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test_coco.py
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import os
import datetime
import random
import time
import cv2
import numpy as np
import logging
import argparse
import math
from visdom import Visdom
import os.path as osp
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.multiprocessing as mp
import torch.distributed as dist
from torch.utils.data.distributed import DistributedSampler
from torch.cuda.amp import autocast
from tensorboardX import SummaryWriter
from model import HMNetAMP
from util import dataset
from util import transform, transform_tri, config
from util.util import AverageMeter, poly_learning_rate, intersectionAndUnionGPU, get_model_para_number, setup_seed, \
get_logger, get_save_path, \
is_same_model, fix_bn, sum_list, check_makedirs
import matplotlib.pyplot as plt
cv2.ocl.setUseOpenCL(False)
cv2.setNumThreads(0)
val_manual_seed = 123
setup_seed(val_manual_seed, False)
seed_array = [321]
val_num = len(seed_array)
def get_parser():
parser = argparse.ArgumentParser(description='PyTorch Few-Shot Semantic Segmentation')
parser.add_argument('--arch', type=str, default='HMNetAMP')
parser.add_argument('--viz', action='store_true', default=False)
parser.add_argument('--config', type=str, default='config/coco/coco_split3_resnet50.yaml',
help='config file') # coco/coco_split0_resnet50.yaml
parser.add_argument('--episode', help='number of test episodes', type=int, default=4000)
parser.add_argument('--opts', help='see config/ade20k/ade20k_pspnet50.yaml for all options', default=None,
nargs=argparse.REMAINDER)
args = parser.parse_args()
assert args.config is not None
cfg = config.load_cfg_from_cfg_file(args.config)
cfg = config.merge_cfg_from_args(cfg, args)
if args.opts is not None:
cfg = config.merge_cfg_from_list(cfg, args.opts)
return cfg
def get_model(args):
model = eval(args.arch).OneModel(args)
optimizer, optimizer_swin = model.get_optim(model, args, LR=args.base_lr)
model = model.cuda()
# Resume
get_save_path(args)
check_makedirs(args.snapshot_path)
check_makedirs(args.result_path)
if args.weight:
weight_path = osp.join(args.snapshot_path, args.weight)
if os.path.isfile(weight_path):
logger.info("=> loading checkpoint '{}'".format(weight_path))
checkpoint = torch.load(weight_path, map_location=torch.device('cpu'))
args.start_epoch = checkpoint['epoch']
new_param = checkpoint['state_dict']
try:
model.load_state_dict(new_param)
except RuntimeError: # 1GPU loads mGPU model
for key in list(new_param.keys()):
new_param[key[7:]] = new_param.pop(key)
model.load_state_dict(new_param)
optimizer.load_state_dict(checkpoint['optimizer'])
optimizer_swin.load_state_dict(checkpoint['optimizer_swin'])
logger.info("=> loaded checkpoint '{}' (epoch {})".format(weight_path, checkpoint['epoch']))
else:
logger.info("=> no checkpoint found at '{}'".format(weight_path))
# Get model para.
total_number, learnable_number = get_model_para_number(model)
print('Number of Parameters: %d' % (total_number))
print('Number of Learnable Parameters: %d' % (learnable_number))
time.sleep(5)
return model, optimizer, optimizer_swin
def main():
global args, logger, writer
args = get_parser()
logger = get_logger()
args.distributed = True if torch.cuda.device_count() > 1 else False
print(args)
if args.manual_seed is not None:
setup_seed(args.manual_seed, args.seed_deterministic)
assert args.classes > 1
assert args.zoom_factor in [1, 2, 4, 8]
assert (args.train_h - 1) % 8 == 0 and (args.train_w - 1) % 8 == 0
logger.info("=> creating model ...")
model, optimizer, optimizer_swin = get_model(args)
logger.info(model)
# ---------------------- DATASET ----------------------
value_scale = 255
mean = [0.485, 0.456, 0.406]
mean = [item * value_scale for item in mean]
std = [0.229, 0.224, 0.225]
std = [item * value_scale for item in std]
# Val
if args.evaluate:
if args.resized_val:
val_transform = transform.Compose([
transform.Resize(size=args.val_size),
transform.ToTensor(),
transform.Normalize(mean=mean, std=std)])
else:
val_transform = transform.Compose([
transform.test_Resize(size=args.val_size),
transform.ToTensor(),
transform.Normalize(mean=mean, std=std)])
if args.data_set == 'pascal' or args.data_set == 'coco':
val_data = dataset.SemData(split=args.split, shot=args.shot, data_root=args.data_root,
data_list=args.val_list, transform=val_transform, mode='val',
ann_type=args.ann_type, data_set=args.data_set, use_split_coco=args.use_split_coco)
val_loader = torch.utils.data.DataLoader(val_data, batch_size=args.batch_size_val, shuffle=False,
num_workers=args.workers, pin_memory=False, sampler=None)
# ---------------------- VAL ----------------------
start_time = time.time()
FBIoU_array = np.zeros(val_num)
mIoU_array = np.zeros(val_num)
pIoU_array = np.zeros(val_num)
for val_id in range(val_num):
val_seed = seed_array[val_id]
print('Val: [{}/{}] \t Seed: {}'.format(val_id + 1, val_num, val_seed))
fb_iou, miou, piou = validate(val_loader, model, val_seed, args.episode)
FBIoU_array[val_id], mIoU_array[val_id], pIoU_array[val_id] = \
fb_iou, miou, piou
total_time = time.time() - start_time
t_m, t_s = divmod(total_time, 60)
t_h, t_m = divmod(t_m, 60)
total_time = '{:02d}h {:02d}m {:02d}s'.format(int(t_h), int(t_m), int(t_s))
print('\nTotal running time: {}'.format(total_time))
print('Seed0: {}'.format(val_manual_seed))
print('Seed: {}'.format(seed_array))
print('mIoU: {}'.format(np.round(mIoU_array, 4)))
print('FBIoU: {}'.format(np.round(FBIoU_array, 4)))
print('pIoU: {}'.format(np.round(pIoU_array, 4)))
print('-' * 43)
print('Best_Seed_m: {} \t Best_Seed_F: {} \t Best_Seed_p: {}'.format(seed_array[mIoU_array.argmax()],
seed_array[FBIoU_array.argmax()],
seed_array[pIoU_array.argmax()]))
print('Best_mIoU: {:.4f} \t Best_FBIoU: {:.4f} \t Best_pIoU: {:.4f}'.format(
mIoU_array.max(), FBIoU_array.max(), pIoU_array.max()))
print('Mean_mIoU: {:.4f} \t Mean_FBIoU: {:.4f} \t Mean_pIoU: {:.4f}'.format(
mIoU_array.mean(), FBIoU_array.mean(), pIoU_array.mean()))
def validate(val_loader, model, val_seed, episode):
logger.info('>>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>')
batch_time = AverageMeter()
model_time = AverageMeter()
data_time = AverageMeter()
loss_meter = AverageMeter()
intersection_meter = AverageMeter() # final
union_meter = AverageMeter()
target_meter = AverageMeter()
if args.data_set == 'pascal':
test_num = 1000
split_gap = 5
elif args.data_set == 'coco':
test_num = episode
split_gap = 20
class_intersection_meter = [0] * split_gap
class_union_meter = [0] * split_gap
setup_seed(val_seed, args.seed_deterministic)
criterion = nn.CrossEntropyLoss(ignore_index=args.ignore_label)
model.eval()
end = time.time()
val_start = end
assert test_num % args.batch_size_val == 0
db_epoch = math.ceil(test_num / (len(val_loader) - args.batch_size_val))
iter_num = 0
for e in range(db_epoch):
for i, (input, target, s_input, s_mask, subcls, ori_label) in enumerate(val_loader):
if iter_num * args.batch_size_val >= test_num:
break
iter_num += 1
data_time.update(time.time() - end)
s_input = s_input.cuda(non_blocking=True)
s_mask = s_mask.cuda(non_blocking=True)
input = input.cuda(non_blocking=True)
target = target.cuda(non_blocking=True)
ori_label = ori_label.cuda(non_blocking=True)
start_time = time.time()
with autocast():
output = model(s_x=s_input, s_y=s_mask, x=input, y_m=target, cat_idx=subcls)
model_time.update(time.time() - start_time)
if args.ori_resize:
output = F.interpolate(output, size=ori_label.size()[-2:], mode='bilinear', align_corners=True)
target = ori_label.long()
output = F.interpolate(output, size=target.size()[1:], mode='bilinear', align_corners=True)
loss = criterion(output, target)
output = output.max(1)[1]
subcls = subcls[0].cpu().numpy()[0]
intersection, union, new_target = intersectionAndUnionGPU(output, target, args.classes, args.ignore_label)
intersection, union, new_target = intersection.cpu().numpy(), union.cpu().numpy(), new_target.cpu().numpy()
intersection_meter.update(intersection), union_meter.update(union), target_meter.update(new_target)
class_intersection_meter[subcls] += intersection[1]
class_union_meter[subcls] += union[1]
accuracy = sum(intersection_meter.val) / (sum(target_meter.val) + 1e-10)
loss_meter.update(loss.item(), input.size(0))
batch_time.update(time.time() - end)
end = time.time()
remain_iter = test_num / args.batch_size_val - iter_num
remain_time = remain_iter * batch_time.avg
t_m, t_s = divmod(remain_time, 60)
t_h, t_m = divmod(t_m, 60)
remain_time = '{:02d}:{:02d}:{:02d}'.format(int(t_h), int(t_m), int(t_s))
if ((i + 1) % round((test_num / 100)) == 0):
logger.info('Test: [{}/{}] '
'Data {data_time.val:.3f} ({data_time.avg:.3f}) '
'Batch {batch_time.val:.3f} ({batch_time.avg:.3f}) '
'Remain {remain_time} '
'Loss {loss_meter.val:.4f} ({loss_meter.avg:.4f}) '
'Accuracy {accuracy:.4f}.'.format(iter_num * args.batch_size_val, test_num,
data_time=data_time,
batch_time=batch_time,
remain_time=remain_time,
loss_meter=loss_meter,
accuracy=accuracy))
val_time = time.time() - val_start
iou_class = intersection_meter.sum / (union_meter.sum + 1e-10)
mIoU = np.mean(iou_class)
class_iou_class = []
class_miou = 0
for i in range(len(class_intersection_meter)):
class_iou = class_intersection_meter[i] / (class_union_meter[i] + 1e-10)
class_iou_class.append(class_iou)
class_miou += class_iou
class_miou = class_miou * 1.0 / len(class_intersection_meter)
logger.info('meanIoU---Val result: mIoU {:.4f}.'.format(class_miou)) # final
logger.info('<<<<<<< Novel Results <<<<<<<')
for i in range(split_gap):
logger.info('Class_{} Result: iou {:.4f}.'.format(i + 1, class_iou_class[i]))
logger.info('FBIoU---Val result: FBIoU {:.4f}.'.format(mIoU))
for i in range(args.classes):
logger.info('Class_{} Result: iou_f {:.4f}.'.format(i, iou_class[i]))
logger.info('<<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<')
print('total time: {:.4f}, avg inference time: {:.4f}, count: {}'.format(val_time, model_time.avg, test_num))
return mIoU, class_miou, iou_class[1]
if __name__ == '__main__':
main()