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train_pl.py
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train_pl.py
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import os
import sys
import json
import math
import random
import cv2
with open('config_pl/SETTINGS.json', 'r') as f:
config = json.load(f)
print("CONFIG LOADED ..")
timm_path = config['TIMM_PATH']
sys.path.append(timm_path)
import timm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import model_selection
from sklearn.metrics import mean_squared_error
from tqdm import tqdm
import glob
import albumentations as A
from albumentations.pytorch import ToTensorV2
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset,DataLoader
from torch import optim
from torchvision import transforms
from transformers import get_cosine_schedule_with_warmup
import wandb
import warnings
warnings.filterwarnings('ignore')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device
if not os.path.exists(config['OUTPUT_DIR']):
os.makedirs(config['OUTPUT_DIR'])
def set_seed(seed):
random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
def mixup(x, y, alpha=1.0):
if alpha > 0:
lam = np.random.beta(alpha, alpha)
else:
lam = 1
batch_size = x.size()[0]
index = torch.randperm(batch_size).to(device)
mixed_x = lam * x + (1 - lam) * x[index, :]
y_a, y_b = y, y[index]
return mixed_x, y_a, y_b, lam
def mixup_criterion(criterion, pred, y_a, y_b, lam):
return lam * criterion(pred, y_a) + (1 - lam) * criterion(pred, y_b)
class Pets(Dataset):
def __init__(self , df,augs = None):
self.df = df
self.augs = augs
def __len__(self):
return len(self.df)
def __getitem__(self,idx):
img_src = self.df.loc[idx,'path']
image = cv2.imread(img_src)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
transformed = self.augs(image=image)
image = transformed['image']
target = torch.tensor(self.df['Pawpularity'].values[idx],dtype = torch.long)
return image, target
class Model(nn.Module):
def __init__(self,pretrained):
super().__init__()
self.backbone = timm.create_model(config['MODEL_NAME'], pretrained=True, num_classes=0, drop_rate=0.0, drop_path_rate=0.0,global_pool='')
self.pool = nn.AdaptiveAvgPool2d(1)
self.fc3_A = nn.Linear(config['NUM_NEURONS'],12)
self.fc3_B = nn.Linear(config['NUM_NEURONS'],1)
self.do = nn.Dropout(p=0.3)
def forward(self,image):
image = self.backbone(image)
if(len(image.shape) == 4):#for efficientnet models
image = self.pool(image)
image = image.view(image.shape[0], -1)
image = self.do(image)
dec2 = self.fc3_B(image)
dec1 = self.fc3_A(image)
return F.sigmoid(dec1) , dec2
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 train_one_epoch(train_loader,model,optimizer,criterion,e,epochs,scheduler):
losses = AverageMeter()
scores = AverageMeter()
model.train()
global_step = 0
loop = tqdm(enumerate(train_loader),total = len(train_loader))
for step,(image,labels) in loop:
image = image.to(device)
labels= labels.to(device)/100.
_ , output2 = model(image)
batch_size = labels.size(0)
'''if torch.rand(1)[0] < 0.5:
image, targets_a, targets_b, lam = mixup(image, labels.view(-1, 1))
loss2 = mixup_criterion(criterion, output2, targets_a, targets_b, lam)
else:
loss2 = criterion(output2,labels.unsqueeze(1))'''
loss2 = criterion(output2,labels.unsqueeze(1))
loss = loss2
output2 = output2.sigmoid()
out = output2.cpu().detach().numpy()
targets = labels.cpu().detach().numpy()
rmse = mean_squared_error(targets*100,out*100, squared=False)
losses.update(loss.item(), batch_size)
scores.update(rmse.item(), batch_size)
loss.backward(retain_graph = True)
'''optimizer.step()
optimizer.zero_grad()'''
optimizer.first_step(zero_grad=True)
criterion(model(image)[1], labels.unsqueeze(1).float()).backward()
optimizer.second_step(zero_grad=True)
scheduler.step()
global_step += 1
loop.set_description(f"Epoch {e+1}/{epochs}")
loop.set_postfix(loss = loss.item(), rmse= rmse.item(), stage = 'train')
return losses.avg,scores.avg
def val_one_epoch(loader,model,optimizer,criterion):
losses = AverageMeter()
scores = AverageMeter()
model.eval()
global_step = 0
loop = tqdm(enumerate(loader),total = len(loader))
for step,(image,labels) in loop:
image = image.to(device)
labels = labels.to(device)/100.
batch_size = labels.size(0)
with torch.no_grad():
output1, output2 = model(image)
loss2 = criterion(output2,labels.unsqueeze(1))
loss = loss2
output2 = output2.sigmoid()
out = output2.cpu().detach().numpy()
targets = labels.cpu().detach().numpy()
rmse = mean_squared_error(targets*100, out*100, squared=False)
losses.update(loss.item(), batch_size)
scores.update(rmse.item(), batch_size)
loop.set_postfix(loss = loss.item(), rmse = rmse.item(), stage = 'valid')
return losses.avg,scores.avg
class SAM(torch.optim.Optimizer):
def __init__(self, params, base_optimizer, rho=0.05, **kwargs):
assert rho >= 0.0, f"Invalid rho, should be non-negative: {rho}"
defaults = dict(rho=rho, **kwargs)
super(SAM, self).__init__(params, defaults)
self.base_optimizer = base_optimizer(self.param_groups, **kwargs)
self.param_groups = self.base_optimizer.param_groups
@torch.no_grad()
def first_step(self, zero_grad=False):
grad_norm = self._grad_norm()
for group in self.param_groups:
scale = group["rho"] / (grad_norm + 1e-12)
for p in group["params"]:
if p.grad is None: continue
e_w = p.grad * scale.to(p)
p.add_(e_w) # climb to the local maximum "w + e(w)"
self.state[p]["e_w"] = e_w
if zero_grad: self.zero_grad()
@torch.no_grad()
def second_step(self, zero_grad=False):
for group in self.param_groups:
for p in group["params"]:
if p.grad is None: continue
p.sub_(self.state[p]["e_w"]) # get back to "w" from "w + e(w)"
self.base_optimizer.step() # do the actual "sharpness-aware" update
if zero_grad: self.zero_grad()
def step(self, closure=None):
raise NotImplementedError("SAM doesn't work like the other optimizers, you should first call `first_step` and the `second_step`; see the documentation for more info.")
def _grad_norm(self):
shared_device = self.param_groups[0]["params"][0].device # put everything on the same device, in case of model parallelism
norm = torch.norm(
torch.stack([
p.grad.norm(p=2).to(shared_device)
for group in self.param_groups for p in group["params"]
if p.grad is not None
]),
p=2
)
return norm
def fit(m, fn, training_batch_size = config['TRAIN_BATCH_SIZE'], validation_batch_size = config['VAL_BATCH_SIZE']):
train_data = df[df.fold != fn]
train_data = train_data[["path" , "Pawpularity"]]
plx = pl[["path" ,f'pf{fn}']]
plx['Pawpularity'] = plx[f'pf{fn}']
plx = plx[(plx.Pawpularity < 35) | (plx.Pawpularity> 60)]
train_data = pd.concat([train_data , plx])
val_data = df[df.fold == fn]
train_data = Pets(train_data.reset_index(drop=True) , augs = train_aug)
val_data = Pets(val_data.reset_index(drop=True) , augs = val_aug)
train_loader = DataLoader(train_data, shuffle=True, pin_memory=True, drop_last=True, batch_size=training_batch_size, num_workers=4)
valid_loader = DataLoader(val_data, shuffle=False, pin_memory=True, drop_last=False, batch_size=validation_batch_size, num_workers=4)
criterion= nn.BCEWithLogitsLoss()
#optimizer = optim.AdamW(m.parameters(), lr = config['LR'], weight_decay = config['WEIGHT_DECAY'])
base_optimizer = optim.AdamW # define an optimizer for the "sharpness-aware" update
optimizer = SAM(m.parameters(), base_optimizer, lr=config['LR'] , weight_decay =config['WEIGHT_DECAY'])
wandb.watch(model, criterion, log="all", log_freq=10)
num_train_steps = math.ceil(len(train_loader))
num_warmup_steps = num_train_steps * (config['EPOCHS']//2 + 1)
num_training_steps = int(num_train_steps * config['EPOCHS'])
sch = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps = num_warmup_steps, num_training_steps = num_training_steps)
loop = range(config['EPOCHS'])
for e in loop:
train_loss, train_rmse = train_one_epoch(train_loader, m, optimizer, criterion, e, config['EPOCHS'], sch)
print(f"Avg train loss {train_loss} - Avg train rmse {train_rmse}" )
val_loss,val_rmse= val_one_epoch(valid_loader, m, optimizer, criterion)
model_path = config['OUTPUT_DIR'] + f"{config['MODEL_NAME']} - {config['PET_CLASS']} - Fold {fn} - val rmse {format(val_rmse,'.4f')}.pth"
if e == 0:
best_rmse = val_rmse
last_model = model_path
print(f"Avg val loss {val_loss} - Avg val rmse {val_rmse}")
if val_rmse < best_rmse:
if os.path.exists(last_model):
os.remove(last_model)
last_model = model_path
best_rmse = val_rmse
print("Saving best model!")
torch.save(m.state_dict(), model_path)
wandb.log({"Train RMSE": train_rmse, "Val RMSE": val_rmse, "Train loss": train_loss, "Val Loss": val_loss, "Epoch": e})
if __name__ == '__main__':
set_seed(42)
df = pd.read_csv(f"{config['DATA_DIR']}train.csv")
'''y = df.Pawpularity.values
kf = model_selection.StratifiedKFold(n_splits = config['NUM_FOLDS'], random_state=42, shuffle=True)
for f,(t,v) in enumerate(kf.split(X=df,y=y)):
df.loc[v,'fold'] = f'''
num_bins = int(np.ceil(2*((len(df))**(1./3))))
df['bins'] = pd.cut(df['Pawpularity'], bins=num_bins, labels=False)
df['fold'] = -1
strat_kfold = model_selection.StratifiedKFold(n_splits=10, random_state=42, shuffle=True)
for i, (_, train_index) in enumerate(strat_kfold.split(df.index, df['bins'])):
df.iloc[train_index, -1] = i
df['fold'] = df['fold'].astype('int')
df['path'] = [f"{config['TRAIN_IMAGES_PATH']}{x}.jpg" for x in df["Id"].values]
pl = pd.read_csv(f"{config['DATA_DIR']}p_labelling_v2.csv")
pl['path'] = [f"{config['TRAIN_IMAGES_PATH_PL']}{x}-1.jpg" for x in pl["PetID"].values]
train_aug = A.Compose(
[ A.RandomResizedCrop(config['IMAGE_SIZE'],config['IMAGE_SIZE'],p = config['CROP']),
A.Resize(config['IMAGE_SIZE'],config['IMAGE_SIZE'],p = config['RESIZE']),
A.HorizontalFlip(p = config['H_FLIP']),
A.VerticalFlip(p= config['V_FLIP']),
A.RandomBrightnessContrast(p = config['BRIGHT_CONTRAST']),
A.HueSaturationValue(
hue_shift_limit=0.2, sat_shift_limit=0.2, val_shift_limit=0.2, p=config['HUE']),
A.ShiftScaleRotate(shift_limit=0.1, scale_limit=0.1, rotate_limit=30, p=config['SCALE']),
A.Cutout(max_h_size=int(config['IMAGE_SIZE'] * 0.21), max_w_size=int(config['IMAGE_SIZE'] * 0.21), num_holes=4, p=config['CUTOUT']),
A.Normalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
ToTensorV2()
]
)
val_aug = A.Compose(
[
A.Resize(config['IMAGE_SIZE'],config['IMAGE_SIZE'],p = config['RESIZE']),
A.Normalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
ToTensorV2()
]
)
with wandb.init(project="Pawpularity NN", entity = config['WANDB_ENTITY']):
for i in range(config['START_FOLD'], config['NUM_FOLDS']):
model = Model(True)
model= model.to(device)
fit(model, i)