def __init__(self):
super(Agent, self).__init__()
self.p = Parameters()
self.lane_detection_network = lane_detection_network()
self.setup_optimizer()
self.current_epoch = 0
def __init__(self):
super(Agent, self).__init__()
self.p = Parameters()
self.lane_detection_network = lane_detection_network()
self.lane_val = None
self.current_epoch = 0
self.hard_sampling = hard_sampling.hard_sampling()
print("model parameters: ")
print(self.count_parameters(self.lane_detection_network))
def __init__(self, current_epoch=0):
super(Agent, self).__init__()
self.p = Parameters()
self.lane_detection_network = lane_detection_network()
self.setup_optimizer()
self.current_epoch = current_epoch
self.hard_sampling = hard_sampling.hard_sampling()
print("model parameters: ")
print(self.count_parameters(self.lane_detection_network))
'''
Convert trained model into onnx.
'''
import torch
import torch.onnx
from hourglass_network import lane_detection_network
# (True)Convert to onnx mode.
# (False)Check converted onnx model mode.
convert = True
save_dir = '/media/data4/yg/PINet_new-master/CurveLanes/onnx_models/'
if convert == True:
model = lane_detection_network()
weights_path = '/media/data4/yg/PINet_new-master/CurveLanes/savefile/32_tensor(1.1001)_lane_detection_network.pkl'
# Load the weights from a file (.pth or .pkl usually)
state_dict = torch.load(weights_path)
# Load the weights now into a model net architecture.
model.load_state_dict(state_dict)
# Create the right input shape.
sample_batch_size = 1
channel = 3
height = 256
width = 512
dummy_input = torch.randn(sample_batch_size, channel, height, width)