def main(unused_argv):
# Load training and eval data
train_data, train_labels = data_utils.get_train_data()
eval_data, eval_labels = data_utils.get_eval_data()
# Create the Estimator
mnist_classifier = tf.estimator.Estimator(
model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model")
# Set up logging for predictions
# Log the values in the "Softmax" tensor with label "probabilities"
tensors_to_log = {"probabilities": "softmax_tensor"}
logging_hook = tf.train.LoggingTensorHook(tensors=tensors_to_log,
every_n_iter=50)
# Train the model
train_input_fn = tf.estimator.inputs.numpy_input_fn(x={"x": train_data},
y=train_labels,
batch_size=100,
num_epochs=None,
shuffle=True)
mnist_classifier.train(input_fn=train_input_fn,
steps=20000,
hooks=[logging_hook])
# Evaluate the model and print results
eval_input_fn = tf.estimator.inputs.numpy_input_fn(x={"x": eval_data},
y=eval_labels,
num_epochs=1,
shuffle=False)
eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
print(eval_results)
def main(unused_argv):
# Load training and eval data
train_data, train_labels = data_utils.get_train_data(
file_dir, height, width, img_format)
eval_data, eval_labels = data_utils.get_eval_data(file_dir, height, width,
img_format)
train_labels = train_labels - 1
eval_labels = eval_labels - 1
# Create the Estimator
run_config = tf.estimator.RunConfig(save_summary_steps=None,
save_checkpoints_secs=None)
pitch2d_predictor = tf.estimator.Estimator(model_fn=cnn_model_fn,
config=run_config)
# Set up logging for predictions
# Log the values in the "Softmax" tensor with label "probabilities"
tensors_to_log = {"probabilities": "softmax_tensor"}
logging_hook = tf.train.LoggingTensorHook(tensors=tensors_to_log,
every_n_iter=50)
# Train the model
train_input_fn = tf.estimator.inputs.numpy_input_fn(x={"x": train_data},
y=train_labels,
batch_size=32,
num_epochs=None,
shuffle=True)
pitch2d_predictor.train(input_fn=train_input_fn,
steps=20000,
hooks=[logging_hook])
# Evaluate the model and print results
eval_input_fn = tf.estimator.inputs.numpy_input_fn(x={"x": eval_data},
y=eval_labels,
num_epochs=1,
shuffle=False)
eval_results = pitch2d_predictor.evaluate(input_fn=eval_input_fn)
print(eval_results)
def main(unused_argv):
# Time start
start_time = time.time()
# Load training and eval data
train_data, train_labels = data_utils.get_train_data(
data_dir, height, width, "color")
eval_data, eval_labels = data_utils.get_eval_data(data_dir, height, width,
"color")
# Create the Estimator
pitch2d_predictor = tf.estimator.Estimator(
model_fn=model_fn, model_dir="/tmp/pitch2d_alexnet_np")
# Set up logging for predictions
# Log the values in the "Softmax" tensor with label "probabilities"
tensors_to_log = {"probabilities": "softmax_tensor"}
logging_hook = tf.train.LoggingTensorHook(tensors=tensors_to_log,
every_n_iter=50)
# Train the model
train_input_fn = tf.estimator.inputs.numpy_input_fn(x={"x": train_data},
y=train_labels,
batch_size=128,
num_epochs=128,
shuffle=True,
num_threads=4)
pitch2d_predictor.train(input_fn=train_input_fn, hooks=[logging_hook])
# Evaluate the model and print results
eval_input_fn = tf.estimator.inputs.numpy_input_fn(x={"x": eval_data},
y=eval_labels,
num_epochs=None,
shuffle=False)
eval_results = pitch2d_predictor.evaluate(input_fn=eval_input_fn)
print(eval_results)
# End timing
end_time = time.time()
print("{} seconds elapsed".format(end_time - start_time))
# we train our model again (this time fine-tuning the top 2 inception blocks
# alongside the top Dense layers
model.fit_generator(data_utils.get_batch_data(),
epochs=2,
steps_per_epoch=2000)
model.save('MSL_tuned_inceptionnetV3.h5')
test_batch_size = 64
total_test_batch = 500
avg = 0
predicted_y = np.array([])
actual_y = np.array([])
for i in range(total_test_batch):
evalX, evalY = data_utils.get_eval_data()
loss, acc = model.evaluate(evalX, evalY, batch_size=64)
y1 = model.predict(evalX, batch_size=64)
y1 = np.argmax(y1, axis=1)
y2 = np.argmax(evalY, axis=1)
predicted_y = np.hstack((predicted_y, y1))
actual_y = np.hstack((actual_y, y2))
print("loss: ", loss)
print("acc: ", acc)
avg += acc
print("avg: ", avg / total_test_batch)
arr = np.vstack((actual_y, predicted_y)).T
np.savetxt(
'/home/cse/mtech/mcs162557/Replicate_MSL/Results/UPD_MSL_Multinomial.txt',
arr)
def evaluate(config_task, ids, model, outdir='eval_out', epoch_num=0):
"""
evalutation
"""
files = load_files(ids)
files = list(files)
datDir = os.path.join(config.prepData_dir, config_task.task, "Tr")
dices_list = []
# files = files[:2] # debugging.
logger.info('Evaluating epoch{} for {}--- {} cases:\n{}'.format(
epoch_num, config_task.task, len(files),
str([obj['id'] for obj in files])))
for obj in tqdm(files, desc='Eval epoch{}'.format(epoch_num)):
ID = obj['id']
# logger.info('evaluating {}:'.format(ID))
obj['im'] = os.path.join(config.base_dir, config_task.task, "imagesTr",
ID)
obj['gt'] = os.path.join(config.base_dir, config_task.task, "labelsTr",
ID)
img_path = os.path.join(config.base_dir, config_task.task, "imagesTr",
ID)
gt_path = os.path.join(config.base_dir, config_task.task, "labelsTr",
ID)
data = get_eval_data(obj, datDir)
# final_label, probs = segment_one_image(config_task, data, model) # final_label: d, h, w, num_classes
try:
final_label = segment_one_image(
config_task, data, model,
ID) # final_label: d, h, w, num_classes
save_to_nii(final_label,
filename=ID + '.nii.gz',
refer_file_path=img_path,
outdir=outdir,
mode="label",
prefix='Epoch{}_'.format(epoch_num))
gt = sitk.GetArrayFromImage(sitk.ReadImage(gt_path)) # d, h, w
# treat cancer as organ for Task03_Liver and Task07_Pancreas
if config_task.task in ['Task03_Liver', 'Task07_Pancreas']:
gt[gt == 2] = 1
# cal dices
dices = multiClassDice(gt, final_label, config_task.num_class)
dices_list.append(dices)
tinies.sureDir(outdir)
fo = open(os.path.join(outdir,
'{}_eval_res.csv'.format(config_task.task)),
mode='a+')
wo = csv.writer(fo, delimiter=',')
wo.writerow([epoch_num, tinies.datestr(), ID] + dices)
fo.flush()
## for tensorboard visualization
tb_img = sitk.GetArrayFromImage(sitk.ReadImage(img_path)) # d,h,w
if tb_img.ndim == 4:
tb_img = tb_img[0, ...]
train.tb_images([tb_img, gt, final_label], [False, True, True],
['image', 'GT', 'PS'],
epoch_num * config.step_per_epoch,
tag='Eval_{}_epoch_{}_dices_{}'.format(
ID, epoch_num, str(dices)))
except Exception as e:
logger.info('{}'.format(str(e)))
labels = config_task.labels
dices_all = np.asarray(dices_list)
dices_mean = dices_all.mean(axis=0)
logger.info('Eval mean dices:')
dices_res = {}
for i in range(config_task.num_class):
tag = labels[str(i)]
dices_res[tag] = dices_mean[i]
logger.info(' {}, {}'.format(tag, dices_mean[i]))
return dices_res