Merge branch 'master' of https://github.com/lindawangg/covid19-classifier

README.md

@@ -18,7 +18,7 @@ The COVID-19 pandemic continues to have a devastating effect on the health and w
 
 For a detailed description of the methodology behind COVID-Net and a full description of the COVIDx dataset, please click [here](assets/COVIDNet_CXR.pdf).
 
-Currently, the COVID-Net team is working on COVID-RiskNet, a deep neural network tailored for COVID-19 risk stratification.  Stay tuned as we make it available soon.
+Currently, the COVID-Net team is working on **COVID-RiskNet**, a deep neural network tailored for COVID-19 risk stratification.  Currently this is available as a work-in-progress via included `train_risknet.py` script, help to contribute data and we can improve this tool.
 
 If you would like to **contribute COVID-19 x-ray images**, please submit to https://figure1.typeform.com/to/lLrHwv. Lets all work together to stop the spread of COVID-19!
 
@@ -138,6 +138,15 @@ TF training script from a pretrained model:
 3. To inference, `python inference.py --weightspath models/COVIDNet-CXR-Large --metaname model.meta_eval --ckptname model-8485 --imagepath assets/ex-covid.jpeg`
 4. For more options and information, `python inference.py --help`
 
+### Steps for Training COVIDNet-Risk
+
+COVIDNet-Risk uses the same architecture as the existing COVIDNet - but instead it predicts the *"number of days since symptom onset"\** for a diagnosed COVID-19 patient based on their chest radiography (same data as COVIDNet). By performing offset stratification, we aim to provide an estimate of prognosis for the patient. Note that the initial dataset is fairly small at the time of writing and we hope to see more results as data increases.
+
+1. Complete data creation and training for COVIDNet (see Training above)
+2. run `train_risknet.py` (see `-h` for argument help)
+
+*\* note that definition varies between data sources*
+
 ## Results
 These are the final results for COVIDNet-CXR Small and COVIDNet-CXR Large.
 
@@ -221,6 +230,7 @@ These are the final results for COVIDNet-CXR Small and COVIDNet-CXR Large.
 </table></div>
 
 ## Pretrained Models
+**Information on these new models to be released soon**
 
 |  Type | COVID-19 Sensitivity | # Params (M) | MACs (G) |        Model        |
 |:-----:|:--------------------:|:------------:|:--------:|:-------------------:|

train_risknet.py

+"""Perform transfer-learning for offset stratification with a provided COVID-Net
+
+From the trained weights of a COVID-Net for COVID-19 identification in radiographs,
+this tool performs transfer learning to re-use these weights for stratification of
+patient offset (# of days since symptoms began *)
+
+Steps to use this:
+1. follow instructions for building data dir with train & test subdirs
+2. train your network with the train_tf.py script
+3. run this script and pass the path to your trained network (defaults should suffice)
+
+(*) FIXME: It seems that the definition of offset varies between data sources! (account for this)
+TODO: Make this script more general so that it can be used to transfer learn for other applications
+
+paul@darwinai.ca
+"""
+import argparse
+from collections import namedtuple
+import cv2
+import os
+from typing import List, Tuple, Dict, Any
+
+import numpy as np
+import pandas as pd
+from sklearn.metrics import confusion_matrix
+import tensorflow as tf
+
+from data import BalanceDataGenerator
+
+
+# We will create a checkpoint which has initial values for these variables
+VARS_TO_FORGET = [
+    'dense_3/kernel:0',
+    'dense_3/bias:0',
+    'dense_2/kernel:0',
+    'dense_2/bias:0',
+    'dense_1/kernel:0',
+    'dense_1/bias:0',
+]
+IMAGE_SHAPE = (224, 224, 3)
+INPUT_TENSOR_NAME = "input_1:0"
+OUTPUT_TENSOR_NAME = "dense_3/Softmax:0"
+SAMPLE_WEIGHTS = "dense_3_sample_weights:0"
+
+
+def get_parse_fn(num_classes: int, augment: bool = False):
+    def parse_function(imagepath: str, label: int):
+        """Parse a single element of the stratification dataset"""
+        # TODO add augmentation here ideally
+        image_decoded = tf.image.resize_images(
+            tf.image.decode_jpeg(tf.io.read_file(imagepath), IMAGE_SHAPE[-1]), IMAGE_SHAPE[:2])
+        return (
+            tf.image.convert_image_dtype(image_decoded, dtype=tf.float32) / 255.0, # x
+            tf.one_hot(label, num_classes), # y
+            tf.convert_to_tensor(1.0, dtype=tf.float32), # sample_weights TODO: verify this is right
+        )
+    return parse_function
+
+
+def parse_split(split_txt_path: str) -> Tuple[List[str], List[int]]:
+    """Read the offsets for COVID patients based on the files in our split"""
+    # FIXME: ideally we should just store the offset in the split as well or read it from CSV by id.
+    # FIXME: we need to add pretrained weights + .txts for split with well-distributed offset.
+    files, labels = [], [],
+    for split_entry in open(split_txt_path).readlines():
+        _, image_file, diagnosis = split_entry.strip().split() # TODO: txts should just contain ids
+        if diagnosis == 'COVID-19':
+            patient = csv[csv["filename"] == image_file]
+            recorded_offset = patient['offset'].item()
+            if not np.isnan(recorded_offset):
+                offset = stratify(int(recorded_offset))
+                image_path = os.path.abspath(
+                    os.path.join(args.chestxraydir, 'images', image_file))
+                assert os.path.exists(image_path), "Missing file {}".format(image_path)
+                files.append(image_path)
+                labels.append(offset)
+    return files, labels
+
+
+def eval_net(sess: tf.Session, dataset_dict: Dict[str, Any], test_files: List[str],
+             test_labels: List[int]) -> None:
+    """Evaluate the network"""
+    # Reset eval iterator
+    sess.run(dataset_dict['iterator'].initializer)
+
+    # Eval
+    preds, all_labels = [], []
+    num_evaled = 0
+    while True:
+        try:
+            images, labels, sample_weights = sess.run(dataset_dict['gn_op'])
+            pred = sess.run(
+                OUTPUT_TENSOR_NAME,
+                feed_dict={INPUT_TENSOR_NAME: images, SAMPLE_WEIGHTS: sample_weights}
+            )
+            preds.append(np.array(pred).argmax(axis=1))
+            num_evaled += len(pred)
+            all_labels.extend(np.array(labels).argmax(axis=1))
+        except tf.errors.OutOfRangeError:
+            print("\tevaluated {} images.".format(num_evaled))
+            break
+
+    matrix = confusion_matrix(all_labels, np.concatenate(preds)).astype('float')
+    per_class_acc = [
+        matrix[i,i]/np.sum(matrix[i,:]) if np.sum(matrix[i,:]) else 0 for i in range(len(matrix))
+    ]
+    print("confusion matrix:\n{}\nper-class accuracies:\n{}".format(matrix, per_class_acc))
+
+
+if __name__ == "__main__":
+
+    # Input args NOTE: the params here differ from thise in train_tf.py - we are fine-tuning
+    parser = argparse.ArgumentParser(description='COVIDNet-Risk Transfer Learning Script (offset).')
+    parser.add_argument('--classes', default=4, type=int,
+                        help='Number of classes to stratify offset into.')
+    parser.add_argument('--stratification', type=int, nargs='+', default=[3, 5, 10],
+                        help='Stratification points (days), i.e. "5 10" produces stratification of'
+                        ': 0o <-0c-> 5o <-1c-> 10o -2c-> via >= comparison (o=offset, c=class).')
+    parser.add_argument('--epochs', default=10, type=int,
+                        help='Number of epochs (less since we\'re effectively fine-tuning).')
+    parser.add_argument('--lr', default=0.000002, type=float, help='Learning rate.')
+    parser.add_argument('--batch-size', default=8, type=int, help='Train batch-size')
+    parser.add_argument('--eval-batch-size', default=8, type=int, help='Eval batch-size')
+    parser.add_argument('--evaliterval', default=3, type=int,
+                        help='# of epochs to train before running evaluation. NOTE: we only save'
+                        'after evaluation. This can be disabled when more test data is available')
+    parser.add_argument('--input-weights-dir', default='models/COVIDNetv2', type=str,
+                        help='Path to input folder containing a trained COVID-Netv2 checkpoint')
+    parser.add_argument('--input-meta-name', default='model.meta', type=str,
+                        help='Name of meta file within <input-weights-dir>')
+    parser.add_argument('--outputdir', default='models/COVIDNet-Risk', type=str,
+                        help='Path to output folder.')
+    parser.add_argument('--trainfile', default='train_COVIDx.txt', type=str,
+                        help='Name of train file. NOTE: stock split is insufficient at this time.')
+    parser.add_argument('--testfile', default='test_COVIDx.txt', type=str,
+                        help='Name of test file. NOTE: stock split is insufficient at this time.')
+    parser.add_argument('--name', default='COVIDNet-Risk', type=str,
+                        help='Name of folder to store training checkpoints.')
+    parser.add_argument('--chestxraydir', default='../covid-chestxray-dataset', type=str,
+                        help='Path to the chestxray images directory for COVID-19 patients.')
+    args = parser.parse_args()
+
+    # Check inputs
+    assert os.path.exists(args.input_weights_dir), "Missing file {}".format(args.input_weights_dir)
+    assert os.path.exists(os.path.join(args.input_weights_dir, args.input_meta_name)), \
+        "Missing file {}".format(args.input_meta_name)
+
+    # Format and define a stratification method based on our points
+    # TODO we could do a different amount of stratification but we have to add our own dense layers
+    assert len(args.stratification) == 3, "Must pass exactly 3 offset stratification points"
+    if args.stratification[0] != 0:
+        stratification = np.array([0, *args.stratification])
+    else:
+        stratification = np.array(args.stratification)
+    num_classes = len(stratification)
+    stratify = lambda offset: np.where(offset >= stratification)[0][-1]
+
+    # Read CSV of dataset
+    assert os.path.exists(args.chestxraydir), "please clone "\
+        "https://github.com/ieee8023/covid-chestxray-dataset and pass path to dir as --chestxraydir"
+    csv = pd.read_csv(os.path.join(args.chestxraydir, "metadata.csv"), nrows=None)
+
+    # Get the image filepaths and labels for training and testing split
+    train_files, train_labels = parse_split(args.trainfile)
+    assert len(train_files) >= 0 and len(train_files) == len(train_labels)
+    test_files, test_labels = parse_split(args.testfile)
+    assert len(test_files) >= 0 and len(test_labels) == len(test_files)
+    print("collected {} training and {} test cases for transfer-learning".format(
+        len(train_files), len(test_files)))
+
+    # Init augmentation fn - FIXME: we need a way to put this in a parse_fn for tf.data.dataset
+    # augmentation_fn = tf.keras.preprocessing.image.ImageDataGenerator(
+    #     featurewise_center=False,
+    #     featurewise_std_normalization=False,
+    #     rotation_range=10,
+    #     width_shift_range=0.1,
+    #     height_shift_range=0.1,
+    #     horizontal_flip=True,
+    #     brightness_range=(0.9, 1.1),
+    #     fill_mode='constant',
+    #     cval=0.,
+    # )
+    # < define generator from augmentation_fn + cv loads? >
+    # dataset = tf.data.Dataset.from_generator(lambda: generator,
+    #                                       output_types=(tf.float32, tf.float32, tf.float32),
+    #                                       output_shapes=([batch_size, 224, 224, 3],
+    #                                                      [batch_size, 3],
+    #                                                      [batch_size]))
+
+    # Output path creation for this run with lr param in name
+    train_dir = os.path.join(args.outputdir, args.name + '-lr' + str(args.lr))
+    os.makedirs(args.outputdir, exist_ok=True)
+    os.makedirs(train_dir)
+    print('Output: ' + train_dir)
+
+    # Train
+    graph = tf.Graph()
+    with tf.Session(graph=graph) as sess:
+
+        # Import meta graph
+        tf.train.import_meta_graph(os.path.join(args.input_weights_dir, args.input_meta_name))
+
+        # Restore pre-trained vars which are not in our VARS_TO_FORGET list
+        restore_vars_list, init_vars_list = [], []
+        for var in graph.get_collection(tf.GraphKeys.GLOBAL_VARIABLES):
+            if var.name in VARS_TO_FORGET:
+                init_vars_list.append(var)
+            else:
+                restore_vars_list.append(var)
+        restore_saver = tf.train.Saver(var_list=restore_vars_list)
+        restore_saver.restore(sess, tf.train.latest_checkpoint(args.input_weights_dir))
+        existing_vars = sess.graph.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
+
+        # Get some I/O tensors
+        image_tensor = graph.get_tensor_by_name(INPUT_TENSOR_NAME)
+        labels_tensor = graph.get_tensor_by_name("dense_3_target:0")
+        sample_weights = graph.get_tensor_by_name(SAMPLE_WEIGHTS)
+        pred_tensor = graph.get_tensor_by_name("dense_3/MatMul:0")
+
+        # Define tf.datasets
+        datasets = {}
+        for is_training, files, labels in zip(
+                [True, False], [train_files, test_files], [train_labels, test_labels]):
+
+            dataset = tf.data.Dataset.from_tensor_slices((files, labels))
+            dataset = dataset.map(get_parse_fn(num_classes))
+            if is_training:
+                dataset = dataset.shuffle(15)
+            dataset = dataset.batch(args.batch_size if is_training else args.eval_batch_size)
+            if is_training:
+                dataset = dataset.repeat()
+            iterator = dataset.make_initializable_iterator()
+            datasets['train' if is_training else 'test'] = {
+                'dataset': dataset,
+                'iterator': iterator,
+                'gn_op': iterator.get_next(),
+            }
+
+        # Define loss and optimizer
+        loss_op = tf.reduce_mean(
+            tf.nn.softmax_cross_entropy_with_logits_v2(
+                logits=pred_tensor, labels=labels_tensor) * sample_weights
+        )
+        optimizer = tf.train.AdamOptimizer(learning_rate=args.lr)
+        train_op = optimizer.minimize(loss_op)
+        optim_vars = list(
+            set(sess.graph.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)) - set(existing_vars))
+
+        # Initialize the optimizer + dsi + vars in our VARS_TO_FORGET list
+        sess.run(tf.variables_initializer(optim_vars + init_vars_list))
+
+        # save base model
+        saver = tf.train.Saver()
+        saver.save(sess, os.path.join(train_dir, 'model'))
+        print('Saved pre-trained model with re-initialized output layers.')
+        print('Baseline eval:')
+        eval_net(sess, datasets['test'], test_files, test_labels)
+
+        # Training cycle
+        # TODO: we need a training method that we can re-use. below very similar to train_tf.py
+        # FIXME: we need to consider freezing vars for all but dense layers.
+        print('Transfer Learning Started.')
+        print('\ttrain samples: {}\n\ttest samples:  {}\n\tstratification: {}\n'.format(
+            len(train_files), len(test_files), args.stratification))
+        sess.run(datasets['train']['iterator'].initializer)
+        num_batches = len(train_files) // args.batch_size
+        progbar = tf.keras.utils.Progbar(num_batches)
+        for epoch in range(args.epochs):
+
+            # Train
+            print("Fine-Tuning on 1 epoch = {} images.".format(len(train_files)))
+            for i in range(num_batches):
+
+                batch_x, batch_y, weights = sess.run(datasets['train']['gn_op'])
+                sess.run(
+                    train_op,
+                    feed_dict={
+                        image_tensor: batch_x,
+                        labels_tensor: batch_y,
+                        sample_weights: weights,
+                    }
+                )
+                progbar.update(i + 1)
+
+            # Evaluate + save
+            if epoch % args.evaliterval == 0:
+                pred = sess.run(pred_tensor, feed_dict={image_tensor:batch_x})
+                loss = sess.run(
+                    loss_op,
+                    feed_dict={
+                        pred_tensor: pred,
+                        labels_tensor: batch_y,
+                        sample_weights: weights,
+                    }
+               )
+                print("Epoch:", '%04d' % (epoch + 1), "Minibatch loss=", "{:.9f}".format(loss))
+                eval_net(sess, datasets['test'], test_files, test_labels)
+                saver.save(
+                    sess,
+                    os.path.join(train_dir, 'model'),
+                    global_step=epoch + 1,
+                    write_meta_graph=False
+                )
+                print('Saving checkpoint at epoch {}'.format(epoch + 1))
+
+    print("Transfer Learning Finished!\n\tcheckpoint: '{}'".format(train_dir))