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9627ce0 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 | import os
import numpy as np
import torch
import torch.nn.functional as F
from src.interface.model_interface import MInterface_base
from src.model.finetune_model import UniModel
from scipy.stats import spearmanr
from sklearn.metrics import roc_auc_score, f1_score
from sklearn.preprocessing import binarize
from src.utils.metrics import f1_score_max
class MInterface(MInterface_base):
def __init__(self, model_name=None, loss=None, lr=None, **kargs):
super().__init__()
self.save_hyperparameters()
self.load_model()
self._context = {
"validation": {
"logits": [],
"labels": [],
"metric": []
},
"test": {
"logits": [],
"labels": [],
"metric": []
}
}
os.makedirs(os.path.join(self.hparams.res_dir, self.hparams.ex_name), exist_ok=True)
def forward(self, batch, batch_idx):
ret = self.model(batch)
return ret
def training_step(self, batch, batch_idx, **kwargs):
ret = self(batch, batch_idx)
loss = ret['loss']
self.log("train_loss", ret['loss'], on_step=True, on_epoch=True, prog_bar=True)
return loss
def validation_step(self, batch, batch_idx):
ret = self(batch, batch_idx)
loss = ret['loss']
log_dict = {'val_loss': loss}
if self.hparams.task_type in [
"contact"
]:
self._context["validation"]["metric"].append(
self.metrics(
ret['logits'][...,0].float().cpu(),
ret['label'].float().cpu(),
ret['attention_mask'].float().cpu(),
name="valid"
))
elif self.hparams.task_type in [
"residual_classification"
]:
self._context["validation"]["metric"].append(
self.metrics(
ret['logits'].float().cpu().numpy(),
ret['label'].float().cpu().numpy(),
ret['attention_mask'].float().cpu().numpy(),
name="valid"
))
else:
self._context["validation"]["logits"].append(ret['logits'].float().cpu().numpy())
self._context["validation"]["labels"].append(ret['label'].float().cpu().numpy())
self.log_dict(log_dict)
return self.log_dict
def on_validation_epoch_end(self):
# Make the Progress Bar leave there
self.print('')
# compute metrics and reset
if self.hparams.task_type in ["contact"]:
value = torch.tensor(self._context["validation"]["metric"]).mean()
metric = {f"valid_Top(L/5)": value}
self._context["validation"]["metric"] = []
elif self.hparams.task_type in [
"residual_classification"
]:
value = torch.tensor(self._context["validation"]["metric"]).mean()
metric = {f"valid_acc": value}
self._context["validation"]["metric"] = []
else:
metric = self.metrics(
self._context["validation"]["logits"],
self._context["validation"]["labels"],
# self._context["validation"]["attn_mask"],
name="valid"
)
self._context["validation"]["logits"] = []
self._context["validation"]["labels"] = []
self._context["validation"]["metric"] = []
self.log_dict(metric)
return self.log_dict
def test_step(self, batch, batch_idx):
ret = self(batch, batch_idx)
loss = ret['loss']
if self.hparams.task_type in ["contact"]:
self._context["test"]["metric"].append(self.metrics(
ret['logits'][...,0].float().cpu(),
ret['label'].float().cpu(),
ret['attention_mask'].float().cpu(),
name="test"
))
elif self.hparams.task_type in [
"residual_classification"
]:
self._context["test"]["metric"].append(
self.metrics(
ret['logits'].float().cpu().numpy(),
ret['label'].float().cpu().numpy(),
ret['attention_mask'].float().cpu().numpy(),
name="test"
))
else:
self._context["test"]["logits"].append(ret['logits'].float().cpu().numpy())
self._context["test"]["labels"].append(ret['label'].float().cpu().numpy())
return self.log_dict
def on_test_epoch_end(self):
# Make the Progress Bar leave there
self.print('')
# compute metrics and reset
if self.hparams.task_type in ["contact"]:
value = torch.tensor(self._context["test"]["metric"]).mean()
metric = {f"test_Top(L/5)": value}
self._context["test"]["metric"] = []
elif self.hparams.task_type in [
"residual_classification"
]:
value = torch.tensor(self._context["test"]["metric"]).mean()
metric = {f"test_acc": value}
self._context["test"]["metric"] = []
else:
metric = self.metrics(
self._context["test"]["logits"],
self._context["test"]["labels"],
# self._context["test"]["attn_mask"],
name="test"
)
self._context["test"]["logits"] = []
self._context["test"]["labels"] = []
self._context["test"]["attn_mask"] = []
self.log_dict(metric)
return self.log_dict
def load_model(self):
self.model = UniModel(
self.hparams.pretrain_model_name,
self.hparams.task_type,
self.hparams.finetune_type,
self.hparams.num_classes,
peft_type = self.hparams.peft_type,
lora_r = self.hparams.lora_r,
lora_alpha = self.hparams.lora_alpha,
lora_dropout = self.hparams.lora_dropout
)
def metrics(self, preds, target, attn_mask=None, name='default'):
if self.hparams.task_type == "classification":
preds, target = np.vstack(preds), np.hstack(target)
preds = np.argmax(preds, axis=-1)
acc = (preds == target).mean()
return {f"{name}_acc": acc}
elif self.hparams.task_type == "residual_classification":
target_valid = []
for i in range(len(target)):
target_valid.append(target[i])
# target_valid.append(target[i][attn_mask[i].astype(bool)])
preds, target = np.vstack(preds), np.hstack(target_valid)
preds = np.argmax(preds, axis=-1)
acc = (preds == target).mean()
return acc
# return {f"{name}_acc": acc}
elif self.hparams.task_type in ["binary_classification", "pair_binary_classification"]:
preds, target = np.vstack(preds), np.concatenate(target, axis=0)
auroc = roc_auc_score(target, preds)
return {f"{name}_auroc": auroc}
elif self.hparams.task_type in ["regression", "pair_regression"]:
preds, target = np.hstack(preds), np.hstack(target)
return {f"{name}_spearman": spearmanr(target, preds).statistic}
elif self.hparams.task_type == "multi_labels_classification":
preds, target = np.vstack(preds), np.vstack(target)
f1_max = f1_score_max(torch.tensor(preds), torch.tensor(target)).item()
return {f"{name}_f1_max": f1_max}
elif self.hparams.task_type == "contact":
from src.model.finetune_model import top_L_div_5_precision
# preds = torch.cat([torch.from_numpy(one) for one in preds], dim=0)
# target = torch.cat([torch.from_numpy(one) for one in target], dim=0)
# attn_mask = torch.cat([torch.from_numpy(one) for one in attn_mask], dim=0)
metrics = top_L_div_5_precision(preds, target, attn_mask)
return metrics['Top(L/5)']
def on_save_checkpoint(self, checkpoint):
state = checkpoint["state_dict"]
if self.hparams.finetune_type == "lora":
for name in list(state.keys()):
if "lora" not in name:
state.pop(name)
return state
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