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Create sotabench.py

5.0
Glenn Jocher 4 лет назад
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d68179953d
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import argparse
import glob
import json
import os
import shutil
from pathlib import Path

import numpy as np
import torch
import yaml
from tqdm import tqdm

from models.experimental import attempt_load
from utils.datasets import create_dataloader
from utils.general import (
coco80_to_coco91_class, check_dataset, check_file, check_img_size, compute_loss, non_max_suppression, scale_coords,
xyxy2xywh, clip_coords, plot_images, xywh2xyxy, box_iou, output_to_target, ap_per_class, set_logging)
from utils.torch_utils import select_device, time_synchronized


from sotabencheval.object_detection import COCOEvaluator
from sotabencheval.utils import is_server

DATA_ROOT = './.data/vision/coco' if is_server() else '../coco' # sotabench data dir


def test(data,
weights=None,
batch_size=16,
imgsz=640,
conf_thres=0.001,
iou_thres=0.6, # for NMS
save_json=False,
single_cls=False,
augment=False,
verbose=False,
model=None,
dataloader=None,
save_dir='',
merge=False,
save_txt=False):
# Initialize/load model and set device
training = model is not None
if training: # called by train.py
device = next(model.parameters()).device # get model device

else: # called directly
set_logging()
device = select_device(opt.device, batch_size=batch_size)
merge, save_txt = opt.merge, opt.save_txt # use Merge NMS, save *.txt labels
if save_txt:
out = Path('inference/output')
if os.path.exists(out):
shutil.rmtree(out) # delete output folder
os.makedirs(out) # make new output folder

# Remove previous
for f in glob.glob(str(Path(save_dir) / 'test_batch*.jpg')):
os.remove(f)

# Load model
model = attempt_load(weights, map_location=device) # load FP32 model
imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size

# Multi-GPU disabled, incompatible with .half() https://github.com/ultralytics/yolov5/issues/99
# if device.type != 'cpu' and torch.cuda.device_count() > 1:
# model = nn.DataParallel(model)

# Half
half = device.type != 'cpu' # half precision only supported on CUDA
if half:
model.half()

# Configure
model.eval()
with open(data) as f:
data = yaml.load(f, Loader=yaml.FullLoader) # model dict
check_dataset(data) # check
nc = 1 if single_cls else int(data['nc']) # number of classes
iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95
niou = iouv.numel()

# Dataloader
if not training:
img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
_ = model(img.half() if half else img) if device.type != 'cpu' else None # run once
path = data['test'] if opt.task == 'test' else data['val'] # path to val/test images
dataloader = create_dataloader(path, imgsz, batch_size, model.stride.max(), opt,
hyp=None, augment=False, cache=True, pad=0.5, rect=True)[0]

seen = 0
names = model.names if hasattr(model, 'names') else model.module.names
coco91class = coco80_to_coco91_class()
s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
loss = torch.zeros(3, device=device)
jdict, stats, ap, ap_class = [], [], [], []
evaluator = COCOEvaluator(root=DATA_ROOT, model_name=opt.weights.replace('.pt', ''))
for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
img = img.to(device, non_blocking=True)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
targets = targets.to(device)
nb, _, height, width = img.shape # batch size, channels, height, width
whwh = torch.Tensor([width, height, width, height]).to(device)

# Disable gradients
with torch.no_grad():
# Run model
t = time_synchronized()
inf_out, train_out = model(img, augment=augment) # inference and training outputs
t0 += time_synchronized() - t

# Compute loss
if training: # if model has loss hyperparameters
loss += compute_loss([x.float() for x in train_out], targets, model)[1][:3] # GIoU, obj, cls

# Run NMS
t = time_synchronized()
output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres, merge=merge)
t1 += time_synchronized() - t

# Statistics per image
for si, pred in enumerate(output):
labels = targets[targets[:, 0] == si, 1:]
nl = len(labels)
tcls = labels[:, 0].tolist() if nl else [] # target class
seen += 1

if pred is None:
if nl:
stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
continue

# Append to text file
if save_txt:
gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]] # normalization gain whwh
x = pred.clone()
x[:, :4] = scale_coords(img[si].shape[1:], x[:, :4], shapes[si][0], shapes[si][1]) # to original
for *xyxy, conf, cls in x:
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
with open(str(out / Path(paths[si]).stem) + '.txt', 'a') as f:
f.write(('%g ' * 5 + '\n') % (cls, *xywh)) # label format

# Clip boxes to image bounds
clip_coords(pred, (height, width))

# Append to pycocotools JSON dictionary
if save_json:
# [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
image_id = Path(paths[si]).stem
box = pred[:, :4].clone() # xyxy
scale_coords(img[si].shape[1:], box, shapes[si][0], shapes[si][1]) # to original shape
box = xyxy2xywh(box) # xywh
box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
for p, b in zip(pred.tolist(), box.tolist()):
result = {'image_id': int(image_id) if image_id.isnumeric() else image_id,
'category_id': coco91class[int(p[5])],
'bbox': [round(x, 3) for x in b],
'score': round(p[4], 5)}
jdict.append(result)

#evaluator.add([result])
#if evaluator.cache_exists:
# break

# # Assign all predictions as incorrect
# correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device)
# if nl:
# detected = [] # target indices
# tcls_tensor = labels[:, 0]
#
# # target boxes
# tbox = xywh2xyxy(labels[:, 1:5]) * whwh
#
# # Per target class
# for cls in torch.unique(tcls_tensor):
# ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1) # prediction indices
# pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(-1) # target indices
#
# # Search for detections
# if pi.shape[0]:
# # Prediction to target ious
# ious, i = box_iou(pred[pi, :4], tbox[ti]).max(1) # best ious, indices
#
# # Append detections
# detected_set = set()
# for j in (ious > iouv[0]).nonzero(as_tuple=False):
# d = ti[i[j]] # detected target
# if d.item() not in detected_set:
# detected_set.add(d.item())
# detected.append(d)
# correct[pi[j]] = ious[j] > iouv # iou_thres is 1xn
# if len(detected) == nl: # all targets already located in image
# break
#
# # Append statistics (correct, conf, pcls, tcls)
# stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))

# # Plot images
# if batch_i < 1:
# f = Path(save_dir) / ('test_batch%g_gt.jpg' % batch_i) # filename
# plot_images(img, targets, paths, str(f), names) # ground truth
# f = Path(save_dir) / ('test_batch%g_pred.jpg' % batch_i)
# plot_images(img, output_to_target(output, width, height), paths, str(f), names) # predictions

evaluator.add(jdict)
evaluator.save()

# # Compute statistics
# stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy
# if len(stats) and stats[0].any():
# p, r, ap, f1, ap_class = ap_per_class(*stats)
# p, r, ap50, ap = p[:, 0], r[:, 0], ap[:, 0], ap.mean(1) # [P, R, AP@0.5, AP@0.5:0.95]
# mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
# nt = np.bincount(stats[3].astype(np.int64), minlength=nc) # number of targets per class
# else:
# nt = torch.zeros(1)
#
# # Print results
# pf = '%20s' + '%12.3g' * 6 # print format
# print(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
#
# # Print results per class
# if verbose and nc > 1 and len(stats):
# for i, c in enumerate(ap_class):
# print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
#
# # Print speeds
# t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (imgsz, imgsz, batch_size) # tuple
# if not training:
# print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t)
#
# # Save JSON
# if save_json and len(jdict):
# f = 'detections_val2017_%s_results.json' % \
# (weights.split(os.sep)[-1].replace('.pt', '') if isinstance(weights, str) else '') # filename
# print('\nCOCO mAP with pycocotools... saving %s...' % f)
# with open(f, 'w') as file:
# json.dump(jdict, file)
#
# try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
# from pycocotools.coco import COCO
# from pycocotools.cocoeval import COCOeval
#
# imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files]
# cocoGt = COCO(glob.glob('../coco/annotations/instances_val*.json')[0]) # initialize COCO ground truth api
# cocoDt = cocoGt.loadRes(f) # initialize COCO pred api
# cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
# cocoEval.params.imgIds = imgIds # image IDs to evaluate
# cocoEval.evaluate()
# cocoEval.accumulate()
# cocoEval.summarize()
# map, map50 = cocoEval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5)
# except Exception as e:
# print('ERROR: pycocotools unable to run: %s' % e)
#
# # Return results
# model.float() # for training
# maps = np.zeros(nc) + map
# for i, c in enumerate(ap_class):
# maps[c] = ap[i]
# return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t


if __name__ == '__main__':
parser = argparse.ArgumentParser(prog='test.py')
parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
parser.add_argument('--data', type=str, default='data/coco.yaml', help='*.data path')
parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch')
parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.65, help='IOU threshold for NMS')
parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
parser.add_argument('--task', default='val', help="'val', 'test', 'study'")
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')
parser.add_argument('--augment', action='store_true', help='augmented inference')
parser.add_argument('--merge', action='store_true', help='use Merge NMS')
parser.add_argument('--verbose', action='store_true', help='report mAP by class')
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
opt = parser.parse_args()
opt.save_json |= opt.data.endswith('coco.yaml')
opt.data = check_file(opt.data) # check file
print(opt)

if opt.task in ['val', 'test']: # run normally
test(opt.data,
opt.weights,
opt.batch_size,
opt.img_size,
opt.conf_thres,
opt.iou_thres,
opt.save_json,
opt.single_cls,
opt.augment,
opt.verbose)

elif opt.task == 'study': # run over a range of settings and save/plot
for weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
f = 'study_%s_%s.txt' % (Path(opt.data).stem, Path(weights).stem) # filename to save to
x = list(range(320, 800, 64)) # x axis
y = [] # y axis
for i in x: # img-size
print('\nRunning %s point %s...' % (f, i))
r, _, t = test(opt.data, weights, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json)
y.append(r + t) # results and times
np.savetxt(f, y, fmt='%10.4g') # save
os.system('zip -r study.zip study_*.txt')
# utils.general.plot_study_txt(f, x) # plot

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