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source_uncertain_tester.py
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source_uncertain_tester.py
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import argparse
import json
import os
from pprint import pprint
import matplotlib
import numpy as np
import torch
from PIL import Image
from torch.autograd import Variable
from torch.utils import data
from tqdm import tqdm
matplotlib.use('Agg')
from argmyparse import add_additional_params_to_args
from datasets import get_dataset, AVAILABLE_DATASET_LIST
from loss import CrossEntropyLoss2d
from models.model_util import get_full_uncertain_model
from transform import get_img_transform, get_lbl_transform
from util import mkdir_if_not_exist, save_dic_to_json, check_if_done, save_colorized_lbl, exec_eval, calc_entropy, \
get_class_weight_from_file, set_debugger_org_frc
from matplotlib import pyplot as plt
set_debugger_org_frc()
parser = argparse.ArgumentParser(description='Adapt tester for validation data')
parser.add_argument('tgt_dataset', type=str, choices=AVAILABLE_DATASET_LIST)
parser.add_argument('--split', type=str, default='val', help="'val' or 'test') is used")
parser.add_argument('trained_checkpoint', type=str, metavar="PTH")
parser.add_argument('--outdir', type=str, default="test_output",
help='output directory')
parser.add_argument('--test_img_shape', default=None, nargs=2,
help="W H, FOR Valid(2048, 1024) Test(1280, 720)")
parser.add_argument("---saves_prob", action="store_true",
help='whether you save probability tensors')
args = parser.parse_args()
args = add_additional_params_to_args(args)
if not os.path.exists(args.trained_checkpoint):
raise OSError("%s does not exist!" % args.resume)
checkpoint = torch.load(args.trained_checkpoint)
try:
train_args = checkpoint['args'] # Load args!
except KeyError:
from easydict import EasyDict as edict
train_args = edict(json.load(open("train_output/city_only_4ch/param_res152.json", 'r')))
weight = get_class_weight_from_file(n_class=train_args.n_class, weight_filename=train_args.loss_weights_file,
add_bg_loss=train_args.add_bg_loss)
if torch.cuda.is_available():
weight = weight.cuda()
criterion = CrossEntropyLoss2d(weight)
# model = get_full_model(train_args.net, train_args.res, train_args.n_class, train_args.input_ch)
model = get_full_uncertain_model(net=train_args.net, res=train_args.res, n_class=train_args.n_class,
input_ch=train_args.input_ch,
n_dropout=train_args.n_dropout, criterion=criterion, is_data_parallel=False)
try:
model.load_state_dict(checkpoint['state_dict'])
except:
model.load_state_dict(checkpoint)
print ("----- train args ------")
pprint(train_args.__dict__, indent=4)
print ("-" * 50)
args.train_img_shape = train_args.train_img_shape
print("=> loaded checkpoint '{}'".format(args.trained_checkpoint))
indir, infn = os.path.split(args.trained_checkpoint)
trained_mode = indir.split(os.path.sep)[-2]
args.mode = "%s---%s-%s" % (trained_mode, args.tgt_dataset, args.split)
model_name = infn.replace(".pth", "")
base_outdir = os.path.join(args.outdir, args.mode, model_name)
mkdir_if_not_exist(base_outdir)
json_fn = os.path.join(base_outdir, "param.json")
check_if_done(json_fn)
args.machine = os.uname()[1]
save_dic_to_json(args.__dict__, json_fn)
train_img_shape = tuple([int(x) for x in args.train_img_shape])
test_img_shape = tuple([int(x) for x in args.test_img_shape])
if "crop_size" in train_args.__dict__.keys() and train_args.crop_size > 0:
train_img_shape = test_img_shape
print ("train_img_shape was set to the same as test_img_shape")
if "normalize_way" in train_args.__dict__.keys():
img_transform = get_img_transform(img_shape=train_img_shape, normalize_way=train_args.normalize_way)
else:
img_transform = get_img_transform(img_shape=train_img_shape)
if "background_id" in train_args.__dict__.keys():
label_transform = get_lbl_transform(img_shape=train_img_shape, n_class=train_args.n_class,
background_id=train_args.background_id)
else:
label_transform = get_lbl_transform(img_shape=train_img_shape, n_class=train_args.n_class)
tgt_dataset = get_dataset(dataset_name=args.tgt_dataset, split=args.split, img_transform=img_transform,
label_transform=label_transform, test=True, input_ch=train_args.input_ch)
target_loader = data.DataLoader(tgt_dataset, batch_size=1, pin_memory=True)
if torch.cuda.is_available():
model.cuda()
model.eval()
total_ent = 0.
for index, (imgs, labels, paths) in tqdm(enumerate(target_loader)):
path = paths[0]
imgs = Variable(imgs)
if torch.cuda.is_available():
imgs = imgs.cuda()
preds, std = model(imgs)
total_ent += calc_entropy(preds).data.cpu().numpy()[0]
if train_args.net == "psp":
preds = preds[0]
if args.saves_prob:
# Save probability tensors
prob_outdir = os.path.join(base_outdir, "prob")
mkdir_if_not_exist(prob_outdir)
prob_outfn = os.path.join(prob_outdir, path.split('/')[-1].replace('png', 'npy'))
np.save(prob_outfn, preds[0].data.cpu().numpy())
# Save predicted pixel labels(pngs)
if train_args.add_bg_loss:
pred = preds[0, :train_args.n_class].data.max(0)[1].cpu()
else:
pred = preds[0, :train_args.n_class - 1].data.max(0)[1].cpu()
img = Image.fromarray(np.uint8(pred.numpy()))
img = img.resize(test_img_shape, Image.NEAREST)
label_outdir = os.path.join(base_outdir, "label")
mkdir_if_not_exist(label_outdir)
label_fn = os.path.join(label_outdir, path.split('/')[-1])
img.save(label_fn)
# Save visualized predicted pixel labels(pngs)
vis_outdir = os.path.join(base_outdir, "vis")
mkdir_if_not_exist(vis_outdir)
vis_fn = os.path.join(vis_outdir, path.split('/')[-1])
save_colorized_lbl(img, vis_fn, args.tgt_dataset)
# Save std
aleatoric_uncertainty_outdir = os.path.join(base_outdir, "aleatoric_uncertainty")
mkdir_if_not_exist(aleatoric_uncertainty_outdir)
aleatoric_uncertainty_fn = os.path.join(aleatoric_uncertainty_outdir, path.split('/')[-1])
std_im = std.data.cpu().numpy()[0]
# std_im = std_im.transpose([1, 2, 0]).sum(axis=2)
# std_im = std_im[0]
plt.imshow(std_im, cmap="jet")
plt.savefig(aleatoric_uncertainty_fn)
exec_eval(args.tgt_dataset, label_outdir)
ave_ent = total_ent / len(target_loader)
print ("average entropy: %s" % ave_ent)
with open(os.path.join(base_outdir, "ave_ent_%s.txt" % ave_ent), "w") as f:
f.write(str(ave_ent))