adding basic CI [stale]

.github/workflows/ci-tests.yml

@@ -0,0 +1,34 @@
+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
+name: CI Testing
+
+on:
+  push:
+    branches: [ "master" ]
+  pull_request:
+    branches: [ "master" ]
+
+jobs:
+
+  testing:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.8", "3.10"]
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        pip install -r requirements.txt -f "https://download.pytorch.org/whl/cpu/torch_stable.html"
+        pip list
+    #- name: Test with pytest
+    #  run: pytest -v .
+    - name: Runing main
+      run: python main.py --epochs=2

README.md

@@ -1,7 +1,9 @@
 Attention-based Deep Multiple Instance Learning
 ================================================
 
-by Maximilian Ilse (<ilse.maximilian@gmail.com>), Jakub M. Tomczak (<jakubmkt@gmail.com>) and Max Welling
+by [Maximilian Ilse](ilse.maximilian@gmail.com), [Jakub M. Tomczak](jakubmkt@gmail.com) and Max Welling
+
+[![CI Testing](https://github.com/Borda/AttentionDeepMIL/actions/workflows/ci-tests.yml/badge.svg?event=push)](https://github.com/Borda/AttentionDeepMIL/actions/workflows/ci-tests.yml)
 
 Overview
 --------

dataloader.py

@@ -52,7 +52,7 @@ def _create_bags(self):
         labels_list = []
 
         for i in range(self.num_bag):
-            bag_length = np.int(self.r.normal(self.mean_bag_length, self.var_bag_length, 1))
+            bag_length = int(self.r.normal(self.mean_bag_length, self.var_bag_length, 1))
             if bag_length < 1:
                 bag_length = 1
 

mnist_bags_loader.py

@@ -1,7 +1,6 @@
 """Pytorch Dataset object that loads perfectly balanced MNIST dataset in bag form."""
 
 import numpy as np
-import matplotlib.pyplot as plt
 import torch
 import torch.utils.data as data_utils
 from torchvision import datasets, transforms
@@ -47,7 +46,7 @@ def _form_bags(self):
                 labels = batch_data[1]
 
             while valid_bags_counter < self.num_bag:
-                bag_length = np.int(self.r.normal(self.mean_bag_length, self.var_bag_length, 1))
+                bag_length = int(self.r.normal(self.mean_bag_length, self.var_bag_length, 1))
                 if bag_length < 1:
                     bag_length = 1
                 indices = torch.LongTensor(self.r.randint(0, self.num_in_train, bag_length))
@@ -99,7 +98,7 @@ def _form_bags(self):
                 labels = batch_data[1]
 
             while valid_bags_counter < self.num_bag:
-                bag_length = np.int(self.r.normal(self.mean_bag_length, self.var_bag_length, 1))
+                bag_length = int(self.r.normal(self.mean_bag_length, self.var_bag_length, 1))
                 if bag_length < 1:
                     bag_length = 1
                 indices = torch.LongTensor(self.r.randint(0, self.num_in_test, bag_length))

requirements.txt

@@ -0,0 +1,3 @@
+numpy
+torch >=2.0.0
+torchvision