Tworzenie evaluation.
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@ -25,3 +25,5 @@ COPY ./dlgssdpytorch.py ./
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RUN chmod +x dlgssdpytorch.py
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COPY ./create_dataset.py ./
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RUN chmod +x create_dataset.py
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COPY ./evaluation.py ./
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RUN chmod +x evaluation.py
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39
Jenkinsfile_evaluation
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39
Jenkinsfile_evaluation
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@ -0,0 +1,39 @@
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pipeline {
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agent any
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parameters {
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buildSelector(
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defaultSelector: lastSuccessful(),
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description: 'Which build to use for copying artifacts',
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name: 'BUILD_SELECTOR')
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}
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stages {
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stage('checkout') {
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steps {
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checkout([$class: 'GitSCM', branches: [[name: '*/master']], doGenerateSubmoduleConfigurations: false, extensions: [], submoduleCfg: [], userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s426206/ium_426206.git']]])
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}
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}
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stage('Copy artifact') {
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steps {
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copyArtifacts filter: 'model.pt', fingerprintArtifacts: false, projectName: 's426206-training', selector: buildParameter('BUILD_SELECTOR')
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}
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}
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stage('docker') {
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steps {
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script {
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def img = docker.build('rokoch/ium:01')
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img.inside {
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sh 'chmod +x evaluation.py'
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sh 'python3 ./evaluation.py >>> metryki.txt'
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}
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}
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}
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}
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stage('end') {
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steps {
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//Zarchiwizuj wynik
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archiveArtifacts 'model.pt'
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}
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}
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}
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}
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@ -101,15 +101,8 @@ for epoch in range(n_epochs):
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print(f"[{epoch+1}] Training loss: {training_loss:.3f}\t Validation loss: {validation_loss:.3f}")
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# Checks model's parameters
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print("Model's state_dict:")
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for param_tensor in model.state_dict():
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print(param_tensor, "\t", model.state_dict()[param_tensor])
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# Print optimizer's state_dict
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print("Optimizer's state_dict:")
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for var_name in optimizer.state_dict():
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print(var_name, "\t", optimizer.state_dict()[var_name])
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print("Mean squared error for training: ", np.mean(losses))
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print("Mean squared error for validating: ", np.mean(val_losses))
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torch.save(model, 'model.pt')
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torch.save({
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'model_state_dict': model.state_dict(),
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'optimizer_state_dict': optimizer.state_dict(),
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'loss': lr,
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}, 'model.pt')
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41
evaluation.py
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41
evaluation.py
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@ -0,0 +1,41 @@
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import torch
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import numpy as np
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from datetime import datetime
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import torch.nn as nn
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import torch.optim as optim
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#from torch.utils.data import Dataset, TensorDataset, DataLoader
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class LayerLinearRegression(nn.Module):
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def __init__(self):
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super().__init__()
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# Instead of our custom parameters, we use a Linear layer with single input and single output
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self.linear = nn.Linear(1, 1)
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def forward(self, x):
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# Now it only takes a call to the layer to make predictions
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return self.linear(x)
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checkpoint = torch.load('model.pt')
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model = LayerLinearRegression()
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optimizer = optim.SGD(model.parameters(), lr=checkpoint['loss'])
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model.load_state_dict(checkpoint['model_state_dict'])
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optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
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model.eval()
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now = datetime.now()
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print("\n-----------{}-----------".format(now.strftime("%d/%m/%Y, %H:%M:%S")))
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# Checks model's parameters
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print("Model's state_dict:")
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for param_tensor in model.state_dict():
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print(param_tensor, "\t", model.state_dict()[param_tensor])
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# Print optimizer's state_dict
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print("Optimizer's state_dict:")
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for var_name in optimizer.state_dict():
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print(var_name, "\t", optimizer.state_dict()[var_name])
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#print("Mean squared error for training: ", np.mean(losses))
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#print("Mean squared error for validating: ", np.mean(val_losses))
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print("----------------------\n")
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