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Start adding pose decoder

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Piv
2021-08-03 20:25:19 +09:30
parent 2372b906df
commit a111f89722
1 changed files with 28 additions and 11 deletions
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39
unsupervised/models.py
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@@ -19,7 +19,7 @@ def wrap_mobilenet_nnconv5_for_utrain(model):
def res_layer(inputs, out_channels, down_sample=None, stride=1, normalisation=layers.BatchNormalization, def res_layer(inputs, out_channels, down_sample=None, stride=1, normalisation=layers.BatchNormalization,
activation=layers.ReLU): activation=layers.ReLU, name=None):
x = layers.Conv2D(out_channels, 3, padding='same', strides=stride)(inputs) x = layers.Conv2D(out_channels, 3, padding='same', strides=stride)(inputs)
x = normalisation()(x) x = normalisation()(x)
x = activation()(x) x = activation()(x)
@@ -29,12 +29,12 @@ def res_layer(inputs, out_channels, down_sample=None, stride=1, normalisation=la
if down_sample is not None: if down_sample is not None:
inputs = down_sample inputs = down_sample
x = layers.Add()([x, inputs]) x = layers.Add()([x, inputs])
x = activation()(x) x = activation(name=name)(x)
return x return x
def res_block(inputs, out_channels, num_blocks=1, stride=1, normalisation=layers.BatchNormalization, def res_block(inputs, out_channels, num_blocks=1, stride=1, normalisation=layers.BatchNormalization,
activation=layers.ReLU): activation=layers.ReLU, name=None):
down_sample = None down_sample = None
if stride != 1 or inputs.shape[-1] != out_channels: if stride != 1 or inputs.shape[-1] != out_channels:
down_sample = layers.Conv2D(out_channels, 1, stride, padding='same')(inputs) down_sample = layers.Conv2D(out_channels, 1, stride, padding='same')(inputs)
@@ -42,7 +42,7 @@ def res_block(inputs, out_channels, num_blocks=1, stride=1, normalisation=layers
x = res_layer(inputs, out_channels, down_sample, stride, normalisation, activation) x = res_layer(inputs, out_channels, down_sample, stride, normalisation, activation)
for i in range(1, num_blocks): for i in range(1, num_blocks):
x = res_layer(x, out_channels, None, 1, normalisation, activation) x = res_layer(x, out_channels, None, 1, normalisation, activation, name)
return x return x
@@ -55,18 +55,35 @@ def resnet_18(shape=(224, 224, 6)):
inputs = layers.Input(shape) inputs = layers.Input(shape)
x = keras.layers.Conv2D(64, 7, 2, padding='same')(inputs) x = keras.layers.Conv2D(64, 7, 2, padding='same')(inputs)
x = layers.BatchNormalization()(x) x = layers.BatchNormalization()(x)
x = layers.ReLU()(x) skip_1 = layers.ReLU(name="res_1")(x)
x = layers.MaxPooling2D(3, 2, 'same')(x) x = layers.MaxPooling2D(3, 2, 'same')(skip_1)
x = res_block(x, 64, 2) skip_2 = res_block(x, 64, 2, name="res_2")
x = res_block(x, 128, 2, 2) skip_3 = res_block(skip_2, 128, 2, 2, name="res_3")
x = res_block(x, 256, 2, 2) skip_4 = res_block(skip_3, 256, 2, 2, name="res_4")
x = res_block(x, 512, 2, 2) skip_5 = res_block(skip_4, 512, 2, 2, name="res_5")
return keras.Model(inputs=inputs, outputs=x) return keras.Model(inputs=inputs, outputs=[skip_1, skip_2, skip_3, skip_4, skip_5])
# TODO Monodepth and sfm learner both solve the posenet on all source images. So for the case of monodepth, it would need
# 9 as the input (3 images - target and 2 source images) and would produce 2 6DOF poses
def pose_net(shape=(224, 224, 6), encoder=resnet_18): def pose_net(shape=(224, 224, 6), encoder=resnet_18):
resnet = encoder(shape=shape) resnet = encoder(shape=shape)
for layer in resnet.layers:
layer.trainable = True
# Concatenate every skip connection
cat_skips = [layers.ReLU()(layers.Conv2D(256, 1)(encode_output[-1])) for encode_output in resnet.outputs]
cat_skips = layers.Concatenate(1)(cat_skips)
x = layers.Conv2D(256, 3, padding='same')(cat_skips)
x = layers.ReLU()(x)
x = layers.Conv2D(256, 3, padding='same')
x = layers.ReLU()(x)
x = layers.Conv2D(256, 12, 1)(x)
# Decoder # Decoder
pass pass