PointMLP/analysis.py

import fvcore.common
import fvcore.nn
import torch
from fvcore.nn import FlopCountAnalysis

from classification_ScanObjectNN.models import pointMLPElite

model = pointMLPElite()
model.eval()
# model = deit_tiny_patch16_224()

inputs = torch.randn((1, 3, 1024))
k = 1024.0
flops = FlopCountAnalysis(model, inputs).total()
print(f"Flops : {flops}")
flops = flops / (k**3)
print(f"Flops : {flops:.1f}G")
params = fvcore.nn.parameter_count(model)[""]
print(f"Params : {params}")
params = params / (k**2)
print(f"Params : {params:.1f}M")