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test.py
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test.py
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import sys
import os
import argparse
import time
import cjson
import threading
sys.path.append(os.path.abspath("caffe-fm/python"))
sys.path.append(os.path.abspath("python_layers"))
sys.path.append(os.getcwd())
import caffe
from IPython import embed
import numpy as np
import setproctitle
import cv2
from pycocotools.cocoeval import COCOeval
from alchemy.utils.image import resize_blob, visualize_masks
from alchemy.utils.timer import Timer
from alchemy.utils.mask import encode, crop, iou
from alchemy.utils.load_config import load_config
from alchemy.utils.progress_bar import printProgress
import config
import utils
from utils import gen_masks
from config import *
'''
python test.py gpu_id model [--debug=False] [--init_weights=*.caffemodel] [--useDet=False] \
[--test_num=5000] [--dataset=val2014] [--debug=False] [--end=5000] \
'''
def parse_args():
parser = argparse.ArgumentParser('train net')
parser.add_argument('gpu_id', type=int)
parser.add_argument('model', type=str)
parser.add_argument('--useCats', dest='useCats', type=str, default='False')
parser.add_argument('--debug', dest='debug', type=str, default='False')
parser.add_argument('--init_weights', dest='init_weights', type=str,
default=None)
parser.add_argument('--dataset', dest='dataset', type=str,
default='val2014')
parser.add_argument('--end', dest='end', type=int, default=5000)
args = parser.parse_args()
args.useCats = args.useCats == 'True'
args.debug = args.debug == 'True'
return args
if __name__ == '__main__':
args = parse_args()
caffe.set_mode_gpu()
caffe.set_device(int(args.gpu_id))
setproctitle.setproctitle(args.model)
net = caffe.Net(
'models/' + args.model + ".test.prototxt",
'params/' + args.init_weights,
caffe.TEST)
# surgeries
interp_layers = [layer for layer in net.params.keys() if 'up' in layer]
utils.interp(net, interp_layers)
if os.path.exists("configs/%s.json" % args.model):
load_config("configs/%s.json" % args.model)
else:
print "Specified config does not exists, use the default config..."
time.sleep(2)
timer = Timer()
config.ANNOTATION_TYPE = args.dataset
config.IMAGE_SET = "val2014"
from spiders.coco_ssm_spider import COCOSSMDemoSpider
spider = COCOSSMDemoSpider()
spider.dataset.sort(key=lambda item: int(item.image_path[-10:-4]))
ds = spider.dataset[:args.end]
timer.tic()
results = []
for i in range(len(ds)):
spider.fetch()
img = spider.img_blob
image_id = int(ds[i].image_path[-10:-4])
# gen mask
ret = gen_masks(net, img, config,
dest_shape=(spider.origin_height, spider.origin_width),
image=args.debug)
if args.useCats:
ret_masks, ret_scores, ret_cats = ret
else:
ret_masks, ret_scores = ret
printProgress(i, len(ds), prefix='Progress: ', suffix='Complete', barLength=50)
for _ in range(len(ret_masks)):
cat = 1
if args.useCats:
cat = spider.dataset.getCatIds()[int(ret_cats[_].argmax())]
score = float(ret_cats[_].max())
else:
score = float(ret_scores[_])
objn = float(ret_scores[_])
results.append({
'image_id': image_id,
'category_id': cat,
'segmentation': encode(ret_masks[_]),
'score': score,
'objn': objn
})
with open('results/%s.json' % args.model, "wb") as f:
f.write(cjson.encode(results))