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generate_plabel_cityscapes_SYNTHIA.py
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import argparse
import scipy
from scipy import ndimage
import numpy as np
import sys
import re
from packaging import version
import torch
from torch.autograd import Variable
import torchvision.models as models
import torch.nn.functional as F
from torch.utils import data, model_zoo
from model.deeplab import Res_Deeplab
from model.deeplab_multi import DeeplabMulti
from model.deeplab_vgg import DeeplabVGG
from dataset.cityscapes_dataset import cityscapesDataSet
from collections import OrderedDict
import os
from PIL import Image
from utils.tool import fliplr
import matplotlib.pyplot as plt
import torch.nn as nn
import yaml
torch.backends.cudnn.benchmark=True
IMG_MEAN = np.array((104.00698793,116.66876762,122.67891434), dtype=np.float32)
DATA_DIRECTORY = './data/Cityscapes/data'
DATA_LIST_PATH = './dataset/cityscapes_list/train.txt'
SAVE_PATH = './data/Cityscapes/data/pseudo_SYNTHIA/train'
if not os.path.isdir('./data/Cityscapes/data/pseudo_SYNTHIA/'):
os.mkdir('./data/Cityscapes/data/pseudo_SYNTHIA/')
os.mkdir(SAVE_PATH)
IGNORE_LABEL = 255
NUM_CLASSES = 19
NUM_STEPS = 2975 # Number of images in the validation set.
RESTORE_FROM = 'http://vllab.ucmerced.edu/ytsai/CVPR18/GTA2Cityscapes_multi-ed35151c.pth'
RESTORE_FROM_VGG = 'http://vllab.ucmerced.edu/ytsai/CVPR18/GTA2Cityscapes_vgg-ac4ac9f6.pth'
RESTORE_FROM_ORC = 'http://vllab1.ucmerced.edu/~whung/adaptSeg/cityscapes_oracle-b7b9934.pth'
SET = 'train' # We generate pseudo label for training set
MODEL = 'DeeplabMulti'
palette = [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]
zero_pad = 256 * 3 - len(palette)
for i in range(zero_pad):
palette.append(0)
def colorize_mask(mask):
# mask: numpy array of the mask
new_mask = Image.fromarray(mask.astype(np.uint8)).convert('P')
new_mask.putpalette(palette)
return new_mask
def get_arguments():
"""Parse all the arguments provided from the CLI.
Returns:
A list of parsed arguments.
"""
parser = argparse.ArgumentParser(description="DeepLab-ResNet Network")
parser.add_argument("--model", type=str, default=MODEL,
help="Model Choice (DeeplabMulti/DeeplabVGG/Oracle).")
parser.add_argument("--data-dir", type=str, default=DATA_DIRECTORY,
help="Path to the directory containing the Cityscapes dataset.")
parser.add_argument("--data-list", type=str, default=DATA_LIST_PATH,
help="Path to the file listing the images in the dataset.")
parser.add_argument("--ignore-label", type=int, default=IGNORE_LABEL,
help="The index of the label to ignore during the training.")
parser.add_argument("--num-classes", type=int, default=NUM_CLASSES,
help="Number of classes to predict (including background).")
parser.add_argument("--restore-from", type=str, default=RESTORE_FROM,
help="Where restore model parameters from.")
parser.add_argument("--gpu", type=int, default=0,
help="choose gpu device.")
parser.add_argument("--batchsize", type=int, default=12,
help="choose gpu device.")
parser.add_argument("--set", type=str, default=SET,
help="choose evaluation set.")
parser.add_argument("--save", type=str, default=SAVE_PATH,
help="Path to save result.")
return parser.parse_args()
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
config_path = os.path.join(os.path.dirname(args.restore_from),'opts.yaml')
with open(config_path, 'r') as stream:
config = yaml.load(stream)
args.model = config['model']
print('ModelType:%s'%args.model)
print('NormType:%s'%config['norm_style'])
gpu0 = args.gpu
batchsize = args.batchsize
model_name = os.path.basename( os.path.dirname(args.restore_from) )
#args.save += model_name
if not os.path.exists(args.save):
os.makedirs(args.save)
if args.model == 'DeepLab':
model = DeeplabMulti(num_classes=args.num_classes, use_se = config['use_se'], train_bn = False, norm_style = config['norm_style'])
elif args.model == 'Oracle':
model = Res_Deeplab(num_classes=args.num_classes)
if args.restore_from == RESTORE_FROM:
args.restore_from = RESTORE_FROM_ORC
elif args.model == 'DeeplabVGG':
model = DeeplabVGG(num_classes=args.num_classes)
if args.restore_from == RESTORE_FROM:
args.restore_from = RESTORE_FROM_VGG
if args.restore_from[:4] == 'http' :
saved_state_dict = model_zoo.load_url(args.restore_from)
else:
saved_state_dict = torch.load(args.restore_from)
try:
model.load_state_dict(saved_state_dict)
except:
model = torch.nn.DataParallel(model)
model.load_state_dict(saved_state_dict)
model = torch.nn.DataParallel(model)
model.eval()
model.cuda(gpu0)
testloader = data.DataLoader(cityscapesDataSet(args.data_dir, args.data_list, crop_size=(512, 1024), resize_size=(1024, 512), mean=IMG_MEAN, scale=False, mirror=False, set=args.set),
batch_size=batchsize, shuffle=False, pin_memory=True, num_workers=4)
scale = 1.25
testloader2 = data.DataLoader(cityscapesDataSet(args.data_dir, args.data_list, crop_size=(round(512*scale), round(1024*scale) ), resize_size=( round(1024*scale), round(512*scale)), mean=IMG_MEAN, scale=False, mirror=False, set=args.set),
batch_size=batchsize, shuffle=False, pin_memory=True, num_workers=4)
if version.parse(torch.__version__) >= version.parse('0.4.0'):
interp = nn.Upsample(size=(1024, 2048), mode='bilinear', align_corners=True)
else:
interp = nn.Upsample(size=(1024, 2048), mode='bilinear')
sm = torch.nn.Softmax(dim = 1)
for index, img_data in enumerate(zip(testloader, testloader2) ):
batch, batch2 = img_data
image, _, _, name = batch
image2, _, _, name2 = batch2
print(image.shape)
inputs = image.cuda()
inputs2 = image2.cuda()
print('\r>>>>Extracting feature...%04d/%04d'%(index*batchsize, NUM_STEPS), end='')
if args.model == 'DeepLab':
with torch.no_grad():
output1, output2 = model(inputs)
output_batch = interp(sm(0.5* output1 + output2))
output1, output2 = model(fliplr(inputs))
output1, output2 = fliplr(output1), fliplr(output2)
output_batch += interp(sm(0.5 * output1 + output2))
del output1, output2, inputs
output1, output2 = model(inputs2)
output_batch += interp(sm(0.5* output1 + output2))
output1, output2 = model(fliplr(inputs2))
output1, output2 = fliplr(output1), fliplr(output2)
output_batch += interp(sm(0.5 * output1 + output2))
del output1, output2, inputs2
output_batch = sm(output_batch) #new add
output_batch = output_batch.cpu().data.numpy()
elif args.model == 'DeeplabVGG' or args.model == 'Oracle':
output_batch = model(Variable(image).cuda())
output_batch = interp(output_batch).cpu().data.numpy()
#output_batch = output_batch.transpose(0,2,3,1)
#output_batch = np.asarray(np.argmax(output_batch, axis=3), dtype=np.uint8)
output_batch = output_batch.transpose(0,2,3,1)
score_batch = np.max(output_batch, axis=3)
score_batch = np.asarray(np.round(score_batch*100), dtype=np.uint8)
output_batch = np.asarray(np.argmax(output_batch, axis=3), dtype=np.uint8)
output_batch[score_batch<3.6] = 255 #3.6 = 4*0.9
for i in range(output_batch.shape[0]):
output = output_batch[i,:,:]
score = score_batch[i,:,:]
output_col = colorize_mask(output)
output = Image.fromarray(output)
score = Image.fromarray(score)
name_tmp = name[i].split('/')[-1]
dir_name = name[i].split('/')[-2]
save_path = args.save + '/' + dir_name
#save_path = re.replace(save_path, 'leftImg8bit', 'pseudo')
#print(save_path)
if not os.path.isdir(save_path):
os.mkdir(save_path)
output.save('%s/%s' % (save_path, name_tmp))
score.save('%s/%s_score.png' % (save_path, name_tmp))
print('%s/%s' % (save_path, name_tmp))
output_col.save('%s/%s_color.png' % (save_path, name_tmp.split('.')[0]))
return args.save
if __name__ == '__main__':
with torch.no_grad():
save_path = main()
#os.system('python compute_iou.py ./data/Cityscapes/data/gtFine/train %s'%save_path)