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Change 'dice_coefficient' to calculate Dice score rather than pixel accuracy #51

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Change 'dice_coefficient' to calculate Dice score rather than pixel accuracy #51

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8028484
Merge pull request #1 from jakeret/master
May 11, 2017
48ad75c
Change dice coefficient calculation
ethanbb May 11, 2017
0d6777e
Merge branch 'master' of https://github.com/ethanbb/tf_unet
ethanbb May 11, 2017
3d8cf8f
Merge branch 'master' of https://github.com/jakeret/tf_unet
ethanbb Jun 5, 2018
cb77c48
Fix dice_coefficient denominator name and add iou
ethanbb Jun 5, 2018
828db79
Add documentation for iou
ethanbb Jun 7, 2018
adb031c
Fix syntax errors
ethanbb Jun 8, 2018
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20 changes: 13 additions & 7 deletions tf_unet/unet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,12 +2,12 @@
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
#
# tf_unet is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
#
# You should have received a copy of the GNU General Public License
# along with tf_unet. If not, see <http://www.gnu.org/licenses/>.

Expand Down Expand Up @@ -204,7 +204,8 @@ def __init__(self, channels=3, n_class=2, cost="cross_entropy", cost_kwargs={},

def _get_cost(self, logits, cost_name, cost_kwargs):
"""
Constructs the cost function, either cross_entropy, weighted cross_entropy or dice_coefficient.
Constructs the cost function, either cross_entropy, weighted cross_entropy,
dice_coefficient, or iou (intersection over union).
Optional arguments are:
class_weights: weights for the different classes in case of multi-class imbalance
regularizer: power of the L2 regularizers added to the loss function
Expand All @@ -230,12 +231,17 @@ def _get_cost(self, logits, cost_name, cost_kwargs):
else:
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=flat_logits,
labels=flat_labels))
elif cost_name == "dice_coefficient":
elif cost_name == "dice_coefficient" or cost_name == "iou":
eps = 1e-5
prediction = pixel_wise_softmax_2(logits)
intersection = tf.reduce_sum(prediction * self.y)
union = eps + tf.reduce_sum(prediction) + tf.reduce_sum(self.y)
loss = -(2 * intersection / (union))
A_intersect_B = tf.reduce_sum(prediction * self.y, axis=[0, 1, 2])
A_plus_B = tf.reduce_sum(prediction, axis=[0, 1, 2]) + tf.reduce_sum(self.y, axis=[0, 1, 2])
if cost_name == "dice_coefficient":
denominator = A_plus_B
else: # intersection over union
A_union_B = A_plus_B - A_intersect_B
denominator = A_union_B
loss = tf.reduce_sum(-(2 * A_intersect_B / (eps + denominator)))

else:
raise ValueError("Unknown cost function: " % cost_name)
Expand Down