def main(args):
modelpath = args.loadDir + args.loadModel
weightspath = args.loadDir + args.loadWeights
print ("Loading model: " + modelpath)
print ("Loading weights: " + weightspath)
model = Net(NUM_CLASSES)
model = torch.nn.DataParallel(model)
if (not args.cpu):
model = model.cuda()
#model.load_state_dict(torch.load(args.state))
#model.load_state_dict(torch.load(weightspath)) #not working if missing key
def load_my_state_dict(model, state_dict): #custom function to load model when not all dict elements
own_state = model.state_dict()
for name, param in state_dict.items():
if name not in own_state:
continue
own_state[name].copy_(param)
return model
model = load_my_state_dict(model, torch.load(weightspath))
print ("Model and weights LOADED successfully")
model.eval()
if(not os.path.exists(args.datadir)):
print ("Error: datadir could not be loaded")
loader = DataLoader(cityscapes(args.datadir, input_transform_cityscapes, target_transform_cityscapes, subset=args.subset),
num_workers=args.num_workers, batch_size=args.batch_size, shuffle=False)
for step, (images, labels, filename, filenameGt) in enumerate(loader):
if (not args.cpu):
images = images.cuda()
#labels = labels.cuda()
inputs = Variable(images)
#targets = Variable(labels)
with torch.no_grad():
outputs = model(inputs)
label = outputs[0].max(0)[1].byte().cpu().data
label_cityscapes = cityscapes_trainIds2labelIds(label.unsqueeze(0))
#print (numpy.unique(label.numpy())) #debug
filenameSave = "./save_results/" + filename[0].split("leftImg8bit/")[1]
os.makedirs(os.path.dirname(filenameSave), exist_ok=True)
#image_transform(label.byte()).save(filenameSave)
label_cityscapes.save(filenameSave)
print (step, filenameSave)
def main(args):
modelpath = args.loadDir + args.loadModel
weightspath = args.loadDir + args.loadWeights
print("Loading model: " + modelpath)
print("Loading weights: " + weightspath)
model = Net(NUM_CLASSES)
model = torch.nn.DataParallel(model)
if (not args.cpu):
model = model.cuda()
def load_my_state_dict(
model, state_dict
): #custom function to load model when not all dict elements
own_state = model.state_dict()
for name, param in state_dict.items():
if name not in own_state:
continue
own_state[name].copy_(param)
return model
model = load_my_state_dict(model, torch.load(weightspath))
print("Model and weights LOADED successfully")
model.eval()
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
image = Image.open(args.test_img).convert('RGB')
image = image.resize((480, 480)) #
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
images = transform(image).unsqueeze(0).to(device)
output = model(images)
label = output[0].max(0)[1].byte().cpu().data
label = label.numpy()
mask = get_color_pallete(label, 'ade20k')
outname = args.test_img.split('.')[0] + '.png'
mask.save(os.path.join('./', outname))
def __init__(self):
super().__init__('segmentation_publisher')
self.bridge = CvBridge()
self.pub_seg = self.create_publisher(Image, '/LEDNet/segmented_image')
##self.pub_seg = self.create_publisher(Image, '/amsl/demo/segmentation')
self.sub = self.create_subscription(CompressedImage, '/usb_cam/image_raw/compressed', self.callback)
#Please fix "weightspath" if you use other workspace.
self.weightspath = '/home/amsl/ros2_ws_2/src/segmentation_publisher/model_001/model_best.pth'
self.model = Net(NUM_CLASSES)
self.model = torch.nn.DataParallel(self.model)
self.model = self.model.cuda()
self.model = load_state(self.model, torch.load(self.weightspath))
self.model.eval()
print("Ready")
self.count = 0
def main(args):
modelpath = args.loadDir + args.loadModel
weightspath = args.loadDir + args.loadWeights
print("Loading model: " + modelpath)
print("Loading weights: " + weightspath)
model = Net(NUM_CLASSES)
model = torch.nn.DataParallel(model)
if (not args.cpu):
model = model.cuda()
#model.load_state_dict(torch.load(args.state))
#model.load_state_dict(torch.load(weightspath)) #not working if missing key
def load_my_state_dict(
model, state_dict
): #custom function to load model when not all dict elements
own_state = model.state_dict()
for name, param in state_dict.items():
if name not in own_state:
continue
own_state[name].copy_(param)
return model
model = load_my_state_dict(model, torch.load(weightspath))
print("Model and weights LOADED successfully")
model.eval()
if (not os.path.exists(args.datadir)):
print("Error: datadir could not be loaded")
loader = DataLoader(KITTI(args.datadir,
input_transform_cityscapes,
target_transform_cityscapes,
subset=args.subset),
num_workers=args.num_workers,
batch_size=args.batch_size,
shuffle=False)
# input_transform_cityscapes = Compose([ Resize((512, 1024), Image.BILINEAR), ToTensor(),
# Normalize([.485, .456, .406], [.229, .224, .225]),])
# with open(image_path_city('/home/gongyiqun', '4.png'), 'rb') as f:
# images = load_image(f).convert('RGB')
# images = input_transform_cityscapes(images)
# For visualizer:
# must launch in other window "python3.6 -m visdom.server -port 8097"
# and access localhost:8097 to see it
if (args.visualize):
vis = visdom.Visdom()
for step, (images, filename) in enumerate(loader):
if (not args.cpu):
images = images.cuda()
#labels = labels.cuda()
inputs = Variable(images)
#targets = Variable(labels)
with torch.no_grad():
outputs = model(inputs)
label = outputs[0].max(0)[1].byte().cpu().data
#label_cityscapes = cityscapes_trainIds2labelIds(label.unsqueeze(0))
label_color = Colorize()(label.unsqueeze(0))
filenameSave = "./save_color(KITTI)/" + filename[0].split(
"leftImg8bit/")[1]
# filenameSave = "./save_color/"+"Others"
os.makedirs(os.path.dirname(filenameSave), exist_ok=True)
#image_transform(label.byte()).save(filenameSave)
label_save = ToPILImage()(label_color)
label_save = label_save.resize((1242, 375),
Image.BILINEAR) # For KITTI only
label_save.save(filenameSave)
if (args.visualize):
vis.image(label_color.numpy())
print(step, filenameSave)
def main(args):
weightspath = args.loadDir + args.loadWeights
print("Loading weights: " + weightspath)
model = Net(NUM_CLASSES)
model = torch.nn.DataParallel(model)
if (not args.cpu):
model = model.cuda()
def load_my_state_dict(
model, state_dict
): # custom function to load model when not all dict elements
own_state = model.state_dict()
for name, param in state_dict.items():
if name not in own_state:
continue
own_state[name].copy_(param)
return model
model = load_my_state_dict(model, torch.load(weightspath)) # 加载权重
print("Model and weights LOADED successfully")
model.eval()
if (not os.path.exists(args.datadir)):
print("Error: datadir could not be loaded")
# 读取数据集目录
loader = DataLoader(KITTI(args.datadir,
input_transform_cityscapes,
subset=args.subset),
num_workers=args.num_workers,
batch_size=args.batch_size,
shuffle=False)
# For visualizer:
# must launch in other window "python3.6 -m visdom.server -port 8097"
# and access localhost:8097 to see it
for step, (images, filename) in enumerate(loader): # 迭代图片
time_start = time.clock()
if (not args.cpu):
images = images.cuda()
inputs = Variable(images)
with torch.no_grad():
outputs = model(
inputs) # output 是[1,20,512,1024], 对应于20个类别的置信度，和图像大小
time_elapsed = (time.clock() - time_start)
time_start = time.clock()
instance, confidence, confidence_color, label_color = PostProcess(
20, 1226, 370)(outputs) # 太费时了这个， 需要简化
time_process = (time.clock() - time_start)
# out_json = {"instance":instance,
# "confidence":confidence
# }
yaml = YAML()
code = yaml.load(inp)
code['instance'] = instance # 改变inp格式
code['confidence'] = confidence
yaml.indent(mapping=6, sequence=4, offset=2) # 设置yaml空格格式
# 设置保存路径名字
filenameSave = "/home/gongyiqun/project/output/08/" + filename[
0].split(args.subset)[1]
filenameSave_bel = filenameSave.split(".png")[0] + "_bel.png"
filenameSave_inf = filenameSave.split(".png")[0] + ".yaml"
os.makedirs(os.path.dirname(filenameSave), exist_ok=True)
with open(filenameSave_inf, 'w') as f:
yaml.dump(code, f)
# json.dump(out, f)
# label_save = ToPILImage()(label_color)
# label_save = label_save.resize((1242, 375), Image.BILINEAR) # For KITTI only
# label_save.save(filenameSave)
# cv2.imwrite(filenameSave, label_color)
# cv2.imwrite(filenameSave_bel, confidence_color)
print(step, filenameSave, time_elapsed, time_process)
class Segmentation(Node):
def __init__(self):
super().__init__('segmentation_publisher')
self.bridge = CvBridge()
self.pub_seg = self.create_publisher(Image, '/LEDNet/segmented_image')
##self.pub_seg = self.create_publisher(Image, '/amsl/demo/segmentation')
self.sub = self.create_subscription(CompressedImage, '/usb_cam/image_raw/compressed', self.callback)
#Please fix "weightspath" if you use other workspace.
self.weightspath = '/home/amsl/ros2_ws_2/src/segmentation_publisher/model_001/model_best.pth'
self.model = Net(NUM_CLASSES)
self.model = torch.nn.DataParallel(self.model)
self.model = self.model.cuda()
self.model = load_state(self.model, torch.load(self.weightspath))
self.model.eval()
print("Ready")
self.count = 0
def callback(self, oimg):
try:
#if you want to save images and labels ,please uncomment following codes(No.1 to No.4).
#NO.1 #write_image_name = "image_" + str(self.count) + ".jpg"
#No.2 #write_label_name = "label_" + str(self.count) + ".jpg"
oimg_b = bytes(oimg.data)
np_arr = np.fromstring(oimg_b, np.uint8)
img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
#No.3 #cv2.imwrite("/home/amsl/images/output/seg_pub/image/" + write_image_name, img)
img_size = img.shape
image = PIL_Image.fromarray(img)
image = image.resize((1024,512),PIL_Image.NEAREST)
image = ToTensor()(image)
image = torch.Tensor(np.array([image.numpy()]))
image = image.cuda()
input_image = Variable(image)
with torch.no_grad():
output_image = self.model(input_image)
label = output_image[0].max(0)[1].byte().cpu().data
label_color = Colorize()(label.unsqueeze(0))
label_pub = ToPILImage()(label_color)
label_pub = label_pub.resize((img_size[1],img_size[0]),PIL_Image.NEAREST)
label_pub = np.asarray(label_pub)
#show label.
#plt.imshow(label_pub)
#plt.pause(0.001)
#No.4 #cv2.imwrite("/home/amsl/images/output/seg_pub/label/" + write_label_name, label_pub)
#self.pub_seg.publish(self.bridge.cv2_to_imgmsg(label_pub, "bgr8"))
self.pub_seg.publish(self.bridge.cv2_to_imgmsg(label_pub, "rgb8"))
print("published")
self.count += 1
except CvBridgeError as e:
print(e)
def main(args):
modelpath = args.loadDir + args.loadModel
weightspath = args.loadDir + args.loadWeights
print ("Loading model: " + modelpath)
print ("Loading weights: " + weightspath)
model = Net(NUM_CLASSES)
#model = torch.nn.DataParallel(model)
if (not args.cpu):
model = torch.nn.DataParallel(model).cuda()
def load_my_state_dict(model, state_dict): #custom function to load model when not all dict elements
own_state = model.state_dict()
for name, param in state_dict.items():
if name not in own_state:
if name.startswith("module."):
own_state[name.split("module.")[-1]].copy_(param)
else:
print(name, " not loaded")
continue
else:
own_state[name].copy_(param)
return model
model = load_my_state_dict(model, torch.load(weightspath, map_location=lambda storage, loc: storage))
print ("Model and weights LOADED successfully")
model.eval()
if(not os.path.exists(args.datadir)):
print ("Error: datadir could not be loaded")
loader = DataLoader(cityscapes(args.datadir, input_transform_cityscapes, target_transform_cityscapes, subset=args.subset), num_workers=args.num_workers, batch_size=args.batch_size, shuffle=False)
iouEvalVal = iouEval(NUM_CLASSES)
start = time.time()
for step, (images, labels, filename, filenameGt) in enumerate(loader):
if (not args.cpu):
images = images.cuda()
labels = labels.cuda()
inputs = Variable(images)
with torch.no_grad():
outputs = model(inputs)
iouEvalVal.addBatch(outputs.max(1)[1].unsqueeze(1).data, labels)
filenameSave = filename[0].split("leftImg8bit/")[1]
print (step, filenameSave)
iouVal, iou_classes = iouEvalVal.getIoU()
iou_classes_str = []
for i in range(iou_classes.size(0)):
iouStr = getColorEntry(iou_classes[i])+'{:0.2f}'.format(iou_classes[i]*100) + '\033[0m'
iou_classes_str.append(iouStr)
print("---------------------------------------")
print("Took ", time.time()-start, "seconds")
print("=======================================")
#print("TOTAL IOU: ", iou * 100, "%")
print("Per-Class IoU:")
print(iou_classes_str[0], "Road")
print(iou_classes_str[1], "sidewalk")
print(iou_classes_str[2], "building")
print(iou_classes_str[3], "wall")
print(iou_classes_str[4], "fence")
print(iou_classes_str[5], "pole")
print(iou_classes_str[6], "traffic light")
print(iou_classes_str[7], "traffic sign")
print(iou_classes_str[8], "vegetation")
print(iou_classes_str[9], "terrain")
print(iou_classes_str[10], "sky")
print(iou_classes_str[11], "person")
print(iou_classes_str[12], "rider")
print(iou_classes_str[13], "car")
print(iou_classes_str[14], "truck")
print(iou_classes_str[15], "bus")
print(iou_classes_str[16], "train")
print(iou_classes_str[17], "motorcycle")
print(iou_classes_str[18], "bicycle")
print("=======================================")
iouStr = getColorEntry(iouVal)+'{:0.2f}'.format(iouVal*100) + '\033[0m'
print ("MEAN IoU: ", iouStr, "%")