"""
this module is a cancer classifier model
which contains
 1. model

"""

import tensorflow as tf
import tio
import skopt
import utility as util
import logging
from components import encoder as cnn
from components import unet_based_annotator as annotator
logging.getLogger().setLevel(logging.INFO)

default_params = {
    "unet_filters_first":64,
    "unet_n_downsample":3,
    "kernel_size":3,
    "conv_stride":1,
    "unet_rate":2,
    "cnn_filters_first":16,
    "cnn_n_downsample":4,
    "cnn_rate":2,
    'cnn_kernel': 5,
}

def model_fn(features, labels, mode, params, config):
    """
    this function is a model_fn for tensorflow
    """

    if not params.keys():
        params = default_params

    seg = annotator(features['raw'], params["unet_filters_first"], params["unet_n_downsample"],
                    params["unet_rate"], params["kernel_size"], params["conv_stride"], True)

    predictions = {
        "annotation": tf.sigmoid(seg),
    }

    # Configure the Prediction Op (for PREDICT mode)
    if mode == tf.estimator.ModeKeys.PREDICT:
        # Estimators will save summaries while training session but not in eval or predict,
        #  so saver hook above is useful for eval and predict
        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)

    seg_size = tf.cast(seg.get_shape()[1], tf.int32)

    # crop labes and features
    # assuming they have the same sizes
    label_size = tf.cast(labels['annotation'].get_shape()[1], tf.int32)
    diff_half = tf.cast(( label_size-seg_size )/2, tf.int32)
    labels['annotation'] = tf.image.crop_to_bounding_box(labels['annotation'], diff_half, diff_half, seg_size, seg_size)
    features_cropped = tf.image.crop_to_bounding_box(features['raw'], diff_half, diff_half, seg_size, seg_size)

    labels_int = util.tf_threshold(labels['annotation'], 0.5, 1, tf.uint8)
    loss = tf.losses.sigmoid_cross_entropy(labels_int, logits=seg)

    tf.summary.image("input", features_cropped)
    tf.summary.image("seg_sigmoid", predictions['annotation'])
    tf.summary.image("label", labels['annotation'])

    # Configure the Training Op (for TRAIN mode)
    if mode == tf.estimator.ModeKeys.TRAIN:
        optimizer = tf.train.AdamOptimizer()
        train_op = optimizer.minimize(
            loss=loss, global_step=tf.train.get_global_step())
        return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op, training_hooks=[])

    # Add evaluation metrics (for EVAL mode)
    summary_saver_hook = tf.train.SummarySaverHook(
        save_steps=config.save_summary_steps,
        output_dir=config.model_dir+'eval' if config.model_dir[-1]=='/' else config.model_dir+'/eval',
        summary_op=tf.summary.merge_all())
    # Estimators will save summaries while training session but not in eval or predict,
    #  so saver hook above is useful for eval and predict
    eval_metric_ops = {
        "accuracy": tf.metrics.accuracy(
            labels=labels['annotation'], predictions=predictions["annotation"]
        )
    }
    return tf.estimator.EstimatorSpec(
        mode=mode, loss=loss, eval_metric_ops=eval_metric_ops, evaluation_hooks=[summary_saver_hook]
    )

730b626e5996e49e7b43dc910462fb3ab7682c71
<mask token>


def model_fn(features, labels, mode, params, config):
    """
    this function is a model_fn for tensorflow
    """
    if not params.keys():
        params = default_params
    seg = annotator(features['raw'], params['unet_filters_first'], params[
        'unet_n_downsample'], params['unet_rate'], params['kernel_size'],
        params['conv_stride'], True)
    predictions = {'annotation': tf.sigmoid(seg)}
    if mode == tf.estimator.ModeKeys.PREDICT:
        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
    seg_size = tf.cast(seg.get_shape()[1], tf.int32)
    label_size = tf.cast(labels['annotation'].get_shape()[1], tf.int32)
    diff_half = tf.cast((label_size - seg_size) / 2, tf.int32)
    labels['annotation'] = tf.image.crop_to_bounding_box(labels[
        'annotation'], diff_half, diff_half, seg_size, seg_size)
    features_cropped = tf.image.crop_to_bounding_box(features['raw'],
        diff_half, diff_half, seg_size, seg_size)
    labels_int = util.tf_threshold(labels['annotation'], 0.5, 1, tf.uint8)
    loss = tf.losses.sigmoid_cross_entropy(labels_int, logits=seg)
    tf.summary.image('input', features_cropped)
    tf.summary.image('seg_sigmoid', predictions['annotation'])
    tf.summary.image('label', labels['annotation'])
    if mode == tf.estimator.ModeKeys.TRAIN:
        optimizer = tf.train.AdamOptimizer()
        train_op = optimizer.minimize(loss=loss, global_step=tf.train.
            get_global_step())
        return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=
            train_op, training_hooks=[])
    summary_saver_hook = tf.train.SummarySaverHook(save_steps=config.
        save_summary_steps, output_dir=config.model_dir + 'eval' if config.
        model_dir[-1] == '/' else config.model_dir + '/eval', summary_op=tf
        .summary.merge_all())
    eval_metric_ops = {'accuracy': tf.metrics.accuracy(labels=labels[
        'annotation'], predictions=predictions['annotation'])}
    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops
        =eval_metric_ops, evaluation_hooks=[summary_saver_hook])

<mask token>
logging.getLogger().setLevel(logging.INFO)
<mask token>


def model_fn(features, labels, mode, params, config):
    """
    this function is a model_fn for tensorflow
    """
    if not params.keys():
        params = default_params
    seg = annotator(features['raw'], params['unet_filters_first'], params[
        'unet_n_downsample'], params['unet_rate'], params['kernel_size'],
        params['conv_stride'], True)
    predictions = {'annotation': tf.sigmoid(seg)}
    if mode == tf.estimator.ModeKeys.PREDICT:
        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
    seg_size = tf.cast(seg.get_shape()[1], tf.int32)
    label_size = tf.cast(labels['annotation'].get_shape()[1], tf.int32)
    diff_half = tf.cast((label_size - seg_size) / 2, tf.int32)
    labels['annotation'] = tf.image.crop_to_bounding_box(labels[
        'annotation'], diff_half, diff_half, seg_size, seg_size)
    features_cropped = tf.image.crop_to_bounding_box(features['raw'],
        diff_half, diff_half, seg_size, seg_size)
    labels_int = util.tf_threshold(labels['annotation'], 0.5, 1, tf.uint8)
    loss = tf.losses.sigmoid_cross_entropy(labels_int, logits=seg)
    tf.summary.image('input', features_cropped)
    tf.summary.image('seg_sigmoid', predictions['annotation'])
    tf.summary.image('label', labels['annotation'])
    if mode == tf.estimator.ModeKeys.TRAIN:
        optimizer = tf.train.AdamOptimizer()
        train_op = optimizer.minimize(loss=loss, global_step=tf.train.
            get_global_step())
        return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=
            train_op, training_hooks=[])
    summary_saver_hook = tf.train.SummarySaverHook(save_steps=config.
        save_summary_steps, output_dir=config.model_dir + 'eval' if config.
        model_dir[-1] == '/' else config.model_dir + '/eval', summary_op=tf
        .summary.merge_all())
    eval_metric_ops = {'accuracy': tf.metrics.accuracy(labels=labels[
        'annotation'], predictions=predictions['annotation'])}
    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops
        =eval_metric_ops, evaluation_hooks=[summary_saver_hook])

<mask token>
logging.getLogger().setLevel(logging.INFO)
default_params = {'unet_filters_first': 64, 'unet_n_downsample': 3,
    'kernel_size': 3, 'conv_stride': 1, 'unet_rate': 2, 'cnn_filters_first':
    16, 'cnn_n_downsample': 4, 'cnn_rate': 2, 'cnn_kernel': 5}


def model_fn(features, labels, mode, params, config):
    """
    this function is a model_fn for tensorflow
    """
    if not params.keys():
        params = default_params
    seg = annotator(features['raw'], params['unet_filters_first'], params[
        'unet_n_downsample'], params['unet_rate'], params['kernel_size'],
        params['conv_stride'], True)
    predictions = {'annotation': tf.sigmoid(seg)}
    if mode == tf.estimator.ModeKeys.PREDICT:
        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
    seg_size = tf.cast(seg.get_shape()[1], tf.int32)
    label_size = tf.cast(labels['annotation'].get_shape()[1], tf.int32)
    diff_half = tf.cast((label_size - seg_size) / 2, tf.int32)
    labels['annotation'] = tf.image.crop_to_bounding_box(labels[
        'annotation'], diff_half, diff_half, seg_size, seg_size)
    features_cropped = tf.image.crop_to_bounding_box(features['raw'],
        diff_half, diff_half, seg_size, seg_size)
    labels_int = util.tf_threshold(labels['annotation'], 0.5, 1, tf.uint8)
    loss = tf.losses.sigmoid_cross_entropy(labels_int, logits=seg)
    tf.summary.image('input', features_cropped)
    tf.summary.image('seg_sigmoid', predictions['annotation'])
    tf.summary.image('label', labels['annotation'])
    if mode == tf.estimator.ModeKeys.TRAIN:
        optimizer = tf.train.AdamOptimizer()
        train_op = optimizer.minimize(loss=loss, global_step=tf.train.
            get_global_step())
        return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=
            train_op, training_hooks=[])
    summary_saver_hook = tf.train.SummarySaverHook(save_steps=config.
        save_summary_steps, output_dir=config.model_dir + 'eval' if config.
        model_dir[-1] == '/' else config.model_dir + '/eval', summary_op=tf
        .summary.merge_all())
    eval_metric_ops = {'accuracy': tf.metrics.accuracy(labels=labels[
        'annotation'], predictions=predictions['annotation'])}
    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops
        =eval_metric_ops, evaluation_hooks=[summary_saver_hook])

<mask token>
import tensorflow as tf
import tio
import skopt
import utility as util
import logging
from components import encoder as cnn
from components import unet_based_annotator as annotator
logging.getLogger().setLevel(logging.INFO)
default_params = {'unet_filters_first': 64, 'unet_n_downsample': 3,
    'kernel_size': 3, 'conv_stride': 1, 'unet_rate': 2, 'cnn_filters_first':
    16, 'cnn_n_downsample': 4, 'cnn_rate': 2, 'cnn_kernel': 5}


def model_fn(features, labels, mode, params, config):
    """
    this function is a model_fn for tensorflow
    """
    if not params.keys():
        params = default_params
    seg = annotator(features['raw'], params['unet_filters_first'], params[
        'unet_n_downsample'], params['unet_rate'], params['kernel_size'],
        params['conv_stride'], True)
    predictions = {'annotation': tf.sigmoid(seg)}
    if mode == tf.estimator.ModeKeys.PREDICT:
        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
    seg_size = tf.cast(seg.get_shape()[1], tf.int32)
    label_size = tf.cast(labels['annotation'].get_shape()[1], tf.int32)
    diff_half = tf.cast((label_size - seg_size) / 2, tf.int32)
    labels['annotation'] = tf.image.crop_to_bounding_box(labels[
        'annotation'], diff_half, diff_half, seg_size, seg_size)
    features_cropped = tf.image.crop_to_bounding_box(features['raw'],
        diff_half, diff_half, seg_size, seg_size)
    labels_int = util.tf_threshold(labels['annotation'], 0.5, 1, tf.uint8)
    loss = tf.losses.sigmoid_cross_entropy(labels_int, logits=seg)
    tf.summary.image('input', features_cropped)
    tf.summary.image('seg_sigmoid', predictions['annotation'])
    tf.summary.image('label', labels['annotation'])
    if mode == tf.estimator.ModeKeys.TRAIN:
        optimizer = tf.train.AdamOptimizer()
        train_op = optimizer.minimize(loss=loss, global_step=tf.train.
            get_global_step())
        return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=
            train_op, training_hooks=[])
    summary_saver_hook = tf.train.SummarySaverHook(save_steps=config.
        save_summary_steps, output_dir=config.model_dir + 'eval' if config.
        model_dir[-1] == '/' else config.model_dir + '/eval', summary_op=tf
        .summary.merge_all())
    eval_metric_ops = {'accuracy': tf.metrics.accuracy(labels=labels[
        'annotation'], predictions=predictions['annotation'])}
    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops
        =eval_metric_ops, evaluation_hooks=[summary_saver_hook])

"""
this module is a cancer classifier model
which contains
 1. model

"""

import tensorflow as tf
import tio
import skopt
import utility as util
import logging
from components import encoder as cnn
from components import unet_based_annotator as annotator
logging.getLogger().setLevel(logging.INFO)

default_params = {
    "unet_filters_first":64,
    "unet_n_downsample":3,
    "kernel_size":3,
    "conv_stride":1,
    "unet_rate":2,
    "cnn_filters_first":16,
    "cnn_n_downsample":4,
    "cnn_rate":2,
    'cnn_kernel': 5,
}

def model_fn(features, labels, mode, params, config):
    """
    this function is a model_fn for tensorflow
    """

    if not params.keys():
        params = default_params

    seg = annotator(features['raw'], params["unet_filters_first"], params["unet_n_downsample"],
                    params["unet_rate"], params["kernel_size"], params["conv_stride"], True)

    predictions = {
        "annotation": tf.sigmoid(seg),
    }

    # Configure the Prediction Op (for PREDICT mode)
    if mode == tf.estimator.ModeKeys.PREDICT:
        # Estimators will save summaries while training session but not in eval or predict,
        #  so saver hook above is useful for eval and predict
        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)

    seg_size = tf.cast(seg.get_shape()[1], tf.int32)

    # crop labes and features
    # assuming they have the same sizes
    label_size = tf.cast(labels['annotation'].get_shape()[1], tf.int32)
    diff_half = tf.cast(( label_size-seg_size )/2, tf.int32)
    labels['annotation'] = tf.image.crop_to_bounding_box(labels['annotation'], diff_half, diff_half, seg_size, seg_size)
    features_cropped = tf.image.crop_to_bounding_box(features['raw'], diff_half, diff_half, seg_size, seg_size)

    labels_int = util.tf_threshold(labels['annotation'], 0.5, 1, tf.uint8)
    loss = tf.losses.sigmoid_cross_entropy(labels_int, logits=seg)

    tf.summary.image("input", features_cropped)
    tf.summary.image("seg_sigmoid", predictions['annotation'])
    tf.summary.image("label", labels['annotation'])

    # Configure the Training Op (for TRAIN mode)
    if mode == tf.estimator.ModeKeys.TRAIN:
        optimizer = tf.train.AdamOptimizer()
        train_op = optimizer.minimize(
            loss=loss, global_step=tf.train.get_global_step())
        return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op, training_hooks=[])

    # Add evaluation metrics (for EVAL mode)
    summary_saver_hook = tf.train.SummarySaverHook(
        save_steps=config.save_summary_steps,
        output_dir=config.model_dir+'eval' if config.model_dir[-1]=='/' else config.model_dir+'/eval',
        summary_op=tf.summary.merge_all())
    # Estimators will save summaries while training session but not in eval or predict,
    #  so saver hook above is useful for eval and predict
    eval_metric_ops = {
        "accuracy": tf.metrics.accuracy(
            labels=labels['annotation'], predictions=predictions["annotation"]
        )
    }
    return tf.estimator.EstimatorSpec(
        mode=mode, loss=loss, eval_metric_ops=eval_metric_ops, evaluation_hooks=[summary_saver_hook]
    )
