From 733fbc9bea4d59c1b06a9b1463bdb50956084873 Mon Sep 17 00:00:00 2001
From: s47700 <s47700@bht-berlin.de>
Date: Tue, 26 Mar 2024 14:15:23 +0100
Subject: [PATCH] Upload New File
---
test/TEST_MODEL_with_DATASET_1_.py | 182 +++++++++++++++++++++++++++++
1 file changed, 182 insertions(+)
create mode 100644 test/TEST_MODEL_with_DATASET_1_.py
diff --git a/test/TEST_MODEL_with_DATASET_1_.py b/test/TEST_MODEL_with_DATASET_1_.py
new file mode 100644
index 0000000..2dc5873
--- /dev/null
+++ b/test/TEST_MODEL_with_DATASET_1_.py
@@ -0,0 +1,182 @@
+import torch
+import torchvision
+import torchvision.transforms as transforms
+import torch.nn as nn
+
+
+
+import os
+import sys
+import numpy as np
+import time
+import yaml
+import cv2
+import matplotlib.pyplot as plt
+
+"""
+Author: Jenö Faist, Paul Judis
+Refernces: LFI-3 cnn.py
+"""
+
+"""
+This File for Testing a Trained Model with the Kegal "Early detection of 3D printing issues" Set
+Link to Original Dataset: https://www.kaggle.com/datasets/gauravduttakiit/early-detection-of-3d-printing-issues?select=train
+Wearning: The Test Dataset for what ever reason is not labeled
+so pleas use this Modified Kegal Dataset with the Labeld Test Dataset Labeled by Hand.
+Link to the Test Dataset Labeled: ??? TODO ???
+
+Because the Test Dataset was hand labeled there is a MANUAL_MODE so that you can MANUAL specifice
+by pressing "f" if the current image is a file print or not !
+MANUAL_MODE is Perfered because probably the Test Dataset was labeled porley be hand.
+"""
+
+if __name__ == '__main__':
+
+ """
+ Setting up from where to load the Dataset and Model !
+ """
+ absolutepath = os.path.dirname(__file__)
+ test_set_PATH = absolutepath+'/DATASETS/early_3D_Kegel_SET/test'
+ model_save_PATH = absolutepath+'/COMPLETE_MODELS/3D_DEC_MODEL_MIXR18_18E_64B.pt'
+ MANUAL_MODE = True
+
+ #Consol
+ print("STARTING CNN TESTING")
+ print("--------------------")
+ print("Cuda Version: " + torch.version.cuda)
+ print("Cuda: "+str(torch.cuda.is_available()))
+ print("GPU: "+str(torch.cuda.get_device_name()))
+ print("MANUAL_MODE: "+str(MANUAL_MODE))
+ print("--------------------")
+ print("Loading Model...")
+
+
+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+ """
+ Specifing which Model we using for testing the Data this!
+ THIS MUST BE CHANGED DEPENDING OF WHICH MODEL YOU ARE USING !!!
+ """
+
+ model = torchvision.models.resnet18(weights='DEFAULT')
+ num_ftrs = model.fc.in_features
+ model.fc = nn.Linear(num_ftrs, 2)
+
+
+ """
+ Loading Model and Dataset if possible else the programm stops
+ """
+
+ try:
+ model.load_state_dict(torch.load(model_save_PATH),strict=False)
+ model.eval()
+ print("[✓] Model Loaded [✓]")
+ except:
+ print("[!?] No Model Found [?!]")
+ exit(1)
+
+ model.eval()
+ model = model.to(device)
+
+ print("Loading Test Dataset") # Console
+
+ transform = transforms.Compose([
+ transforms.ToTensor(),
+ transforms.Resize((256,256)),
+ ])
+
+ test_dataset = None
+ try:
+ test_dataset = torchvision.datasets.ImageFolder(
+ root=test_set_PATH,
+ transform=transform
+ )
+ print("[✓] Test Dataset Loaded [✓]") # Console
+ except:
+ print("[!?] Test Dataset couldn't be loaded [?!]") # Console
+ exit(1)
+
+ print("--------------------") # Console
+
+ running_accrucay = 0
+ count = 0
+
+
+ data_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=0)
+ len_test_set = len(data_loader)
+
+ with torch.no_grad():
+
+ for batch_idx, (inputs, labels) in enumerate(data_loader):
+ count +=1
+ """
+ Compute current Network Output for the current Image
+ """
+ img = (inputs[0].squeeze()).numpy()
+ inputs = inputs.to(device)
+ labels = labels.to(device)
+
+ outputs = model(inputs)
+ _, preds = torch.max(outputs, 1)
+
+ """
+ MANUAL_MODE shows the current Image and you can press "f" to say if the current image is a failed print or
+ not (not when f not pressed), this is compared to the network output to specifice accurarcy!
+ ELSE normal labeling of the Test Dataset is used to calculate accuracy
+ """
+ if(MANUAL_MODE):
+
+ norm_image = cv2.normalize(img, None, alpha = 0, beta = 255, norm_type = cv2.NORM_MINMAX, dtype = cv2.CV_64F)
+
+ norm_image = norm_image.astype(np.uint8)
+ norm_image = np.transpose(norm_image, (1, 2, 0))
+
+
+ cv2.imshow('TEST MODEL PRESS "F" TO MARK AS FAIL',norm_image)
+ k =cv2.waitKey(20)
+
+ FAILED_PRINT = False
+
+ if(k==ord("f")):
+ FAILED_PRINT = True
+
+ if(preds[0].item() == 1 and FAILED_PRINT):
+ running_accrucay +=1
+ elif(preds[0].item() == 0 and not FAILED_PRINT):
+ running_accrucay +=1
+
+ """
+ JUST FOR VISUALS
+ """
+
+ sym = "NO"
+ sym_2 = "NO"
+ if(preds[0].item() == 1):
+ sym = "YES"
+ if FAILED_PRINT:
+ sym_2 = "YES"
+
+
+ sys.stdout.write("\033[K")
+ #DEBUG
+ # " NETWORK VALUES: " + str(outputs[0][0].item())+","+str(outputs[0][1].item())
+ print("CNN FAIL DETECTED: " + sym + " USER FAIL DETECTED: " + sym_2 + " CURRENT ACCURACY: "+str(int(100*(running_accrucay/count)))+"%", end='\r')
+ else:
+ if(labels == preds[0].item()):
+ running_accrucay += 1
+ sys.stdout.write("\033[K")
+ print("Testing Image "+str(batch_idx)+"/"+str(len_test_set)+" CURRENT ACCURACY: "+str(int(100*(running_accrucay/count)))+"%", end='\r')
+
+sys.stdout.write("\033[K")
+print("TESTING FINISHED ACCURACY: "+str(int(100*(running_accrucay/count)))+"%", end='\r')
+
+
+
+
+
+
+
+
+
+
+
--
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