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perf.py
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# Results on a 2021 Macbook Pro M1
# AVG POOLING ((2048, 2048, 64), uint8)
# 2x2 1 mip: 0.695s, 3856.07 MVx/sec, N=10
# 2x2 2 mip: 0.998s, 2685.80 MVx/sec, N=10
# 2x2x2 1 mip: 0.600s, 4468.55 MVx/sec, N=10
# 2x2x2 2 mip: 0.935s, 2867.80 MVx/sec, N=10
# 2x2x2 1 mip sparse: 1.929s, 1389.47 MVx/sec, N=10
# 2x2x2 2 mip sparse: 2.128s, 1259.58 MVx/sec, N=10
# MODE POOLING RANDOM ((2048, 2048, 64), uint64)
# 2x2 1 mip: 2.533s, 1057.87 MVx/sec, N=10
# 2x2 2 mip: 2.522s, 1062.69 MVx/sec, N=10
# 2x2 1 mip sparse: 20.673s, 129.64 MVx/sec, N=10
# 2x2 2 mip sparse: 32.836s, 81.62 MVx/sec, N=10
# 2x2x2 1 mip: 7.956s, 336.85 MVx/sec, N=10
# 2x2x2 2 mip: 8.980s, 298.45 MVx/sec, N=10
# 2x2x2 1 mip sparse: 7.932s, 337.87 MVx/sec, N=10
# 2x2x2 2 mip sparse: 9.121s, 293.84 MVx/sec, N=10
# MODE POOLING CONNECTOMICS ((512,512,512), uint64)
# 2x2 1 mip: 1.243s, 1078.09 MVx/sec, N=10
# 2x2 2 mip: 1.300s, 1030.90 MVx/sec, N=10
# 2x2 1 mip sparse: 9.168s, 146.15 MVx/sec, N=10
# 2x2 2 mip sparse: 19.350s, 69.25 MVx/sec, N=10
# 2x2x2 1 mip: 0.681s, 1966.74 MVx/sec, N=10
# 2x2x2 2 mip: 0.748s, 1790.60 MVx/sec, N=10
# 2x2x2 1 mip sparse: 0.656s, 2041.96 MVx/sec, N=10
# 2x2x2 2 mip sparse: 0.762s, 1758.42 MVx/sec, N=10
import numpy as np
import tinybrain
import time
def result(label, dt, data, N):
voxels = data.size
mvx = voxels // (10 ** 6)
print(f"{label}: {dt:02.3f}s, {N * mvx / dt:.2f} MVx/sec, N={N}")
def benchmark_avg_pooling():
data = np.random.randint(0,255, size=(2048,2048,64), dtype=np.uint8)
data = np.asfortranarray(data)
N = 10
print(f"AVG POOLING ({data.shape}, {data.dtype})")
start = time.time()
for _ in range(N):
tinybrain.downsample_with_averaging(data, (2,2,1), num_mips=1)
end = time.time()
result("2x2 1 mip", end - start, data, N)
start = time.time()
for _ in range(N):
tinybrain.downsample_with_averaging(data, (2,2,1), num_mips=2)
end = time.time()
result("2x2 2 mip", end - start, data, N)
start = time.time()
for _ in range(N):
tinybrain.downsample_with_averaging(data, (2,2,2), num_mips=1)
end = time.time()
result("2x2x2 1 mip", end - start, data, N)
start = time.time()
for _ in range(N):
tinybrain.downsample_with_averaging(data, (2,2,2), num_mips=2)
end = time.time()
result("2x2x2 2 mip", end - start, data, N)
start = time.time()
for _ in range(N):
tinybrain.downsample_with_averaging(data, (2,2,2), num_mips=1, sparse=True)
end = time.time()
result("2x2x2 1 mip sparse", end - start, data, N)
start = time.time()
for _ in range(N):
tinybrain.downsample_with_averaging(data, (2,2,2), num_mips=2, sparse=True)
end = time.time()
result("2x2x2 2 mip sparse", end - start, data, N)
def benchmark_mode_pooling():
data = np.random.randint(1000,1255, size=(2048,2048,64), dtype=np.uint64)
data = np.asfortranarray(data)
N = 10
print(f"MODE POOLING ({data.shape}, {data.dtype})")
start = time.time()
for _ in range(N):
tinybrain.downsample_segmentation(data, (2,2,1), num_mips=1)
end = time.time()
result("2x2 1 mip", end - start, data, N)
start = time.time()
for _ in range(N):
tinybrain.downsample_segmentation(data, (2,2,1), num_mips=2)
end = time.time()
result("2x2 2 mip", end - start, data, N)
start = time.time()
for _ in range(N):
tinybrain.downsample_segmentation(data, (2,2,1), num_mips=1, sparse=True)
end = time.time()
result("2x2 1 mip sparse", end - start, data, N)
start = time.time()
for _ in range(N):
tinybrain.downsample_segmentation(data, (2,2,1), num_mips=2, sparse=True)
end = time.time()
result("2x2 2 mip sparse", end - start, data, N)
start = time.time()
for _ in range(N):
tinybrain.downsample_segmentation(data, (2,2,2), num_mips=1)
end = time.time()
result("2x2x2 1 mip", end - start, data, N)
start = time.time()
for _ in range(N):
tinybrain.downsample_segmentation(data, (2,2,2), num_mips=2)
end = time.time()
result("2x2x2 2 mip", end - start, data, N)
start = time.time()
for _ in range(N):
tinybrain.downsample_segmentation(data, (2,2,2), num_mips=1, sparse=True)
end = time.time()
result("2x2x2 1 mip sparse", end - start, data, N)
start = time.time()
for _ in range(N):
tinybrain.downsample_segmentation(data, (2,2,2), num_mips=2, sparse=True)
end = time.time()
result("2x2x2 2 mip sparse", end - start, data, N)
benchmark_avg_pooling()
benchmark_mode_pooling()