45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
import numpy as np
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DATA_DIR = "../data"
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OUT_DIR = "./out"
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MODEL_DIR = "./models"
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NB_THREADS = 1024
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NB_THREADS_2D = (32, 32)
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NB_THREADS_3D = (16, 16, 4)
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M = int(np.log2(NB_THREADS_2D[1]))
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# Save state to avoid recalculation on restart
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SAVE_STATE = True
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# Redo the state even if it's already saved
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FORCE_REDO = False
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# Use NJIT to greatly accelerate runtime
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COMPILE_WITH_C = True
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# Use GPU to greatly accelerate runtime (as priority over NJIT)
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GPU_BOOSTED = True
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# Depending on what you set, the output label will be as follow :
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# | COMPILE_WITH_C | GPU_BOOSTED | LABEL |
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# |----------------|-------------|-------|
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# | True | True | GPU |
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# | True | False | CPU |
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# | False | True | PGPU |
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# | False | False | PY |
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# Number of weak classifiers
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# TS = [1]
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# TS = [1, 5, 10]
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TS = [1, 5, 10, 25, 50]
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# TS = [1, 5, 10, 25, 50, 100, 200]
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# TS = [1, 5, 10, 25, 50, 100, 200, 300]
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# TS = [1, 5, 10, 25, 50, 100, 200, 300, 400, 500, 1000]
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# Enable verbose output (for debugging purposes)
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__DEBUG = False
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# Debugging options
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if __DEBUG:
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IDX_INSPECT = 4548
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IDX_INSPECT_OFFSET = 100
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np.seterr(all = 'raise')
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# Debug option (image width * log_10(length) + extra characters)
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np.set_printoptions(linewidth = 19 * 6 + 3)
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