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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
"""
@Author : Peike Li
@Contact : peike.li@yahoo.com
@File : evaluate.py
@Time : 8/4/19 3:36 PM
@Desc :
@License : This source code is licensed under the license found in the
LICENSE file in the root directory of this source tree.
"""
import os
import argparse
import numpy as np
import torch
from torch.utils import data
from tqdm import tqdm
from PIL import Image as PILImage
import torchvision.transforms as transforms
import torch.backends.cudnn as cudnn
import networks
from datasets.datasets import LIPDataValSet
from utils.miou import compute_mean_ioU
from utils.transforms import BGR2RGB_transform
from utils.transforms import transform_parsing
def get_arguments():
"""Parse all the arguments provided from the CLI.
Returns:
A list of parsed arguments.
"""
parser = argparse.ArgumentParser(description="Self Correction for Human Parsing")
# Network Structure
parser.add_argument("--arch", type=str, default='resnet101')
# Data Preference
parser.add_argument("--data-dir", type=str, default='./data/LIP')
parser.add_argument("--batch-size", type=int, default=1)
parser.add_argument("--input-size", type=str, default='473,473')
parser.add_argument("--num-classes", type=int, default=20)
parser.add_argument("--ignore-label", type=int, default=255)
parser.add_argument("--random-mirror", action="store_true")
parser.add_argument("--random-scale", action="store_true")
# Evaluation Preference
parser.add_argument("--log-dir", type=str, default='./log')
parser.add_argument("--model-restore", type=str, default='./log/checkpoint.pth.tar')
parser.add_argument("--gpu", type=str, default='0', help="choose gpu device.")
parser.add_argument("--save-results", action="store_true", help="whether to save the results.")
parser.add_argument("--flip", action="store_true", help="random flip during the test.")
parser.add_argument("--multi-scales", type=str, default='1', help="multiple scales during the test")
return parser.parse_args()
def get_palette(num_cls):
""" Returns the color map for visualizing the segmentation mask.
Args:
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 multi_scale_testing(model, batch_input_im, crop_size=[473, 473], flip=True, multi_scales=[1]):
flipped_idx = (15, 14, 17, 16, 19, 18)
if len(batch_input_im.shape) > 4:
batch_input_im = batch_input_im.squeeze()
if len(batch_input_im.shape) == 3:
batch_input_im = batch_input_im.unsqueeze(0)
interp = torch.nn.Upsample(size=crop_size, mode='bilinear', align_corners=True)
ms_outputs = []
for s in multi_scales:
interp_im = torch.nn.Upsample(scale_factor=s, mode='bilinear', align_corners=True)
scaled_im = interp_im(batch_input_im)
parsing_output = model(scaled_im)
parsing_output = parsing_output[0][-1]
output = parsing_output[0]
if flip:
flipped_output = parsing_output[1]
flipped_output[14:20, :, :] = flipped_output[flipped_idx, :, :]
output += flipped_output.flip(dims=[-1])
output *= 0.5
output = interp(output.unsqueeze(0))
ms_outputs.append(output[0])
ms_fused_parsing_output = torch.stack(ms_outputs)
ms_fused_parsing_output = ms_fused_parsing_output.mean(0)
ms_fused_parsing_output = ms_fused_parsing_output.permute(1, 2, 0) # HWC
parsing = torch.argmax(ms_fused_parsing_output, dim=2)
parsing = parsing.data.cpu().numpy()
ms_fused_parsing_output = ms_fused_parsing_output.data.cpu().numpy()
return parsing, ms_fused_parsing_output
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
multi_scales = [float(i) for i in args.multi_scales.split(',')]
gpus = [int(i) for i in args.gpu.split(',')]
assert len(gpus) == 1
if not args.gpu == 'None':
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
cudnn.benchmark = True
cudnn.enabled = True
h, w = map(int, args.input_size.split(','))
input_size = [h, w]
model = networks.init_model(args.arch, num_classes=args.num_classes, pretrained=None)
IMAGE_MEAN = model.mean
IMAGE_STD = model.std
INPUT_SPACE = model.input_space
print('image mean: {}'.format(IMAGE_MEAN))
print('image std: {}'.format(IMAGE_STD))
print('input space:{}'.format(INPUT_SPACE))
if INPUT_SPACE == 'BGR':
print('BGR Transformation')
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=IMAGE_MEAN,
std=IMAGE_STD),
])
if INPUT_SPACE == 'RGB':
print('RGB Transformation')
transform = transforms.Compose([
transforms.ToTensor(),
BGR2RGB_transform(),
transforms.Normalize(mean=IMAGE_MEAN,
std=IMAGE_STD),
])
# Data loader
lip_test_dataset = LIPDataValSet(args.data_dir, 'val', crop_size=input_size, transform=transform, flip=args.flip)
num_samples = len(lip_test_dataset)
print('Totoal testing sample numbers: {}'.format(num_samples))
testloader = data.DataLoader(lip_test_dataset, batch_size=args.batch_size, shuffle=False, pin_memory=True)
# Load model weight
state_dict = torch.load(args.model_restore)['state_dict']
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
model.load_state_dict(new_state_dict)
model.cuda()
model.eval()
sp_results_dir = os.path.join(args.log_dir, 'sp_results')
if not os.path.exists(sp_results_dir):
os.makedirs(sp_results_dir)
palette = get_palette(20)
parsing_preds = []
scales = np.zeros((num_samples, 2), dtype=np.float32)
centers = np.zeros((num_samples, 2), dtype=np.int32)
with torch.no_grad():
for idx, batch in enumerate(tqdm(testloader)):
image, meta = batch
if (len(image.shape) > 4):
image = image.squeeze()
im_name = meta['name'][0]
c = meta['center'].numpy()[0]
s = meta['scale'].numpy()[0]
w = meta['width'].numpy()[0]
h = meta['height'].numpy()[0]
scales[idx, :] = s
centers[idx, :] = c
parsing, logits = multi_scale_testing(model, image.cuda(), crop_size=input_size, flip=args.flip,
multi_scales=multi_scales)
if args.save_results:
parsing_result = transform_parsing(parsing, c, s, w, h, input_size)
parsing_result_path = os.path.join(sp_results_dir, im_name + '.png')
output_im = PILImage.fromarray(np.asarray(parsing_result, dtype=np.uint8))
output_im.putpalette(palette)
output_im.save(parsing_result_path)
parsing_preds.append(parsing)
assert len(parsing_preds) == num_samples
mIoU = compute_mean_ioU(parsing_preds, scales, centers, args.num_classes, args.data_dir, input_size)
print(mIoU)
return
if __name__ == '__main__':
main()
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