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https://github.com/sbrl/research-rainfallradar
synced 2024-11-22 01:12:59 +00:00
add optional PATH_CHECKPOINT env var
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1BE5172E637709C2
1 changed files with 114 additions and 108 deletions
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@ -28,6 +28,8 @@ STEPS_PER_EPOCH = int(os.environ["STEPS_PER_EPOCH"]) if "STEPS_PER_EPOCH" in os.
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DIR_OUTPUT=os.environ["DIR_OUTPUT"] if "DIR_OUTPUT" in os.environ else f"output/{datetime.utcnow().date().isoformat()}_deeplabv3plus_rainfall_TEST"
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PATH_CHECKPOINT = os.environ["PATH_CHECKPOINT"] if "PATH_CHECKPOINT" in os.environ else None
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if not os.path.exists(DIR_OUTPUT):
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os.makedirs(os.path.join(DIR_OUTPUT, "checkpoints"))
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@ -38,6 +40,7 @@ logger.info(f"> PATH_HEIGHTMAP {PATH_HEIGHTMAP}")
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logger.info(f"> PATH_COLOURMAP {PATH_COLOURMAP}")
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logger.info(f"> STEPS_PER_EPOCH {STEPS_PER_EPOCH}")
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logger.info(f"> DIR_OUTPUT {DIR_OUTPUT}")
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logger.info(f"> PATH_CHECKPOINT {PATH_CHECKPOINT}")
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dataset_train, dataset_validate = dataset_mono(
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@ -60,73 +63,75 @@ logger.info("Validation Dataset:", dataset_validate)
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# ██ ██ ██ ██ ██ ██ ██ ██ ██
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# ██ ██ ██████ ██████ ███████ ███████
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def convolution_block(
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block_input,
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num_filters=256,
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kernel_size=3,
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dilation_rate=1,
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padding="same",
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use_bias=False,
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):
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x = tf.keras.layers.Conv2D(
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num_filters,
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kernel_size=kernel_size,
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dilation_rate=dilation_rate,
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if PATH_CHECKPOINT is None:
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def convolution_block(
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block_input,
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num_filters=256,
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kernel_size=3,
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dilation_rate=1,
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padding="same",
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use_bias=use_bias,
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kernel_initializer=tf.keras.initializers.HeNormal(),
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)(block_input)
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x = tf.keras.layers.BatchNormalization()(x)
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return tf.nn.relu(x)
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use_bias=False,
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):
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x = tf.keras.layers.Conv2D(
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num_filters,
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kernel_size=kernel_size,
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dilation_rate=dilation_rate,
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padding="same",
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use_bias=use_bias,
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kernel_initializer=tf.keras.initializers.HeNormal(),
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)(block_input)
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x = tf.keras.layers.BatchNormalization()(x)
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return tf.nn.relu(x)
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def DilatedSpatialPyramidPooling(dspp_input):
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dims = dspp_input.shape
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x = tf.keras.layers.AveragePooling2D(pool_size=(dims[-3], dims[-2]))(dspp_input)
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x = convolution_block(x, kernel_size=1, use_bias=True)
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out_pool = tf.keras.layers.UpSampling2D(
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size=(dims[-3] // x.shape[1], dims[-2] // x.shape[2]), interpolation="bilinear",
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)(x)
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def DilatedSpatialPyramidPooling(dspp_input):
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dims = dspp_input.shape
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x = tf.keras.layers.AveragePooling2D(pool_size=(dims[-3], dims[-2]))(dspp_input)
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x = convolution_block(x, kernel_size=1, use_bias=True)
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out_pool = tf.keras.layers.UpSampling2D(
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size=(dims[-3] // x.shape[1], dims[-2] // x.shape[2]), interpolation="bilinear",
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)(x)
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out_1 = convolution_block(dspp_input, kernel_size=1, dilation_rate=1)
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out_6 = convolution_block(dspp_input, kernel_size=3, dilation_rate=6)
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out_12 = convolution_block(dspp_input, kernel_size=3, dilation_rate=12)
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out_18 = convolution_block(dspp_input, kernel_size=3, dilation_rate=18)
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out_1 = convolution_block(dspp_input, kernel_size=1, dilation_rate=1)
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out_6 = convolution_block(dspp_input, kernel_size=3, dilation_rate=6)
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out_12 = convolution_block(dspp_input, kernel_size=3, dilation_rate=12)
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out_18 = convolution_block(dspp_input, kernel_size=3, dilation_rate=18)
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x = tf.keras.layers.Concatenate(axis=-1)([out_pool, out_1, out_6, out_12, out_18])
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output = convolution_block(x, kernel_size=1)
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return output
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x = tf.keras.layers.Concatenate(axis=-1)([out_pool, out_1, out_6, out_12, out_18])
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output = convolution_block(x, kernel_size=1)
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return output
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def DeeplabV3Plus(image_size, num_classes, num_channels=3):
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model_input = tf.keras.Input(shape=(image_size, image_size, num_channels))
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resnet50 = tf.keras.applications.ResNet50(
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weights="imagenet" if num_channels == 3 else None,
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include_top=False, input_tensor=model_input
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)
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x = resnet50.get_layer("conv4_block6_2_relu").output
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x = DilatedSpatialPyramidPooling(x)
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def DeeplabV3Plus(image_size, num_classes, num_channels=3):
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model_input = tf.keras.Input(shape=(image_size, image_size, num_channels))
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resnet50 = tf.keras.applications.ResNet50(
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weights="imagenet" if num_channels == 3 else None,
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include_top=False, input_tensor=model_input
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)
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x = resnet50.get_layer("conv4_block6_2_relu").output
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x = DilatedSpatialPyramidPooling(x)
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input_a = tf.keras.layers.UpSampling2D(
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size=(image_size // 4 // x.shape[1], image_size // 4 // x.shape[2]),
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interpolation="bilinear",
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)(x)
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input_b = resnet50.get_layer("conv2_block3_2_relu").output
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input_b = convolution_block(input_b, num_filters=48, kernel_size=1)
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input_a = tf.keras.layers.UpSampling2D(
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size=(image_size // 4 // x.shape[1], image_size // 4 // x.shape[2]),
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interpolation="bilinear",
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)(x)
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input_b = resnet50.get_layer("conv2_block3_2_relu").output
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input_b = convolution_block(input_b, num_filters=48, kernel_size=1)
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x = tf.keras.layers.Concatenate(axis=-1)([input_a, input_b])
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x = convolution_block(x)
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x = convolution_block(x)
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x = tf.keras.layers.UpSampling2D(
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size=(image_size // x.shape[1], image_size // x.shape[2]),
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interpolation="bilinear",
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)(x)
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model_output = tf.keras.layers.Conv2D(num_classes, kernel_size=(1, 1), padding="same")(x)
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return tf.keras.Model(inputs=model_input, outputs=model_output)
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x = tf.keras.layers.Concatenate(axis=-1)([input_a, input_b])
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x = convolution_block(x)
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x = convolution_block(x)
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x = tf.keras.layers.UpSampling2D(
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size=(image_size // x.shape[1], image_size // x.shape[2]),
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interpolation="bilinear",
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)(x)
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model_output = tf.keras.layers.Conv2D(num_classes, kernel_size=(1, 1), padding="same")(x)
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return tf.keras.Model(inputs=model_input, outputs=model_output)
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model = DeeplabV3Plus(image_size=IMAGE_SIZE, num_classes=NUM_CLASSES, num_channels=8)
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summarywriter(model, os.path.join(DIR_OUTPUT, "summary.txt"))
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model = DeeplabV3Plus(image_size=IMAGE_SIZE, num_classes=NUM_CLASSES, num_channels=8)
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summarywriter(model, os.path.join(DIR_OUTPUT, "summary.txt"))
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@ -137,63 +142,64 @@ summarywriter(model, os.path.join(DIR_OUTPUT, "summary.txt"))
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# ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██
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# ██ ██ ██ ██ ██ ██ ██ ████ ██ ██ ████ ██████
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loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
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model.compile(
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optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
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loss=loss,
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metrics=["accuracy"],
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)
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logger.info(">>> Beginning training")
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history = model.fit(dataset_train,
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validation_data=dataset_validate,
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epochs=25,
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callbacks=[
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tf.keras.callbacks.CSVLogger(
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filename=os.path.join(DIR_OUTPUT, "metrics.tsv"),
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separator="\t"
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),
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CallbackCustomModelCheckpoint(
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model_to_checkpoint=model,
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filepath=os.path.join(
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DIR_OUTPUT,
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"checkpoints"
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"checkpoint_e{epoch:d}_loss{loss:.3f}.hdf5"
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if PATH_CHECKPOINT is None:
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loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
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model.compile(
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optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
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loss=loss,
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metrics=["accuracy"],
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)
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logger.info(">>> Beginning training")
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history = model.fit(dataset_train,
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validation_data=dataset_validate,
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epochs=25,
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callbacks=[
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tf.keras.callbacks.CSVLogger(
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filename=os.path.join(DIR_OUTPUT, "metrics.tsv"),
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separator="\t"
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),
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monitor="loss"
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),
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],
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steps_per_epoch=STEPS_PER_EPOCH,
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)
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logger.info(">>> Training complete")
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logger.info(">>> Plotting graphs")
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CallbackCustomModelCheckpoint(
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model_to_checkpoint=model,
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filepath=os.path.join(
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DIR_OUTPUT,
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"checkpoints"
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"checkpoint_e{epoch:d}_loss{loss:.3f}.hdf5"
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),
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monitor="loss"
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),
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],
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steps_per_epoch=STEPS_PER_EPOCH,
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)
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logger.info(">>> Training complete")
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logger.info(">>> Plotting graphs")
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plt.plot(history.history["loss"])
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plt.title("Training Loss")
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plt.ylabel("loss")
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plt.xlabel("epoch")
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plt.savefig(os.path.join(DIR_OUTPUT, "loss.png"))
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plt.close()
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plt.plot(history.history["loss"])
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plt.title("Training Loss")
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plt.ylabel("loss")
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plt.xlabel("epoch")
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plt.savefig(os.path.join(DIR_OUTPUT, "loss.png"))
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plt.close()
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plt.plot(history.history["accuracy"])
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plt.title("Training Accuracy")
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plt.ylabel("accuracy")
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plt.xlabel("epoch")
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plt.savefig(os.path.join(DIR_OUTPUT, "acc.png"))
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plt.close()
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plt.plot(history.history["accuracy"])
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plt.title("Training Accuracy")
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plt.ylabel("accuracy")
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plt.xlabel("epoch")
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plt.savefig(os.path.join(DIR_OUTPUT, "acc.png"))
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plt.close()
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plt.plot(history.history["val_loss"])
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plt.title("Validation Loss")
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plt.ylabel("val_loss")
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plt.xlabel("epoch")
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plt.savefig(os.path.join(DIR_OUTPUT, "val_loss.png"))
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plt.close()
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plt.plot(history.history["val_loss"])
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plt.title("Validation Loss")
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plt.ylabel("val_loss")
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plt.xlabel("epoch")
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plt.savefig(os.path.join(DIR_OUTPUT, "val_loss.png"))
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plt.close()
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plt.plot(history.history["val_accuracy"])
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plt.title("Validation Accuracy")
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plt.ylabel("val_accuracy")
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plt.xlabel("epoch")
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plt.savefig(os.path.join(DIR_OUTPUT, "val_acc.png"))
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plt.close()
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plt.plot(history.history["val_accuracy"])
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plt.title("Validation Accuracy")
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plt.ylabel("val_accuracy")
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plt.xlabel("epoch")
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plt.savefig(os.path.join(DIR_OUTPUT, "val_acc.png"))
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plt.close()
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