File size: 2,433 Bytes
5d2fa0b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | import timm
import torch
import torch.nn as nn
class PlantDiseaseModel(nn.Module):
def __init__(self, config, num_classes):
super().__init__()
self.backbone_name = config.model.backbone
self.model = timm.create_model(
self.backbone_name,
pretrained=config.model.pretrained,
num_classes=num_classes,
drop_rate=config.model.dropout,
drop_path_rate=config.model.drop_path,
)
if config.model.freeze_backbone:
self._freeze_backbone()
if config.model.freeze_bn:
self.freeze_bn()
def _freeze_backbone(self):
for param in self.model.parameters():
param.requires_grad = False
if hasattr(self.model, "get_classifier"):
classifier = self.model.get_classifier()
for param in classifier.parameters():
param.requires_grad = True
else:
for name, param in self.model.named_parameters():
if "head" in name or "classifier" in name:
param.requires_grad = True
def freeze_bn(self):
for module in self.model.modules():
if isinstance(module, (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d)):
module.eval()
if module.weight is not None:
module.weight.requires_grad = False
if module.bias is not None:
module.bias.requires_grad = False
def forward(self, x):
return self.model(x)
def get_param_groups(model, base_lr, head_lr, weight_decay):
if hasattr(model.model, "get_classifier"):
head = model.model.get_classifier()
head_params = list(head.parameters())
head_param_ids = set(id(p) for p in head_params)
else:
# fallback
head_params = []
for name, p in model.named_parameters():
if any(k in name for k in ["head", "classifier"]):
head_params.append(p)
head_param_ids = set(id(p) for p in head_params)
head_params = [p for p in head_params if p.requires_grad]
backbone_params = [
p for p in model.parameters() if id(p) not in head_param_ids and p.requires_grad
]
return [
{"params": backbone_params, "lr": base_lr, "weight_decay": weight_decay},
{"params": head_params, "lr": head_lr, "weight_decay": weight_decay},
]
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