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compression_resnet.py
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import torch
import torch.nn as nn
import torchvision
import torch.nn.utils.prune as prune
import resnet
import torch.nn.functional as F
class Round(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
return torch.round(input)
@staticmethod
def backward(ctx, grad_output):
grad_input = grad_output.clone()
return grad_input
# map the float-point values to codewords
class emb(torch.autograd.Function):
@staticmethod
def forward(ctx, input, embb):
x_expanded = input.unsqueeze(-1)
emb_expanded = embb
dist = torch.norm(x_expanded - emb_expanded, 2, 1)
_, argmin = dist.min(-1)
emb_t = embb.t()
x = emb_t[argmin,:]
return x,argmin
@staticmethod
def backward(ctx, grad_output, argmin = None):
grad_input = grad_output.clone()
return grad_input, None
class split_resnet1(nn.Module):
def __init__(self,args):
super().__init__()
self.hidden_channel = args.hid_dim
self.scale = 2 ** args.bit
self.resnet = resnet.resnet18()
model_dict = torch.load(args.load)
self.resnet.load_state_dict(model_dict['model_state_dict'])
self.conv1 = nn.Conv2d(64,self.hidden_channel,kernel_size = 3,stride=1,padding=1)
#self.conv1_1 = nn.Conv2d(64,32,kernel_size = 3,stride=1,padding=1)
self.conv2 = nn.Conv2d(self.hidden_channel,64,kernel_size = 3,stride=1,padding=1)
#self.conv2_1 = nn.Conv2d(32,64,kernel_size = 3,stride=1,padding=1)
self.elu = nn.ELU()
self.Tanh = nn.Tanh()
for para in self.resnet.parameters():
para.requires_grad = False
def forward(self, x, flag):
output = self.resnet.conv1(x)
B = output.size()[0]
scale = self.scale
output = self.Tanh(self.conv1(output))
output_ori = torch.reshape(output,(B,-1))
output = Round().apply(scale*(output_ori + 1)/2.0)
output = (output * 2)/scale - 1
regular_term = torch.norm(output-output_ori)**2 / B
output = torch.reshape(output,(B,self.hidden_channel,32,32))
output = self.elu(self.conv2(output))
output = self.resnet.conv2_x(output)
output = self.resnet.conv3_x(output)
output = self.resnet.conv4_x(output)
output = self.resnet.conv5_x(output)
output = self.resnet.avg_pool(output)
output = output.view(output.size(0), -1)
output = self.resnet.fc(output)
return output, regular_term
class split_resnet2(nn.Module):
def __init__(self,args):
super().__init__()
self.hidden_channel = args.hid_dim
self.resnet = resnet.resnet18()
model_dict = torch.load(args.load)
self.resnet.load_state_dict(model_dict['model_state_dict'])
self.scale = 2 ** args.bit
print('scale',self.scale,'bit',args.bit)
self.conv1 = nn.Conv2d(64,self.hidden_channel,kernel_size = 3,stride=1,padding=1)
self.conv2 = nn.Conv2d(self.hidden_channel,64,kernel_size = 3,stride=1,padding=1)
self.elu = nn.ELU()
self.Tanh = nn.Tanh()
for para in self.resnet.parameters():
para.requires_grad = False
def forward(self, x, flag):
output = self.resnet.conv1(x)
output = self.resnet.conv2_x(output)
B = output.size()[0]
scale = self.scale
output = self.Tanh(self.conv1(output))
output_ori = torch.reshape(output,(B,-1))
output = Round().apply(scale*(output_ori + 1)/2.0)
output = (output * 2)/scale - 1
regular_term = torch.norm(output-output_ori)**2 / B
output = torch.reshape(output,(B,self.hidden_channel,32,32))
output = self.elu(self.conv2(output))
output = self.resnet.conv3_x(output)
output = self.resnet.conv4_x(output)
output = self.resnet.conv5_x(output)
output = self.resnet.avg_pool(output)
output = output.view(output.size(0), -1)
output = self.resnet.fc(output)
return output, regular_term
class split_resnet3(nn.Module):
def __init__(self,args):
super().__init__()
self.hidden_channel = args.hid_dim
self.scale = 2 ** args.bit
self.resnet = resnet.resnet18()
model_dict = torch.load(args.load)
self.resnet.load_state_dict(model_dict['model_state_dict'])
self.conv1 = nn.Conv2d(128,self.hidden_channel,kernel_size = 3,stride=1,padding=1)
self.conv2 = nn.Conv2d(self.hidden_channel,128,kernel_size = 3,stride=1,padding=1)
for para in self.resnet.parameters():
para.requires_grad = False
self.elu = nn.ELU()
self.Tanh = nn.Tanh()
def forward(self, x, flag):
output = self.resnet.conv1(x)
output = self.resnet.conv2_x(output)
output = self.resnet.conv3_x(output)
B = output.size()[0]
scale = self.scale
output = self.Tanh(self.conv1(output))
output_ori = torch.reshape(output,(B,-1))
output = Round().apply(scale*(output_ori + 1)/2.0)
output = (output * 2)/scale - 1
regular_term = torch.norm(output-output_ori)**2 / B
output = torch.reshape(output,(B,self.hidden_channel,16,16))
output = self.elu(self.conv2(output))
output = self.resnet.conv4_x(output)
output = self.resnet.conv5_x(output)
output = self.resnet.avg_pool(output)
output = output.view(output.size(0), -1)
output = self.resnet.fc(output)
return output, regular_term
class split_resnet4(nn.Module):
def __init__(self,args):
super().__init__()
self.hidden_channel = args.hid_dim
self.resnet = resnet.resnet18()
model_dict = torch.load(args.load)
self.resnet.load_state_dict(model_dict['model_state_dict'])
self.encode1 = nn.Linear(16384,self.hidden_channel)
self.encode2 = nn.Linear(self.hidden_channel,self.hidden_channel)
self.decode1 = nn.Linear(self.hidden_channel,self.hidden_channel)
self.decode2 = nn.Linear(self.hidden_channel,16384)
self.dropout = torch.nn.Dropout(p = 0.25)
self.elu = nn.ELU()
self.Tanh = nn.Tanh()
#learned codeword
self.weight = nn.Parameter(torch.rand(self.hidden_channel,2**args.bit))
self.batchnorm1 = nn.BatchNorm2d(8)
self.batchnorm2 = nn.BatchNorm2d(256)
for para in self.resnet.parameters():
para.requires_grad = False
def forward(self, x, flag):
output = self.resnet.conv1(x)
output = self.resnet.conv2_x(output)
output = self.resnet.conv3_x(output)
output = self.resnet.conv4_x(output)
B = output.size()[0]
output = torch.reshape(output,(B,-1))
output = self.elu(self.encode1(output))
output = self.dropout(output)
output = self.Tanh(self.encode2(output))
output_tmp = output
if flag == 1:
output_emb, argmin = emb().apply(output, self.weight.detach())
selected_emb = self.weight.t()[argmin.detach(),:]
regular_term = F.mse_loss(selected_emb,output_tmp.detach()) + 0.5 * F.mse_loss(selected_emb.detach(),output_tmp)
elif flag == 0:
output_emb = output_tmp
_, argmin = emb().apply(output, self.weight.detach())
selected_emb = self.weight.t()[argmin.detach(),:]
regular_term = F.mse_loss(selected_emb,output_tmp.detach())
output = self.elu(self.decode1(output_emb))
output = self.dropout(output)
output = self.elu(self.decode2(output))
output = torch.reshape(output,(B,256,8,8))
output = self.resnet.conv5_x(output)
output = self.resnet.avg_pool(output)
output = output.view(output.size(0), -1)
output = self.resnet.fc(output)
return output, 0.1 * regular_term
class split_resnet5(nn.Module):
def __init__(self,args):
super().__init__()
self.hidden_channel = args.hid_dim
self.resnet = resnet.resnet18()
model_dict = torch.load(args.load)
self.resnet.load_state_dict(model_dict['model_state_dict'])
self.encode1 = nn.Linear(512,self.hidden_channel)
self.decode2 = nn.Linear(self.hidden_channel,512)
self.elu = nn.ELU()
self.Tanh = nn.Tanh()
#learned codeword
self.weight = nn.Parameter(torch.rand(self.hidden_channel,2**args.bit))
for para in self.resnet.parameters():
para.requires_grad = False
def forward(self, x, flag):
output = self.resnet.conv1(x)
output = self.resnet.conv2_x(output)
output = self.resnet.conv3_x(output)
output = self.resnet.conv4_x(output)
B = output.size()[0]
output = self.resnet.conv5_x(output)
output = self.resnet.avg_pool(output)
output = output.view(output.size(0), -1)
output = self.Tanh(self.encode1(output))
output_tmp = output
if flag == 1:
output_emb, argmin = emb().apply(output, self.weight.detach())
selected_emb = self.weight.t()[argmin.detach(),:]
regular_term = F.mse_loss(selected_emb,output_tmp.detach()) + 0.5 * F.mse_loss(selected_emb.detach(),output_tmp)
elif flag == 0:
output_emb = output_tmp
_, argmin = emb().apply(output, self.weight.detach())
selected_emb = self.weight.t()[argmin.detach(),:]
regular_term = F.mse_loss(selected_emb,output_tmp.detach())
output = self.elu(self.decode2(output_emb))
output = self.resnet.fc(output)
return output, regular_term