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dlr eo: cheese it by upsampling and then downsampling again

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Starbeamrainbowlabs 2023-02-23 16:47:00 +00:00
parent 96b94ec55b
commit 9f1cee2927
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
2 changed files with 9 additions and 8 deletions
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7
aimodel/src/deeplabv3_plus_test_rainfall.py
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@ -121,15 +121,16 @@ if PATH_CHECKPOINT is None:
def DeeplabV3Plus(image_size, num_classes, num_channels=3): def DeeplabV3Plus(image_size, num_classes, num_channels=3):
model_input = tf.keras.Input(shape=(image_size, image_size, num_channels)) model_input = tf.keras.Input(shape=(image_size, image_size, num_channels))
x = tf.keras.layers.UpSampling2D(size=2)(model_input)
resnet50 = tf.keras.applications.ResNet50( resnet50 = tf.keras.applications.ResNet50(
weights="imagenet" if num_channels == 3 else None, weights="imagenet" if num_channels == 3 else None,
include_top=False, input_tensor=model_input include_top=False, input_tensor=x
) )
x = resnet50.get_layer("conv4_block6_2_relu").output x = resnet50.get_layer("conv4_block6_2_relu").output
x = DilatedSpatialPyramidPooling(x) x = DilatedSpatialPyramidPooling(x)
input_a = tf.keras.layers.UpSampling2D( input_a = tf.keras.layers.UpSampling2D(
size=(image_size // 4 // x.shape[1], image_size // 4 // x.shape[2]), size=(image_size // 4 // x.shape[1], image_size // 4 // x.shape[2]), # <--- UPSAMPLE after pyramid
interpolation="bilinear", interpolation="bilinear",
)(x) )(x)
input_b = resnet50.get_layer("conv2_block3_2_relu").output input_b = resnet50.get_layer("conv2_block3_2_relu").output
@ -139,7 +140,7 @@ if PATH_CHECKPOINT is None:
x = convolution_block(x) x = convolution_block(x)
x = convolution_block(x) x = convolution_block(x)
x = tf.keras.layers.UpSampling2D( x = tf.keras.layers.UpSampling2D(
size=(image_size // x.shape[1], image_size // x.shape[2]), size=(image_size // x.shape[1], image_size // x.shape[2]), # <--- UPSAMPLE at end
interpolation="bilinear", interpolation="bilinear",
)(x) )(x)
model_output = tf.keras.layers.Conv2D(num_classes, kernel_size=(1, 1), padding="same")(x) model_output = tf.keras.layers.Conv2D(num_classes, kernel_size=(1, 1), padding="same")(x)

2
aimodel/src/lib/dataset/dataset_mono.py
View file

@ -94,7 +94,7 @@ def parse_item(metadata, output_size=100, input_size="same", water_threshold=0.1
# ONE-HOT [LOSS cross entropy] # ONE-HOT [LOSS cross entropy]
# water = tf.cast(tf.math.greater_equal(water, water_threshold), dtype=tf.int32) # water = tf.cast(tf.math.greater_equal(water, water_threshold), dtype=tf.int32)
# water = tf.one_hot(water, water_bins, axis=-1, dtype=tf.int32) # water = tf.one_hot(water, water_bins, axis=-1, dtype=tf.int32)
# SPARSE [LOSS dice] # SPARSE [LOSS dice / sparse cross entropy]
water = tf.cast(tf.math.greater_equal(water, water_threshold), dtype=tf.float32) water = tf.cast(tf.math.greater_equal(water, water_threshold), dtype=tf.float32)
if do_remove_isolated_pixels: if do_remove_isolated_pixels:
water = remove_isolated_pixels(water) water = remove_isolated_pixels(water)