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evaluate.py
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evaluate.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 tqdm import tqdm
from networks.CDGNet import Res_Deeplab
from dataset.datasets import LIPDataSet
import os
import torchvision.transforms as transforms
from utils.miou import compute_mean_ioU
from copy import deepcopy
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_ccihp_pallete():
palette = [
120, 120, 120,
127, 0, 0,
254, 0, 0,
0, 84, 0,
169, 0, 50,
254, 84, 0,
255, 0, 84,
0, 118, 220,
84, 84, 0,
0, 84, 84,
84, 50, 0,
51, 85, 127,
0, 127, 0,
0, 0, 254,
50, 169, 220,
0, 254, 254,
84, 254, 169,
169, 254, 84,
254, 254, 0,
254, 169, 0,
102, 254, 0,
182, 255, 0
]
return palette
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 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)
idx = 0
interp = torch.nn.Upsample(size=(input_size[0], input_size[1]), mode='bilinear', align_corners=True)
with torch.no_grad():
loop = tqdm(valloader, position = 0, leave = True)
for index, batch in enumerate(valloader):
image, meta = batch
num_images = image.size(0)
if index % 100 == 0:
print('%d processed' % (index * num_images))
c = meta['center'].numpy()
s = meta['scale'].numpy()
scales[idx:idx + num_images, :] = s[:, :]
centers[idx:idx + num_images, :] = c[:, :]
#====================================================================================
org_img = image.numpy()
normal_img = org_img
flipped_img = org_img[:,:,:,::-1]
fused_img = np.concatenate( (normal_img,flipped_img), axis=0 )
with torch.no_grad():
outputs = model( torch.from_numpy(fused_img).cuda())
prediction = interp( outputs[0][-1].cpu()).data.numpy().transpose(0, 2, 3, 1) #N,H,W,C
single_out = prediction[:num_images,:,:,:]
single_out_flip = np.zeros( single_out.shape )
single_out_tmp = prediction[num_images:, :,:,:]
for c in range(14):
single_out_flip[:,:, :, c] = single_out_tmp[:, :, :, c]
single_out_flip[:, :, :, 14] = single_out_tmp[:, :, :, 15]
single_out_flip[:, :, :, 15] = single_out_tmp[:, :, :, 14]
single_out_flip[:, :, :, 16] = single_out_tmp[:, :, :, 17]
single_out_flip[:, :, :, 17] = single_out_tmp[:, :, :, 16]
single_out_flip[:, :, :, 18] = single_out_tmp[:, :, :, 19]
single_out_flip[:, :, :, 19] = single_out_tmp[:, :, :, 18]
single_out_flip = single_out_flip[:, :, ::-1, :]
# Fuse two outputs
single_out = ( single_out+single_out_flip ) / 2
parsing_preds[idx:idx + num_images, :, :] = np.asarray(np.argmax(single_out, axis=3), dtype=np.uint8)
#====================================================================================
# outputs = model(image.cuda())
# if gpus > 1:
# for output in outputs:
# parsing = output[0][-1]
# nums = len(parsing)
# parsing = interp(parsing).data.cpu().numpy()
# parsing = parsing.transpose(0, 2, 3, 1) # NCHW NHWC
# parsing_preds[idx:idx + nums, :, :] = np.asarray(np.argmax(parsing, axis=3), dtype=np.uint8)
# idx += nums
# else:
# parsing = outputs[0][-1]
# parsing = interp(parsing).data.cpu().numpy()
# parsing = parsing.transpose(0, 2, 3, 1) # NCHW NHWC
# parsing_preds[idx:idx + num_images, :, :] = np.asarray(np.argmax(parsing, axis=3), dtype=np.uint8)
idx += num_images
parsing_preds = parsing_preds[:num_samples, :, :]
print(f'Parsing preds: {parsing_preds.shape}')
return parsing_preds, fused_img, scales, centers
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 = LIPDataSet(args.data_dir, 'val', crop_size=input_size, transform=transform)
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 = 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')
# 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'))
#=================================================================
mIoU = compute_mean_ioU(parsing_preds, scales, centers, args.num_classes, args.data_dir, input_size)
print(mIoU)
if __name__ == '__main__':
main()