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@A-Kerim
A-Kerim authored Oct 5, 2022
1 parent 921ba61 commit 00b4c76
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Showing 2 changed files with 45 additions and 54 deletions.
12 changes: 6 additions & 6 deletions datasets/AWSS.py
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Original file line number Diff line number Diff line change
Expand Up @@ -96,10 +96,10 @@ def get_images_paths(self,image_dir, percentage=100, nTrain_samples=None, params
for filename in glob.iglob(image_dir + '/**/RGB_Shaky_Fr_*.png', recursive=True):
if filename.find("RGB_Shaky_Fr_") != -1:
# ------------------------------
img = filename.replace('/home/kerim/Silver_Project/Silver_Recordings/', '')
img = filename.replace('/home/kerim/Silver_Project/AWSS/', '')
temp = img.split('/')
seq_name, frame_name = temp[0], temp[3]
xml_path = '/home/kerim/Silver_Project/Silver_Recordings/' + seq_name + '/Annotations/Textual/TextualInfo.xml'
xml_path = '/home/kerim/Silver_Project/AWSS/' + seq_name + '/Annotations/Textual/TextualInfo.xml'

root_node = ET.parse(xml_path).getroot()

Expand Down Expand Up @@ -169,8 +169,8 @@ def __init__(self, root, split='train', mode='fine', target_type='semantic', tra

FLAG = True#for HRNet
if split =="test" and FLAG:
print("".join("{} {}\n".format(x.replace("/home/kerim/Silver_Project/Silver_Recordings/",''),
y.replace("/home/kerim/Silver_Project/Silver_Recordings/",'')) for x, y in zip(self.images, self.targets)))
print("".join("{} {}\n".format(x.replace("/home/kerim/Silver_Project/AWSS/",''),
y.replace("/home/kerim/Silver_Project/AWSS/",'')) for x, y in zip(self.images, self.targets)))

pass
def __len__(self):
Expand Down Expand Up @@ -198,10 +198,10 @@ def __getitem__(self, index):

# get the weather and time labels
# ------------------------------
img = self.images[index].replace('/home/kerim/Silver_Project/Silver_Recordings/', '')
img = self.images[index].replace('/home/kerim/Silver_Project/AWSS/', '')
temp = img.split('/')
seq_name, frame_name = temp[0], temp[3]
xml_path = '/home/kerim/Silver_Project/Silver_Recordings/' + seq_name + '/Annotations/Textual/TextualInfo.xml'
xml_path = '/home/kerim/Silver_Project/AWSS/' + seq_name + '/Annotations/Textual/TextualInfo.xml'

root_node = ET.parse(xml_path).getroot()

Expand Down
87 changes: 39 additions & 48 deletions main.py
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Original file line number Diff line number Diff line change
@@ -1,22 +1,25 @@
# The model is weather and time aware!
# This models is trained on: AWSS + CS 1:1
# low level features i.e module.backbone.low_level_features (Atrous Conv.) are frozen when training on CS
# Multi-task learning two losses Segmentation loss and weather_time loss, propagated separtely.
# weather awareness just of the Atrous Convolution
# ------------------------
# Please note that our code is based on DeepLabV3+ pytorch implementation.
# --------------------------
import torch
import torch.nn as nn
import numpy as np
import random
import os
from tqdm import tqdm
import network
import utils
import os
import random
import argparse
import numpy as np

from torch.utils import data

# from New_Exps.Ours.network.utils import Weather_Classifier
from datasets import VOCSegmentation, Cityscapes, ACDC, AWSS
from datasets import Cityscapes, ACDC, AWSS
from utils import ext_transforms as et
from metrics import StreamSegMetrics

import torch
import torch.nn as nn
from utils.visualizer import Visualizer

from PIL import Image
import matplotlib
import matplotlib.pyplot as plt
Expand Down Expand Up @@ -45,7 +48,7 @@ def get_argparser():
parser.add_argument("--output_stride", type=int, default=16, choices=[8, 16])

# Train Options
parser.add_argument("--test_only", action='store_true', default=False)#Change from False to True Kerim
parser.add_argument("--test_only", action='store_true', default=False)
parser.add_argument("--save_val_results", action='store_true', default=True,
help="save segmentation results to \"./results\"")
parser.add_argument("--total_itrs", type=int, default=30e3,
Expand Down Expand Up @@ -254,15 +257,12 @@ def main(ACDC_test_class = None,n_itrs=10000000,MODE=12345):
opts.ACDC_test_class = ACDC_test_class
opts.finetune = False
opts.pretrained_model = None
# MODE = 0#train on cityscapes
# MODE = 1#finetune pretrained cityscapes on AWSS
# MODE = 10#test on cityscapes
# MODE = 0#train on cityscapes and AWSS
# MODE = 11#test on cityscapes
# MODE = 20#test on acdc
# MODE = 21 # test on acdc
opts.data_root_cs = "/home/kerim/DataSets/SemanticSegmentation/cityscapes"
opts.data_root_acdc = "/home/kerim/DataSets/SemanticSegmentation/ACDC"
opts.data_root_awss = "/home/kerim/Silver_Project/Silver_Recordings"
# MODE = 21#test on acdc
opts.data_root_cs = "/home/kerim/DataSets/SemanticSegmentation/cityscapes"#Update as necessary
opts.data_root_acdc = "/home/kerim/DataSets/SemanticSegmentation/ACDC"#Update as necessary
opts.data_root_awss = "/home/kerim/Silver_Project/AWSS"#Update as necessary
opts.total_itrs = n_itrs
opts.test_class = None
opts.val_batch_size = 8
Expand Down Expand Up @@ -293,9 +293,6 @@ def main(ACDC_test_class = None,n_itrs=10000000,MODE=12345):
opts.batch_size = 4
opts.output_stride = 16
opts.crop_val = True
# opts.val_interval = 2

# opts.num_classes = 11


if opts.dataset.lower() == 'voc':
Expand Down Expand Up @@ -599,7 +596,6 @@ def save_ckpt(path):
if cur_itrs >= opts.total_itrs:
return

# The model is ours V01
if __name__ == '__main__':

ACDC_classes = ['rain','fog','snow','night']
Expand All @@ -613,47 +609,42 @@ def save_ckpt(path):
exit()
main(MODE=MODE)

# --------------------------------------------------------------- V02 --------------
# V02: The model is weather and time aware!
# This models is trained on: AWSS + CS 1:1
# low level features i.e module.backbone.low_level_features (Atrous Conv.) are frozen when training on CS
# Multi-task learning two losses Segmentation loss and weather_time loss, propagated separtely.
# weather awareness just of the Atrous Convolution
# --------------------------------------------------------------- CS
# Expected Output
# =================
# Cityscapes
# ------------
# Overall Acc: 0.939875
# Mean Acc: 0.825837
# FreqW Acc: 0.890178
# Mean IoU: 0.746920
#
# [0.95335767 0.72550871 0.88743945 0.50785172 0.44274712 0.60158601 0.87598127 0.87561615 0.70870125 0.89040882]
# ------------------------------------------------------------------- ACDC

# Per-class IoU: [0.95335767 0.72550871 0.88743945 0.50785172 0.44274712 0.60158601 0.87598127 0.87561615 0.70870125 0.89040882]
# -------------------------------------------------------------------
# ACDC
# ------------
# ACDC (Rain)
# Overall Acc: 0.877963
# Mean Acc: 0.667648
# FreqW Acc: 0.791779
# Mean IoU: 0.566379
#
# [0.76403344 0.36642211 0.72458654 0.31330346 0.32441966 0.40964191 0.81764442 0.9160704 0.40519023 0.62248053]

# Per-class IoU: [0.76403344 0.36642211 0.72458654 0.31330346 0.32441966 0.40964191 0.81764442 0.9160704 0.40519023 0.62248053]
# ---
# ACDC (Fog)
# Overall Acc: 0.899750
# Mean Acc: 0.697483
# FreqW Acc: 0.826015
# Mean IoU: 0.599675
#
# [0.89968568 0.61364265 0.72526159 0.30592753 0.36195622 0.40860551 0.82659963 0.91106372 0.34895574 0.59505032]

# Per-class IoU: [0.89968568 0.61364265 0.72526159 0.30592753 0.36195622 0.40860551 0.82659963 0.91106372 0.34895574 0.59505032]
# ---
# ACDC (Snow)
# Overall Acc: 0.813452
# Mean Acc: 0.597240
# FreqW Acc: 0.690171
# Mean IoU: 0.502845
#
# [0.72744211 0.27268236 0.63459407 0.28343183 0.2544351 0.42002753 0.75314912 0.75577437 0.34386931 0.58304499]

# Per-class IoU: [0.72744211 0.27268236 0.63459407 0.28343183 0.2544351 0.42002753 0.75314912 0.75577437 0.34386931 0.58304499]
# ---
# ACDC (Night)
# Overall Acc: 0.589835
# Mean Acc: 0.365503
# FreqW Acc: 0.423166
# Mean IoU: 0.271261
#
# [0.75924663 0.34913799 0.43090425 0.08629936 0.10791564 0.08757231 0.37398767 0.04645397 0.18224199 0.28884528]


# Per-class IoU: [0.75924663 0.34913799 0.43090425 0.08629936 0.10791564 0.08757231 0.37398767 0.04645397 0.18224199 0.28884528]

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