BugNIST2D challenge submission helpers

Chapter09/netEval.py

+# -*- coding: utf-8 -*-
+"""
+Created on Tue Mar 28 14:30:09 2023
+
+@author: Pawel Pieta, papi@dtu.dk
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+import glob
+import skimage.io
+import time
+from collections.abc import Callable
+
+from sampleNetwork import netFunc
+
+def netEval(netFunc: Callable[np.array], dataPath: str, targetPath: str) -> tuple[float,float]:
+    """
+    Evaluates the accuracy of a classification model using provided dataset.
+    
+    Parameters
+    ----------
+    netFunc - function of the network that takes an image and outputs a predicted class,\n
+    dataPath - path to a folder with data,\n
+    targetPath - path to a file with target labels (either .txt or .npy).
+    """
+    
+    assert callable(netFunc), "The first argument is not callable, it should be a network function."
+    assert os.path.exists(dataPath), f"Provided path: {dataPath} does not exist."
+    assert os.path.exists(targetPath), f"Provided path: {targetPath} does not exist."
+    
+    ext = os.path.splitext(targetPath)[-1]
+    assert ext == '.txt' or ext == '.npy', f"Target path extension file {ext} is not supported, use .txt or .npy"
+    
+    if ext == '.txt':
+        with open(targetPath) as f:
+            targetList = np.array(f.readlines()).astype(int)
+    else:
+        targetList = np.load(targetPath)
+    
+    
+    # Read in the images
+    imgList = glob.glob(dataPath+'/*.png')
+    assert imgList, f"No .png images found in folder {targetPath}"
+    imgsArr = np.array([skimage.io.imread(fname) for fname in imgList])
+    
+    # Execute network
+    t0 = time.time()
+    predList = netFunc(imgsArr)
+    t1 = time.time()
+    
+    # Calculate accuracy and execution time
+    accuracy = np.sum(np.equal(predList,targetList))/len(targetList)
+    execTime = t1-t0
+    
+    return accuracy, execTime
+        
+    
+
+if __name__ == "__main__":
+    
+    targetPath = 'C:/DTU_local/AdvancedImageAnalysis/Data/bugNIST2D/train_targets.txt'
+    dataPath = 'C:/DTU_local/AdvancedImageAnalysis/Data/bugNIST2D/train' 
+    
+    accuracy, execTime = netEval(netFunc, dataPath, targetPath)
+    
+    print(f"Achieved accuracy: {np.round(accuracy,4)}")
+    print(f"Network execution time: {np.round(execTime,4)}s")
\ No newline at end of file

Chapter09/sampleNetwork.py

+# -*- coding: utf-8 -*-
+"""
+Created on Tue Mar 28 14:31:20 2023
+
+@author: Pawel Pieta, papi@dtu.dk
+"""
+import numpy as np
+from collections.abc import Iterable
+
+def netFunc(images: np.array) -> Iterable[int]:
+    '''
+    Loads a feed forward neural network, and predicts the labels of each image in an array.
+
+    Parameters
+    ----------
+    images : numpy array
+        An array with grayscale images loaded with "skimage.io.imread(fname,as_gray=True)",
+        float datatype with pixel values between 0 and 1. The array has \n
+        dimension N x H x W x C where N is the number of images, H is the height of the images \n
+        , W is the width of the images and C is the number of channels in each image.
+
+    Returns
+    -------
+    class_indices : Iterable[int]
+        An iterable (e.g., a list) of the class indices representing the predictions of the network. The valid indices range from 0 to 11.
+
+    '''
+    
+    # Specify here the main path to where you keep your model weights, (in case we need to modify it)
+    # try to make the path relative to this code file location, 
+    # e.g "../weights/v1/" instead of "C:/AdvImg/week10/weights/v1"
+    MODEL_PATH = "../weights/v1/"
+    
+            
+    # Change this to return a list of indices predicted by your model
+    class_indices = np.random.randint(0,12, size=images.shape[0])
+
+    return class_indices
+
+