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dataset_mono: adjust to suit DeepLabV3+ too

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Starbeamrainbowlabs 2022-12-13 18:37:38 +00:00
parent 440e693dfc
commit eb47f8f544
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
2 changed files with 21 additions and 8 deletions
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26
aimodel/src/lib/dataset/dataset_mono.py
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@ -15,11 +15,17 @@ from .parse_heightmap import parse_heightmap
# TO PARSE: # TO PARSE:
def parse_item(metadata, shape_water_desired=[100,100], water_threshold=0.1, water_bins=2, heightmap=None): def parse_item(metadata, output_size=100, input_size="same", water_threshold=0.1, water_bins=2, heightmap=None):
if input_size == "same":
input_size = output_size # This is almost always the case with e.g. the DeepLabV3+ model
water_height_source, water_width_source = metadata["waterdepth"] water_height_source, water_width_source = metadata["waterdepth"]
water_height_target, water_width_target = shape_water_desired water_offset_x = math.ceil((water_width_source - output_size) / 2)
water_offset_x = math.ceil((water_width_source - water_width_target) / 2) water_offset_y = math.ceil((water_height_source - output_size) / 2)
water_offset_y = math.ceil((water_height_source - water_height_target) / 2)
_, rainfall_height_source, rainfall_width_source = metadata["rainfallradar"]
rainfall_offset_x = math.ceil((rainfall_width_source - input_size) / 2)
rainfall_offset_y = math.ceil((rainfall_height_source - input_size) / 2)
print("DEBUG DATASET:rainfall shape", metadata["rainfallradar"]) print("DEBUG DATASET:rainfall shape", metadata["rainfallradar"])
print("DEBUG DATASET:water shape", metadata["waterdepth"]) print("DEBUG DATASET:water shape", metadata["waterdepth"])
@ -42,7 +48,6 @@ def parse_item(metadata, shape_water_desired=[100,100], water_threshold=0.1, wat
water = tf.io.parse_tensor(parsed["waterdepth"], out_type=tf.float32) water = tf.io.parse_tensor(parsed["waterdepth"], out_type=tf.float32)
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))
@ -56,14 +61,21 @@ def parse_item(metadata, shape_water_desired=[100,100], water_threshold=0.1, wat
rainfall = tf.transpose(rainfall, [2, 1, 0]) rainfall = tf.transpose(rainfall, [2, 1, 0])
if heightmap is not None: if heightmap is not None:
rainfall = tf.concat([rainfall, heightmap], axis=-1) rainfall = tf.concat([rainfall, heightmap], axis=-1)
if input_size is not None:
rainfall = tf.image.crop_to_bounding_box(rainfall,
offset_width=rainfall_offset_x,
offset_height=rainfall_offset_y,
target_width=input_size,
target_height=input_size,
)
# 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=1] water = tf.expand_dims(water, axis=-1) # [width, height] → [width, height, channels=1]
water = tf.image.crop_to_bounding_box(water, water = tf.image.crop_to_bounding_box(water,
offset_width=water_offset_x, offset_width=water_offset_x,
offset_height=water_offset_y, offset_height=water_offset_y,
target_width=water_width_target, target_width=output_size,
target_height=water_height_target target_height=output_size
) )
print("DEBUG:dataset BEFORE_SQUEEZE water", water.shape) print("DEBUG:dataset BEFORE_SQUEEZE water", water.shape)

3
aimodel/src/subcommands/train_mono.py
View file

@ -59,7 +59,8 @@ def run(args):
dirpath_input=args.input, dirpath_input=args.input,
batch_size=args.batch_size, batch_size=args.batch_size,
water_threshold=args.water_threshold, water_threshold=args.water_threshold,
shape_water_desired=[args.water_size, args.water_size], output_size=args.water_size,
input_size=None,
filepath_heightmap=args.heightmap filepath_heightmap=args.heightmap
) )
dataset_metadata = read_metadata(args.input) dataset_metadata = read_metadata(args.input)