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ddeeplabv3+: fix imports/pathing errors

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Starbeamrainbowlabs 2022-12-13 13:38:27 +00:00
parent fc43f145c2
commit 4d8ce792c9
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
1 changed files with 15 additions and 15 deletions
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30
aimodel/src/deeplabv3_plus_test.py
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@ -88,23 +88,23 @@ def convolution_block(
padding="same",
use_bias=False,
):
x = layers.Conv2D(
x = tf.keras.layers.Conv2D(
num_filters,
kernel_size=kernel_size,
dilation_rate=dilation_rate,
padding="same",
use_bias=use_bias,
kernel_initializer=keras.initializers.HeNormal(),
kernel_initializer=tf.keras.initializers.HeNormal(),
)(block_input)
x = layers.BatchNormalization()(x)
x = tf.keras.layers.BatchNormalization()(x)
return tf.nn.relu(x)
def DilatedSpatialPyramidPooling(dspp_input):
dims = dspp_input.shape
x = layers.AveragePooling2D(pool_size=(dims[-3], dims[-2]))(dspp_input)
x = tf.keras.layers.AveragePooling2D(pool_size=(dims[-3], dims[-2]))(dspp_input)
x = convolution_block(x, kernel_size=1, use_bias=True)
out_pool = layers.UpSampling2D(
out_pool = tf.keras.layers.UpSampling2D(
size=(dims[-3] // x.shape[1], dims[-2] // x.shape[2]), interpolation="bilinear",
)(x)
@ -113,35 +113,35 @@ def DilatedSpatialPyramidPooling(dspp_input):
out_12 = convolution_block(dspp_input, kernel_size=3, dilation_rate=12)
out_18 = convolution_block(dspp_input, kernel_size=3, dilation_rate=18)
x = layers.Concatenate(axis=-1)([out_pool, out_1, out_6, out_12, out_18])
x = tf.keras.layers.Concatenate(axis=-1)([out_pool, out_1, out_6, out_12, out_18])
output = convolution_block(x, kernel_size=1)
return output
def DeeplabV3Plus(image_size, num_classes):
model_input = keras.Input(shape=(image_size, image_size, 3))
resnet50 = keras.applications.ResNet50(
model_input = tf.keras.Input(shape=(image_size, image_size, 3))
resnet50 = tf.keras.applications.ResNet50(
weights="imagenet", include_top=False, input_tensor=model_input
)
x = resnet50.get_layer("conv4_block6_2_relu").output
x = DilatedSpatialPyramidPooling(x)
input_a = layers.UpSampling2D(
input_a = tf.keras.layers.UpSampling2D(
size=(image_size // 4 // x.shape[1], image_size // 4 // x.shape[2]),
interpolation="bilinear",
)(x)
input_b = resnet50.get_layer("conv2_block3_2_relu").output
input_b = convolution_block(input_b, num_filters=48, kernel_size=1)
x = layers.Concatenate(axis=-1)([input_a, input_b])
x = tf.keras.layers.Concatenate(axis=-1)([input_a, input_b])
x = convolution_block(x)
x = convolution_block(x)
x = layers.UpSampling2D(
x = tf.keras.layers.UpSampling2D(
size=(image_size // x.shape[1], image_size // x.shape[2]),
interpolation="bilinear",
)(x)
model_output = layers.Conv2D(num_classes, kernel_size=(1, 1), padding="same")(x)
return keras.Model(inputs=model_input, outputs=model_output)
model_output = tf.keras.layers.Conv2D(num_classes, kernel_size=(1, 1), padding="same")(x)
return tf.keras.Model(inputs=model_input, outputs=model_output)
model = DeeplabV3Plus(image_size=IMAGE_SIZE, num_classes=NUM_CLASSES)
@ -156,9 +156,9 @@ model.summary()
# ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██
# ██ ██ ██ ██ ██ ██ ██ ████ ██ ██ ████ ██████
loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(
optimizer=keras.optimizers.Adam(learning_rate=0.001),
optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
loss=loss,
metrics=["accuracy"],
)