1 Commits

Author SHA1 Message Date
  Hobe eecceac20d Rewrite mapping table to speed up 3 years ago
1 changed files with 42 additions and 89 deletions
Unified View
  1. +42
    -89
      SimBinaryNetpytorch/models/binarized_modules.py

+ 42
- 89
SimBinaryNetpytorch/models/binarized_modules.py View File

@ -16,6 +16,25 @@ def Binarize(tensor,quant_mode='det'):
return tensor.add_(1).div_(2).add_(torch.rand(tensor.size()).add(-0.5)).clamp_(0,1).round().mul_(2).add_(-1) return tensor.add_(1).div_(2).add_(torch.rand(tensor.size()).add(-0.5)).clamp_(0,1).round().mul_(2).add_(-1)
def Ninarize(tensor, quant_number, quant_mode='det'):
#return tensor.add(1).mul(quant_number+1).div(2).floor().clamp(0, quant_number).mul(2).add(-quant_number)
return tensor.add(quant_number).mul(quant_number+1).div(2*quant_number).floor().clamp(0, quant_number).mul(2).add(-quant_number)
LUT = torch.Tensor([-63, -62, -61, -60,
-59, -58, -57, -56, -55, -54, -53, -52, -51, -50,
-49, -48, -47, -46, -45, -44, -43, -42, -41, -40,
-39, -38, -37, -36, -35, -35, -35, -35, -33, -33,
-31, -31, -29, -29, -29, -27, -27, -25, -25, -25,
-25, -23, -21, -21, -19, -19, -17, -17, -17, -13,
-13, -11, -11, -9, -9, -7, -6, -5, -4, -2,
-1, 1, 2, 4, 4, 6, 8, 8, 10,
10, 12, 12, 16, 16, 16, 16, 18, 20, 20,
24, 24, 24, 26, 26, 28, 28, 28, 30, 30,
32, 32, 34, 34, 34, 34, 36, 37, 38, 39,
40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
60, 61, 62, 63]).long()
LUT_OFFSET = 63
class HingeLoss(nn.Module): class HingeLoss(nn.Module):
@ -195,19 +214,23 @@ class CimSimConv2d(nn.Conv2d):
super(CimSimConv2d, self).__init__(*kargs, **kwargs) super(CimSimConv2d, self).__init__(*kargs, **kwargs)
self.device = device self.device = device
nn.init.uniform_(self.weight.data, a = -1., b = 1.)
def forward(self, input): def forward(self, input):
if not hasattr(self.weight,'org'): if not hasattr(self.weight,'org'):
self.weight.org=self.weight.data.clone() self.weight.org=self.weight.data.clone()
self.weight.data=Binarize(self.weight.org)
#print('In:', torch.max(self.weight.org), torch.min(self.weight.org))
#self.weight.data=Binarize(self.weight.org)
self.weight.data=Ninarize(self.weight.org, 1)
#print('out:', torch.max(self.weight.data), torch.min(self.weight.data))
#scale = max(torch.max(input), -torch.min(input)) / 63 #scale = max(torch.max(input), -torch.min(input)) / 63
#if scale != 0: #if scale != 0:
# input = torch.round(input / scale) # input = torch.round(input / scale)
#''' random error #''' random error
#out = nn.functional.conv2d(input, self.weight, None, self.stride,
# self.padding, self.dilation, self.groups)
#out = torch.round(out / 64 * 36 / 64)
out = nn.functional.conv2d(input, self.weight, None, self.stride,
self.padding, self.dilation, self.groups)
out = torch.round(out / 64)
#randrange = (self.weight.size()[1] // 4) #randrange = (self.weight.size()[1] // 4)
#for _ in range(randrange): #for _ in range(randrange):
# out += torch.randint(-1, 1, out.size(), device=device) # out += torch.randint(-1, 1, out.size(), device=device)
@ -224,11 +247,20 @@ class CimSimConv2d(nn.Conv2d):
if torch.max(out2) < 32: if torch.max(out2) < 32:
out2 = out2 * 2 out2 = out2 * 2
''' '''
#print ('in, weight, out')
'''
print ('round')
#print (torch.max(input), torch.min(input))
#print (torch.sum(input), torch.sum(input))
#print (torch.max(self.weight), torch.min(self.weight))
#print (torch.sum(self.weight), torch.sum(self.weight))
print (torch.max(out), torch.min(out))
print (torch.max(out2), torch.min(out2))
#'''
out2 = out2 * 4 out2 = out2 * 4
out2[out2 > 63] = 63 out2[out2 > 63] = 63
out2[out2 < -63] = -63 out2[out2 < -63] = -63
#print (self.weight.data.size()) #print (self.weight.data.size())
#print (torch.max(out2), torch.min(out2))
#print (torch.max(out-out2), torch.min(out-out2)) #print (torch.max(out-out2), torch.min(out-out2))
#out = nn.functional.conv2d(input, self.weight, None, self.stride, #out = nn.functional.conv2d(input, self.weight, None, self.stride,
# self.padding, self.dilation, self.groups) # self.padding, self.dilation, self.groups)
@ -246,9 +278,11 @@ class CimSimConv2d(nn.Conv2d):
out_channel = weight.size()[0] out_channel = weight.size()[0]
out_width = input_a.size()[2] - 2 * (weight.size()[2] // 2) out_width = input_a.size()[2] - 2 * (weight.size()[2] // 2)
out_height = input_a.size()[3] - 2 * (weight.size()[3] // 2) out_height = input_a.size()[3] - 2 * (weight.size()[3] // 2)
simout = torch.zeros(batch_size, out_channel, out_width, out_height, dtype = input_a.dtype).to(device)
simout = torch.zeros(batch_size, out_channel, out_width, out_height, dtype = input_a.dtype).to(input_a.device)
first = True first = True
#''' Mapping Table #''' Mapping Table
global LUT
LUT = LUT.to(input_a.device)
if weight.size()[2] == 7: if weight.size()[2] == 7:
kernel_group = 1 kernel_group = 1
else: else:
@ -257,9 +291,9 @@ class CimSimConv2d(nn.Conv2d):
binary_weight_split = torch.split(weight, kernel_group, dim=1) binary_weight_split = torch.split(weight, kernel_group, dim=1)
for i in range(len(Digital_input_split)): for i in range(len(Digital_input_split)):
temp_output = nn.functional.conv2d(Digital_input_split[i], binary_weight_split[i], None, self.stride, self.padding, self.dilation, self.groups) temp_output = nn.functional.conv2d(Digital_input_split[i], binary_weight_split[i], None, self.stride, self.padding, self.dilation, self.groups)
#temp_output = torch.round(temp_output / 64 * 36 / 64)
temp_output = torch.round(temp_output / 64) temp_output = torch.round(temp_output / 64)
temp_output = Mapping.apply(temp_output)
temp_output += LUT_OFFSET
temp_output = LUT[temp_output.long()]
simout += temp_output + 2 simout += temp_output + 2
#print (torch.max(simout), torch.min(simout)) #print (torch.max(simout), torch.min(simout))
#''' #'''
@ -340,84 +374,3 @@ class CimSimConv2d(nn.Conv2d):
raw_sum += (row + adjust + 2) raw_sum += (row + adjust + 2)
#print (raw_sum) #print (raw_sum)
return raw_sum return raw_sum
class Mapping(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
output = input.clone()
output[input==-1] = -4
output[input==-2] = -5
output[input==-3] = -6
output[input==-4] = -7
output[input==-5] = -9
output[input==-6] = -9
output[input==-7] = -11
output[input==-8] = -11
output[input==-9] = -13
output[input==-10] = -13
output[input==-11] = -17
output[input==-12] = -17
output[input==-13] = -17
output[input==-14] = -19
output[input==-15] = -19
output[input==-16] = -21
output[input==-17] = -21
output[input==-18] = -23
output[input==-19] = -25
output[input==-20] = -25
output[input==-21] = -25
output[input==-22] = -25
output[input==-23] = -27
output[input==-24] = -27
output[input==-25] = -29
output[input==-26] = -29
output[input==-27] = -29
output[input==-28] = -31
output[input==-29] = -31
output[input==-30] = -33
output[input==-31] = -33
output[input==-32] = -35
output[input==-33] = -35
output[input==-34] = -35
#output[input==-35] = -35
output[input==0] = -2
output[input==1] = -1
output[input==2] = 1
output[input==3] = 2
#output[input==4] = 4
output[input==5] = 4
#output[input==6] = 6
output[input==7] = 8
#output[input==8] = 8
output[input==9] = 10
#output[input==10] = 10
output[input==11] = 12
#output[input==12] = 12
output[input==13] = 16
output[input==14] = 16
output[input==15] = 16
#output[input==16] = 16
output[input==17] = 18
output[input==18] = 20
output[input==19] = 20
output[input==20] = 24
output[input==21] = 24
output[input==22] = 24
output[input==23] = 26
output[input==24] = 26
output[input==25] = 28
output[input==26] = 28
output[input==27] = 28
output[input==28] = 30
output[input==29] = 30
output[input==30] = 32
output[input==31] = 32
output[input==32] = 34
output[input==33] = 34
output[input==34] = 34
output[input==35] = 34
return output
def backward(ctx, grad_output):
return grad_output

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