thsunkid · September 5, 2021 15:11 · thsunkid · Sep 5, 2021
diff --git a/gwnet.py b/gwnet.py
 import os
 import math
 import time
 import random

 import numpy as np
 import pandas as pd

 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from torch.optim import Adam, SGD
 from torch.nn.parameter import Parameter
 from torch.utils.data import DataLoader, Dataset
 from torch.optim.lr_scheduler import CosineAnnealingLR

 import torch_xla
 import torch_xla.distributed.parallel_loader as pl
 import torch_xla.core.xla_model as xm
 import torch_xla.distributed.xla_multiprocessing as xmp

 from tqdm.auto import tqdm
 from sklearn.metrics import roc_auc_score
 from sklearn.model_selection import StratifiedKFold

 import timm
 import albumentations
 from albumentations.pytorch import ToTensorV2
 from nnAudio.Spectrogram import CQT1992v2

 OUTPUT_DIR = './'

 # ====================================================
 # CFG
 # ====================================================
 class CFG:
    num_workers=4
    model_name='tf_efficientnet_b7_ns'
    epochs=1
    T_max=3
    lr=1e-4
    min_lr=1e-6
    batch_size=48
    weight_decay=1e-6
    gradient_accumulation_steps=1
    max_grad_norm=1000
    qtransform_params={"sr": 2048, "fmin": 20, "fmax": 1024, "hop_length": 32, "bins_per_octave": 8, "n_bins":None}
    target_size=1
    train=True

 # ====================================================
 # Utils
 # ====================================================
 def get_score(y_true, y_pred):
    score = roc_auc_score(y_true, y_pred)
    return score


 def seed_torch(seed=42):
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.backends.cudnn.deterministic = True


 # ====================================================
 # Dataset
 # ====================================================
 class TrainDataset(Dataset):
    def __init__(self, transform=None):
        self.wave_transform = CQT1992v2(**CFG.qtransform_params)
        self.transform = transform
        
    def __len__(self):
        return 2000
    
    def apply_qtransform(self, waves, transform):
        waves = np.hstack(waves)
        waves = waves / np.max(waves)
        waves = torch.from_numpy(waves).float()
        image = transform(waves)
        return image

    def __getitem__(self, idx):
        waves = np.random.randn(3,4096)
        image = self.apply_qtransform(waves, self.wave_transform)
        image = image.squeeze().numpy()
        if self.transform:
            image = self.transform(image=image)['image']
        label = torch.tensor(1).float()
        return image, label


 # ====================================================
 # Transforms
 # ====================================================
 def get_transforms(*, data):
    
    if data == 'train':
        return albumentations.Compose([
            ToTensorV2(),
        ])

    elif data == 'valid':
        return albumentations.Compose([
            ToTensorV2(),
        ])


 # ====================================================
 # MODEL
 # ====================================================
 class CustomModel(nn.Module):
    def __init__(self, cfg, pretrained=False):
        super().__init__()
        self.cfg = cfg
        self.model = timm.create_model(self.cfg.model_name, pretrained=pretrained, in_chans=1)
        self.n_features = self.model.classifier.in_features
        self.model.classifier = nn.Linear(self.n_features, self.cfg.target_size)

    def forward(self, x):
        output = self.model(x)
        return output


 # ====================================================
 # Helper functions
 # ====================================================
 class AverageMeter(object):
    """Computes and stores the average and current value"""
    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count


 def loss_fn(outputs, targets):
    return nn.BCEWithLogitsLoss()(outputs, targets.view(-1, 1))

 def train_fn(fold, train_loader, model, criterion, optimizer, epoch, scheduler, device):
    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses = AverageMeter()
    scores = AverageMeter()
    # switch to train mode
    model.train()
    start = end = time.time()
    global_step = 0
    xm.master_print("Training time ... ")
    train_loader = tqdm(train_loader)
    for step, (images, labels) in enumerate(train_loader):
        # measure data loading time
        data_time.update(time.time() - end)
        images = images.to(device)
        labels = labels.to(device)
        batch_size = labels.size(0)
        y_preds = model(images)
        loss = criterion(y_preds.view(-1), labels)
        # record loss
        losses.update(loss.item(), batch_size)
        if CFG.gradient_accumulation_steps > 1:
            loss = loss / CFG.gradient_accumulation_steps

        loss.backward()
        grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), CFG.max_grad_norm)
        if (step + 1) % CFG.gradient_accumulation_steps == 0:
            xm.optimizer_step(optimizer)
            optimizer.zero_grad()
            global_step += 1
        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()
    return losses.avg


 def valid_fn(valid_loader, model, criterion, device):
    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses = AverageMeter()
    scores = AverageMeter()
    # switch to evaluation mode
    model.eval()
    preds = [] 
    start = end = time.time()
    valid_labels = []
    for step, (images, labels) in enumerate(valid_loader):
        # measure data loading time
        data_time.update(time.time() - end)
        images = images.to(device)
        labels = labels.to(device)
        batch_size = labels.size(0)
        # compute loss
        with torch.no_grad():
            y_preds = model(images)
        xm.mark_step()
        loss = loss_fn(y_preds, labels)
        losses.update(loss.item(), batch_size)
        # record accuracy
        preds.append(y_preds.sigmoid().to('cpu').numpy())
        valid_labels.append(labels.to('cpu').numpy())
        if CFG.gradient_accumulation_steps > 1:
            loss = loss / CFG.gradient_accumulation_steps
        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()
    preds = np.concatenate(preds)
    valid_labels = np.concatenate(valid_labels)
    score = get_score(valid_labels, preds)
    return losses.avg, preds, score

 # ====================================================
 # Train loop
 # ====================================================
 def train_loop(tid):

    device = xm.xla_device()
    train_dataset = TrainDataset(transform=get_transforms(data='train'))
    valid_dataset = TrainDataset(transform=get_transforms(data='train'))

    train_sampler = torch.utils.data.distributed.DistributedSampler(
        train_dataset,
        num_replicas=xm.xrt_world_size(),
        rank=xm.get_ordinal(),
        shuffle=True)

    valid_sampler = torch.utils.data.distributed.DistributedSampler(
        valid_dataset,
        num_replicas=xm.xrt_world_size(),
        rank=xm.get_ordinal(),
        shuffle=False,
        )

    train_loader = DataLoader(train_dataset,
                              batch_size=CFG.batch_size,
                              sampler=train_sampler,
                              num_workers=CFG.num_workers, pin_memory=True, drop_last=True)
    valid_loader = DataLoader(valid_dataset, 
                              batch_size=CFG.batch_size,
                              sampler=valid_sampler,
                              num_workers=CFG.num_workers, pin_memory=True, drop_last=False)
    xm.master_print("Dataloader")

    # ====================================================
    # model & optimizer
    # ====================================================
    model = CustomModel(CFG, pretrained=True)
    model.to(device)

    optimizer = Adam(model.parameters(), lr=CFG.lr, weight_decay=CFG.weight_decay, amsgrad=False)
    scheduler = CosineAnnealingLR(optimizer, T_max=CFG.T_max, eta_min=CFG.min_lr, last_epoch=-1)

    criterion = nn.BCEWithLogitsLoss()
    best_score = 0.
    best_loss = np.inf
    fold = 0
    
    for epoch in range(CFG.epochs):
        start_time = time.time()
        
        # train
        para_loader = pl.ParallelLoader(train_loader, [device])
        avg_loss = train_fn(fold, para_loader.per_device_loader(device), model, criterion, optimizer, epoch, scheduler, device)

        # eval
        para_loader = pl.ParallelLoader(valid_loader, [device])
        avg_val_loss, preds, score = valid_fn(para_loader.per_device_loader(device), model, criterion, device)
        
        scheduler.step()
        elapsed = time.time() - start_time
        if score > best_score:
            best_score = score
            xm.save(model.state_dict(), OUTPUT_DIR+f'{CFG.model_name}_fold{fold}_best_score.pth')
        
        if avg_val_loss < best_loss:
            best_loss = avg_val_loss
            xm.save(model.state_dict(), OUTPUT_DIR+f'{CFG.model_name}_fold{fold}_best_score.pth')

    return best_score, best_loss

 if __name__ == "__main__":
    seed_torch()
    torch.set_default_tensor_type('torch.FloatTensor')
    xmp.spawn(train_loop, args=(), nprocs=None)
	import os
	import math
	import time
	import random

	import numpy as np
	import pandas as pd

	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	from torch.optim import Adam, SGD
	from torch.nn.parameter import Parameter
	from torch.utils.data import DataLoader, Dataset
	from torch.optim.lr_scheduler import CosineAnnealingLR

	import torch_xla
	import torch_xla.distributed.parallel_loader as pl
	import torch_xla.core.xla_model as xm
	import torch_xla.distributed.xla_multiprocessing as xmp

	from tqdm.auto import tqdm
	from sklearn.metrics import roc_auc_score
	from sklearn.model_selection import StratifiedKFold

	import timm
	import albumentations
	from albumentations.pytorch import ToTensorV2
	from nnAudio.Spectrogram import CQT1992v2

	OUTPUT_DIR = './'

	# ====================================================
	# CFG
	# ====================================================
	class CFG:
	num_workers=4
	model_name='tf_efficientnet_b7_ns'
	epochs=1
	T_max=3
	lr=1e-4
	min_lr=1e-6
	batch_size=48
	weight_decay=1e-6
	gradient_accumulation_steps=1
	max_grad_norm=1000
	qtransform_params={"sr": 2048, "fmin": 20, "fmax": 1024, "hop_length": 32, "bins_per_octave": 8, "n_bins":None}
	target_size=1
	train=True

	# ====================================================
	# Utils
	# ====================================================
	def get_score(y_true, y_pred):
	score = roc_auc_score(y_true, y_pred)
	return score


	def seed_torch(seed=42):
	random.seed(seed)
	os.environ['PYTHONHASHSEED'] = str(seed)
	np.random.seed(seed)
	torch.manual_seed(seed)
	torch.cuda.manual_seed(seed)
	torch.backends.cudnn.deterministic = True


	# ====================================================
	# Dataset
	# ====================================================
	class TrainDataset(Dataset):
	def __init__(self, transform=None):
	self.wave_transform = CQT1992v2(**CFG.qtransform_params)
	self.transform = transform

	def __len__(self):
	return 2000

	def apply_qtransform(self, waves, transform):
	waves = np.hstack(waves)
	waves = waves / np.max(waves)
	waves = torch.from_numpy(waves).float()
	image = transform(waves)
	return image

	def __getitem__(self, idx):
	waves = np.random.randn(3,4096)
	image = self.apply_qtransform(waves, self.wave_transform)
	image = image.squeeze().numpy()
	if self.transform:
	image = self.transform(image=image)['image']
	label = torch.tensor(1).float()
	return image, label


	# ====================================================
	# Transforms
	# ====================================================
	def get_transforms(*, data):

	if data == 'train':
	return albumentations.Compose([
	ToTensorV2(),
	])

	elif data == 'valid':
	return albumentations.Compose([
	ToTensorV2(),
	])


	# ====================================================
	# MODEL
	# ====================================================
	class CustomModel(nn.Module):
	def __init__(self, cfg, pretrained=False):
	super().__init__()
	self.cfg = cfg
	self.model = timm.create_model(self.cfg.model_name, pretrained=pretrained, in_chans=1)
	self.n_features = self.model.classifier.in_features
	self.model.classifier = nn.Linear(self.n_features, self.cfg.target_size)

	def forward(self, x):
	output = self.model(x)
	return output


	# ====================================================
	# Helper functions
	# ====================================================
	class AverageMeter(object):
	"""Computes and stores the average and current value"""
	def __init__(self):
	self.reset()

	def reset(self):
	self.val = 0
	self.avg = 0
	self.sum = 0
	self.count = 0

	def update(self, val, n=1):
	self.val = val
	self.sum += val * n
	self.count += n
	self.avg = self.sum / self.count


	def loss_fn(outputs, targets):
	return nn.BCEWithLogitsLoss()(outputs, targets.view(-1, 1))

	def train_fn(fold, train_loader, model, criterion, optimizer, epoch, scheduler, device):
	batch_time = AverageMeter()
	data_time = AverageMeter()
	losses = AverageMeter()
	scores = AverageMeter()
	# switch to train mode
	model.train()
	start = end = time.time()
	global_step = 0
	xm.master_print("Training time ... ")
	train_loader = tqdm(train_loader)
	for step, (images, labels) in enumerate(train_loader):
	# measure data loading time
	data_time.update(time.time() - end)
	images = images.to(device)
	labels = labels.to(device)
	batch_size = labels.size(0)
	y_preds = model(images)
	loss = criterion(y_preds.view(-1), labels)
	# record loss
	losses.update(loss.item(), batch_size)
	if CFG.gradient_accumulation_steps > 1:
	loss = loss / CFG.gradient_accumulation_steps

	loss.backward()
	grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), CFG.max_grad_norm)
	if (step + 1) % CFG.gradient_accumulation_steps == 0:
	xm.optimizer_step(optimizer)
	optimizer.zero_grad()
	global_step += 1
	# measure elapsed time
	batch_time.update(time.time() - end)
	end = time.time()
	return losses.avg


	def valid_fn(valid_loader, model, criterion, device):
	batch_time = AverageMeter()
	data_time = AverageMeter()
	losses = AverageMeter()
	scores = AverageMeter()
	# switch to evaluation mode
	model.eval()
	preds = []
	start = end = time.time()
	valid_labels = []
	for step, (images, labels) in enumerate(valid_loader):
	# measure data loading time
	data_time.update(time.time() - end)
	images = images.to(device)
	labels = labels.to(device)
	batch_size = labels.size(0)
	# compute loss
	with torch.no_grad():
	y_preds = model(images)
	xm.mark_step()
	loss = loss_fn(y_preds, labels)
	losses.update(loss.item(), batch_size)
	# record accuracy
	preds.append(y_preds.sigmoid().to('cpu').numpy())
	valid_labels.append(labels.to('cpu').numpy())
	if CFG.gradient_accumulation_steps > 1:
	loss = loss / CFG.gradient_accumulation_steps
	# measure elapsed time
	batch_time.update(time.time() - end)
	end = time.time()
	preds = np.concatenate(preds)
	valid_labels = np.concatenate(valid_labels)
	score = get_score(valid_labels, preds)
	return losses.avg, preds, score

	# ====================================================
	# Train loop
	# ====================================================
	def train_loop(tid):

	device = xm.xla_device()
	train_dataset = TrainDataset(transform=get_transforms(data='train'))
	valid_dataset = TrainDataset(transform=get_transforms(data='train'))

	train_sampler = torch.utils.data.distributed.DistributedSampler(
	train_dataset,
	num_replicas=xm.xrt_world_size(),
	rank=xm.get_ordinal(),
	shuffle=True)

	valid_sampler = torch.utils.data.distributed.DistributedSampler(
	valid_dataset,
	num_replicas=xm.xrt_world_size(),
	rank=xm.get_ordinal(),
	shuffle=False,
	)

	train_loader = DataLoader(train_dataset,
	batch_size=CFG.batch_size,
	sampler=train_sampler,
	num_workers=CFG.num_workers, pin_memory=True, drop_last=True)
	valid_loader = DataLoader(valid_dataset,
	batch_size=CFG.batch_size,
	sampler=valid_sampler,
	num_workers=CFG.num_workers, pin_memory=True, drop_last=False)
	xm.master_print("Dataloader")

	# ====================================================
	# model & optimizer
	# ====================================================
	model = CustomModel(CFG, pretrained=True)
	model.to(device)

	optimizer = Adam(model.parameters(), lr=CFG.lr, weight_decay=CFG.weight_decay, amsgrad=False)
	scheduler = CosineAnnealingLR(optimizer, T_max=CFG.T_max, eta_min=CFG.min_lr, last_epoch=-1)

	criterion = nn.BCEWithLogitsLoss()
	best_score = 0.
	best_loss = np.inf
	fold = 0

	for epoch in range(CFG.epochs):
	start_time = time.time()

	# train
	para_loader = pl.ParallelLoader(train_loader, [device])
	avg_loss = train_fn(fold, para_loader.per_device_loader(device), model, criterion, optimizer, epoch, scheduler, device)

	# eval
	para_loader = pl.ParallelLoader(valid_loader, [device])
	avg_val_loss, preds, score = valid_fn(para_loader.per_device_loader(device), model, criterion, device)

	scheduler.step()
	elapsed = time.time() - start_time
	if score > best_score:
	best_score = score
	xm.save(model.state_dict(), OUTPUT_DIR+f'{CFG.model_name}_fold{fold}_best_score.pth')

	if avg_val_loss < best_loss:
	best_loss = avg_val_loss
	xm.save(model.state_dict(), OUTPUT_DIR+f'{CFG.model_name}_fold{fold}_best_score.pth')

	return best_score, best_loss

	if __name__ == "__main__":
	seed_torch()
	torch.set_default_tensor_type('torch.FloatTensor')
	xmp.spawn(train_loop, args=(), nprocs=None)