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Poprawiona ewaluacja, tworzenie wykresu.

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This commit is contained in:
Jan Nowak 2021-05-13 22:20:14 +02:00
parent 7fd26e40e1
commit 2dce7b1d0a
5 changed files with 107 additions and 44 deletions
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25
Jenkinsfile_evaluation
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@ -15,6 +15,8 @@ pipeline {
stage('Copy artifact') { stage('Copy artifact') {
steps { steps {
copyArtifacts filter: 'model.pt', fingerprintArtifacts: false, projectName: 's426206-training', selector: buildParameter('BUILD_SELECTOR') 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') { stage('docker') {
@ -23,7 +25,7 @@ pipeline {
def img = docker.build('rokoch/ium:01') def img = docker.build('rokoch/ium:01')
img.inside { img.inside {
sh 'chmod +x evaluation.py' sh 'chmod +x evaluation.py'
sh 'python3 ./evaluation.py >>> metryki.txt' sh 'python3 ./evaluation.py >> metrics.tsv'
} }
} }
} }
@ -32,8 +34,27 @@ pipeline {
stage('end') { stage('end') {
steps { steps {
//Zarchiwizuj wynik //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'
}
}
} }

13
Jenkinsfile_train
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@ -46,18 +46,21 @@ pipeline {
post { post {
success { success {
//Wysłanie maila //Wysłanie maila
emailext body: 'SUCCESS', emailext body: 'Success train',
subject: 's426206', subject: 's426206 train',
to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms' 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 { 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 { 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 { 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'
} }
} }
} }

43
dlgssdpytorch.py
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@ -15,10 +15,10 @@ lr = args.lr
n_epochs = args.epochs n_epochs = args.epochs
train_dataset = torch.load('train_dataset.pt') 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) train_loader = DataLoader(dataset=train_dataset)
val_loader = DataLoader(dataset=val_dataset) #val_loader = DataLoader(dataset=val_dataset)
class LayerLinearRegression(nn.Module): class LayerLinearRegression(nn.Module):
def __init__(self): def __init__(self):
@ -79,27 +79,28 @@ for epoch in range(n_epochs):
# After finishing training steps for all mini-batches, # After finishing training steps for all mini-batches,
# it is time for evaluation! # 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... # # We tell PyTorch to NOT use autograd...
# Do you remember why? # # Do you remember why?
with torch.no_grad(): # with torch.no_grad():
val_losses = [] # val_losses = []
# Uses loader to fetch one mini-batch for validation # # Uses loader to fetch one mini-batch for validation
for x_val, y_val in val_loader: # for x_val, y_val in val_loader:
# Again, sends data to same device as model # # Again, sends data to same device as model
# x_val = x_val.to(device) # # x_val = x_val.to(device)
# y_val = y_val.to(device) # # y_val = y_val.to(device)
model.eval() # model.eval()
# Makes predictions # # Makes predictions
yhat = model(x_val) # yhat = model(x_val)
# Computes validation loss # # Computes validation loss
val_loss = loss_fn(y_val, yhat) # val_loss = loss_fn(y_val, yhat)
val_losses.append(val_loss.item()) # val_losses.append(val_loss.item())
validation_loss = np.mean(val_losses) # validation_loss = np.mean(val_losses)
validation_losses.append(validation_loss) # validation_losses.append(validation_loss)
print(f"[{epoch+1}] Training loss: {training_loss:.3f}\t Validation loss: {validation_loss:.3f}") # print(f"[{epoch+1}] Training loss: {training_loss:.3f}\t Validation loss: {validation_loss:.3f}")
print(f"[{epoch+1}] Training loss: {training_loss:.3f}\t")
torch.save({ torch.save({
'model_state_dict': model.state_dict(), 'model_state_dict': model.state_dict(),

54
evaluation.py
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@ -3,7 +3,7 @@ import numpy as np
from datetime import datetime from datetime import datetime
import torch.nn as nn import torch.nn as nn
import torch.optim as optim 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): class LayerLinearRegression(nn.Module):
def __init__(self): def __init__(self):
@ -18,24 +18,46 @@ class LayerLinearRegression(nn.Module):
checkpoint = torch.load('model.pt') checkpoint = torch.load('model.pt')
model = LayerLinearRegression() 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']) 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() 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")

16
plot.py Normal file
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@ -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()