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group_cifar.py
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import argparse
import os
import torch
from torch.autograd import Variable
from torchvision import models
import sys
import numpy as np
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from prune_cifar import *
import logging
class GroupResNet(torch.nn.Module):
def __init__(self, model):
super(GroupResNet, self).__init__()
self.conv1 = model.conv1
self.bn1 = model.bn1
self.layer1 = model.layer1
self.layer2 = model.layer2
self.layer3 = model.layer3
self.avgpool = model.avgpool
self.linear = model.linear
self.n_clusters = model.n_clusters
self.pruning_target_layers = model.pruning_target_layers
self.grouping_target_layers = model.grouping_target_layers
self.pruning_rate = model.pruning_rate
self.kernel_gcd = model.kernel_gcd
# self.pruning_rate = 0.4375
# self.kernel_gcd = 16
print(f'GroupResNet pruning rate: {self.pruning_rate}; kernel_gcd: {self.kernel_gcd}')
# imagenet_target_layers = [4, 5, 6, 7]
# # tiny_imagenet_target_layers = [2, 3, 4, 5]
# tiny_imagenet_target_layers = [2, 3, 4]
# cifar10_target_layers = [2, 3, 4]
# if dataset == 'imagenet':
# target_layers = imagenet_target_layers
# elif dataset == 'tiny_imagenet':
# target_layers = tiny_imagenet_target_layers
# elif dataset == 'cifar10':
# target_layers = cifar10_target_layers
# else:
# print(f'Invalid dataset input {dataset}.')
# sys.exit(0)
global modules
for layer, (name, modules) in enumerate(model._modules.items()):
#self.modules = modules
if layer in self.grouping_target_layers:
for sublayer, (name,submodule) in enumerate(modules._modules.items()):
self.out_list = submodule.out_list
self.layer_list = []
self.in_list = []
self.in_planes = submodule.conv1.in_channels
self.planes = submodule.conv1.out_channels
for subsublayer, (name, module) in enumerate(submodule._modules.items()):
if isinstance(module, torch.nn.modules.conv.Conv2d):
new_conv, number_of_unpruned_kernels = make_new_conv(module, group_info = (self.n_clusters, self.pruning_rate, self.kernel_gcd))
old_weights = module.weight.cuda()
old_out_channels, old_in_channels, old_kernel_size, old_kernel_size = old_weights.data.size()
original_old_weights_shape = old_weights.data.size()
old_weights = old_weights.data.cpu().numpy()
new_weights = torch.zeros(module.out_channels, module.in_channels * number_of_unpruned_kernels // self.kernel_gcd, module.kernel_size[0], module.kernel_size[1])
new_weights = new_weights.data.cpu().numpy()
d_out = old_out_channels // self.n_clusters
self.j_list = []
if subsublayer == 0:
conv_num = 0
elif subsublayer == 2:
conv_num = 1
for i in range(self.n_clusters):
wi = old_weights[i * d_out:(i + 1) * d_out, :, :, :]
m = 0
# print('i is', i)
for j in range(old_in_channels):
# if not np.all(wi[:, j, :, :] == 0):
if j in submodule.preserved_kernel_index[conv_num][i]:
# print(f'{layer, sublayer, subsublayer}; i: {i}; j: {j}; m: {m}; {original_old_weights_shape} --> {new_weights.shape}; module.in_channels {module.in_channels}')
# first = new_weights[i * d_out:(i + 1) * d_out, m, :, :]
# second = wi[:, j, :, :]
new_weights[i * d_out:(i + 1) * d_out, m, :, :] = wi[:, j, :, :]
self.j_list.append(j)
m = m + 1
# print(f'i: {i}; m: {m}')
self.j_list = np.array(self.j_list)
self.j_list = torch.from_numpy(self.j_list)
self.in_list.append(self.j_list.cuda())
# print(f'j_list: {self.j_list} ({len(self.j_list)})')
new_weights = Variable(torch.from_numpy(new_weights))
self.new_weight = torch.nn.Parameter(new_weights)
self.new_weight.data = self.new_weight.type(torch.FloatTensor)
self.new_weight.data = self.new_weight.data.cuda()
new_conv.weight = self.new_weight
# print(new_conv.weight.size())
self.layer_list.append(new_conv)
# print(self.layer_list[0])
# print('--------------', self.in_list[0].size())
# print(f'{layer}-{sublayer} in_list: {self.in_list}')
modules[sublayer] = GroupBasicblock(submodule, self.in_list, self.out_list, self.layer_list, self.in_planes, self.planes)
# modules[sublayer] = GroupBasicblock(submodule, submodule.preserved_kernel_index, self.out_list, self.layer_list, self.in_planes, self.planes)
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.linear(x)
return x
class GroupBasicblock(torch.nn.Module):
expansion = 4
def __init__(self, module, in_list, out_list, layer_list, in_planes, planes, stride=1, shortcut=None):
super(GroupBasicblock, self).__init__()
self.in_list = in_list
self.out_list = out_list
self.conv1 = layer_list[0]
self.bn1 = module.bn1
self.conv2 = layer_list[1]
self.bn2 = module.bn2
self.shortcut = shortcut
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = module.shortcut
def forward(self, x):
residual = x
# print('x', x)
# print('x.shape', x.shape)
# print('self.in_list[0]', self.in_list[0])
# print('self.in_list[0].shape', self.in_list[0].shape)
out = torch.index_select(x, 1, self.in_list[0])
# print('out.size()', out.size())
out = self.conv1(out)
out = torch.index_select(out, 1, self.out_list[0])
out = self.bn1(out)
out = F.relu(out)
out = torch.index_select(out, 1, self.in_list[1])
out = self.conv2(out)
out = torch.index_select(out, 1, self.out_list[1])
out = self.bn2(out)
if self.shortcut is not None:
residual = self.shortcut(x)
out += residual
out = F.relu(out)
return out
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='group.py')
parser.add_argument('--model', type=str,
help='path of pruned model')
parser.add_argument('--output', default='', type=str,
help='path of grouped model')
args = parser.parse_args()
model = torch.load(args.model).cuda()
newmodel = GroupResNet(model).cuda()
# print(newmodel)
torch.save(newmodel, args.output)