I used the code to test yolov7 pose estimation.
import matplotlib.pyplot as plt
import torch
import cv2
from torchvision import transforms
import numpy as np
from utils.datasets import letterbox
from utils.general import non_max_suppression_kpt
from utils.plots import output_to_keypoint, plot_skeleton_kpts
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
weigths = torch.load('yolov7-w6-pose.pt', map_location=device)
model = weigths['model']
_ = model.float().eval()
if torch.cuda.is_available():
model.half().to(device)
image = cv2.imread('./person.jpg')
image = letterbox(image, 960, stride=32, auto=True)[0] #original is stride=64
image_ = image.copy()
image = transforms.ToTensor()(image)
image = torch.tensor(np.array([image.numpy()]))
if torch.cuda.is_available():
image = image.half().to(device)
output, _ = model(image)
output = non_max_suppression_kpt(output, 0.25, 0.65, nc=model.yaml['nc'], nkpt=model.yaml['nkpt'], kpt_label=True)
with torch.no_grad():
output = output_to_keypoint(output)
nimg = image[0].permute(1, 2, 0) * 255
nimg = nimg.cpu().numpy().astype(np.uint8)
nimg = cv2.cvtColor(nimg, cv2.COLOR_RGB2BGR)
for idx in range(output.shape[0]):
plot_skeleton_kpts(nimg, output[idx, 7:].T, 3)
%matplotlib inline
plt.figure(figsize=(8,8))
plt.axis('off')
plt.imshow(nimg)
plt.show()
When I change stride to 32:
image = letterbox(image, 960, stride=64, auto=True)[0] to
image = letterbox(image, 960, stride=32, auto=True)[0]
The error message is:RuntimeError: Sizes of tensors must match except in dimension 2. Got 24 and 23 (The offending index is 0)
Why can't I use stride=32?
