added layers and model_run python files

Backend/dist_cal/layers.py

+#
+# MIT License
+#
+# Copyright (c) 2018 Matteo Poggi m.poggi@unibo.it
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import tensorflow.compat.v1  as tf
+tf.disable_v2_behavior()
+# Hand-made leaky relu
+def leaky_relu(x, alpha=0.2):
+  return tf.maximum(x, alpha * x)
+
+# 2D convolution wrapper
+def conv2d_leaky(x, kernel_shape, bias_shape, strides=1, relu=True, padding='SAME'):
+  # Conv2D
+  weights = tf.get_variable("weights", kernel_shape, initializer=tf.initializers.glorot_uniform(), dtype=tf.float32)
+  biases = tf.get_variable("biases", bias_shape, initializer=tf.truncated_normal_initializer(), dtype=tf.float32)
+  output = tf.nn.conv2d(x, weights, strides=[1, strides, strides, 1], padding=padding)
+  output = tf.nn.bias_add(output, biases)
+  # ReLU (if required)
+  if relu:
+    output = leaky_relu(output, 0.2)
+  return output
+
+# 2D deconvolution wrapper
+def deconv2d_leaky(x, kernel_shape, bias_shape, strides=1, relu=True, padding='SAME'):
+  # Conv2D
+  weights = tf.get_variable("weights", kernel_shape, initializer=tf.initializers.glorot_uniform(), dtype=tf.float32)
+  biases = tf.get_variable("biases", bias_shape, initializer=tf.truncated_normal_initializer(), dtype=tf.float32)
+  x_shape = tf.shape(x)
+  outputShape = [x_shape[0],x_shape[1]*strides,x_shape[2]*strides,kernel_shape[2]]  
+  output = tf.nn.conv2d_transpose(x, weights, output_shape=outputShape, strides=[1, strides, strides, 1], padding=padding)
+  output = tf.nn.bias_add(output, biases)
+  # ReLU (if required)
+  if relu:
+    output = leaky_relu(output, 0.2)
+  return output