layer_channel_prune.py

from models import *
from utils.utils import *
import numpy as np
from copy import deepcopy
from test import test
from terminaltables import AsciiTable
import time
from utils.prune_utils import *
import argparse



if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
    parser.add_argument('--data', type=str, default='data/coco.data', help='*.data file path')
    parser.add_argument('--weights', type=str, default='weights/last.pt', help='sparse model weights')
    parser.add_argument('--shortcuts', type=int, default=8, help='how many shortcut layers will be pruned,\
        pruning one shortcut will also prune two CBL,yolove has 23 shortcuts')
    parser.add_argument('--global_percent', type=float, default=0.95, help='global channel prune percent')
    parser.add_argument('--layer_keep', type=float, default=0.01, help='channel keep percent per layer')
    parser.add_argument('--img_size', type=int, default=320, help='inference size (pixels)')
    opt = parser.parse_args()
    print(opt)

    img_size = opt.img_size
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = Darknet(opt.cfg, (img_size, img_size)).to(device)

    if opt.weights.endswith(".pt"):
        model.load_state_dict(torch.load(opt.weights, map_location=device)['model'])
    else:
        _ = load_darknet_weights(model, opt.weights)
    print('\nloaded weights from ',opt.weights)


    eval_model = lambda model:test(model=model, cfg=opt.cfg, data=opt.data, batch_size=16, img_size=img_size)
    obtain_num_parameters = lambda model:sum([param.nelement() for param in model.parameters()])

    print("\nlet's test the original model first:")
    with torch.no_grad():
        origin_model_metric = eval_model(model)
    origin_nparameters = obtain_num_parameters(model)


    ##############################################################
    #先剪通道
    print("we will prune the channels first")


    CBL_idx, Conv_idx, prune_idx, _, _= parse_module_defs2(model.module_defs)



    bn_weights = gather_bn_weights(model.module_list, prune_idx)

    sorted_bn = torch.sort(bn_weights)[0]
    sorted_bn, sorted_index = torch.sort(bn_weights)
    thresh_index = int(len(bn_weights) * opt.global_percent)
    thresh = sorted_bn[thresh_index].cuda()

    print(f'Global Threshold should be less than {thresh:.4f}.')




    #%%
    def obtain_filters_mask(model, thre, CBL_idx, prune_idx):

        pruned = 0
        total = 0
        num_filters = []
        filters_mask = []
        for idx in CBL_idx:
            bn_module = model.module_list[idx][1]
            if idx in prune_idx:

                weight_copy = bn_module.weight.data.abs().clone()
                
                channels = weight_copy.shape[0] #
                min_channel_num = int(channels * opt.layer_keep) if int(channels * opt.layer_keep) > 0 else 1
                mask = weight_copy.gt(thresh).float()
                
                if int(torch.sum(mask)) < min_channel_num: 
                    _, sorted_index_weights = torch.sort(weight_copy,descending=True)
                    mask[sorted_index_weights[:min_channel_num]]=1. 
                remain = int(mask.sum())
                pruned = pruned + mask.shape[0] - remain

                print(f'layer index: {idx:>3d} \t total channel: {mask.shape[0]:>4d} \t '
                        f'remaining channel: {remain:>4d}')
            else:
                mask = torch.ones(bn_module.weight.data.shape)
                remain = mask.shape[0]

            total += mask.shape[0]
            num_filters.append(remain)
            filters_mask.append(mask.clone())

        prune_ratio = pruned / total
        print(f'Prune channels: {pruned}\tPrune ratio: {prune_ratio:.3f}')

        return num_filters, filters_mask

    num_filters, filters_mask = obtain_filters_mask(model, thresh, CBL_idx, prune_idx)
    CBLidx2mask = {idx: mask for idx, mask in zip(CBL_idx, filters_mask)}
    CBLidx2filters = {idx: filters for idx, filters in zip(CBL_idx, num_filters)}

    for i in model.module_defs:
        if i['type'] == 'shortcut':
            i['is_access'] = False

    print('merge the mask of layers connected to shortcut!')
    merge_mask(model, CBLidx2mask, CBLidx2filters)



    def prune_and_eval(model, CBL_idx, CBLidx2mask):
        model_copy = deepcopy(model)

        for idx in CBL_idx:
            bn_module = model_copy.module_list[idx][1]
            mask = CBLidx2mask[idx].cuda()
            bn_module.weight.data.mul_(mask)

        with torch.no_grad():
            mAP = eval_model(model_copy)[0][2]

        print(f'mask the gamma as zero, mAP of the model is {mAP:.4f}')


    prune_and_eval(model, CBL_idx, CBLidx2mask)


    for i in CBLidx2mask:
        CBLidx2mask[i] = CBLidx2mask[i].clone().cpu().numpy()


    pruned_model = prune_model_keep_size2(model, prune_idx, CBL_idx, CBLidx2mask)
    print("\nnow prune the model but keep size,(actually add offset of BN beta to following layers), let's see how the mAP goes")

    with torch.no_grad():
        eval_model(pruned_model)

    for i in model.module_defs:
        if i['type'] == 'shortcut':
            i.pop('is_access')

    compact_module_defs = deepcopy(model.module_defs)
    for idx in CBL_idx:
        assert compact_module_defs[idx]['type'] == 'convolutional'
        compact_module_defs[idx]['filters'] = str(CBLidx2filters[idx])


    compact_model1 = Darknet([model.hyperparams.copy()] + compact_module_defs, (img_size, img_size)).to(device)
    compact_nparameters1 = obtain_num_parameters(compact_model1)

    init_weights_from_loose_model(compact_model1, pruned_model, CBL_idx, Conv_idx, CBLidx2mask)

    print('testing the channel pruned model...')
    with torch.no_grad():
        compact_model_metric1 = eval_model(compact_model1)


    #########################################################
    #再剪层
    print('\nnow we prune shortcut layers and corresponding CBLs')



    CBL_idx, Conv_idx, shortcut_idx = parse_module_defs4(compact_model1.module_defs)
    print('all shortcut_idx:', [i + 1 for i in shortcut_idx])


    bn_weights = gather_bn_weights(compact_model1.module_list, shortcut_idx)

    sorted_bn = torch.sort(bn_weights)[0]


    # highest_thre = torch.zeros(len(shortcut_idx))
    # for i, idx in enumerate(shortcut_idx):
    #     highest_thre[i] = compact_model1.module_list[idx][1].weight.data.abs().max().clone()
    # _, sorted_index_thre = torch.sort(highest_thre)
    
    #这里更改了选层策略，由最大值排序改为均值排序，均值一般表现要稍好，但不是绝对，可以自己切换尝试；前面注释的四行为原策略。
    bn_mean = torch.zeros(len(shortcut_idx))
    for i, idx in enumerate(shortcut_idx):
        bn_mean[i] = compact_model1.module_list[idx][1].weight.data.abs().mean().clone()
    _, sorted_index_thre = torch.sort(bn_mean)
    
    prune_shortcuts = torch.tensor(shortcut_idx)[[sorted_index_thre[:opt.shortcuts]]]
    prune_shortcuts = [int(x) for x in prune_shortcuts]

    index_all = list(range(len(compact_model1.module_defs)))
    index_prune = []
    for idx in prune_shortcuts:
        index_prune.extend([idx - 1, idx, idx + 1])
    index_remain = [idx for idx in index_all if idx not in index_prune]

    print('These shortcut layers and corresponding CBL will be pruned :', index_prune)





    def prune_and_eval2(model, prune_shortcuts=[]):
        model_copy = deepcopy(model)
        for idx in prune_shortcuts:
            for i in [idx, idx-1]:
                bn_module = model_copy.module_list[i][1]

                mask = torch.zeros(bn_module.weight.data.shape[0]).cuda()
                bn_module.weight.data.mul_(mask)
         

        with torch.no_grad():
            mAP = eval_model(model_copy)[0][2]

        print(f'simply mask the BN Gama of to_be_pruned CBL as zero, now the mAP is {mAP:.4f}')


    prune_and_eval2(compact_model1, prune_shortcuts)





    #%%
    def obtain_filters_mask2(model, CBL_idx, prune_shortcuts):

        filters_mask = []
        for idx in CBL_idx:
            bn_module = model.module_list[idx][1]
            mask = np.ones(bn_module.weight.data.shape[0], dtype='float32')
            filters_mask.append(mask.copy())
        CBLidx2mask = {idx: mask for idx, mask in zip(CBL_idx, filters_mask)}
        for idx in prune_shortcuts:
            for i in [idx, idx - 1]:
                bn_module = model.module_list[i][1]
                mask = np.zeros(bn_module.weight.data.shape[0], dtype='float32')
                CBLidx2mask[i] = mask.copy()
        return CBLidx2mask


    CBLidx2mask = obtain_filters_mask2(compact_model1, CBL_idx, prune_shortcuts)



    pruned_model = prune_model_keep_size2(compact_model1, CBL_idx, CBL_idx, CBLidx2mask)

    with torch.no_grad():
        mAP = eval_model(pruned_model)[0][2]
    print("after transfering the offset of pruned CBL's activation, map is {}".format(mAP))


    compact_module_defs = deepcopy(compact_model1.module_defs)


    for module_def in compact_module_defs:
        if module_def['type'] == 'route':
            from_layers = [int(s) for s in module_def['layers'].split(',')]
            if len(from_layers) == 2:
                count = 0
                for i in index_prune:
                    if i <= from_layers[1]:
                        count += 1
                from_layers[1] = from_layers[1] - count
                from_layers = ', '.join([str(s) for s in from_layers])
                module_def['layers'] = from_layers

    compact_module_defs = [compact_module_defs[i] for i in index_remain]
    compact_model2 = Darknet([compact_model1.hyperparams.copy()] + compact_module_defs, (img_size, img_size)).to(device)
    for i, index in enumerate(index_remain):
        compact_model2.module_list[i] = pruned_model.module_list[index]

    compact_nparameters2 = obtain_num_parameters(compact_model2)

    print('testing the final model')
    with torch.no_grad():
        compact_model_metric2 = eval_model(compact_model2)





    ################################################################
    #剪枝完毕，测试速度


    random_input = torch.rand((1, 3, img_size, img_size)).to(device)

    def obtain_avg_forward_time(input, model, repeat=200):

        model.eval()
        start = time.time()
        with torch.no_grad():
            for i in range(repeat):
                output = model(input)
        avg_infer_time = (time.time() - start) / repeat

        return avg_infer_time, output

    print('testing inference time...')
    pruned_forward_time, output = obtain_avg_forward_time(random_input, model)
    compact_forward_time1, compact_output1 = obtain_avg_forward_time(random_input, compact_model1)
    compact_forward_time2, compact_output2 = obtain_avg_forward_time(random_input, compact_model2)



    


    metric_table = [
        ["Metric", "Before", "After prune channels", "After prune layers(final)"],
        ["mAP", f'{origin_model_metric[0][2]:.6f}', f'{compact_model_metric1[0][2]:.6f}', f'{compact_model_metric2[0][2]:.6f}'],
        ["Parameters", f"{origin_nparameters}", f"{compact_nparameters1}", f"{compact_nparameters2}"],
        ["Inference", f'{pruned_forward_time:.4f}', f'{compact_forward_time1:.4f}', f'{compact_forward_time2:.4f}']
    ]
    print(AsciiTable(metric_table).table)


    pruned_cfg_name = opt.cfg.replace('/', f'/prune_{opt.global_percent}_keep_{opt.layer_keep}_{opt.shortcuts}_shortcut_')
    pruned_cfg_file = write_cfg(pruned_cfg_name, [model.hyperparams.copy()] + compact_module_defs)
    print(f'Config file has been saved: {pruned_cfg_file}')

    compact_model_name = opt.weights.replace('/', f'/prune_{opt.global_percent}_keep_{opt.layer_keep}_{opt.shortcuts}_shortcut_')
    if compact_model_name.endswith('.pt'):
        compact_model_name = compact_model_name.replace('.pt', '.weights')
    save_weights(compact_model2, path=compact_model_name)
    print(f'Compact model has been saved: {compact_model_name}')