wehub-resource-sync
916 lines
32 KiB
Python
916 lines
32 KiB
Python
# coding=utf-8
|
|
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
|
# 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.
|
|
""" Paddle BiT model. Also supports backbone for ViT hybrid."""
|
|
|
|
import collections
|
|
import math
|
|
from typing import Optional, Tuple
|
|
|
|
import numpy as np
|
|
import paddle
|
|
import paddle.nn.functional as F
|
|
from paddle import Tensor, nn
|
|
from paddle.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
|
|
|
from ...utils.initializer import kaiming_normal_, ones_, zeros_
|
|
from ..activations import ACT2FN
|
|
from ..model_outputs import (
|
|
BackboneOutput,
|
|
BaseModelOutputWithNoAttention,
|
|
BaseModelOutputWithPoolingAndNoAttention,
|
|
ImageClassifierOutputWithNoAttention,
|
|
)
|
|
from ..model_utils import BackboneMixin, PretrainedModel
|
|
from .configuration import BitConfig
|
|
|
|
__all__ = [
|
|
"BitPretrainedModel",
|
|
"BitModel",
|
|
"BitForImageClassification",
|
|
"BitBackbone",
|
|
]
|
|
|
|
|
|
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
|
|
Paddle 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,
|
|
epsilon=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_attr=bias,
|
|
)
|
|
if is_dynamic:
|
|
self.pad = DynamicPad2d(kernel_size, stride, dilation)
|
|
else:
|
|
self.pad = None
|
|
self.epsilon = epsilon
|
|
|
|
def forward(self, hidden_state):
|
|
if self.pad is not None:
|
|
hidden_state = self.pad(hidden_state)
|
|
w = self.weight
|
|
v, m = paddle.var(w, axis=[1, 2, 3], keepdim=True, unbiased=False), paddle.mean(
|
|
w, axis=[1, 2, 3], keepdim=True
|
|
)
|
|
w = (w - m) / paddle.sqrt(v + self.epsilon)
|
|
|
|
hidden_state = F.conv2d(
|
|
hidden_state, w, self.bias, self._stride, self._padding, self._dilation, self._groups, self._data_format
|
|
)
|
|
return hidden_state
|
|
|
|
|
|
class BitGroupNormActivation(nn.GroupNorm):
|
|
r"""
|
|
A module that combines group normalization with an activation function.
|
|
"""
|
|
|
|
def __init__(self, config, num_channels, epsilon=1e-5, apply_activation=True):
|
|
super(BitGroupNormActivation, self).__init__(config.num_groups, num_channels, epsilon=epsilon)
|
|
if apply_activation:
|
|
self.activation = ACT2FN[config.hidden_act]
|
|
else:
|
|
self.activation = nn.Identity()
|
|
|
|
def forward(self, hidden_state):
|
|
hidden_state = super().forward(hidden_state)
|
|
hidden_state = self.activation(hidden_state)
|
|
return hidden_state
|
|
|
|
|
|
class DynamicPad2d(nn.Layer):
|
|
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.shape[-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 = F.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,
|
|
):
|
|
# must be 1
|
|
assert dilation == 1
|
|
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, ceil_mode=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 super().forward(hidden_states)
|
|
|
|
|
|
class BitEmbeddings(nn.Layer):
|
|
"""
|
|
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,
|
|
epsilon=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.Pad2D(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
|
|
|
|
|
|
def drop_path(input, drop_prob: float = 0.0, training: bool = False):
|
|
"""
|
|
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 + paddle.rand(shape, dtype=input.dtype)
|
|
random_tensor = paddle.floor(random_tensor) # binarize
|
|
output = (input / keep_prob) * random_tensor
|
|
return output
|
|
|
|
|
|
class BitDropPath(nn.Layer):
|
|
"""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: paddle.Tensor) -> paddle.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.Layer):
|
|
"""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, epsilon=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, epsilon=1e-8, padding=config.global_padding
|
|
)
|
|
|
|
self.norm3 = BitGroupNormActivation(config, mid_channels)
|
|
self.conv3 = WeightStandardizedConv2D(
|
|
mid_channels, out_channels, 1, epsilon=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.Layer):
|
|
"""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, epsilon=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,
|
|
epsilon=1e-8,
|
|
padding=config.global_padding,
|
|
)
|
|
self.norm2 = BitGroupNormActivation(config, num_channels=mid_chs)
|
|
self.conv3 = WeightStandardizedConv2D(mid_chs, out_channels, 1, epsilon=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.Layer):
|
|
def __init__(
|
|
self,
|
|
config,
|
|
in_channels,
|
|
out_channels,
|
|
stride=1,
|
|
preact=True,
|
|
):
|
|
super().__init__()
|
|
self.conv = WeightStandardizedConv2D(
|
|
in_channels, out_channels, 1, stride=stride, epsilon=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.Layer):
|
|
"""
|
|
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_sublayer(
|
|
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.Layer):
|
|
def __init__(self, config: BitConfig):
|
|
super().__init__()
|
|
self.stages = nn.LayerList([])
|
|
|
|
prev_chs = config.embedding_size
|
|
|
|
# These needs to stay hardcoded
|
|
current_stride = 4
|
|
dilation = 1
|
|
|
|
layer_dropouts = [
|
|
x.tolist()
|
|
for x in paddle.to_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_sublayer(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):
|
|
kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu")
|
|
elif isinstance(module, (nn.BatchNorm2D, nn.GroupNorm)):
|
|
ones_(module.weight)
|
|
zeros_(module.bias)
|
|
|
|
|
|
class BitModel(BitPretrainedModel):
|
|
"""
|
|
The bare BiT model outputting raw features without any specific head on top.
|
|
|
|
This model inherits from :class:`~paddlenlp.transformers.model_utils.PretrainedModel`.
|
|
Refer to the superclass documentation for the generic methods.
|
|
|
|
This model is also a Paddle `paddle.nn.Layer <https://www.paddlepaddle.org.cn/documentation
|
|
/docs/zh/api/paddle/nn/Layer_cn.html>`__ subclass. Use it as a regular Paddle Layer
|
|
and refer to the Paddle documentation for all matter related to general usage and behavior.
|
|
|
|
Args:
|
|
config (:class:`BitConfig`):
|
|
An instance of BitConfig used to construct BitModel.
|
|
"""
|
|
|
|
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))
|
|
|
|
def forward(
|
|
self, pixel_values: Tensor, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None
|
|
) -> BaseModelOutputWithPoolingAndNoAttention:
|
|
r"""
|
|
The BitModel forward method, overrides the `__call__()` special method.
|
|
|
|
Args:
|
|
pixel_values (`paddle.Tensor` of shape `(batch_size, num_channels, height, width)`):
|
|
Pixel values. Pixel values can be obtained using [`BitImageProcessor`]. 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 to return a :class:`BaseModelOutputWithPoolingAndNoAttention` object. If `False`, the output
|
|
will be a tuple of tensors. Defaults to `None`.
|
|
"""
|
|
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):
|
|
"""
|
|
BiT Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
|
|
ImageNet.
|
|
|
|
This model inherits from :class:`~paddlenlp.transformers.model_utils.PretrainedModel`.
|
|
Refer to the superclass documentation for the generic methods.
|
|
|
|
This model is also a Paddle `paddle.nn.Layer <https://www.paddlepaddle.org.cn/documentation
|
|
/docs/zh/api/paddle/nn/Layer_cn.html>`__ subclass. Use it as a regular Paddle Layer
|
|
and refer to the Paddle documentation for all matter related to general usage and behavior.
|
|
|
|
Args:
|
|
config (:class:`BitConfig`):
|
|
An instance of BitConfig used to construct BitForImageClassification.
|
|
"""
|
|
|
|
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(),
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[paddle.Tensor] = None,
|
|
labels: Optional[paddle.Tensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> ImageClassifierOutputWithNoAttention:
|
|
r"""
|
|
The BitForImageClassification forward method, overrides the `__call__()` special method.
|
|
|
|
Args:
|
|
pixel_values (`paddle.Tensor` of shape `(batch_size, num_channels, height, width)`):
|
|
Pixel values. Pixel values can be obtained using [`BitImageProcessor`]. See [`BitImageProcessor.__call__`]
|
|
for details.
|
|
labels (`paddle.Tensor` 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).
|
|
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 to return a :class:`ImageClassifierOutputWithNoAttention` object. If `False`, the output
|
|
will be a tuple of tensors. Defaults to `None`.
|
|
"""
|
|
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 == paddle.int64 or labels.dtype == paddle.int32):
|
|
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.reshape([-1, self.num_labels]), labels.flatten())
|
|
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):
|
|
"""
|
|
BiT backbone, to be used with frameworks like DETR and MaskFormer.
|
|
|
|
This model is also a Paddle `paddle.nn.Layer <https://www.paddlepaddle.org.cn/documentation
|
|
/docs/zh/api/paddle/nn/Layer_cn.html>`__ subclass. Use it as a regular Paddle Layer
|
|
and refer to the Paddle documentation for all matter related to general usage and behavior.
|
|
|
|
Args:
|
|
config (:class:`DPTConfig`):
|
|
An instance of DPTConfig used to construct BitBackbone.
|
|
"""
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.stage_names = config.stage_names
|
|
self.bit = BitModel(config)
|
|
|
|
self.out_features = config.out_features if config.out_features is not None else [self.stage_names[-1]]
|
|
|
|
out_feature_channels = {}
|
|
out_feature_channels["stem"] = config.embedding_size
|
|
for idx, stage in enumerate(self.stage_names[1:]):
|
|
out_feature_channels[stage] = config.hidden_sizes[idx]
|
|
|
|
self.out_feature_channels = out_feature_channels
|
|
|
|
@property
|
|
def channels(self):
|
|
return [self.out_feature_channels[name] for name in self.out_features]
|
|
|
|
def forward(
|
|
self, pixel_values: Tensor, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None
|
|
) -> BackboneOutput:
|
|
r"""
|
|
The BitBackbone forward method, overrides the `__call__()` special method.
|
|
|
|
Args:
|
|
pixel_values (`paddle.Tensor` of shape `(batch_size, num_channels, height, width)`):
|
|
Pixel values. Pixel values can be obtained using [`BitImageProcessor`]. 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 to return a :class:`BackboneOutput` object. If `False`, the output
|
|
will be a tuple of tensors. Defaults to `None`.
|
|
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from paddlenlp.transformers import BitImageProcessor, BitBackbone
|
|
>>> import paddle
|
|
>>> from PIL import Image
|
|
>>> import requests
|
|
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
|
|
>>> processor = BitImageProcessor.from_pretrained("google/bit-50")
|
|
>>> model = BitBackbone.from_pretrained("google/bit-50")
|
|
|
|
>>> inputs = processor(image, return_tensors="pd")
|
|
>>> 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,
|
|
)
|