【pytorch-ssd目标检测】验证自己创建的数据集

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制作类似pascal voc格式的目标检测数据集：https://www.cnblogs.com/xiximayou/p/12546061.html

训练自己创建的数据集：https://www.cnblogs.com/xiximayou/p/12546556.html

它的代码中的eval.py实际上使用的是test.txt里面的数据。

直接看修改后的代码：eval.py

"""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
from data import MASK_ROOT, MASKAnnotationTransform, MASKDetection, BaseTransform
from data import MASK_CLASSES as labelmap
import torch.utils.data as data

from ssd import build_ssd

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/ssd300_mAP_77.43_v2.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=0.01, type=float,
help='Detection confidence threshold')
parser.add_argument('--top_k', default=5, 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('--mask_root', default=MASK_ROOT,
#                    help='Location of MASK root directory')
parser.add_argument('--cleanup', default=True, type=str2bool,
help='Cleanup and remove results files following eval')

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
mask_root="/content/drive/My Drive/pytorch_ssd/data/maskornot"
annopath = os.path.join(mask_root, 'Annotations', '%s.xml')
imgpath = os.path.join(mask_root, 'JPEGImages', '%s.jpg')
imgsetpath = os.path.join(mask_root, 'ImageSets',
'Main', '{:s}.txt')
devkit_path = mask_root
#print(annopath,imgpath,imgsetpath)

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')
#print(filedir)
if not os.path.exists(filedir):
os.mkdir(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} MASK 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 >= 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 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)
#print(class_recs)
for d in range(nd):
#print(image_ids[d])
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, 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 > 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('ssd300_5000', 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()
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
#print(num_classes)                     # +1 for background
net = build_ssd('test', 300, num_classes)            # initialize SSD
net.load_state_dict(torch.load(args.trained_model))
net.eval()
print('Finished loading model!')
# load data
dataset = MASKDetection(mask_root, set_type,
BaseTransform(300, dataset_mean),
MASKAnnotationTransform())
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, 300,
thresh=args.confidence_threshold)

开始执行：

!python eval.py --trained_model weights/ssd300_MASK_5000.pth --top_k 1

运行结果：

Finished loading model!
/pytorch/torch/csrc/autograd/python_function.cpp:622: UserWarning: Legacy autograd function with non-static forward method is deprecated and will be removed in 1.3. Please use new-style autograd function with static forward method. (Example: https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function)
im_detect: 1/80 2.040s
。。。。。。
/pytorch/torch/csrc/autograd/python_function.cpp:622: UserWarning: Legacy autograd function with non-static forward method is deprecated and will be removed in 1.3. Please use new-style autograd function with static forward method. (Example: https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function)
im_detect: 80/80 0.068s
Evaluating detections
Writing mask MASK results file
Writing nomask MASK results file
VOC07 metric? Yes
AP for mask = 0.8982
AP for nomask = 0.0629
Mean AP = 0.4806
~~~~~~~~
Results:
0.898
0.063
0.481
~~~~~~~~

--------------------------------------------------------------
Results computed with the **unofficial** Python eval code.
Results should be very close to the official MATLAB eval code.
--------------------------------------------------------------

由于我标注的数据集中很少有nomask（即不戴口罩的），因此nomask的AP较低也很正常。最后看看都生成了哪些文件：