from collections import OrderedDict from functools import partial from typing import Callable, Optional import torch.nn as nn import torch from torch import Tensor def drop_path(x, drop_prob: float = 0., training: bool = False): """ Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). "Deep Networks with Stochastic Depth", https://arxiv.org/pdf/1603.09382.pdf This function is taken from the rwightman. It can be seen here: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py#L140 """ if drop_prob == 0. or not training: return x keep_prob = 1 - drop_prob shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) random_tensor.floor_() # binarize output = x.div(keep_prob) * random_tensor return output class DropPath(nn.Module): """ Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). "Deep Networks with Stochastic Depth", https://arxiv.org/pdf/1603.09382.pdf """ def __init__(self, drop_prob=None): super(DropPath, self).__init__() self.drop_prob = drop_prob def forward(self, x): return drop_path(x, self.drop_prob, self.training) class ConvBNAct(nn.Module): def __init__(self, in_planes: int, out_planes: int, kernel_size: int = 3, stride: int = 1, groups: int = 1, norm_layer: Optional[Callable[..., nn.Module]] = None, activation_layer: Optional[Callable[..., nn.Module]] = None): super(ConvBNAct, self).__init__() padding = (kernel_size - 1) // 2 if norm_layer is None: norm_layer = nn.BatchNorm2d if activation_layer is None: activation_layer = nn.SiLU # alias Swish (torch>=1.7) self.conv = nn.Conv2d(in_channels=in_planes, out_channels=out_planes, kernel_size=kernel_size, stride=stride, padding=padding, groups=groups, bias=False) self.bn = norm_layer(out_planes) self.act = activation_layer() def forward(self, x): result = self.conv(x) result = self.bn(result) result = self.act(result) return result class SqueezeExcite(nn.Module): def __init__(self, input_c: int, # block input channel expand_c: int, # block expand channel se_ratio: float = 0.25): super(SqueezeExcite, self).__init__() squeeze_c = int(input_c * se_ratio) self.conv_reduce = nn.Conv2d(expand_c, squeeze_c, 1) self.act1 = nn.SiLU() # alias Swish self.conv_expand = nn.Conv2d(squeeze_c, expand_c, 1) self.act2 = nn.Sigmoid() def forward(self, x: Tensor) -> Tensor: scale = x.mean((2, 3), keepdim=True) scale = self.conv_reduce(scale) scale = self.act1(scale) scale = self.conv_expand(scale) scale = self.act2(scale) return scale * x class MBConv(nn.Module): def __init__(self, kernel_size: int, input_c: int, out_c: int, expand_ratio: int, stride: int, se_ratio: float, drop_rate: float, norm_layer: Callable[..., nn.Module]): super(MBConv, self).__init__() if stride not in [1, 2]: raise ValueError("illegal stride value.") self.has_shortcut = (stride == 1 and input_c == out_c) activation_layer = nn.SiLU # alias Swish expanded_c = input_c * expand_ratio # 在EfficientNetV2中，MBConv中不存在expansion=1的情况所以conv_pw肯定存在 assert expand_ratio != 1 # Point-wise expansion self.expand_conv = ConvBNAct(input_c, expanded_c, kernel_size=1, norm_layer=norm_layer, activation_layer=activation_layer) # Depth-wise convolution self.dwconv = ConvBNAct(expanded_c, expanded_c, kernel_size=kernel_size, stride=stride, groups=expanded_c, norm_layer=norm_layer, activation_layer=activation_layer) self.se = SqueezeExcite(input_c, expanded_c, se_ratio) if se_ratio > 0 else nn.Identity() # Point-wise linear projection self.project_conv = ConvBNAct(expanded_c, out_planes=out_c, kernel_size=1, norm_layer=norm_layer, activation_layer=nn.Identity) # 注意这里没有激活函数，所有传入Identity self.out_channels = out_c # 只有在使用shortcut连接时才使用dropout层 self.drop_rate = drop_rate if self.has_shortcut and drop_rate > 0: self.dropout = DropPath(drop_rate) def forward(self, x: Tensor) -> Tensor: result = self.expand_conv(x) result = self.dwconv(result) result = self.se(result) result = self.project_conv(result) if self.has_shortcut: if self.drop_rate > 0: result = self.dropout(result) result += x return result class FusedMBConv(nn.Module): def __init__(self, kernel_size: int, input_c: int, out_c: int, expand_ratio: int, stride: int, se_ratio: float, drop_rate: float, norm_layer: Callable[..., nn.Module]): super(FusedMBConv, self).__init__() assert stride in [1, 2] assert se_ratio == 0 self.has_shortcut = stride == 1 and input_c == out_c self.drop_rate = drop_rate self.has_expansion = expand_ratio != 1 activation_layer = nn.SiLU # alias Swish expanded_c = input_c * expand_ratio # 只有当expand ratio不等于1时才有expand conv if self.has_expansion: # Expansion convolution self.expand_conv = ConvBNAct(input_c, expanded_c, kernel_size=kernel_size, stride=stride, norm_layer=norm_layer, activation_layer=activation_layer) self.project_conv = ConvBNAct(expanded_c, out_c, kernel_size=1, norm_layer=norm_layer, activation_layer=nn.Identity) # 注意没有激活函数 else: # 当只有project_conv时的情况 self.project_conv = ConvBNAct(input_c, out_c, kernel_size=kernel_size, stride=stride, norm_layer=norm_layer, activation_layer=activation_layer) # 注意有激活函数 self.out_channels = out_c # 只有在使用shortcut连接时才使用dropout层 self.drop_rate = drop_rate if self.has_shortcut and drop_rate > 0: self.dropout = DropPath(drop_rate) def forward(self, x: Tensor) -> Tensor: if self.has_expansion: result = self.expand_conv(x) result = self.project_conv(result) else: result = self.project_conv(x) if self.has_shortcut: if self.drop_rate > 0: result = self.dropout(result) result += x return result class EfficientNetV2(nn.Module): def __init__(self, model_cnf: list, num_classes: int = 1000, num_features: int = 1280, dropout_rate: float = 0.2, drop_connect_rate: float = 0.2): super(EfficientNetV2, self).__init__() for cnf in model_cnf: assert len(cnf) == 8 norm_layer = partial(nn.BatchNorm2d, eps=1e-3, momentum=0.1) stem_filter_num = model_cnf[0][4] self.stem = ConvBNAct(3, stem_filter_num, kernel_size=3, stride=2, norm_layer=norm_layer) # 激活函数默认是SiLU total_blocks = sum([i[0] for i in model_cnf]) block_id = 0 blocks = [] for cnf in model_cnf: repeats = cnf[0] op = FusedMBConv if cnf[-2] == 0 else MBConv for i in range(repeats): blocks.append(op(kernel_size=cnf[1], input_c=cnf[4] if i == 0 else cnf[5], out_c=cnf[5], expand_ratio=cnf[3], stride=cnf[2] if i == 0 else 1, se_ratio=cnf[-1], drop_rate=drop_connect_rate * block_id / total_blocks, norm_layer=norm_layer)) block_id += 1 self.blocks = nn.Sequential(*blocks) head_input_c = model_cnf[-1][-3] head = OrderedDict() head.update({"project_conv": ConvBNAct(head_input_c, num_features, kernel_size=1, norm_layer=norm_layer)}) # 激活函数默认是SiLU head.update({"avgpool": nn.AdaptiveAvgPool2d(1)}) head.update({"flatten": nn.Flatten()}) if dropout_rate > 0: head.update({"dropout": nn.Dropout(p=dropout_rate, inplace=True)}) head.update({"classifier": nn.Linear(num_features, num_classes)}) self.head = nn.Sequential(head) # initial weights for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode="fan_out") if m.bias is not None: nn.init.zeros_(m.bias) elif isinstance(m, nn.BatchNorm2d): nn.init.ones_(m.weight) nn.init.zeros_(m.bias) elif isinstance(m, nn.Linear): nn.init.normal_(m.weight, 0, 0.01) nn.init.zeros_(m.bias) def forward(self, x: Tensor) -> Tensor: x = self.stem(x) x = self.blocks(x) x = self.head(x) return x def efficientnetv2_s(num_classes: int = 1000): """ EfficientNetV2 https://arxiv.org/abs/2104.00298 """ # train_size: 300, eval_size: 384 # repeat, kernel, stride, expansion, in_c, out_c, operator, se_ratio model_config = [[2, 3, 1, 1, 24, 24, 0, 0], [4, 3, 2, 4, 24, 48, 0, 0], [4, 3, 2, 4, 48, 64, 0, 0], [6, 3, 2, 4, 64, 128, 1, 0.25], [9, 3, 1, 6, 128, 160, 1, 0.25], [15, 3, 2, 6, 160, 256, 1, 0.25]] model = EfficientNetV2(model_cnf=model_config, num_classes=num_classes, dropout_rate=0.2) return model def efficientnetv2_m(num_classes: int = 1000): """ EfficientNetV2 https://arxiv.org/abs/2104.00298 """ # train_size: 384, eval_size: 480 # repeat, kernel, stride, expansion, in_c, out_c, operator, se_ratio model_config = [[3, 3, 1, 1, 24, 24, 0, 0], [5, 3, 2, 4, 24, 48, 0, 0], [5, 3, 2, 4, 48, 80, 0, 0], [7, 3, 2, 4, 80, 160, 1, 0.25], [14, 3, 1, 6, 160, 176, 1, 0.25], [18, 3, 2, 6, 176, 304, 1, 0.25], [5, 3, 1, 6, 304, 512, 1, 0.25]] model = EfficientNetV2(model_cnf=model_config, num_classes=num_classes, dropout_rate=0.3) return model def efficientnetv2_l(num_classes: int = 1000): """ EfficientNetV2 https://arxiv.org/abs/2104.00298 """ # train_size: 384, eval_size: 480 # repeat, kernel, stride, expansion, in_c, out_c, operator, se_ratio model_config = [[4, 3, 1, 1, 32, 32, 0, 0], [7, 3, 2, 4, 32, 64, 0, 0], [7, 3, 2, 4, 64, 96, 0, 0], [10, 3, 2, 4, 96, 192, 1, 0.25], [19, 3, 1, 6, 192, 224, 1, 0.25], [25, 3, 2, 6, 224, 384, 1, 0.25], [7, 3, 1, 6, 384, 640, 1, 0.25]] model = EfficientNetV2(model_cnf=model_config, num_classes=num_classes, dropout_rate=0.4) return model