1067 lines
39 KiB
Python
1067 lines
39 KiB
Python
# coding=utf-8
|
|
# Copyright 2022 Apple Inc. 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.
|
|
#
|
|
# Original license: https://github.com/apple/ml-cvnets/blob/main/LICENSE
|
|
""" PyTorch MobileViT model."""
|
|
|
|
|
|
import math
|
|
from typing import Dict, Optional, Set, Tuple, Union
|
|
|
|
import torch
|
|
import torch.utils.checkpoint
|
|
from torch import nn
|
|
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
|
|
|
from ...activations import ACT2FN
|
|
from ...modeling_outputs import (
|
|
BaseModelOutputWithNoAttention,
|
|
BaseModelOutputWithPoolingAndNoAttention,
|
|
ImageClassifierOutputWithNoAttention,
|
|
SemanticSegmenterOutput,
|
|
)
|
|
from ...modeling_utils import PreTrainedModel
|
|
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
|
|
from ...utils import (
|
|
add_code_sample_docstrings,
|
|
add_start_docstrings,
|
|
add_start_docstrings_to_model_forward,
|
|
logging,
|
|
replace_return_docstrings,
|
|
)
|
|
from .configuration_mobilevit import MobileViTConfig
|
|
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
|
|
# General docstring
|
|
_CONFIG_FOR_DOC = "MobileViTConfig"
|
|
|
|
# Base docstring
|
|
_CHECKPOINT_FOR_DOC = "apple/mobilevit-small"
|
|
_EXPECTED_OUTPUT_SHAPE = [1, 640, 8, 8]
|
|
|
|
# Image classification docstring
|
|
_IMAGE_CLASS_CHECKPOINT = "apple/mobilevit-small"
|
|
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
|
|
|
|
|
|
from ..deprecated._archive_maps import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
|
|
|
|
|
|
def make_divisible(value: int, divisor: int = 8, min_value: Optional[int] = None) -> int:
|
|
"""
|
|
Ensure that all layers have a channel count that is divisible by `divisor`. This function is taken from the
|
|
original TensorFlow repo. It can be seen here:
|
|
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
|
|
"""
|
|
if min_value is None:
|
|
min_value = divisor
|
|
new_value = max(min_value, int(value + divisor / 2) // divisor * divisor)
|
|
# Make sure that round down does not go down by more than 10%.
|
|
if new_value < 0.9 * value:
|
|
new_value += divisor
|
|
return int(new_value)
|
|
|
|
|
|
class MobileViTConvLayer(nn.Module):
|
|
def __init__(
|
|
self,
|
|
config: MobileViTConfig,
|
|
in_channels: int,
|
|
out_channels: int,
|
|
kernel_size: int,
|
|
stride: int = 1,
|
|
groups: int = 1,
|
|
bias: bool = False,
|
|
dilation: int = 1,
|
|
use_normalization: bool = True,
|
|
use_activation: Union[bool, str] = True,
|
|
) -> None:
|
|
super().__init__()
|
|
padding = int((kernel_size - 1) / 2) * dilation
|
|
|
|
if in_channels % groups != 0:
|
|
raise ValueError(f"Input channels ({in_channels}) are not divisible by {groups} groups.")
|
|
if out_channels % groups != 0:
|
|
raise ValueError(f"Output channels ({out_channels}) are not divisible by {groups} groups.")
|
|
|
|
self.convolution = nn.Conv2d(
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=kernel_size,
|
|
stride=stride,
|
|
padding=padding,
|
|
dilation=dilation,
|
|
groups=groups,
|
|
bias=bias,
|
|
padding_mode="zeros",
|
|
)
|
|
|
|
if use_normalization:
|
|
self.normalization = nn.BatchNorm2d(
|
|
num_features=out_channels,
|
|
eps=1e-5,
|
|
momentum=0.1,
|
|
affine=True,
|
|
track_running_stats=True,
|
|
)
|
|
else:
|
|
self.normalization = None
|
|
|
|
if use_activation:
|
|
if isinstance(use_activation, str):
|
|
self.activation = ACT2FN[use_activation]
|
|
elif isinstance(config.hidden_act, str):
|
|
self.activation = ACT2FN[config.hidden_act]
|
|
else:
|
|
self.activation = config.hidden_act
|
|
else:
|
|
self.activation = None
|
|
|
|
def forward(self, features: torch.Tensor) -> torch.Tensor:
|
|
features = self.convolution(features)
|
|
if self.normalization is not None:
|
|
features = self.normalization(features)
|
|
if self.activation is not None:
|
|
features = self.activation(features)
|
|
return features
|
|
|
|
|
|
class MobileViTInvertedResidual(nn.Module):
|
|
"""
|
|
Inverted residual block (MobileNetv2): https://arxiv.org/abs/1801.04381
|
|
"""
|
|
|
|
def __init__(
|
|
self, config: MobileViTConfig, in_channels: int, out_channels: int, stride: int, dilation: int = 1
|
|
) -> None:
|
|
super().__init__()
|
|
expanded_channels = make_divisible(int(round(in_channels * config.expand_ratio)), 8)
|
|
|
|
if stride not in [1, 2]:
|
|
raise ValueError(f"Invalid stride {stride}.")
|
|
|
|
self.use_residual = (stride == 1) and (in_channels == out_channels)
|
|
|
|
self.expand_1x1 = MobileViTConvLayer(
|
|
config, in_channels=in_channels, out_channels=expanded_channels, kernel_size=1
|
|
)
|
|
|
|
self.conv_3x3 = MobileViTConvLayer(
|
|
config,
|
|
in_channels=expanded_channels,
|
|
out_channels=expanded_channels,
|
|
kernel_size=3,
|
|
stride=stride,
|
|
groups=expanded_channels,
|
|
dilation=dilation,
|
|
)
|
|
|
|
self.reduce_1x1 = MobileViTConvLayer(
|
|
config,
|
|
in_channels=expanded_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=1,
|
|
use_activation=False,
|
|
)
|
|
|
|
def forward(self, features: torch.Tensor) -> torch.Tensor:
|
|
residual = features
|
|
|
|
features = self.expand_1x1(features)
|
|
features = self.conv_3x3(features)
|
|
features = self.reduce_1x1(features)
|
|
|
|
return residual + features if self.use_residual else features
|
|
|
|
|
|
class MobileViTMobileNetLayer(nn.Module):
|
|
def __init__(
|
|
self, config: MobileViTConfig, in_channels: int, out_channels: int, stride: int = 1, num_stages: int = 1
|
|
) -> None:
|
|
super().__init__()
|
|
|
|
self.layer = nn.ModuleList()
|
|
for i in range(num_stages):
|
|
layer = MobileViTInvertedResidual(
|
|
config,
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
stride=stride if i == 0 else 1,
|
|
)
|
|
self.layer.append(layer)
|
|
in_channels = out_channels
|
|
|
|
def forward(self, features: torch.Tensor) -> torch.Tensor:
|
|
for layer_module in self.layer:
|
|
features = layer_module(features)
|
|
return features
|
|
|
|
|
|
class MobileViTSelfAttention(nn.Module):
|
|
def __init__(self, config: MobileViTConfig, hidden_size: int) -> None:
|
|
super().__init__()
|
|
|
|
if hidden_size % config.num_attention_heads != 0:
|
|
raise ValueError(
|
|
f"The hidden size {hidden_size,} is not a multiple of the number of attention "
|
|
f"heads {config.num_attention_heads}."
|
|
)
|
|
|
|
self.num_attention_heads = config.num_attention_heads
|
|
self.attention_head_size = int(hidden_size / config.num_attention_heads)
|
|
self.all_head_size = self.num_attention_heads * self.attention_head_size
|
|
|
|
self.query = nn.Linear(hidden_size, self.all_head_size, bias=config.qkv_bias)
|
|
self.key = nn.Linear(hidden_size, self.all_head_size, bias=config.qkv_bias)
|
|
self.value = nn.Linear(hidden_size, self.all_head_size, bias=config.qkv_bias)
|
|
|
|
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
|
|
|
|
def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
|
|
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
|
|
x = x.view(*new_x_shape)
|
|
return x.permute(0, 2, 1, 3)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
mixed_query_layer = self.query(hidden_states)
|
|
|
|
key_layer = self.transpose_for_scores(self.key(hidden_states))
|
|
value_layer = self.transpose_for_scores(self.value(hidden_states))
|
|
query_layer = self.transpose_for_scores(mixed_query_layer)
|
|
|
|
# Take the dot product between "query" and "key" to get the raw attention scores.
|
|
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
|
|
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
|
|
|
|
# Normalize the attention scores to probabilities.
|
|
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
|
|
|
|
# This is actually dropping out entire tokens to attend to, which might
|
|
# seem a bit unusual, but is taken from the original Transformer paper.
|
|
attention_probs = self.dropout(attention_probs)
|
|
|
|
context_layer = torch.matmul(attention_probs, value_layer)
|
|
|
|
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
|
|
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
|
|
context_layer = context_layer.view(*new_context_layer_shape)
|
|
return context_layer
|
|
|
|
|
|
class MobileViTSelfOutput(nn.Module):
|
|
def __init__(self, config: MobileViTConfig, hidden_size: int) -> None:
|
|
super().__init__()
|
|
self.dense = nn.Linear(hidden_size, hidden_size)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class MobileViTAttention(nn.Module):
|
|
def __init__(self, config: MobileViTConfig, hidden_size: int) -> None:
|
|
super().__init__()
|
|
self.attention = MobileViTSelfAttention(config, hidden_size)
|
|
self.output = MobileViTSelfOutput(config, hidden_size)
|
|
self.pruned_heads = set()
|
|
|
|
def prune_heads(self, heads: Set[int]) -> None:
|
|
if len(heads) == 0:
|
|
return
|
|
heads, index = find_pruneable_heads_and_indices(
|
|
heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
|
|
)
|
|
|
|
# Prune linear layers
|
|
self.attention.query = prune_linear_layer(self.attention.query, index)
|
|
self.attention.key = prune_linear_layer(self.attention.key, index)
|
|
self.attention.value = prune_linear_layer(self.attention.value, index)
|
|
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
|
|
|
|
# Update hyper params and store pruned heads
|
|
self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
|
|
self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
|
|
self.pruned_heads = self.pruned_heads.union(heads)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
self_outputs = self.attention(hidden_states)
|
|
attention_output = self.output(self_outputs)
|
|
return attention_output
|
|
|
|
|
|
class MobileViTIntermediate(nn.Module):
|
|
def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int) -> None:
|
|
super().__init__()
|
|
self.dense = nn.Linear(hidden_size, intermediate_size)
|
|
if isinstance(config.hidden_act, str):
|
|
self.intermediate_act_fn = ACT2FN[config.hidden_act]
|
|
else:
|
|
self.intermediate_act_fn = config.hidden_act
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.intermediate_act_fn(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class MobileViTOutput(nn.Module):
|
|
def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int) -> None:
|
|
super().__init__()
|
|
self.dense = nn.Linear(intermediate_size, hidden_size)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
hidden_states = hidden_states + input_tensor
|
|
return hidden_states
|
|
|
|
|
|
class MobileViTTransformerLayer(nn.Module):
|
|
def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int) -> None:
|
|
super().__init__()
|
|
self.attention = MobileViTAttention(config, hidden_size)
|
|
self.intermediate = MobileViTIntermediate(config, hidden_size, intermediate_size)
|
|
self.output = MobileViTOutput(config, hidden_size, intermediate_size)
|
|
self.layernorm_before = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps)
|
|
self.layernorm_after = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
attention_output = self.attention(self.layernorm_before(hidden_states))
|
|
hidden_states = attention_output + hidden_states
|
|
|
|
layer_output = self.layernorm_after(hidden_states)
|
|
layer_output = self.intermediate(layer_output)
|
|
layer_output = self.output(layer_output, hidden_states)
|
|
return layer_output
|
|
|
|
|
|
class MobileViTTransformer(nn.Module):
|
|
def __init__(self, config: MobileViTConfig, hidden_size: int, num_stages: int) -> None:
|
|
super().__init__()
|
|
|
|
self.layer = nn.ModuleList()
|
|
for _ in range(num_stages):
|
|
transformer_layer = MobileViTTransformerLayer(
|
|
config,
|
|
hidden_size=hidden_size,
|
|
intermediate_size=int(hidden_size * config.mlp_ratio),
|
|
)
|
|
self.layer.append(transformer_layer)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
for layer_module in self.layer:
|
|
hidden_states = layer_module(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class MobileViTLayer(nn.Module):
|
|
"""
|
|
MobileViT block: https://arxiv.org/abs/2110.02178
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
config: MobileViTConfig,
|
|
in_channels: int,
|
|
out_channels: int,
|
|
stride: int,
|
|
hidden_size: int,
|
|
num_stages: int,
|
|
dilation: int = 1,
|
|
) -> None:
|
|
super().__init__()
|
|
self.patch_width = config.patch_size
|
|
self.patch_height = config.patch_size
|
|
|
|
if stride == 2:
|
|
self.downsampling_layer = MobileViTInvertedResidual(
|
|
config,
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
stride=stride if dilation == 1 else 1,
|
|
dilation=dilation // 2 if dilation > 1 else 1,
|
|
)
|
|
in_channels = out_channels
|
|
else:
|
|
self.downsampling_layer = None
|
|
|
|
self.conv_kxk = MobileViTConvLayer(
|
|
config,
|
|
in_channels=in_channels,
|
|
out_channels=in_channels,
|
|
kernel_size=config.conv_kernel_size,
|
|
)
|
|
|
|
self.conv_1x1 = MobileViTConvLayer(
|
|
config,
|
|
in_channels=in_channels,
|
|
out_channels=hidden_size,
|
|
kernel_size=1,
|
|
use_normalization=False,
|
|
use_activation=False,
|
|
)
|
|
|
|
self.transformer = MobileViTTransformer(
|
|
config,
|
|
hidden_size=hidden_size,
|
|
num_stages=num_stages,
|
|
)
|
|
|
|
self.layernorm = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps)
|
|
|
|
self.conv_projection = MobileViTConvLayer(
|
|
config, in_channels=hidden_size, out_channels=in_channels, kernel_size=1
|
|
)
|
|
|
|
self.fusion = MobileViTConvLayer(
|
|
config, in_channels=2 * in_channels, out_channels=in_channels, kernel_size=config.conv_kernel_size
|
|
)
|
|
|
|
def unfolding(self, features: torch.Tensor) -> Tuple[torch.Tensor, Dict]:
|
|
patch_width, patch_height = self.patch_width, self.patch_height
|
|
patch_area = int(patch_width * patch_height)
|
|
|
|
batch_size, channels, orig_height, orig_width = features.shape
|
|
|
|
new_height = int(math.ceil(orig_height / patch_height) * patch_height)
|
|
new_width = int(math.ceil(orig_width / patch_width) * patch_width)
|
|
|
|
interpolate = False
|
|
if new_width != orig_width or new_height != orig_height:
|
|
# Note: Padding can be done, but then it needs to be handled in attention function.
|
|
features = nn.functional.interpolate(
|
|
features, size=(new_height, new_width), mode="bilinear", align_corners=False
|
|
)
|
|
interpolate = True
|
|
|
|
# number of patches along width and height
|
|
num_patch_width = new_width // patch_width
|
|
num_patch_height = new_height // patch_height
|
|
num_patches = num_patch_height * num_patch_width
|
|
|
|
# convert from shape (batch_size, channels, orig_height, orig_width)
|
|
# to the shape (batch_size * patch_area, num_patches, channels)
|
|
patches = features.reshape(
|
|
batch_size * channels * num_patch_height, patch_height, num_patch_width, patch_width
|
|
)
|
|
patches = patches.transpose(1, 2)
|
|
patches = patches.reshape(batch_size, channels, num_patches, patch_area)
|
|
patches = patches.transpose(1, 3)
|
|
patches = patches.reshape(batch_size * patch_area, num_patches, -1)
|
|
|
|
info_dict = {
|
|
"orig_size": (orig_height, orig_width),
|
|
"batch_size": batch_size,
|
|
"channels": channels,
|
|
"interpolate": interpolate,
|
|
"num_patches": num_patches,
|
|
"num_patches_width": num_patch_width,
|
|
"num_patches_height": num_patch_height,
|
|
}
|
|
return patches, info_dict
|
|
|
|
def folding(self, patches: torch.Tensor, info_dict: Dict) -> torch.Tensor:
|
|
patch_width, patch_height = self.patch_width, self.patch_height
|
|
patch_area = int(patch_width * patch_height)
|
|
|
|
batch_size = info_dict["batch_size"]
|
|
channels = info_dict["channels"]
|
|
num_patches = info_dict["num_patches"]
|
|
num_patch_height = info_dict["num_patches_height"]
|
|
num_patch_width = info_dict["num_patches_width"]
|
|
|
|
# convert from shape (batch_size * patch_area, num_patches, channels)
|
|
# back to shape (batch_size, channels, orig_height, orig_width)
|
|
features = patches.contiguous().view(batch_size, patch_area, num_patches, -1)
|
|
features = features.transpose(1, 3)
|
|
features = features.reshape(
|
|
batch_size * channels * num_patch_height, num_patch_width, patch_height, patch_width
|
|
)
|
|
features = features.transpose(1, 2)
|
|
features = features.reshape(
|
|
batch_size, channels, num_patch_height * patch_height, num_patch_width * patch_width
|
|
)
|
|
|
|
if info_dict["interpolate"]:
|
|
features = nn.functional.interpolate(
|
|
features, size=info_dict["orig_size"], mode="bilinear", align_corners=False
|
|
)
|
|
|
|
return features
|
|
|
|
def forward(self, features: torch.Tensor) -> torch.Tensor:
|
|
# reduce spatial dimensions if needed
|
|
if self.downsampling_layer:
|
|
features = self.downsampling_layer(features)
|
|
|
|
residual = features
|
|
|
|
# local representation
|
|
features = self.conv_kxk(features)
|
|
features = self.conv_1x1(features)
|
|
|
|
# convert feature map to patches
|
|
patches, info_dict = self.unfolding(features)
|
|
|
|
# learn global representations
|
|
patches = self.transformer(patches)
|
|
patches = self.layernorm(patches)
|
|
|
|
# convert patches back to feature maps
|
|
features = self.folding(patches, info_dict)
|
|
|
|
features = self.conv_projection(features)
|
|
features = self.fusion(torch.cat((residual, features), dim=1))
|
|
return features
|
|
|
|
|
|
class MobileViTEncoder(nn.Module):
|
|
def __init__(self, config: MobileViTConfig) -> None:
|
|
super().__init__()
|
|
self.config = config
|
|
|
|
self.layer = nn.ModuleList()
|
|
self.gradient_checkpointing = False
|
|
|
|
# segmentation architectures like DeepLab and PSPNet modify the strides
|
|
# of the classification backbones
|
|
dilate_layer_4 = dilate_layer_5 = False
|
|
if config.output_stride == 8:
|
|
dilate_layer_4 = True
|
|
dilate_layer_5 = True
|
|
elif config.output_stride == 16:
|
|
dilate_layer_5 = True
|
|
|
|
dilation = 1
|
|
|
|
layer_1 = MobileViTMobileNetLayer(
|
|
config,
|
|
in_channels=config.neck_hidden_sizes[0],
|
|
out_channels=config.neck_hidden_sizes[1],
|
|
stride=1,
|
|
num_stages=1,
|
|
)
|
|
self.layer.append(layer_1)
|
|
|
|
layer_2 = MobileViTMobileNetLayer(
|
|
config,
|
|
in_channels=config.neck_hidden_sizes[1],
|
|
out_channels=config.neck_hidden_sizes[2],
|
|
stride=2,
|
|
num_stages=3,
|
|
)
|
|
self.layer.append(layer_2)
|
|
|
|
layer_3 = MobileViTLayer(
|
|
config,
|
|
in_channels=config.neck_hidden_sizes[2],
|
|
out_channels=config.neck_hidden_sizes[3],
|
|
stride=2,
|
|
hidden_size=config.hidden_sizes[0],
|
|
num_stages=2,
|
|
)
|
|
self.layer.append(layer_3)
|
|
|
|
if dilate_layer_4:
|
|
dilation *= 2
|
|
|
|
layer_4 = MobileViTLayer(
|
|
config,
|
|
in_channels=config.neck_hidden_sizes[3],
|
|
out_channels=config.neck_hidden_sizes[4],
|
|
stride=2,
|
|
hidden_size=config.hidden_sizes[1],
|
|
num_stages=4,
|
|
dilation=dilation,
|
|
)
|
|
self.layer.append(layer_4)
|
|
|
|
if dilate_layer_5:
|
|
dilation *= 2
|
|
|
|
layer_5 = MobileViTLayer(
|
|
config,
|
|
in_channels=config.neck_hidden_sizes[4],
|
|
out_channels=config.neck_hidden_sizes[5],
|
|
stride=2,
|
|
hidden_size=config.hidden_sizes[2],
|
|
num_stages=3,
|
|
dilation=dilation,
|
|
)
|
|
self.layer.append(layer_5)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
output_hidden_states: bool = False,
|
|
return_dict: bool = True,
|
|
) -> Union[tuple, BaseModelOutputWithNoAttention]:
|
|
all_hidden_states = () if output_hidden_states else None
|
|
|
|
for i, layer_module in enumerate(self.layer):
|
|
if self.gradient_checkpointing and self.training:
|
|
hidden_states = self._gradient_checkpointing_func(
|
|
layer_module.__call__,
|
|
hidden_states,
|
|
)
|
|
else:
|
|
hidden_states = layer_module(hidden_states)
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
|
|
|
|
return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states, hidden_states=all_hidden_states)
|
|
|
|
|
|
class MobileViTPreTrainedModel(PreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = MobileViTConfig
|
|
base_model_prefix = "mobilevit"
|
|
main_input_name = "pixel_values"
|
|
supports_gradient_checkpointing = True
|
|
|
|
def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
|
|
"""Initialize the weights"""
|
|
if isinstance(module, (nn.Linear, nn.Conv2d)):
|
|
# Slightly different from the TF version which uses truncated_normal for initialization
|
|
# cf https://github.com/pytorch/pytorch/pull/5617
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
|
|
|
|
MOBILEVIT_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 ([`MobileViTConfig`]): 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.
|
|
"""
|
|
|
|
MOBILEVIT_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
|
|
[`MobileViTImageProcessor.__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 MobileViT model outputting raw hidden-states without any specific head on top.",
|
|
MOBILEVIT_START_DOCSTRING,
|
|
)
|
|
class MobileViTModel(MobileViTPreTrainedModel):
|
|
def __init__(self, config: MobileViTConfig, expand_output: bool = True):
|
|
super().__init__(config)
|
|
self.config = config
|
|
self.expand_output = expand_output
|
|
|
|
self.conv_stem = MobileViTConvLayer(
|
|
config,
|
|
in_channels=config.num_channels,
|
|
out_channels=config.neck_hidden_sizes[0],
|
|
kernel_size=3,
|
|
stride=2,
|
|
)
|
|
|
|
self.encoder = MobileViTEncoder(config)
|
|
|
|
if self.expand_output:
|
|
self.conv_1x1_exp = MobileViTConvLayer(
|
|
config,
|
|
in_channels=config.neck_hidden_sizes[5],
|
|
out_channels=config.neck_hidden_sizes[6],
|
|
kernel_size=1,
|
|
)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def _prune_heads(self, heads_to_prune):
|
|
"""Prunes heads of the model.
|
|
heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel
|
|
"""
|
|
for layer_index, heads in heads_to_prune.items():
|
|
mobilevit_layer = self.encoder.layer[layer_index]
|
|
if isinstance(mobilevit_layer, MobileViTLayer):
|
|
for transformer_layer in mobilevit_layer.transformer.layer:
|
|
transformer_layer.attention.prune_heads(heads)
|
|
|
|
@add_start_docstrings_to_model_forward(MOBILEVIT_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: Optional[torch.Tensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, 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
|
|
|
|
if pixel_values is None:
|
|
raise ValueError("You have to specify pixel_values")
|
|
|
|
embedding_output = self.conv_stem(pixel_values)
|
|
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
if self.expand_output:
|
|
last_hidden_state = self.conv_1x1_exp(encoder_outputs[0])
|
|
|
|
# global average pooling: (batch_size, channels, height, width) -> (batch_size, channels)
|
|
pooled_output = torch.mean(last_hidden_state, dim=[-2, -1], keepdim=False)
|
|
else:
|
|
last_hidden_state = encoder_outputs[0]
|
|
pooled_output = None
|
|
|
|
if not return_dict:
|
|
output = (last_hidden_state, pooled_output) if pooled_output is not None else (last_hidden_state,)
|
|
return 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(
|
|
"""
|
|
MobileViT model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
|
|
ImageNet.
|
|
""",
|
|
MOBILEVIT_START_DOCSTRING,
|
|
)
|
|
class MobileViTForImageClassification(MobileViTPreTrainedModel):
|
|
def __init__(self, config: MobileViTConfig) -> None:
|
|
super().__init__(config)
|
|
|
|
self.num_labels = config.num_labels
|
|
self.mobilevit = MobileViTModel(config)
|
|
|
|
# Classifier head
|
|
self.dropout = nn.Dropout(config.classifier_dropout_prob, inplace=True)
|
|
self.classifier = (
|
|
nn.Linear(config.neck_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(MOBILEVIT_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.Tensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, 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 regression loss is computed (Mean-Square loss). 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.mobilevit(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(self.dropout(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 MobileViTASPPPooling(nn.Module):
|
|
def __init__(self, config: MobileViTConfig, in_channels: int, out_channels: int) -> None:
|
|
super().__init__()
|
|
|
|
self.global_pool = nn.AdaptiveAvgPool2d(output_size=1)
|
|
|
|
self.conv_1x1 = MobileViTConvLayer(
|
|
config,
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
use_normalization=True,
|
|
use_activation="relu",
|
|
)
|
|
|
|
def forward(self, features: torch.Tensor) -> torch.Tensor:
|
|
spatial_size = features.shape[-2:]
|
|
features = self.global_pool(features)
|
|
features = self.conv_1x1(features)
|
|
features = nn.functional.interpolate(features, size=spatial_size, mode="bilinear", align_corners=False)
|
|
return features
|
|
|
|
|
|
class MobileViTASPP(nn.Module):
|
|
"""
|
|
ASPP module defined in DeepLab papers: https://arxiv.org/abs/1606.00915, https://arxiv.org/abs/1706.05587
|
|
"""
|
|
|
|
def __init__(self, config: MobileViTConfig) -> None:
|
|
super().__init__()
|
|
|
|
in_channels = config.neck_hidden_sizes[-2]
|
|
out_channels = config.aspp_out_channels
|
|
|
|
if len(config.atrous_rates) != 3:
|
|
raise ValueError("Expected 3 values for atrous_rates")
|
|
|
|
self.convs = nn.ModuleList()
|
|
|
|
in_projection = MobileViTConvLayer(
|
|
config,
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=1,
|
|
use_activation="relu",
|
|
)
|
|
self.convs.append(in_projection)
|
|
|
|
self.convs.extend(
|
|
[
|
|
MobileViTConvLayer(
|
|
config,
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=3,
|
|
dilation=rate,
|
|
use_activation="relu",
|
|
)
|
|
for rate in config.atrous_rates
|
|
]
|
|
)
|
|
|
|
pool_layer = MobileViTASPPPooling(config, in_channels, out_channels)
|
|
self.convs.append(pool_layer)
|
|
|
|
self.project = MobileViTConvLayer(
|
|
config, in_channels=5 * out_channels, out_channels=out_channels, kernel_size=1, use_activation="relu"
|
|
)
|
|
|
|
self.dropout = nn.Dropout(p=config.aspp_dropout_prob)
|
|
|
|
def forward(self, features: torch.Tensor) -> torch.Tensor:
|
|
pyramid = []
|
|
for conv in self.convs:
|
|
pyramid.append(conv(features))
|
|
pyramid = torch.cat(pyramid, dim=1)
|
|
|
|
pooled_features = self.project(pyramid)
|
|
pooled_features = self.dropout(pooled_features)
|
|
return pooled_features
|
|
|
|
|
|
class MobileViTDeepLabV3(nn.Module):
|
|
"""
|
|
DeepLabv3 architecture: https://arxiv.org/abs/1706.05587
|
|
"""
|
|
|
|
def __init__(self, config: MobileViTConfig) -> None:
|
|
super().__init__()
|
|
self.aspp = MobileViTASPP(config)
|
|
|
|
self.dropout = nn.Dropout2d(config.classifier_dropout_prob)
|
|
|
|
self.classifier = MobileViTConvLayer(
|
|
config,
|
|
in_channels=config.aspp_out_channels,
|
|
out_channels=config.num_labels,
|
|
kernel_size=1,
|
|
use_normalization=False,
|
|
use_activation=False,
|
|
bias=True,
|
|
)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
features = self.aspp(hidden_states[-1])
|
|
features = self.dropout(features)
|
|
features = self.classifier(features)
|
|
return features
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
MobileViT model with a semantic segmentation head on top, e.g. for Pascal VOC.
|
|
""",
|
|
MOBILEVIT_START_DOCSTRING,
|
|
)
|
|
class MobileViTForSemanticSegmentation(MobileViTPreTrainedModel):
|
|
def __init__(self, config: MobileViTConfig) -> None:
|
|
super().__init__(config)
|
|
|
|
self.num_labels = config.num_labels
|
|
self.mobilevit = MobileViTModel(config, expand_output=False)
|
|
self.segmentation_head = MobileViTDeepLabV3(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(MOBILEVIT_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=SemanticSegmenterOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, SemanticSegmenterOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
|
|
Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy).
|
|
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> import requests
|
|
>>> import torch
|
|
>>> from PIL import Image
|
|
>>> from transformers import AutoImageProcessor, MobileViTForSemanticSegmentation
|
|
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
|
|
>>> image_processor = AutoImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-small")
|
|
>>> model = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-small")
|
|
|
|
>>> inputs = image_processor(images=image, return_tensors="pt")
|
|
|
|
>>> with torch.no_grad():
|
|
... outputs = model(**inputs)
|
|
|
|
>>> # logits are of shape (batch_size, num_labels, height, width)
|
|
>>> logits = outputs.logits
|
|
```"""
|
|
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
|
|
|
|
outputs = self.mobilevit(
|
|
pixel_values,
|
|
output_hidden_states=True, # we need the intermediate hidden states
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
encoder_hidden_states = outputs.hidden_states if return_dict else outputs[1]
|
|
|
|
logits = self.segmentation_head(encoder_hidden_states)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
if self.config.num_labels == 1:
|
|
raise ValueError("The number of labels should be greater than one")
|
|
else:
|
|
# upsample logits to the images' original size
|
|
upsampled_logits = nn.functional.interpolate(
|
|
logits, size=labels.shape[-2:], mode="bilinear", align_corners=False
|
|
)
|
|
loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index)
|
|
loss = loss_fct(upsampled_logits, labels)
|
|
|
|
if not return_dict:
|
|
if output_hidden_states:
|
|
output = (logits,) + outputs[1:]
|
|
else:
|
|
output = (logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return SemanticSegmenterOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states if output_hidden_states else None,
|
|
attentions=None,
|
|
)
|