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convert_FPGA.py
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import argparse
from pathlib import Path
import struct
from models import * # set ONNX_EXPORT in models.py
from torch import save
from utils.utils import *
def _create_parser():
parser = argparse.ArgumentParser()
parser.add_argument('--cfg', type=str, default='cfg/yolov4tiny/yolov4-tiny-quant.cfg', help='cfg file path')
parser.add_argument('--data', type=str, default='data/coco2017.data', help='coco.data file path')
parser.add_argument('--weights', type=str, default='weights/yolov4-tiny-best.weights', help='path to weights file')
parser.add_argument('--source', type=str, default='data/samples', help='source') # input file/folder, 0 for webcam
parser.add_argument('--output', type=str, default='pt_models/intuitus.pt', help='output folder') # output folder
parser.add_argument('--img_size', type=int, default=416, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.6, help='object confidence threshold')
parser.add_argument('--nms-thres', type=float, default=0.8, help='iou threshold for non-maximum suppression')
parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) or cpu')
parser.add_argument('--quantized', type=int, default=5,
help='0:quantization way one Ternarized weight and 8bit activation')
parser.add_argument('--a-bit', type=int, default=8,
help='a-bit')
parser.add_argument('--w-bit', type=int, default=6,
help='w-bit')
parser.add_argument('--FPGA', type=bool, default=True, help='FPGA')
parser.add_argument('--reorder', action='store_true', help='reorder')
parser.add_argument('--TN', type=int, default=8, help='TN')
parser.add_argument('--TM', type=int, default=64, help='TN')
return parser.parse_args()
def convert(opt):
img_size = (320, 192) if ONNX_EXPORT else opt.img_size # (320, 192) or (416, 256) or (608, 352) for (height, width)
#weights, half = opt.weights, opt.half
weights = opt.weights
# Initialize
device = torch_utils.select_device(device='cpu' if ONNX_EXPORT else opt.device)
# Initialize model
model = Darknet(opt.cfg, img_size, quantized=opt.quantized, a_bit=opt.a_bit, w_bit=opt.w_bit,
FPGA=opt.FPGA)
# Load weights
attempt_download(weights)
if weights.endswith('.pt'): # pytorch format
model.load_state_dict(torch.load(weights, map_location=device)['model'])
else: # darknet format
_ = load_darknet_weights(model, weights, FPGA=opt.FPGA)
if opt.quantized == 0:
save_weights(model, path='weights/' + opt.cfg.split('/')[-1].replace('.cfg', '') + '-best.weights')
else:
w_file = open('weights/' + opt.cfg.split('/')[-1].replace('.cfg', '') + '_weights.bin', 'wb')
b_file = open('weights/' + opt.cfg.split('/')[-1].replace('.cfg', '') + '_bias.bin', 'wb')
if opt.quantized == 1:
w_scale = open('weights/' + opt.cfg.split('/')[-1].replace('.cfg', '') + '_w_scale.bin', 'wb')
a_scale = open('weights/' + opt.cfg.split('/')[-1].replace('.cfg', '') + '_a_scale.bin', 'wb')
b_scale = open('weights/' + opt.cfg.split('/')[-1].replace('.cfg', '') + '_b_scale.bin', 'wb')
for _, (mdef, module) in enumerate(zip(model.module_defs, model.module_list)):
print(mdef)
if mdef['type'] == 'convolutional':
conv_layer = module[0]
# 使用BN训练中量化，融合BN参数 --> Use BN training to quantize and integrate BN parameters
if mdef['quant'] != 0:
weight, bias = conv_layer.BN_fuse()
else:
bias = None
weight = conv_layer.weight
if opt.quantized == 1:
# 得到缩放因子 --> Get zoom factor
activate_scale = -math.log(conv_layer.activation_quantizer.scale.cpu().data.numpy()[0], 2)
weight_scale = -math.log(conv_layer.weight_quantizer.scale.cpu().data.numpy()[0], 2)
a = struct.pack('<i', int(activate_scale))
a_scale.write(a)
a = struct.pack('<i', int(weight_scale))
w_scale.write(a)
# 处理weights
if mdef['quant'] != 0:
para, weight_exp_shift = conv_layer.weight_quantizer.get_quantize_value(weight)
else:
para = weight
if opt.reorder:
# 重排序参数 --> Reordering parameters
print("use reorder!")
shape_output = para.shape[0]
shape_input = para.shape[1]
num_TN = int(shape_input / opt.TN)
remainder_TN = shape_input % opt.TN
num_TM = int(shape_output / opt.TM)
remainder_TM = shape_output % opt.TM
first = True
for j in range(num_TM):
for k in range(num_TN):
temp = para[j * opt.TM:(j + 1) * opt.TM, k * opt.TN:(k + 1) * opt.TN, :, :]
temp = temp.view(temp.shape[0], temp.shape[1], temp.shape[2] * temp.shape[3])
temp = temp.permute(2, 0, 1).contiguous().view(-1)
if first:
reorder_para = temp.clone().cpu().data.numpy()
first = False
else:
reorder_para = np.append(reorder_para, temp.cpu().data.numpy())
temp = para[j * opt.TM:(j + 1) * opt.TM, num_TN * opt.TN:num_TN * opt.TN + remainder_TN, :, :]
temp = temp.view(temp.shape[0], temp.shape[1], temp.shape[2] * temp.shape[3])
temp = temp.permute(2, 0, 1).contiguous().view(-1)
if first:
reorder_para = temp.clone().cpu().data.numpy()
first = False
else:
reorder_para = np.append(reorder_para, temp.cpu().data.numpy())
for k in range(num_TN):
temp = para[num_TM * opt.TM:num_TM * opt.TM + remainder_TM, k * opt.TN:(k + 1) * opt.TN, :, :]
temp = temp.view(temp.shape[0], temp.shape[1], temp.shape[2] * temp.shape[3])
temp = temp.permute(2, 0, 1).contiguous().view(-1)
if first:
reorder_para = temp.clone().cpu().data.numpy()
first = False
else:
reorder_para = np.append(reorder_para, temp.cpu().data.numpy())
temp = para[num_TM * opt.TM:num_TM * opt.TM + remainder_TM,
num_TN * opt.TN:num_TN * opt.TN + remainder_TN, :, :]
temp = temp.view(temp.shape[0], temp.shape[1], temp.shape[2] * temp.shape[3])
temp = temp.permute(2, 0, 1).contiguous().view(-1)
if first:
reorder_para = temp.clone().cpu().data.numpy()
first = False
else:
reorder_para = np.append(reorder_para, temp.cpu().data.numpy())
para_flatten = reorder_para
else:
para_flatten = para.cpu().data.numpy().flatten() # 展开
# 存储weights --> Store weights
for i in para_flatten:
if opt.w_bit == 16:
# Dorefa量化为非对称量化 Google量化为对称量化 --> Dorefa quantification is asymmetric quantization Google quantification is symmetric quantification
if opt.quantized == 1:
a = struct.pack('<h', int(i))
if opt.quantized == 2:
a = struct.pack('<H', int(i))
elif opt.w_bit == 8:
# Dorefa量化为非对称量化 Google量化为对称量化 --> Dorefa quantification is asymmetric quantization Google quantification is symmetric quantification
if opt.quantized == 1:
a = struct.pack('b', int(i))
if opt.quantized == 2:
a = struct.pack('B', int(i))
else:
a = struct.pack('<f', i)
w_file.write(a)
# 处理bias --> Deal with bias
if bias != None:
# 生成量化后的参数 --> Generate quantized parameters
para = conv_layer.bias_quantizer.get_quantize_value(bias,weight_exp_shift)
if opt.quantized == 1:
bias_scale = -math.log(conv_layer.bias_quantizer.scale.cpu().data.numpy()[0], 2)
a = struct.pack('<i', int(bias_scale))
b_scale.write(a)
# print(para.shape)
para_flatten = para.cpu().data.numpy().flatten() # 展开
# 存储bias --> Storage bias
for i in para_flatten:
if opt.w_bit == 16:
# Dorefa量化为非对称量化 Google量化为对称量化 --> Dorefa quantification is asymmetric quantization Google quantification is symmetric quantification
if opt.quantized == 1:
a = struct.pack('<h', int(i))
if opt.quantized == 2:
a = struct.pack('<H', int(i))
elif opt.w_bit == 8:
# Dorefa量化为非对称量化 Google量化为对称量化 --> Dorefa quantification is asymmetric quantization Google quantification is symmetric quantification
if opt.quantized == 1:
a = struct.pack('b', int(i))
if opt.quantized == 2:
a = struct.pack('B', int(i))
else:
a = struct.pack('<f', i)
b_file.write(a)
if opt.quantized == 1:
w_scale.close()
a_scale.close()
b_scale.close()
w_file.close()
b_file.close()
# Eval mode
print(model)
model.to(device).eval()
out_path = Path(opt.output).absolute()
save(model,str(out_path))
if __name__ == '__main__':
opt = _create_parser()
print(opt)
with torch.no_grad():
convert(opt)