valid_patch.py

from darknet import Darknet
import dataset_patch_eval
import torch
from torch.autograd import Variable
from torchvision import datasets, transforms
from utils import *
import os
import pdb

use_cuda = True
gpus = sys.argv[7]
if use_cuda:
    os.environ['CUDA_VISIBLE_DEVICES'] = gpus

def valid(datacfg, cfgfile, weightfile, outfile, valid_images):
    options = read_data_cfg(datacfg)
    # valid_images = options['valid']   # Changed - passed as command line argument
    # valid_images = sys.argv[4]
    name_list = options['names']

    classname = sys.argv[5]
    result_prefix = sys.argv[6]
    prefix = result_prefix
    names = load_class_names(name_list)

    with open(valid_images) as fp:
        tmp_files = fp.readlines()
        valid_files = [item.rstrip() for item in tmp_files]

    m = Darknet(cfgfile)
    m.print_network()
    m.load_weights(weightfile)
    m.cuda()
    m.eval()

    valid_dataset = dataset_patch_eval.listDataset(valid_images, shape=(m.width, m.height),
                       shuffle=False,
                       transform=transforms.Compose([
                           transforms.ToTensor(),
                       ]))
    valid_batchsize = 2
    assert(valid_batchsize > 1)

    kwargs = {'num_workers': 4, 'pin_memory': True}
    valid_loader = torch.utils.data.DataLoader(
        valid_dataset, batch_size=valid_batchsize, shuffle=False, **kwargs)

    fps = [0]*m.num_classes
    if not os.path.exists(result_prefix):
        os.makedirs(result_prefix)

    # print('results/no_class_overlap/' + classname)
    for i in range(m.num_classes):
        buf = '%s/%s%s.txt' % (prefix, outfile, names[i])
        fps[i] = open(buf, 'w')

    lineId = -1

    conf_thresh = 0.005
    nms_thresh = 0.45
    for batch_idx, (data, target, dummy) in enumerate(valid_loader):  # Changed
        data = data.cuda()
        data = Variable(data, volatile = True)
        output = m(data).data
        batch_boxes = get_region_boxes(output, conf_thresh, m.num_classes, m.anchors, m.num_anchors, 0, 1)
        for i in range(output.size(0)):
            lineId = lineId + 1
            fileId = os.path.basename(valid_files[lineId]).split('.')[0]
            width, height = get_image_size(valid_files[lineId])
            # pdb.set_trace()
            #width, height = 416, 416
            print(valid_files[lineId], width, height)
            boxes = batch_boxes[i]
            boxes = nms(boxes, nms_thresh)
            for box in boxes:
                x1 = (box[0] - box[2]/2.0) * width
                y1 = (box[1] - box[3]/2.0) * height
                x2 = (box[0] + box[2]/2.0) * width
                y2 = (box[1] + box[3]/2.0) * height

                det_conf = box[4]
                for j in range((len(box)-5)//2):
                    cls_conf = box[5+2*j]
                    cls_id = box[6+2*j]
                    prob =det_conf * cls_conf
                    fps[cls_id].write('%s %f %f %f %f %f\n' % (fileId, prob, x1, y1, x2, y2))

    for i in range(m.num_classes):
        fps[i].close()

if __name__ == '__main__':
    import sys
    if len(sys.argv) == 8:
        datacfg = sys.argv[1]
        cfgfile = sys.argv[2]
        weightfile = sys.argv[3]
        outfile = 'comp4_det_test_'
        valid_images = sys.argv[4]
        valid(datacfg, cfgfile, weightfile, outfile, valid_images)
    else:
        print('Usage:')
        print(' python valid_patch.py datacfg cfgfile weightfile valid_images result_prefix classname gpu')