Tworzenie evaluation.

Dockerfile

@@ -25,3 +25,5 @@ COPY ./dlgssdpytorch.py ./
 RUN chmod +x dlgssdpytorch.py
 COPY ./create_dataset.py ./
 RUN chmod +x create_dataset.py
+COPY ./evaluation.py ./
+RUN chmod +x evaluation.py

Jenkinsfile_evaluation

@@ -0,0 +1,39 @@
+pipeline {
+	agent any 
+    parameters {
+      buildSelector(
+          defaultSelector: lastSuccessful(),
+          description: 'Which build to use for copying artifacts',
+          name: 'BUILD_SELECTOR')
+    }
+	stages {
+		stage('checkout') {
+			steps {
+				checkout([$class: 'GitSCM', branches: [[name: '*/master']], doGenerateSubmoduleConfigurations: false, extensions: [], submoduleCfg: [], userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s426206/ium_426206.git']]])
+			}
+		}
+        stage('Copy artifact') {
+                steps {
+                    copyArtifacts filter: 'model.pt', fingerprintArtifacts: false, projectName: 's426206-training', selector: buildParameter('BUILD_SELECTOR')
+                }
+            }		
+		stage('docker') {
+			steps {
+                script {
+                    def img = docker.build('rokoch/ium:01')
+                    img.inside {
+                        sh 'chmod +x evaluation.py'
+                        sh 'python3 ./evaluation.py >>> metryki.txt'
+                    }
+                }
+			}
+		}
+		
+		stage('end') {
+            steps {
+				//Zarchiwizuj wynik
+				archiveArtifacts 'model.pt'					
+			}
+		}
+	}
+}

dlgssdpytorch.py

@@ -101,15 +101,8 @@ for epoch in range(n_epochs):
 
     print(f"[{epoch+1}] Training loss: {training_loss:.3f}\t Validation loss: {validation_loss:.3f}")
 
-# Checks model's parameters
-print("Model's state_dict:")
-for param_tensor in model.state_dict():
-    print(param_tensor, "\t", model.state_dict()[param_tensor])
-
-# Print optimizer's state_dict
-print("Optimizer's state_dict:")
-for var_name in optimizer.state_dict():
-    print(var_name, "\t", optimizer.state_dict()[var_name])
-print("Mean squared error for training: ", np.mean(losses))
-print("Mean squared error for validating: ", np.mean(val_losses))   
-torch.save(model, 'model.pt')
+torch.save({
+            'model_state_dict': model.state_dict(),
+            'optimizer_state_dict': optimizer.state_dict(),
+                'loss': lr,
+            }, 'model.pt')

evaluation.py

@@ -0,0 +1,41 @@
+import torch
+import numpy as np
+from datetime import datetime
+import torch.nn as nn
+import torch.optim as optim
+#from torch.utils.data import Dataset, TensorDataset, DataLoader
+
+class LayerLinearRegression(nn.Module):
+    def __init__(self):
+        super().__init__()
+        # Instead of our custom parameters, we use a Linear layer with single input and single output
+        self.linear = nn.Linear(1, 1)
+
+    def forward(self, x):
+        # Now it only takes a call to the layer to make predictions
+        return self.linear(x)
+
+checkpoint = torch.load('model.pt')
+
+model = LayerLinearRegression()
+optimizer = optim.SGD(model.parameters(), lr=checkpoint['loss'])
+
+model.load_state_dict(checkpoint['model_state_dict'])
+optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
+
+model.eval()
+
+now = datetime.now()
+print("\n-----------{}-----------".format(now.strftime("%d/%m/%Y, %H:%M:%S")))
+# Checks model's parameters
+print("Model's state_dict:")
+for param_tensor in model.state_dict():
+    print(param_tensor, "\t", model.state_dict()[param_tensor])
+
+# Print optimizer's state_dict
+print("Optimizer's state_dict:")
+for var_name in optimizer.state_dict():
+    print(var_name, "\t", optimizer.state_dict()[var_name])
+#print("Mean squared error for training: ", np.mean(losses))
+#print("Mean squared error for validating: ", np.mean(val_losses))   
+print("----------------------\n")