def net_load(model_name):
Net = NET_FCN.Net(NumClasses=NUM_CLASSES)
Net.load_state_dict(torch.load(liquid_segmentation_model))
Net.eval()
Net.half()
return Net
def net_load(model_name):
Net=NET_FCN.Net(NumClasses=NUM_CLASSES, UseGPU=False)
Net.load_state_dict(torch.load(liquid_segmentation_model, map_location='cpu'))
Net.eval()
# Net.half()
return Net
#......................Input Parameters...................................................................
Image_Dir = "Data_Zoo/Materials_In_Vessels/Test_Images_All//" #Images for evaluation
Label_Dir = "Data_Zoo/Materials_In_Vessels/FillLevelLabels" #Ground truth per pixel annotation for the images in Image dir
Trained_model_path = "TrainedModelWeights/FillLevelRecognitionNetWeights.torch" # "Path to trained net weights
NUM_CLASSES = 3 #Number of classes the net predicts
#Classes = ["BackGround", "Empty Vessel","Liquid","Solid"] #names of classe the net predic
#Classes=["Background","Vessel"] #Classes predicted for vessel region prediction
Classes = ["BackGround", "Empty Vessel region", "Filled Vessel region"] #
#Classes=["BackGround","Empty Vessel region","liquid","Solid"]
#Classes=["BackGround","Vessel","Liquid","Liquid Phase two","Suspension", "Emulsion","Foam","Solid","Gel","Powder","Granular","Bulk","Bulk Liquid","Solid Phase two","Vapor"]
# .........................Build FCN Net...............................................................................................
Net = FCN.Net(NumClasses=NUM_CLASSES) #Build Net
Net.load_state_dict(torch.load(Trained_model_path)) # Load Traine model
print("Model weights loaded from: " + Trained_model_path)
Net.eval()
Net.half()
# -------------------------Data reader for validation image-----------------------------------------------------------------------------------------------------------------------------
ValidReader = Data_Reader.Data_Reader(
Image_Dir, GTLabelDir=Label_Dir, BatchSize=1
) # build reader that will be used to load images and labels from validation set
#--------------------Sum of intersection from all validation images for all classes and sum of union for all images and all classes----------------------------------------------------------------------------------
Union = np.float64(np.zeros(len(Classes))) #Sum of union
Intersection = np.float64(np.zeros(len(Classes))) #Sum of Intersection
fim = 0
print("Start Evaluating intersection over union for " +
MaxPixel = 34000 # Max pixel image can have (to keep oom out of memory problems) if the image larger it will be resized. Reduce if
TrainLossTxtFile = TrainedModelWeightDir + "TrainLoss.txt" #Where train losses will be writen
ValidLossTxtFile = TrainedModelWeightDir + "ValidationLoss.txt" # Where validation losses will be writen
Batch_Size = 1 # Number of images per training iteration (keep small to avoid out of memory problems)
Weight_Decay = 1e-4 # Weight for the weight decay loss function
MAX_ITERATION = int(80010) # Max number of training iteration
NUM_CLASSES = 4 #Number of classes the model predict
UpdateEncoderBatchNormStatistics = False
#---------------------Create and Initiate net and create optimizer------------------------------------------------------------------------------------
Net = NET_FCN.Net(
NumClasses=NUM_CLASSES,
PreTrainedModelPath=Pretrained_Encoder_Weights,
UpdateEncoderBatchNormStatistics=UpdateEncoderBatchNormStatistics
) # Create net and load pretrained encoder path
if Trained_model_path != "": # Optional initiate full net by loading a pretrained net
Net.load_state_dict(torch.load(Trained_model_path))
#optimizer=torch.optim.SGD(params=Net.parameters(),lr=Learning_Rate,weight_decay=Weight_Decay,momentum=0.5)
optimizer = torch.optim.Adam(
params=Net.parameters(), lr=Learning_Rate,
weight_decay=Weight_Decay) # Create adam optimizer
#----------------------------------------Create reader for data set--------------------------------------------------------------------------------------------------------------
TrainReader = Data_Reader.Data_Reader(
ImageDir=Train_Image_Dir, GTLabelDir=Train_Label_Dir,
BatchSize=Batch_Size) #Reader for training data