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cxr_validator_model.py
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cxr_validator_model.py
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import os
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID' # see issue #152
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import json
import tqdm
import pandas as pd
import tensorflow as tf
from sklearn.utils import shuffle
##### MIMIC Dataset ####################################################################################################
csv_root = 'preprocessing/mimic'
img_root = '/data/datasets/chest_xray/MIMIC-CXR/mimic-cxr-jpg-2.0.0.physionet.org'
train_reports = pd.read_csv(os.path.join(csv_root, 'MIMIC_AP_PA_train.csv')).values
cxr_train_image_paths = [os.path.join(img_root, path) for path in train_reports[:, 0]]
validate_reports = pd.read_csv(os.path.join(csv_root, 'MIMIC_AP_PA_validate.csv')).values
cxr_validate_image_paths = [os.path.join(img_root, path) for path in train_reports[:, 0]]
##### COCO Dataset #####################################################################################################
def get_coco_paths(coco_root, dataset):
# Read the json file
with open(os.path.join(coco_root, f'annotations/captions_{dataset}.json'), 'r') as f:
annotations = json.load(f)
# Store captions and image names in vectors
coco_image_paths = []
for annot in tqdm.tqdm(annotations['annotations']):
image_id = annot['image_id']
full_coco_image_path = os.path.join(coco_root, dataset, f'{image_id:012d}.jpg')
coco_image_paths.append(full_coco_image_path)
return coco_image_paths
MS_COCO_ROOT = '/data/datasets/MS-COCO/2017'
coco_train_image_paths = get_coco_paths(MS_COCO_ROOT, dataset='train2017')
coco_validate_image_paths = get_coco_paths(MS_COCO_ROOT, dataset='val2017')
##### Train/Validate Image-Path/Ground-Truth pairs #####################################################################
all_train_image_paths = cxr_train_image_paths + coco_train_image_paths
all_train_image_labels = [1] * len(cxr_train_image_paths) + [0] * len(coco_train_image_paths)
all_train_image_paths, all_train_image_labels = \
shuffle(all_train_image_paths, all_train_image_labels)
all_validate_image_paths = cxr_validate_image_paths + coco_validate_image_paths
all_validate_image_labels = [1] * len(cxr_validate_image_paths) + [0] * len(coco_validate_image_paths)
all_validate_image_paths, all_validate_image_labels = \
shuffle(all_validate_image_paths, all_validate_image_labels)
##### Tensorflow Dataloader ############################################################################################
def parse_function(filename, label):
# Read entire contents of image
image_string = tf.io.read_file(filename)
# Don't use tf.image.decode_image, or the output shape will be undefined
image = tf.io.decode_jpeg(image_string, channels=3)
# This will convert to float values in [0, 1]
image = tf.image.convert_image_dtype(image, tf.float32)
# Resize image with padding to 224x224
image = tf.image.resize_with_pad(image, 224, 224, method=tf.image.ResizeMethod.BILINEAR)
# Convert image to grayscale
image = tf.image.rgb_to_grayscale(image)
return image, label
train_dataset = tf.data.Dataset.from_tensor_slices((all_train_image_paths, all_train_image_labels))
train_dataset = train_dataset.shuffle(len(train_dataset))
train_dataset = train_dataset.map(parse_function, num_parallel_calls=tf.data.experimental.AUTOTUNE)
train_dataset = train_dataset.batch(32)
train_dataset = train_dataset.prefetch(tf.data.experimental.AUTOTUNE)
validate_dataset = tf.data.Dataset.from_tensor_slices((all_validate_image_paths, all_validate_image_labels))
validate_dataset = validate_dataset.shuffle(len(validate_dataset))
validate_dataset = validate_dataset.map(parse_function, num_parallel_calls=tf.data.experimental.AUTOTUNE)
validate_dataset = validate_dataset.batch(8)
validate_dataset = validate_dataset.prefetch(tf.data.experimental.AUTOTUNE)
##### Network Definition ###############################################################################################
model = tf.keras.Sequential([
tf.keras.applications.InceptionResNetV2(include_top=False, weights=None, input_shape=(224,224,1)),
tf.keras.layers.GlobalAveragePooling2D(),
tf.keras.layers.Dense(1024, activation='relu'),
tf.keras.layers.Dense(1)
])
model.compile(
optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
metrics=['accuracy']
)
checkpoint = tf.keras.callbacks.ModelCheckpoint(
filepath='checkpoints/cxr_validator_model.tf',
monitor='val_accuracy', verbose=1, save_best_only=True)
model.fit(train_dataset, epochs=20, callbacks=[checkpoint])
model.fit(train_dataset,
validation_data=validate_dataset,
steps_per_epoch=len(train_dataset),
validation_steps=len(validate_dataset),
epochs=100,
callbacks=[checkpoint])
########################################################################################################################