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movilenetv2_caffe.py
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movilenetv2_caffe.py
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# # -*- coding: utf-8 -*-
# import caffe
# import caffe.proto.caffe_pb2 as caffe_pb2
# from caffe import layers as L, params as P
# import numpy as np
# from utils import conv2d, depthwise_conv2d, bottleneck,heartmap,subnet,deconv_relu
#
# from utils import get_npy, decode_npy_model, decode_pth_model
# import os
# os.environ['CUDA_VISIBLE_DEVICES'] = '1'
#
# def mobilenetv2_centernet_inference(netspec, input_node):
# layer_cfg = [['conv2d', 32, 3, 2, 'relu', False, True, 'conv1'],
# ['bottleneck_0_0', 32, 16, 1, 'LinearBottleneck0_0'],
# ['bottleneck_1_0', 96,24, 2, 'LinearBottleneck1_0'],
# ['bottleneck_1_1', 144,24, 1, 'LinearBottleneck1_1'],
# ['bottleneck_2_0', 144,32, 2, 'LinearBottleneck2_0'],
# ['bottleneck_2_1', 192,32, 1, 'LinearBottleneck2_1'],
# ['bottleneck_3_0', 192,64, 2, 'LinearBottleneck3_0'],
# ['bottleneck_3_1', 384,64, 1, 'LinearBottleneck3_1'],
# ['bottleneck_3_2', 384,64, 1, 'LinearBottleneck3_2'],
# ['bottleneck_4_0', 384,96, 1, 'LinearBottleneck4_0'],
# ['bottleneck_4_1', 576,96, 1, 'LinearBottleneck4_1'],
# ['bottleneck_4_2', 576,96, 1, 'LinearBottleneck4_2'],
# ['bottleneck_5_0', 576,160, 2, 'LinearBottleneck5_0'],
# ['bottleneck_5_1', 960,160, 1, 'LinearBottleneck5_1'],
# ['bottleneck_6_0', 960,320, 1, 'LinearBottleneck6_0']]
# heartmap_cfg=[['heartmap', 32, 'MapHeatmap_2'],
# ['heartmap', 96, 'MapHeatmap_4'],
# ['heartmap', 320, 'MapHeatmap_6']]
#
# n = netspec
# blobs_lst = []
# layer = layer_cfg[0]
# n.conv = conv2d(n, input_node, num_output=32, kernel_size=3,
# stride=2,
# activation_fn='relu',
# bias_term=False,
# use_bn=True,
# scope='conv1')
# resnet_block = False
# layer = layer_cfg[1]
# n.bottleneck_0_0 = bottleneck(n, n.conv, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
# resnet_block=resnet_block)
# layer = layer_cfg[2]
# n.bottleneck_1_0 = bottleneck(n, n.bottleneck_0_0, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
# resnet_block=resnet_block)
# layer = layer_cfg[3]
# n.bottleneck_1_1 = bottleneck(n, n.bottleneck_1_0, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
# resnet_block=resnet_block)
# layer = layer_cfg[4]
# n.bottleneck_2_0 = bottleneck(n, n.bottleneck_1_1, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
# resnet_block=resnet_block)
# layer = layer_cfg[5]
# n.bottleneck_2_1 = bottleneck(n, n.bottleneck_2_0, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
# resnet_block=resnet_block)
# layer = layer_cfg[6]
# n.bottleneck_3_0 = bottleneck(n, n.bottleneck_2_1, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
# resnet_block=resnet_block)
# layer = layer_cfg[7]
# n.bottleneck_3_1 = bottleneck(n, n.bottleneck_3_0, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
# resnet_block=resnet_block)
# layer = layer_cfg[8]
# n.bottleneck_3_2 = bottleneck(n, n.bottleneck_3_1, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
# resnet_block=resnet_block)
# layer = layer_cfg[9]
# n.bottleneck_4_0 = bottleneck(n, n.bottleneck_3_2, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
# resnet_block=resnet_block)
# layer = layer_cfg[10]
# n.bottleneck_4_1 = bottleneck(n, n.bottleneck_4_0, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
# resnet_block=resnet_block)
# layer = layer_cfg[11]
# n.bottleneck_4_2 = bottleneck(n, n.bottleneck_4_1, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
# resnet_block=resnet_block)
# layer = layer_cfg[12]
# n.bottleneck_5_0 = bottleneck(n, n.bottleneck_4_2, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
# resnet_block=resnet_block)
# layer = layer_cfg[13]
# n.bottleneck_5_1 = bottleneck(n, n.bottleneck_5_0, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
# resnet_block=resnet_block)
# layer = layer_cfg[14]
# n.bottleneck_6_0 = bottleneck(n, n.bottleneck_5_1, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
# resnet_block=resnet_block)
#
# n.deconv1 = L.de
# heartmap_layer = heartmap_cfg[0]
# n.hm1, n.wh1, n.reg1 = subnet(n, n.bottleneck_2_1, c_i=heartmap_layer[1], scope=heartmap_layer[2])
# heartmap_layer = heartmap_cfg[1]
# n.hm2, n.wh2, n.reg2 = subnet(n, n.bottleneck_4_2, c_i=heartmap_layer[1], scope=heartmap_layer[2])
# heartmap_layer = heartmap_cfg[2]
# n.hm3, n.wh3, n.reg3 = subnet(n, n.bottleneck_6_0, c_i=heartmap_layer[1], scope=heartmap_layer[2])
#
# return n
#
#
# def create_model(depth_coe=1.):
# n = caffe.NetSpec()
#
# n.data = L.Input(shape=[dict(dim=[1, 3, 512, 512])], ntop=1)
# n = mobilenetv2_centernet_inference(n, n.data)
#
# return n
#
#
#
# def parse_caffemodel(caffemodel):
# MODEL_FILE = '/home/amax/workspace/pytorch_caffe/deploy.prototxt'
# # 预先训练好的caffe模型
# PRETRAIN_FILE = caffemodel
#
# # 保存参数的文件
# params_txt = 'params.txt'
# pf = open(params_txt, 'w')
#
# # 让caffe以测试模式读取网络参数
# net = caffe.Net(MODEL_FILE, PRETRAIN_FILE, caffe.TEST)
#
# # 遍历每一层
# for param_name in net.params.keys():
# # 权重参数
# weight = net.params[param_name][0].data
# # 偏置参数
# bias = net.params[param_name][1].data
#
# # 该层在prototxt文件中对应“top”的名称
# pf.write(param_name)
# pf.write('\n')
#
# # 写权重参数
# pf.write('\n' + param_name + '_weight:\n\n')
# # 权重参数是多维数组,为了方便输出,转为单列数组
# weight.shape = (-1, 1)
#
# for w in weight:
# pf.write('%ff, ' % w)
#
# # 写偏置参数
# pf.write('\n\n' + param_name + '_bias:\n\n')
# # 偏置参数是多维数组,为了方便输出,转为单列数组
# bias.shape = (-1, 1)
# for b in bias:
# pf.write('%ff, ' % b)
#
# pf.write('\n\n')
#
# pf.close()
#
# print('--')
#
# def gen_prototxt(model_name='MobileNet_CenterNet'):
# net = create_model()
# with open('%s.prototxt' % model_name, 'w') as f:
# f.write(str(net.to_proto()))
#
# def save_conv2caffe(weights=None, biases=None, conv_param=None):
# if conv_param is not None:
# if biases is not None:
# conv_param[1].data[...] = biases
# if weights is not None:
# conv_param[0].data[...] = weights
#
#
# def save_fc2caffe(weights, biases, fc_param):
# print(biases.size(), weights.size())
# print(fc_param[1].data.shape)
# print(fc_param[0].data.shape)
# fc_param[1].data[...] = biases
# fc_param[0].data[...] = weights
#
#
# def save_bn2caffe(running_mean=None, running_var=None, bn_param=None):
# if bn_param is not None:
# if running_mean is not None:
# bn_param[0].data[...] = running_mean
# if running_var is not None:
# bn_param[1].data[...] = running_var
# bn_param[2].data[...] = np.array([1.0])
#
#
# def save_scale2caffe(weights=None, biases=None, scale_param=None):
# if scale_param is not None:
# if biases is not None:
# scale_param[1].data[...] = biases
# if weights is not None:
# scale_param[0].data[...] = weights
# def map_torch_bn_layer_to_caffe_bn(bn_layer_name):
# layer_name = bn_layer_name.replace('bn', 'conv')
# lst = layer_name.split('.')
# if 'run' in layer_name:
# new_lst = lst[2:-1]+['BatchNorm']
# else:
# new_lst = lst[2:-1] + ['scale']
# caffe_bn_layer_name = '/'.join(new_lst)
# return caffe_bn_layer_name
#
#
#
# def save_caffemodel(model_name,pth_path=None):
# # if meta_file is not None and ckpt_file is not None:
# # convert_meta_to_npy(meta_file, ckpt_file, npy_file)
# pth_path = '/data1/exp/ctdet/mobilenetv2/model_last.pth'
# data_dict = decode_pth_model(pth_path)
# # data_dict = decode_npy_model(npy_file)
# keys = list(data_dict.keys())
# # var_name_lst = [key for key in keys if 'pfld_inference' in key]
# var_name_lst = keys
#
# net = caffe.Net('./%s.prototxt' % model_name, caffe.TEST)
#
# # idx_w_notBN = {'weight': 0, 'depthwise_weight': 0, 'bias': 1}
# # idx_w_BN = {'running_mean': 0, 'running_var': 1}
#
# for var_name in var_name_lst:
# if 'bottleneck' in var_name: # bottleneck layer
# if 'conv' in var_name:
# layer_name = '/'.join(var_name.split('.')[2:-1])
# if 'weight' in var_name:
# weight = data_dict[var_name]
# save_conv2caffe(weights=weight,conv_param=net.params[layer_name])
# elif 'bias' in var_name:
# bias = data_dict[var_name]
# save_conv2caffe(biases=bias,conv_param=net.params[layer_name])
# elif 'bn' in var_name:
# layer_name = map_torch_bn_layer_to_caffe_bn(var_name)
# if 'mean' in var_name:
# mean = data_dict[var_name]
# save_bn2caffe(running_mean=mean, bn_param=net.params[layer_name])
# elif 'var' in var_name:
# var = data_dict[var_name]
# save_bn2caffe(running_var=var, bn_param=net.params[layer_name])
# elif 'weight' in var_name:
# weight = data_dict[var_name]
# save_scale2caffe(weights=weight,scale_param=net.params[layer_name])
# elif 'bias' in var_name:
# bias = data_dict[var_name]
# save_scale2caffe(biases=bias,scale_param=net.params[layer_name])
# else:
# continue
# else:
# continue
# elif 'mapheatmap' in var_name: # heatmap layer
# layer_num = var_name.split('.')[-2]
# if layer_num in ['0','4']:
# #conv with bias
# layer_name='/'.join(var_name.split('.')[1:-1])
# if 'weight' in var_name:
# weight = data_dict[var_name]
# save_conv2caffe(weights=weight,conv_param=net.params[layer_name])
# elif 'bias' in var_name:
# bias = data_dict[var_name]
# save_conv2caffe(biases=bias, conv_param=net.params[layer_name])
#
# elif layer_num=='1':
# # bn
# if 'run' in var_name:
# layer_name = '/'.join(var_name.split('.')[1:3]+['0/BatchNorm'])
# else:
# layer_name = '/'.join(var_name.split('.')[1:3]+['0/scale'])
# if 'mean' in var_name:
# mean = data_dict[var_name]
# save_bn2caffe(running_mean=mean, bn_param=net.params[layer_name])
# elif 'var' in var_name:
# var = data_dict[var_name]
# save_bn2caffe(running_var=var, bn_param=net.params[layer_name])
# elif 'weight' in var_name:
# weight = data_dict[var_name]
# save_scale2caffe(weights=weight, scale_param=net.params[layer_name])
# elif 'bias' in var_name:
# bias = data_dict[var_name]
# save_scale2caffe(biases=bias, scale_param=net.params[layer_name])
# else:
# continue
# elif layer_num=='2':
# # conv without bias
# layer_name='/'.join(var_name.split('.')[1:-1])
# if 'weight' in var_name:
# weight = data_dict[var_name]
# save_conv2caffe(weights=weight,conv_param=net.params[layer_name])
# else:
# continue
# elif 'conv1' in var_name:
# if 'weight' in var_name:
# weight = data_dict[var_name]
# layer_name = var_name.split('.')[0]
# save_conv2caffe(weights=weight,conv_param= net.params[layer_name])
# elif 'bn1' in var_name:
# if 'run' in var_name:
# layer_name = 'conv1/BatchNorm'
# else:
# layer_name = 'conv1/scale'
# if 'mean' in var_name:
# mean = data_dict[var_name]
# save_bn2caffe(running_mean=mean, bn_param=net.params[layer_name])
# elif 'var' in var_name:
# var = data_dict[var_name]
# save_bn2caffe(running_var=var, bn_param=net.params[layer_name])
# elif 'weight' in var_name:
# weight = data_dict[var_name]
# save_scale2caffe(weights=weight, scale_param=net.params[layer_name])
# elif 'bias' in var_name:
# bias = data_dict[var_name]
# save_scale2caffe(biases=bias, scale_param=net.params[layer_name])
# else:
# continue
#
#
# net.save('./%s.caffemodel' % model_name)
#
#
# def test_model(model_name):
# import cv2
# import torch
# from mobilenetv2 import MobileNetV2
# checkpoint_path = '/data1/exp/ctdet/mobilenetv2/model_last.pth'
# image = cv2.imread('540e3f90874dfa66.jpg')
# # image = np.random.randn(112, 112, 3)*255
# # image = image.astype(np.uint8)
# input = cv2.cvtColor(image.copy(), cv2.COLOR_BGR2RGB)
# # input = image.copy()[...,::-1]
# input = cv2.resize(input, (512, 512))
# # debug
# # input = input[:8, :8, :]
# input = input.astype(np.float32) / 256.0
# input = np.expand_dims(input, 0)
# torch_input = input.copy().transpose((0, 3, 1, 2))
# tensor_input = torch.from_numpy(torch_input)
# input_ = input.copy()
# heads = {'hm': 6, 'wh': 2, 'reg': 2}
# num_layers = 34
# model3 = MobileNetV2(heads, head_conv=64)
# model3.load_state_dict(torch.load(checkpoint_path)['state_dict'])
#
# pytorch_result = model3(tensor_input)
#
#
# net = caffe.Net('./%s.prototxt' % model_name, './%s.caffemodel' % model_name, caffe.TEST)
# input_ = input.transpose((0, 3, 1, 2))
#
# net.blobs['data'].data[...] = input_
# output_ = net.forward()
# # 把数据经过xxx层后的结果输出来
# out = net.blobs['Convolution1'].data[0]
# # print(output_)
# keys = list(output_.keys())
# print(output_[keys[0]].shape)
# caffe_output = output_[keys[0]]
#
# def cal_MPA(caffe_output, cmp_output):
# try:
# error = np.abs(caffe_output - cmp_output)
# except:
# cmp_output = cmp_output.transpose((0, 3, 1, 2))
# error = np.abs(caffe_output - cmp_output)
# zeros = np.zeros_like(error)
# error = np.where(np.less(error, 1e-5), zeros, error)
# print('error: ', np.sum(error))
# MPA = np.max(error) / np.max(np.abs(cmp_output)) * 100.
# print('MPA: %f' % MPA)
#
# cmp_output = pytorch_result
# cal_MPA(caffe_output, cmp_output)
#
# bin_file = '/data2/SharedVMs/nfs_sync/model_speed_test/mobileResult.bin'
# hisi_result = np.fromfile(bin_file, dtype=np.float32)
# hisi_result = np.reshape(hisi_result, [1, 196])
# cal_MPA(caffe_output, hisi_result)
#
# caffe_output.astype(dtype=np.float32)
# caffe_output.tofile('./data/caffe_varify_output.bin')
#
#
#
# print('Done.')
#
#
# def main():
# model_name = 'MobileNet_CenterNet'
# # gen_pfld_prototxt(model_name=model_name)
# npy_file = './mobilenet_centernet.npy'
# # meta_file = './TF_model/model.meta'
# # ckpt_file = './TF_model/model.ckpt-312'
# # save_caffemodel(npy_file, model_name=model_name,
# # meta_file=meta_file, ckpt_file=ckpt_file)
# save_caffemodel(model_name)
# test_model(model_name)
#
#
# if __name__ == '__main__':
# gen_prototxt()
# main()
# -*- coding: utf-8 -*-
import caffe
import caffe.proto.caffe_pb2 as caffe_pb2
from caffe import layers as L, params as P
import numpy as np
from utils import conv2d, depthwise_conv2d, bottleneck, heartmap, subnet, deconv
from utils import get_npy, decode_npy_model, decode_pth_model
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
def mobilenetv2_centernet_inference(netspec, input_node):
layer_cfg = [['conv2d', 32, 3, 2, 'relu', False, True, 'conv1'],
['bottleneck_0_0', 32, 16, 1, 'LinearBottleneck0_0'],
['bottleneck_1_0', 96, 24, 2, 'LinearBottleneck1_0'],
['bottleneck_1_1', 144, 24, 1, 'LinearBottleneck1_1'],
['bottleneck_2_0', 144, 32, 2, 'LinearBottleneck2_0'],
['bottleneck_2_1', 192, 32, 1, 'LinearBottleneck2_1'],
['bottleneck_3_0', 192, 64, 2, 'LinearBottleneck3_0'],
['bottleneck_3_1', 384, 64, 1, 'LinearBottleneck3_1'],
['bottleneck_3_2', 384, 64, 1, 'LinearBottleneck3_2'],
['bottleneck_4_0', 384, 96, 1, 'LinearBottleneck4_0'],
['bottleneck_4_1', 576, 96, 1, 'LinearBottleneck4_1'],
['bottleneck_4_2', 576, 96, 1, 'LinearBottleneck4_2'],
['bottleneck_5_0', 576, 160, 2, 'LinearBottleneck5_0'],
['bottleneck_5_1', 960, 160, 1, 'LinearBottleneck5_1'],
['bottleneck_6_0', 960, 320, 1, 'LinearBottleneck6_0']]
deconv_cfg = [['deconv', 160, 'deconv1'],
['deconv', 160, 'deconv2'],
['deconv', 64, 'deconv3']]
n = netspec
blobs_lst = []
layer = layer_cfg[0]
n.conv = conv2d(n, input_node, num_output=32, kernel_size=3,
stride=2,
activation_fn='relu',
bias_term=False,
use_bn=True,
scope='conv1')
resnet_block = [False,False,True,False,True,False,True,True,False,True,True,False,True,False]
layer = layer_cfg[1]
n.bottleneck_0_0 = bottleneck(n, n.conv, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
resnet_block=resnet_block[0])
layer = layer_cfg[2]
n.bottleneck_1_0 = bottleneck(n, n.bottleneck_0_0, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
resnet_block=resnet_block[1])
layer = layer_cfg[3]
n.bottleneck_1_1 = bottleneck(n, n.bottleneck_1_0, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
resnet_block=resnet_block[2])
layer = layer_cfg[4]
n.bottleneck_2_0 = bottleneck(n, n.bottleneck_1_1, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
resnet_block=resnet_block[3])
layer = layer_cfg[5]
n.bottleneck_2_1 = bottleneck(n, n.bottleneck_2_0, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
resnet_block=resnet_block[4])
layer = layer_cfg[6]
n.bottleneck_3_0 = bottleneck(n, n.bottleneck_2_1, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
resnet_block=resnet_block[5])
layer = layer_cfg[7]
n.bottleneck_3_1 = bottleneck(n, n.bottleneck_3_0, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
resnet_block=resnet_block[6])
layer = layer_cfg[8]
n.bottleneck_3_2 = bottleneck(n, n.bottleneck_3_1, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
resnet_block=resnet_block[7])
layer = layer_cfg[9]
n.bottleneck_4_0 = bottleneck(n, n.bottleneck_3_2, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
resnet_block=resnet_block[8])
layer = layer_cfg[10]
n.bottleneck_4_1 = bottleneck(n, n.bottleneck_4_0, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
resnet_block=resnet_block[9])
layer = layer_cfg[11]
n.bottleneck_4_2 = bottleneck(n, n.bottleneck_4_1, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
resnet_block=resnet_block[10])
layer = layer_cfg[12]
n.bottleneck_5_0 = bottleneck(n, n.bottleneck_4_2, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
resnet_block=resnet_block[11])
layer = layer_cfg[13]
n.bottleneck_5_1 = bottleneck(n, n.bottleneck_5_0, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
resnet_block=resnet_block[12])
layer = layer_cfg[14]
n.bottleneck_6_0 = bottleneck(n, n.bottleneck_5_1, c_e=layer[1], c_o=layer[2], stride=layer[3], scope=layer[4],
resnet_block=resnet_block[13])
deconv_layer = deconv_cfg[0]
n.deconv1 = deconv(n,n.bottleneck_6_0,deconv_layer[1], scope=deconv_layer[2])
deconv_layer = deconv_cfg[1]
n.deconv2 = deconv(n, n.deconv1, deconv_layer[1], scope=deconv_layer[2])
deconv_layer = deconv_cfg[2]
n.deconv3 = deconv(n, n.deconv2, deconv_layer[1], scope=deconv_layer[2])
n.hm, n.wh, n.reg = subnet(n, n.deconv3, c_i=64, scope='heatmap_layer')
return n
def create_model(depth_coe=1.):
n = caffe.NetSpec()
n.data = L.Input(shape=[dict(dim=[1, 3, 512, 512])], ntop=1)
n = mobilenetv2_centernet_inference(n, n.data)
return n
def parse_caffemodel(caffemodel):
MODEL_FILE = '/home/amax/workspace/pytorch_caffe/deploy.prototxt'
# 预先训练好的caffe模型
PRETRAIN_FILE = caffemodel
# 保存参数的文件
params_txt = 'params.txt'
pf = open(params_txt, 'w')
# 让caffe以测试模式读取网络参数
net = caffe.Net(MODEL_FILE, PRETRAIN_FILE, caffe.TEST)
# 遍历每一层
for param_name in net.params.keys():
# 权重参数
weight = net.params[param_name][0].data
# 偏置参数
bias = net.params[param_name][1].data
# 该层在prototxt文件中对应“top”的名称
pf.write(param_name)
pf.write('\n')
# 写权重参数
pf.write('\n' + param_name + '_weight:\n\n')
# 权重参数是多维数组,为了方便输出,转为单列数组
weight.shape = (-1, 1)
for w in weight:
pf.write('%ff, ' % w)
# 写偏置参数
pf.write('\n\n' + param_name + '_bias:\n\n')
# 偏置参数是多维数组,为了方便输出,转为单列数组
bias.shape = (-1, 1)
for b in bias:
pf.write('%ff, ' % b)
pf.write('\n\n')
pf.close()
print('--')
def gen_prototxt(model_name='new_MobileNet_CenterNet'):
net = create_model()
with open('%s.prototxt' % model_name, 'w') as f:
f.write(str(net.to_proto()))
def save_conv2caffe(weights=None, biases=None, conv_param=None):
if conv_param is not None:
if biases is not None:
conv_param[1].data[...] = biases
if weights is not None:
conv_param[0].data[...] = weights
def save_deconv2caffe(weights=None, biases=None, deconv_param=None):
if deconv_param is not None:
if biases is not None:
deconv_param[1].data[...] = biases
if weights is not None:
deconv_param[0].data[...] = weights
def save_fc2caffe(weights, biases, fc_param):
print(biases.size(), weights.size())
print(fc_param[1].data.shape)
print(fc_param[0].data.shape)
fc_param[1].data[...] = biases
fc_param[0].data[...] = weights
def save_bn2caffe(running_mean=None, running_var=None, bn_param=None):
if bn_param is not None:
if running_mean is not None:
bn_param[0].data[...] = running_mean
if running_var is not None:
bn_param[1].data[...] = running_var
bn_param[2].data[...] = np.array([1.0])
def save_scale2caffe(weights=None, biases=None, scale_param=None):
if scale_param is not None:
if biases is not None:
scale_param[1].data[...] = biases
if weights is not None:
scale_param[0].data[...] = weights
def map_torch_bn_layer_to_caffe_bn(bn_layer_name):
layer_name = bn_layer_name.replace('bn', 'conv')
lst = layer_name.split('.')
if 'run' in layer_name:
new_lst = lst[2:-1] + ['BatchNorm']
else:
new_lst = lst[2:-1] + ['scale']
caffe_bn_layer_name = '/'.join(new_lst)
return caffe_bn_layer_name
def save_caffemodel(model_name, pth_path=None):
# if meta_file is not None and ckpt_file is not None:
# convert_meta_to_npy(meta_file, ckpt_file, npy_file)
pth_path = '/data1/exp/ctdet/default/model_best.pth'
data_dict = decode_pth_model(pth_path)
# data_dict = decode_npy_model(npy_file)
keys = list(data_dict.keys())
# var_name_lst = [key for key in keys if 'pfld_inference' in key]
var_name_lst = keys
net = caffe.Net('./%s.prototxt' % model_name, caffe.TEST)
# idx_w_notBN = {'weight': 0, 'depthwise_weight': 0, 'bias': 1}
# idx_w_BN = {'running_mean': 0, 'running_var': 1}
for var_name in var_name_lst:
if 'bottleneck' in var_name: # bottleneck layer
if 'conv' in var_name:
layer_name = '/'.join(var_name.split('.')[2:-1])
if 'weight' in var_name:
weight = data_dict[var_name]
save_conv2caffe(weights=weight, conv_param=net.params[layer_name])
elif 'bias' in var_name:
bias = data_dict[var_name]
save_conv2caffe(biases=bias, conv_param=net.params[layer_name])
elif 'bn' in var_name:
layer_name = map_torch_bn_layer_to_caffe_bn(var_name)
if 'mean' in var_name:
mean = data_dict[var_name]
save_bn2caffe(running_mean=mean, bn_param=net.params[layer_name])
elif 'var' in var_name:
var = data_dict[var_name]
save_bn2caffe(running_var=var, bn_param=net.params[layer_name])
elif 'weight' in var_name:
weight = data_dict[var_name]
save_scale2caffe(weights=weight, scale_param=net.params[layer_name])
elif 'bias' in var_name:
bias = data_dict[var_name]
save_scale2caffe(biases=bias, scale_param=net.params[layer_name])
else:
continue
else:
continue
elif 'mapheatmap' in var_name: # heatmap layer
layer_num = var_name.split('.')[-2]
if layer_num in ['0', '4']:
# conv with bias
layer_name = '/'.join(var_name.split('.')[1:-1])
if 'weight' in var_name:
weight = data_dict[var_name]
save_conv2caffe(weights=weight, conv_param=net.params[layer_name])
elif 'bias' in var_name:
bias = data_dict[var_name]
save_conv2caffe(biases=bias, conv_param=net.params[layer_name])
elif layer_num == '1':
# bn
if 'run' in var_name:
layer_name = '/'.join(var_name.split('.')[1:3] + ['0/BatchNorm'])
else:
layer_name = '/'.join(var_name.split('.')[1:3] + ['0/scale'])
if 'mean' in var_name:
mean = data_dict[var_name]
save_bn2caffe(running_mean=mean, bn_param=net.params[layer_name])
elif 'var' in var_name:
var = data_dict[var_name]
save_bn2caffe(running_var=var, bn_param=net.params[layer_name])
elif 'weight' in var_name:
weight = data_dict[var_name]
save_scale2caffe(weights=weight, scale_param=net.params[layer_name])
elif 'bias' in var_name:
bias = data_dict[var_name]
save_scale2caffe(biases=bias, scale_param=net.params[layer_name])
else:
continue
elif layer_num == '2':
# conv without bias
layer_name = '/'.join(var_name.split('.')[1:-1])
if 'weight' in var_name:
weight = data_dict[var_name]
save_conv2caffe(weights=weight, conv_param=net.params[layer_name])
else:
continue
elif 'conv1' in var_name:
if 'weight' in var_name:
weight = data_dict[var_name]
layer_name = var_name.split('.')[0]
save_conv2caffe(weights=weight, conv_param=net.params[layer_name])
elif 'bn1' in var_name:
if 'run' in var_name:
layer_name = 'conv1/BatchNorm'
else:
layer_name = 'conv1/scale'
if 'mean' in var_name:
mean = data_dict[var_name]
save_bn2caffe(running_mean=mean, bn_param=net.params[layer_name])
elif 'var' in var_name:
var = data_dict[var_name]
save_bn2caffe(running_var=var, bn_param=net.params[layer_name])
elif 'weight' in var_name:
weight = data_dict[var_name]
save_scale2caffe(weights=weight, scale_param=net.params[layer_name])
elif 'bias' in var_name:
bias = data_dict[var_name]
save_scale2caffe(biases=bias, scale_param=net.params[layer_name])
else:
continue
# elif 'deconv' in var_name:
# layer_name = var_name.split('.')[0]
# if 'weight' in var_name:
# weight = data_dict[var_name]
# save_deconv2caffe(weights=weight,deconv_param=net.params[layer_name])
# else:
# bias = data_dict[var_name]
# save_deconv2caffe(biases=bias,deconv_param=net.params[layer_name])
elif var_name.split('.')[0] in ['wh','hm','reg']:
layer_name = '/'.join(['heatmap_layer']+var_name.split('.')[:2])
if 'weight' in var_name:
weight=data_dict[var_name]
save_conv2caffe(weights=weight,conv_param=net.params[layer_name])
else:
bias = data_dict[var_name]
save_conv2caffe(biases=bias,conv_param=net.params[layer_name])
weight1 = data_dict['deconv1.weight']
bias1 = data_dict['deconv1.bias']
net.params['deconv1'][0].data[...]=weight1
net.params['deconv1'][1].data[...]=bias1
weight2 = data_dict['deconv2.weight']
bias2 = data_dict['deconv2.bias']
net.params['deconv2'][0].data[...] = weight2
net.params['deconv2'][1].data[...] = bias2
weight3 = data_dict['deconv3.weight']
bias3 = data_dict['deconv3.bias']
net.params['deconv3'][0].data[...] = weight3
net.params['deconv3'][1].data[...] = bias3
net.save('./%s.caffemodel' % model_name)
def test_model(model_name):
import cv2
import torch
from mobilenetv2 import MobileNetV2
checkpoint_path = '/data1/exp/ctdet/default/model_best.pth'
image = cv2.imread('540e3f90874dfa66.jpg')
# image = np.random.randn(112, 112, 3)*255
# image = image.astype(np.uint8)
input = cv2.cvtColor(image.copy(), cv2.COLOR_BGR2RGB)
# input = image.copy()[...,::-1]
input = cv2.resize(input, (512, 512))
# debug
# input = input[:8, :8, :]
input = input.astype(np.float32) / 256.0
input = np.expand_dims(input, 0)
torch_input = input.copy().transpose((0, 3, 1, 2))
tensor_input = torch.from_numpy(torch_input)
input_ = input.copy()
heads = {'hm': 6, 'wh': 2, 'reg': 2}
num_layers = 34
model3 = MobileNetV2(heads, head_conv=64)
data_dict = torch.load(checkpoint_path)['state_dict']
# data_dict.pop('bn1.weight')
# data_dict.pop('bn1.bias')
model3.load_state_dict(data_dict)
model3.train(False)
pytorch_result = model3(tensor_input)
net = caffe.Net('./%s.prototxt' % model_name, './%s.caffemodel' % model_name, caffe.TEST)
input_ = input.transpose((0, 3, 1, 2))
net.blobs['data'].data[...] = input_
output_ = net.forward()
# output_ = net.forward(end='deconv1') # 获取指定层的输出
# print(output_)
keys = list(output_.keys())
print(output_[keys[0]].shape)
caffe_output = output_[keys[0]]
def cal_MPA(caffe_output, cmp_output):
try:
error = np.abs(caffe_output - cmp_output)
except:
cmp_output = cmp_output.transpose((0, 3, 1, 2))
error = np.abs(caffe_output - cmp_output)
zeros = np.zeros_like(error)
error = np.where(np.less(error, 1e-5), zeros, error)
print('error: ', np.sum(error))
MPA = np.max(error) / np.max(np.abs(cmp_output)) * 100.
print('MPA: %f' % MPA)
# cmp_output = pytorch_result.cpu().detach().numpy()
# cal_MPA(caffe_output, cmp_output)
for k,val in output_.items():
cmp_output = pytorch_result[0][k].cpu().detach().numpy()
cal_MPA(val, cmp_output)
# bin_file = '/data2/SharedVMs/nfs_sync/model_speed_test/mobileResult.bin'
# hisi_result = np.fromfile(bin_file, dtype=np.float32)
# hisi_result = np.reshape(hisi_result, [1, 196])
# cal_MPA(caffe_output, hisi_result)
#
# caffe_output.astype(dtype=np.float32)
# caffe_output.tofile('./data/caffe_varify_output.bin')
print('Done.')
def main():
model_name = 'new_MobileNet_CenterNet'
# gen_pfld_prototxt(model_name=model_name)
# npy_file = './mobilenet_centernet.npy'
# meta_file = './TF_model/model.meta'
# ckpt_file = './TF_model/model.ckpt-312'
# save_caffemodel(npy_file, model_name=model_name,
# meta_file=meta_file, ckpt_file=ckpt_file)
save_caffemodel(model_name)
test_model(model_name)
if __name__ == '__main__':
gen_prototxt()
main()