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convnextv2.py
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'''
Function:
Implementation of ConvNeXtV2
Author:
Zhenchao Jin
'''
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from .bricks.normalization.grn import GRN
from .bricks.dropout.droppath import DropPath
from .bricks import NormalizationBuilder, BuildActivation
'''DEFAULT_MODEL_URLS'''
DEFAULT_MODEL_URLS = {
'convnextv2_atto_1k_224_fcmae': 'https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_atto_1k_224_fcmae.pt',
'convnextv2_femto_1k_224_fcmae': 'https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_femto_1k_224_fcmae.pt',
'convnextv2_pico_1k_224_fcmae': 'https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_pico_1k_224_fcmae.pt',
'convnextv2_nano_1k_224_fcmae': 'https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_nano_1k_224_fcmae.pt',
'convnextv2_tiny_1k_224_fcmae': 'https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_tiny_1k_224_fcmae.pt',
'convnextv2_base_1k_224_fcmae': 'https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_base_1k_224_fcmae.pt',
'convnextv2_large_1k_224_fcmae': 'https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_large_1k_224_fcmae.pt',
'convnextv2_huge_1k_224_fcmae': 'https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_huge_1k_224_fcmae.pt',
'convnextv2_atto_1k_224_ema': 'https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt',
'convnextv2_femto_1k_224_ema': 'https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt',
'convnextv2_pico_1k_224_ema': 'https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt',
'convnextv2_nano_1k_224_ema': 'https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt',
'convnextv2_tiny_1k_224_ema': 'https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt',
'convnextv2_base_1k_224_ema': 'https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt',
'convnextv2_large_1k_224_ema': 'https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt',
'convnextv2_huge_1k_224_ema': 'https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt',
'convnextv2_nano_22k_224_ema': 'https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt',
'convnextv2_nano_22k_384_ema': 'https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt',
'convnextv2_tiny_22k_224_ema': 'https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt',
'convnextv2_tiny_22k_384_ema': 'https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt',
'convnextv2_base_22k_224_ema': 'https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt',
'convnextv2_base_22k_384_ema': 'https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt',
'convnextv2_large_22k_224_ema': 'https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt',
'convnextv2_large_22k_384_ema': 'https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt',
'convnextv2_huge_22k_384_ema': 'https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt',
'convnextv2_huge_22k_512_ema': 'https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt',
}
'''AUTO_ASSERT_STRUCTURE_TYPES'''
AUTO_ASSERT_STRUCTURE_TYPES = {}
'''LayerNorm'''
class LayerNorm(nn.Module):
def __init__(self, normalized_shape, eps=1e-6, data_format='channels_last'):
super(LayerNorm, self).__init__()
# assert
assert data_format in ['channels_last', 'channels_first']
# set attributes
self.eps = eps
self.data_format = data_format
self.normalized_shape = (normalized_shape, )
# build parameters
self.weight = nn.Parameter(torch.ones(normalized_shape))
self.bias = nn.Parameter(torch.zeros(normalized_shape))
'''forward'''
def forward(self, x):
if self.data_format == 'channels_last':
return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
else:
u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.eps)
x = self.weight[:, None, None] * x + self.bias[:, None, None]
return x
'''BuildNormalization'''
BuildNormalization = NormalizationBuilder(
requires_register_modules={'LayerNormConvNeXtV2': LayerNorm}
).build
'''ConvNeXtV2Block'''
class ConvNeXtV2Block(nn.Module):
def __init__(self, dim, drop_path=0., norm_cfg=None, act_cfg=None):
super(ConvNeXtV2Block, self).__init__()
self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim)
self.norm = BuildNormalization(placeholder=dim, norm_cfg=norm_cfg)
self.pwconv1 = nn.Linear(dim, 4 * dim)
self.act = BuildActivation(act_cfg)
self.grn = GRN(4 * dim)
self.pwconv2 = nn.Linear(4 * dim, dim)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
'''forward'''
def forward(self, x):
identity = x
x = self.dwconv(x)
x = x.permute(0, 2, 3, 1)
x = self.norm(x)
x = self.pwconv1(x)
x = self.act(x)
x = self.grn(x)
x = self.pwconv2(x)
x = x.permute(0, 3, 1, 2)
x = identity + self.drop_path(x)
return x
'''ConvNeXtV2'''
class ConvNeXtV2(nn.Module):
arch_settings = {
'atto': {'depths': [2, 2, 6, 2], 'dims': [40, 80, 160, 320]},
'femto': {'depths': [2, 2, 6, 2], 'dims': [48, 96, 192, 384]},
'pico': {'depths': [2, 2, 6, 2], 'dims': [64, 128, 256, 512]},
'nano': {'depths': [2, 2, 8, 2], 'dims': [80, 160, 320, 640]},
'tiny': {'depths': [3, 3, 9, 3], 'dims': [96, 192, 384, 768]},
'base': {'depths': [3, 3, 27, 3], 'dims': [128, 256, 512, 1024]},
'large': {'depths': [3, 3, 27, 3], 'dims': [192, 384, 768, 1536]},
'huge': {'depths': [3, 3, 27, 3], 'dims': [352, 704, 1408, 2816]},
}
def __init__(self, structure_type, in_channels=3, arch='tiny', drop_path_rate=0., out_indices=(0, 1, 2, 3), norm_cfg={'type': 'LayerNormConvNeXtV2', 'eps': 1e-6},
act_cfg={'type': 'GELU'}, pretrained=True, pretrained_model_path=''):
super(ConvNeXtV2, self).__init__()
assert arch in self.arch_settings
arch = self.arch_settings[arch]
# set attributes
self.structure_type = structure_type
self.in_channels = in_channels
self.depths = arch['depths']
self.dims = arch['dims']
self.drop_path_rate = drop_path_rate
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
self.pretrained = pretrained
self.pretrained_model_path = pretrained_model_path
if isinstance(out_indices, int):
out_indices = [out_indices]
for i, index in enumerate(out_indices):
if index < 0:
out_indices[i] = 4 + index
assert out_indices[i] >= 0, f'invalid out_indices {index}'
self.out_indices = out_indices
# assert
if structure_type in AUTO_ASSERT_STRUCTURE_TYPES:
for key, value in AUTO_ASSERT_STRUCTURE_TYPES[structure_type].items():
assert hasattr(self, key) and (getattr(self, key) == value)
# build downsample_layers
self.downsample_layers = nn.ModuleList()
norm_cfg['data_format'] = 'channels_first'
stem = nn.Sequential(
nn.Conv2d(in_channels, self.dims[0], kernel_size=4, stride=4),
BuildNormalization(placeholder=self.dims[0], norm_cfg=norm_cfg),
)
self.downsample_layers.append(stem)
for i in range(3):
downsample_layer = nn.Sequential(
BuildNormalization(placeholder=self.dims[i], norm_cfg=norm_cfg),
nn.Conv2d(self.dims[i], self.dims[i+1], kernel_size=2, stride=2),
)
self.downsample_layers.append(downsample_layer)
# build stages
dp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths))]
self.stages = nn.ModuleList()
cur = 0
for i in range(4):
norm_cfg['data_format'] = 'channels_last'
stage = nn.Sequential(
*[ConvNeXtV2Block(dim=self.dims[i], drop_path=dp_rates[cur + j], norm_cfg=norm_cfg, act_cfg=act_cfg) for j in range(self.depths[i])]
)
self.stages.append(stage)
cur += self.depths[i]
if i in self.out_indices:
norm_cfg['data_format'] = 'channels_first'
norm_layer = BuildNormalization(placeholder=self.dims[i], norm_cfg=norm_cfg)
self.add_module(f'norm{i}', norm_layer)
# load pretrained weights
if pretrained:
self.loadpretrainedweights(structure_type, pretrained_model_path)
'''forward'''
def forward(self, x):
outs = []
for i, stage in enumerate(self.stages):
x = self.downsample_layers[i](x)
x = stage(x)
if i in self.out_indices:
norm_layer = getattr(self, f'norm{i}')
outs.append(norm_layer(x).contiguous())
return tuple(outs)
'''loadpretrainedweights'''
def loadpretrainedweights(self, structure_type, pretrained_model_path=''):
if pretrained_model_path and os.path.exists(pretrained_model_path):
checkpoint = torch.load(pretrained_model_path, map_location='cpu')
else:
checkpoint = model_zoo.load_url(DEFAULT_MODEL_URLS[structure_type], 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
state_dict_convert = {}
for key, value in state_dict.items():
state_dict_convert[key.replace('backbone.', '')] = value
if 'grn.gamma' in key:
state_dict_convert[key] = value.reshape(1, 1, 1, -1)
if 'grn.beta' in key:
state_dict_convert[key] = value.reshape(1, 1, 1, -1)
self.load_state_dict(state_dict_convert, strict=False)