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07cb7d3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | from deep_learning.data.oxford_pets import SegmentationDataSource
from deep_learning.env.resolve import resolve_env, resolve_path, resolve_saved
from deep_learning.models.segmentation import SegmentationModelBuilder
from deep_learning.pipeline import (
SupervisedModelPipeline,
PipelineRunner
)
from deep_learning.pipeline.specs.configs import CheckpointConfig, CheckpointLoadRules, TrainingRule
pipeline = resolve_env(
# 开发配置
SupervisedModelPipeline(
name="segmentation",
data_source=SegmentationDataSource(
images_path=resolve_path("data/dev/oxford_pets/images"),
annotations_path=resolve_path("data/dev/oxford_pets/annotations/trimaps"),
image_size=(200, 200),
batch_size=2,
validation_batches=1,
example_count=5,
example_output_dir=resolve_path("local/examples/segmentation")
),
model_builder=SegmentationModelBuilder(
image_size=(200, 200),
num_classes=3,
model_filters=(8,)
),
training_rule=TrainingRule(
epochs=1,
steps_per_epoch=None
)
),
# 生产配置
SupervisedModelPipeline(
name="segmentation",
data_source=SegmentationDataSource(
images_path=resolve_path("~/.keras/datasets/vgg_perts_images_extracted/images"),
annotations_path=resolve_path("~/.keras/datasets/vgg_pets_annotations_extracted/annotations/trimaps"),
image_size=(200, 200),
batch_size=64,
validation_batches=15,
example_count=5,
example_output_dir=resolve_path("local/examples/segmentation")
),
model_builder=SegmentationModelBuilder(
image_size=(200, 200),
num_classes=3,
model_filters=(64, 128, 256)
),
training_rule=TrainingRule(
epochs=50,
steps_per_epoch=None
),
checkpoint_load_rules=CheckpointLoadRules(
export=CheckpointConfig(epoch=26),
test=CheckpointConfig(dirs=[resolve_saved("models/segmentation")], suffix=".keras")
)
)
)
pipeline_runner = PipelineRunner(pipeline)
if __name__ == "__main__":
pipeline_runner()
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