diff --git a/SimBinaryNetpytorch/models/binarized_modules.py b/SimBinaryNetpytorch/models/binarized_modules.py index 9555959..dbc837a 100644 --- a/SimBinaryNetpytorch/models/binarized_modules.py +++ b/SimBinaryNetpytorch/models/binarized_modules.py @@ -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) +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): @@ -195,19 +214,23 @@ class CimSimConv2d(nn.Conv2d): super(CimSimConv2d, self).__init__(*kargs, **kwargs) self.device = device + nn.init.uniform_(self.weight.data, a = -1., b = 1.) def forward(self, input): if not hasattr(self.weight,'org'): 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 #if scale != 0: # input = torch.round(input / scale) #''' 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) #for _ in range(randrange): # out += torch.randint(-1, 1, out.size(), device=device) @@ -224,11 +247,20 @@ class CimSimConv2d(nn.Conv2d): if torch.max(out2) < 32: 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 > 63] = 63 out2[out2 < -63] = -63 #print (self.weight.data.size()) - #print (torch.max(out2), torch.min(out2)) #print (torch.max(out-out2), torch.min(out-out2)) #out = nn.functional.conv2d(input, self.weight, None, self.stride, # self.padding, self.dilation, self.groups) @@ -246,9 +278,11 @@ class CimSimConv2d(nn.Conv2d): out_channel = weight.size()[0] out_width = input_a.size()[2] - 2 * (weight.size()[2] // 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 #''' Mapping Table + global LUT + LUT = LUT.to(input_a.device) if weight.size()[2] == 7: kernel_group = 1 else: @@ -257,9 +291,9 @@ class CimSimConv2d(nn.Conv2d): binary_weight_split = torch.split(weight, kernel_group, dim=1) 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 = torch.round(temp_output / 64 * 36 / 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 #print (torch.max(simout), torch.min(simout)) #''' @@ -340,84 +374,3 @@ class CimSimConv2d(nn.Conv2d): raw_sum += (row + adjust + 2) #print (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