Yolov5模型加载与推理的简化代码，只需要一个文件即可，不依赖其他文件。
如果报下列错误，请下载精简版的models文件夹。下载地址：Yolov5最简推理代码-深度学习文档类资源-CSDN下载。
File "D:\ProgramData\Anaconda3\lib\site-packages\torch\serialization.py", line 853, in _load
result = unpickler.load()
ModuleNotFoundError: No module named 'models'这个错误是因为torch保存了模型存储路径和结构，但是并没有保存计算过程。因此，需要models文件中对模型的定义。
File "D:\ProgramData\Anaconda3\lib\site-packages\torch\serialization.py", line 853, in _load
result = unpickler.load()
ModuleNotFoundError: No module named 'models'
Yolov5最简推理代码如下所示：
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 1 16:12:34 2022
@author: suiyingy
"""
import cv2
import torch
import torchvision
import numpy as np
import time
import os
def xywh2xyxy(x):
# Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x
y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x
y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y
return y
def xyxy2xywh(x):
# Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center
y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center
y[:, 2] = x[:, 2] - x[:, 0] # width
y[:, 3] = x[:, 3] - x[:, 1] # height
return y
def box_iou(box1, box2):
# https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
"""
Return intersection-over-union (Jaccard index) of boxes.
Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
Arguments:
box1 (Tensor[N, 4])
box2 (Tensor[M, 4])
Returns:
iou (Tensor[N, M]): the NxM matrix containing the pairwise
IoU values for every element in boxes1 and boxes2
"""
def box_area(box):
# box = 4xn
return (box[2] - box[0]) * (box[3] - box[1])
area1 = box_area(box1.T)
area2 = box_area(box2.T)
# inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
return inter / (area1[:, None] + area2 - inter) # iou = inter / (area1 + area2 - inter)
def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False,
labels=(), max_det=300):
"""Runs Non-Maximum Suppression (NMS) on inference results
Returns:
list of detections, on (n,6) tensor per image [xyxy, conf, cls]
"""
nc = prediction.shape[2] - 5 # number of classes
xc = prediction[..., 4] > conf_thres # candidates
# Checks
assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'
assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
# Settings
min_wh, max_wh = 2, 7680 # (pixels) minimum and maximum box width and height
max_nms = 30000 # maximum number of boxes into torchvision.ops.nms()
time_limit = 10.0 # seconds to quit after
redundant = True # require redundant detections
multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img)
merge = False # use merge-NMS
t = time.time()
output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0]
for xi, x in enumerate(prediction): # image index, image inference
# Apply constraints
x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height
x = x[xc[xi]] # confidence
# Cat apriori labels if autolabelling
if labels and len(labels[xi]):
lb = labels[xi]
v = torch.zeros((len(lb), nc + 5), device=x.device)
v[:, :4] = lb[:, 1:5] # box
v[:, 4] = 1.0 # conf
v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls
x = torch.cat((x, v), 0)
# If none remain process next image
if not x.shape[0]:
continue
# Compute conf
x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf
# Box (center x, center y, width, height) to (x1, y1, x2, y2)
box = xywh2xyxy(x[:, :4])
# Detections matrix nx6 (xyxy, conf, cls)
if multi_label:
i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
else: # best class only
conf, j = x[:, 5:].max(1, keepdim=True)
x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
# Filter by class
if classes is not None:
x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
# Apply finite constraint
# if not torch.isfinite(x).all():
# x = x[torch.isfinite(x).all(1)]
# Check shape
n = x.shape[0] # number of boxes
if not n: # no boxes
continue
elif n > max_nms: # excess boxes
x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence
# Batched NMS
c = x[:, 5:6] * (0 if agnostic else max_wh) # classes
boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores
i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS
if i.shape[0] > max_det: # limit detections
i = i[:max_det]
if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean)
# update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix
weights = iou * scores[None] # box weights
x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes
if redundant:
i = i[iou.sum(1) > 1] # require redundancy
output[xi] = x[i]
if (time.time() - t) > time_limit:
break # time limit exceeded
return output
def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
# Rescale coords (xyxy) from img1_shape to img0_shape
if ratio_pad is None: # calculate from img0_shape
gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
else:
gain = ratio_pad[0][0]
pad = ratio_pad[1]
coords[:, [0, 2]] -= pad[0] # x padding
coords[:, [1, 3]] -= pad[1] # y padding
coords[:, :4] /= gain
clip_coords(coords, img0_shape)
return coords
def clip_coords(boxes, shape):
# Clip bounding xyxy bounding boxes to image shape (height, width)
if isinstance(boxes, torch.Tensor): # faster individually
boxes[:, 0].clamp_(0, shape[1]) # x1
boxes[:, 1].clamp_(0, shape[0]) # y1
boxes[:, 2].clamp_(0, shape[1]) # x2
boxes[:, 3].clamp_(0, shape[0]) # y2
else: # np.array (faster grouped)
boxes[:, [0, 2]] = boxes[:, [0, 2]].clip(0, shape[1]) # x1, x2
boxes[:, [1, 3]] = boxes[:, [1, 3]].clip(0, shape[0]) # y1, y2
def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
# Resize and pad image while meeting stride-multiple constraints
shape = im.shape[:2] # current shape [height, width]
if isinstance(new_shape, int):
new_shape = (new_shape, new_shape)
# Scale ratio (new / old)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
if not scaleup: # only scale down, do not scale up (for better val mAP)
r = min(r, 1.0)
# Compute padding
ratio = r, r # width, height ratios
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
if auto: # minimum rectangle
dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding
elif scaleFill: # stretch
dw, dh = 0.0, 0.0
new_unpad = (new_shape[1], new_shape[0])
ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
dw /= 2 # divide padding into 2 sides
dh /= 2
if shape[::-1] != new_unpad: # resize
im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
return im, ratio, (dw, dh)
def preprocess(path, img_size, stride, auto):
img_bgr = cv2.imread(path) # BGR
assert img_bgr is not None, f'Image Not Found {path}'
# Padded resize
img_rgb = letterbox(img_bgr, img_size, stride=stride, auto=auto)[0]
# Convert
img_rgb = img_rgb.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
img_rgb = np.ascontiguousarray(img_rgb)
return img_rgb, img_bgr
class Detect():
def __init__(self, weights='yolov5s.pt'):
self.device = 'cpu'
self.weights = weights
self.model = None
self.imgsz = (640, 640)
self.conf_thres=0.25
self.iou_thres=0.45
self.save_txt=True
self.save_conf=True
self.save_dir = './detect/'
if not os.path.exists(self.save_dir):
os.makedirs(self.save_dir)
if torch.cuda.is_available() and torch.cuda.device_count() > 1:
self.device = torch.device('cuda:0')
self.init_model()
self.stride = max(int(self.model.stride.max()), 32)
def init_model(self):
ckpt = torch.load(self.weights, map_location=self.device) # load
ckpt = (ckpt.get('ema') or ckpt['model']).float() # FP32 model
fuse = True
self.model = ckpt.fuse().eval() if fuse else ckpt.eval() # fused or un-fused model in eval mode
self.model.float()
def infer_image(self, image_path):
im, im0 = preprocess(image_path, img_size=self.imgsz, stride=self.stride, auto=True)
im = torch.from_numpy(im).to(self.device).float() / 255
if len(im.shape) == 3:
im = im[None] # expand for batch dim
# Inference
pred = self.model(im, augment=False, visualize=False)[0]
# NMS
pred = non_max_suppression(pred, self.conf_thres, self.iou_thres, None, False, max_det=1000)
det = pred[0]
fn = image_path.rsplit('/', 1)[-1]
txt_path = self.save_dir + fn.split('.', 1)[0] + '.txt'
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()
# Write results
for *xyxy, conf, cls in reversed(det):
if self.save_txt: # Write to file
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
line = (cls, *xywh, conf) if self.save_conf else (cls, *xywh) # label format
with open(txt_path, 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
if __name__ == "__main__":
detect = Detect('best.pt')
detect.infer_image('test.png')
Yolov5模型加载与推理的简化代码，只需要一个文件即可，不依赖其他文件。
如果报下列错误，请下载精简版的models文件夹。下载地址：Yolov5最简推理代码-深度学习文档类资源-CSDN下载。
File "D:\ProgramData\Anaconda3\lib\site-packages\torch\serialization.py", line 853, in _load
result = unpickler.load()
ModuleNotFoundError: No module named 'models'这个错误是因为torch保存了模型存储路径和结构，但是并没有保存计算过程。因此，需要models文件中对模型的定义。
File "D:\ProgramData\Anaconda3\lib\site-packages\torch\serialization.py", line 853, in _load
result = unpickler.load()
ModuleNotFoundError: No module named 'models'
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