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https://github.com/sbrl/research-rainfallradar
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dlr: steps per execution is ugh
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1 changed files with 3 additions and 2 deletions
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@ -52,7 +52,8 @@ LEARNING_RATE = float(os.environ["LEARNING_RATE"]) if "LEARNING_RATE" in os.envi
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WATER_THRESHOLD = float(os.environ["WATER_THRESHOLD"]) if "WATER_THRESHOLD" in os.environ else 0.1
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WATER_THRESHOLD = float(os.environ["WATER_THRESHOLD"]) if "WATER_THRESHOLD" in os.environ else 0.1
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UPSAMPLE = int(os.environ["UPSAMPLE"]) if "UPSAMPLE" in os.environ else 2
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UPSAMPLE = int(os.environ["UPSAMPLE"]) if "UPSAMPLE" in os.environ else 2
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STEPS_PER_EXECUTION = int(os.environ["STEPS_PER_EXECUTION"]) if "STEPS_PER_EXECUTION" in os.environ else 16
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STEPS_PER_EXECUTION = int(os.environ["STEPS_PER_EXECUTION"]) if "STEPS_PER_EXECUTION" in os.environ else 1
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JIT_COMPILE = True if "JIT_COMPILE" in os.environ else False
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JIT_COMPILE = True if "JIT_COMPILE" in os.environ else False
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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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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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@ -158,7 +159,7 @@ if PATH_CHECKPOINT is None:
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x = backbone.get_layer("conv4_block6_2_relu").output
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x = backbone.get_layer("conv4_block6_2_relu").output
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x = DilatedSpatialPyramidPooling(x)
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x = DilatedSpatialPyramidPooling(x)
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input_a = tf.keras.layers.UpSampling2D(
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input_a = tf.keras.layers.UpSampling2D(
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size=(image_size // 4 // x.shape[1] * 2, image_size // 4 // x.shape[2] * 2), # <--- UPSAMPLE after pyramid
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size=(image_size // 4 // x.shape[1] * 2, image_size // 4 // x.shape[2] * 2), # <--- UPSAMPLE after pyramid
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interpolation="bilinear",
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interpolation="bilinear",
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