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https://github.com/sbrl/research-rainfallradar
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dataset: namespace → dict
Python is so annoying
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1 changed files with 2 additions and 2 deletions
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@ -20,13 +20,13 @@ def parse_item(metadata):
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})
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})
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rainfall = tf.io.parse_tensor(parsed["rainfallradar"], out_type=tf.float32)
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rainfall = tf.io.parse_tensor(parsed["rainfallradar"], out_type=tf.float32)
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water = tf.io.parse_tensor(parsed["waterdepth"], out_type=tf.float32)
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water = tf.io.parse_tensor(parsed["waterdepth"], out_type=tf.float32)
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# [channels, width, height] → [width, height, channels] - ref ConvNeXt does not support data_format=channels_first
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# [channels, width, height] → [width, height, channels] - ref ConvNeXt does not support data_format=channels_first
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rainfall = tf.transpose(rainfall, [1, 2, 0])
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rainfall = tf.transpose(rainfall, [1, 2, 0])
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# [width, height] → [width, height, channels]
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# [width, height] → [width, height, channels]
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water = tf.expand_dims(water, axis=-1)
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water = tf.expand_dims(water, axis=-1)
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# BUG: AttributeError: 'dict' object has no attribute 'waterdepth
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# BUG: AttributeError: 'dict' object has no attribute 'waterdepth
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rainfall = tf.image.resize(rainfall, tf.constant(metadata.waterdepth))
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rainfall = tf.image.resize(rainfall, tf.constant(metadata["waterdepth"]))
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# TODO: The shape of the resulting tensor can't be statically determined, so we need to reshape here
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# TODO: The shape of the resulting tensor can't be statically determined, so we need to reshape here
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print("DEBUG:dataset ITEM rainfall:shape", rainfall.shape, "water:shape", water.shape)
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print("DEBUG:dataset ITEM rainfall:shape", rainfall.shape, "water:shape", water.shape)
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