def __init__(self, select_largest=False, keep_all=True):
self.pnet = PNet().to(device)
self.rnet = RNet().to(device)
self.onet = ONet().to(device)
self.pnet.eval()
self.rnet.eval()
self.onet.eval()
self.refrence = get_reference_facial_points(default_square=True)
def __init__(self):
self.pnet = PNet().to(device)
self.rnet = RNet().to(device)
self.onet = ONet().to(device)
self.pnet.eval()
self.rnet.eval()
self.onet.eval()
self.refrence = get_reference_facial_points(default_square=True)
def __init__(self, device='cpu'):
self.device = torch.device("cuda:0" if device == 'cuda' else "cpu")
self.pnet = PNet().to(device)
self.rnet = RNet().to(device)
self.onet = ONet().to(device)
self.pnet.eval()
self.rnet.eval()
self.onet.eval()
self.refrence = get_reference_facial_points(default_square=True)
def __init__(self, weight_path='mtcnn_pytorch/src/weights'):
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.pnet = PNet(weight_path).to(self.device)
self.rnet = RNet(weight_path).to(self.device)
self.onet = ONet(weight_path).to(self.device)
self.pnet.eval()
self.rnet.eval()
self.onet.eval()
self.refrence = get_reference_facial_points(default_square=True)
def __init__(self,
device=devi,
thresholds=[0.3, 0.6, 0.8],
nms_thresholds=[0.6, 0.6, 0.6]):
self.pnet = PNet().to(device)
self.rnet = RNet().to(device)
self.onet = ONet().to(device)
self.onet.eval()
self.rnet.eval()
self.onet.eval()
self.refrence = get_reference_facial_points(default_square=True)
self.device = device
self.thresholds = thresholds
self.nms_thresholds = nms_thresholds
def __init__(self):
self.pnet = PNet()
self.rnet = RNet()
self.onet = ONet()
self.onet.eval()
from mtcnn_pytorch.src.get_nets import PNet, RNet, ONet
import torch
from PIL import Image
from config import get_config
# model definitions in get_nets.py
if __name__ == '__main__':
conf = get_config(False)
device = 'gpu'
# P-Net
model = PNet().to(device) # to device
model.eval() # to eval mode
example = torch.ones([1, 3, 100, 300]).to(device)
traced = torch.jit.trace(model, example)
traced.save(str(conf.save_path / 'pnet-gpu.pt'))
a, b = model(example)
print('P-Net')
print('Input size {}'.format(
example.size())) # torch.Size([1, 3, 112, 112])
print('A size {}'.format(a.size())) # torch.Size([1, 4, 51, 51])
print('B size {}'.format(b.size())) # torch.Size([1, 2, 51, 51])
# R-Net
model = RNet().to(device) # to device
model.eval() # to eval mode
example = torch.ones([1, 3, 24, 24]).to(device)