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slim_prune.py
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slim_prune.py
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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('--global_percent', type=float, default=0.8, 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=416, 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)
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_model = Darknet([model.hyperparams.copy()] + compact_module_defs, (img_size, img_size)).to(device)
compact_nparameters = obtain_num_parameters(compact_model)
init_weights_from_loose_model(compact_model, pruned_model, CBL_idx, Conv_idx, CBLidx2mask)
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, pruned_output = obtain_avg_forward_time(random_input, pruned_model)
compact_forward_time, compact_output = obtain_avg_forward_time(random_input, compact_model)
print('testing the final model...')
with torch.no_grad():
compact_model_metric = eval_model(compact_model)
metric_table = [
["Metric", "Before", "After"],
["mAP", f'{origin_model_metric[0][2]:.6f}', f'{compact_model_metric[0][2]:.6f}'],
["Parameters", f"{origin_nparameters}", f"{compact_nparameters}"],
["Inference", f'{pruned_forward_time:.4f}', f'{compact_forward_time:.4f}']
]
print(AsciiTable(metric_table).table)
pruned_cfg_name = opt.cfg.replace('/', f'/prune_{opt.global_percent}_keep_{opt.layer_keep}_')
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}_')
if compact_model_name.endswith('.pt'):
compact_model_name = compact_model_name.replace('.pt', '.weights')
save_weights(compact_model, path=compact_model_name)
print(f'Compact model has been saved: {compact_model_name}')