Tensorflow 2.0

[alext234]

Hi do you plan to upgrade to TF2.0?

[josephius]

You can still query the model from TF2.0. Just use tf.compat.v1 to create a session and such.

I've also been able train an EfficientNetB0 using the efficientnet library with the dataset provided here.

[alext234]

This mod works for Google Colab which has TF2.0

for eval.py

from sklearn.metrics import confusion_matrix
import numpy as np

# import tensorflow as tf
import tensorflow.compat.v1 as tf
#To make tf 2.0 compatible with tf1.0 code, we disable the tf2.0 functionalities
tf.disable_eager_execution()

import os, argparse
import cv2

parser = argparse.ArgumentParser(description='COVID-Net Evaluation')
parser.add_argument('--weightspath', default='output', type=str, help='Path to output folder')
parser.add_argument('--metaname', default='model.meta', type=str, help='Name of ckpt meta file')
parser.add_argument('--ckptname', default='model', type=str, help='Name of model ckpts')
parser.add_argument('--testfile', default='test_COVIDx.txt', type=str, help='Name of testfile')
parser.add_argument('--testfolder', default='test', type=str, help='Folder where test data is located')

args = parser.parse_args()

mapping = {'normal': 0, 'pneumonia': 1, 'COVID-19': 2}

sess = tf.Session()
tf.get_default_graph()
saver = tf.train.import_meta_graph(os.path.join(args.weightspath, args.metaname))
saver.restore(sess, os.path.join(args.weightspath, args.ckptname))

graph = tf.get_default_graph()

image_tensor = graph.get_tensor_by_name("input_1:0")
pred_tensor = graph.get_tensor_by_name("dense_3/Softmax:0")

file = open(args.testfile, 'r')
testfile = file.readlines()
y_test = []
pred = []
for i in range(len(testfile)):
    line = testfile[i].split()
    x = cv2.imread(os.path.join('data', args.testfolder, line[1]))
    x = cv2.resize(x, (224, 224))
    x = x.astype('float32') / 255.0
    y_test.append(mapping[line[2]])
    pred.append(np.array(sess.run(pred_tensor, feed_dict={image_tensor: np.expand_dims(x, axis=0)})).argmax(axis=1))
y_test = np.array(y_test)
pred = np.array(pred)

matrix = confusion_matrix(y_test, pred)
matrix = matrix.astype('float')
#cm_norm = matrix / matrix.sum(axis=1)[:, np.newaxis]
print(matrix)
#class_acc = np.array(cm_norm.diagonal())
class_acc = [matrix[i,i]/np.sum(matrix[i,:]) if np.sum(matrix[i,:]) else 0 for i in range(len(matrix))]
print('Sens Normal: {0:.3f}, Pneumonia: {1:.3f}, COVID-19: {2:.3f}'.format(class_acc[0],
                                                                           class_acc[1],
                                                                           class_acc[2]))
ppvs = [matrix[i,i]/np.sum(matrix[:,i]) if np.sum(matrix[:,i]) else 0 for i in range(len(matrix))]
print('PPV Normal: {0:.3f}, Pneumonia {1:.3f}, COVID-19: {2:.3f}'.format(ppvs[0],
                                                                         ppvs[1],
                                                                         ppvs[2]))