File size: 751 Bytes
eb5a5f6 673c9f2 eb5a5f6 673c9f2 eb5a5f6 |
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 |
import torch
from math_model import QuantConv2d
torch.manual_seed(0)
batch_size = 1
out_ch = 8
in_ch = 4
k = 3
h = 5
w = 5
quant_params = {
'smoothquant_mul': torch.rand((in_ch,)),
'smoothquant_mul_shape': (1,in_ch,1,1),
'weight_scale': torch.rand((out_ch,)),
'weight_scale_shape': (out_ch,1,1,1),
'weight_zp': torch.randint(-255, 0, (out_ch,)),
'weight_zp_shape': (out_ch,1,1,1),
'input_scale': torch.rand((1,)),
'input_scale_shape': tuple(),
'input_zp': torch.zeros((1,)),
'input_zp_shape': tuple(),
}
print(quant_params)
l = QuantConv2d(in_ch, out_ch, k, quant_params)
i = torch.rand((batch_size,in_ch,h,w))
o_qdq = l(i)
o_qop = l(i, qop=True)
print(o_qdq.shape)
print(o_qop.shape)
print(o_qdq - o_qop)
|