def pipeline(opt):
solver_and_prototxt('/ssd/yqian/prune/model/ResNet50', opt.compress_layer,
opt.compress_rate, opt.compress_block)
index, w = get_index(list(opt.compress_rate), opt.compress_layer, opt.gpu,
list(opt.compress_block), opt.method)
compress_net(index, w, opt.compress_layer, list(opt.compress_block),
list(opt.compress_rate))
weight[:, i, :, :] *= w[i]
elif next_layer == 'fc6':
# fc6
weight = weight.reshape([4096, 512, 7, 7])
weight = weight[:, index, :, :]
for i in range(weight.shape[1]):
weight[:, i, :, :] *= w[i]
weight = weight.reshape([4096, -1])
else:
# fc7 or fc8 layer
weight = weight[:, index]
for i in range(weight.shape[1]):
weight[:, i] *= w[i]
net_new.params[next_layer][0].data[...] = weight
net_new.params[next_layer][1].data[...] = bias
net_new.save('model.caffemodel')
print 'OK!'
if __name__ == '__main__':
compress_layer = int(sys.argv[1])
gpu = int(sys.argv[2])
compress_rate = float(sys.argv[3])
solver_and_prototxt(compress_layer, compress_rate)
index, w = get_index(compress_rate, compress_layer, gpu)
compress_net(index, w, compress_layer)
opt = parser.parse_args()
return opt
def pipeline(opt):
solver_and_prototxt('/ssd/yqian/prune/model/ResNet50', opt.compress_layer,
opt.compress_rate, opt.compress_block)
index, w = get_index(list(opt.compress_rate), opt.compress_layer, opt.gpu,
list(opt.compress_block), opt.method)
compress_net(index, w, opt.compress_layer, list(opt.compress_block),
list(opt.compress_rate))
if __name__ == '__main__':
# compress_layer = int(sys.argv[1])
# compress_block = int(sys.argv[2])
# compress_rate = float(sys.argv[3])
# gpu = int(sys.argv[4])
opt = get_opt()
solver_and_prototxt('/ssd/yqian/prune/model/ResNet50', opt.compress_layer,
opt.compress_rate, opt.compress_block)
index, w = get_index(opt.compress_rate, opt.compress_layer, opt.gpu,
list(opt.compress_block))
compress_net(index, w, opt.compress_layer, list(opt.compress_block),
opt.compress_rate)
index, w)
else:
# copy block
net_new = copy_net(net_new, net, layer_name + '_branch2a')
net_new = copy_net(net_new, net, layer_name + '_branch2b')
net_new = copy_net(net_new, net, layer_name + '_branch2c')
# branch 1
candidate = ['2a', '3a', '4a', '5a']
for i in range(0, 4):
layer_name = candidate[i]
net_new = copy_net(net_new, net, layer_name + '_branch1')
# fc
net_new.params['fc1000'][0].data[...] = net.params['fc1000'][0].data
net_new.params['fc1000'][1].data[...] = net.params['fc1000'][1].data
net_new.save('model.caffemodel')
print 'OK!'
if __name__ == '__main__':
compress_layer = int(sys.argv[1])
compress_block = int(sys.argv[2])
compress_rate = float(sys.argv[3])
gpu = int(sys.argv[4])
solver_and_prototxt(compress_layer, compress_rate, compress_block)
index, w = get_index(compress_rate, compress_layer, gpu, compress_block)
compress_net(index, w, compress_layer, compress_block)