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dpl: fix moar crashes

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Starbeamrainbowlabs 2023-01-05 19:03:44 +00:00
parent fefeb5d531
commit 4563fe6b27
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
2 changed files with 9 additions and 4 deletions
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2
aimodel/src/deeplabv3_plus_test_rainfall.py
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@ -123,7 +123,7 @@ def DeeplabV3Plus(image_size, num_classes, num_channels=3):
return tf.keras.Model(inputs=model_input, outputs=model_output) return tf.keras.Model(inputs=model_input, outputs=model_output)
model = DeeplabV3Plus(image_size=IMAGE_SIZE, num_classes=NUM_CLASSES) model = DeeplabV3Plus(image_size=IMAGE_SIZE, num_classes=NUM_CLASSES, num_channels=8)
summarywriter(model, os.path.join(DIR_OUTPUT, "summary.txt")) summarywriter(model, os.path.join(DIR_OUTPUT, "summary.txt"))

11
aimodel/src/lib/dataset/dataset.py
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@ -26,7 +26,7 @@ def parse_item(metadata, shape_water_desired, dummy_label=True):
}) })
rainfall = tf.io.parse_tensor(parsed["rainfallradar"], out_type=tf.float32) rainfall = tf.io.parse_tensor(parsed["rainfallradar"], out_type=tf.float32)
water = tf.io.parse_tensor(parsed["waterdepth"], out_type=tf.float32) water = tf.io.parse_tensor(parsed["waterdepth"], out_type=tf.float32)
# [channels, width, height] → [width, height, channels] - ref ConvNeXt does not support data_format=channels_first # [channels, height, width] → [height, width, channels] - ref ConvNeXt does not support data_format=channels_first
rainfall = tf.reshape(rainfall, tf.constant(metadata["rainfallradar"], dtype=tf.int32)) rainfall = tf.reshape(rainfall, tf.constant(metadata["rainfallradar"], dtype=tf.int32))
water = tf.reshape(water, tf.constant(metadata["waterdepth"], dtype=tf.int32)) water = tf.reshape(water, tf.constant(metadata["waterdepth"], dtype=tf.int32))
@ -34,8 +34,13 @@ def parse_item(metadata, shape_water_desired, dummy_label=True):
rainfall = tf.transpose(rainfall, [1, 2, 0]) # channels_first → channels_last rainfall = tf.transpose(rainfall, [1, 2, 0]) # channels_first → channels_last
# rainfall = tf.image.resize(rainfall, tf.cast(tf.constant(metadata["rainfallradar"]) / 2, dtype=tf.int32)) # rainfall = tf.image.resize(rainfall, tf.cast(tf.constant(metadata["rainfallradar"]) / 2, dtype=tf.int32))
water = tf.expand_dims(water, axis=-1) # [width, height] → [width, height, channels] water = tf.expand_dims(water, axis=-1) # [height, width] → [height, width, channels]
water = tf.image.crop_to_bounding_box(water, water_offset_x, water_offset_y, water_width_target, water_height_target) water = tf.image.crop_to_bounding_box(water,
offset_height=water_offset_y,
offset_width =water_offset_x,
target_height=water_height_target,
target_width =water_width_target,
)
print("DEBUG:dataset ITEM rainfall:shape", rainfall.shape, "water:shape", water.shape) print("DEBUG:dataset ITEM rainfall:shape", rainfall.shape, "water:shape", water.shape)
# TODO: Any other additional parsing here, since multiple .map() calls are not optimal # TODO: Any other additional parsing here, since multiple .map() calls are not optimal