def load_model(self):
cnn_arch = self.hparams['graph_model_params']['cnn_params']['arch']
model = MOTMPNet(self.hparams['graph_model_params']).cuda()
cnn_model = resnet50_fc256(10, loss='xent', pretrained=True).cuda()
load_pretrained_weights(cnn_model,
osp.join(OUTPUT_PATH, self.hparams['graph_model_params']['cnn_params']['model_weights_path'][cnn_arch]))
cnn_model.return_embeddings = True
return model, cnn_model
def load_model(self):
model = MOTMPNet(self.hparams["graph_model_params"]).cuda()
cnn_model = resnet50_fc256(10, loss="xent", pretrained=True).cuda()
load_pretrained_weights(
cnn_model,
self.reid_weights_path,
)
cnn_model.return_embeddings = True
return model, cnn_model
def load_model(self):
cnn_arch = self.hparams["graph_model_params"]["cnn_params"]["arch"]
if (
"multi" not in self.hparams["graph_model_params"]
or not self.hparams["graph_model_params"]["multi"]
):
model = MOTMPNet(self.hparams["graph_model_params"]).cuda()
else:
model = CombinedMOTMPNet(self.hparams["graph_model_params"]).cuda()
cnn_model = resnet50_fc256(10, loss="xent", pretrained=True).cuda()
load_pretrained_weights(
cnn_model,
osp.join(
OUTPUT_PATH,
self.hparams["graph_model_params"]["cnn_params"]["model_weights_path"][
cnn_arch
],
),
)
cnn_model.return_embeddings = True
return model, cnn_model