def from_opts(cls, opt) -> InferencePipeline:
"""Load all trained models required from the locations indicated in opt."""
pinet = create_model(opt).eval()
args = DEFAULT_ARGS
args.opts = ['TEST.MODEL_FILE', opt.pose_estimator]
pose_estimator = PoseEstimator(args, opt.gpu_ids != [])
segmentator = SegmentationModel(opt.segmentation_model, bool(opt.gpu_ids))
return cls(pose_estimator, pinet, segmentator, opt)
from options.test_options import TestOptions
from data.data_loader import CreateDataLoader
from models.PINet20 import create_model
from util.visualizer import Visualizer
from util import html
import time
opt = TestOptions().parse()
opt.nThreads = 1 # test code only supports nThreads = 1
opt.batchSize = 1 # test code only supports batchSize = 1
opt.serial_batches = True # no shuffle
opt.no_flip = True # no flip
data_loader = CreateDataLoader(opt)
dataset = data_loader.load_data()
model = create_model(opt)
visualizer = Visualizer(opt)
# create website
web_dir = os.path.join(opt.results_dir, opt.name,
'%s_%s' % (opt.phase, opt.which_epoch))
webpage = html.HTML(
web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' %
(opt.name, opt.phase, opt.which_epoch))
print(opt.how_many)
print(len(dataset))
model = model.eval()
print(model.training)