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[Feature] Segformer backbone re-implementation (#594) 

* [Feature]Segformer re-implementation

* Using act_cfg and norm_cfg to control activation and normalization

* Split this PR into several little PRs

* Fix lint error

* Remove SegFormerHead

* parameters init refactor

* 1. Refactor segformer backbone parameters init;

2. Remove rebundant functions and unit tests;

* Remove rebundant codes

* 1. Remove rebundant codes;

2. Modify module name;

* Refactor the backbone of segformer using mmcv.cnn.bricks.transformer.py

* Fix some code logic bugs.

* Add mit_convert.py to match pretrain keys of segformer.

* Resolve some comments.

* 1. Add some assert to ensure right params;

2. Support flexible peconv position;

* Add pe_index assert and fix unit test.

* 1. Add doc string for MixVisionTransformer;

2. Add some unit tests for MixVisionTransformer;

* Use hw_shape to pass shape of feature map.

* 1. Fix doc string of MixVisionTransformer;

2. Simplify MixFFN;

3. Modify H, W to hw_shape;

* Add more unit tests.

* Add doc string for shape convertion functions.

* Add some unit tests to improve code coverage.

* Fix Segformer backbone pretrain weights match bug.

* resolve the shape convertion functions doc string.

* Add pad_to_patch_size arg.

* Modify default value of pad_to_patch_size arg.
pull/1801/head
sennnnn 2021-07-20 00:40:40 +08:00 committed by GitHub
parent f6246d6eaa
commit 095ed243c0
9 changed files with 578 additions and 11 deletions
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3
mmseg/models/backbones/__init__.py
View File

@ -1,6 +1,7 @@
from .cgnet import CGNet
from .fast_scnn import FastSCNN
from .hrnet import HRNet
from .mit import MixVisionTransformer
from .mobilenet_v2 import MobileNetV2
from .mobilenet_v3 import MobileNetV3
from .resnest import ResNeSt
@ -13,5 +14,5 @@ from .vit import VisionTransformer
__all__ = [
'ResNet', 'ResNetV1c', 'ResNetV1d', 'ResNeXt', 'HRNet', 'FastSCNN',
'ResNeSt', 'MobileNetV2', 'UNet', 'CGNet', 'MobileNetV3',
'VisionTransformer', 'SwinTransformer'
'VisionTransformer', 'SwinTransformer', 'MixVisionTransformer'
]

416
mmseg/models/backbones/mit.py 100644
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@ -0,0 +1,416 @@
import math
import warnings
import torch
import torch.nn as nn
from mmcv.cnn import (Conv2d, build_activation_layer, build_norm_layer,
constant_init, normal_init, trunc_normal_init)
from mmcv.cnn.bricks.drop import build_dropout
from mmcv.cnn.bricks.transformer import MultiheadAttention
from mmcv.runner import BaseModule, ModuleList, Sequential, _load_checkpoint
from ...utils import get_root_logger
from ..builder import BACKBONES
from ..utils import PatchEmbed, mit_convert, nchw_to_nlc, nlc_to_nchw
class MixFFN(BaseModule):
"""An implementation of MixFFN of Segformer.
The differences between MixFFN & FFN:
1. Use 1X1 Conv to replace Linear layer.
2. Introduce 3X3 Conv to encode positional information.
Args:
embed_dims (int): The feature dimension. Same as
`MultiheadAttention`. Defaults: 256.
feedforward_channels (int): The hidden dimension of FFNs.
Defaults: 1024.
act_cfg (dict, optional): The activation config for FFNs.
Default: dict(type='ReLU')
ffn_drop (float, optional): Probability of an element to be
zeroed in FFN. Default 0.0.
dropout_layer (obj:`ConfigDict`): The dropout_layer used
when adding the shortcut.
init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
Default: None.
"""
def __init__(self,
embed_dims,
feedforward_channels,
act_cfg=dict(type='GELU'),
ffn_drop=0.,
dropout_layer=None,
init_cfg=None):
super(MixFFN, self).__init__(init_cfg)
self.embed_dims = embed_dims
self.feedforward_channels = feedforward_channels
self.act_cfg = act_cfg
self.activate = build_activation_layer(act_cfg)
in_channels = embed_dims
fc1 = Conv2d(
in_channels=in_channels,
out_channels=feedforward_channels,
kernel_size=1,
stride=1,
bias=True)
# 3x3 depth wise conv to provide positional encode information
pe_conv = Conv2d(
in_channels=feedforward_channels,
out_channels=feedforward_channels,
kernel_size=3,
stride=1,
padding=(3 - 1) // 2,
bias=True,
groups=feedforward_channels)
fc2 = Conv2d(
in_channels=feedforward_channels,
out_channels=in_channels,
kernel_size=1,
stride=1,
bias=True)
drop = nn.Dropout(ffn_drop)
layers = [fc1, pe_conv, self.activate, drop, fc2, drop]
self.layers = Sequential(*layers)
self.dropout_layer = build_dropout(
dropout_layer) if dropout_layer else torch.nn.Identity()
def forward(self, x, hw_shape, identity=None):
out = nlc_to_nchw(x, hw_shape)
out = self.layers(out)
out = nchw_to_nlc(out)
if identity is None:
identity = x
return identity + self.dropout_layer(out)
class EfficientMultiheadAttention(MultiheadAttention):
"""An implementation of Efficient Multi-head Attention of Segformer.
This module is modified from MultiheadAttention which is a module from
mmcv.cnn.bricks.transformer.
Args:
embed_dims (int): The embedding dimension.
num_heads (int): Parallel attention heads.
attn_drop (float): A Dropout layer on attn_output_weights.
Default: 0.0.
proj_drop (float): A Dropout layer after `nn.MultiheadAttention`.
Default: 0.0.
dropout_layer (obj:`ConfigDict`): The dropout_layer used
when adding the shortcut. Default: None.
init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
Default: None.
batch_first (bool): Key, Query and Value are shape of
(batch, n, embed_dim)
or (n, batch, embed_dim). Default: False.
qkv_bias (bool): enable bias for qkv if True. Default True.
norm_cfg (dict): Config dict for normalization layer.
Default: dict(type='LN').
sr_ratio (int): The ratio of spatial reduction of Efficient Multi-head
Attention of Segformer. Default: 1.
"""
def __init__(self,
embed_dims,
num_heads,
attn_drop=0.,
proj_drop=0.,
dropout_layer=None,
init_cfg=None,
batch_first=True,
qkv_bias=False,
norm_cfg=dict(type='LN'),
sr_ratio=1):
super().__init__(
embed_dims,
num_heads,
attn_drop,
proj_drop,
dropout_layer=dropout_layer,
init_cfg=init_cfg,
batch_first=batch_first,
bias=qkv_bias)
self.sr_ratio = sr_ratio
if sr_ratio > 1:
self.sr = Conv2d(
in_channels=embed_dims,
out_channels=embed_dims,
kernel_size=sr_ratio,
stride=sr_ratio)
# The ret[0] of build_norm_layer is norm name.
self.norm = build_norm_layer(norm_cfg, embed_dims)[1]
def forward(self, x, hw_shape, identity=None):
x_q = x
if self.sr_ratio > 1:
x_kv = nlc_to_nchw(x, hw_shape)
x_kv = self.sr(x_kv)
x_kv = nchw_to_nlc(x_kv)
x_kv = self.norm(x_kv)
else:
x_kv = x
if identity is None:
identity = x_q
out = self.attn(query=x_q, key=x_kv, value=x_kv)[0]
return identity + self.dropout_layer(self.proj_drop(out))
class TransformerEncoderLayer(BaseModule):
"""Implements one encoder layer in Segformer.
Args:
embed_dims (int): The feature dimension.
num_heads (int): Parallel attention heads.
feedforward_channels (int): The hidden dimension for FFNs.
drop_rate (float): Probability of an element to be zeroed.
after the feed forward layer. Default 0.0.
attn_drop_rate (float): The drop out rate for attention layer.
Default 0.0.
drop_path_rate (float): stochastic depth rate. Default 0.0.
qkv_bias (bool): enable bias for qkv if True.
Default: True.
act_cfg (dict): The activation config for FFNs.
Defalut: dict(type='GELU').
norm_cfg (dict): Config dict for normalization layer.
Default: dict(type='LN').
batch_first (bool): Key, Query and Value are shape of
(batch, n, embed_dim)
or (n, batch, embed_dim). Default: False.
init_cfg (dict, optional): Initialization config dict.
Default:None.
sr_ratio (int): The ratio of spatial reduction of Efficient Multi-head
Attention of Segformer. Default: 1.
"""
def __init__(self,
embed_dims,
num_heads,
feedforward_channels,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
qkv_bias=True,
act_cfg=dict(type='GELU'),
norm_cfg=dict(type='LN'),
batch_first=True,
sr_ratio=1):
super(TransformerEncoderLayer, self).__init__()
# The ret[0] of build_norm_layer is norm name.
self.norm1 = build_norm_layer(norm_cfg, embed_dims)[1]
self.attn = EfficientMultiheadAttention(
embed_dims=embed_dims,
num_heads=num_heads,
attn_drop=attn_drop_rate,
proj_drop=drop_rate,
dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
batch_first=batch_first,
qkv_bias=qkv_bias,
norm_cfg=norm_cfg,
sr_ratio=sr_ratio)
# The ret[0] of build_norm_layer is norm name.
self.norm2 = build_norm_layer(norm_cfg, embed_dims)[1]
self.ffn = MixFFN(
embed_dims=embed_dims,
feedforward_channels=feedforward_channels,
ffn_drop=drop_rate,
dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
act_cfg=act_cfg)
def forward(self, x, hw_shape):
x = self.attn(self.norm1(x), hw_shape, identity=x)
x = self.ffn(self.norm2(x), hw_shape, identity=x)
return x
@BACKBONES.register_module()
class MixVisionTransformer(BaseModule):
"""The backbone of Segformer.
A PyTorch implement of : `SegFormer: Simple and Efficient Design for
Semantic Segmentation with Transformers` -
https://arxiv.org/pdf/2105.15203.pdf
Args:
in_channels (int): Number of input channels. Default: 3.
embed_dims (int): Embedding dimension. Default: 768.
num_stags (int): The num of stages. Default: 4.
num_layers (Sequence[int]): The layer number of each transformer encode
layer. Default: [3, 4, 6, 3].
num_heads (Sequence[int]): The attention heads of each transformer
encode layer. Default: [1, 2, 4, 8].
patch_sizes (Sequence[int]): The patch_size of each overlapped patch
embedding. Default: [7, 3, 3, 3].
strides (Sequence[int]): The stride of each overlapped patch embedding.
Default: [4, 2, 2, 2].
sr_ratios (Sequence[int]): The spatial reduction rate of each
transformer encode layer. Default: [8, 4, 2, 1].
out_indices (Sequence[int] | int): Output from which stages.
Default: (0, 1, 2, 3).
mlp_ratio (int): ratio of mlp hidden dim to embedding dim.
Default: 4.
qkv_bias (bool): Enable bias for qkv if True. Default: True.
drop_rate (float): Probability of an element to be zeroed.
Default 0.0
attn_drop_rate (float): The drop out rate for attention layer.
Default 0.0
drop_path_rate (float): stochastic depth rate. Default 0.0
norm_cfg (dict): Config dict for normalization layer.
Default: dict(type='LN')
act_cfg (dict): The activation config for FFNs.
Defalut: dict(type='GELU').
pretrain_style (str): Choose to use official or mmcls pretrain weights.
Default: official.
pretrained (str, optional): model pretrained path. Default: None.
init_cfg (dict or list[dict], optional): Initialization config dict.
Default: None.
"""
def __init__(self,
in_channels=3,
embed_dims=64,
num_stages=4,
num_layers=[3, 4, 6, 3],
num_heads=[1, 2, 4, 8],
patch_sizes=[7, 3, 3, 3],
strides=[4, 2, 2, 2],
sr_ratios=[8, 4, 2, 1],
out_indices=(0, 1, 2, 3),
mlp_ratio=4,
qkv_bias=True,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
act_cfg=dict(type='GELU'),
norm_cfg=dict(type='LN', eps=1e-6),
pretrain_style='official',
pretrained=None,
init_cfg=None):
super().__init__()
assert pretrain_style in [
'official', 'mmcls'
], 'we only support official weights or mmcls weights.'
if isinstance(pretrained, str) or pretrained is None:
warnings.warn('DeprecationWarning: pretrained is a deprecated, '
'please use "init_cfg" instead')
else:
raise TypeError('pretrained must be a str or None')
self.embed_dims = embed_dims
self.num_stages = num_stages
self.num_layers = num_layers
self.num_heads = num_heads
self.patch_sizes = patch_sizes
self.strides = strides
self.sr_ratios = sr_ratios
assert num_stages == len(num_layers) == len(num_heads) \
== len(patch_sizes) == len(strides) == len(sr_ratios)
self.out_indices = out_indices
assert max(out_indices) < self.num_stages
self.pretrain_style = pretrain_style
self.pretrained = pretrained
self.init_cfg = init_cfg
# transformer encoder
dpr = [
x.item()
for x in torch.linspace(0, drop_path_rate, sum(num_layers))
] # stochastic num_layer decay rule
cur = 0
self.layers = ModuleList()
for i, num_layer in enumerate(num_layers):
embed_dims_i = embed_dims * num_heads[i]
patch_embed = PatchEmbed(
in_channels=in_channels,
embed_dims=embed_dims_i,
kernel_size=patch_sizes[i],
stride=strides[i],
padding=patch_sizes[i] // 2,
pad_to_patch_size=False,
norm_cfg=norm_cfg)
layer = ModuleList([
TransformerEncoderLayer(
embed_dims=embed_dims_i,
num_heads=num_heads[i],
feedforward_channels=mlp_ratio * embed_dims_i,
drop_rate=drop_rate,
attn_drop_rate=attn_drop_rate,
drop_path_rate=dpr[cur + idx],
qkv_bias=qkv_bias,
act_cfg=act_cfg,
norm_cfg=norm_cfg,
sr_ratio=sr_ratios[i]) for idx in range(num_layer)
])
in_channels = embed_dims_i
# The ret[0] of build_norm_layer is norm name.
norm = build_norm_layer(norm_cfg, embed_dims_i)[1]
self.layers.append(ModuleList([patch_embed, layer, norm]))
cur += num_layer
def init_weights(self):
if self.pretrained is None:
for m in self.modules():
if isinstance(m, nn.Linear):
trunc_normal_init(m.weight, std=.02)
if m.bias is not None:
constant_init(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
constant_init(m.bias, 0)
constant_init(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[
1] * m.out_channels
fan_out //= m.groups
normal_init(m.weight, 0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
constant_init(m.bias, 0)
elif isinstance(self.pretrained, str):
logger = get_root_logger()
checkpoint = _load_checkpoint(
self.pretrained, logger=logger, map_location='cpu')
if 'state_dict' in checkpoint:
state_dict = checkpoint['state_dict']
elif 'model' in checkpoint:
state_dict = checkpoint['model']
else:
state_dict = checkpoint
if self.pretrain_style == 'official':
# Because segformer backbone is not support by mmcls,
# so we need to convert pretrain weights to match this
# implementation.
state_dict = mit_convert(state_dict)
self.load_state_dict(state_dict, False)
def forward(self, x):
outs = []
for i, layer in enumerate(self.layers):
x, H, W = layer[0](x), layer[0].DH, layer[0].DW
hw_shape = (H, W)
for block in layer[1]:
x = block(x, hw_shape)
x = layer[2](x)
x = nlc_to_nchw(x, hw_shape)
if i in self.out_indices:
outs.append(x)
return outs

1
mmseg/models/backbones/swin.py
View File

@ -628,6 +628,7 @@ class SwinTransformer(BaseModule):
conv_type='Conv2d',
kernel_size=patch_size,
stride=strides[0],
pad_to_patch_size=True,
norm_cfg=norm_cfg if patch_norm else None,
init_cfg=None)

1
mmseg/models/backbones/vit.py
View File

@ -210,6 +210,7 @@ class VisionTransformer(BaseModule):
conv_type='Conv2d',
kernel_size=patch_size,
stride=patch_size,
pad_to_patch_size=True,
norm_cfg=norm_cfg if patch_norm else None,
init_cfg=None,
)

5
mmseg/models/utils/__init__.py
View File

@ -1,14 +1,15 @@
from .ckpt_convert import swin_convert, vit_convert
from .ckpt_convert import mit_convert, swin_convert, vit_convert
from .embed import PatchEmbed
from .inverted_residual import InvertedResidual, InvertedResidualV3
from .make_divisible import make_divisible
from .res_layer import ResLayer
from .se_layer import SELayer
from .self_attention_block import SelfAttentionBlock
from .shape_convert import nchw_to_nlc, nlc_to_nchw
from .up_conv_block import UpConvBlock
__all__ = [
'ResLayer', 'SelfAttentionBlock', 'make_divisible', 'InvertedResidual',
'UpConvBlock', 'InvertedResidualV3', 'SELayer', 'vit_convert',
'swin_convert', 'PatchEmbed'
'mit_convert', 'swin_convert', 'PatchEmbed', 'nchw_to_nlc', 'nlc_to_nchw'
]

49
mmseg/models/utils/ckpt_convert.py
View File

@ -1,5 +1,7 @@
from collections import OrderedDict
import torch
def swin_convert(ckpt):
new_ckpt = OrderedDict()
@ -88,3 +90,50 @@ def vit_convert(ckpt):
new_ckpt[new_k] = v
return new_ckpt
def mit_convert(ckpt):
new_ckpt = OrderedDict()
# Process the concat between q linear weights and kv linear weights
for k, v in ckpt.items():
if k.startswith('head'):
continue
elif k.startswith('patch_embed'):
stage_i = int(k.split('.')[0].replace('patch_embed', ''))
new_k = k.replace(f'patch_embed{stage_i}', f'layers.{stage_i-1}.0')
new_v = v
if 'proj.' in new_k:
new_k = new_k.replace('proj.', 'projection.')
elif k.startswith('block'):
stage_i = int(k.split('.')[0].replace('block', ''))
new_k = k.replace(f'block{stage_i}', f'layers.{stage_i-1}.1')
new_v = v
if 'attn.q.' in new_k:
sub_item_k = k.replace('q.', 'kv.')
new_k = new_k.replace('q.', 'attn.in_proj_')
new_v = torch.cat([v, ckpt[sub_item_k]], dim=0)
elif 'attn.kv.' in new_k:
continue
elif 'attn.proj.' in new_k:
new_k = new_k.replace('proj.', 'attn.out_proj.')
elif 'attn.sr.' in new_k:
new_k = new_k.replace('sr.', 'sr.')
elif 'mlp.' in new_k:
string = f'{new_k}-'
new_k = new_k.replace('mlp.', 'ffn.layers.')
if 'fc1.weight' in new_k or 'fc2.weight' in new_k:
new_v = v.reshape((*v.shape, 1, 1))
new_k = new_k.replace('fc1.', '0.')
new_k = new_k.replace('dwconv.dwconv.', '1.')
new_k = new_k.replace('fc2.', '4.')
string += f'{new_k} {v.shape}-{new_v.shape}'
# print(string)
elif k.startswith('norm'):
stage_i = int(k.split('.')[0].replace('norm', ''))
new_k = k.replace(f'norm{stage_i}', f'layers.{stage_i-1}.2')
new_v = v
else:
new_k = k
new_v = v
new_ckpt[new_k] = new_v
return new_ckpt

26
mmseg/models/utils/embed.py
View File

@ -19,6 +19,8 @@ class PatchEmbed(BaseModule):
Default: None (Default to be equal with kernel_size).
padding (int): The padding length of embedding conv. Default: 0.
dilation (int): The dilation rate of embedding conv. Default: 1.
pad_to_patch_size (bool, optional): Whether to pad feature map shape
to multiple patch size. Default: True.
norm_cfg (dict, optional): Config dict for normalization layer.
init_cfg (`mmcv.ConfigDict`, optional): The Config for initialization.
Default: None.
@ -32,6 +34,7 @@ class PatchEmbed(BaseModule):
stride=16,
padding=0,
dilation=1,
pad_to_patch_size=True,
norm_cfg=None,
init_cfg=None):
super(PatchEmbed, self).__init__()
@ -42,7 +45,9 @@ class PatchEmbed(BaseModule):
if stride is None:
stride = kernel_size
# The default setting of patch size is eaual to kernel size.
self.pad_to_patch_size = pad_to_patch_size
# The default setting of patch size is equal to kernel size.
patch_size = kernel_size
if isinstance(patch_size, int):
patch_size = to_2tuple(patch_size)
@ -56,7 +61,7 @@ class PatchEmbed(BaseModule):
self.patch_size = patch_size
# Use conv layer to embed
conv_type = conv_type or dict(type='Conv2d')
conv_type = conv_type or 'Conv2d'
self.projection = build_conv_layer(
dict(type=conv_type),
in_channels=in_channels,
@ -73,12 +78,17 @@ class PatchEmbed(BaseModule):
def forward(self, x):
H, W = x.shape[2], x.shape[3]
if H % self.patch_size[0] != 0:
x = F.pad(x,
(0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
if W % self.patch_size[1] != 0:
x = F.pad(x,
(0, self.patch_size[1] - W % self.patch_size[1], 0, 0))
# TODO: Process overlapping op
if self.pad_to_patch_size:
# Modify H, W to multiple of patch size.
if H % self.patch_size[0] != 0:
x = F.pad(
x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
if W % self.patch_size[1] != 0:
x = F.pad(
x, (0, self.patch_size[1] - W % self.patch_size[1], 0, 0))
x = self.projection(x)
self.DH, self.DW = x.shape[2], x.shape[3]
x = x.flatten(2).transpose(1, 2)

28
mmseg/models/utils/shape_convert.py 100644
View File

@ -0,0 +1,28 @@
def nlc_to_nchw(x, hw_shape):
"""Convert [N, L, C] shape tensor to [N, C, H, W] shape tensor.
Args:
x (Tensor): The input tensor of shape [N, L, C] before convertion.
hw_shape (Sequence[int]): The height and width of output feature map.
Returns:
Tensor: The output tensor of shape [N, C, H, W] after convertion.
"""
H, W = hw_shape
assert len(x.shape) == 3
B, L, C = x.shape
assert L == H * W, 'The seq_len doesn\'t match H, W'
return x.transpose(1, 2).reshape(B, C, H, W)
def nchw_to_nlc(x):
"""Flatten [N, C, H, W] shape tensor to [N, L, C] shape tensor.
Args:
x (Tensor): The input tensor of shape [N, C, H, W] before convertion.
Returns:
Tensor: The output tensor of shape [N, L, C] after convertion.
"""
assert len(x.shape) == 4
return x.flatten(2).transpose(1, 2).contiguous()

60
tests/test_models/test_backbones/test_mit.py 100644
View File

@ -0,0 +1,60 @@
import pytest
import torch
from mmseg.models.backbones import MixVisionTransformer
from mmseg.models.backbones.mit import EfficientMultiheadAttention, MixFFN
def test_mit():
with pytest.raises(AssertionError):
# It's only support official style and mmcls style now.
MixVisionTransformer(pretrain_style='timm')
with pytest.raises(TypeError):
# Pretrained represents pretrain url and must be str or None.
MixVisionTransformer(pretrained=123)
# Test normal input
H, W = (224, 224)
temp = torch.randn((1, 3, H, W))
model = MixVisionTransformer(
embed_dims=32, num_heads=[1, 2, 5, 8], out_indices=(0, 1, 2, 3))
model.init_weights()
outs = model(temp)
assert outs[0].shape == (1, 32, H // 4, W // 4)
assert outs[1].shape == (1, 64, H // 8, W // 8)
assert outs[2].shape == (1, 160, H // 16, W // 16)
assert outs[3].shape == (1, 256, H // 32, W // 32)
# Test non-squared input
H, W = (224, 320)
temp = torch.randn((1, 3, H, W))
outs = model(temp)
assert outs[0].shape == (1, 32, H // 4, W // 4)
assert outs[1].shape == (1, 64, H // 8, W // 8)
assert outs[2].shape == (1, 160, H // 16, W // 16)
assert outs[3].shape == (1, 256, H // 32, W // 32)
# Test MixFFN
FFN = MixFFN(128, 512)
hw_shape = (32, 32)
token_len = 32 * 32
temp = torch.randn((1, token_len, 128))
# Self identity
out = FFN(temp, hw_shape)
assert out.shape == (1, token_len, 128)
# Out identity
outs = FFN(temp, hw_shape, temp)
assert out.shape == (1, token_len, 128)
# Test EfficientMHA
MHA = EfficientMultiheadAttention(128, 2)
hw_shape = (32, 32)
token_len = 32 * 32
temp = torch.randn((1, token_len, 128))
# Self identity
out = MHA(temp, hw_shape)
assert out.shape == (1, token_len, 128)
# Out identity
outs = MHA(temp, hw_shape, temp)
assert out.shape == (1, token_len, 128)