templates.py

x_template = '''
# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------

import os
from datasets.imdb import imdb
import datasets.ds_utils as ds_utils
import xml.etree.ElementTree as ET
import numpy as np
import scipy.sparse
import scipy.io as sio
import utils.cython_bbox
import cPickle
import subprocess
import uuid

from voc_eval import voc_eval
#+
from MMICC_eval import MMICC_eval #F1 F2
import errno

from fast_rcnn.config import cfg

class MMICC(imdb): #F3
    def __init__(self, image_set, devkit_path): #M
        imdb.__init__(self, image_set) #M

        self._image_set = image_set
        self._devkit_path = devkit_path #M
        self._data_path = os.path.join(self._devkit_path, 'data') #M
        self._classes = ('__background__', # always index 0
                         QYCC) #M F4

        self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))
        self._image_ext = '.jpg'
        self._image_index = self._load_image_set_index()
        # Default to roidb handler
        self._roidb_handler = self.selective_search_roidb
        self._salt = str(uuid.uuid4())
        self._comp_id = 'comp4'

        # DATASET specific config options
        self.config = {'cleanup'     : True,
                       'use_salt'    : True,
                       'use_diff'    : False,
                       'top_k' : 2000, #M
                       'matlab_eval' : False,
                       'rpn_file'    : None,
                       'min_size'    : 2}

        assert os.path.exists(self._devkit_path), \\
                'Devkit path does not exist: {}'.format(self._devkit_path)
        assert os.path.exists(self._data_path), \\
                'Path does not exist: {}'.format(self._data_path)

    def image_path_at(self, i):
        """
        Return the absolute path to image i in the image sequence.
        """
        return self.image_path_from_index(self._image_index[i])

    def image_path_from_index(self, index):
        """
        Construct an image path from the image's "index" identifier.
        """
        image_path = os.path.join(self._data_path, 'JPEGImages',
                                  index + self._image_ext)
        assert os.path.exists(image_path), \\
                'Path does not exist: {}'.format(image_path)
        return image_path

    def _load_image_set_index(self):
        """
        Load the indexes listed in this dataset's image set file.
        """
        # Example path to image set file:
        # self._data_path + /ImageSets/Main/val.txt
        image_set_file = os.path.join(self._data_path, 'ImageSets', 'Main',
                                      self._image_set + '.txt') #leave unchanged
        assert os.path.exists(image_set_file), \\
                'Path does not exist: {}'.format(image_set_file)
        with open(image_set_file) as f:
            image_index = [x.strip() for x in f.readlines()]
        return image_index

    def _get_default_path(self):
        """
        Return the default path where PASCAL VOC is expected to be installed.
        """
        return os.path.join(cfg.DATA_DIR, 'VOCdevkit' + self._year)

    def gt_roidb(self):
        """
        Return the database of ground-truth regions of interest.

        This function loads/saves from/to a cache file to speed up future calls.
        """
        cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
        if os.path.exists(cache_file):
            with open(cache_file, 'rb') as fid:
                roidb = cPickle.load(fid)
            print '{} gt roidb loaded from {}'.format(self.name, cache_file)
            return roidb

        gt_roidb = [self._load_pascal_annotation(index)
                    for index in self.image_index]
        with open(cache_file, 'wb') as fid:
            cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL)
        print 'wrote gt roidb to {}'.format(cache_file)

        return gt_roidb

    def selective_search_roidb(self):
        """
        Return the database of selective search regions of interest.
        Ground-truth ROIs are also included.

        This function loads/saves from/to a cache file to speed up future calls.
        """
        cache_file = os.path.join(self.cache_path,
                                  self.name + '_selective_search_roidb.pkl')

        if os.path.exists(cache_file):
            with open(cache_file, 'rb') as fid:
                roidb = cPickle.load(fid)
            print '{} ss roidb loaded from {}'.format(self.name, cache_file)
            return roidb

        if int(self._year) == 2007 or self._image_set != 'test':
            gt_roidb = self.gt_roidb()
            ss_roidb = self._load_selective_search_roidb(gt_roidb)
            roidb = imdb.merge_roidbs(gt_roidb, ss_roidb)
        else:
            roidb = self._load_selective_search_roidb(None)
        with open(cache_file, 'wb') as fid:
            cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL)
        print 'wrote ss roidb to {}'.format(cache_file)

        return roidb

    def rpn_roidb(self):
        if int(self._year) == 2007 or self._image_set != 'test':
            gt_roidb = self.gt_roidb()
            rpn_roidb = self._load_rpn_roidb(gt_roidb)
            roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb)
        else:
            roidb = self._load_rpn_roidb(None)

        return roidb

    def _load_rpn_roidb(self, gt_roidb):
        filename = self.config['rpn_file']
        print 'loading {}'.format(filename)
        assert os.path.exists(filename), \\
               'rpn data not found at: {}'.format(filename)
        with open(filename, 'rb') as f:
            box_list = cPickle.load(f)
        return self.create_roidb_from_box_list(box_list, gt_roidb)

    def _load_selective_search_roidb(self, gt_roidb):
        filename = os.path.abspath(os.path.join(cfg.DATA_DIR,
                                                'selective_search_data',
                                                self.name + '.mat'))
        assert os.path.exists(filename), \\
               'Selective search data not found at: {}'.format(filename)
        raw_data = sio.loadmat(filename)['boxes'].ravel()

        box_list = []
        for i in xrange(raw_data.shape[0]):
            boxes = raw_data[i][:, (1, 0, 3, 2)] - 1
            keep = ds_utils.unique_boxes(boxes)
            boxes = boxes[keep, :]
            keep = ds_utils.filter_small_boxes(boxes, self.config['min_size'])
            boxes = boxes[keep, :]
            box_list.append(boxes)

        return self.create_roidb_from_box_list(box_list, gt_roidb)

    def _load_pascal_annotation(self, index):
        """
        Load image and bounding boxes info from XML file in the PASCAL VOC
        format.
        """
        filename = os.path.join(self._data_path, 'Annotations', index + '.xml')
        tree = ET.parse(filename)
        objs = tree.findall('object')

        #M: find img size
        img_size = tree.find('size')
        img_width = int(img_size.find('width').text)
        img_height = int(img_size.find('height').text)

        if not self.config['use_diff']:
            # Exclude the samples labeled as difficult
            non_diff_objs = [
                obj for obj in objs if int(obj.find('difficult').text) == 0]
            # if len(non_diff_objs) != len(objs):
            #     print 'Removed {} difficult objects'.format(
            #         len(objs) - len(non_diff_objs))
            objs = non_diff_objs
        num_objs = len(objs)

        boxes = np.zeros((num_objs, 4), dtype=np.uint16)
        gt_classes = np.zeros((num_objs), dtype=np.int32)
        overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
        # "Seg" area for pascal is just the box area
        seg_areas = np.zeros((num_objs), dtype=np.float32)

        # Load object bounding boxes into a data frame.
        for ix, obj in enumerate(objs):
            bbox = obj.find('bndbox')
            # Make pixel indexes 0-based
            x1 = float(bbox.find('xmin').text) - 1
            y1 = float(bbox.find('ymin').text) - 1
            x2 = float(bbox.find('xmax').text) - 1
            y2 = float(bbox.find('ymax').text) - 1

            #M: clip
            x1 = np.clip(x1, 0.0, img_width - 1.0)
            x2 = np.clip(x2, 0.0, img_width - 1.0)
            y1 = np.clip(y1, 0.0, img_height - 1.0)
            y2 = np.clip(y2, 0.0, img_height - 1.0)



            cls = self._class_to_ind[obj.find('name').text.lower().strip()]
            boxes[ix, :] = [x1, y1, x2, y2]
            gt_classes[ix] = cls
            overlaps[ix, cls] = 1.0
            seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)

        overlaps = scipy.sparse.csr_matrix(overlaps)

        return {'boxes' : boxes,
                'gt_classes': gt_classes,
                'gt_overlaps' : overlaps,
                'flipped' : False,
                'seg_areas' : seg_areas}

    def _get_comp_id(self):
        comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt']
            else self._comp_id)
        return comp_id

    def _get_MMICC_results_file_template(self): #F5
        # VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
        filename = self._get_comp_id() + '_det_' + self._image_set + '_{:s}.txt'
        try:
            os.mkdir(self._devkit_path + '/results')
        except OSError as e:
            if e.errno == errno.EEXIST:
                pass
            else:
                raise e
        path = os.path.join(
            self._devkit_path,
            'results',
            filename)

        # old version
       # path = os.path.join(
       #     self._devkit_path,
        #    'results',
        #    'VOC' + self._year,
       #     'Main',
         #   filename)
        #

        return path

    def _write_MMICC_results_file(self, all_boxes): #F6
        for cls_ind, cls in enumerate(self.classes):
            if cls == '__background__':
                continue
            print 'Writing {} VOC results file'.format(cls)
            filename = self._get_MMICC_results_file_template().format(cls)  #F7
            with open(filename, 'wt') as f:
                for im_ind, index in enumerate(self.image_index):
	            dets = all_boxes[cls_ind][im_ind]
                    if dets == []:
                        continue
                    # the VOCdevkit expects 1-based indices
                    for k in xrange(dets.shape[0]):
                        f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\\n'.
				format(index, dets[k, -1],
                                       dets[k, 0] + 1, dets[k, 1] + 1,
                                       dets[k, 2] + 1, dets[k, 3] + 1))

    def _do_python_eval(self, output_dir = 'output'):
        annopath = os.path.join(
            self._data_path,
            'Annotations',
            '{:s}.xml')
        imagesetfile = os.path.join(
            self._data_path,
            'ImageSets',
            'Main',
            self._image_set + '.txt')

        cachedir = os.path.join(self._devkit_path, 'annotations_cache')
        aps = []
        # The PASCAL VOC metric changed in 2010

        if not os.path.isdir(output_dir):
            os.mkdir(output_dir)
        for i, cls in enumerate(self._classes):
            if cls == '__background__':
                continue
            filename = self._get_MMICC_results_file_template().format(cls) #F8
            rec, prec, ap = MMICC_eval( #F9
                filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5)
            aps += [ap]
            print('AP for {} = {:.4f}'.format(cls, ap))
            with open(os.path.join(output_dir, cls + '_pr.pkl'), 'w') as f:
                cPickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
        print('Mean AP = {:.4f}'.format(np.mean(aps)))
        print('~~~~~~~~')
        print('Results:')
        for ap in aps:
            print('{:.3f}'.format(ap))
        print('{:.3f}'.format(np.mean(aps)))
        print('~~~~~~~~')
        print('')
        print('--------------------------------------------------------------')
        print('Results computed with the **unofficial** Python eval code.')
        print('Results should be very close to the official MATLAB eval code.')
        print('Recompute with `./tools/reval.py --matlab ...` for your paper.')
        print('-- Thanks, The Management')
        print('--------------------------------------------------------------')

    def _do_matlab_eval(self, output_dir='output'):
        print '-----------------------------------------------------'
        print 'Computing results with the official MATLAB eval code.'
        print '-----------------------------------------------------'
        path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets',
                            'VOCdevkit-matlab-wrapper')
        cmd = 'cd {} && '.format(path)
        cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB)
        cmd += '-r "dbstop if error; '
        cmd += 'voc_eval(\\\'{:s}\\\',\\\'{:s}\\\',\\\'{:s}\\\',\\\'{:s}\\\'); quit;"' \\
               .format(self._devkit_path, self._get_comp_id(),
                       self._image_set, output_dir)
        print('Running:\\n{}'.format(cmd))
        status = subprocess.call(cmd, shell=True)

    def evaluate_detections(self, all_boxes, output_dir):
        self._write_MMICC_results_file(all_boxes) #F10
        self._do_python_eval(output_dir)
        if self.config['matlab_eval']:
            self._do_matlab_eval(output_dir)
        if self.config['cleanup']:
            for cls in self._classes:
                if cls == '__background__':
                    continue
                filename = self._get_MMICC_results_file_template().format(cls)#F11
                os.remove(filename)

    def competition_mode(self, on):
        if on:
            self.config['use_salt'] = False
            self.config['cleanup'] = False
        else:
            self.config['use_salt'] = True
            self.config['cleanup'] = True

if __name__ == '__main__':
    from datasets.pascal_voc import pascal_voc
    d = pascal_voc('trainval', '2007')
    res = d.roidb
    from IPython import embed; embed()
'''

eval_template='''
# --------------------------------------------------------
# Fast/er R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Bharath Hariharan
# --------------------------------------------------------

import xml.etree.ElementTree as ET
import os
import cPickle
import numpy as np

def parse_rec(filename):
    """ Parse a xml file """
    tree = ET.parse(filename)
    objects = []
    for obj in tree.findall('object'):
        obj_struct = {}
        obj_struct['name'] = obj.find('name').text
        obj_struct['pose'] = obj.find('pose').text
        obj_struct['truncated'] = int(obj.find('truncated').text)
        obj_struct['difficult'] = int(obj.find('difficult').text)
        bbox = obj.find('bndbox')

	#orignal x,y in VOC is int. But x,y in here may be float
        # obj_struct['bbox'] = [int(bbox.find('xmin').text),
        #                      int(bbox.find('ymin').text),
        #                      int(bbox.find('xmax').text),
        #                      int(bbox.find('ymax').text)]
        #
        obj_struct['bbox'] = [float(bbox.find('xmin').text),
                              float(bbox.find('ymin').text),
                              float(bbox.find('xmax').text),
                              float(bbox.find('ymax').text)]
        objects.append(obj_struct)

    return objects

# modify
def MMICC_ap(rec, prec, use_07_metric=False):
    """ ap = MMICC_ap(rec, prec, [use_07_metric])
    Compute VOC AP given precision and recall.
    If use_07_metric is true, uses the
    VOC 07 11 point method (default:False).
    """
    if use_07_metric:
        # 11 point metric
        ap = 0.
        for t in np.arange(0., 1.1, 0.1):
            if np.sum(rec >= t) == 0:
                p = 0
            else:
                p = np.max(prec[rec >= t])
            ap = ap + p / 11.
    else:
        # correct AP calculation
        # first append sentinel values at the end
        mrec = np.concatenate(([0.], rec, [1.]))
        mpre = np.concatenate(([0.], prec, [0.]))

        # compute the precision envelope
        for i in range(mpre.size - 1, 0, -1):
            mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])

        # to calculate area under PR curve, look for points
        # where X axis (recall) changes value
        i = np.where(mrec[1:] != mrec[:-1])[0]

        # and sum (\Delta recall) * prec
        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
    return ap

def MMICC_eval(detpath,
             annopath,
             imagesetfile,
             classname,
             cachedir,
             ovthresh=0.5,
             use_07_metric=False):
    """rec, prec, ap = MMICC_eval(detpath,
                                annopath,
                                imagesetfile,
                                classname,
                                [ovthresh],
                                [use_07_metric])

    Top level function that does the PASCAL VOC evaluation.

    detpath: Path to detections
        detpath.format(classname) should produce the detection results file.
    annopath: Path to annotations
        annopath.format(imagename) should be the xml annotations file.
    imagesetfile: Text file containing the list of images, one image per line.
    classname: Category name (duh)
    cachedir: Directory for caching the annotations
    [ovthresh]: Overlap threshold (default = 0.5)
    [use_07_metric]: Whether to use VOC07's 11 point AP computation
        (default False)
    """
    # assumes detections are in detpath.format(classname)
    # assumes annotations are in annopath.format(imagename)
    # assumes imagesetfile is a text file with each line an image name
    # cachedir caches the annotations in a pickle file

    # first load gt
    if not os.path.isdir(cachedir):
        os.mkdir(cachedir)
    cachefile = os.path.join(cachedir, 'annots.pkl')
    # read list of images
    with open(imagesetfile, 'r') as f:
        lines = f.readlines()
    imagenames = [x.strip() for x in lines]

    if not os.path.isfile(cachefile):
        # load annots
        recs = {}
        for i, imagename in enumerate(imagenames):
            recs[imagename] = parse_rec(annopath.format(imagename))
            if i % 100 == 0:
                print 'Reading annotation for {:d}/{:d}'.format(
                    i + 1, len(imagenames))
        # save
        print 'Saving cached annotations to {:s}'.format(cachefile)
        with open(cachefile, 'w') as f:
            cPickle.dump(recs, f)
    else:
        # load
        with open(cachefile, 'r') as f:
            recs = cPickle.load(f)

    # extract gt objects for this class
    class_recs = {}
    npos = 0
    for imagename in imagenames:
        R = [obj for obj in recs[imagename] if obj['name'] == classname]
        bbox = np.array([x['bbox'] for x in R])
        difficult = np.array([x['difficult'] for x in R]).astype(np.bool)
        det = [False] * len(R)
        npos = npos + sum(~difficult)
        class_recs[imagename] = {'bbox': bbox,
                                 'difficult': difficult,
                                 'det': det}

    # read dets
    detfile = detpath.format(classname)
    with open(detfile, 'r') as f:
        lines = f.readlines()

    splitlines = [x.strip().split(' ') for x in lines]
    image_ids = [x[0] for x in splitlines]
    confidence = np.array([float(x[1]) for x in splitlines])
    BB = np.array([[float(z) for z in x[2:]] for x in splitlines])

    # sort by confidence
    sorted_ind = np.argsort(-confidence)
    sorted_scores = np.sort(-confidence)
    BB = BB[sorted_ind, :]
    image_ids = [image_ids[x] for x in sorted_ind]

    # go down dets and mark TPs and FPs
    nd = len(image_ids)
    tp = np.zeros(nd)
    fp = np.zeros(nd)
    for d in range(nd):
        R = class_recs[image_ids[d]]
        bb = BB[d, :].astype(float)
        ovmax = -np.inf
        BBGT = R['bbox'].astype(float)

        if BBGT.size > 0:
            # compute overlaps
            # intersection
            ixmin = np.maximum(BBGT[:, 0], bb[0])
            iymin = np.maximum(BBGT[:, 1], bb[1])
            ixmax = np.minimum(BBGT[:, 2], bb[2])
            iymax = np.minimum(BBGT[:, 3], bb[3])
            iw = np.maximum(ixmax - ixmin + 1., 0.)
            ih = np.maximum(iymax - iymin + 1., 0.)
            inters = iw * ih

            # union
            uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
                   (BBGT[:, 2] - BBGT[:, 0] + 1.) *
                   (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)

            overlaps = inters / uni
            ovmax = np.max(overlaps)
            jmax = np.argmax(overlaps)

        if ovmax > ovthresh:
            if not R['difficult'][jmax]:
                if not R['det'][jmax]:
                    tp[d] = 1.
                    R['det'][jmax] = 1
                else:
                    fp[d] = 1.
        else:
            fp[d] = 1.

    # compute precision recall
    fp = np.cumsum(fp)
    tp = np.cumsum(tp)
    rec = tp / float(npos)
    # avoid divide by zero in case the first detection matches a difficult
    # ground truth
    prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
    ap = MMICC_ap(rec, prec, use_07_metric)

    return rec, prec, ap
'''

fac_template='''
# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------

"""Factory method for easily getting imdbs by name."""

__sets = {}

from datasets.pascal_voc import pascal_voc
from datasets.coco import coco
from datasets.MMICC import MMICC
import numpy as np


#Set up MMICC
MMICC_devkit_path = 'DEVKITPATH'
for split in ['train', 'val', 'test']:
    name = '{}_{}'.format('MMICC', split)
    __sets[name] = (lambda split=split: MMICC(split, MMICC_devkit_path))


# Set up voc_<year>_<split> using selective search "fast" mode
for year in ['2007', '2012']:
    for split in ['train', 'val', 'trainval', 'test']:
        name = 'voc_{}_{}'.format(year, split)
        __sets[name] = (lambda split=split, year=year: pascal_voc(split, year))

# Set up coco_2014_<split>
for year in ['2014']:
    for split in ['train', 'val', 'minival', 'valminusminival']:
        name = 'coco_{}_{}'.format(year, split)
        __sets[name] = (lambda split=split, year=year: coco(split, year))

# Set up coco_2015_<split>
for year in ['2015']:
    for split in ['test', 'test-dev']:
        name = 'coco_{}_{}'.format(year, split)
        __sets[name] = (lambda split=split, year=year: coco(split, year))

def get_imdb(name):
    """Get an imdb (image database) by name."""
    if not __sets.has_key(name):
        raise KeyError('Unknown dataset: {}'.format(name))
    return __sets[name]()

def list_imdbs():
    """List all registered imdbs."""
    return __sets.keys()
'''

sh_template = '''
#!/bin/bash
# Usage:
# ./experiments/scripts/faster_rcnn_end2end.sh GPU NET DATASET [options args to {train,test}_net.py]
# DATASET is either pascal_voc or coco.
#
# Example:
# ./experiments/scripts/faster_rcnn_end2end.sh 0 VGG_CNN_M_1024 pascal_voc \
#   --set EXP_DIR foobar RNG_SEED 42 TRAIN.SCALES "[400, 500, 600, 700]"

set -x
set -e

export PYTHONUNBUFFERED="True"

GPU_ID=$1
NET=$2
NET_lc=${NET,,}
DATASET=$3


array=( $@ )
len=${#array[@]}
EXTRA_ARGS=${array[@]:3:$len}
EXTRA_ARGS_SLUG=${EXTRA_ARGS// /_}

case $DATASET in
  pascal_voc)
    TRAIN_IMDB="voc_2007_trainval"
    TEST_IMDB="voc_2007_test"
    PT_DIR="pascal_voc"
    ITERS=70000
    ;;
  MMICC)
    TRAIN_IMDB="MMICC_train"
    TEST_IMDB="MMICC_test"
    PT_DIR="MMICC"
    ITERS=70000
    ;;
  coco)
    # This is a very long and slow training schedule
    # You can probably use fewer iterations and reduce the
    # time to the LR drop (set in the solver to 350,000 iterations).
    TRAIN_IMDB="coco_2014_train"
    TEST_IMDB="coco_2014_minival"
    PT_DIR="coco"
    ITERS=490000
    ;;
  *)
    echo "No dataset given"
    exit
    ;;
esac

LOG="experiments/logs/faster_rcnn_end2end_${NET}_${EXTRA_ARGS_SLUG}.txt.`date +'%Y-%m-%d_%H-%M-%S'`"
exec &> >(tee -a "$LOG")
echo Logging output to "$LOG"


echo "GPU_ID ${GPU_ID}"
echo "PT_DIR ${PT_DIR}"
echo "NET ${NET}"
echo "TRAIN_IMDB ${TRAIN_IMDB}"


time ./tools/train_net.py --gpu ${GPU_ID} \
  --solver models/${PT_DIR}/${NET}/faster_rcnn_end2end/solver.prototxt \
  --weights data/imagenet_models/${NET}.v2.caffemodel \
  --imdb ${TRAIN_IMDB} \
  --iters ${ITERS} \
  --cfg experiments/cfgs/MMICC_end2end.yml \
  ${EXTRA_ARGS}

set +x
NET_FINAL=`grep -B 1 "done solving" ${LOG} | grep "Wrote snapshot" | awk '{print $4}'`
set -x

time ./tools/test_net.py --gpu ${GPU_ID} \
  --def models/${PT_DIR}/${NET}/faster_rcnn_end2end/test.prototxt \
  --net ${NET_FINAL} \
  --imdb ${TEST_IMDB} \
  --cfg experiments/cfgs/faster_rcnn_end2end.yml \
  ${EXTRA_ARGS}

'''

short_fac_template = '''
#Set up MMICC
from datasets.MMICC import MMICC
MMICC_devkit_path = 'DEVKITPATH'
for split in ['train', 'val', 'test']:
    name = '{}_{}'.format('MMICC', split)
    __sets[name] = (lambda split=split: MMICC(split, MMICC_devkit_path))
'''


cfg_template = '''
EXP_DIR: MMICC
TRAIN:
  HAS_RPN: True
  IMS_PER_BATCH: 1
  BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True
  RPN_POSITIVE_OVERLAP: 0.7
  RPN_BATCHSIZE: 256
  PROPOSAL_METHOD: gt
  BG_THRESH_LO: 0.0
TEST:
  HAS_RPN: True
'''