Add resnet18

unsupervised/models.py

@@ -0,0 +1,78 @@
+import tensorflow.keras as keras
+import tensorflow.keras.layers as layers
+
+
+def wrap_mobilenet_nnconv5_for_utrain(model):
+    """
+    Wraps a fast depth model for use in unsupervised training.
+
+    This just exposes the lower disparity layers as outputs, so they can be used to train at different scales/image
+    resolutions.
+    :param model:
+    :return:
+    """
+    input = model.input
+    disp_1 = model.get_layer('conv_pw_%d_relu' % 15).output
+    disp_2 = model.get_layer('conv_pw_%d_relu' % 16).output
+    disp_3 = model.get_layer('conv_pw_%d_relu' % 17).output
+    return keras.Model(input, outputs=[disp_1, disp_2, disp_3, model.output])
+
+
+def res_layer(inputs, out_channels, down_sample=None, stride=1, normalisation=layers.BatchNormalization,
+              activation=layers.ReLU):
+    x = layers.Conv2D(out_channels, 3, padding='same', strides=stride)(inputs)
+    x = normalisation()(x)
+    x = activation()(x)
+    x = layers.Conv2D(out_channels, 3, padding='same', strides=1)(x)
+    x = normalisation()(x)
+    # Residual skip connection. Downsample inputs if necessary
+    if down_sample is not None:
+        inputs = down_sample
+    x = layers.Add()([x, inputs])
+    x = activation()(x)
+    return x
+
+
+def res_block(inputs, out_channels, num_blocks=1, stride=1, normalisation=layers.BatchNormalization,
+              activation=layers.ReLU):
+    down_sample = None
+    if stride != 1 or inputs.shape[-1] != out_channels:
+        down_sample = layers.Conv2D(out_channels, 1, stride, padding='same')(inputs)
+        down_sample = normalisation()(down_sample)
+
+    x = res_layer(inputs, out_channels, down_sample, stride, normalisation, activation)
+    for i in range(1, num_blocks):
+        x = res_layer(x, out_channels, None, 1, normalisation, activation)
+    return x
+
+
+def resnet_18(shape=(224, 224, 6)):
+    """
+    Build the ResNet 18 network (encoder for the pose network)
+    :param shape: Input shape. Note this should support 2 images for the pose net, so 6 channels in that case
+    :return: Resnet encoder (ResNet18)
+    """
+    inputs = layers.Input(shape)
+    x = keras.layers.Conv2D(64, 7, 2, padding='same')(inputs)
+    x = layers.BatchNormalization()(x)
+    x = layers.ReLU()(x)
+    x = layers.MaxPooling2D(3, 2, 'same')(x)
+    x = res_block(x, 64, 2)
+    x = res_block(x, 128, 2, 2)
+    x = res_block(x, 256, 2, 2)
+    x = res_block(x, 512, 2, 2)
+    return keras.Model(inputs=inputs, outputs=x)
+
+
+def pose_net(shape=(224, 224, 6), encoder=resnet_18):
+    resnet = encoder(shape=shape)
+
+    # Decoder
+
+    pass
+
+
+if __name__ == '__main__':
+    # import fast_depth_functional as fd
+    # wrap_mobilenet_nnconv5_for_utrain(fd.mobilenet_nnconv5()).summary()
+    pose_net()