import unittest
import torch
from evaluate.coco_eval import run_eval
from lib.network.rtpose_vgg import get_model, use_vgg
from torch import load
#Notice, if you using the
with torch.autograd.no_grad():
# this path is with respect to the root of the project
weight_name = '/home/tensorboy/Downloads/pose_model.pth'
state_dict = torch.load(weight_name)
model = get_model(trunk='vgg19')
#model = torch.nn.DataParallel(model).cuda()
model.load_state_dict(state_dict)
model.eval()
model.float()
model = model.cuda()
# The choice of image preprocessing include: 'rtpose', 'inception', 'vgg' and 'ssd'.
# If you use the converted model from caffe, it is 'rtpose' preprocess, the model trained in
# this repo used 'vgg' preprocess
run_eval(image_dir= '/home/tensorboy/data/coco/images/val2017', anno_file = '/home/tensorboy/data/coco/annotations/person_keypoints_val2017.json', vis_dir = '/home/tensorboy/data/coco/images/vis_val2017', model=model, preprocess='rtpose')
import unittest
import torch
from evaluate.coco_eval import run_eval
from network.rtpose_vgg import get_model, use_vgg
from torch import load
#Notice, if you using the
with torch.autograd.no_grad():
# this path is with respect to the root of the project
weight_name = './network/weight/best_pose.pth'
state_dict = torch.load(weight_name)
model = get_model(trunk='vgg19')
model = torch.nn.DataParallel(model).cuda()
model.load_state_dict(state_dict)
model.eval()
model.float()
model = model.cuda()
# The choice of image preprocessing include: 'rtpose', 'inception', 'vgg' and 'ssd'.
# If you use the converted model from caffe, it is 'rtpose' preprocess, the model trained in
# this repo used 'vgg' preprocess
run_eval(image_dir='/data/coco/images/',
anno_dir='/data/coco',
vis_dir='/data/coco/vis',
image_list_txt='./evaluate/image_info_val2014_1k.txt',
model=model,
preprocess='vgg')
import unittest
import torch
from evaluate.coco_eval import run_eval
from network.rtpose_vgg import get_model, use_vgg
from torch import load
#Notice, if you using the
with torch.autograd.no_grad():
# this path is with respect to the root of the project
# weight_name = './network/weight/best_pose.pth'
weight_name = '/home/jie/kiktech-seg/OpenPoseV1/network/weight/pose_model.pth'
state_dict = torch.load(weight_name)
model = get_model(trunk='vgg19')
model.load_state_dict(state_dict)
model = torch.nn.DataParallel(model).cuda()
model.eval()
model.float()
model = model.cuda()
# The choice of image preprocessing include: 'rtpose', 'inception', 'vgg' and 'ssd'.
# If you use the converted model from caffe, it is 'rtpose' preprocess, the model trained in
# this repo used 'vgg' preprocess
run_eval(image_dir='/home/jie/dataset/COCO/images/',
anno_dir='/home/jie/dataset/COCO/',
vis_dir='./coco_vis',
image_list_txt='image_info_val2014_1k.txt',
model=model, preprocess='vgg')
from lib.network.openpose import OpenPose_Model, use_vgg
from torch import load
#Notice, if you using the
with torch.autograd.no_grad():
# this path is with respect to the root of the project
weight_name = '/data/rtpose/rtpose_lr001/1/_ckpt_epoch_82.ckpt'
state_dict = torch.load(weight_name)['state_dict']
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[6:]
new_state_dict[name] = v
model = get_model(trunk='vgg19')
#model = openpose = OpenPose_Model(l2_stages=4, l1_stages=2, paf_out_channels=38, heat_out_channels=19)
#model = torch.nn.DataParallel(model).cuda()
model.load_state_dict(new_state_dict)
model.eval()
model.float()
model = model.cuda()
# The choice of image preprocessing include: 'rtpose', 'inception', 'vgg' and 'ssd'.
# If you use the converted model from caffe, it is 'rtpose' preprocess, the model trained in
# this repo used 'vgg' preprocess
run_eval(image_dir='/data/coco/images/val2017',
anno_file='/data/coco/annotations/person_keypoints_val2017.json',
vis_dir='/data/coco/images/vis_val2017',
model=model,
preprocess='vgg')
