adaptive_trainer_v3.py

#!/home/aredwann/anaconda3/envs/MSHViT3/bin/python
import os
from argparse import ArgumentParser

import torch
import pytorch_lightning as pl
from pytorch_lightning.callbacks import ModelCheckpoint, LearningRateMonitor
from pytorch_lightning.loggers import WandbLogger
from metrics import F2CIW, F1Normal

import wandb

wandb.login()

from core import backbones
from core import heads
from core import tokenizers
from core import blocks
from core import optimizer
from core import scheduler
from core import class_weight
from core import datamodules
from core import transforms
from core import ema
from nets.basenet import BaseNet

import torch
import torch.nn as nn
import torch.nn.functional as F



class Model(pl.LightningModule):
    def __init__(self, num_classes, loss_weight, sigmoid_loss, args):
        super().__init__()

        self.save_hyperparameters()

        # get network architecture
        self.rgbNet = BaseNet(num_classes, sigmoid_loss, args.baseline_model_path, args)
        self.optiNet = BaseNet(num_classes, sigmoid_loss, args.optical_model_path, args)
        self.rgb_backbone, self.rgb_head = self.rgbNet.getArchi()
        self.opti_backbone, self.opti_head = self.optiNet.getArchi()

        # Main training loss setup
        self.attention_layers = ['layer3']
        self.criterion_labels = self.validation_criterion_labels = torch.nn.BCEWithLogitsLoss(weight=loss_weight)
        self.criterion_feats = self.validation_criterion_feats = torch.nn.MSELoss()
        self.criterion_alpha = torch.nn.L1Loss()

        self.autocast_loss = True
        self.sinkhorn_head = args.head_model == "MultiScaleViTHead" and "Sinkhorn" in args.tokenizer_layer

        # Setup accuracy logger
        self.valid_f2ciw = F2CIW()
        self.valid_f1normal = F1Normal()

        self.alpha_val = torch.tensor(0.5)
        self.alpha = torch.clamp(self.alpha_val, 0.0, 1.0)
        self.alphaLearningRate = args.lr


        if args.model_ema:
            self.backbone_ema = ema.JointModelEMA(self.rgb_backbone, self.opti_backbone, decay=args.model_ema_decay,
                                             device='cpu' if args.model_ema_force_cpu else None)
            self.head_ema = ema.JointModelEMA(self.rgb_head, self.opti_head, decay=args.model_ema_decay,
                                         device='cpu' if args.model_ema_force_cpu else None)

            self.valid_f2ciw_ema = F2CIW()
            self.valid_f1normal_ema = F1Normal()

        self.all_time_f2ciw = 0.
        self.all_time_f1normal = 0.

    def ema_forward(self, x1, x2):
        feat_map1 = self.backbone_ema.rgb_module(x1)
        logits1 = self.head_ema.rgb_module(feat_map1)
        feat_map2 = self.backbone_ema.opti_module(x2)
        logits2 = self.head_ema.opti_module(feat_map2)
        return logits1, feat_map1, logits2, feat_map2

    def forward(self, x1, x2):
        feat_map1 = self.rgb_backbone(x1)
        logits1 = self.rgb_head(feat_map1)
        feat_map2 = self.opti_backbone(x2)
        logits2 = self.opti_head(feat_map2)
        return logits1, feat_map1, logits2, feat_map2

    def training_step(self, batch, batch_idx):
        x1, x2, y, _ = batch
        logits1, feat_map1, logits2, feat_map2 = self(x1, x2)

        if not self.autocast_loss:
            logits1 = logits1.float()
            logits2 = logits2.float()

        with torch.cuda.amp.autocast(enabled=self.autocast_loss):
            rgb_loss = self.criterion_labels(logits1, y)
            opti_loss = self.criterion_labels(logits2, y)
            attention_loss = sum([self.criterion_feats(feat_map1[attention_layer], feat_map2[attention_layer]) for attention_layer in self.attention_layers])

            # alpha val is unbounded scalar value where alpha is clipped between 0, 1
            # alpha_desire = self.alpha_val - self.alphaLearningRate * (rgb_loss - opti_loss)
            # alpha_loss = self.criterion_alpha(self.alpha, alpha_desire)
            # total_loss = rgb_loss +  opti_loss + attention_loss + alpha_loss

            self.alpha_val = self.alpha_val - self.alphaLearningRate * (rgb_loss - opti_loss)
            self.alpha = torch.clamp(self.alpha_val, 0.0, 1.0)
            total_loss = rgb_loss + opti_loss + attention_loss


                         

        self.log('rgb_loss', rgb_loss, on_step=True, on_epoch=True, sync_dist=True, prog_bar=True)
        self.log('opti_loss', opti_loss, on_step=True, on_epoch=True, sync_dist=True, prog_bar=True)
        self.log('attention_loss', attention_loss, on_step=True, on_epoch=True, sync_dist=True, prog_bar=True)
        self.log('alpha', self.alpha, on_step=True, on_epoch=True, sync_dist=True, prog_bar=True)
        self.log('train_loss', total_loss, on_step=True, on_epoch=True, sync_dist=True, prog_bar=True)

        return total_loss

    def validation_step(self, batch, batch_idx):
        x1, x2, y, _ = batch
        logits1, feat_map1, logits2, feat_map2 = self(x1, x2)

        if not self.autocast_loss:
            logits1 = logits1.float()
            logits2 = logits2.float()

        with torch.cuda.amp.autocast(enabled=self.autocast_loss):

            rgb_loss = self.criterion_labels(logits1, y)
            opti_loss = self.criterion_labels(logits2, y)
            attention_loss = sum(
                [self.criterion_feats(feat_map1[attention_layer], feat_map2[attention_layer]) for attention_layer in
                 self.attention_layers])
            # alpha val is unbounded scalar value where alpha is clipped between 0, 1
            total_loss = rgb_loss + opti_loss + attention_loss


        self.log('val_loss', total_loss, on_step=False, on_epoch=True, sync_dist=True, prog_bar=False)

        # alpha = 0.5
        y_hat = self.alpha * logits1.sigmoid() + (1 - self.alpha) * logits2.sigmoid()

        self.valid_f2ciw(y_hat, y)
        self.valid_f1normal(y_hat, y)
        self.log('val_F2CIW', self.valid_f2ciw, on_step=False, on_epoch=True, prog_bar=False)
        self.log('val_F1Normal', self.valid_f1normal, on_step=False, on_epoch=True, prog_bar=False)

        if self.hparams.args.model_ema and not self.hparams.args.model_ema_force_cpu:
            logits1_ema, feat_map1_ema, logits2_ema, feat_map2_ema = self.ema_forward(x1, x2)
            if not self.autocast_loss:
                logits1_ema = logits1_ema.float()
                logits2_ema = logits2_ema.float()
            with torch.cuda.amp.autocast(enabled=self.autocast_loss):
                total_loss_ema = self.criterion_labels(logits1_ema, y) + \
                                 self.criterion_labels(logits2_ema, y) + \
                                 self.criterion_feats( feat_map1_ema[self.attention_layer], feat_map2_ema[self.attention_layer])
            # rgb_feat_maps[self.attention_layer], opti_feat_maps[self.attention_layer]
            self.log('val_loss_ema', total_loss_ema, on_step=False, on_epoch=True, sync_dist=True, prog_bar=False)

            y_hat_ema = self.alpha * logits1_ema.sigmoid() + (1 - self.alpha) * logits2_ema.sigmoid()
            self.valid_f2ciw_ema(y_hat_ema, y)
            self.valid_f1normal_ema(y_hat_ema, y)
            self.log('val_F2CIW_ema', self.valid_f2ciw_ema, on_step=False, on_epoch=True, prog_bar=False)
            self.log('val_F1Normal_ema', self.valid_f1normal_ema, on_step=False, on_epoch=True, prog_bar=False)

        
        return total_loss

    def on_before_zero_grad(self, *args, **kwargs):
        if self.hparams.args.model_ema:
            self.backbone_ema.update(self.rgb_backbone, self.opti_backbone)
            self.head_ema.update(self.rgb_head, self.opti_head)

    def on_after_backward(self, *args, **kwargs):
        if self.trainer.global_step < self.hparams.args.freeze_cluster_niters and self.sinkhorn_head:
            self.rgb_head.reset_tokenizer_grad()
            self.opti_head.reset_tokenizer_grad()
            self.alpha_val = torch.tensor(0.5)

    def validation_epoch_end(self, *args, **kwargs):

        if self.hparams.args.model_ema and not self.hparams.args.model_ema_force_cpu:
            max_f2ciw = max(self.trainer.logged_metrics["val_F2CIW"], self.trainer.logged_metrics["val_F2CIW_ema"])
            max_f1normal = max(self.trainer.logged_metrics["val_F1Normal"],
                               self.trainer.logged_metrics["val_F1Normal_ema"])
        else:
            max_f2ciw = self.trainer.logged_metrics["val_F2CIW"]
            max_f1normal = self.trainer.logged_metrics["val_F1Normal"]
        self.log("valid_max_F2CIW", max_f2ciw, on_step=False, on_epoch=True, prog_bar=False, sync_dist=True)
        self.log("valid_max_F1Normal", max_f1normal, on_step=False, on_epoch=True, prog_bar=False, sync_dist=True)

        if max_f2ciw > self.all_time_f2ciw:
            self.all_time_f2ciw = max_f2ciw
        self.log("all_time_f2ciw", self.all_time_f2ciw, on_step=False, on_epoch=True, prog_bar=False, sync_dist=True)

        if max_f1normal > self.all_time_f1normal:
            self.all_time_f1normal = max_f1normal
        self.log("all_time_f1normal", self.all_time_f1normal, on_step=False, on_epoch=True, prog_bar=False,
                 sync_dist=True)

    def configure_optimizers(self):
        print("len(train_loader) {}".format(len(self.train_dataloader())))
        print("len(val_loader) {}".format(len(self.val_dataloader())))

        if self.hparams.args.freeze_layer_index != "None":
            for idx, child in enumerate(self.rgb_backbone.backbone.children()):
                if idx <= self.rgb_backbone.freeze_index[self.hparams.args.freeze_layer_index]:
                    for param in child.parameters():
                        param.requires_grad = False
            # repeat for opti
            for idx, child in enumerate(self.opti_backbone.backbone.children()):
                if idx <= self.opti_backbone.freeze_index[self.hparams.args.freeze_layer_index]:
                    for param in child.parameters():
                        param.requires_grad = False

        rgb_params_backbone = optimizer.adjusted_parameter_setting(self.rgb_backbone, self.hparams.args.lr,
                                                                   self.hparams.args.weight_decay)
        rgb_params_head = optimizer.adjusted_parameter_setting(self.rgb_head, self.hparams.args.lr,
                                                               self.hparams.args.weight_decay)

        opti_params_backbone = optimizer.adjusted_parameter_setting(self.opti_backbone, self.hparams.args.lr,
                                                                    self.hparams.args.weight_decay)
        opti_params_head = optimizer.adjusted_parameter_setting(self.opti_head, self.hparams.args.lr,
                                                                self.hparams.args.weight_decay)
        params = rgb_params_backbone + rgb_params_head + opti_params_backbone + opti_params_head

        opt_args = {"optim": "SGD", "lr": 0., "weight_decay": 0., "momentum": self.hparams.args.momentum,
                    "nesterov": self.hparams.args.nesterov}

        scheduler_args = {"lr_schedule": "Step",
                          "schedule_int": "epoch",
                          "lr_steps": self.hparams.args.lr_steps,
                          "lr_gamma": self.hparams.args.lr_gamma
                          }

        optim = optimizer.get_optimizer(params, opt_args)
        sched = scheduler.get_lr_scheduler(optim, scheduler_args)

        sched = {"scheduler": sched,
                 "interval": "epoch",
                 "frequency": 1}

        return [optim], [sched]

    @staticmethod
    def add_model_specific_args(parent_parser):
        parser = parent_parser.add_argument_group("Optimization")
        parser.add_argument('--lr', type=float, default=0.1)
        parser.add_argument('--weight_decay', type=float, default=0.0001)
        parser.add_argument('--momentum', type=float, default=0.9)
        parser.add_argument('--nesterov', action='store_true')
        parser.add_argument('--lr_gamma', type=float, default=0.01)
        parser.add_argument('--lr_steps', nargs='+', type=int, default=[20, 30])
        parser.add_argument('--effective_beta', type=float, default=0.9999)

        parser = parent_parser.add_argument_group("Backbone")
        parser.add_argument('--backbone_model', type=str, choices=backbones.VALID_BACKBONES)
        parser.add_argument('--img_size', type=int, choices=[224, 299, 336, 384, 448, 576, 640])
        parser.add_argument('--backbone_feature_maps', nargs='+', type=str,
                            choices=["stem", "layer1", "layer2", "layer3", "layer4"])
        parser.add_argument('--freeze_layer_index', type=str, default="None",
                            choices=["None", "stem", "layer1", "layer2", "layer3", "layer4"])
        parser.add_argument('--head_model', type=str, choices=heads.HEADS)

        parser = parent_parser.add_argument_group("BaseHead")
        parser.add_argument('--sigmoid_loss', action='store_true')
        parser.add_argument('--tresnet_init', action='store_true')
        parser.add_argument('--global_pool', type=str, default="avg", choices=["avg", "max"])

        parser = parent_parser.add_argument_group("MultiScaleViT")
        parser.add_argument('--use_mean_token', action='store_true')
        parser.add_argument('--freeze_cluster_niters', type=int, default=0)
        parser.add_argument('--token_dim', type=int)
        parser.add_argument('--norm_layer', default=None)
        parser.add_argument('--act_layer', default=None)
        parser.add_argument('--transformer_depth', nargs='+', type=int)
        parser.add_argument('--representation_size', type=int, default=None)
        parser.add_argument('--block_drop', type=float)
        parser.add_argument('--tokenizer_layer', type=str, default="Patchify", choices=tokenizers.TOKENIZERS)
        parser.add_argument('--block_type', type=str, default="TransformerBlock", choices=blocks.BLOCKS)
        parser.add_argument('--use_pos_embed', action='store_true')
        parser.add_argument('--num_heads', nargs='+', type=int)
        parser.add_argument('--qkv_bias', action='store_true')
        parser.add_argument('--mlp_ratio', nargs='+', type=float)
        parser.add_argument('--proj_drop', nargs='+', type=float)
        parser.add_argument('--attn_drop', nargs='+', type=float)
        parser.add_argument('--pos_embed_drop', type=float)
        parser.add_argument('--shared_tower', action='store_true')

        parser.add_argument('--multiscale_method', type=str,
                            choices=["SharedTokenizer", "SeparateScale", "CrossScale-CNN", "CrossScale-Token",
                                     "CrossScale-VIT"])
        parser.add_argument('--late_fusion', action='store_true')
        parser.add_argument('--cross_scale_all', action='store_true')
        parser.add_argument('--shared_tokenizer', action='store_true')
        parser.add_argument('--no_vit_layers', nargs='+', type=str,
                            choices=["stem", "layer1", "layer2", "layer3", "layer4"])

        parser.add_argument('--cross_block_type', type=str, default="MHSABlock", choices=blocks.BLOCKS)
        parser.add_argument('--cross_num_heads', type=int)
        parser.add_argument('--cross_mlp_ratio', type=float)
        parser.add_argument('--cross_qkv_bias', action='store_true')
        parser.add_argument('--cross_proj_drop', type=float)
        parser.add_argument('--cross_attn_drop', type=float)
        parser.add_argument('--cross_block_drop', type=float)

        parser = parent_parser.add_argument_group("Tokenizers")
        parser.add_argument('--num_clusters', nargs='+', type=int)
        parser.add_argument('--l2_normalize', action='store_true')
        parser.add_argument('--patch_size', nargs='+', type=int)
        parser.add_argument('--sinkhorn_eps', nargs='+', type=float)
        parser.add_argument('--sinkhorn_iters', nargs='+', type=int)

        # Model Exponential Moving Average
        parser = parent_parser.add_argument_group("Model EMA")
        parser.add_argument('--model_ema', action='store_true')
        parser.add_argument('--model_ema_force_cpu', action='store_true')
        parser.add_argument('--model_ema_decay', type=float, default=0.9997)

        return parent_parser


def main(args):
    args.seed = pl.seed_everything(args.seed)

    train_transform = transforms.create_sewerml_train_transformations(
        {"img_size": args.img_size, "model_name": args.backbone_model})
    eval_transform = transforms.create_sewerml_eval_transformations(
        {"img_size": args.img_size, "model_name": args.backbone_model})

    dm = datamodules.get_joint_datamodule(dataset=args.dataset, batch_size=args.batch_size, workers=args.workers,
                                          ann_root=args.ann_root, rgb_data_root=args.rgb_data_root,
                                          opti_data_root=args.opti_data_root, train_transform=train_transform,
                                          eval_transform=eval_transform)

    dm.prepare_data()
    dm.setup("fit")

    weights = class_weight.effective_samples(dm.train_dataset.labels, dm.num_classes, args.effective_beta)
    model = Model(num_classes=dm.num_classes, loss_weight=weights, sigmoid_loss=args.sigmoid_loss, args=args)

    # Setup Logger
    version = "version_" + str(args.log_version)
    model_name = args.dataset + "_" + args.backbone_model + "_" + args.head_model + "_" + args.block_type + "_" + args.tokenizer_layer
    print("-" * 15 + model_name + "-" * 15)

    os.makedirs(args.log_save_dir, exist_ok=True)
    logger_path = os.path.join(args.log_save_dir, model_name, "version_" + str(args.log_version))
    os.makedirs(logger_path, exist_ok=True)

    logger = WandbLogger(project=args.wandb_project,  # group runs in "MNIST" project
                         log_model='all',
                         save_dir=logger_path,
                         version=version,
                         name=model_name,
                         **{"group": args.wandb_group})  # log all new checkpoints during training

    logger_path = os.path.join(args.log_save_dir, model_name, "version_" + str(args.log_version))
    if args.monitor_metric:
        monitor = "valid_max_F2CIW"
        # monitor = "valid_max_F1Normal"
        filename = '{epoch:02d}-{%s:.4f}' % monitor
        # filename = 'vmF1N-last'
        mode = "max"

    else:
        monitor = "val_loss"
        filename = '{epoch:02d}-{val_loss:.4f}'
        mode = "min"

    checkpoint_callback = ModelCheckpoint(
        dirpath=os.path.join(logger_path),
        filename=filename,
        save_top_k=args.save_top_k,
        save_last=args.save_last,
        verbose=True,
        monitor=monitor,
        mode=mode,
        every_n_val_epochs=1
    )

    lr_monitor = LearningRateMonitor(logging_interval='epoch')

    callbacks = [checkpoint_callback, lr_monitor]

    if args.deterministic:
        args.benchmark = False
    trainer = pl.Trainer.from_argparse_args(args, terminate_on_nan=True, logger=logger, callbacks=callbacks)

    try:
        trainer.fit(model, dm)
    except Exception as e:
        print(e)
        with open(os.path.join(logger_path, "error.txt"), "w") as f:
            f.write(str(e))


def run_cli():
    parser = ArgumentParser()
    parser = pl.Trainer.add_argparse_args(parser)

    # figure out which model to use
    parser.add_argument('--conda_env', type=str, default='')
    parser.add_argument('--notification_email', type=str, default='')
    parser.add_argument('--ann_root', type=str, default='./annotations_sewerml')

    parser.add_argument('--rgb_data_root', type=str, default='')
    parser.add_argument('--opti_data_root', type=str, default='')
    parser.add_argument("--baseline_model_path", type=str)
    parser.add_argument("--optical_model_path", type=str)

    parser.add_argument('--batch_size', type=int, default=64, help="Size of the batch per GPU")
    parser.add_argument('--workers', type=int, default=8)
    parser.add_argument('--log_save_dir', type=str, default="")
    parser.add_argument('--log_version', type=int, default=1)
    parser.add_argument('--seed', type=int, default=None)
    parser.add_argument('--dataset', type=str, choices=["SewerML", "SewerMLJoint"], default="SewerMLJoint")
    parser.add_argument('--wandb_project', type=str, default="")
    parser.add_argument('--wandb_group', type=str, default="")
    parser.add_argument('--monitor_metric', action='store_true')
    parser.add_argument('--save_last', action='store_true')
    parser.add_argument('--save_top_k', type=int, default=1)

    parser = Model.add_model_specific_args(parser)
    args = parser.parse_args()

    main(args)


if __name__ == "__main__":
    run_cli()