File size: 5,390 Bytes
f34af6f | 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 | from collections import defaultdict
import PIL
import logging
import time, os
import torch
import torchmetrics
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pytorch_lightning import LightningModule
from torchmetrics.classification import BinaryAccuracy
from torchmetrics import Accuracy, AUROC, AveragePrecision
from models.classifier import ProtClassifier
from utils.flows import Interpolant
import wandb
from sklearn.metrics import roc_auc_score
class ClasfModule(LightningModule):
def __init__(self, cfg):
super().__init__()
self._print_logger = logging.getLogger(__name__)
self._exp_cfg = cfg.experiment
self._model_cfg = cfg.model
self._data_cfg = cfg.data
self._interpolant_cfg = cfg.interpolant
# Set-up prediction model
self.model = ProtClassifier(cfg.model)
# Set-up interpolant
self.interpolant = Interpolant(cfg.interpolant)
self.crossent = torch.nn.CrossEntropyLoss()
self.accuracy = Accuracy(task="multiclass", num_classes=2)
self.val_output = defaultdict(list)
self.save_hyperparameters()
def _log_scalar(
self,
key,
value,
on_step=True,
on_epoch=False,
prog_bar=True,
batch_size=None,
sync_dist=False,
rank_zero_only=True
):
if sync_dist and rank_zero_only:
raise ValueError('Unable to sync dist when rank_zero_only=True')
self.log(
key,
value,
on_step=on_step,
on_epoch=on_epoch,
prog_bar=prog_bar,
batch_size=batch_size,
sync_dist=sync_dist,
rank_zero_only=rank_zero_only
)
def model_step(self, batch):
# Get class label
cls = batch["class"].squeeze()
# Step the batch through flow
self.interpolant.set_device(batch['res_mask'].device)
noisy_batch = self.interpolant.corrupt_batch(batch)
alphas = noisy_batch["t"]
num_batch = alphas.shape[0]
# Calculate logits with classifier
logits = self.model(noisy_batch)
# Cross-entropy loss
crent_loss = self.crossent(logits.squeeze(0), cls)
probs = torch.softmax(logits, dim=-1)
cls_pred = torch.argmax(logits, dim=-1)
#print(cls_pred)
#print(f"Shape: {cls_pred.shape}")
#print("-"*30)
#print(cls)
#print(f"Shape: {cls.shape}")
acc = (cls_pred == cls).cpu().numpy().mean()
#print(acc)
#acc = self.accuracy(cls_pred, cls.unsqueeze(0))
if self.stage == 'val':
self.val_output['cls'].append(cls)
self.val_output['logits'].append(logits)
self.val_output['alphas'].append(alphas)
self._log_scalar(f"{self.stage}/accuracy", acc, on_epoch=True, batch_size=num_batch)
self._log_scalar(f"{self.stage}/celoss", crent_loss, on_epoch=True, batch_size=num_batch)
return {
"cross_entropy": crent_loss.mean()
}
def on_train_start(self):
self._epoch_start_time = time.time()
def on_train_epoch_end(self):
epoch_time = (time.time() - self._epoch_start_time) / 60.0
self.log(
'train/epoch_time_minutes',
epoch_time,
on_step=False,
on_epoch=True,
prog_bar=False
)
self._epoch_start_time = time.time()
def training_step(self, batch):
step_start_time = time.time()
self.stage = 'train'
batch_loss = self.model_step(batch)
num_batch = batch['res_mask'].shape[0]
total_losses = {
k: torch.mean(v) for k, v in batch_loss.items()
}
"""
for k, v in total_losses.items():
self._log_scalar(
f"train/{k}", v, prog_bar=False, batch_size=num_batch)
"""
# Training throughput
self._log_scalar(
"train/length", batch['res_mask'].shape[1], prog_bar=False, batch_size=num_batch)
self._log_scalar(
"train/batch_size", num_batch, prog_bar=False)
step_time = time.time() - step_start_time
self._log_scalar(
"train/examples_per_second", num_batch / step_time)
train_loss = (
total_losses["cross_entropy"]
)
self._log_scalar(
"train/loss", train_loss, batch_size=num_batch)
return train_loss
def validation_step(self, batch):
self.stage = 'val'
num_batch = batch['res_mask'].shape[0]
batch_loss = self.model_step(batch)
total_losses = {
k: torch.mean(v) for k, v in batch_loss.items()
}
val_loss = (
total_losses["cross_entropy"]
)
self._log_scalar(
"val/loss", val_loss, prog_bar=False, batch_size=num_batch, on_epoch=True)
return {
'val/loss': val_loss,
}
def configure_optimizers(self):
return torch.optim.AdamW(
params=self.model.parameters(),
**self._exp_cfg.optimizer
)
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