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# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Copyright (c) 2023 Image Processing Research Group of University Federico II of Naples ('GRIP-UNINA').
#
# All rights reserved.
# This work should only be used for nonprofit purposes.
#
# By downloading and/or using any of these files, you implicitly agree to all the
# terms of the license, as specified in the document LICENSE.txt
# (included in this package) and online at
# http://www.grip.unina.it/download/LICENSE_OPEN.txt
"""
Created in September 2022
@author: fabrizio.guillaro
"""
import logging
import os
import time
import numpy as np
from tqdm import tqdm
import torch
from torch.nn import functional as F
from lib.utils import AverageMeter
from lib.utils import get_confusion_matrix, get_confusion_matrix_1ch
from lib.utils import adjust_learning_rate as default_adjust_learning_rate
def train(epoch, num_epoch, epoch_iters, base_lr, num_iters,
trainloader, optimizer, model, writer_dict,
adjust_learning_rate=default_adjust_learning_rate):
# Training
model.train()
batch_time = AverageMeter()
avg_loss = AverageMeter()
tic = time.time()
cur_iters = epoch*epoch_iters
writer = writer_dict['writer']
global_steps = writer_dict['train_global_steps']
for i_iter, (rgbs, labels) in enumerate(tqdm(trainloader)):
rgbs = rgbs.cuda()
labels = labels.long().cuda()
losses, *_ = model(labels=labels, rgbs=rgbs)
loss = losses.mean()
model.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - tic)
tic = time.time()
# update average loss
avg_loss.update(loss.item())
lr = adjust_learning_rate(optimizer, base_lr, num_iters, i_iter+cur_iters)
print_loss = avg_loss.average()
msg = 'Epoch: [{}/{}], Time: {:.2f}, ' \
'lr: {:.6f}, Loss: {:.6f}' .format(
epoch, num_epoch, batch_time.average(), lr, print_loss)
logging.info(msg)
writer.add_scalar('train_loss', print_loss, global_steps)
writer.add_scalar('learning_rate', lr, global_steps)
global_steps += 1
writer_dict['train_global_steps'] = global_steps
def validate(config, testloader, model, writer_dict, valid_set="valid"):
model.eval()
avg_loss = AverageMeter()
confusion_matrix = np.zeros((config.DATASET.NUM_CLASSES, config.DATASET.NUM_CLASSES))
confusion_matrix_CONF = np.zeros((config.DATASET.NUM_CLASSES, config.DATASET.NUM_CLASSES))
# PRED metrics
avg_mse = AverageMeter()
avg_mIoU = AverageMeter()
avg_p_mIoU = AverageMeter()
avg_mIoU_s = AverageMeter() # smoothed
avg_p_mIoU_s = AverageMeter() # smoothed
avg_IoU_1_s = AverageMeter() # smoothed
avg_p_F1 = AverageMeter()
avg_p_F1_s = AverageMeter()
# CONF metrics
c_avg_mse = AverageMeter()
c_avg_mIoU = AverageMeter()
c_avg_mIoU_s = AverageMeter() # smoothed
# DET metrics
avg_det_tpr = AverageMeter()
avg_det_tnr = AverageMeter()
with torch.no_grad():
for it, (rgb, label) in enumerate(tqdm(testloader)):
size = label.size()
rgb = rgb.cuda()
label = label.long().cuda()
losses, pred, conf, det = model(labels=label, rgbs=rgb)
if pred is not None:
pred = F.upsample(input=pred, size=(size[-2], size[-1]), mode='bilinear')
pred_prob = F.softmax(pred, dim=1)
if conf is not None:
conf = F.upsample(input=conf, size=(size[-2], size[-1]), mode='bilinear')
tcp = pred_prob[:,1]*(label==1) + pred_prob[:,0]*(label==0)
loss = losses.mean()
avg_loss.update(loss.item())
smooth = 1.
# PRED METRICS
current_confusion_matrix = get_confusion_matrix(
label,
pred,
size,
config.DATASET.NUM_CLASSES,
config.TRAIN.IGNORE_LABEL)
val_mse = torch.mean((pred_prob[:,1]-label)**2).item()
avg_mse.update(val_mse)
confusion_matrix += current_confusion_matrix
TN = current_confusion_matrix[0, 0]
FN = current_confusion_matrix[1, 0]
FP = current_confusion_matrix[0, 1]
TP = current_confusion_matrix[1, 1]
pos = current_confusion_matrix.sum(1) # ground truth label count
res = current_confusion_matrix.sum(0) # prediction count
tp = np.diag(current_confusion_matrix) # Intersection part
# mIoU
IoU_array = (tp / np.maximum(1.0, pos + res - tp)) # Union part
mean_IoU = IoU_array.mean() # mean of the classes IoU
avg_mIoU.update(mean_IoU)
# mIoU_s
IoU_array_smooth = ((tp + smooth)/ (pos + res - tp + smooth)) # Union part
mean_IoU_smooth = IoU_array_smooth.mean() # mean of the classes IoU
avg_mIoU_s.update(mean_IoU_smooth)
avg_IoU_1_s.update(IoU_array_smooth[1]) # IoU of class 1
# p_mIoU
p_mIoU = 0.5 * (FN / np.maximum(1.0, FN + TP + TN)) + 0.5 * (FP / np.maximum(1.0, FP + TP + TN))
avg_p_mIoU.update(np.maximum(mean_IoU, p_mIoU))
# p_mIoU_smooth
p_mIoU_smooth = 0.5 * ((FN + smooth)/ (FN + TP + TN + smooth)) + 0.5 * ((FP + smooth)/ (FP + TP + TN + smooth))
avg_p_mIoU_s.update(np.maximum(mean_IoU_smooth, p_mIoU_smooth))
# p_F1
F1 = 2 * TP / np.maximum(2 * TP + FN + FP, 1.0)
p_F1 = 2 * FN / np.maximum(2 * TP + FN + TN, 1.0)
avg_p_F1.update(np.maximum(F1, p_F1))
# p_F1_smooth
F1_s = (2 * TP + smooth) / (2 * TP + FN + FP + smooth)
p_F1_s = (2 * FN + smooth) / (2 * TP + FN + TN + smooth)
avg_p_F1_s.update(np.maximum(F1_s, p_F1_s))
# CONF metrics
check_conf = 'conf_head' in config.MODEL.EXTRA.MODULES
if check_conf:
current_confusion_matrix_CONF = get_confusion_matrix_1ch(
tcp > 0.5,
conf,
size,
config.DATASET.NUM_CLASSES,
config.TRAIN.IGNORE_LABEL)
val_mse = torch.mean((torch.sigmoid(conf[:, 0]) - tcp) ** 2).item()
c_avg_mse.update(val_mse)
confusion_matrix_CONF += current_confusion_matrix_CONF
c_pos = current_confusion_matrix_CONF.sum(1) # ground truth label count
c_res = current_confusion_matrix_CONF.sum(0) # prediction count
c_tp = np.diag(current_confusion_matrix_CONF) # Intersection part
# mIoU (conf)
c_IoU_array = (c_tp / np.maximum(1.0, c_pos + c_res - c_tp)) # Union part
c_mean_IoU = c_IoU_array.mean() # mean of the classes IoU
c_avg_mIoU.update(c_mean_IoU)
# mIoU_s (conf)
c_IoU_array_smooth = ((c_tp + smooth) / (c_pos + c_res - c_tp + smooth)) # Union part
c_mean_IoU_smooth = c_IoU_array_smooth.mean() # mean of the classes IoU
c_avg_mIoU_s.update(c_mean_IoU_smooth)
# DET metrics
if det is not None:
det = det[:,0].cpu().numpy()
else:
det = np.max(pred[:,1].cpu().numpy(), axis=(1,2))
target_det = torch.count_nonzero(label * (label >= 0), (-1, -2)) > 3
target_det = target_det.cpu().numpy()
if np.any(target_det>0.5):
avg_det_tpr.update(np.sum(det[target_det>0.5]>0), np.sum(target_det>0.5))
if np.any(target_det<0.5):
avg_det_tnr.update(np.sum(det[target_det<0.5]<0), np.sum(target_det<0.5))
confusion_matrix = torch.from_numpy(confusion_matrix).cuda()
confusion_matrix = confusion_matrix.cpu().numpy()
pos = confusion_matrix.sum(1)
res = confusion_matrix.sum(0)
tp = np.diag(confusion_matrix)
pixel_acc = tp.sum() / pos.sum()
IoU_array = (tp / np.maximum(1.0, pos + res - tp))
mean_IoU = IoU_array.mean()
IoU_array_smooth = ((tp + smooth)/ (pos + res - tp + smooth)) # Union part
mean_IoU_smooth = IoU_array_smooth.mean() # mean of the classes IoU
print_loss = avg_loss.average()
try:
bacc = (avg_det_tpr.average()+avg_det_tnr.average())/2
except:
bacc = 0
writer = writer_dict['writer']
global_steps = writer_dict['valid_global_steps']
metric_dict = {
'loss' : print_loss,
'mIoU' : mean_IoU,
'mIoU_smooth' : mean_IoU_smooth,
'avg_mIoU' : avg_mIoU.average(),
'avg_mIoU_smooth' : avg_mIoU_s.average(),
'avg_det_tpr' : avg_det_tpr.average(),
'avg_det_tnr' : avg_det_tnr.average(),
'avg_det_bacc' : bacc,
'avg_mse' : avg_mse.average(),
'avg_IoU_1_smooth' : avg_IoU_1_s.average(),
'avg_p-mIoU' : avg_p_mIoU.average(),
'avg_p-mIoU_smooth' : avg_p_mIoU_s.average(),
'avg_p-F1' : avg_p_F1.average(),
'avg_p-F1_smooth' : avg_p_F1_s.average(),
'pixel_acc' : pixel_acc,
}
if check_conf:
metric_dict['avg_mse_CONF'] = c_avg_mse.average()
metric_dict['avg_mIoU_CONF'] = c_avg_mIoU.average()
metric_dict['avg_mIoU_smooth_CONF'] = c_avg_mIoU_s.average()
for metric in metric_dict:
writer.add_scalar(valid_set + '_' + metric, metric_dict[metric], global_steps)
writer_dict['valid_global_steps'] = global_steps + 1
return metric_dict, IoU_array, confusion_matrix
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