问题遇到的现象和发生背景
在vitis-ai的vai_q_pytorch下运行yolov5模型，出现了报错
以下是使用的量化脚本的代码
from pytorch_nndct.apis import torch_quantizer
import argparse
import os
import sys
from pathlib import Path
from threading import Thread
import numpy as np
import torch
from tqdm import tqdm
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0] # YOLOv5 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 models.common import DetectMultiBackend
from utils.callbacks import Callbacks
from utils.dataloaders import create_dataloader
from utils.general import (LOGGER, check_dataset, check_img_size, check_requirements, check_yaml,
coco80_to_coco91_class, colorstr, emojis, increment_path, non_max_suppression, print_args,
scale_coords, 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 select_device, time_sync
device = torch.device("cpu")
BATCHSIZE=1
parser = argparse.ArgumentParser()
parser.add_argument('--data_dir',default="datasets/val",
help='Data set directory, when quant_mode=calib, it is for calibration, while quant_mode=test it is for evaluation')
parser.add_argument('--model_dir',default="weights",
help='Trained model file path. Download pretrained model from the following url and put it in model_dir specified path: https://download.pytorch.org/models/resnet18-5c106cde.pth')
parser.add_argument('--subset_len',default=1,type=int,
help='subset_len to evaluate model, using the whole validation dataset if it is not set')
parser.add_argument('--batch_size',default=BATCHSIZE,type=int,
help='input data batch size to evaluate model')
parser.add_argument('--quant_mode',default='float',choices=['float', 'calib', 'test'],
help='quantization mode. 0: no quantization, evaluate float model, calib: quantize, test: evaluate quantized model')
parser.add_argument('--fast_finetune',dest='fast_finetune',default=False,action='store_true',
help='fast finetune model before calibration')
parser.add_argument('--deploy',dest='deploy',action='store_true',default=False,
help='export xmodel for deployment')
args, _ = parser.parse_known_args()
def process_batch(detections, labels, iouv):
"""
Return correct predictions matrix. Both sets of boxes are in (x1, y1, x2, y2) format.
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 = torch.zeros(detections.shape[0], iouv.shape[0], dtype=torch.bool, device=iouv.device)
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 correct
def _make_grid(anchors,stride,nx=20, ny=20, i=0):
# d = anchors[i].device
t = anchors[i].dtype
shape = 1, 3, ny, nx, 2
y, x = torch.arange(ny, device=device, dtype=t), torch.arange(nx, device=device, dtype=t)
yv, xv = torch.meshgrid(y,x)
grid = torch.stack((xv, yv), 2).expand(shape) - 0.5 # add grid offset, i.e. y = 2.0 * x - 0.5
anchor_grid = (anchors[i] * stride[i]).view((1, 3, 1, 1, 2)).expand(shape)
return grid, anchor_grid
@torch.no_grad()
def evaluate(
model,
batch_size=BATCHSIZE, # batch size
imgsz=640, # inference size (pixels)
conf_thres=0.5, # confidence threshold
iou_thres=0.1, # NMS IoU threshold
workers=8, # max dataloader workers (per RANK in DDP mode)
verbose=False, # verbose output
save_txt=False, # save results to *.txt
project=ROOT / 'runs/quant', # save to project/name
name='exp', # save to project/name
exist_ok=False, # existing project/name ok, do not increment
plots=True,
callbacks=Callbacks(),
):
# 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
model = model.to(device)
# stride, pt= model.stride, model.pt
imgsz = check_img_size(imgsz, s=32) # check image size
# Configure
model.eval()
nc =2
iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95
niou = iouv.numel()
names=['tomato']
dataloader = create_dataloader(args.data_dir, imgsz=imgsz, batch_size=batch_size, pad=0.5,stride=32,
workers=workers, prefix=colorstr(f'quant: '))[0]
seen = 0
# confusion_matrix = ConfusionMatrix(nc=nc)
names = {k: v for k, v in enumerate(names)}
class_map = list(range(1000))
s = ('%20s' + '%11s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
dt, p, r, f1, mp, mr, map50, map = [0.0, 0.0, 0.0], 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
jdict, stats, ap, ap_class = [], [], [], []
pbar = tqdm(dataloader, desc=s, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar
for batch_i, (im, targets, paths, shapes) in enumerate(pbar):
# targets (tensor): (n_gt_all_batch, [img_index clsid cx cy w h )
# shapes (tensor): (b, [(h_raw, w_raw), (hw_ratios, wh_paddings)])
t1 = time_sync()
im = im.to(device, non_blocking=True)
targets = targets.to(device)
im = im.float() # uint8 to fp16/32
im /= 255 # 0 - 255 to 0.0 - 1.0
nb, _, height, width = im.shape # batch size, channels, height, width
t2 = time_sync()
dt[0] += t2 - t1
# Inference
# out, train_out =model(im) # inference, loss outputs
x=model(im)
# ckpt = torch.load(file_path, map_location=device)
# paras=ckpt['model'].yaml
nc = 2 # number of classfication
no = nc+5 #each anchor's output，include nc(class)+conf(1)+xywh(4)，故nc+5
anchors = [[1.25, 1.625, 2, 3.75, 4.125, 2.875], [1.875, 3.8125, 3.875, 2.8125, 3.6875, 7.4375], [3.625, 2.8125, 4.875, 6.1875, 11.65625, 10.1875]]
nl = 3 # number of detection layers
na = 3 # number of anchors
grid = [torch.zeros(1)] * nl # init grid
anchors = torch.tensor(anchors).float().view(nl, -1, 2)
# register_buffer('anchors', a)
anchor_grid=[torch.zeros(1)] * nl
stride = [8, 16, 32]
z = []
for i in range(nl):
print(x[i].shape)
bs, _, ny, nx, _no= x[i].shape
# x[i] = x[i].view(bs, na, no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
if grid[i].shape[2:4] != x[i].shape[2:4]:
grid[i], anchor_grid[i] = _make_grid(anchors,stride,nx, ny, i)
# grid[i]= _make_grid(nx, ny, i)
y = x[i].sigmoid() # (tensor): (b, self.na, h, w, self.no)
y[..., 0:2] = (y[..., 0:2] * 2 + grid[i]) * stride[i] # xy
y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * anchor_grid[i] # wh
z.append(y.view(bs, -1, no)) # z (list[P3_pred]): Torch.Size(b, n_anchors, self.no)
out,train_out = torch.cat(z, 1), x
dt[1] += time_sync() - t2
# Loss
# loss += compute_loss([x.float() for x in train_out], targets)[1] # box, obj, cls, theta
# NMS
t3 = time_sync()
out = non_max_suppression(out, conf_thres, iou_thres, multi_label=True) # list*(n, [cxcylsθ, conf, cls]) θ ∈ [-pi/2, pi/2)
dt[2] += time_sync() - t3
targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels
# Metrics
for si, pred in enumerate(out):
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((3, 0), device=device)))
continue
# Predictions
# if single_cls:
# pred[:, 5] = 0
predn = pred.clone()
scale_coords(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred
# Evaluate
if nl:
tbox = xywh2xyxy(labels[:, 1:5]) # target boxes
scale_coords(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' / (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:
plot_images(im, targets, paths, save_dir / f'val_batch{batch_i}_labels.jpg', names) # labels
plot_images(im, output_to_target(out), paths, save_dir / f'val_batch{batch_i}_pred.jpg', names) # pred
callbacks.run('on_val_batch_end')
# 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) # 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(int), minlength=nc) # number of targets per class
else:
nt = torch.zeros(1)
# Print results
pf = '%20s' + '%11i' * 2 + '%11.3g' * 4 # print format
LOGGER.info(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
# Print results per class
if (verbose or (nc < 50)) 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 / 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')
# Save JSON
# Return results
# model.float() # for training
# 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]
return (mp, mr, map50, map), maps, t
def quantization(title='optimize',file_path=''):
# data_dir = args.data_dir
quant_mode = args.quant_mode
finetune = args.fast_finetune
deploy = args.deploy
batch_size = args.batch_size
subset_len = args.subset_len
if quant_mode != 'test' and deploy:
deploy = False
print(r'Warning: Exporting xmodel needs to be done in quantization test mode, turn off it in this running!')
if deploy and (batch_size != 1 or subset_len != 1):
print(
r'Warning: Exporting xmodel needs batch size to be 1 and only 1 iteration of inference, change them automatically!')
batch_size = 1
subset_len = 1
# model.load_state_dict(torch.load(file_path))
# model=torch.load(file_path,map_location=device)['model'].float()
model = DetectMultiBackend(file_path)
# ckpt = torch.load(file_path, map_location=device) # load checkpoint
# model = Model(ckpt['model'].yaml, ch=3, nc=16).to(device) # create
# exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else [] # exclude keys
# csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32
# csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect
# model.load_state_dict(csd, strict=False) # load
input = torch.randn([1, 3, 640, 640],device=device)
if quant_mode == 'float':
quant_model = model
else:
quantizer = torch_quantizer(
quant_mode, model, (input), device=device,bitwidth=8)
quant_model = quantizer.quant_model
quant_model = quant_model.to(device)
# names = ['plane', 'baseball-diamond', 'bridge', 'ground-track-field', 'small-vehicle', 'large-vehicle', 'ship',
# 'tennis-court', 'basketball-court', 'storage-tank', 'soccer-ball-field', 'roundabout', 'harbor',
# 'swimming-pool', 'helicopter', 'container-crane']
# val_loader = create_dataloader("dataset/quant_dir", imgsz=1024, batch_size=batch_size, names=names, pad=0.5,prefix=colorstr('val: '))[0]
# ft_loader = create_dataloader("dataset/quant_dir", imgsz=1024, batch_size=batch_size, names=names, pad=0.5,prefix=colorstr('val: '))[0]
# fast finetune model or load finetuned parameter before test
if finetune == True:
# ft_loader, _ = load_data(
# subset_len=1024,
# train=False,
# batch_size=batch_size,
# sample_method='None',
# data_dir=data_dir,
# model_name=model_name)
if quant_mode == 'calib':
quantizer.fast_finetune(evaluate, (quant_model,))
elif quant_mode == 'test':
quantizer.load_ft_param()
print(evaluate(model=quant_model))
# handle quantization result
if quant_mode == 'calib':
quantizer.export_quant_config()
print('\n\n ========== Calibration Completed! ==========\n\n')
if deploy:
quantizer.export_xmodel(deploy_check=False)
print('\n\n ========== Xmodel export Completed! ==========\n\n')
if __name__ == '__main__':
#model_name = "yolov5_s_v6.1"
#file_path = os.path.join(args.model_dir, model_name + '.pt')
model_name = "bestt"
file_path = os.path.join(args.model_dir, model_name + '.pt')
feature_test = ' float model evaluation'
if args.quant_mode != 'float':
feature_test = ' quantization'
# force to merge BN with CONV for better quantization accuracy
args.optimize = 1
feature_test += ' with optimization'
else:
feature_test = ' float model evaluation'
title = model_name + feature_test
print("-------- Start {} test ".format(model_name))
# calibration or evaluation
quantization(
title=title,
file_path=file_path)
print("-------- End of {} test ".format(model_name))
运行结果及详细报错内容
Traceback (most recent call last):
File "/opt/vitis_ai/conda/envs/vitis-ai-pytorch/lib/python3.7/site-packages/pytorch_nndct/nn/modules/module_template.py", line 131, in forward
output = caller(*args, **kwargs)
TypeError: meshgrid() got an unexpected keyword argument 'tensors'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "quantize.py", line 364, in <module>
file_path=file_path)
File "quantize.py", line 332, in quantization
print(evaluate(model=quant_model))
File "/opt/vitis_ai/conda/envs/vitis-ai-pytorch/lib/python3.7/site-packages/torch/autograd/grad_mode.py", line 28, in decorate_context
return func(*args, **kwargs)
File "quantize.py", line 146, in evaluate
x=model(im)
File "/opt/vitis_ai/conda/envs/vitis-ai-pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "quantize_result/DetectMultiBackend.py", line 449, in forward
output_module_189_0,output_module_189_1 = self.module_189(tensors=[output_module_187,output_module_185], indexing='ij')
File "/opt/vitis_ai/conda/envs/vitis-ai-pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/vitis_ai/conda/envs/vitis-ai-pytorch/lib/python3.7/site-packages/pytorch_nndct/nn/modules/module_template.py", line 137, in forward
output = caller(*inputs)
File "/opt/vitis_ai/conda/envs/vitis-ai-pytorch/lib/python3.7/site-packages/torch/functional.py", line 430, in meshgrid
return _meshgrid(*tensors, indexing=indexing)
File "/opt/vitis_ai/conda/envs/vitis-ai-pytorch/lib/python3.7/site-packages/torch/functional.py", line 445, in _meshgrid
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
TypeError: expected Tensor as element 0 in argument 0, but got list
解答思路和尝试过的方法
尝试过改meshgrid函数，即删掉torch，以为是该函数的问题，但发现还是会出现一样的报错。然后接着按错误的提示找到量化脚本中报错的行数，但是一个是文件路径、一个是打印信息，只能是模型推断的问题，但是不懂python，因此不知道x这个是否类型有问题，也尝试过输出x的类型，但都失败
