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961cf0c | 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 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 | import torch
import torch.nn as nn
import timm
from torchvision import models
from transformers import AutoModel
from tab_transformer import TabTransformer
class loadModels:
# ======================================================
# Utilitário: controle de fine-tuning do backbone
# ======================================================
@staticmethod
def set_backbone_train_mode(model, mode="frozen_weights", last_n_layers=1):
if mode == "frozen_weights":
for p in model.parameters():
p.requires_grad = False
elif mode == "unfrozen_weights":
for p in model.parameters():
p.requires_grad = True
elif mode == "last_layer_unfrozen_weights":
for p in model.parameters():
p.requires_grad = False
children = list(model.children())
for layer in children[-last_n_layers:]:
for p in layer.parameters():
p.requires_grad = True
else:
raise ValueError(f"Invalid backbone_train_mode: {mode}")
# ======================================================
# Image encoders
# ======================================================
@staticmethod
def loadModelImageEncoder(
cnn_model_name: str,
backbone_train_mode: str = "frozen_weights"
):
# ------------------------------
# TorchVision CNNs
# ------------------------------
if cnn_model_name == "resnet-18":
model = models.resnet18(pretrained=True)
cnn_dim = 512
model.fc = nn.Identity()
loadModels.set_backbone_train_mode(
model, backbone_train_mode, last_n_layers=1
)
elif cnn_model_name == "resnet-50":
model = models.resnet50(pretrained=True)
cnn_dim = 2048
model.fc = nn.Identity()
loadModels.set_backbone_train_mode(
model, backbone_train_mode, last_n_layers=1
)
elif cnn_model_name == "densenet169":
model = models.densenet169(pretrained=True)
cnn_dim = 1664
model.classifier = nn.Identity()
if backbone_train_mode == "last_layer_unfrozen_weights":
for p in model.parameters():
p.requires_grad = False
for p in model.features.denseblock4.parameters():
p.requires_grad = True
else:
loadModels.set_backbone_train_mode(model, backbone_train_mode)
elif cnn_model_name == "mobilenet-v2":
model = models.mobilenet_v2(pretrained=True)
cnn_dim = 1280
model.classifier = nn.Identity()
loadModels.set_backbone_train_mode(
model, backbone_train_mode, last_n_layers=1
)
elif cnn_model_name == "efficientnet-b0":
model = models.efficientnet_b0(pretrained=True)
cnn_dim = 1280
model.classifier = nn.Identity()
loadModels.set_backbone_train_mode(
model, backbone_train_mode, last_n_layers=1
)
elif cnn_model_name == "efficientnet-b4":
model = models.efficientnet_b4(pretrained=True)
cnn_dim = 1792
model.classifier = nn.Identity()
loadModels.set_backbone_train_mode(
model, backbone_train_mode, last_n_layers=1
)
elif cnn_model_name == "efficientnet-b7":
model = models.efficientnet_b7(pretrained=True)
cnn_dim = 2560
model.classifier = nn.Identity()
loadModels.set_backbone_train_mode(
model, backbone_train_mode, last_n_layers=1
)
# ------------------------------
# timm models (ViT / Hybrid)
# ------------------------------
elif cnn_model_name in timm.list_models(pretrained=True):
model = timm.create_model(cnn_model_name, pretrained=True)
model.reset_classifier(0)
cnn_dim = model.num_features
if backbone_train_mode == "last_layer_unfrozen_weights":
for p in model.parameters():
p.requires_grad = False
# estratégia genérica: último estágio
if hasattr(model, "stages"):
for p in model.stages[-1].parameters():
p.requires_grad = True
elif hasattr(model, "blocks"):
for p in model.blocks[-1].parameters():
p.requires_grad = True
else:
loadModels.set_backbone_train_mode(model, backbone_train_mode)
else:
raise ValueError(f"Backbone '{cnn_model_name}' não implementado.")
return model, cnn_dim
# ======================================================
# Text encoders
# ======================================================
@staticmethod
def loadTextModelEncoder(
text_model_encoder: str,
train_mode: str = "frozen_weights"
):
# ------------------------------
# HuggingFace Transformers
# ------------------------------
if text_model_encoder in ["bert-base-uncased", "gpt2"]:
model = AutoModel.from_pretrained(text_model_encoder)
output_dim = model.config.hidden_size
if train_mode == "unfrozen_weights":
for p in model.parameters():
p.requires_grad = True
else:
for p in model.parameters():
p.requires_grad = False
return model, output_dim, output_dim
# ------------------------------
# TabTransformer
# ------------------------------
elif text_model_encoder == "tab-transformer":
categorical_indices = list(range(82))
output_dim = 85
model = TabTransformer(
categorical_cardinalities=categorical_indices,
num_continuous=4,
output_dim=output_dim
)
return model, output_dim, output_dim
else:
raise ValueError(f"Text encoder '{text_model_encoder}' não suportado.") |