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# coding=utf-8 | |
# Copyright 2022 Google AI and The HuggingFace Inc. team. All rights reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
""" PyTorch BiT model. Also supports backbone for ViT hybrid.""" | |
import collections | |
import math | |
from typing import Optional, Tuple | |
import numpy as np | |
import torch | |
import torch.utils.checkpoint | |
from torch import Tensor, nn | |
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss | |
from ...activations import ACT2FN | |
from ...modeling_outputs import ( | |
BackboneOutput, | |
BaseModelOutputWithNoAttention, | |
BaseModelOutputWithPoolingAndNoAttention, | |
ImageClassifierOutputWithNoAttention, | |
) | |
from ...modeling_utils import PreTrainedModel | |
from ...utils import ( | |
add_code_sample_docstrings, | |
add_start_docstrings, | |
add_start_docstrings_to_model_forward, | |
logging, | |
replace_return_docstrings, | |
) | |
from ...utils.backbone_utils import BackboneMixin | |
from .configuration_bit import BitConfig | |
logger = logging.get_logger(__name__) | |
# General docstring | |
_CONFIG_FOR_DOC = "BitConfig" | |
# Base docstring | |
_CHECKPOINT_FOR_DOC = "google/bit-50" | |
_EXPECTED_OUTPUT_SHAPE = [1, 2048, 7, 7] | |
# Image classification docstring | |
_IMAGE_CLASS_CHECKPOINT = "google/bit-50" | |
_IMAGE_CLASS_EXPECTED_OUTPUT = "tiger cat" | |
BIT_PRETRAINED_MODEL_ARCHIVE_LIST = [ | |
"google/bit-50", | |
# See all BiT models at https://huggingface.co/models?filter=bit | |
] | |
def get_padding_value(padding=None, kernel_size=7, stride=1, dilation=1) -> Tuple[Tuple, bool]: | |
r""" | |
Utility function to get the tuple padding value given the kernel_size and padding. | |
Args: | |
padding (Union[`str`, `int`], *optional*): | |
Padding value, can be either `"same"`, `"valid"`. If a different value is provided the default padding from | |
PyTorch is used. | |
kernel_size (`int`, *optional*, defaults to 7): | |
Kernel size of the convolution layers. | |
stride (`int`, *optional*, defaults to 1): | |
Stride value of the convolution layers. | |
dilation (`int`, *optional*, defaults to 1): | |
Dilation value of the convolution layers. | |
""" | |
dynamic = False | |
if padding is None: | |
padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2 | |
return padding, dynamic | |
if isinstance(padding, str): | |
# for any string padding, the padding will be calculated for you, one of three ways | |
padding = padding.lower() | |
if padding == "same": | |
# TF compatible 'SAME' padding, has a performance and GPU memory allocation impact | |
if stride == 1 and (dilation * (kernel_size - 1)) % 2 == 0: | |
# static case, no extra overhead | |
padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2 | |
else: | |
# dynamic 'SAME' padding, has runtime/GPU memory overhead | |
padding = 0 | |
dynamic = True | |
elif padding == "valid": | |
# 'VALID' padding, same as padding=0 | |
padding = 0 | |
else: | |
# Default to PyTorch style 'same'-ish symmetric padding | |
padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2 | |
return padding, dynamic | |
class WeightStandardizedConv2d(nn.Conv2d): | |
"""Conv2d with Weight Standardization. Includes TensorFlow compatible SAME padding. Used for ViT Hybrid model. | |
Paper: [Micro-Batch Training with Batch-Channel Normalization and Weight | |
Standardization](https://arxiv.org/abs/1903.10520v2) | |
""" | |
def __init__( | |
self, | |
in_channel, | |
out_channels, | |
kernel_size, | |
stride=1, | |
padding="SAME", | |
dilation=1, | |
groups=1, | |
bias=False, | |
eps=1e-6, | |
): | |
padding, is_dynamic = get_padding_value(padding, kernel_size, stride=stride, dilation=dilation) | |
super().__init__( | |
in_channel, | |
out_channels, | |
kernel_size, | |
stride=stride, | |
padding=padding, | |
dilation=dilation, | |
groups=groups, | |
bias=bias, | |
) | |
if is_dynamic: | |
self.pad = DynamicPad2d(kernel_size, stride, dilation) | |
else: | |
self.pad = None | |
self.eps = eps | |
def forward(self, hidden_state): | |
if self.pad is not None: | |
hidden_state = self.pad(hidden_state) | |
weight = nn.functional.batch_norm( | |
self.weight.reshape(1, self.out_channels, -1), None, None, training=True, momentum=0.0, eps=self.eps | |
).reshape_as(self.weight) | |
hidden_state = nn.functional.conv2d( | |
hidden_state, weight, self.bias, self.stride, self.padding, self.dilation, self.groups | |
) | |
return hidden_state | |
class BitGroupNormActivation(nn.GroupNorm): | |
r""" | |
A module that combines group normalization with an activation function. | |
""" | |
def __init__(self, config, num_channels, eps=1e-5, affine=True, apply_activation=True): | |
super(BitGroupNormActivation, self).__init__(config.num_groups, num_channels, eps=eps, affine=affine) | |
if apply_activation: | |
self.activation = ACT2FN[config.hidden_act] | |
else: | |
self.activation = nn.Identity() | |
def forward(self, hidden_state): | |
hidden_state = nn.functional.group_norm(hidden_state, self.num_groups, self.weight, self.bias, self.eps) | |
hidden_state = self.activation(hidden_state) | |
return hidden_state | |
class DynamicPad2d(nn.Module): | |
r""" | |
A module that wraps dynamic padding of any input, given the parameters of the convolutional layer and the input | |
hidden states. | |
""" | |
def __init__(self, kernel_size, stride, dilation, value=0): | |
super().__init__() | |
# Safety checkers | |
if isinstance(kernel_size, int): | |
kernel_size = (kernel_size, kernel_size) | |
if isinstance(stride, int): | |
stride = (stride, stride) | |
if isinstance(dilation, int): | |
dilation = (dilation, dilation) | |
self.kernel_size = kernel_size | |
self.stride = stride | |
self.dilation = dilation | |
self.value = value | |
def compute_padding(x, kernel_size, stride, dilation): | |
return max((math.ceil(x / stride) - 1) * stride + (kernel_size - 1) * dilation + 1 - x, 0) | |
self.compute_padding = compute_padding | |
def __call__(self, input): | |
# Get width and height | |
input_height, input_width = input.size()[-2:] | |
# Compute the padding values | |
padding_height = self.compute_padding(input_height, self.kernel_size[0], self.stride[0], self.dilation[0]) | |
padding_width = self.compute_padding(input_width, self.kernel_size[1], self.stride[1], self.dilation[1]) | |
# apply pad | |
if padding_height > 0 or padding_width > 0: | |
input = nn.functional.pad( | |
input, | |
[ | |
padding_width // 2, | |
padding_width - padding_width // 2, | |
padding_height // 2, | |
padding_height - padding_height // 2, | |
], | |
value=self.value, | |
) | |
return input | |
class BitMaxPool2d(nn.MaxPool2d): | |
"""Tensorflow like 'SAME' wrapper for 2D max pooling""" | |
def __init__( | |
self, | |
kernel_size: int, | |
stride=None, | |
dilation=1, | |
ceil_mode=False, | |
padding=(0, 0), | |
padding_value=0, | |
use_dynamic_padding=True, | |
): | |
kernel_size = kernel_size if isinstance(kernel_size, collections.abc.Iterable) else (kernel_size, kernel_size) | |
stride = stride if isinstance(stride, collections.abc.Iterable) else (stride, stride) | |
dilation = dilation if isinstance(dilation, collections.abc.Iterable) else (dilation, dilation) | |
super().__init__(kernel_size, stride, padding, dilation, ceil_mode) | |
if use_dynamic_padding: | |
self.pad = DynamicPad2d(kernel_size, stride, dilation, padding_value) | |
else: | |
self.pad = nn.Identity() | |
def forward(self, hidden_states): | |
hidden_states = self.pad(hidden_states) | |
return nn.functional.max_pool2d( | |
hidden_states, self.kernel_size, self.stride, self.padding, self.dilation, self.ceil_mode | |
) | |
class BitEmbeddings(nn.Module): | |
""" | |
BiT Embeddings (stem) composed of a single aggressive convolution. | |
""" | |
def __init__(self, config: BitConfig): | |
super().__init__() | |
self.convolution = WeightStandardizedConv2d( | |
config.num_channels, | |
config.embedding_size, | |
kernel_size=7, | |
stride=2, | |
eps=1e-8, | |
padding=config.global_padding, | |
) | |
self.pooler = BitMaxPool2d(kernel_size=3, stride=2, use_dynamic_padding=config.embedding_dynamic_padding) | |
# Use the same padding strategy as convolutional layers | |
if config.global_padding is not None and config.global_padding.upper() == "SAME": | |
self.pad = nn.Identity() | |
else: | |
self.pad = nn.ConstantPad2d(padding=(1, 1, 1, 1), value=0.0) | |
if not config.layer_type == "preactivation": | |
self.norm = BitGroupNormActivation(config, num_channels=config.embedding_size) | |
else: | |
self.norm = nn.Identity() | |
self.num_channels = config.num_channels | |
def forward(self, pixel_values: Tensor) -> Tensor: | |
num_channels = pixel_values.shape[1] | |
if num_channels != self.num_channels: | |
raise ValueError( | |
"Make sure that the channel dimension of the pixel values match with the one set in the configuration." | |
) | |
embedding = self.convolution(pixel_values) | |
embedding = self.pad(embedding) | |
embedding = self.norm(embedding) | |
embedding = self.pooler(embedding) | |
return embedding | |
# Copied from transformers.models.convnext.modeling_convnext.drop_path | |
def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor: | |
""" | |
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). | |
Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks, | |
however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... | |
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the | |
layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the | |
argument. | |
""" | |
if drop_prob == 0.0 or not training: | |
return input | |
keep_prob = 1 - drop_prob | |
shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets | |
random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device) | |
random_tensor.floor_() # binarize | |
output = input.div(keep_prob) * random_tensor | |
return output | |
# Copied from transformers.models.beit.modeling_beit.BeitDropPath with Beit->Bit | |
class BitDropPath(nn.Module): | |
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" | |
def __init__(self, drop_prob: Optional[float] = None) -> None: | |
super().__init__() | |
self.drop_prob = drop_prob | |
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: | |
return drop_path(hidden_states, self.drop_prob, self.training) | |
def extra_repr(self) -> str: | |
return "p={}".format(self.drop_prob) | |
def make_div(value, divisor=8): | |
min_value = divisor | |
new_value = max(min_value, int(value + divisor / 2) // divisor * divisor) | |
if new_value < 0.9 * value: | |
new_value += divisor | |
return new_value | |
class BitPreActivationBottleneckLayer(nn.Module): | |
"""Pre-activation (v2) bottleneck block. | |
Follows the implementation of "Identity Mappings in Deep Residual Networks": | |
https://github.com/KaimingHe/resnet-1k-layers/blob/master/resnet-pre-act.lua | |
Except it puts the stride on 3x3 conv when available. | |
""" | |
def __init__( | |
self, | |
config, | |
in_channels, | |
out_channels=None, | |
bottle_ratio=0.25, | |
stride=1, | |
dilation=1, | |
first_dilation=None, | |
groups=1, | |
drop_path_rate=0.0, | |
is_first_layer=False, | |
): | |
super().__init__() | |
first_dilation = first_dilation or dilation | |
out_channels = out_channels or in_channels | |
mid_channels = make_div(out_channels * bottle_ratio) | |
if is_first_layer: | |
self.downsample = BitDownsampleConv( | |
config, | |
in_channels, | |
out_channels, | |
stride=stride, | |
preact=True, | |
) | |
else: | |
self.downsample = None | |
self.norm1 = BitGroupNormActivation(config, in_channels) | |
self.conv1 = WeightStandardizedConv2d(in_channels, mid_channels, 1, eps=1e-8, padding=config.global_padding) | |
self.norm2 = BitGroupNormActivation(config, num_channels=mid_channels) | |
self.conv2 = WeightStandardizedConv2d( | |
mid_channels, mid_channels, 3, stride=stride, groups=groups, eps=1e-8, padding=config.global_padding | |
) | |
self.norm3 = BitGroupNormActivation(config, mid_channels) | |
self.conv3 = WeightStandardizedConv2d(mid_channels, out_channels, 1, eps=1e-8, padding=config.global_padding) | |
self.drop_path = BitDropPath(drop_path_rate) if drop_path_rate > 0 else nn.Identity() | |
def forward(self, hidden_states): | |
hidden_states_preact = self.norm1(hidden_states) | |
# shortcut branch | |
shortcut = hidden_states | |
if self.downsample is not None: | |
shortcut = self.downsample(hidden_states_preact) | |
# residual branch | |
hidden_states = self.conv1(hidden_states_preact) | |
hidden_states = self.conv2(self.norm2(hidden_states)) | |
hidden_states = self.conv3(self.norm3(hidden_states)) | |
hidden_states = self.drop_path(hidden_states) | |
return hidden_states + shortcut | |
class BitBottleneckLayer(nn.Module): | |
"""Non Pre-activation bottleneck block, equivalent to V1.5/V1b bottleneck. Used for ViT Hybrid.""" | |
def __init__( | |
self, | |
config, | |
in_channels, | |
out_channels=None, | |
bottle_ratio=0.25, | |
stride=1, | |
dilation=1, | |
first_dilation=None, | |
groups=1, | |
drop_path_rate=0.0, | |
is_first_layer=False, | |
): | |
super().__init__() | |
first_dilation = first_dilation or dilation | |
out_channels = out_channels or in_channels | |
mid_chs = make_div(out_channels * bottle_ratio) | |
if is_first_layer: | |
self.downsample = BitDownsampleConv( | |
config, | |
in_channels, | |
out_channels, | |
stride=stride, | |
preact=False, | |
) | |
else: | |
self.downsample = None | |
self.conv1 = WeightStandardizedConv2d(in_channels, mid_chs, 1, eps=1e-8, padding=config.global_padding) | |
self.norm1 = BitGroupNormActivation(config, num_channels=mid_chs) | |
self.conv2 = WeightStandardizedConv2d( | |
mid_chs, | |
mid_chs, | |
3, | |
stride=stride, | |
dilation=first_dilation, | |
groups=groups, | |
eps=1e-8, | |
padding=config.global_padding, | |
) | |
self.norm2 = BitGroupNormActivation(config, num_channels=mid_chs) | |
self.conv3 = WeightStandardizedConv2d(mid_chs, out_channels, 1, eps=1e-8, padding=config.global_padding) | |
self.norm3 = BitGroupNormActivation(config, num_channels=out_channels, apply_activation=False) | |
self.drop_path = BitDropPath(drop_path_rate) if drop_path_rate > 0 else nn.Identity() | |
self.activation = ACT2FN[config.hidden_act] | |
def forward(self, hidden_states): | |
# shortcut branch | |
shortcut = hidden_states | |
if self.downsample is not None: | |
shortcut = self.downsample(hidden_states) | |
# residual | |
hidden_states = self.conv1(hidden_states) | |
hidden_states = self.norm1(hidden_states) | |
hidden_states = self.conv2(hidden_states) | |
hidden_states = self.norm2(hidden_states) | |
hidden_states = self.conv3(hidden_states) | |
hidden_states = self.norm3(hidden_states) | |
hidden_states = self.drop_path(hidden_states) | |
hidden_states = self.activation(hidden_states + shortcut) | |
return hidden_states | |
class BitDownsampleConv(nn.Module): | |
def __init__( | |
self, | |
config, | |
in_channels, | |
out_channels, | |
stride=1, | |
preact=True, | |
): | |
super().__init__() | |
self.conv = WeightStandardizedConv2d( | |
in_channels, out_channels, 1, stride=stride, eps=1e-8, padding=config.global_padding | |
) | |
self.norm = ( | |
nn.Identity() | |
if preact | |
else BitGroupNormActivation(config, num_channels=out_channels, apply_activation=False) | |
) | |
def forward(self, x): | |
return self.norm(self.conv(x)) | |
class BitStage(nn.Module): | |
""" | |
A ResNet v2 stage composed by stacked layers. | |
""" | |
def __init__( | |
self, | |
config, | |
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" | |
supports_gradient_checkpointing = True | |
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) | |
def _set_gradient_checkpointing(self, module, value=False): | |
if isinstance(module, BitModel): | |
module.gradient_checkpointing = value | |
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. | |
""" | |
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() | |
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, | |
) | |
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() | |
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) | |
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() | |
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, | |
) | |