
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def __init__(self, config: DeiTConfig):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
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if isinstance(config.hidden_act, str):
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self.intermediate_act_fn = ACT2FN[config.hidden_act]
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else:
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self.intermediate_act_fn = config.hidden_act
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.intermediate_act_fn(hidden_states)
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return hidden_states
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# Copied from transformers.models.vit.modeling_vit.ViTOutput with ViT->DeiT
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class DeiTOutput(nn.Module):
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def __init__(self, config: DeiTConfig):
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super().__init__()
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self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = hidden_states + input_tensor
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return hidden_states
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# Copied from transformers.models.vit.modeling_vit.ViTLayer with ViT->DeiT,VIT->DEIT
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class DeiTLayer(GradientCheckpointingLayer):
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"""This corresponds to the Block class in the timm implementation."""
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def __init__(self, config: DeiTConfig):
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super().__init__()
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self.chunk_size_feed_forward = config.chunk_size_feed_forward
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self.seq_len_dim = 1
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self.attention = DeiTAttention(config)
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self.intermediate = DeiTIntermediate(config)
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self.output = DeiTOutput(config)
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self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states_norm = self.layernorm_before(hidden_states)
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attention_output = self.attention(hidden_states_norm)
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# first residual connection
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hidden_states = attention_output + hidden_states
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# in DeiT, layernorm is also applied after self-attention
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layer_output = self.layernorm_after(hidden_states)
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layer_output = self.intermediate(layer_output)
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# second residual connection is done here
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layer_output = self.output(layer_output, hidden_states)
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return layer_output
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# Copied from transformers.models.vit.modeling_vit.ViTEncoder with ViT->DeiT
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class DeiTEncoder(nn.Module):
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def __init__(self, config: DeiTConfig):
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super().__init__()
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self.config = config
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self.layer = nn.ModuleList([DeiTLayer(config) for _ in range(config.num_hidden_layers)])
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self.gradient_checkpointing = False
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def forward(self, hidden_states: torch.Tensor) -> BaseModelOutput:
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for i, layer_module in enumerate(self.layer):
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hidden_states = layer_module(hidden_states)
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return BaseModelOutput(last_hidden_state=hidden_states)
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@auto_docstring
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class DeiTPreTrainedModel(PreTrainedModel):
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config: DeiTConfig
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base_model_prefix = "deit"
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main_input_name = "pixel_values"
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input_modalities = ("image",)
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supports_gradient_checkpointing = True
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_no_split_modules = ["DeiTLayer"]
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_supports_sdpa = True
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_supports_flash_attn = True
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_supports_flex_attn = True
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_supports_attention_backend = True
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_can_record_outputs = {
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"hidden_states": DeiTLayer,
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"attentions": DeiTSelfAttention,
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}
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@torch.no_grad()
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def _init_weights(self, module: nn.Linear | nn.Conv2d | nn.LayerNorm) -> None:
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"""Initialize the weights"""
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if isinstance(module, (nn.Linear, nn.Conv2d)):
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init.trunc_normal_(module.weight, mean=0.0, std=self.config.initializer_range)
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if module.bias is not None:
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init.zeros_(module.bias)
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elif isinstance(module, nn.LayerNorm):
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init.zeros_(module.bias)
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init.ones_(module.weight)
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elif isinstance(module, DeiTEmbeddings):
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init.zeros_(module.cls_token)
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init.zeros_(module.position_embeddings)
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init.zeros_(module.distillation_token)
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if module.mask_token is not None:
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init.zeros_(module.mask_token)
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@auto_docstring
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class DeiTModel(DeiTPreTrainedModel):
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def __init__(self, config: DeiTConfig, add_pooling_layer: bool = True, use_mask_token: bool = False) -> None:
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r"""
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add_pooling_layer (bool, *optional*, defaults to `True`):
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Whether to add a pooling layer
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use_mask_token (`bool`, *optional*, defaults to `False`):
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Whether to use a mask token for masked image modeling.
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"""
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super().__init__(config)
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self.config = config
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self.embeddings = DeiTEmbeddings(config, use_mask_token=use_mask_token)
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self.encoder = DeiTEncoder(config)
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self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.pooler = DeiTPooler(config) if add_pooling_layer else None
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# Initialize weights and apply final processing
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self.post_init()
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def get_input_embeddings(self) -> DeiTPatchEmbeddings:
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return self.embeddings.patch_embeddings
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@check_model_inputs(tie_last_hidden_states=False)
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@auto_docstring
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def forward(
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self,
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pixel_values: torch.Tensor | None = None,
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bool_masked_pos: torch.BoolTensor | None = None,
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interpolate_pos_encoding: bool = False,
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**kwargs: Unpack[TransformersKwargs],
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) -> BaseModelOutputWithPooling:
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r"""
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bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*):
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Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
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"""
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if pixel_values is None:
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raise ValueError("You have to specify pixel_values")
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# TODO: maybe have a cleaner way to cast the input (from `ImageProcessor` side?)
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expected_dtype = self.embeddings.patch_embeddings.projection.weight.dtype
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if pixel_values.dtype != expected_dtype:
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pixel_values = pixel_values.to(expected_dtype)
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embedding_output = self.embeddings(
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pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding
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)
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encoder_outputs: BaseModelOutput = self.encoder(embedding_output)
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sequence_output = encoder_outputs.last_hidden_state
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sequence_output = self.layernorm(sequence_output)
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pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
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return BaseModelOutputWithPooling(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
)
|
|
|
|
|
|
# Copied from transformers.models.vit.modeling_vit.ViTPooler with ViT->DeiT
|
|
class DeiTPooler(nn.Module):
|
|
def __init__(self, config: DeiTConfig):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.pooler_output_size)
|
|
self.activation = ACT2FN[config.pooler_act]
|
|
|
|
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
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
DeiT Model with a decoder on top for masked image modeling, as proposed in [SimMIM](https://huggingface.co/papers/2111.09886).
|
|
|
|
<Tip>
|
|
|
|
Note that we provide a script to pre-train this model on custom data in our [examples
|
|
directory](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining).
|
|
|
|
</Tip>
|
|
"""
|
|
)
|
|
class DeiTForMaskedImageModeling(DeiTPreTrainedModel):
|
|
def __init__(self, config: DeiTConfig) -> None:
|
|
super().__init__(config)
|
|
|
|
self.deit = DeiTModel(config, add_pooling_layer=False, use_mask_token=True)
|
|
|
|
self.decoder = nn.Sequential(
|
|
nn.Conv2d(
|
|
in_channels=config.hidden_size,
|
|
out_channels=config.encoder_stride**2 * config.num_channels,
|
|
kernel_size=1,
|
|
),
|
|
nn.PixelShuffle(config.encoder_stride),
|
|
)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
pixel_values: torch.Tensor | None = None,
|
|
bool_masked_pos: torch.BoolTensor | None = None,
|
|
interpolate_pos_encoding: bool = False,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> MaskedImageModelingOutput:
|
|
r"""
|
|
bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`):
|
|
Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
|
|
|
|
Examples:
|
|
```python
|
|
>>> from transformers import AutoImageProcessor, DeiTForMaskedImageModeling
|
|
>>> import torch
|
|
>>> from PIL import Image
|
|
>>> import httpx
|
|
>>> from io import BytesIO
|
|
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> with httpx.stream("GET", url) as response:
|
|
... image = Image.open(BytesIO(response.read()))
|
|
|
|
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224")
|
|
>>> model = DeiTForMaskedImageModeling.from_pretrained("facebook/deit-base-distilled-patch16-224")
|
|
|
|
>>> num_patches = (model.config.image_size // model.config.patch_size) ** 2
|
|
>>> pixel_values = image_processor(images=image, return_tensors="pt").pixel_values
|
|
>>> # create random boolean mask of shape (batch_size, num_patches)
|
|
>>> bool_masked_pos = torch.randint(low=0, high=2, size=(1, num_patches)).bool()
|
|
|
|
>>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
|
|
>>> loss, reconstructed_pixel_values = outputs.loss, outputs.reconstruction
|
|
>>> list(reconstructed_pixel_values.shape)
|
|
[1, 3, 224, 224]
|
|
```"""
|
|
|
|
outputs: BaseModelOutputWithPooling = self.deit(
|
|
pixel_values,
|
|
bool_masked_pos=bool_masked_pos,
|
|
interpolate_pos_encoding=interpolate_pos_encoding,
|
|
**kwargs,
|
|
)
|
|
|
|
sequence_output = outputs.last_hidden_state
|
|
|
|
# Reshape to (batch_size, num_channels, height, width)
|
|
sequence_output = sequence_output[:, 1:-1]
|
|
batch_size, sequence_length, num_channels = sequence_output.shape
|
|
height = width = int(sequence_length**0.5)
|
|
sequence_output = sequence_output.permute(0, 2, 1).reshape(batch_size, num_channels, height, width)
|
|
|
|
# Reconstruct pixel values
|
|
reconstructed_pixel_values = self.decoder(sequence_output)
|
|
|
|
masked_im_loss = None
|
|
if bool_masked_pos is not None:
|
|
size = self.config.image_size // self.config.patch_size
|
|
bool_masked_pos = bool_masked_pos.reshape(-1, size, size)
|
|
mask = (
|
|
bool_masked_pos.repeat_interleave(self.config.patch_size, 1)
|
|
.repeat_interleave(self.config.patch_size, 2)
|
|
.unsqueeze(1)
|
|
.contiguous()
|
|
)
|
|
reconstruction_loss = nn.functional.l1_loss(pixel_values, reconstructed_pixel_values, reduction="none")
|
|
masked_im_loss = (reconstruction_loss * mask).sum() / (mask.sum() + 1e-5) / self.config.num_channels
|
|
|
|
return MaskedImageModelingOutput(
|
|
loss=masked_im_loss,
|
|
reconstruction=reconstructed_pixel_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
DeiT Model transformer with an image classification head on top (a linear layer on top of the final hidden state of
|
|
the [CLS] token) e.g. for ImageNet.
|
|
"""
|
|
)
|
|
class DeiTForImageClassification(DeiTPreTrainedModel):
|
|
def __init__(self, config: DeiTConfig) -> None:
|
|
super().__init__(config)
|
|
|
|
self.num_labels = config.num_labels
|
|
self.deit = DeiTModel(config, add_pooling_layer=False)
|
|
|
|
# Classifier head
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity()
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
pixel_values: torch.Tensor | None = None,
|
|
labels: torch.Tensor | None = None,
|
|
interpolate_pos_encoding: bool = False,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> ImageClassifierOutput:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the image 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).
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoImageProcessor, DeiTForImageClassification
|
|
>>> import torch
|
|
>>> from PIL import Image
|
|
>>> import httpx
|
|
>>> from io import BytesIO
|
|
|
|
>>> torch.manual_seed(3) # doctest: +IGNORE_RESULT
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> with httpx.stream("GET", url) as response:
|
|
... image = Image.open(BytesIO(response.read()))
|
|
|
|
>>> # note: we are loading a DeiTForImageClassificationWithTeacher from the hub here,
|
|
>>> # so the head will be randomly initialized, hence the predictions will be random
|
|
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224")
|
|
>>> model = DeiTForImageClassification.from_pretrained("facebook/deit-base-distilled-patch16-224")
|
|
|
|
>>> inputs = image_processor(images=image, return_tensors="pt")
|
|
>>> outputs = model(**inputs)
|
|
>>> logits = outputs.logits
|
|
>>> # model predicts one of the 1000 ImageNet classes
|
|
>>> predicted_class_idx = logits.argmax(-1).item()
|
|
>>> print("Predicted class:", model.config.id2label[predicted_class_idx])
|
|
Predicted class: Polaroid camera, Polaroid Land camera
|
|
```"""
|
|
|
|
outputs: BaseModelOutputWithPooling = self.deit(
|
|
pixel_values,
|
|
interpolate_pos_encoding=interpolate_pos_encoding,
|
|
**kwargs,
|
|
)
|
|
|
|
sequence_output = outputs.last_hidden_state
|
|
|
|
logits = self.classifier(sequence_output[:, 0, :])
|
|
# we don't use the distillation token
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(labels, logits, self.config, **kwargs)
|
|
|
|
return ImageClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@dataclass
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
Output type of [`DeiTForImageClassificationWithTeacher`].
|
|
"""
|
|
)
|
|
class DeiTForImageClassificationWithTeacherOutput(ModelOutput):
|
|
r"""
|
|
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
|
|
Prediction scores as the average of the cls_logits and distillation logits.
|
|
cls_logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
|
|
Prediction scores of the classification head (i.e. the linear layer on top of the final hidden state of the
|
|
class token).
|
|
distillation_logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
|
|
Prediction scores of the distillation head (i.e. the linear layer on top of the final hidden state of the
|
|
distillation token).
|
|
"""
|
|
|
|
logits: torch.FloatTensor | None = None
|
|
cls_logits: torch.FloatTensor | None = None
|
|
distillation_logits: torch.FloatTensor | None = None
|
|
hidden_states: tuple[torch.FloatTensor] | None = None
|
|
attentions: tuple[torch.FloatTensor] | None = None
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
DeiT Model transformer with image classification heads on top (a linear layer on top of the final hidden state of
|
|
the [CLS] token and a linear layer on top of the final hidden state of the distillation token) e.g. for ImageNet.
|
|
|
|
.. warning::
|
|
|
|
This model supports inference-only. Fine-tuning with distillation (i.e. with a teacher) is not yet
|
|
supported.
|
|
"""
|
|
)
|
|
class DeiTForImageClassificationWithTeacher(DeiTPreTrainedModel):
|
|
def __init__(self, config: DeiTConfig) -> None:
|
|
super().__init__(config)
|
|
|
|
self.num_labels = config.num_labels
|
|
self.deit = DeiTModel(config, add_pooling_layer=False)
|
|
|
|
# Classifier heads
|
|
self.cls_classifier = (
|
|
nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity()
|
|
)
|
|
self.distillation_classifier = (
|
|
nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity()
|
|
)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
pixel_values: torch.Tensor | None = None,
|
|
interpolate_pos_encoding: bool = False,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> DeiTForImageClassificationWithTeacherOutput:
|
|
outputs: BaseModelOutputWithPooling = self.deit(
|
|
pixel_values,
|
|
interpolate_pos_encoding=interpolate_pos_encoding,
|
|
**kwargs,
|
|
)
|
|
|
|
sequence_output = outputs.last_hidden_state
|
|
|
|
cls_logits = self.cls_classifier(sequence_output[:, 0, :])
|
|
distillation_logits = self.distillation_classifier(sequence_output[:, 1, :])
|
|
|
|
# during inference, return the average of both classifier predictions
|
|
logits = (cls_logits + distillation_logits) / 2
|
|
|
|
return DeiTForImageClassificationWithTeacherOutput(
|
|
logits=logits,
|
|
cls_logits=cls_logits,
|
|
distillation_logits=distillation_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
__all__ = [
|
|
"DeiTForImageClassification",
|
|
"DeiTForImageClassificationWithTeacher",
|
|
"DeiTForMaskedImageModeling",
|
|
"DeiTModel",
|
|
"DeiTPreTrainedModel",
|
|
]
|