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1_test_dcn.py
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1_test_dcn.py
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#! /usr/bin/env python
# coding=utf-8
# ================================================================
#
# Author : miemie2013
# Created date: 2020-10-15 14:50:03
# Description : pytorch_ppyolo。测试ema实现是否成功。
#
# ================================================================
import datetime
import json
from collections import deque
import paddle.fluid as fluid
import paddle.fluid.layers as P
import sys
import time
import shutil
import math
import copy
import random
import threading
import numpy as np
import os
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.regularizer import L2Decay
from paddle.fluid.initializer import Constant
from paddle.fluid.regularizer import L2Decay
from paddle.fluid.layers.learning_rate_scheduler import _decay_step_counter
from paddle.fluid.optimizer import ExponentialMovingAverage
import torch
from model.custom_layers import Conv2dUnit, DCNv2
from collections import OrderedDict
class MyNet(torch.nn.Module):
def __init__(self):
super(MyNet, self).__init__()
self.conv1 = torch.nn.Conv2d(3, 8, kernel_size=1, stride=1, padding=0, bias=True)
self.act1 = torch.nn.LeakyReLU(0.1)
self.dcnv2 = DCNv2(8, 512, filter_size=3, stride=2, padding=1, bias_attr=False)
def __call__(self, input_tensor):
x = self.conv1(input_tensor)
x = self.act1(x)
x = self.dcnv2(x)
return x
if __name__ == '__main__':
use_gpu = False
lr = 0.1
startup_prog = fluid.Program()
train_prog = fluid.Program()
with fluid.program_guard(train_prog, startup_prog):
with fluid.unique_name.guard():
inputs = P.data(name='input_1', shape=[-1, 3, 32, 32], append_batch_size=False, dtype='float32')
conv01_out_tensor = fluid.layers.conv2d(input=inputs, num_filters=8, filter_size=1, stride=1, padding=0,
param_attr=ParamAttr(name="conv01_weights"),
bias_attr=ParamAttr(name="conv01_bias", initializer=fluid.initializer.NormalInitializer(loc=0.0, scale=1.0, seed=0)))
act01_out_tensor = fluid.layers.leaky_relu(conv01_out_tensor, alpha=0.1)
filter_size = 3
filters = 512
stride = 2
padding = 1
conv_name = 'dcnv2'
offset_mask = fluid.layers.conv2d(
input=act01_out_tensor,
num_filters=filter_size * filter_size * 3,
filter_size=filter_size,
stride=stride,
padding=padding,
act=None,
# param_attr=ParamAttr(initializer=Constant(0.0), name=conv_name + "_conv_offset.w_0"),
param_attr=ParamAttr(name=conv_name + "_conv_offset.w_0"),
bias_attr=ParamAttr(initializer=Constant(0.0), name=conv_name + "_conv_offset.b_0"),
name=conv_name + "_conv_offset")
offset = offset_mask[:, :filter_size**2 * 2, :, :]
mask = offset_mask[:, filter_size**2 * 2:, :, :]
mask = fluid.layers.sigmoid(mask)
conv02_out_tensor = fluid.layers.deformable_conv(input=act01_out_tensor, offset=offset, mask=mask,
num_filters=filters,
filter_size=filter_size,
stride=stride,
padding=padding,
groups=1,
deformable_groups=1,
im2col_step=1,
param_attr=ParamAttr(name=conv_name + "_weights"),
bias_attr=False,
name=conv_name + ".conv2d.output.1")
# 建立损失函数
y_true = P.data(name='y_true', shape=[-1, 1, 16, 16], append_batch_size=False, dtype='float32')
# 先把差值逐项平方，可以用P.pow()这个op，也可以用python里的运算符**。
mseloss = P.pow(y_true - conv02_out_tensor, 2)
mseloss = P.reduce_mean(mseloss) # 再求平均，即mse损失函数
# 优化器
optimizer = fluid.optimizer.SGD(learning_rate=lr)
optimizer.minimize(mseloss)
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
# 重新建立一次网络，用相同的张量名，不用写损失层
inputs = P.data(name='input_1', shape=[-1, 3, 32, 32], append_batch_size=False, dtype='float32')
conv01_out_tensor = fluid.layers.conv2d(input=inputs, num_filters=8, filter_size=1, stride=1, padding=0,
param_attr=ParamAttr(name="conv01_weights"),
bias_attr=ParamAttr(name="conv01_bias", initializer=fluid.initializer.NormalInitializer(loc=0.0, scale=1.0, seed=0)))
act01_out_tensor = fluid.layers.leaky_relu(conv01_out_tensor, alpha=0.1)
filter_size = 3
filters = 512
stride = 2
padding = 1
conv_name = 'dcnv2'
offset_mask = fluid.layers.conv2d(
input=act01_out_tensor,
num_filters=filter_size * filter_size * 3,
filter_size=filter_size,
stride=stride,
padding=padding,
act=None,
# param_attr=ParamAttr(initializer=Constant(0.0), name=conv_name + "_conv_offset.w_0"),
param_attr=ParamAttr(name=conv_name + "_conv_offset.w_0"),
bias_attr=ParamAttr(initializer=Constant(0.0), name=conv_name + "_conv_offset.b_0"),
name=conv_name + "_conv_offset")
offset = offset_mask[:, :filter_size**2 * 2, :, :]
mask = offset_mask[:, filter_size**2 * 2:, :, :]
mask = fluid.layers.sigmoid(mask)
conv02_out_tensor = fluid.layers.deformable_conv(input=act01_out_tensor, offset=offset, mask=mask,
num_filters=filters,
filter_size=filter_size,
stride=stride,
padding=padding,
groups=1,
deformable_groups=1,
im2col_step=1,
param_attr=ParamAttr(name=conv_name + "_weights"),
bias_attr=False,
name=conv_name + ".conv2d.output.1")
eval_fetch_list = [conv02_out_tensor]
eval_prog = eval_prog.clone(for_test=True)
# 参数初始化
gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
place = fluid.CUDAPlace(gpu_id) if use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
compiled_eval_prog = fluid.compiler.CompiledProgram(eval_prog)
# pytorch搭建的神经网络的权重。初始值是paddle相同层的初始值。为了模拟paddle训练过程。
# 1.卷积层
paddle_conv01_weights = np.array(fluid.global_scope().find_var('conv01_weights').get_tensor())
paddle_conv01_bias = np.array(fluid.global_scope().find_var('conv01_bias').get_tensor())
# 2.激活层
# 3.卷积层
paddle_conv02_weights = np.array(fluid.global_scope().find_var('dcnv2_conv_offset.w_0').get_tensor())
paddle_conv02_bias = np.array(fluid.global_scope().find_var('dcnv2_conv_offset.b_0').get_tensor())
paddle_conv02_dcn_weights = np.array(fluid.global_scope().find_var('dcnv2_weights').get_tensor())
# 6.激活层
# 7.损失函数层，没有权重。
# pytorch搭建的神经网络
myNet = MyNet()
# myNet = myNet.cuda()
loss_fn = torch.nn.MSELoss(reduce=True, size_average=True)
# loss_fn = loss_fn.cuda()
optimizer2 = torch.optim.SGD(filter(lambda p: p.requires_grad, myNet.parameters()), lr=lr) # requires_grad==True 的参数才可以被更新
# 初始化自己网络的权重
myNet.conv1.weight.data = torch.Tensor(np.copy(paddle_conv01_weights))
myNet.conv1.bias.data = torch.Tensor(np.copy(paddle_conv01_bias))
myNet.dcnv2.conv_offset.weight.data = torch.Tensor(np.copy(paddle_conv02_weights))
myNet.dcnv2.conv_offset.bias.data = torch.Tensor(np.copy(paddle_conv02_bias))
myNet.dcnv2.dcn_weight.data = torch.Tensor(np.copy(paddle_conv02_dcn_weights))
myNet.train() # 切换到训练模式
# 只训练8步
for step in range(8):
print('------------------ step %d ------------------' % step)
# ==================== train ====================
batch_data = np.random.normal(loc=0, scale=1, size=(2, 3, 32, 32)).astype(np.float32)
y_true_arr = np.random.normal(loc=0, scale=1, size=(2, 512, 16, 16)).astype(np.float32)
paddle_mseloss_out, paddle_conv01_out, paddle_conv02_out = exe.run(train_prog, feed={"input_1": batch_data, "y_true": y_true_arr, },
fetch_list=[mseloss, conv01_out_tensor, conv02_out_tensor])
print('train_forward:')
# python代码模拟训练过程，与paddle的输出校验。我们希望和飞桨有相同的输出。
batch_data = torch.Tensor(batch_data)
y_true_arr = torch.Tensor(y_true_arr)
my_act02_out = myNet(batch_data)
my_mseloss_out = loss_fn(my_act02_out, y_true_arr)
# 更新权重
optimizer2.zero_grad() # 清空上一步的残余更新参数值
my_mseloss_out.backward() # 误差反向传播, 计算参数更新值
optimizer2.step() # 将参数更新值施加到 net 的 parameters 上
_my_mseloss_out = my_mseloss_out.cpu().data.numpy()
diff_mseloss_out = np.sum((paddle_mseloss_out - _my_mseloss_out)**2)
print('diff_mseloss_out=%.6f' % diff_mseloss_out) # 若是0，则表示成功模拟出PaddlePaddle bn层的输出结果
print('\nbackward:')
# 和飞桨更新后的权重校验。
paddle_conv01_weights = np.array(fluid.global_scope().find_var('conv01_weights').get_tensor())
paddle_conv01_bias = np.array(fluid.global_scope().find_var('conv01_bias').get_tensor())
paddle_conv02_weights = np.array(fluid.global_scope().find_var('dcnv2_conv_offset.w_0').get_tensor())
paddle_conv02_bias = np.array(fluid.global_scope().find_var('dcnv2_conv_offset.b_0').get_tensor())
paddle_conv02_dcn_weights = np.array(fluid.global_scope().find_var('dcnv2_weights').get_tensor())
diff_conv01_weights = np.sum((paddle_conv01_weights - myNet.conv1.weight.data.numpy())**2)
print('diff_conv01_weights=%.6f' % diff_conv01_weights) # 若是0，则表示成功模拟出权重更新
diff_conv01_bias = np.sum((paddle_conv01_bias - myNet.conv1.bias.data.numpy())**2)
print('diff_conv01_bias=%.6f' % diff_conv01_bias) # 若是0，则表示成功模拟出权重更新
print('\nDCNv2:')
diff_conv02_weights = np.sum((paddle_conv02_weights - myNet.dcnv2.conv_offset.weight.data.numpy())**2)
print('diff_conv02_weights=%.6f' % diff_conv02_weights) # 若是0，则表示成功模拟出权重更新
diff_conv02_bias = np.sum((paddle_conv02_bias - myNet.dcnv2.conv_offset.bias.data.numpy())**2)
print('diff_conv02_bias=%.6f' % diff_conv02_bias) # 若是0，则表示成功模拟出权重更新
diff_conv02_dcn_weights = np.sum((paddle_conv02_dcn_weights - myNet.dcnv2.dcn_weight.data.numpy())**2)
print('diff_conv02_dcn_weights=%.6f' % diff_conv02_dcn_weights) # 若是0，则表示成功模拟出权重更新
# ==================== test ====================
test_data = np.random.normal(loc=0, scale=1, size=(2, 3, 32, 32)).astype(np.float32)
_conv02_out_tensor, = exe.run(compiled_eval_prog, feed={"input_1": test_data, }, fetch_list=eval_fetch_list)
# 自己网络的test
print('\ntest_forward:')
myNet.eval() # 切换到验证模式
test_data = torch.Tensor(test_data)
my_test_conv02_out_ = myNet(test_data)
my_test_conv02_out = my_test_conv02_out_.cpu().data.numpy()
myNet.train() # 切换到训练模式
d1 = np.sum((_conv02_out_tensor - my_test_conv02_out)**2)
print('d1=%.6f' % d1) # 若是0，则表示成功模拟出推理