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main.py
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main.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
from typing import OrderedDict
import argparse
import json
import logging
import math
import os
# import pdb
from os.path import exists, join, split
import threading
from datetime import datetime
import time
import numpy as np
import shutil
import sys
from PIL import Image
import torch
from torch import nn
import torch.backends.cudnn as cudnn
from torch.nn.modules import transformer
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
from tensorboardX import SummaryWriter
from min_norm_solvers import MinNormSolver
import drn
import data_transforms as transforms
from model.models import DPTSegmentationModel, DPTSegmentationModelMultiHead, TransferNet, CerberusSegmentationModelMultiHead
from model.transforms import PrepareForNet
try:
from modules import batchnormsync
except ImportError:
pass
FORMAT = "[%(asctime)-15s %(filename)s:%(lineno)d %(funcName)s] %(message)s"
logging.basicConfig(format=FORMAT, filename='./'+ datetime.now().strftime("%Y%m%d_%H%M%S") + '.txt')
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
TASK =None # 'ATTRIBUTE', 'AFFORDANCE', 'SEGMENTATION'
TRANSFER_FROM_TASK = None #'ATTRIBUTE', 'AFFORDANCE', 'SEGMENTATION', or None to unable transfer
CITYSCAPE_PALETTE = np.asarray([
[128, 64, 128],
[244, 35, 232],
[70, 70, 70],
[102, 102, 156],
[190, 153, 153],
[153, 153, 153],
[250, 170, 30],
[220, 220, 0],
[107, 142, 35],
[152, 251, 152],
[70, 130, 180],
[220, 20, 60],
[255, 0, 0],
[0, 0, 142],
[0, 0, 70],
[0, 60, 100],
[0, 80, 100],
[0, 0, 230],
[119, 11, 32],
[0, 0, 0]], dtype=np.uint8)
NYU40_PALETTE = np.asarray([
[0, 0, 0],
[0, 0, 80],
[0, 0, 160],
[0, 0, 240],
[0, 80, 0],
[0, 80, 80],
[0, 80, 160],
[0, 80, 240],
[0, 160, 0],
[0, 160, 80],
[0, 160, 160],
[0, 160, 240],
[0, 240, 0],
[0, 240, 80],
[0, 240, 160],
[0, 240, 240],
[80, 0, 0],
[80, 0, 80],
[80, 0, 160],
[80, 0, 240],
[80, 80, 0],
[80, 80, 80],
[80, 80, 160],
[80, 80, 240],
[80, 160, 0],
[80, 160, 80],
[80, 160, 160],
[80, 160, 240], [80, 240, 0], [80, 240, 80], [80, 240, 160], [80, 240, 240],
[160, 0, 0], [160, 0, 80], [160, 0, 160], [160, 0, 240], [160, 80, 0],
[160, 80, 80], [160, 80, 160], [160, 80, 240]], dtype=np.uint8)
AFFORDANCE_PALETTE = np.asarray([
[0, 0, 0],
[255, 255, 255]], dtype=np.uint8)
task_list = None
middle_task_list = None
if TASK == 'ATTRIBUTE':
task_list = ['Wood','Painted','Paper','Glass','Brick','Metal','Flat','Plastic','Textured','Glossy','Shiny']
FILE_DESCRIPTION = '_attribute'
PALETTE = AFFORDANCE_PALETTE
EVAL_METHOD = 'mIoU'
elif TASK == 'AFFORDANCE':
task_list = ['L','M','R','S','W']
FILE_DESCRIPTION = '_affordance'
PALETTE = AFFORDANCE_PALETTE
EVAL_METHOD = 'mIoU'
elif TASK =='SEGMENTATION':
task_list = ['Segmentation']
FILE_DESCRIPTION = ''
PALETTE = NYU40_PALETTE
EVAL_METHOD = 'mIoUAll'
else:
task_list = None
FILE_DESCRIPTION = ''
PALETTE = None
EVAL_METHOD = None
if TRANSFER_FROM_TASK == 'ATTRIBUTE':
middle_task_list = ['Wood','Painted','Paper','Glass','Brick','Metal','Flat','Plastic','Textured','Glossy','Shiny']
elif TRANSFER_FROM_TASK == 'AFFORDANCE':
middle_task_list = ['L','M','R','S','W']
elif TRANSFER_FROM_TASK =='SEGMENTATION':
middle_task_list = ['Segmentation']
elif TRANSFER_FROM_TASK is None:
pass
if TRANSFER_FROM_TASK is not None:
TENSORBOARD_WRITER = SummaryWriter(comment='From_'+TRANSFER_FROM_TASK+'_TO_'+TASK)
elif TASK is not None:
TENSORBOARD_WRITER = SummaryWriter(comment=TASK)
else:
TENSORBOARD_WRITER = SummaryWriter(comment='Nontype')
def downsampling(x, size=None, scale=None, mode='nearest'):
if size is None:
size = (int(scale * x.size(2)) , int(scale * x.size(3)))
h = torch.arange(0,size[0]) / (size[0] - 1) * 2 - 1
w = torch.arange(0,size[1]) / (size[1] - 1) * 2 - 1
grid = torch.zeros(size[0] , size[1] , 2)
grid[: , : , 0] = w.unsqueeze(0).repeat(size[0] , 1)
grid[: , : , 1] = h.unsqueeze(0).repeat(size[1] , 1).transpose(0 , 1)
grid = grid.unsqueeze(0).repeat(x.size(0),1,1,1)
if x.is_cuda:
grid = grid.cuda()
return torch.nn.functional.grid_sample(x , grid , mode = mode)
def fill_up_weights(up):
w = up.weight.data
f = math.ceil(w.size(2) / 2)
c = (2 * f - 1 - f % 2) / (2. * f)
for i in range(w.size(2)):
for j in range(w.size(3)):
w[0, 0, i, j] = \
(1 - math.fabs(i / f - c)) * (1 - math.fabs(j / f - c))
for c in range(1, w.size(0)):
w[c, 0, :, :] = w[0, 0, :, :]
class SegList(torch.utils.data.Dataset):
def __init__(self, data_dir, phase, transforms, list_dir=None,
out_name=False):
self.list_dir = data_dir if list_dir is None else list_dir
self.data_dir = data_dir
self.out_name = out_name
self.phase = phase
self.transforms = transforms
self.image_list = None
self.label_list = None
self.bbox_list = None
self.read_lists()
def __getitem__(self, index):
data = [Image.open(join(self.data_dir, self.image_list[index]))]
data = np.array(data[0])
if len(data.shape) == 2:
data = np.stack([data , data , data] , axis = 2)
data = [Image.fromarray(data)]
if self.label_list is not None:
data.append(Image.open(join(self.data_dir, self.label_list[index])))
data = list(self.transforms(*data))
if self.out_name:
if self.label_list is None:
data.append(data[0][0, :, :])
data.append(self.image_list[index])
return tuple(data)
def __len__(self):
return len(self.image_list)
def read_lists(self):
image_path = join(self.list_dir, self.phase + FILE_DESCRIPTION+ '_images.txt')
label_path = join(self.list_dir, self.phase + FILE_DESCRIPTION+ '_labels.txt')
assert exists(image_path)
self.image_list = [line.strip() for line in open(image_path, 'r')]
if exists(label_path):
self.label_list = [line.strip() for line in open(label_path, 'r')]
assert len(self.image_list) == len(self.label_list)
class ConcatSegList(torch.utils.data.Dataset):
def __init__(self, at, af, seg):
self.at = at
self.af = af
self.seg = seg
def __getitem__(self, index):
return (self.at[index], self.af[index], self.seg[index])
def __len__(self):
return len(self.at)
class SegMultiHeadList(torch.utils.data.Dataset):
def __init__(self, data_dir, phase, transforms, list_dir=None,
out_name=False):
self.list_dir = data_dir if list_dir is None else list_dir
self.data_dir = data_dir
self.out_name = out_name
self.phase = phase
self.transforms = transforms
self.image_list = None
self.label_list = None
self.bbox_list = None
self.read_lists()
def __getitem__(self, index):
data = [Image.open(join(self.data_dir, self.image_list[index]))]
data = np.array(data[0])
if len(data.shape) == 2:
data = np.stack([data , data , data] , axis = 2)
data = [Image.fromarray(data)]
label_data = list()
if self.label_list is not None:
for it in self.label_list[index].split(','):
label_data.append(Image.open(join(self.data_dir, it)))
data.append(label_data)
data = list(self.transforms(*data))
if self.out_name:
if self.label_list is None:
data.append(data[0][0, :, :])
data.append(self.image_list[index])
return tuple(data)
def __len__(self):
return len(self.image_list)
def read_lists(self):
image_path = join(self.list_dir, self.phase + FILE_DESCRIPTION+ '_images.txt')
label_path = join(self.list_dir, self.phase + FILE_DESCRIPTION+ '_labels.txt')
assert exists(image_path)
self.image_list = [line.strip() for line in open(image_path, 'r')]
if exists(label_path):
self.label_list = [line.strip() for line in open(label_path, 'r')]
assert len(self.image_list) == len(self.label_list)
class SegListMS(torch.utils.data.Dataset):
def __init__(self, data_dir, phase, transforms, scales, list_dir=None):
self.list_dir = data_dir if list_dir is None else list_dir
self.data_dir = data_dir
self.phase = phase
self.transforms = transforms
self.image_list = None
self.label_list = None
self.bbox_list = None
self.read_lists()
self.scales = scales
def __getitem__(self, index):
data = [Image.open(join(self.data_dir, self.image_list[index]))]
w, h = 640, 480
data = np.array(data[0])
if len(data.shape) == 2:
data = np.stack([data , data , data] , axis = 2)
data = [Image.fromarray(data)]
if self.label_list is not None:
data.append(Image.open(join(self.data_dir, self.label_list[index])))
out_data = list(self.transforms(*data))
ms_images = [self.transforms(data[0].resize((round(int(w * s)/32) * 32 , round(int(h * s)/32) * 32),
Image.BICUBIC))[0]
for s in self.scales]
out_data.append(self.image_list[index])
out_data.extend(ms_images)
return tuple(out_data)
def __len__(self):
return len(self.image_list)
def read_lists(self):
image_path = join(self.list_dir, self.phase + FILE_DESCRIPTION+ '_images.txt')
label_path = join(self.list_dir, self.phase + FILE_DESCRIPTION+ '_labels.txt')
assert exists(image_path)
self.image_list = [line.strip() for line in open(image_path, 'r')]
if exists(label_path):
self.label_list = [line.strip() for line in open(label_path, 'r')]
assert len(self.image_list) == len(self.label_list)
class SegListMSMultiHead(torch.utils.data.Dataset):
def __init__(self, data_dir, phase, transforms, scales, list_dir=None):
self.list_dir = data_dir if list_dir is None else list_dir
self.data_dir = data_dir
self.phase = phase
self.transforms = transforms
self.image_list = None
self.label_list = None
self.bbox_list = None
self.read_lists()
self.scales = scales
def __getitem__(self, index):
data = [Image.open(join(self.data_dir, self.image_list[index]))]
w, h = 640, 480
data = np.array(data[0])
if len(data.shape) == 2:
data = np.stack([data , data , data] , axis = 2)
data = [Image.fromarray(data)]
label_data = list()
if self.label_list is not None:
for it in self.label_list[index].split(','):
label_data.append(Image.open(join(self.data_dir, it)))
data.append(label_data)
out_data = list(self.transforms(*data))
ms_images = [self.transforms(data[0].resize((round(int(w * s)/32) * 32 , round(int(h * s)/32) * 32),
Image.BICUBIC))[0]
for s in self.scales]
out_data.append(self.image_list[index])
out_data.extend(ms_images)
return tuple(out_data)
def __len__(self):
return len(self.image_list)
def read_lists(self):
image_path = join(self.list_dir, self.phase + FILE_DESCRIPTION+ '_images.txt')
label_path = join(self.list_dir, self.phase + FILE_DESCRIPTION+ '_labels.txt')
assert exists(image_path)
self.image_list = [line.strip() for line in open(image_path, 'r')]
if exists(label_path):
self.label_list = [line.strip() for line in open(label_path, 'r')]
assert len(self.image_list) == len(self.label_list)
def validate(val_loader, model, criterion, eval_score=None, print_freq=10, transfer_model=None, epoch=None):
batch_time = AverageMeter()
losses = AverageMeter()
losses_array = list()
for it in task_list:
losses_array.append(AverageMeter())
score = AverageMeter()
# switch to evaluate mode
model.eval()
if transfer_model is not None:
transfer_model.eval()
end = time.time()
for i, (input, target) in enumerate(val_loader):
with torch.no_grad():
input = input.cuda()
input_var = torch.autograd.Variable(input, volatile=True)
target_var = list()
for idx in range(len(target)):
target[idx] = target[idx].cuda(non_blocking=True)
target_var.append(torch.autograd.Variable(target[idx], volatile=True))
# compute output
if transfer_model is not None:
_, features = model(input_var)
output = transfer_model(features)
elif transfer_model is None:
output, _ = model(input_var)
softmaxf = nn.LogSoftmax()
loss_array = list()
for idx in range(len(output)):
output[idx] = softmaxf(output[idx])
loss_array.append(criterion(output[idx],target_var[idx]))
loss = sum(loss_array)
# measure accuracy and record loss
losses.update(loss.item(), input.size(0))
for idx, it in enumerate(task_list):
(losses_array[idx]).update((loss_array[idx]).item(), input.size(0))
scores_array = list()
for idx in range(len(output)):
scores_array.append(eval_score(output[idx], target_var[idx]))
score.update(np.nanmean(scores_array), input.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % print_freq == 0:
logger.info('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Score {score.val:.3f} ({score.avg:.3f})'.format(
i, len(val_loader), batch_time=batch_time, loss=losses,
score=score))
TENSORBOARD_WRITER.add_scalar('val_loss_average', losses.avg, global_step=epoch)
TENSORBOARD_WRITER.add_scalar('val_score_average', score.avg, global_step=epoch)
logger.info(' * Score {top1.avg:.3f}'.format(top1=score))
return score.avg
def validate_cerberus(val_loader, model, criterion, eval_score=None, print_freq=10, transfer_model=None, epoch=None):
task_list_array = [['Wood','Painted','Paper','Glass','Brick','Metal','Flat','Plastic','Textured','Glossy','Shiny'],
['L','M','R','S','W'],
['Segmentation']]
batch_time_list = list()
losses_list = list()
losses_array_list = list()
score_list = list()
score = AverageMeter()
for i in range(3):
batch_time_list.append(AverageMeter())
losses_list.append(AverageMeter())
losses_array = list()
for it in task_list_array[i]:
losses_array.append(AverageMeter())
losses_array_list.append(losses_array)
score_list.append(AverageMeter())
# switch to evaluate mode
model.eval()
# if transfer_model is not None:
# transfer_model.eval()
end = time.time()
for i, pairs in enumerate(val_loader):
for index, (input,target) in enumerate(pairs):
with torch.no_grad():
input = input.cuda()
input_var = torch.autograd.Variable(input, volatile=True)
target_var = list()
for idx in range(len(target)):
target[idx] = target[idx].cuda(non_blocking=True)
target_var.append(torch.autograd.Variable(target[idx], volatile=True))
# compute output
output, _, _ = model(input_var, index)
softmaxf = nn.LogSoftmax()
loss_array = list()
for idx in range(len(output)):
output[idx]= softmaxf(output[idx])
loss_array.append(criterion(output[idx],target_var[idx]))
loss = sum(loss_array)
# measure accuracy and record loss
losses_list[index].update(loss.item(), input.size(0))
for idx, it in enumerate(task_list_array[index]):
(losses_array_list[index][idx]).update((loss_array[idx]).item(), input.size(0))
scores_array = list()
if index < 2:
for idx in range(len(output)):
scores_array.append(eval_score(output[idx], target_var[idx]))
elif index == 2:
for idx in range(len(output)):
scores_array.append(mIoUAll(output[idx], target_var[idx]))
else:
assert 0 == 1
tmp = np.nanmean(scores_array)
if not np.isnan(tmp):
score_list[index].update(tmp, input.size(0))
else:
pass
# measure elapsed time
batch_time_list[index].update(time.time() - end)
end = time.time()
if i % print_freq == 0:
logger.info('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Score {score.val:.3f} ({score.avg:.3f})'.format(
i, len(val_loader), batch_time=batch_time_list[index], loss=losses_list[index],
score=score_list[index]))
score.update(np.nanmean([score_list[0].val, score_list[1].val, score_list[2].val]))
if i % print_freq == 0:
logger.info('total score is:{score.val:.3f} ({score.avg:.3f})'.format(
score = score
))
for idx, item in enumerate(['attribute','affordance','segmentation']):
TENSORBOARD_WRITER.add_scalar('val_'+ item +'_loss_average', losses_list[idx].avg, global_step=epoch)
TENSORBOARD_WRITER.add_scalar('val_'+ item +'_score_average', score_list[idx].avg, global_step=epoch)
logger.info(' * Score {top1.avg:.3f}'.format(top1=score))
TENSORBOARD_WRITER.add_scalar('val_score_average', score.avg, global_step=epoch)
return score.avg
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 accuracy(output, target):
"""Computes the precision@k for the specified values of k"""
# batch_size = target.size(0) * target.size(1) * target.size(2)
_, pred = output.max(1)
pred = pred.view(1, -1)
target = target.view(1, -1)
correct = pred.eq(target)
correct = correct[target != 255]
correct = correct.view(-1)
try:
score = correct.float().sum(0).mul(100.0 / correct.size(0))
return score.item()
except:
return 0
def mIoU(output, target):
"""Computes the iou for the specified values of k"""
num_classes = output.shape[1]
hist = np.zeros((num_classes, num_classes))
_, pred = output.max(1)
pred = pred.cpu().data.numpy()
target = target.cpu().data.numpy()
hist += fast_hist(pred.flatten(), target.flatten(), num_classes)
ious = per_class_iu(hist) * 100
return round(np.nanmean(ious[1]), 2)
def mIoUAll(output, target):
"""Computes the iou for the specified values of k"""
num_classes = output.shape[1]
hist = np.zeros((num_classes, num_classes))
_, pred = output.max(1)
pred = pred.cpu().data.numpy()
target = target.cpu().data.numpy()
hist += fast_hist(pred.flatten(), target.flatten(), num_classes)
ious = per_class_iu(hist) * 100
return round(np.nanmean(ious), 2)
def train(train_loader, model, criterion, optimizer, epoch,
eval_score=None, print_freq=1, transfer_model=None, transfer_optim=None):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
losses_array = list()
for it in task_list:
losses_array.append(AverageMeter())
scores = AverageMeter()
# switch to train mode
model.train()
if transfer_model is not None:
model.eval()
for param in model.parameters():
param.requires_grad = False
transfer_model.train()
end = time.time()
for i, (input, target) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
input = input.cuda()
input_var = torch.autograd.Variable(input)
target_var = list()
for idx in range(len(target)):
target[idx] = target[idx].cuda()
target_var.append(torch.autograd.Variable(target[idx]))
# compute output
if transfer_model is None:
output, _ = model(input_var)
elif transfer_model is not None:
_, features = model(input_var)
output = transfer_model(features)
softmaxf = nn.LogSoftmax()
loss_array = list()
assert len(output) == len(target)
for idx in range(len(output)):
output[idx] = softmaxf(output[idx])
loss_array.append(criterion(output[idx],target_var[idx]))
loss = sum(loss_array)
# measure accuracy and record loss
losses.update(loss.item(), input.size(0))
for idx, it in enumerate(task_list):
(losses_array[idx]).update((loss_array[idx]).item(), input.size(0))
scores_array = list()
for idx in range(len(output)):
scores_array.append(eval_score(output[idx], target_var[idx]))
scores.update(np.nanmean(scores_array), input.size(0))
# compute gradient and do SGD step
if transfer_optim is not None:
transfer_optim.zero_grad()
elif transfer_optim is None:
optimizer.zero_grad()
loss.backward()
if transfer_optim is not None:
transfer_optim.step()
elif transfer_optim is None:
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % print_freq == 0:
losses_info = ''
for idx, it in enumerate(task_list):
losses_info += 'Loss_{0} {loss.val:.4f} ({loss.avg:.4f})\t'.format(it, loss=losses_array[idx])
TENSORBOARD_WRITER.add_scalar('train_task_' + it + '_loss_val', losses_array[idx].val,
global_step= epoch * len(train_loader) + i)
TENSORBOARD_WRITER.add_scalar('train_task_' + it + '_loss_average', losses_array[idx].avg,
global_step= epoch * len(train_loader) + i)
logger.info('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'{loss_info}'
'Score {top1.val:.3f} ({top1.avg:.3f})'.format(
epoch, i, len(train_loader), batch_time=batch_time,
data_time=data_time, loss=losses,loss_info=losses_info,
top1=scores))
TENSORBOARD_WRITER.add_scalar('train_loss_val', losses.val, global_step= epoch * len(train_loader) + i)
TENSORBOARD_WRITER.add_scalar('train_loss_average', losses.avg, global_step= epoch * len(train_loader) + i)
TENSORBOARD_WRITER.add_scalar('train_scores_val', scores.val, global_step= epoch * len(train_loader) + i)
TENSORBOARD_WRITER.add_scalar('train_scores_val', scores.avg, global_step= epoch * len(train_loader) + i)
TENSORBOARD_WRITER.add_scalar('train_epoch_loss_average', losses.avg, global_step= epoch)
TENSORBOARD_WRITER.add_scalar('train_epochscores_val', scores.avg, global_step= epoch)
def train_cerberus(train_loader, model, criterion, optimizer, epoch,
eval_score=None, print_freq=1): # transfer_model=None, transfer_optim=None):
task_list_array = [['Wood','Painted','Paper','Glass','Brick','Metal','Flat','Plastic','Textured','Glossy','Shiny'],
['L','M','R','S','W'],
['Segmentation']]
root_task_list_array = ['At', 'Af', 'Seg']
batch_time_list = list()
data_time_list = list()
losses_list = list()
losses_array_list = list()
scores_list = list()
for i in range(3):
batch_time_list.append(AverageMeter())
data_time_list.append(AverageMeter())
losses_list.append(AverageMeter())
losses_array = list()
for it in task_list_array[i]:
losses_array.append(AverageMeter())
losses_array_list.append(losses_array)
scores_list.append(AverageMeter())
model.train()
end = time.time()
moo = True
for i, in_tar_name_pair in enumerate(train_loader):
if moo :
grads = {}
task_loss_array = []
for index, (input, target, name) in enumerate(in_tar_name_pair):
# measure data loading time
data_time_list[index].update(time.time() - end)
if moo:
input = input.cuda()
input_var = torch.autograd.Variable(input)
target_var = list()
for idx in range(len(target)):
target[idx] = target[idx].cuda()
target_var.append(torch.autograd.Variable(target[idx]))
# compute output
output, _, _ = model(input_var, index)
# if transfer_model is not None:
# output = transfer_model(output)
softmaxf = nn.LogSoftmax()
loss_array = list()
assert len(output) == len(target)
for idx in range(len(output)):
output[idx] = softmaxf(output[idx])
loss_raw = criterion(output[idx],target_var[idx])
loss_enhance = loss_raw
if torch.isnan(loss_enhance):
print("nan")
logger.info('loss_raw is: {0}'.format(loss_raw))
logger.info('loss_enhance is: {0}'.format(loss_enhance))
exit(0)
# loss_array.append(loss_enhance)
else:
loss_array.append(loss_enhance)
local_loss = sum(loss_array)
local_loss_enhance = local_loss
# backward for gradient calculate
for cnt in model.pretrained.parameters():
cnt.grad = None
model.scratch.layer1_rn.weight.grad = None
model.scratch.layer2_rn.weight.grad = None
model.scratch.layer3_rn.weight.grad = None
model.scratch.layer4_rn.weight.grad = None
local_loss_enhance.backward()
grads[root_task_list_array[index]] = []
for par_name, cnt in model.pretrained.named_parameters():
if cnt.grad is not None:
grads[root_task_list_array[index]].append(Variable(cnt.grad.data.clone(),requires_grad = False))
grads[root_task_list_array[index]].append(Variable(model.scratch.layer1_rn.weight.grad.data.clone(), requires_grad = False))
grads[root_task_list_array[index]].append(Variable(model.scratch.layer2_rn.weight.grad.data.clone(), requires_grad = False))
grads[root_task_list_array[index]].append(Variable(model.scratch.layer3_rn.weight.grad.data.clone(), requires_grad = False))
grads[root_task_list_array[index]].append(Variable(model.scratch.layer4_rn.weight.grad.data.clone(), requires_grad = False))
else:
pass
if moo:
if torch.isnan(local_loss_enhance):
print("nan")
logger.info('loss_raw is: {0}'.format(local_loss))
logger.info('loss_enhance is: {0}'.format(local_loss_enhance))
exit(0)
# loss_array.append(loss_enhance)
else:
task_loss_array.append(local_loss_enhance)
# measure accuracy and record loss
losses_list[index].update(local_loss_enhance.item(), input.size(0))
for idx, it in enumerate(task_list_array[index]):
(losses_array_list[index][idx]).update((loss_array[idx]).item(), input.size(0))
scores_array = list()
if index < 2:
for idx in range(len(output)):
scores_array.append(eval_score(output[idx], target_var[idx]))
elif index == 2:
for idx in range(len(output)):
scores_array.append(mIoUAll(output[idx], target_var[idx]))
else:
assert 0 == 1
scores_list[index].update(np.nanmean(scores_array), input.size(0))
# compute gradient and do SGD step
if index == 2:
if moo:
del input, target, input_var, target_var
task_loss_array_new = []
for index_new, (input_new, target_new, _) in enumerate(in_tar_name_pair):
input_var_new = torch.autograd.Variable(input_new.cuda())
target_var_new = [torch.autograd.Variable(target_new[idx].cuda()) for idx in range(len(target_new))]
output_new, _, _ = model(input_var_new, index_new)
loss_array_new = [criterion(softmaxf(output_new[idx]),target_var_new[idx]) \
for idx in range(len(output_new))]
local_loss_new = sum(loss_array_new)
task_loss_array_new.append(local_loss_new)
assert len(task_loss_array_new) == 3
sol, min_norm = MinNormSolver.find_min_norm_element([grads[cnt] for cnt in root_task_list_array])
logger.info('scale is: |{0}|\t|{1}|\t|{2}|\t'.format(sol[0], sol[1], sol[2]))
loss_new = 0
loss_new = sol[0] * task_loss_array_new[0] + sol[1] * task_loss_array_new[1] \
+ sol[2] * task_loss_array_new[2]
optimizer.zero_grad()
loss_new.backward()
optimizer.step()
else:
assert len(task_loss_array) == 3
loss = sum(task_loss_array)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if moo:
# measure elapsed time
batch_time_list[index].update(time.time() - end)
end = time.time()
if i % print_freq == 0:
losses_info = ''
for idx, it in enumerate(task_list_array[index]):
losses_info += 'Loss_{0} {loss.val:.4f} ({loss.avg:.4f}) \t'.format(it, loss=losses_array_list[index][idx])
TENSORBOARD_WRITER.add_scalar('train_task_'+ it +'_loss_val', losses_array_list[index][idx].val,
global_step= epoch * len(train_loader) + i)
TENSORBOARD_WRITER.add_scalar('train_task_'+ it +'_loss_avg', losses_array_list[index][idx].avg,
global_step= epoch * len(train_loader) + i)
logger.info('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'{loss_info}'
'Score {top1.val:.3f} ({top1.avg:.3f})'.format(
epoch, i, len(train_loader), batch_time=batch_time_list[index],
data_time=data_time_list[index], loss=losses_list[index],loss_info=losses_info,
top1=scores_list[index]))
logger.info('File name is: {}'.format(','.join(name)))
TENSORBOARD_WRITER.add_scalar('train_'+ str(index) +'_losses_val', losses_list[index].val,
global_step= epoch * len(train_loader) + i)
TENSORBOARD_WRITER.add_scalar('train_'+ str(index) +'_losses_avg', losses_list[index].avg,
global_step= epoch * len(train_loader) + i)
TENSORBOARD_WRITER.add_scalar('train_'+ str(index) +'_score_val', scores_list[index].val,
global_step= epoch * len(train_loader) + i)
TENSORBOARD_WRITER.add_scalar('train_'+ str(index) +'_score_avg', scores_list[index].avg,
global_step= epoch * len(train_loader) + i)
for i in range(3):
TENSORBOARD_WRITER.add_scalar('train_epoch_loss_average', losses_list[index].avg, global_step= epoch)
TENSORBOARD_WRITER.add_scalar('train_epoch_scores_val', scores_list[index].avg, global_step= epoch)
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
torch.save(state, filename)
if is_best:
shutil.copyfile(filename, 'model_best.pth.tar')
def train_seg(args):
batch_size = args.batch_size
num_workers = args.workers
crop_size = args.crop_size
print(' '.join(sys.argv))
for k, v in args.__dict__.items():
print(k, ':', v)
if len(task_list) == 1:
single_model = DPTSegmentationModel(args.classes, backbone="vitb_rn50_384")
else:
single_model = DPTSegmentationModelMultiHead(args.classes, task_list, backbone="vitb_rn50_384")
model = single_model.cuda()
if args.trans:
if len(middle_task_list) == 1:
single_model = DPTSegmentationModel(40, backbone="vitb_rn50_384")
else:
single_model = DPTSegmentationModelMultiHead(2, middle_task_list, backbone="vitb_rn50_384")
model = single_model.cuda()
model_trans = TransferNet(middle_task_list, task_list)
model_trans = model_trans.cuda()
criterion = nn.NLLLoss2d(ignore_index=255)
criterion.cuda()
# Data loading code
data_dir = args.data_dir
info = json.load(open(join(data_dir, 'info.json'), 'r'))
normalize = transforms.Normalize(mean=info['mean'],
std=info['std'])
t = []
if args.random_rotate > 0:
t.append(transforms.RandomRotateMultiHead(args.random_rotate))
if args.random_scale > 0:
t.append(transforms.RandomScaleMultiHead(args.random_scale))
t.extend([transforms.RandomCropMultiHead(crop_size),
transforms.RandomHorizontalFlipMultiHead(),
transforms.ToTensorMultiHead(),
normalize])
train_loader = torch.utils.data.DataLoader(
SegMultiHeadList(data_dir, 'train', transforms.Compose(t)),
batch_size=batch_size, shuffle=True, num_workers=num_workers,
pin_memory=True, drop_last=True
)
val_loader = torch.utils.data.DataLoader(
SegMultiHeadList(data_dir, 'val', transforms.Compose([
transforms.RandomCropMultiHead(crop_size),
transforms.ToTensorMultiHead(),
normalize,
])),
batch_size=1, shuffle=False, num_workers=num_workers,
pin_memory=True, drop_last=True
)
# define loss function (criterion) and pptimizer
optimizer = torch.optim.SGD(single_model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.trans:
trans_optim = torch.optim.SGD(model_trans.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
cudnn.benchmark = True
best_prec1 = 0
start_epoch = 0
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
for name, param in checkpoint['state_dict'].items():
# name = name[7:]
model.state_dict()[name].copy_(param)