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val_trt.py
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val_trt.py
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import argparse
import json
import os
import sys
from pathlib import Path
import numpy as np
import torch
from tqdm import tqdm
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0] # YOLO root directory
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
from utils.callbacks import Callbacks
from utils.dataloaders import create_dataloader
from utils.general import (LOGGER, TQDM_BAR_FORMAT, Profile, check_dataset, check_requirements,
check_yaml, coco80_to_coco91_class, colorstr, increment_path, non_max_suppression,
print_args, scale_boxes, xywh2xyxy, xyxy2xywh)
from utils.metrics import ConfusionMatrix, ap_per_class, box_iou
from utils.plots import output_to_target, plot_images, plot_val_study
from utils.torch_utils import smart_inference_mode
import pycuda.autoinit
import pycuda.driver as cuda
import tensorrt as trt
class HostDeviceMem(object):
def __init__(self, host_mem, device_mem):
self.host = host_mem
self.device = device_mem
def __str__(self):
return "Host:\n" + str(self.host) + "\nDevice:\n" + str(self.device)
def __repr__(self):
return self.__str__()
## https://docs.nvidia.com/deeplearning/tensorrt/migration-guide/index.html
def do_inference(context, inputs, outputs, stream):
# Transfer input data to the GPU.
[cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs]
# Run inference.
context.execute_async_v3(stream_handle=stream.handle)
# Transfer predictions back from the GPU.
[cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs]
# Synchronize the stream
stream.synchronize()
# Return only the host outputs.
return [out.host for out in outputs]
def allocate_buffers(engine):
'''
Allocates all buffers required for an engine, i.e. host/device inputs/outputs.
'''
inputs = []
outputs = []
bindings = []
stream = cuda.Stream()
for i in range(engine.num_io_tensors):
tensor_name = engine.get_tensor_name(i)
size = trt.volume(engine.get_tensor_shape(tensor_name))
dtype = trt.nptype(engine.get_tensor_dtype(tensor_name))
# Allocate host and device buffers
host_mem = cuda.pagelocked_empty(size, dtype) # page-locked memory buffer (won't swapped to disk)
device_mem = cuda.mem_alloc(host_mem.nbytes)
# Append the device buffer address to device bindings.
# When cast to int, it's a linear index into the context's memory (like memory address).
bindings.append(int(device_mem))
# Append to the appropriate input/output list.
if engine.get_tensor_mode(tensor_name) == trt.TensorIOMode.INPUT:
inputs.append(HostDeviceMem(host_mem, device_mem))
else:
outputs.append(HostDeviceMem(host_mem, device_mem))
return inputs, outputs, bindings, stream
def save_one_txt(predn, save_conf, shape, file):
# Save one txt result
gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh
for *xyxy, conf, cls in predn.tolist():
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
with open(file, 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
def save_one_json(predn, jdict, path, class_map):
# Save one JSON result {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}
image_id = int(path.stem) if path.stem.isnumeric() else path.stem
box = xyxy2xywh(predn[:, :4]) # xywh
box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
for p, b in zip(predn.tolist(), box.tolist()):
jdict.append({
'image_id': image_id,
'category_id': class_map[int(p[5])],
'bbox': [round(x, 3) for x in b],
'score': round(p[4], 5)})
def process_batch(detections, labels, iouv):
"""
Return correct prediction matrix
Arguments:
detections (array[N, 6]), x1, y1, x2, y2, conf, class
labels (array[M, 5]), class, x1, y1, x2, y2
Returns:
correct (array[N, 10]), for 10 IoU levels
"""
correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)
iou = box_iou(labels[:, 1:], detections[:, :4])
correct_class = labels[:, 0:1] == detections[:, 5]
for i in range(len(iouv)):
x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match
if x[0].shape[0]:
matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou]
if x[0].shape[0] > 1:
matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
# matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
correct[matches[:, 1].astype(int), i] = True
return torch.tensor(correct, dtype=torch.bool, device=iouv.device)
@smart_inference_mode()
def run(
data,
engine_file=None, # model.pt path(s)
batch_size=1, # batch size
imgsz=640, # inference size (pixels)
conf_thres=0.001, # confidence threshold
iou_thres=0.7, # NMS IoU threshold
max_det=300, # maximum detections per image
task='val', # train, val, test, speed or study
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
workers=8, # max dataloader workers (per RANK in DDP mode)
single_cls=False, # treat as single-class dataset
augment=False, # augmented inference
verbose=False, # verbose output
save_txt=False, # save results to *.txt
save_hybrid=False, # save label+prediction hybrid results to *.txt
save_conf=False, # save confidences in --save-txt labels
save_json=False, # save a COCO-JSON results file
project=ROOT / 'runs/val', # save to project/name
name='exp', # save to project/name
exist_ok=False, # existing project/name ok, do not increment
half=True, # use FP16 half-precision inference
dnn=False, # use OpenCV DNN for ONNX inference
min_items=0, # Experimental
model=None,
dataloader=None,
save_dir=Path(''),
plots=True,
callbacks=Callbacks(),
compute_loss=None,
prefix=colorstr('VAL-TRT:')
):
stride=32
logger = trt.Logger(trt.Logger.INFO)
runtime = trt.Runtime(logger)
engine = runtime.deserialize_cuda_engine(open(engine_file, 'rb') .read())
inputs, outputs, bindings, stream = allocate_buffers(engine)
inputshape = [engine.get_tensor_shape(binding) for binding in engine][0]
imgsz=inputshape[3] - stride ## set imgz from engine
outputshape = [engine.get_tensor_shape(binding) for binding in engine][1]
context = engine.create_execution_context()
for i in range(engine.num_io_tensors):
context.set_tensor_address(engine.get_tensor_name(i), bindings[i])
# Initialize/load model and set device
training = False
device = torch.device("cpu")
gs = 32
# Directories
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
data = check_dataset(data) # check
# Configure
is_coco = isinstance(data.get('val'), str) and data['val'].endswith(f'val2017.txt') # COCO dataset
nc = 1 if single_cls else int(data['nc']) # number of classes
iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for [email protected]:0.95
niou = iouv.numel()
# Dataloader
if not training:
task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images
dataloader = create_dataloader(data[task],
imgsz,
batch_size,
stride,
single_cls,
pad=0.5,
rect=True,
workers=workers,
min_items=0,
prefix=colorstr(f'{task}: '))[0]
seen = 0
confusion_matrix = ConfusionMatrix(nc=nc)
#names = model.names if hasattr(model, 'names') else model.module.names # get class names
names=data['names']
if isinstance(names, (list, tuple)): # old format
names = dict(enumerate(names))
class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
s = ('%22s' + '%11s' * 6) % ('Class', 'Images', 'Instances', 'P', 'R', 'mAP50', 'mAP50-95')
tp, fp, p, r, f1, mp, mr, map50, ap50, map = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
dt = Profile(), Profile(), Profile() # profiling times
loss = torch.zeros(3, device=device)
jdict, stats, ap, ap_class = [], [], [], []
callbacks.run('on_val_start')
pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar
for batch_i, (im, targets, paths, shapes) in enumerate(pbar):
callbacks.run('on_val_batch_start')
with dt[0]:
im = (im / 255.0).float() # 0 - 255 to 0.0 - 1.0
# dataloader is setup pad=0.5
input_image = torch.full((inputshape[1], inputshape[2], inputshape[3]), 114 / 255.0, dtype=torch.float32)
im_cp=im.squeeze(0)
input_image[:, :im_cp.size(1), :im_cp.size(2)] = im_cp
targets = targets.to(device)
nb, _, height, width = im.shape # batch size, channels, height, width
# Run model
with dt[1]:
inputs[0].host = input_image.data.numpy()
trt_outputs = do_inference(context, inputs=inputs, outputs=outputs, stream = stream)
preds = torch.Tensor(trt_outputs[0].reshape(outputshape))
# Run NMS
targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device) # to pixels
lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling
with dt[2]:
preds = non_max_suppression(preds,
conf_thres,
iou_thres,
labels=lb,
multi_label=True,
agnostic=single_cls,
max_det=max_det)
# Metrics
for si, pred in enumerate(preds):
labels = targets[targets[:, 0] == si, 1:]
nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions
path, shape = Path(paths[si]), shapes[si][0]
correct = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init
seen += 1
if npr == 0:
if nl:
stats.append((correct, *torch.zeros((2, 0), device=device), labels[:, 0]))
if plots:
confusion_matrix.process_batch(detections=None, labels=labels[:, 0])
continue
# Predictions
if single_cls:
pred[:, 5] = 0
predn = pred.clone()
scale_boxes(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred
# Evaluate
if nl:
tbox = xywh2xyxy(labels[:, 1:5]) # target boxes
scale_boxes(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels
labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels
correct = process_batch(predn, labelsn, iouv)
if plots:
confusion_matrix.process_batch(predn, labelsn)
stats.append((correct, pred[:, 4], pred[:, 5], labels[:, 0])) # (correct, conf, pcls, tcls)
# Save/log
if save_txt:
save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
if save_json:
save_one_json(predn, jdict, path, class_map) # append to COCO-JSON dictionary
callbacks.run('on_val_image_end', pred, predn, path, names, im[si])
# Plot images
if plots and batch_i < 3:
plot_images(im, targets, paths, save_dir / f'val_batch{batch_i}_labels.jpg', names) # labels
plot_images(im, output_to_target(preds), paths, save_dir / f'val_batch{batch_i}_pred.jpg', names) # pred
callbacks.run('on_val_batch_end', batch_i, im, targets, paths, shapes, preds)
# Compute metrics
stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy
if len(stats) and stats[0].any():
tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)
ap50, ap = ap[:, 0], ap.mean(1) # [email protected], [email protected]:0.95
mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
nt = np.bincount(stats[3].astype(int), minlength=nc) # number of targets per class
# Print results
pf = '%22s' + '%11i' * 2 + '%11.3g' * 4 # print format
LOGGER.info(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
if nt.sum() == 0:
LOGGER.warning(f'WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels')
# Print results per class
if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):
for i, c in enumerate(ap_class):
LOGGER.info(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
# Print speeds
t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image
if not training:
shape = (batch_size, 3, imgsz, imgsz)
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t)
# Plots
if plots:
confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
callbacks.run('on_val_end', nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix)
# Save JSON
if save_json and len(jdict):
w = Path(engine_file[0] if isinstance(engine_file, list) else engine_file).stem if engine_file is not None else '' # weights
anno_json = str(Path(data.get('path', '../coco')) / 'annotations/instances_val2017.json') # annotations json
pred_json = str(save_dir / f"{w}_predictions.json") # predictions json
LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...')
with open(pred_json, 'w') as f:
json.dump(jdict, f)
try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
check_requirements('pycocotools')
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
anno = COCO(anno_json) # init annotations api
pred = anno.loadRes(pred_json) # init predictions api
eval = COCOeval(anno, pred, 'bbox')
if is_coco:
eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # image IDs to evaluate
eval.evaluate()
eval.accumulate()
eval.summarize()
map, map50 = eval.stats[:2] # update results ([email protected]:0.95, [email protected])
except Exception as e:
LOGGER.info(f'pycocotools unable to run: {e}')
# Return results
if not training:
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
maps = np.zeros(nc) + map
for i, c in enumerate(ap_class):
maps[c] = ap[i]
eval_mp, eval_mr, eval_map50, eval_map = round(mp, 4), round(mr, 4), round(map50, 4), round(map, 4)
LOGGER.info(f'\n{prefix} Eval TRT - AP: {eval_map} AP50: {eval_map50} Precision: {eval_mp} Recall: {eval_mr}')
return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t
def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--data', type=str, default=ROOT / 'data/coco.yaml', help='dataset.yaml path')
parser.add_argument('--engine-file', type=str, default=ROOT / 'yolo.engine', help='model path(s)')
parser.add_argument('--conf-thres', type=float, default=0.001, help='confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.7, help='NMS IoU threshold')
parser.add_argument('--max-det', type=int, default=300, help='maximum detections per image')
parser.add_argument('--task', default='val', help='train, val, test, speed or study')
parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 ')
parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')
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')
parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt')
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
parser.add_argument('--save-json', action='store_true', help='save a COCO-JSON results file')
parser.add_argument('--project', default=ROOT / 'runs/val_trt', help='save to project/name')
parser.add_argument('--name', default='exp', help='save to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
opt = parser.parse_args()
opt.data = check_yaml(opt.data) # check YAML
opt.save_json |= opt.data.endswith('coco.yaml')
opt.save_txt |= opt.save_hybrid
print_args(vars(opt))
return opt
def main(opt):
#check_requirements(exclude=('tensorboard', 'thop'))
if opt.task in ('train', 'val', 'test'): # run normally
if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466
LOGGER.info(f'WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results')
if opt.save_hybrid:
LOGGER.info('WARNING ⚠️ --save-hybrid will return high mAP from hybrid labels, not from predictions alone')
run(**vars(opt))
else:
engine_file = opt.engine_file if isinstance(opt.engine_file, list) else [opt.engine_file]
opt.half = torch.cuda.is_available() and opt.device != 'cpu' # FP16 for fastest results
if opt.task == 'speed': # speed benchmarks
# python val.py --task speed --data coco.yaml --batch 1 --weights yolo.pt...
opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False
for opt.engine_file in engine_file:
run(**vars(opt), plots=False)
elif opt.task == 'study': # speed vs mAP benchmarks
# python val.py --task study --data coco.yaml --iou 0.7 --weights yolo.pt...
for opt.engine_file in engine_file:
f = f'study_{Path(opt.data).stem}_{Path(opt.engine_file).stem}.txt' # filename to save to
x, y = list(range(256, 1536 + 128, 128)), [] # x axis (image sizes), y axis
for opt.imgsz in x: # img-size
LOGGER.info(f'\nRunning {f} --imgsz {opt.imgsz}...')
r, _, t = run(**vars(opt), plots=False)
y.append(r + t) # results and times
np.savetxt(f, y, fmt='%10.4g') # save
os.system('zip -r study.zip study_*.txt')
plot_val_study(x=x) # plot
if __name__ == "__main__":
opt = parse_opt()
main(opt)