def parse_args():
parser = argparse.ArgumentParser(description='Train keypoints network')
# general
parser.add_argument('--cfg',
help='experiment configure file name',
required=True,
type=str)
args, rest = parser.parse_known_args()
# update config
update_config(args.cfg)
# training
parser.add_argument('--frequent',
help='frequency of logging',
default=config.PRINT_FREQ,
type=int)
parser.add_argument('--gpus', help='gpus', type=str)
parser.add_argument('--workers',
help='num of dataloader workers',
type=int)
args = parser.parse_args()
return args
def parse_args():
parser = argparse.ArgumentParser(description='Train localization network')
# general
#parser.add_argument('--cfg', help='experiment configure file name', required=True, type=str)
parser.add_argument(
'--cfg', default="experiments/charades/2D-TAN-16x16-K5L8-pool.yaml")
args, rest = parser.parse_known_args()
# update config
update_config(args.cfg)
# training
parser.add_argument('--gpus', help='gpus', type=str)
parser.add_argument('--workers',
help='num of dataloader workers',
type=int)
parser.add_argument('--dataDir', help='data path', type=str)
parser.add_argument('--modelDir', help='model path', type=str)
parser.add_argument('--logDir', help='log path', type=str)
parser.add_argument('--verbose',
default=False,
action="store_true",
help='print progress bar')
parser.add_argument('--tag', help='tags shown in log', type=str)
args = parser.parse_args()
return args
def parse_args():
"""
args for training.
"""
parser = argparse.ArgumentParser(description='Train Ocean')
# general
parser.add_argument('--cfg',
type=str,
default='experiments/train/Ocean.yaml',
help='yaml configure file name')
args, rest = parser.parse_known_args()
# update config
update_config(args.cfg)
parser.add_argument('--gpus', type=str, help='gpus')
parser.add_argument('--workers',
type=int,
help='num of dataloader workers')
parser.add_argument('--WORKDIR', type=str, default='')
parser.add_argument('--CHECKPOINT_DIR', type=str, default='')
parser.add_argument('--OUTPUT_DIR', type=str, default='')
parser.add_argument('--SUPERNET_PATH', type=str, default='')
parser.add_argument('--DP', type=int, default=0)
args = parser.parse_args()
return args
def parse_args():
parser = argparse.ArgumentParser(description='Train keypoints network')
# general
# It can be mobilenetv2, shufflenetv2, mnasnet, resnet18, darts, nasnet, pairnas
parser.add_argument('--net',
help='experiment configure file name',
default='pairnas',
type=str)
parser.add_argument(
'--cfg', default='experiments/coco_256x192_d256x3_adam_lr1e-3.ymal')
parser.add_argument('--dataset_path',
default='/raid/huangsh/datasets/MSCOCO2017/')
parser.add_argument('--gpu', type=str, default='0')
parser.add_argument('--flip', help='use flip test', default=True)
parser.add_argument('--post_process',
help='use post process',
action='store_true')
args, rest = parser.parse_known_args()
# update config
update_config(args.cfg)
# training
parser.add_argument('--workers',
help='num of dataloader workers',
type=int,
default=16)
parser.add_argument('--frequent',
help='frequency of logging',
default=config.PRINT_FREQ,
type=int)
parser.add_argument('--use-detect-bbox',
help='use detect bbox',
action='store_true')
parser.add_argument('--shift-heatmap',
help='shift heatmap',
action='store_true')
parser.add_argument('--coco-bbox-file',
help='coco detection bbox file',
type=str)
args = parser.parse_args()
return args
def parse_args():
parser = argparse.ArgumentParser(description='Train keypoints network')
# general
parser.add_argument('--cfg',
help='experiment configure file name',
required=True,
type=str)
args, rest = parser.parse_known_args()
# update config
update_config(args.cfg)
# training
parser.add_argument('--frequent',
help='frequency of logging',
default=config.PRINT_FREQ,
type=int)
parser.add_argument('--gpus', help='gpus', type=str)
parser.add_argument('--workers',
help='num of dataloader workers',
type=int)
parser.add_argument('--model-file', help='model state file', type=str)
parser.add_argument('--use-detect-bbox',
help='use detect bbox',
action='store_true')
parser.add_argument('--flip-test',
help='use flip test',
action='store_true')
parser.add_argument('--post-process',
help='use post process',
action='store_true')
parser.add_argument('--shift-heatmap',
help='shift heatmap',
action='store_true')
parser.add_argument('--coco-bbox-file',
help='coco detection bbox file',
type=str)
args = parser.parse_args()
return args
def pose_inference_on_image(cfg_path, image):
# Load config file
update_config(cfg_path)
torch.backends.cudnn.benchmark = config.CUDNN.BENCHMARK
torch.backends.cudnn.deterministic = config.CUDNN.DETERMINISTIC
torch.backends.cudnn.enabled = config.CUDNN.ENABLED
image_size = config.MODEL.IMAGE_SIZE[0]
# Create Model
model = models.pose3d_resnet.get_pose_net(config, is_train=False)
gpus = [int(i) for i in config.GPUS.split(',')]
model = torch.nn.DataParallel(model, device_ids=gpus)
print('Created model...')
checkpoint = torch.load(config.MODEL.RESUME, map_location='cpu')
model.load_state_dict(checkpoint)
model.eval()
print('Loaded pre-trained weights...')
image = cv2.resize(image, (image_size, image_size))
img_height, img_width, img_channels = image.shape
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
img_patch = convert_cvimg_to_tensor(image)
mean = np.array([123.675, 116.280, 103.530])
std = np.array([58.395, 57.120, 57.375])
# apply normalization
for n_c in range(img_channels):
if mean is not None and std is not None:
img_patch[n_c, :, :] = (img_patch[n_c, :, :] -
mean[n_c]) / std[n_c]
img_patch = torch.from_numpy(img_patch)
preds = model(img_patch[None, ...])
preds = get_joint_location_result(image_size, image_size, preds)[0, :, :3]
return image, preds
def parse_args():
parser = argparse.ArgumentParser(description='Train keypoints network')
# It can be mobilenetv2, shufflenetv2, mnasnet, darts, pairnas, nasnet
parser.add_argument('--net', default='pairnas', type=str)
parser.add_argument('--dataset_path',
default='/raid/huangsh/datasets/MSCOCO2017/')
parser.add_argument(
'--cfg', default='experiments/coco_256x192_d256x3_adam_lr1e-3.ymal')
parser.add_argument('--model', default='posenet')
parser.add_argument('--gpu', type=str, default='0')
args, rest = parser.parse_known_args()
# update config
update_config(args.cfg)
# training
parser.add_argument('--frequent',
help='frequency of logging',
default=config.PRINT_FREQ,
type=int)
parser.add_argument('--workers',
help='num of dataloader workers',
type=int,
default=16)
args = parser.parse_args()
return args
import torch
from torch.utils.data import DataLoader
import lib.models.models as models
from lib.utils import print_speed, load_pretrain, save_model
from lib.dataset import SiamFCDataset
from lib.core.config import config, update_config
from lib.core.function import siamfc_train
parser = argparse.ArgumentParser()
parser.add_argument('--cfg',
required=True,
type=str,
help='yaml configure file name')
args = parser.parse_args()
update_config(args.cfg)
print('Config:')
print(pprint.pformat(config))
print()
model = models.__dict__[config.SIAMFC.TRAIN.MODEL]()
model = load_pretrain(model, config.SIAMFC.TRAIN.PRETRAIN)
trainable_params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(trainable_params,
config.SIAMFC.TRAIN.LR,
momentum=config.SIAMFC.TRAIN.MOMENTUM,
weight_decay=config.SIAMFC.TRAIN.WEIGHT_DECAY)
def parse_args():
parser = argparse.ArgumentParser(
"Train the unsupervised human pose estimation network")
parser.add_argument(
'--cfg',
help="Specify the path of the path of the config(*.yaml)",
default='../cfg/default.yaml')
parser.add_argument(
'--use_gt',
action='store_true',
help='Specify whether to use 2d gt / predictions as inputs')
parser.add_argument('--model_dir',
help='Specify the directory of pretrained model',
default='')
parser.add_argument('--data_dir',
help="Specify the directory of data",
default=config.DATA_DIR)
parser.add_argument('--log_dir',
help='Specify the directory of output',
default=config.LOG_DIR)
parser.add_argument('--dataset_name',
help="Specify which dataset to use",
choices=["h36m", "mpi"],
default="h36m")
parser.add_argument(
'--workers',
help="Specify the number of workers for data loadering",
default=config.NUM_WORKERS)
parser.add_argument('--gpu',
help="Specify the gpu to use for training",
default='')
parser.add_argument('--debug',
action='store_true',
help="Turn on the debug mode")
parser.add_argument(
'--print_info',
action='store_true',
help="Whether to print detailed information in tqdm processing")
parser.add_argument(
'--eval',
action='store_true',
help="Evaluate the model on the dataset(i.e. generate: joint_3d_pre)")
parser.add_argument(
'--eval_suffix',
default=None,
help="Specify the suffix to save predictions on 3D in evaluation mode")
parser.add_argument('--pretrain',
default='',
help="Whether to use pretrain model")
parser.add_argument('--finetune_rotater',
action='store_true',
help="Load pretrained model and finetune rotater")
parser.add_argument('--print_interval', type=int, default=50)
args = parser.parse_args()
if args.cfg:
update_config(args.cfg)
else:
print("Using default config...")
update_dir(args.model_dir, args.log_dir, args.data_dir, args.debug)
return args
import torch
import torchvision.transforms as transforms
import torch.backends.cudnn as cudnn
import cv2
import numpy as np
import matplotlib.pyplot as plt
import argparse
cudnn.benchmark = config.CUDNN.BENCHMARK
torch.backends.cudnn.deterministic = config.CUDNN.DETERMINISTIC
torch.backends.cudnn.enabled = config.CUDNN.ENABLED
config.TEST.FLIP_TEST = True
update_config('pretrained/384x288_d256x3_adam_lr1e-3.yaml')
model = get_pose_net(config, is_train=False)
model.load_state_dict(torch.load('pretrained/pose_resnet_50_384x288.pth.tar'))
gpus = [int(i) for i in config.GPUS.split(',')]
model = torch.nn.DataParallel(model, device_ids=gpus).cuda()
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
toTensor = transforms.Compose([transforms.ToTensor(),
transforms.Normalize(mean, std)])
def get_keypoints(input_image):
'''
Calculates keypoints based on resnet
Input: Image
