通过timm下载模型

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我是靠谱客的博主平淡纸鹤，最近开发中收集的这篇文章主要介绍通过timm下载模型，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

import os
import warnings
from pprint import pprint
from glob import glob
from tqdm import tqdm

import torch
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torchvision.transforms as T
from box import Box
from timm import create_model
from sklearn.model_selection import StratifiedKFold
from torchvision.io import read_image
from torch.utils.data import DataLoader, Dataset
from pytorch_grad_cam import GradCAMPlusPlus
from pytorch_grad_cam.utils.image import show_cam_on_image


import pytorch_lightning as pl
from pytorch_lightning.utilities.seed import seed_everything
from pytorch_lightning import callbacks
from pytorch_lightning.callbacks.progress import ProgressBarBase
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
from pytorch_lightning.loggers import TensorBoardLogger
from pytorch_lightning import LightningDataModule, LightningModule

config = {'seed': 20211111,
          'root': './input/petfinder-pawpularity-score/', 
          'n_splits': 5,
          'epoch': 60,
          'trainer': {
              'gpus': 1,
              'accumulate_grad_batches': 1,
              'progress_bar_refresh_rate': 1,
              'fast_dev_run': False,
              'num_sanity_val_steps': 0,
              'resume_from_checkpoint': None,
          },
          'transform':{
              'name': 'get_default_transforms',
              'image_size': 600
          },
          'train_loader':{
              'batch_size': 2,
              'shuffle': True,
              'num_workers': 4,
              'pin_memory': False,
              'drop_last': True,
          },
          'val_loader': {
              'batch_size': 2,
              'shuffle': False,
              'num_workers': 4,
              'pin_memory': False,
              'drop_last': False
         },
          'model':{
              'name': 'vit_large_patch16_384',
              'output_dim': 1
          },
          'optimizer':{
              'name': 'optim.AdamW',
              'params':{
                  'lr': 1e-5
              },
          },
          'scheduler':{
              'name': 'optim.lr_scheduler.CosineAnnealingWarmRestarts',
              'params':{
                  'T_0': 20,
                  'eta_min': 1e-4,
              }
          },
          'loss': 'nn.BCEWithLogitsLoss',
}
config = Box(config)
pprint(config)


class Model(pl.LightningModule):
    def __init__(self, cfg):
        super().__init__()
        self.cfg = cfg
        self.__build_model()
        self._criterion = eval(self.cfg.loss)()
        self.transform = get_default_transforms()
        self.save_hyperparameters(cfg)

    def __build_model(self):
        self.backbone = create_model(
            self.cfg.model.name, pretrained=True, num_classes=0, in_chans=3
        )
        num_features = self.backbone.num_features
        self.fc = nn.Sequential(
            nn.Dropout(0.5), nn.Linear(num_features, self.cfg.model.output_dim)
        )

    def forward(self, x):
        f = self.backbone(x)
        out = self.fc(f)
        return out

    def training_step(self, batch, batch_idx):
        loss, pred, labels = self.__share_step(batch, 'train')
        return {'loss': loss, 'pred': pred, 'labels': labels}
        
    def validation_step(self, batch, batch_idx):
        loss, pred, labels = self.__share_step(batch, 'val')
        return {'pred': pred, 'labels': labels}
    
    def __share_step(self, batch, mode):
        images, labels = batch
        labels = labels.float() / 100.0
        images = self.transform[mode](images)
        
        if torch.rand(1)[0] < 0.5 and mode == 'train':
            mix_images, target_a, target_b, lam = mixup(images, labels, alpha=0.5)
            logits = self.forward(mix_images).squeeze(1)
            loss = self._criterion(logits, target_a) * lam + 
                (1 - lam) * self._criterion(logits, target_b)
        else:
            logits = self.forward(images).squeeze(1)
            loss = self._criterion(logits, labels)
        
        pred = logits.sigmoid().detach().cpu() * 100.
        labels = labels.detach().cpu() * 100.
        return loss, pred, labels
        
    def training_epoch_end(self, outputs):
        self.__share_epoch_end(outputs, 'train')

    def validation_epoch_end(self, outputs):
        self.__share_epoch_end(outputs, 'val')    
        
    def __share_epoch_end(self, outputs, mode):
        preds = []
        labels = []
        for out in outputs:
            pred, label = out['pred'], out['labels']
            preds.append(pred)
            labels.append(label)
        preds = torch.cat(preds)
        labels = torch.cat(labels)
        metrics = torch.sqrt(((labels - preds) ** 2).mean())
        self.log(f'{mode}_loss', metrics)
    
    def check_gradcam(self, dataloader, target_layer, target_category, reshape_transform=None):
        cam = GradCAMPlusPlus(
            model=self,
            #target_layer=target_layer, 
            target_layers=[target_layer], 
            use_cuda=self.cfg.trainer.gpus, 
            reshape_transform=reshape_transform)
        
        org_images, labels = iter(dataloader).next()
        cam.batch_size = len(org_images)
        images = self.transform['val'](org_images)
        images = images.to(self.device)
        logits = self.forward(images).squeeze(1)
        pred = logits.sigmoid().detach().cpu().numpy() * 100
        labels = labels.cpu().numpy()
        
        grayscale_cam = cam(input_tensor=images, target_category=target_category, eigen_smooth=True)
        org_images = org_images.detach().cpu().numpy().transpose(0, 2, 3, 1) / 255.
        return org_images, grayscale_cam, pred, labels

    def configure_optimizers(self):
        optimizer = eval(self.cfg.optimizer.name)(
            self.parameters(), **self.cfg.optimizer.params
        )
        scheduler = eval(self.cfg.scheduler.name)(
            optimizer,
            **self.cfg.scheduler.params
        )
        return [optimizer], [scheduler]

IMAGENET_MEAN = [0.485, 0.456, 0.406]  # RGB
IMAGENET_STD = [0.229, 0.224, 0.225]  # RGB
def get_default_transforms():
    transform = {
        "train": T.Compose(
            [
                T.RandomHorizontalFlip(),
                T.RandomVerticalFlip(),
                T.RandomAffine(15, translate=(0.1, 0.1), scale=(0.9, 1.1)),
                T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1),
                T.ConvertImageDtype(torch.float),
                T.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD),
            ]
        ),
        "val": T.Compose(
            [
                T.ConvertImageDtype(torch.float),
                T.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD),
            ]
        ),
    }
    return transform

model = Model(config)