Poprawiona ewaluacja, tworzenie wykresu.

Jenkinsfile_evaluation

@@ -15,6 +15,8 @@ pipeline {
         stage('Copy artifact') {
                 steps {
                     copyArtifacts filter: 'model.pt', fingerprintArtifacts: false, projectName: 's426206-training', selector: buildParameter('BUILD_SELECTOR')
+                    copyArtifacts filter: 'val_dataset.pt', fingerprintArtifacts: false, projectName: 's426206-create-dataset', selector: buildParameter('BUILD_SELECTOR')
+                    copyArtifacts filter: 'metrics.tsv', fingerprintArtifacts: false, optional: true, projectName: 's426206-evaluation', selector: buildParameter('BUILD_SELECTOR')
                 }
             }		
 		stage('docker') {
@@ -23,7 +25,7 @@ pipeline {
                     def img = docker.build('rokoch/ium:01')
                     img.inside {
                         sh 'chmod +x evaluation.py'
-                        sh 'python3 ./evaluation.py >>> metryki.txt'
+                        sh 'python3 ./evaluation.py >> metrics.tsv'
                     }
                 }
 			}
@@ -32,8 +34,27 @@ pipeline {
 		stage('end') {
             steps {
 				//Zarchiwizuj wynik
-				archiveArtifacts 'model.pt'					
+				archiveArtifacts 'model.pt, metrics.tsv, plot.png'					
 			}
 		}
 	}
+
+	post {
+		success {
+			//Wysłanie maila
+			emailext body: 'Success evaluation',
+				subject: 's426206 evaluation',
+				to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+
+		}
+		unstable {
+			emailext body: 'Unstable evaluation', subject: 's426206 evaluation', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+		}
+		failure {
+			emailext body: 'Failure evaluation', subject: 's426206 evaluation', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+		}
+		changed {
+			emailext body: 'Changed evaluation', subject: 's426206 evaluation', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+		}
+	}
 }

Jenkinsfile_train

@@ -46,18 +46,21 @@ pipeline {
 	post {
 		success {
 			//Wysłanie maila
-			emailext body: 'SUCCESS',
+			emailext body: 'Success train',
-				subject: 's426206',
+				subject: 's426206 train',
 				to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+			//Uruchamianie innego zadania
+			build job: 's426206-evaluation/master', string(name: 'BUILD_SELECTOR', value: '<StatusBuildSelector plugin="copyartifact@1.46"/>')]
+
 		}
 		unstable {
-			emailext body: 'UNSTABLE', subject: 's426206', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+			emailext body: 'Unstable train', subject: 's426206 train', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
 		}
 		failure {
-			emailext body: 'FAILURE', subject: 's426206', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+			emailext body: 'Failure train', subject: 's426206 train', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
 		}
 		changed {
-			emailext body: 'CHANGED', subject: 's426206', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+			emailext body: 'Changed train', subject: 's426206 train', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
 		}
 	}
 }

dlgssdpytorch.py

@@ -15,10 +15,10 @@ lr = args.lr
 n_epochs = args.epochs
 
 train_dataset = torch.load('train_dataset.pt')
-val_dataset = torch.load('val_dataset.pt')
+#val_dataset = torch.load('val_dataset.pt')
 
 train_loader = DataLoader(dataset=train_dataset)
-val_loader = DataLoader(dataset=val_dataset)
+#val_loader = DataLoader(dataset=val_dataset)
 
 class LayerLinearRegression(nn.Module):
     def __init__(self):
@@ -79,27 +79,28 @@ for epoch in range(n_epochs):
         
     # After finishing training steps for all mini-batches,
     # it is time for evaluation!
+    # Ewaluacja jest już tutaj nie potrzebna bo odbywa sie w evaluation.py. Można jednak włączyć podgląd ewaluacji dla poszczególnych epok.    
+    # # We tell PyTorch to NOT use autograd...
+    # # Do you remember why?
+    # with torch.no_grad():
+    #     val_losses = []
+    #     # Uses loader to fetch one mini-batch for validation
+    #     for x_val, y_val in val_loader:
+    #         # Again, sends data to same device as model
+    #         # x_val = x_val.to(device)
+    #         # y_val = y_val.to(device)
             
-    # We tell PyTorch to NOT use autograd...
+    #         model.eval()
-    # Do you remember why?
+    #         # Makes predictions
-    with torch.no_grad():
+    #         yhat = model(x_val)
-        val_losses = []
+    #         # Computes validation loss
-        # Uses loader to fetch one mini-batch for validation
+    #         val_loss = loss_fn(y_val, yhat)
-        for x_val, y_val in val_loader:
+    #         val_losses.append(val_loss.item())
-            # Again, sends data to same device as model
+    #     validation_loss = np.mean(val_losses)
-            # x_val = x_val.to(device)
+    #     validation_losses.append(validation_loss)
-            # y_val = y_val.to(device)
 
-            model.eval()
+    # print(f"[{epoch+1}] Training loss: {training_loss:.3f}\t Validation loss: {validation_loss:.3f}")
-            # Makes predictions
+    print(f"[{epoch+1}] Training loss: {training_loss:.3f}\t")
-            yhat = model(x_val)
-            # Computes validation loss
-            val_loss = loss_fn(y_val, yhat)
-            val_losses.append(val_loss.item())
-        validation_loss = np.mean(val_losses)
-        validation_losses.append(validation_loss)
-
-    print(f"[{epoch+1}] Training loss: {training_loss:.3f}\t Validation loss: {validation_loss:.3f}")
 
 torch.save({
             'model_state_dict': model.state_dict(),

evaluation.py

@@ -3,7 +3,7 @@ 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
+from torch.utils.data import Dataset, TensorDataset, DataLoader
 
 class LayerLinearRegression(nn.Module):
     def __init__(self):
@@ -18,24 +18,46 @@ class LayerLinearRegression(nn.Module):
 checkpoint = torch.load('model.pt')
 
 model = LayerLinearRegression()
-optimizer = optim.SGD(model.parameters(), lr=checkpoint['loss'])
+#optimizer = optim.SGD(model.parameters(), lr=checkpoint['loss'])
 
 model.load_state_dict(checkpoint['model_state_dict'])
-optimizer.load_state_dict(checkpoint['optimizer_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
+loss_fn = nn.MSELoss(reduction='mean')
-print("Optimizer's state_dict:")
+val_dataset = torch.load('val_dataset.pt')
-for var_name in optimizer.state_dict():
+val_loader = DataLoader(dataset=val_dataset)
-    print(var_name, "\t", optimizer.state_dict()[var_name])
+
-#print("Mean squared error for training: ", np.mean(losses))
+with torch.no_grad():
-#print("Mean squared error for validating: ", np.mean(val_losses))   
+        val_losses = []
-print("----------------------\n")
+        # Uses loader to fetch one mini-batch for validation
+        for x_val, y_val in val_loader:
+            # Again, sends data to same device as model
+            # x_val = x_val.to(device)
+            # y_val = y_val.to(device)
+            
+            model.eval()
+            # Makes predictions
+            yhat = model(x_val)
+            # Computes validation loss
+            val_loss = loss_fn(y_val, yhat)
+            val_losses.append(val_loss.item())
+        validation_loss = np.mean(val_losses)
+
+#now = datetime.now()
+#print("\n-----------{}-----------".format(now.strftime("%d/%m/%Y, %H:%M:%S")))
+#print(f"Mean Squared Error: {validation_loss:.4f}")
+#print("------------------------------------------\n")
+print(f"{validation_loss:.4f}")
+# # 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("----------------------\n")

plot.py

@@ -0,0 +1,16 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+y = []
+with open('metrics.tsv','r') as test_in_file:
+    for line in test_in_file:
+        y.append(float(line.rstrip('\n')))
+
+fig = plt.figure()
+plt.plot(list(range(1,len(y)+1)), y)
+plt.xticks(range(1,len(y)+1))
+plt.ylabel("MSE")
+plt.xlabel("Build number")
+plt.savefig('plot.png')
+plt.show()