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from dataclasses import dataclass, field
from utils import parse_structure
from typing import Any, Dict, Mapping
from .base import BaseSystemConfig, BaseSystem
from torch import nn, Tensor
import os
import torch
import numpy as np
import models
@dataclass
class SimpleClassificationConfig(BaseSystemConfig):
pass
class SimpleClassificationSystem(BaseSystem):
def __init__(self, cfg: Dict, *args: Any, **kwargs: Any) -> BaseSystem:
super().__init__(cfg, *args, **kwargs)
self.cfg:SimpleClassificationConfig = parse_structure(SimpleClassificationConfig, cfg)
self.model: nn.Module = getattr(models, self.cfg.model_type)(self.cfg.model)
def forward(self, x: Tensor) -> Tensor:
return self.model(x)
def training_step(self, batch: Mapping[str, Tensor], batch_idx: int) -> Tensor:
x: Tensor = batch[0]
y: Tensor = batch[1].float()
y_hat: Tensor = self.model(x).squeeze(-1)
loss = self.criterion(y_hat, y)
self.log(
"train/loss",
loss,
on_step=self.cfg.log_on_step,
on_epoch=self.cfg.log_on_epoch,
prog_bar=self.cfg.log_prog_bar,
logger=self.cfg.log_logger
)
self.log_metrics(self.metrics_func(y_hat, y, 'train'))
return loss
def validation_step(self, batch: Mapping[str, Tensor], batch_idx: int) -> Tensor:
x: Tensor = batch[0]
y: Tensor = batch[1].float()
y_hat: Tensor = self.model(x).squeeze(-1)
loss = self.criterion(y_hat, y)
self.log(
"valid/loss",
loss,
on_step=self.cfg.log_on_step,
on_epoch=self.cfg.log_on_epoch,
prog_bar=self.cfg.log_prog_bar,
logger=self.cfg.log_logger
)
self.log_metrics(self.metrics_func(y_hat, y, 'valid'))
return loss
def test_step(self, batch: Mapping[str, Tensor], batch_idx: int) -> Tensor:
x: Tensor = batch[0]
y: Tensor = batch[1].float()
y_hat: Tensor = self.model(x).squeeze(-1)
loss = self.criterion(y_hat, y)
self.log(
"test/loss",
loss,
on_step=self.cfg.log_on_step,
on_epoch=self.cfg.log_on_epoch,
prog_bar=self.cfg.log_prog_bar,
logger=self.cfg.log_logger
)
metrics_dict = self.metrics_func(y_hat, y, 'test')
self.log_metrics(metrics_dict) |