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evaluate_multi.py
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evaluate_multi.py
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import argparse
import numpy as np
import torch
torch.multiprocessing.set_start_method("spawn", force=True)
from torch.utils import data
from networks.CDGNet import Res_Deeplab
from dataset.datasets import LIPDataValSet
import os
import torchvision.transforms as transforms
from utils.miou import compute_mean_ioU
from copy import deepcopy
import cv2
from PIL import Image as PILImage
DATA_DIRECTORY = '/ssd1/liuting14/Dataset/LIP/'
DATA_LIST_PATH = './dataset/list/lip/valList.txt'
IGNORE_LABEL = 255
NUM_CLASSES = 20
SNAPSHOT_DIR = './snapshots/'
INPUT_SIZE = (473,473)
# colour map
COLORS = [(0,0,0)
# 0=background
,(128,0,0),(0,128,0),(128,128,0),(0,0,128),(128,0,128)
# 1=aeroplane, 2=bicycle, 3=bird, 4=boat, 5=bottle
,(0,128,128),(128,128,128),(64,0,0),(192,0,0),(64,128,0)
# 6=bus, 7=car, 8=cat, 9=chair, 10=cow
,(192,128,0),(64,0,128),(192,0,128),(64,128,128),(192,128,128)
# 11=diningtable, 12=dog, 13=horse, 14=motorbike, 15=person
,(0,64,0),(128,64,0),(0,192,0),(128,192,0),(0,64,128)]
# 16=potted plant, 17=sheep, 18=sofa, 19=train, 20=tv/monitor
def get_lip_palette():
palette = [0,0,0,
128,0,0,
255,0,0,
0,85,0,
170,0,51,
255,85,0,
0,0,85,
0,119,221,
85,85,0,
0,85,85,
85,51,0,
52,86,128,
0,128,0,
0,0,255,
51,170,221,
0,255,255,
85,255,170,
170,255,85,
255,255,0,
255,170,0]
return palette
def get_palette(num_cls):
""" Returns the color map for visualizing the segmentation mask.
Inputs:
=num_cls=
Number of classes.
Returns:
The color map.
"""
n = num_cls
palette = [0] * (n * 3)
for j in range(0, n):
lab = j
palette[j * 3 + 0] = 0
palette[j * 3 + 1] = 0
palette[j * 3 + 2] = 0
i = 0
while lab:
palette[j * 3 + 0] |= (((lab >> 0) & 1) << (7 - i))
palette[j * 3 + 1] |= (((lab >> 1) & 1) << (7 - i))
palette[j * 3 + 2] |= (((lab >> 2) & 1) << (7 - i))
i += 1
lab >>= 3
return palette
def get_arguments():
"""Parse all the arguments provided from the CLI.
Returns:
A list of parsed arguments.
"""
parser = argparse.ArgumentParser(description="CE2P Network")
parser.add_argument("--batch-size", type=int, default=1,
help="Number of images sent to the network in one step.")
parser.add_argument("--data-dir", type=str, default=DATA_DIRECTORY,
help="Path to the directory containing the PASCAL VOC dataset.")
parser.add_argument("--dataset", type=str, default='val',
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,
help="Where restore model parameters from.")
parser.add_argument("--gpu", type=str, default='0',
help="choose gpu device.")
parser.add_argument("--input-size", type=str, default=INPUT_SIZE,
help="Comma-separated string with height and width of images.")
return parser.parse_args()
# def scale_image(image, scale):
# image = image[0, :, :, :]
# image = image.transpose((1, 2, 0))
# image = cv2.resize(image, None, fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR)
# image = image.transpose((2, 0, 1))
# return image
def valid(model, valloader, input_size, num_samples, gpus):
model.eval()
parsing_preds = np.zeros((num_samples, input_size[0], input_size[1]),
dtype=np.uint8)
scales = np.zeros((num_samples, 2), dtype=np.float32)
centers = np.zeros((num_samples, 2), dtype=np.int32)
hpreds_lst = []
wpreds_lst = []
idx = 0
interp = torch.nn.Upsample(size=(input_size[0], input_size[1]), mode='bilinear', align_corners=True)
eval_scale=[0.75,1.0,1.25]
# eval_scale=[1.0]
flipped_idx = (15, 14, 17, 16, 19, 18)
with torch.no_grad():
for index, batch in enumerate(valloader):
image, meta = batch
# num_images = image.size(0)
# print( image.size() )
image = image.squeeze()
if index % 10 == 0:
print('%d processd' % (index * 1))
c = meta['center'].numpy()[0]
s = meta['scale'].numpy()[0]
scales[idx, :] = s
centers[idx, :] = c
#====================================================================================
mul_outputs = []
for scale in eval_scale:
interp_img = torch.nn.Upsample(scale_factor=scale, mode='bilinear', align_corners=True)
scaled_img = interp_img( image )
# print( scaled_img.size() )
outputs = model( scaled_img.cuda() )
prediction = outputs[0][-1]
#==========================================================
hPreds = outputs[2][0]
wPreds = outputs[2][1]
hpreds_lst.append( hPreds[0].data.cpu().numpy() )
wpreds_lst.append( wPreds[0].data.cpu().numpy() )
#==========================================================
single_output = prediction[0]
flipped_output = prediction[1]
flipped_output[14:20,:,:]=flipped_output[flipped_idx,:,:]
single_output += flipped_output.flip(dims=[-1])
single_output *=0.5
# print( single_output.size() )
single_output = interp( single_output.unsqueeze(0) )
mul_outputs.append( single_output[0] )
fused_prediction = torch.stack( mul_outputs )
fused_prediction = fused_prediction.mean(0)
fused_prediction = fused_prediction.permute(1, 2, 0) # HWC
fused_prediction = torch.argmax(fused_prediction, dim=2)
fused_prediction = fused_prediction.data.cpu().numpy()
parsing_preds[idx, :, :] = np.asarray(fused_prediction, dtype=np.uint8)
#====================================================================================
idx += 1
parsing_preds = parsing_preds[:num_samples, :, :]
return parsing_preds, scales, centers, hpreds_lst, wpreds_lst
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
os.environ["CUDA_VISIBLE_DEVICES"]=args.gpu
gpus = [int(i) for i in args.gpu.split(',')]
h, w = map(int, args.input_size.split(','))
input_size = (h, w)
model = Res_Deeplab(num_classes=args.num_classes)
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transform = transforms.Compose([
transforms.ToTensor(),
normalize,
])
lip_dataset = LIPDataValSet(args.data_dir, 'val', crop_size=input_size, transform=transform, flip = True )
num_samples = len(lip_dataset)
valloader = data.DataLoader(lip_dataset, batch_size=args.batch_size * len(gpus),
shuffle=False, pin_memory=True)
restore_from = args.restore_from
state_dict = model.state_dict().copy()
state_dict_old = torch.load(restore_from)
for key, nkey in zip(state_dict_old.keys(), state_dict.keys()):
if key != nkey:
# remove the 'module.' in the 'key'
state_dict[key[7:]] = deepcopy(state_dict_old[key])
else:
state_dict[key] = deepcopy(state_dict_old[key])
model.load_state_dict(state_dict)
model.eval()
model.cuda()
parsing_preds, scales, centers, hpredLst, wpredLst = valid(model, valloader, input_size, num_samples, len(gpus))
#=================================================================
# list_path = os.path.join(args.data_dir, args.dataset + '_id.txt')
# val_id = [i_id.strip() for i_id in open(list_path)]
# # pred_root = os.path.join( args.data_dir, 'pred_parsing')
# pred_root = os.path.join( os.getcwd(), 'pred_parsing')
# print( pred_root )
# if not os.path.exists( pred_root ):
# os.makedirs( pred_root )
# palette = get_lip_palette()
# output_parsing = parsing_preds
# for i in range( num_samples ):
# output_image = PILImage.fromarray( output_parsing[i] )
# output_image.putpalette( palette )
# output_image.save( os.path.join( pred_root, str(val_id[i])+'.png'))
# i=0
# for i in range(len( hpredLst )):
# filenameh = os.path.join( pred_root, str( val_id[i] ) + "_h" )
# np.save( filenameh, hpredLst[i] )
# filenamew = os.path.join( pred_root, str( val_id[i] ) + "_w" )
# np.save( filenamew, wpredLst[i] )
#=================================================================
mIoU = compute_mean_ioU(parsing_preds, scales, centers, args.num_classes, args.data_dir, input_size)
print(mIoU)
if __name__ == '__main__':
main()