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baseline_segformer_unet_base_no_augs.py
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baseline_segformer_unet_base_no_augs.py
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import os
from tqdm import tqdm
import numpy as np
import matplotlib.pyplot as plt
import torch
from torch.optim import AdamW
from torch.optim.lr_scheduler import CosineAnnealingLR
from torch.utils.data import Dataset, DataLoader
from torch.cuda.amp import GradScaler
from torch.cuda.amp import autocast
from torch.utils.tensorboard import SummaryWriter
import torchvision.transforms as T
import albumentations as A
from albumentations.pytorch import ToTensorV2
from albumentations import ImageOnlyTransform
import segmentation_models_pytorch as smp
from segmentation_models_pytorch.losses import DiceLoss
from segmentation_models_pytorch.losses import SoftBCEWithLogitsLoss
from transformers import SegformerForSemanticSegmentation
from utils.metrics import dice_avg
class CFG:
exp_name = 'baseline_segformer_256_nvidia_b3'
DATATRAIN_PATH = '../data/ssd_data/vanilla_data/train'
DATAVALID_PATH = '../data/ssd_data/vanilla_data/validation'
PATH_TO_SAVE = f'../models/{exp_name}'
write_to_tensorboard = True
n_epoch = 45
use_amp = False
device = 'cuda:0'
smooth_loss = 1.0
train_loss = SoftBCEWithLogitsLoss(weight=torch.tensor(torch.Tensor([7.31]).to(device)))
#DiceLoss(mode='binary', smooth=smooth_loss)
valid_loss = DiceLoss(mode='binary')
valid_loss_th = DiceLoss(mode='binary', from_logits=False)
backbone = 'nvidia/mit-b3'
n_workers = 2
batch_size = 20
lr = 5e-4
resize = True
resize_value = 256
decoder_attention_type = None
decoder_use_batchnorm = False
flip_augs = False
hflip_p = 0.5
vflip_p = 0.5
rotate_augs = False
rotate_p = 0.5
class ContrailDataset(Dataset):
def __init__(self, dataset_path, data_type):
self.dataset_path = dataset_path
self.imgs = os.listdir(self.dataset_path)
self.data_type = data_type
self.resize_image = T.Resize(CFG.resize_value,interpolation=T.InterpolationMode.BILINEAR,antialias=True)
self.resize_mask = T.Resize(CFG.resize_value,interpolation=T.InterpolationMode.NEAREST,antialias=True)
def __len__(self):
return len(self.imgs)
@staticmethod
def normalize_range(data, bounds):
"""Maps data to the range [0, 1]."""
return (data - bounds[0]) / (bounds[1] - bounds[0])
@staticmethod
def get_false_color(record_data):
__T11_BOUNDS = (243, 303)
_CLOUD_TOP_TDIFF_BOUNDS = (-4, 5)
_TDIFF_BOUNDS = (-4, 2)
N_TIMES_BEFORE = 4
r = ContrailDataset.normalize_range(record_data["band_15"] - record_data["band_14"], _TDIFF_BOUNDS)
g = ContrailDataset.normalize_range(record_data["band_14"] - record_data["band_11"], _CLOUD_TOP_TDIFF_BOUNDS)
b = ContrailDataset.normalize_range(record_data["band_14"], __T11_BOUNDS)
false_color = np.clip(np.stack([r, g, b], axis=2), 0, 1)
img = false_color[..., N_TIMES_BEFORE]
return img
def __getitem__(self, idx):
record_id = self.imgs[idx]
record_dir = os.path.join(self.dataset_path, record_id)
record_data = {}
record_data['band_11'] = np.load(os.path.join(record_dir, 'band_11.npy'))
record_data['band_14'] = np.load(os.path.join(record_dir, 'band_14.npy'))
record_data['band_15'] = np.load(os.path.join(record_dir, 'band_15.npy'))
false_color = ContrailDataset.get_false_color(record_data)
human_pixel_mask = np.load(os.path.join(record_dir,'human_pixel_masks.npy'))
false_color = torch.from_numpy(false_color)
human_pixel_mask = torch.from_numpy(human_pixel_mask)
false_color = torch.moveaxis(false_color,-1,0)
human_pixel_mask = torch.moveaxis(human_pixel_mask,-1,0).squeeze(0)
if CFG.resize:
false_color = self.resize_image(false_color)
if self.data_type == 'train':
if CFG.flip_augs:
random_hflip_p = np.random.uniform(0, 1)
random_vflip_p = np.random.uniform(0, 1)
if random_hflip_p <= CFG.hflip_p:
false_color = torch.flip(false_color, dims=(1,))
human_pixel_mask = torch.flip(human_pixel_mask, dims=(0,))
if random_vflip_p <= CFG.vflip_p:
false_color = torch.flip(false_color, dims=(2,))
human_pixel_mask = torch.flip(human_pixel_mask, dims=(1,))
if CFG.rotate_augs:
angle = np.random.choice([1, 2, 3])
if np.random.uniform(0, 1) < CFG.rotate_p:
false_color = torch.rot90(false_color, k=angle, dims=[1, 2])
human_pixel_mask = torch.rot90(human_pixel_mask, k=angle, dims=[0, 1])
normalize_image = T.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
false_color = normalize_image(false_color)
return false_color, human_pixel_mask.float()
def train_one_epoch(model, optimizer, dataloader, scheduler=None):
model = model.train()
losses = []
scaler = GradScaler(enabled=CFG.use_amp)
for imgs, masks in (pbar := tqdm(dataloader, total=len(dataloader))):
imgs, masks = imgs.to(CFG.device), masks.to(CFG.device).to(torch.float)
#with autocast(CFG.use_amp, dtype=torch.float16):
preds = model(imgs)[0].squeeze(1)
preds, masks = preds.squeeze(), masks.squeeze()
if CFG.resize:
preds = T.Resize(masks.shape[1],interpolation=T.InterpolationMode.BILINEAR,antialias=True)(preds)
pbar.set_description(f'mean dice - {np.mean(losses)}, last dice = {losses[-1:]}')
loss = CFG.train_loss(preds, masks)
#scaler.scale(loss).backward()
loss.backward()
optimizer.step()
#scaler.update()
if scheduler:
scheduler.step()
losses.append(loss.item())
optimizer.zero_grad()
return np.mean(losses)
def get_ths(all_preds, all_masks):
all_preds_sigmoid = torch.nn.Sigmoid()(all_preds)
ths, losses = [], []
for th in np.linspace(0.05, 1.01, 25):
all_preds_cur = (all_preds_sigmoid > th).to(int)
cur_loss = CFG.valid_loss_th(all_preds_cur, all_masks)
ths.append(th)
losses.append(cur_loss.item())
return ths, losses
def valid_one_epoch(model, dataloader):
model = model.eval()
losses = []
all_preds = []
all_masks = []
for imgs, masks in tqdm(dataloader, total=len(dataloader)):
imgs, masks = imgs.to(CFG.device), masks.to(CFG.device)
with torch.no_grad():
preds = model(imgs)[0]
if CFG.resize:
preds = T.Resize(masks.shape[1],interpolation=T.InterpolationMode.BILINEAR,antialias=True)(preds)
loss = CFG.valid_loss(preds, masks)
masks = masks.to('cpu')
preds = preds.to('cpu')
all_preds.append(preds)
all_masks.append(masks)
losses.append(loss.item())
all_preds = torch.stack(all_preds, dim=1).contiguous()
all_masks = torch.stack(all_masks, dim=1).contiguous()
print(all_preds.shape, all_masks.shape)
global_loss = CFG.valid_loss(all_preds, all_masks)
ths, losses_ = get_ths(all_preds, all_masks)
return np.mean(losses), global_loss, (ths, losses_), (all_preds, all_masks)
if __name__ == '__main__':
os.makedirs(CFG.PATH_TO_SAVE, exist_ok=True)
if CFG.write_to_tensorboard:
writer = SummaryWriter(comment=CFG.exp_name)
train_ds = ContrailDataset(CFG.DATATRAIN_PATH, 'train')
valid_ds = ContrailDataset(CFG.DATAVALID_PATH, 'validation')
train_dl = DataLoader(train_ds, batch_size=CFG.batch_size, shuffle=True, num_workers=CFG.n_workers, drop_last=True)
valid_dl = DataLoader(valid_ds, batch_size=CFG.batch_size, shuffle=False, num_workers=CFG.n_workers, drop_last=True)
model = SegformerForSemanticSegmentation.from_pretrained(
CFG.backbone,
return_dict=False,
num_labels=1,
ignore_mismatched_sizes=True,
).to(CFG.device)
#smp.Unet(encoder_name=CFG.backbone, decoder_attention_type=CFG.decoder_attention_type, decoder_use_batchnorm=True).to(CFG.device)
optimizer = AdamW(model.parameters(), lr=5e-4)
scheduler = CosineAnnealingLR(optimizer, T_max=80, eta_min=1e-5, last_epoch=-1)
for i in range(CFG.n_epoch):
loss_train = train_one_epoch(model, optimizer, train_dl, scheduler)
loss_valid, global_loss_valid, (ths, losses_), (all_preds, all_masks) = valid_one_epoch(model, valid_dl)
print(f'EPOCH - {i} : loss_train - {loss_train}, loss_valid - {loss_valid}, global loss valid - {global_loss_valid}')
best_loss_idx = np.argmin(losses_)
print(f'Best th : {ths[best_loss_idx]}, Best dice loss : {losses_[best_loss_idx]}')
if CFG.write_to_tensorboard:
writer.add_scalar('Train BCE', 1. - loss_train, i)
writer.add_scalar('Valid DICE', 1. - loss_valid, i)
writer.add_scalar('Valid TH', ths[best_loss_idx], i)
writer.add_scalar('Valid DICE TH', 1. - losses_[best_loss_idx], i)
torch.save({'model' : model.state_dict(),
'best_th' : ths[best_loss_idx],
'best_dice' : losses_[best_loss_idx],
'resize' : CFG.resize,
'size' : CFG.resize_value},
os.path.join(CFG.PATH_TO_SAVE, f'model_{i}.pth'))
torch.save({'all_preds' : all_preds,
'all_masks' : all_masks,
'best_th' : ths[best_loss_idx],
'best_loss' : losses_[best_loss_idx]},
os.path.join(CFG.PATH_TO_SAVE, f'predictions_{i}.pth'))