RuntimeError: Legacy autograd function with non-static forward method is deprecated. Please use new-style autograd function with static forward method. (Example: https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function)

vishal_Dhull: <pre><code class="lang-auto">"""Adapted from:
    @longcw faster_rcnn_pytorch: https://github.com/longcw/faster_rcnn_pytorch
    @rbgirshick py-faster-rcnn https://github.com/rbgirshick/py-faster-rcnn
    Licensed under The MIT License [see LICENSE for details]
"""

from __future__ import print_function
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
from data import VOC_ROOT, VOCAnnotationTransform, VOCDetection, BaseTransform
from data import VOC_CLASSES as labelmap
import torch.utils.data as data

import sys
import os
import time
import argparse
import numpy as np
import pickle
import cv2

if sys.version_info[0] == 2:
    import xml.etree.cElementTree as ET
else:
    import xml.etree.ElementTree as ET


def str2bool(v):
    return v.lower() in ("yes", "true", "t", "1")


parser = argparse.ArgumentParser(
    description='Single Shot MultiBox Detector Evaluation')
parser.add_argument('--trained_model',
                    default='weights/experiment/320*320/RefineDet320_VOC_310000.pth', type=str,
                    help='Trained state_dict file path to open')
parser.add_argument('--save_folder', default='eval/', type=str,
                    help='File path to save results')
parser.add_argument('--confidence_threshold', default=0.00, type=float,
                    help='Detection confidence threshold')
parser.add_argument('--top_k', default=10, type=int,
                    help='Further restrict the number of predictions to parse')
parser.add_argument('--cuda', default=True, type=str2bool,
                    help='Use cuda to train model')
parser.add_argument('--voc_root', default=VOC_ROOT,
                    help='Location of VOC root directory')
parser.add_argument('--cleanup', default=True, type=str2bool,
                    help='Cleanup and remove results files following eval')
parser.add_argument('--input_size', default='320', choices=['320', '512'],
                    type=str, help='RefineDet320 or RefineDet512')

args = parser.parse_args()

if not os.path.exists(args.save_folder):
    os.mkdir(args.save_folder)

if torch.cuda.is_available():
    if args.cuda:
        torch.set_default_tensor_type('torch.cuda.FloatTensor')
    if not args.cuda:
        print("WARNING: It looks like you have a CUDA device, but aren't using \
              CUDA.  Run with --cuda for optimal eval speed.")
        torch.set_default_tensor_type('torch.FloatTensor')
else:
    torch.set_default_tensor_type('torch.FloatTensor')

annopath = os.path.join(args.voc_root, 'VOC2007', 'Annotations', '%s.xml')
imgpath = os.path.join(args.voc_root, 'VOC2007', 'JPEGImages', '%s.jpg')
imgsetpath = os.path.join(args.voc_root, 'VOC2007', 'ImageSets',
                          'Main', '{:s}.txt')
YEAR = '2007'
devkit_path = args.voc_root + 'VOC' + YEAR
dataset_mean = (104, 117, 123)
set_type = 'test'


class Timer(object):
    """A simple timer."""
    def __init__(self):
        self.total_time = 0.
        self.calls = 0
        self.start_time = 0.
        self.diff = 0.
        self.average_time = 0.

    def tic(self):
        # using time.time instead of time.clock because time time.clock
        # does not normalize for multithreading
        self.start_time = time.time()

    def toc(self, average=True):
        self.diff = time.time() - self.start_time
        self.total_time += self.diff
        self.calls += 1
        self.average_time = self.total_time / self.calls
        if average:
            return self.average_time
        else:
            return self.diff


def parse_rec(filename):
    """ Parse a PASCAL VOC 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')
        obj_struct['bbox'] = [int(bbox.find('xmin').text) - 1,
                              int(bbox.find('ymin').text) - 1,
                              int(bbox.find('xmax').text) - 1,
                              int(bbox.find('ymax').text) - 1]
        objects.append(obj_struct)

    return objects


def get_output_dir(name, phase):
    """Return the directory where experimental artifacts are placed.
    If the directory does not exist, it is created.
    A canonical path is built using the name from an imdb and a network
    (if not None).
    """
    filedir = os.path.join(name, phase)
    if not os.path.exists(filedir):
        os.makedirs(filedir)
    return filedir


def get_voc_results_file_template(image_set, cls):
    # VOCdevkit/VOC2007/results/det_test_aeroplane.txt
    filename = 'det_' + image_set + '_%s.txt' % (cls)
    filedir = os.path.join(devkit_path, 'results')
    if not os.path.exists(filedir):
        os.makedirs(filedir)
    path = os.path.join(filedir, filename)
    return path


def write_voc_results_file(all_boxes, dataset):
    for cls_ind, cls in enumerate(labelmap):
        print('Writing {:s} VOC results file'.format(cls))
        filename = get_voc_results_file_template(set_type, cls)
        with open(filename, 'wt') as f:
            for im_ind, index in enumerate(dataset.ids):
                dets = all_boxes[cls_ind+1][im_ind]
                if dets == []:
                    continue
                # the VOCdevkit expects 1-based indices
                for k in range(dets.shape[0]):
                    f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
                            format(index[1], dets[k, -1],
                                   dets[k, 0] + 1, dets[k, 1] + 1,
                                   dets[k, 2] + 1, dets[k, 3] + 1))


def do_python_eval(output_dir='output', use_07=True):
    cachedir = os.path.join(devkit_path, 'annotations_cache')
    aps = []
    # The PASCAL VOC metric changed in 2010
    use_07_metric = use_07
    print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
    if not os.path.isdir(output_dir):
        os.mkdir(output_dir)
    for i, cls in enumerate(labelmap):
        filename = get_voc_results_file_template(set_type, cls)
        rec, prec, ap = voc_eval(
           filename, annopath, imgsetpath.format(set_type), cls, cachedir,
           ovthresh=0.5, use_07_metric=use_07_metric)
        aps += [ap]
        print('AP for {} = {:.4f}'.format(cls, ap))
        with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
            pickle.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('--------------------------------------------------------------')


def voc_ap(rec, prec, use_07_metric=True):
    """ ap = voc_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:True).
    """
    if use_07_metric:
        # 11 point metric
        ap = 0.
        for t in np.arange(0., 1.1, 0.1):
            if np.sum(rec &gt;= t) == 0:
                p = 0
            else:
                p = np.max(prec[rec &gt;= 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 voc_eval(detpath,
             annopath,
             imagesetfile,
             classname,
             cachedir,
             ovthresh=0.5,
             use_07_metric=True):
    """rec, prec, ap = voc_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 True)
"""
# 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 % (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, 'wb') as f:
            pickle.dump(recs, f)
    else:
        # load
        with open(cachefile, 'rb') as f:
            recs = pickle.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()
    if any(lines) == 1:

        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 &gt; 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, 0.)
                ih = np.maximum(iymax - iymin, 0.)
                inters = iw * ih
                uni = ((bb[2] - bb[0]) * (bb[3] - bb[1]) +
                       (BBGT[:, 2] - BBGT[:, 0]) *
                       (BBGT[:, 3] - BBGT[:, 1]) - inters)
                overlaps = inters / uni
                ovmax = np.max(overlaps)
                jmax = np.argmax(overlaps)

            if ovmax &gt; 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 = voc_ap(rec, prec, use_07_metric)
    else:
        rec = -1.
        prec = -1.
        ap = -1.

    return rec, prec, ap


def test_net(save_folder, net, cuda, dataset, transform, top_k,
             im_size=300, thresh=0.05):
    num_images = len(dataset)
    # all detections are collected into:
    #    all_boxes[cls][image] = N x 5 array of detections in
    #    (x1, y1, x2, y2, score)
    all_boxes = [[[] for _ in range(num_images)]
                 for _ in range(len(labelmap)+1)]

    # timers
    _t = {'im_detect': Timer(), 'misc': Timer()}
    output_dir = get_output_dir('gaijin6_175000', set_type)
    det_file = os.path.join(output_dir, 'detections.pkl')

    for i in range(num_images):
        im, gt, h, w = dataset.pull_item(i)

        x = Variable(im.unsqueeze(0))
        if args.cuda:
            x = x.cuda()
        _t['im_detect'].tic()
        detections = net(x).data
        detect_time = _t['im_detect'].toc(average=False)

        # skip j = 0, because it's the background class
        for j in range(1, detections.size(1)):
            dets = detections[0, j, :]
            mask = dets[:, 0].gt(0.).expand(5, dets.size(0)).t()
            dets = torch.masked_select(dets, mask).view(-1, 5)
            if dets.size(0) == 0:
                continue
            boxes = dets[:, 1:]
            boxes[:, 0] *= w
            boxes[:, 2] *= w
            boxes[:, 1] *= h
            boxes[:, 3] *= h
            scores = dets[:, 0].cpu().numpy()
            """do threshold for scores, delet scores &lt; thresh"""
            scores = scores[np.where(scores &gt; thresh)]
            boxes = boxes[np.where(scores &gt; thresh)]
            if boxes.size(0) == 0:
                continue

            cls_dets = np.hstack((boxes.cpu().numpy(),
                                  scores[:, np.newaxis])).astype(np.float32,
                                                                 copy=False)
            all_boxes[j][i] = cls_dets

        print('im_detect: {:d}/{:d} {:.3f}s'.format(i + 1,
                                                    num_images, detect_time))

    with open(det_file, 'wb') as f:
        pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)

    print('Evaluating detections')
    evaluate_detections(all_boxes, output_dir, dataset)


def evaluate_detections(box_list, output_dir, dataset):
    write_voc_results_file(box_list, dataset)
    do_python_eval(output_dir)


if __name__ == '__main__':
    # load net
    num_classes = len(labelmap) + 1                      # +1 for background
    net = build_multitridentrefinedet('test', int(args.input_size), num_classes)            # initialize SSD
    net.load_state_dict(torch.load(args.trained_model))
    net.eval()
    print('Finished loading model!')
    # load data
    dataset = VOCDetection(args.voc_root, [('2007', set_type)],
                           BaseTransform(int(args.input_size), dataset_mean),
                           VOCAnnotationTransform())
    if args.cuda:
        net = net.cuda()
        cudnn.benchmark = True
    # evaluation
    test_net(args.save_folder, net, args.cuda, dataset,
             BaseTransform(net.size, dataset_mean), args.top_k, int(args.input_size),
             thresh=args.confidence_threshold)
</code></pre>

ptrblck: <p>As the error message points out, you are using a deprecated way to write a custom <code>autograd.Function</code> and would need to use the new approach as described <a href="https://pytorch.org/tutorials/beginner/examples_autograd/two_layer_net_custom_function.html">here</a>.</p>

vishal_Dhull: <p>Hi i am little new to pytorch…If anyone can point out which part of code to change and how, it would be a great help…Thank you</p>

ptrblck: <p>The posted code doesn’t show any custom <code>autograd.Function</code>, so I assume it’s imported from another file. You could search your repository for <code>autograd.Function</code> and should find a definition somewhere.</p>

vishal_Dhull: <p>from torch.autograd import Variable<br>
this is the definition of Variable, the above code is trying to import :</p>
<pre><code class="lang-auto">import torch
from torch._six import with_metaclass


class VariableMeta(type):
    def __instancecheck__(cls, other):
        return isinstance(other, torch.Tensor)


class Variable(with_metaclass(VariableMeta, torch._C._LegacyVariableBase)):
    pass


from torch._C import _ImperativeEngine as ImperativeEngine
Variable._execution_engine = ImperativeEngine()
</code></pre>

ptrblck: <p>This doesn’t seem to be an <code>autograd.Function</code>.<br>
You could run the code line by line in a debugger (or notebook etc.) in order to isolate the line of code which raises the error, if you cannot find the definition (it could be defined in another submodule you are importing).</p>

vishal_Dhull: <p>i think error is in this line of code :</p>
<blockquote>
<p>self.priors.type(type(x.data))</p>
</blockquote>
<p>and this is the forward method of it</p>
<pre><code class="lang-auto">def forward(self):
        mean = []
        for k, f in enumerate(self.feature_maps):
            for i, j in product(range(f), repeat=2):
                f_k = self.image_size / self.steps[k]
                # unit center x,y
                cx = (j + 0.5) / f_k
                cy = (i + 0.5) / f_k

                # aspect_ratio: 1
                # rel size: min_size
                s_k = self.min_sizes[k]/self.image_size
                mean += [cx, cy, s_k, s_k]

                # aspect_ratio: 1
                # rel size: sqrt(s_k * s_(k+1))
                if self.max_sizes:
                    s_k_prime = sqrt(s_k * (self.max_sizes[k]/self.image_size))
                    mean += [cx, cy, s_k_prime, s_k_prime]

                # rest of aspect ratios
                for ar in self.aspect_ratios[k]:
                    mean += [cx, cy, s_k*sqrt(ar), s_k/sqrt(ar)]
                    mean += [cx, cy, s_k/sqrt(ar), s_k*sqrt(ar)]
        # back to torch land
        output = torch.Tensor(mean).view(-1, 4)
        if self.clip:
            output.clamp_(max=1, min=0)
        return output
</code></pre>
<p>i tried to put <span class="mention">@staticmethod</span> above it but it didn’t work</p>
<pre><code class="lang-auto">  self.priors = self.priorbox.forward()
TypeError: forward() missing 1 required positional argument: 'self'

</code></pre>

ptrblck: <p>If this <code>forward</code> method is defined in an <code>autograd.Function</code>, you would not only need to decorate it with <code>staticmethod</code>, but also call the <code>.apply</code> function as described in the linked tutorial in my previous post.</p>