899 lines
31 KiB
Python
899 lines
31 KiB
Python
# coding=utf-8
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# Copyright 2022 Google AI and 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 BiT model. Also supports backbone for ViT hybrid."""
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import collections
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import math
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from typing import Optional, Tuple
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import numpy as np
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import torch
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import torch.utils.checkpoint
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from torch import Tensor, nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from ...activations import ACT2FN
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from ...modeling_outputs import (
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BackboneOutput,
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BaseModelOutputWithNoAttention,
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BaseModelOutputWithPoolingAndNoAttention,
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ImageClassifierOutputWithNoAttention,
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)
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from ...modeling_utils import PreTrainedModel
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from ...utils import (
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add_code_sample_docstrings,
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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 ...utils.backbone_utils import BackboneMixin
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from .configuration_bit import BitConfig
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logger = logging.get_logger(__name__)
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# General docstring
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_CONFIG_FOR_DOC = "BitConfig"
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# Base docstring
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_CHECKPOINT_FOR_DOC = "google/bit-50"
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_EXPECTED_OUTPUT_SHAPE = [1, 2048, 7, 7]
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# Image classification docstring
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_IMAGE_CLASS_CHECKPOINT = "google/bit-50"
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_IMAGE_CLASS_EXPECTED_OUTPUT = "tiger cat"
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from ..deprecated._archive_maps import BIT_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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def get_padding_value(padding=None, kernel_size=7, stride=1, dilation=1) -> Tuple[Tuple, bool]:
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r"""
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Utility function to get the tuple padding value given the kernel_size and padding.
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Args:
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padding (Union[`str`, `int`], *optional*):
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Padding value, can be either `"same"`, `"valid"`. If a different value is provided the default padding from
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PyTorch is used.
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kernel_size (`int`, *optional*, defaults to 7):
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Kernel size of the convolution layers.
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stride (`int`, *optional*, defaults to 1):
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Stride value of the convolution layers.
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dilation (`int`, *optional*, defaults to 1):
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Dilation value of the convolution layers.
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"""
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dynamic = False
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if padding is None:
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padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
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return padding, dynamic
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if isinstance(padding, str):
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# for any string padding, the padding will be calculated for you, one of three ways
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padding = padding.lower()
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if padding == "same":
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# TF compatible 'SAME' padding, has a performance and GPU memory allocation impact
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if stride == 1 and (dilation * (kernel_size - 1)) % 2 == 0:
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# static case, no extra overhead
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padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
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else:
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# dynamic 'SAME' padding, has runtime/GPU memory overhead
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padding = 0
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dynamic = True
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elif padding == "valid":
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# 'VALID' padding, same as padding=0
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padding = 0
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else:
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# Default to PyTorch style 'same'-ish symmetric padding
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padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
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return padding, dynamic
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class WeightStandardizedConv2d(nn.Conv2d):
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"""Conv2d with Weight Standardization. Includes TensorFlow compatible SAME padding. Used for ViT Hybrid model.
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Paper: [Micro-Batch Training with Batch-Channel Normalization and Weight
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Standardization](https://arxiv.org/abs/1903.10520v2)
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"""
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def __init__(
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self,
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in_channel,
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out_channels,
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kernel_size,
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stride=1,
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padding="SAME",
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dilation=1,
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groups=1,
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bias=False,
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eps=1e-6,
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):
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padding, is_dynamic = get_padding_value(padding, kernel_size, stride=stride, dilation=dilation)
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super().__init__(
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in_channel,
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out_channels,
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kernel_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups,
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bias=bias,
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)
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if is_dynamic:
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self.pad = DynamicPad2d(kernel_size, stride, dilation)
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else:
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self.pad = None
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self.eps = eps
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def forward(self, hidden_state):
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if self.pad is not None:
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hidden_state = self.pad(hidden_state)
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weight = nn.functional.batch_norm(
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self.weight.reshape(1, self.out_channels, -1), None, None, training=True, momentum=0.0, eps=self.eps
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).reshape_as(self.weight)
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hidden_state = nn.functional.conv2d(
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hidden_state, weight, self.bias, self.stride, self.padding, self.dilation, self.groups
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)
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return hidden_state
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class BitGroupNormActivation(nn.GroupNorm):
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r"""
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A module that combines group normalization with an activation function.
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"""
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def __init__(self, config, num_channels, eps=1e-5, affine=True, apply_activation=True):
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super(BitGroupNormActivation, self).__init__(config.num_groups, num_channels, eps=eps, affine=affine)
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if apply_activation:
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self.activation = ACT2FN[config.hidden_act]
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else:
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self.activation = nn.Identity()
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def forward(self, hidden_state):
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hidden_state = nn.functional.group_norm(hidden_state, self.num_groups, self.weight, self.bias, self.eps)
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hidden_state = self.activation(hidden_state)
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return hidden_state
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class DynamicPad2d(nn.Module):
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r"""
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A module that wraps dynamic padding of any input, given the parameters of the convolutional layer and the input
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hidden states.
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"""
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def __init__(self, kernel_size, stride, dilation, value=0):
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super().__init__()
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# Safety checkers
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if isinstance(kernel_size, int):
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kernel_size = (kernel_size, kernel_size)
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if isinstance(stride, int):
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stride = (stride, stride)
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if isinstance(dilation, int):
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dilation = (dilation, dilation)
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self.kernel_size = kernel_size
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self.stride = stride
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self.dilation = dilation
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self.value = value
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def compute_padding(x, kernel_size, stride, dilation):
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return max((math.ceil(x / stride) - 1) * stride + (kernel_size - 1) * dilation + 1 - x, 0)
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self.compute_padding = compute_padding
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def __call__(self, input):
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# Get width and height
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input_height, input_width = input.size()[-2:]
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# Compute the padding values
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padding_height = self.compute_padding(input_height, self.kernel_size[0], self.stride[0], self.dilation[0])
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padding_width = self.compute_padding(input_width, self.kernel_size[1], self.stride[1], self.dilation[1])
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# apply pad
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if padding_height > 0 or padding_width > 0:
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input = nn.functional.pad(
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input,
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[
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padding_width // 2,
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padding_width - padding_width // 2,
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padding_height // 2,
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padding_height - padding_height // 2,
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],
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value=self.value,
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)
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return input
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class BitMaxPool2d(nn.MaxPool2d):
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"""Tensorflow like 'SAME' wrapper for 2D max pooling"""
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def __init__(
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self,
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kernel_size: int,
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stride=None,
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dilation=1,
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ceil_mode=False,
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padding=(0, 0),
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padding_value=0,
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use_dynamic_padding=True,
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):
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kernel_size = kernel_size if isinstance(kernel_size, collections.abc.Iterable) else (kernel_size, kernel_size)
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stride = stride if isinstance(stride, collections.abc.Iterable) else (stride, stride)
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dilation = dilation if isinstance(dilation, collections.abc.Iterable) else (dilation, dilation)
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super().__init__(kernel_size, stride, padding, dilation, ceil_mode)
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if use_dynamic_padding:
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self.pad = DynamicPad2d(kernel_size, stride, dilation, padding_value)
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else:
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self.pad = nn.Identity()
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def forward(self, hidden_states):
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hidden_states = self.pad(hidden_states)
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return nn.functional.max_pool2d(
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hidden_states, self.kernel_size, self.stride, self.padding, self.dilation, self.ceil_mode
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)
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class BitEmbeddings(nn.Module):
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"""
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BiT Embeddings (stem) composed of a single aggressive convolution.
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"""
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def __init__(self, config: BitConfig):
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super().__init__()
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self.convolution = WeightStandardizedConv2d(
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config.num_channels,
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config.embedding_size,
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kernel_size=7,
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stride=2,
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eps=1e-8,
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padding=config.global_padding,
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)
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self.pooler = BitMaxPool2d(kernel_size=3, stride=2, use_dynamic_padding=config.embedding_dynamic_padding)
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# Use the same padding strategy as convolutional layers
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if config.global_padding is not None and config.global_padding.upper() == "SAME":
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self.pad = nn.Identity()
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else:
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self.pad = nn.ConstantPad2d(padding=(1, 1, 1, 1), value=0.0)
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if not config.layer_type == "preactivation":
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self.norm = BitGroupNormActivation(config, num_channels=config.embedding_size)
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else:
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self.norm = nn.Identity()
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self.num_channels = config.num_channels
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def forward(self, pixel_values: Tensor) -> Tensor:
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num_channels = pixel_values.shape[1]
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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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embedding = self.convolution(pixel_values)
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embedding = self.pad(embedding)
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embedding = self.norm(embedding)
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embedding = self.pooler(embedding)
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return embedding
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# Copied from transformers.models.convnext.modeling_convnext.drop_path
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def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
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"""
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Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
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Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
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however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
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See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
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layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
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argument.
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"""
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if drop_prob == 0.0 or not training:
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return input
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keep_prob = 1 - drop_prob
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shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
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random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
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random_tensor.floor_() # binarize
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output = input.div(keep_prob) * random_tensor
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return output
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# Copied from transformers.models.beit.modeling_beit.BeitDropPath with Beit->Bit
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class BitDropPath(nn.Module):
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"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
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def __init__(self, drop_prob: Optional[float] = None) -> None:
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super().__init__()
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self.drop_prob = drop_prob
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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return drop_path(hidden_states, self.drop_prob, self.training)
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def extra_repr(self) -> str:
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return "p={}".format(self.drop_prob)
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def make_div(value, divisor=8):
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min_value = divisor
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new_value = max(min_value, int(value + divisor / 2) // divisor * divisor)
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if new_value < 0.9 * value:
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new_value += divisor
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return new_value
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class BitPreActivationBottleneckLayer(nn.Module):
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"""Pre-activation (v2) bottleneck block.
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Follows the implementation of "Identity Mappings in Deep Residual Networks":
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https://github.com/KaimingHe/resnet-1k-layers/blob/master/resnet-pre-act.lua
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Except it puts the stride on 3x3 conv when available.
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"""
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def __init__(
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self,
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config,
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in_channels,
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out_channels=None,
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bottle_ratio=0.25,
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stride=1,
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dilation=1,
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first_dilation=None,
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groups=1,
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drop_path_rate=0.0,
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is_first_layer=False,
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):
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super().__init__()
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first_dilation = first_dilation or dilation
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out_channels = out_channels or in_channels
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mid_channels = make_div(out_channels * bottle_ratio)
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if is_first_layer:
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self.downsample = BitDownsampleConv(
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config,
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in_channels,
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out_channels,
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stride=stride,
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preact=True,
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)
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else:
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self.downsample = None
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self.norm1 = BitGroupNormActivation(config, in_channels)
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self.conv1 = WeightStandardizedConv2d(in_channels, mid_channels, 1, eps=1e-8, padding=config.global_padding)
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self.norm2 = BitGroupNormActivation(config, num_channels=mid_channels)
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self.conv2 = WeightStandardizedConv2d(
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mid_channels, mid_channels, 3, stride=stride, groups=groups, eps=1e-8, padding=config.global_padding
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)
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self.norm3 = BitGroupNormActivation(config, mid_channels)
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self.conv3 = WeightStandardizedConv2d(mid_channels, out_channels, 1, eps=1e-8, padding=config.global_padding)
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self.drop_path = BitDropPath(drop_path_rate) if drop_path_rate > 0 else nn.Identity()
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def forward(self, hidden_states):
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hidden_states_preact = self.norm1(hidden_states)
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# shortcut branch
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shortcut = hidden_states
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if self.downsample is not None:
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shortcut = self.downsample(hidden_states_preact)
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# residual branch
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hidden_states = self.conv1(hidden_states_preact)
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hidden_states = self.conv2(self.norm2(hidden_states))
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hidden_states = self.conv3(self.norm3(hidden_states))
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hidden_states = self.drop_path(hidden_states)
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return hidden_states + shortcut
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class BitBottleneckLayer(nn.Module):
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"""Non Pre-activation bottleneck block, equivalent to V1.5/V1b bottleneck. Used for ViT Hybrid."""
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def __init__(
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self,
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config,
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in_channels,
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out_channels=None,
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bottle_ratio=0.25,
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stride=1,
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dilation=1,
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first_dilation=None,
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groups=1,
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drop_path_rate=0.0,
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is_first_layer=False,
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):
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super().__init__()
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first_dilation = first_dilation or dilation
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out_channels = out_channels or in_channels
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mid_chs = make_div(out_channels * bottle_ratio)
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if is_first_layer:
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self.downsample = BitDownsampleConv(
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config,
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in_channels,
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out_channels,
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stride=stride,
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preact=False,
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)
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else:
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self.downsample = None
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self.conv1 = WeightStandardizedConv2d(in_channels, mid_chs, 1, eps=1e-8, padding=config.global_padding)
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self.norm1 = BitGroupNormActivation(config, num_channels=mid_chs)
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self.conv2 = WeightStandardizedConv2d(
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mid_chs,
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mid_chs,
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3,
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stride=stride,
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dilation=first_dilation,
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groups=groups,
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eps=1e-8,
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padding=config.global_padding,
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)
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self.norm2 = BitGroupNormActivation(config, num_channels=mid_chs)
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self.conv3 = WeightStandardizedConv2d(mid_chs, out_channels, 1, eps=1e-8, padding=config.global_padding)
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self.norm3 = BitGroupNormActivation(config, num_channels=out_channels, apply_activation=False)
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self.drop_path = BitDropPath(drop_path_rate) if drop_path_rate > 0 else nn.Identity()
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self.activation = ACT2FN[config.hidden_act]
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def forward(self, hidden_states):
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# shortcut branch
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shortcut = hidden_states
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if self.downsample is not None:
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shortcut = self.downsample(hidden_states)
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# residual
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hidden_states = self.conv1(hidden_states)
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hidden_states = self.norm1(hidden_states)
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hidden_states = self.conv2(hidden_states)
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hidden_states = self.norm2(hidden_states)
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hidden_states = self.conv3(hidden_states)
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hidden_states = self.norm3(hidden_states)
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hidden_states = self.drop_path(hidden_states)
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hidden_states = self.activation(hidden_states + shortcut)
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return hidden_states
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class BitDownsampleConv(nn.Module):
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def __init__(
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self,
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config,
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in_channels,
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out_channels,
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stride=1,
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preact=True,
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):
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super().__init__()
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self.conv = WeightStandardizedConv2d(
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in_channels, out_channels, 1, stride=stride, eps=1e-8, padding=config.global_padding
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)
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self.norm = (
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nn.Identity()
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if preact
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else BitGroupNormActivation(config, num_channels=out_channels, apply_activation=False)
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)
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def forward(self, x):
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return self.norm(self.conv(x))
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class BitStage(nn.Module):
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"""
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A ResNet v2 stage composed by stacked layers.
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"""
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def __init__(
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self,
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config,
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|
in_channels,
|
|
out_channels,
|
|
stride,
|
|
dilation,
|
|
depth,
|
|
bottle_ratio=0.25,
|
|
layer_dropout=None,
|
|
):
|
|
super().__init__()
|
|
|
|
first_dilation = 1 if dilation in (1, 2) else 2
|
|
|
|
# Get the layer type
|
|
if config.layer_type == "bottleneck":
|
|
layer_cls = BitBottleneckLayer
|
|
else:
|
|
layer_cls = BitPreActivationBottleneckLayer
|
|
|
|
prev_chs = in_channels
|
|
self.layers = nn.Sequential()
|
|
for layer_idx in range(depth):
|
|
# Get the current hyper-parameters
|
|
stride, drop_path_rate, is_first_layer = self._get_updated_hyperparameters(
|
|
layer_idx, stride, layer_dropout
|
|
)
|
|
|
|
self.layers.add_module(
|
|
str(layer_idx),
|
|
layer_cls(
|
|
config,
|
|
prev_chs,
|
|
out_channels,
|
|
stride=stride,
|
|
dilation=dilation,
|
|
bottle_ratio=bottle_ratio,
|
|
first_dilation=first_dilation,
|
|
drop_path_rate=drop_path_rate,
|
|
is_first_layer=is_first_layer,
|
|
),
|
|
)
|
|
prev_chs = out_channels
|
|
first_dilation = dilation
|
|
|
|
def _get_updated_hyperparameters(self, layer_idx, stride, layer_dropout):
|
|
r"""
|
|
Get the new hyper-parameters with respect to the previous ones and the index of the current layer.
|
|
"""
|
|
if layer_dropout:
|
|
drop_path_rate = layer_dropout[layer_idx]
|
|
else:
|
|
drop_path_rate = 0.0
|
|
|
|
if layer_idx != 0:
|
|
stride = 1
|
|
|
|
is_first_layer = layer_idx == 0
|
|
|
|
return stride, drop_path_rate, is_first_layer
|
|
|
|
def forward(self, input: Tensor) -> Tensor:
|
|
hidden_state = input
|
|
for _, layer in enumerate(self.layers):
|
|
hidden_state = layer(hidden_state)
|
|
return hidden_state
|
|
|
|
|
|
class BitEncoder(nn.Module):
|
|
def __init__(self, config: BitConfig):
|
|
super().__init__()
|
|
self.stages = nn.ModuleList([])
|
|
|
|
prev_chs = config.embedding_size
|
|
|
|
# These needs to stay hardcoded
|
|
current_stride = 4
|
|
dilation = 1
|
|
|
|
layer_dropouts = [
|
|
x.tolist()
|
|
for x in torch.Tensor(np.linspace(0, config.drop_path_rate, sum(config.depths))).split(config.depths)
|
|
]
|
|
|
|
for stage_idx, (current_depth, current_hidden_size, layer_dropout) in enumerate(
|
|
zip(config.depths, config.hidden_sizes, layer_dropouts)
|
|
):
|
|
# Get the updated hyper params
|
|
out_channels, stride, dilation = self._get_updated_hyperparameters(
|
|
stage_idx, current_stride, current_hidden_size, dilation, config
|
|
)
|
|
|
|
stage = BitStage(
|
|
config,
|
|
prev_chs,
|
|
out_channels,
|
|
stride=stride,
|
|
dilation=dilation,
|
|
depth=current_depth,
|
|
layer_dropout=layer_dropout,
|
|
)
|
|
|
|
prev_chs = out_channels
|
|
current_stride *= stride
|
|
|
|
self.stages.add_module(str(stage_idx), stage)
|
|
|
|
def _get_updated_hyperparameters(self, stage_idx, current_stride, current_hidden_size, dilation, config):
|
|
out_channels = make_div(current_hidden_size * config.width_factor)
|
|
stride = 1 if stage_idx == 0 else 2
|
|
if current_stride >= config.output_stride:
|
|
dilation *= stride
|
|
stride = 1
|
|
return out_channels, stride, dilation
|
|
|
|
def forward(
|
|
self, hidden_state: Tensor, output_hidden_states: bool = False, return_dict: bool = True
|
|
) -> BaseModelOutputWithNoAttention:
|
|
hidden_states = () if output_hidden_states else None
|
|
|
|
for stage_module in self.stages:
|
|
if output_hidden_states:
|
|
hidden_states = hidden_states + (hidden_state,)
|
|
|
|
hidden_state = stage_module(hidden_state)
|
|
|
|
if output_hidden_states:
|
|
hidden_states = hidden_states + (hidden_state,)
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_state, hidden_states] if v is not None)
|
|
|
|
return BaseModelOutputWithNoAttention(
|
|
last_hidden_state=hidden_state,
|
|
hidden_states=hidden_states,
|
|
)
|
|
|
|
|
|
class BitPreTrainedModel(PreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = BitConfig
|
|
base_model_prefix = "bit"
|
|
main_input_name = "pixel_values"
|
|
|
|
def _init_weights(self, module):
|
|
if isinstance(module, nn.Conv2d):
|
|
nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu")
|
|
elif isinstance(module, (nn.BatchNorm2d, nn.GroupNorm)):
|
|
nn.init.constant_(module.weight, 1)
|
|
nn.init.constant_(module.bias, 0)
|
|
|
|
|
|
BIT_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 ([`BitConfig`]): 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.
|
|
"""
|
|
|
|
BIT_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
|
|
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`BitImageProcessor.__call__`]
|
|
for details.
|
|
|
|
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.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare BiT model outputting raw features without any specific head on top.",
|
|
BIT_START_DOCSTRING,
|
|
)
|
|
class BitModel(BitPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.config = config
|
|
|
|
self.embedder = BitEmbeddings(config)
|
|
|
|
self.encoder = BitEncoder(config)
|
|
self.norm = (
|
|
BitGroupNormActivation(config, num_channels=config.hidden_sizes[-1])
|
|
if config.layer_type == "preactivation"
|
|
else nn.Identity()
|
|
)
|
|
|
|
self.pooler = nn.AdaptiveAvgPool2d((1, 1))
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(BIT_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=BaseModelOutputWithPoolingAndNoAttention,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
modality="vision",
|
|
expected_output=_EXPECTED_OUTPUT_SHAPE,
|
|
)
|
|
def forward(
|
|
self, pixel_values: Tensor, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None
|
|
) -> BaseModelOutputWithPoolingAndNoAttention:
|
|
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
|
|
|
|
embedding_output = self.embedder(pixel_values)
|
|
|
|
encoder_outputs = self.encoder(
|
|
embedding_output, output_hidden_states=output_hidden_states, return_dict=return_dict
|
|
)
|
|
|
|
last_hidden_state = encoder_outputs[0]
|
|
|
|
last_hidden_state = self.norm(last_hidden_state)
|
|
|
|
pooled_output = self.pooler(last_hidden_state)
|
|
|
|
if not return_dict:
|
|
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
|
|
|
|
return BaseModelOutputWithPoolingAndNoAttention(
|
|
last_hidden_state=last_hidden_state,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
BiT Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
|
|
ImageNet.
|
|
""",
|
|
BIT_START_DOCSTRING,
|
|
)
|
|
class BitForImageClassification(BitPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.bit = BitModel(config)
|
|
# classification head
|
|
self.classifier = nn.Sequential(
|
|
nn.Flatten(),
|
|
nn.Linear(config.hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity(),
|
|
)
|
|
# initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(BIT_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_IMAGE_CLASS_CHECKPOINT,
|
|
output_type=ImageClassifierOutputWithNoAttention,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
|
|
)
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> ImageClassifierOutputWithNoAttention:
|
|
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 classification loss is computed (Cross-Entropy).
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.bit(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
|
|
|
|
pooled_output = outputs.pooler_output if return_dict else outputs[1]
|
|
|
|
logits = self.classifier(pooled_output)
|
|
|
|
loss = None
|
|
|
|
if labels is not None:
|
|
if self.config.problem_type is None:
|
|
if self.num_labels == 1:
|
|
self.config.problem_type = "regression"
|
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
|
self.config.problem_type = "single_label_classification"
|
|
else:
|
|
self.config.problem_type = "multi_label_classification"
|
|
if self.config.problem_type == "regression":
|
|
loss_fct = MSELoss()
|
|
if self.num_labels == 1:
|
|
loss = loss_fct(logits.squeeze(), labels.squeeze())
|
|
else:
|
|
loss = loss_fct(logits, labels)
|
|
elif self.config.problem_type == "single_label_classification":
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = BCEWithLogitsLoss()
|
|
loss = loss_fct(logits, labels)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[2:]
|
|
return (loss,) + output if loss is not None else output
|
|
|
|
return ImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
BiT backbone, to be used with frameworks like DETR and MaskFormer.
|
|
""",
|
|
BIT_START_DOCSTRING,
|
|
)
|
|
class BitBackbone(BitPreTrainedModel, BackboneMixin):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
super()._init_backbone(config)
|
|
|
|
self.bit = BitModel(config)
|
|
self.num_features = [config.embedding_size] + config.hidden_sizes
|
|
|
|
# initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(BIT_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=BackboneOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self, pixel_values: Tensor, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None
|
|
) -> BackboneOutput:
|
|
"""
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoImageProcessor, AutoBackbone
|
|
>>> import torch
|
|
>>> from PIL import Image
|
|
>>> import requests
|
|
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
|
|
>>> processor = AutoImageProcessor.from_pretrained("google/resnetnv2-50")
|
|
>>> model = AutoBackbone.from_pretrained("google/resnetnv2-50")
|
|
|
|
>>> inputs = processor(image, return_tensors="pt")
|
|
>>> outputs = model(**inputs)
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
|
|
outputs = self.bit(pixel_values, output_hidden_states=True, return_dict=True)
|
|
|
|
hidden_states = outputs.hidden_states
|
|
|
|
feature_maps = ()
|
|
for idx, stage in enumerate(self.stage_names):
|
|
if stage in self.out_features:
|
|
feature_maps += (hidden_states[idx],)
|
|
|
|
if not return_dict:
|
|
output = (feature_maps,)
|
|
if output_hidden_states:
|
|
output += (outputs.hidden_states,)
|
|
return output
|
|
|
|
return BackboneOutput(
|
|
feature_maps=feature_maps,
|
|
hidden_states=outputs.hidden_states if output_hidden_states else None,
|
|
attentions=None,
|
|
)
|