def create_segmenter(net, pretrained, num_classes): """Create Encoder; for now only ResNet [50,101,152]""" from models.resnet import rf_lw50, rf_lw101, rf_lw152 if str(net) == '50': return rf_lw50(num_classes, imagenet=pretrained) elif str(net) == '101': return rf_lw101(num_classes, imagenet=pretrained) elif str(net) == '152': return rf_lw152(num_classes, imagenet=pretrained) else: raise ValueError("{} is not supported".format(str(net)))
def get_segmenter( enc_backbone, enc_pretrained, num_classes, ): """Create Encoder-Decoder; for now only ResNet [50,101,152] Encoders are supported""" if enc_backbone == "50": return rf_lw50(num_classes, imagenet=enc_pretrained) elif enc_backbone == "101": return rf_lw101(num_classes, imagenet=enc_pretrained) elif enc_backbone == "152": return rf_lw152(num_classes, imagenet=enc_pretrained) else: raise ValueError("{} is not supported".format(str(enc_backbone)))
def create_segmenter(net, pretrained, num_classes): """Create Encoder; for now only ResNet [50,101,152]""" import sys sys.path.append("../") from models.resnet import rf_lw50, rf_lw101, rf_lw152 init_model = '../models/resnet/50_person.ckpt' if str(net) == '50': return rf_lw50(num_classes, model_path=init_model, imagenet=pretrained) elif str(net) == '101': return rf_lw101(num_classes, model_path=init_model, imagenet=pretrained) elif str(net) == '152': return rf_lw152(num_classes, imagenet=pretrained) else: raise ValueError("{} is not supported".format(str(net)))
def get_model(model_name, classes, pre_train=False, mode='train'): if model_name == 'ESpnet_2_8_decoder': from models import Espnet if pre_train: pre_train_path = os.path.join(pre_train) model = Espnet.ESPNet(classes, 2, 8, pre_train_path, mode=mode) else: model = Espnet.ESPNet(classes, 2, 8, mode=mode) elif model_name == 'ESpnet_2_8': from models import Espnet model = Espnet.ESPNet_Encoder(classes, 2, 8) elif model_name == 'EDAnet': from models import EDANet model = EDANet.EDANet(classes) elif model_name == 'ERFnet': from models import ERFnet model = ERFnet.Net(classes) elif model_name == 'Enet': from models import Enet model = Enet.ENet(classes) elif model_name == 'IRRnet_2_8': from models import Irregularity_conv model = Irregularity_conv.ESPNet(classes, 2, 8, mode=mode) elif model_name == 'MOBILE_V2': from models import mobilenet model = mobilenet.mbv2(classes) elif model_name == 'RF_LW_resnet_50': from models import resnet model = resnet.rf_lw50(classes) elif model_name == 'RF_LW_resnet_101': from models import resnet model = resnet.rf_lw101(classes) elif model_name == 'RF_LW_resnet_152': from models import resnet model = resnet.rf_lw152(classes) elif model_name == 'Bisenet': from models import BiSeNet model = BiSeNet.BiSeNet(out_class=classes) elif model_name == 'Basenet': from models import Basenet model = Basenet.Basenet(classes) else: raise NotImplementedError return model
def create_multiNet(net, num_classes, num_depths=10, pretrained=None, task_type=2): from models.multitask import multiNet from models.resnet import rf_lw50, rf_lw101, rf_lw152 if str(net) == 'multi': return multiNet(num_classes, num_depths, task_type) elif str(net) == '50': return rf_lw50(num_classes, imagenet=pretrained) elif str(net) == '101': return rf_lw101(num_classes, imagenet=pretrained) elif str(net) == '152': return rf_lw152(num_classes, imagenet=pretrained) else: raise ValueError("{} is not supported".format(str(net)))
import torch.nn as nn from skimage import io, color from scipy.special import softmax from PIL import Image cmap = np.load('../../utils/cmap.npy') has_cuda = torch.cuda.is_available() img_dir = '../../../test/rgb/0/' # img_dir = "../imgs/ho3d_test/" imgs = glob.glob('{}*[0-9].png'.format(img_dir)) n_classes = 3 # net = rf_lw50(n_classes, pretrained=True).eval() # iter = 0 net = nn.DataParallel(rf_lw50(num_classes=3, pretrained=True)) weights_path = "../../checkpoints/checkpoint_9070.pth.tar" # weights_path = "../../checkpoints.iter1/checkpoint_9254.pth.tar" net.load_state_dict(torch.load(weights_path)["model"]) if has_cuda: net = net.cuda() # cv2.namedWindow("preview") # vc = cv2.VideoCapture(0) # # if vc.isOpened(): # try to get the first frame # rval, frame = vc.read() # else: # rval = False #
import sys sys.path.append('../../') import collections import cv2 from models.resnet import rf_lw50 import cv2 import numpy as np import torch has_cuda = torch.cuda.is_available() n_classes = 11 net = rf_lw50(n_classes, imagenet=False, pretrained=False) cpkt = torch.load("../face/checkpoint.pth.tar")['segmenter'] weights = collections.OrderedDict() for key in cpkt: print(key.split('.', 1)) weights[key.split('.', 1)[1]] = cpkt[key] net.load_state_dict(weights) net = net.cuda() net.eval() dummy_input1 = torch.randn(1, 3, 512, 512) dummy_input1 = dummy_input1.cuda() torch.onnx.export(net, dummy_input1, "refinenet.onnx", verbose=True)