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    

Backend/dist_cal/model_run.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.
+
+from layers import *
+
+class pydnet(object):
+
+    def __init__(self, placeholders=None):
+        self.model_collection = ['PyDnet']    
+        self.placeholders = placeholders
+        self.build_model()
+        self.build_outputs()        
+    
+    def build_model(self):    
+      with tf.variable_scope("pyramid") as scope:
+        pyramid = self.build_pyramid(self.placeholders['im0'])
+      # SCALE 6
+      with tf.variable_scope("L6") as scope:
+        with tf.variable_scope("estimator") as scope:
+          conv6 = self.build_estimator(pyramid[6])
+          self.disp7 = self.get_disp(conv6)
+        with tf.variable_scope("upsampler") as scope:
+          upconv6 = self.bilinear_upsampling_by_deconvolution(conv6)
+      # SCALE 5
+      with tf.variable_scope("L5") as scope:
+        with tf.variable_scope("estimator") as scope:
+          conv5 = self.build_estimator(pyramid[5], upconv6)
+          self.disp6 = self.get_disp(conv5)
+        with tf.variable_scope("upsampler") as scope:
+          upconv5 = self.bilinear_upsampling_by_deconvolution(conv5)
+      # SCALE 4
+      with tf.variable_scope("L4") as scope:
+        with tf.variable_scope("estimator") as scope:
+          conv4 = self.build_estimator(pyramid[4], upconv5)
+          self.disp5 = self.get_disp(conv4)
+        with tf.variable_scope("upsampler") as scope:
+          upconv4 = self.bilinear_upsampling_by_deconvolution(conv4)
+      # SCALE 3
+      with tf.variable_scope("L3") as scope:
+        with tf.variable_scope("estimator") as scope:
+          conv3 = self.build_estimator(pyramid[3], upconv4)
+          self.disp4 = self.get_disp(conv3)
+        with tf.variable_scope("upsampler") as scope:
+          upconv3 = self.bilinear_upsampling_by_deconvolution(conv3)
+      # SCALE 2
+      with tf.variable_scope("L2") as scope:
+        with tf.variable_scope("estimator") as scope:
+          conv2 = self.build_estimator(pyramid[2], upconv3)
+          self.disp3 = self.get_disp(conv2)
+        with tf.variable_scope("upsampler") as scope:
+          upconv2 = self.bilinear_upsampling_by_deconvolution(conv2)
+      # SCALE 1
+      with tf.variable_scope("L1") as scope:
+        with tf.variable_scope("estimator") as scope:
+          conv1 = self.build_estimator(pyramid[1], upconv2)
+          self.disp2 = self.get_disp(conv1)
+
+    # Pyramidal features extraction
+    def build_pyramid(self, input_batch):
+      features = []
+      features.append(input_batch)
+      with tf.variable_scope("conv1a"):
+        conv1a = conv2d_leaky(input_batch, [3, 3, 3, 16], [16], 2, True)
+      with tf.variable_scope("conv1b"):
+        conv1b = conv2d_leaky(conv1a, [3, 3, 16, 16], [16], 1, True)
+      features.append(conv1b)
+      with tf.variable_scope("conv2a"):
+        conv2a = conv2d_leaky(conv1b, [3, 3, 16, 32], [32], 2, True)
+      with tf.variable_scope("conv2b"):
+        conv2b = conv2d_leaky(conv2a, [3, 3, 32, 32], [32], 1, True)
+      features.append(conv2b)
+      with tf.variable_scope("conv3a"):
+        conv3a = conv2d_leaky(conv2b, [3, 3, 32, 64], [64], 2, True)
+      with tf.variable_scope("conv3b"):
+        conv3b = conv2d_leaky(conv3a, [3, 3, 64, 64], [64], 1, True)
+      features.append(conv3b)
+      with tf.variable_scope("conv4a"):
+        conv4a = conv2d_leaky(conv3b, [3, 3, 64, 96], [96], 2, True)
+      with tf.variable_scope("conv4b"):
+        conv4b = conv2d_leaky(conv4a, [3, 3, 96, 96], [96], 1, True)
+      features.append(conv4b)
+      with tf.variable_scope("conv5a"):
+        conv5a = conv2d_leaky(conv4b, [3, 3, 96, 128], [128], 2, True)
+      with tf.variable_scope("conv5b"):
+        conv5b = conv2d_leaky(conv5a, [3, 3, 128, 128], [128], 1, True)
+      features.append(conv5b)
+      with tf.variable_scope("conv6a"):
+        conv6a = conv2d_leaky(conv5b, [3, 3, 128, 192], [192], 2, True)
+      with tf.variable_scope("conv6b"):
+        conv6b = conv2d_leaky(conv6a, [3, 3, 192, 192], [192], 1, True)
+      features.append(conv6b)
+      return features	     
+
+    # Single scale estimator
+    def build_estimator(self, features, upsampled_disp=None):
+      if upsampled_disp is not None:
+        disp2 = tf.concat([features, upsampled_disp], -1)
+      else:
+        disp2 = features
+      with tf.variable_scope("disp-3") as scope:
+        disp3 = conv2d_leaky(disp2, [3, 3, disp2.shape[3], 96], [96], 1, True)
+      with tf.variable_scope("disp-4") as scope:
+        disp4 = conv2d_leaky(disp3, [3, 3, disp3.shape[3], 64], [64], 1, True)
+      with tf.variable_scope("disp-5") as scope:
+        disp5 = conv2d_leaky(disp4, [3, 3, disp4.shape[3], 32], [32], 1, True)
+      with tf.variable_scope("disp-6") as scope:
+        disp6 = conv2d_leaky(disp5, [3, 3, disp5.shape[3], 8], [8], 1, False) # 8 channels for compatibility with @other@ devices
+      return disp6
+      
+    # Upsampling layer
+    def bilinear_upsampling_by_deconvolution(self,x):
+      f = x.get_shape().as_list()[-1]
+      return deconv2d_leaky(x, [2, 2, f, f], f, 2, True)
+
+    # Disparity prediction layer
+    def get_disp(self, x):
+      disp = 0.3 * tf.nn.sigmoid(tf.slice(x, [0,0,0,0], [-1,-1,-1,2]))
+      return disp
+      
+    # Build multi-scale outputs
+    def build_outputs(self):
+      shape = tf.shape(self.placeholders['im0']) 
+      size = [shape[1], shape[2]]          
+      self.results = tf.image.resize_images(self.disp2, size), tf.image.resize_images(self.disp3, size), tf.image.resize_images(self.disp4, size)
+
+
+
+
+
+
+class pydnet2(object):
+
+    def __init__(self, placeholders=None):
+        self.model_collection = ['PyDnet']    
+        self.placeholders = placeholders
+        self.build_model()
+        self.build_outputs()        
+    
+    def build_model(self):    
+      with tf.variable_scope("pyramid") as scope:
+        pyramid = self.build_pyramid(self.placeholders['im0'])
+
+      # SCALE 4
+      with tf.variable_scope("L4") as scope:
+        with tf.variable_scope("estimator") as scope:
+          conv4 = self.build_estimator(pyramid[4])
+          self.disp5 = self.get_disp(conv4)
+        with tf.variable_scope("upsampler") as scope:
+          upconv4 = self.bilinear_upsampling_by_deconvolution(conv4)
+      # SCALE 3
+      with tf.variable_scope("L3") as scope:
+        with tf.variable_scope("estimator") as scope:
+          conv3 = self.build_estimator(pyramid[3], upconv4)
+          self.disp4 = self.get_disp(conv3)
+        with tf.variable_scope("upsampler") as scope:
+          upconv3 = self.bilinear_upsampling_by_deconvolution(conv3)
+      # SCALE 2
+      with tf.variable_scope("L2") as scope:
+        with tf.variable_scope("estimator") as scope:
+          conv2 = self.build_estimator(pyramid[2], upconv3)
+          self.disp3 = self.get_disp(conv2)
+        with tf.variable_scope("upsampler") as scope:
+          upconv2 = self.bilinear_upsampling_by_deconvolution(conv2)
+      # SCALE 1
+      with tf.variable_scope("L1") as scope:
+        with tf.variable_scope("estimator") as scope:
+          conv1 = self.build_estimator(pyramid[1], upconv2)
+          self.disp2 = self.get_disp(conv1)
+
+    # Pyramidal features extraction
+    def build_pyramid(self, input_batch):
+      features = []
+      features.append(input_batch)
+      with tf.variable_scope("conv1a"):
+        conv1a = conv2d_leaky(input_batch, [3, 3, 3, 16], [16], 2, True)
+      with tf.variable_scope("conv1b"):
+        conv1b = conv2d_leaky(conv1a, [3, 3, 16, 16], [16], 1, True)
+      features.append(conv1b)
+      with tf.variable_scope("conv2a"):
+        conv2a = conv2d_leaky(conv1b, [3, 3, 16, 32], [32], 2, True)
+      with tf.variable_scope("conv2b"):
+        conv2b = conv2d_leaky(conv2a, [3, 3, 32, 32], [32], 1, True)
+      features.append(conv2b)
+      with tf.variable_scope("conv3a"):
+        conv3a = conv2d_leaky(conv2b, [3, 3, 32, 64], [64], 2, True)
+      with tf.variable_scope("conv3b"):
+        conv3b = conv2d_leaky(conv3a, [3, 3, 64, 64], [64], 1, True)
+      features.append(conv3b)
+      with tf.variable_scope("conv4a"):
+        conv4a = conv2d_leaky(conv3b, [3, 3, 64, 96], [96], 2, True)
+      with tf.variable_scope("conv4b"):
+        conv4b = conv2d_leaky(conv4a, [3, 3, 96, 96], [96], 1, True)
+      features.append(conv4b)
+      
+      return features	     
+
+    # Single scale estimator
+    def build_estimator(self, features, upsampled_disp=None):
+      if upsampled_disp is not None:
+        disp2 = tf.concat([features, upsampled_disp], -1)
+      else:
+        disp2 = features
+      with tf.variable_scope("disp-3") as scope:
+        disp3 = conv2d_leaky(disp2, [3, 3, disp2.shape[3], 96], [96], 1, True)
+      with tf.variable_scope("disp-4") as scope:
+        disp4 = conv2d_leaky(disp3, [3, 3, disp3.shape[3], 64], [64], 1, True)
+      with tf.variable_scope("disp-5") as scope:
+        disp5 = conv2d_leaky(disp4, [3, 3, disp4.shape[3], 32], [32], 1, True)
+      with tf.variable_scope("disp-6") as scope:
+        disp6 = conv2d_leaky(disp5, [3, 3, disp5.shape[3], 8], [8], 1, False) # 8 channels for compatibility with @other@ devices
+      return disp6
+      
+    # Upsampling layer
+    def bilinear_upsampling_by_deconvolution(self,x):
+      f = x.get_shape().as_list()[-1]
+      return deconv2d_leaky(x, [2, 2, f, f], f, 2, True)
+
+    # Disparity prediction layer
+    def get_disp(self, x):
+      disp = tf.nn.relu(tf.slice(x, [0,0,0,0], [-1,-1,-1,2]))
+      return disp
+      
+    # Build multi-scale outputs
+    def build_outputs(self):
+      shape = tf.shape(self.placeholders['im0']) 
+      size = [shape[1], shape[2]]          
+      self.results = tf.image.resize_images(self.disp2, size), tf.image.resize_images(self.disp3, size), tf.image.resize_images(self.disp4, size)
\ No newline at end of file