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Unverified Commit 9fa209e3 authored by Double_V's avatar Double_V Committed by GitHub
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Merge pull request #5259 from LDOUBLEV/kie_doc 

[tipc] add supplementary chain
parents 05ca1b4b 38372525
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test_tipc/supplementary/config.py 0 → 100644
View file @ 9fa209e3
import numpy as np
import os
import sys
import platform
import yaml
import time
import shutil
import paddle
import paddle.distributed as dist
from tqdm import tqdm
from argparse import ArgumentParser, RawDescriptionHelpFormatter
from utils import get_logger, print_dict
class ArgsParser(ArgumentParser):
def __init__(self):
super(ArgsParser, self).__init__(
formatter_class=RawDescriptionHelpFormatter)
self.add_argument("-c", "--config", help="configuration file to use")
self.add_argument(
"-o", "--opt", nargs='+', help="set configuration options")
self.add_argument(
'-p',
'--profiler_options',
type=str,
default=None,
help='The option of profiler, which should be in format \"key1=value1;key2=value2;key3=value3\".'
)
def parse_args(self, argv=None):
args = super(ArgsParser, self).parse_args(argv)
assert args.config is not None, \
"Please specify --config=configure_file_path."
args.opt = self._parse_opt(args.opt)
return args
def _parse_opt(self, opts):
config = {}
if not opts:
return config
for s in opts:
s = s.strip()
k, v = s.split('=')
config[k] = yaml.load(v, Loader=yaml.Loader)
return config
class AttrDict(dict):
"""Single level attribute dict, NOT recursive"""
def __init__(self, **kwargs):
super(AttrDict, self).__init__()
super(AttrDict, self).update(kwargs)
def __getattr__(self, key):
if key in self:
return self[key]
raise AttributeError("object has no attribute '{}'".format(key))
global_config = AttrDict()
default_config = {'Global': {'debug': False, }}
def load_config(file_path):
"""
Load config from yml/yaml file.
Args:
file_path (str): Path of the config file to be loaded.
Returns: global config
"""
merge_config(default_config)
_, ext = os.path.splitext(file_path)
assert ext in ['.yml', '.yaml'], "only support yaml files for now"
merge_config(yaml.load(open(file_path, 'rb'), Loader=yaml.Loader))
return global_config
def merge_config(config):
"""
Merge config into global config.
Args:
config (dict): Config to be merged.
Returns: global config
"""
for key, value in config.items():
if "." not in key:
if isinstance(value, dict) and key in global_config:
global_config[key].update(value)
else:
global_config[key] = value
else:
sub_keys = key.split('.')
assert (
sub_keys[0] in global_config
), "the sub_keys can only be one of global_config: {}, but get: {}, please check your running command".format(
global_config.keys(), sub_keys[0])
cur = global_config[sub_keys[0]]
for idx, sub_key in enumerate(sub_keys[1:]):
if idx == len(sub_keys) - 2:
cur[sub_key] = value
else:
cur = cur[sub_key]
def preprocess(is_train=False):
FLAGS = ArgsParser().parse_args()
profiler_options = FLAGS.profiler_options
config = load_config(FLAGS.config)
merge_config(FLAGS.opt)
profile_dic = {"profiler_options": FLAGS.profiler_options}
merge_config(profile_dic)
if is_train:
# save_config
save_model_dir = config['save_model_dir']
os.makedirs(save_model_dir, exist_ok=True)
with open(os.path.join(save_model_dir, 'config.yml'), 'w') as f:
yaml.dump(
dict(config), f, default_flow_style=False, sort_keys=False)
log_file = '{}/train.log'.format(save_model_dir)
else:
log_file = None
logger = get_logger(name='root', log_file=log_file)
# check if set use_gpu=True in paddlepaddle cpu version
use_gpu = config['use_gpu']
print_dict(config, logger)
return config, logger
if __name__ == "__main__":
config, logger = preprocess(is_train=False)
# print(config)
test_tipc/supplementary/custom_op/custom_relu_op.cc 0 → 100644
View file @ 9fa209e3
// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// reference from : https://github.com/PaddlePaddle/Paddle-Inference-Demo/blob/master/python/custom-operator/custom_relu_op.cc
#include <iostream>
#include <vector>
#include "paddle/extension.h"
template <typename data_t>
void relu_cpu_forward_kernel(const data_t* x_data,
data_t* out_data,
int64_t x_numel) {
for (int i = 0; i < x_numel; ++i) {
out_data[i] = std::max(static_cast<data_t>(0.), x_data[i]);
}
}
template <typename data_t>
void relu_cpu_backward_kernel(const data_t* grad_out_data,
const data_t* out_data,
data_t* grad_x_data,
int64_t out_numel) {
for (int i = 0; i < out_numel; ++i) {
grad_x_data[i] =
grad_out_data[i] * (out_data[i] > static_cast<data_t>(0) ? 1. : 0.);
}
}
std::vector<paddle::Tensor> relu_cpu_forward(const paddle::Tensor& x) {
auto out = paddle::Tensor(paddle::PlaceType::kCPU);
out.reshape(x.shape());
PD_DISPATCH_FLOATING_TYPES(
x.type(), "relu_cpu_forward", ([&] {
relu_cpu_forward_kernel<data_t>(
x.data<data_t>(), out.mutable_data<data_t>(x.place()), x.size());
}));
return {out};
}
std::vector<paddle::Tensor> relu_cpu_backward(const paddle::Tensor& x,
const paddle::Tensor& out,
const paddle::Tensor& grad_out) {
auto grad_x = paddle::Tensor(paddle::PlaceType::kCPU);
grad_x.reshape(x.shape());
PD_DISPATCH_FLOATING_TYPES(out.type(), "relu_cpu_backward", ([&] {
relu_cpu_backward_kernel<data_t>(
grad_out.data<data_t>(),
out.data<data_t>(),
grad_x.mutable_data<data_t>(x.place()),
out.size());
}));
return {grad_x};
}
std::vector<paddle::Tensor> relu_cuda_forward(const paddle::Tensor& x);
std::vector<paddle::Tensor> relu_cuda_backward(const paddle::Tensor& x,
const paddle::Tensor& out,
const paddle::Tensor& grad_out);
std::vector<paddle::Tensor> ReluForward(const paddle::Tensor& x) {
// TODO(chenweihang): Check Input
if (x.place() == paddle::PlaceType::kCPU) {
return relu_cpu_forward(x);
} else if (x.place() == paddle::PlaceType::kGPU) {
return relu_cuda_forward(x);
} else {
throw std::runtime_error("Not implemented.");
}
}
std::vector<paddle::Tensor> ReluBackward(const paddle::Tensor& x,
const paddle::Tensor& out,
const paddle::Tensor& grad_out) {
// TODO(chenweihang): Check Input
if (x.place() == paddle::PlaceType::kCPU) {
return relu_cpu_backward(x, out, grad_out);
} else if (x.place() == paddle::PlaceType::kGPU) {
return relu_cuda_backward(x, out, grad_out);
} else {
throw std::runtime_error("Not implemented.");
}
}
PD_BUILD_OP(custom_relu)
.Inputs({"X"})
.Outputs({"Out"})
.SetKernelFn(PD_KERNEL(ReluForward));
PD_BUILD_GRAD_OP(custom_relu)
.Inputs({"X", "Out", paddle::Grad("Out")})
.Outputs({paddle::Grad("X")})
.SetKernelFn(PD_KERNEL(ReluBackward));
\ No newline at end of file
test_tipc/supplementary/custom_op/custom_relu_op.cu 0 → 100644
View file @ 9fa209e3
// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// reference https://github.com/PaddlePaddle/Paddle-Inference-Demo/blob/master/python/custom-operator/custom_relu_op.cu
#include "paddle/extension.h"
template <typename data_t>
__global__ void relu_cuda_forward_kernel(const data_t* x,
data_t* y,
const int num) {
int gid = blockIdx.x * blockDim.x + threadIdx.x;
for (int i = gid; i < num; i += blockDim.x * gridDim.x) {
y[i] = max(x[i], static_cast<data_t>(0.));
}
}
template <typename data_t>
__global__ void relu_cuda_backward_kernel(const data_t* dy,
const data_t* y,
data_t* dx,
const int num) {
int gid = blockIdx.x * blockDim.x + threadIdx.x;
for (int i = gid; i < num; i += blockDim.x * gridDim.x) {
dx[i] = dy[i] * (y[i] > 0 ? 1. : 0.);
}
}
std::vector<paddle::Tensor> relu_cuda_forward(const paddle::Tensor& x) {
auto out = paddle::Tensor(paddle::PlaceType::kGPU);
out.reshape(x.shape());
int numel = x.size();
int block = 512;
int grid = (numel + block - 1) / block;
PD_DISPATCH_FLOATING_TYPES(
x.type(), "relu_cuda_forward_kernel", ([&] {
relu_cuda_forward_kernel<data_t><<<grid, block, 0, x.stream()>>>(
x.data<data_t>(), out.mutable_data<data_t>(x.place()), numel);
}));
return {out};
}
std::vector<paddle::Tensor> relu_cuda_backward(const paddle::Tensor& x,
const paddle::Tensor& out,
const paddle::Tensor& grad_out) {
auto grad_x = paddle::Tensor(paddle::PlaceType::kGPU);
grad_x.reshape(x.shape());
int numel = out.size();
int block = 512;
int grid = (numel + block - 1) / block;
PD_DISPATCH_FLOATING_TYPES(
out.type(), "relu_cuda_backward_kernel", ([&] {
relu_cuda_backward_kernel<data_t><<<grid, block, 0, x.stream()>>>(
grad_out.data<data_t>(),
out.data<data_t>(),
grad_x.mutable_data<data_t>(x.place()),
numel);
}));
return {grad_x};
}
test_tipc/supplementary/custom_op/test.py 0 → 100644
View file @ 9fa209e3
import paddle
import paddle.nn as nn
from paddle.vision.transforms import Compose, Normalize
from paddle.utils.cpp_extension import load
from paddle.inference import Config
from paddle.inference import create_predictor
import numpy as np
EPOCH_NUM = 4
BATCH_SIZE = 64
# jit compile custom op
custom_ops = load(
name="custom_jit_ops", sources=["custom_relu_op.cc", "custom_relu_op.cu"])
class LeNet(nn.Layer):
def __init__(self):
super(LeNet, self).__init__()
self.conv1 = nn.Conv2D(
in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=2)
self.max_pool1 = nn.MaxPool2D(kernel_size=2, stride=2)
self.conv2 = nn.Conv2D(
in_channels=6, out_channels=16, kernel_size=5, stride=1)
self.max_pool2 = nn.MaxPool2D(kernel_size=2, stride=2)
self.linear1 = nn.Linear(in_features=16 * 5 * 5, out_features=120)
self.linear2 = nn.Linear(in_features=120, out_features=84)
self.linear3 = nn.Linear(in_features=84, out_features=10)
def forward(self, x):
x = self.conv1(x)
x = custom_ops.custom_relu(x)
x = self.max_pool1(x)
x = custom_ops.custom_relu(x)
x = self.conv2(x)
x = self.max_pool2(x)
x = paddle.flatten(x, start_axis=1, stop_axis=-1)
x = self.linear1(x)
x = custom_ops.custom_relu(x)
x = self.linear2(x)
x = custom_ops.custom_relu(x)
x = self.linear3(x)
return x
# set device
paddle.set_device("gpu")
# model
net = LeNet()
loss_fn = nn.CrossEntropyLoss()
opt = paddle.optimizer.Adam(learning_rate=0.001, parameters=net.parameters())
# data loader
transform = Compose([Normalize(mean=[127.5], std=[127.5], data_format='CHW')])
train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)
train_loader = paddle.io.DataLoader(
train_dataset,
batch_size=BATCH_SIZE,
shuffle=True,
drop_last=True,
num_workers=2)
# train
for epoch_id in range(EPOCH_NUM):
for batch_id, (image, label) in enumerate(train_loader()):
out = net(image)
loss = loss_fn(out, label)
loss.backward()
if batch_id % 300 == 0:
print("Epoch {} batch {}: loss = {}".format(epoch_id, batch_id,
np.mean(loss.numpy())))
opt.step()
opt.clear_grad()
test_tipc/supplementary/data.py 0 → 100644
View file @ 9fa209e3
import numpy as np
import paddle
import os
import cv2
import glob
def transform(data, ops=None):
""" transform """
if ops is None:
ops = []
for op in ops:
data = op(data)
if data is None:
return None
return data
def create_operators(op_param_list, global_config=None):
"""
create operators based on the config
Args:
params(list): a dict list, used to create some operators
"""
assert isinstance(op_param_list, list), ('operator config should be a list')
ops = []
for operator in op_param_list:
assert isinstance(operator,
dict) and len(operator) == 1, "yaml format error"
op_name = list(operator)[0]
param = {} if operator[op_name] is None else operator[op_name]
if global_config is not None:
param.update(global_config)
op = eval(op_name)(**param)
ops.append(op)
return ops
class DecodeImage(object):
""" decode image """
def __init__(self, img_mode='RGB', channel_first=False, **kwargs):
self.img_mode = img_mode
self.channel_first = channel_first
def __call__(self, data):
img = data['image']
if six.PY2:
assert type(img) is str and len(
img) > 0, "invalid input 'img' in DecodeImage"
else:
assert type(img) is bytes and len(
img) > 0, "invalid input 'img' in DecodeImage"
img = np.frombuffer(img, dtype='uint8')
img = cv2.imdecode(img, 1)
if img is None:
return None
if self.img_mode == 'GRAY':
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
elif self.img_mode == 'RGB':
assert img.shape[2] == 3, 'invalid shape of image[%s]' % (img.shape)
img = img[:, :, ::-1]
if self.channel_first:
img = img.transpose((2, 0, 1))
data['image'] = img
data['src_image'] = img
return data
class NormalizeImage(object):
""" normalize image such as substract mean, divide std
"""
def __init__(self, scale=None, mean=None, std=None, order='chw', **kwargs):
if isinstance(scale, str):
scale = eval(scale)
self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
mean = mean if mean is not None else [0.485, 0.456, 0.406]
std = std if std is not None else [0.229, 0.224, 0.225]
shape = (3, 1, 1) if order == 'chw' else (1, 1, 3)
self.mean = np.array(mean).reshape(shape).astype('float32')
self.std = np.array(std).reshape(shape).astype('float32')
def __call__(self, data):
img = data['image']
from PIL import Image
if isinstance(img, Image.Image):
img = np.array(img)
assert isinstance(img,
np.ndarray), "invalid input 'img' in NormalizeImage"
data['image'] = (
img.astype('float32') * self.scale - self.mean) / self.std
return data
class ToCHWImage(object):
""" convert hwc image to chw image
"""
def __init__(self, **kwargs):
pass
def __call__(self, data):
img = data['image']
from PIL import Image
if isinstance(img, Image.Image):
img = np.array(img)
data['image'] = img.transpose((2, 0, 1))
src_img = data['src_image']
from PIL import Image
if isinstance(img, Image.Image):
src_img = np.array(src_img)
data['src_image'] = img.transpose((2, 0, 1))
return data
class SimpleDataset(nn.Dataset):
def __init__(self, config, mode, logger, seed=None):
self.logger = logger
self.mode = mode.lower()
data_dir = config['Train']['data_dir']
imgs_list = self.get_image_list(data_dir)
self.ops = create_operators(cfg['transforms'], None)
def get_image_list(self, img_dir):
imgs = glob.glob(os.path.join(img_dir, "*.png"))
if len(imgs) == 0:
raise ValueError(f"not any images founded in {img_dir}")
return imgs
def __getitem__(self, idx):
return None
test_tipc/supplementary/data_loader.py 0 → 100644
View file @ 9fa209e3
import numpy as np
from paddle.vision.datasets import Cifar100
from paddle.vision.transforms import Normalize
from paddle.fluid.dataloader.collate import default_collate_fn
import signal
import os
from paddle.io import Dataset, DataLoader, DistributedBatchSampler
def term_mp(sig_num, frame):
""" kill all child processes
"""
pid = os.getpid()
pgid = os.getpgid(os.getpid())
print("main proc {} exit, kill process group " "{}".format(pid, pgid))
os.killpg(pgid, signal.SIGKILL)
return
def build_dataloader(mode,
batch_size=4,
seed=None,
num_workers=0,
device='gpu:0'):
normalize = Normalize(
mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], data_format='HWC')
if mode.lower() == "train":
dataset = Cifar100(mode=mode, transform=normalize)
elif mode.lower() in ["test", 'valid', 'eval']:
dataset = Cifar100(mode="test", transform=normalize)
else:
raise ValueError(f"{mode} should be one of ['train', 'test']")
# define batch sampler
batch_sampler = DistributedBatchSampler(
dataset=dataset, batch_size=batch_size, shuffle=False, drop_last=True)
data_loader = DataLoader(
dataset=dataset,
batch_sampler=batch_sampler,
places=device,
num_workers=num_workers,
return_list=True,
use_shared_memory=False)
# support exit using ctrl+c
signal.signal(signal.SIGINT, term_mp)
signal.signal(signal.SIGTERM, term_mp)
return data_loader
# cifar100 = Cifar100(mode='train', transform=normalize)
# data = cifar100[0]
# image, label = data
# reader = build_dataloader('train')
# for idx, data in enumerate(reader):
# print(idx, data[0].shape, data[1].shape)
# if idx >= 10:
# break
test_tipc/supplementary/load_cifar.py 0 → 100644
View file @ 9fa209e3
import pickle as p
import numpy as np
from PIL import Image
def load_CIFAR_batch(filename):
""" load single batch of cifar """
with open(filename, 'rb') as f:
datadict = p.load(f, encoding='bytes')
# 以字典的形式取出数据
X = datadict[b'data']
Y = datadict[b'fine_labels']
try:
X = X.reshape(10000, 3, 32, 32)
except:
X = X.reshape(50000, 3, 32, 32)
Y = np.array(Y)
print(Y.shape)
return X, Y
if __name__ == "__main__":
mode = "train"
imgX, imgY = load_CIFAR_batch(f"./cifar-100-python/{mode}")
with open(f'./cifar-100-python/{mode}_imgs/img_label.txt', 'a+') as f:
for i in range(imgY.shape[0]):
f.write('img' + str(i) + ' ' + str(imgY[i]) + '\n')
for i in range(imgX.shape[0]):
imgs = imgX[i]
img0 = imgs[0]
img1 = imgs[1]
img2 = imgs[2]
i0 = Image.fromarray(img0)
i1 = Image.fromarray(img1)
i2 = Image.fromarray(img2)
img = Image.merge("RGB", (i0, i1, i2))
name = "img" + str(i) + ".png"
img.save(f"./cifar-100-python/{mode}_imgs/" + name, "png")
print("save successfully!")
test_tipc/supplementary/loss.py 0 → 100644
View file @ 9fa209e3
import paddle
import paddle.nn.functional as F
class Loss(object):
"""
Loss
"""
def __init__(self, class_dim=1000, epsilon=None):
assert class_dim > 1, "class_dim=%d is not larger than 1" % (class_dim)
self._class_dim = class_dim
if epsilon is not None and epsilon >= 0.0 and epsilon <= 1.0:
self._epsilon = epsilon
self._label_smoothing = True
else:
self._epsilon = None
self._label_smoothing = False
def _labelsmoothing(self, target):
if target.shape[-1] != self._class_dim:
one_hot_target = F.one_hot(target, self._class_dim)
else:
one_hot_target = target
soft_target = F.label_smooth(one_hot_target, epsilon=self._epsilon)
soft_target = paddle.reshape(soft_target, shape=[-1, self._class_dim])
return soft_target
def _crossentropy(self, input, target, use_pure_fp16=False):
if self._label_smoothing:
target = self._labelsmoothing(target)
input = -F.log_softmax(input, axis=-1)
cost = paddle.sum(target * input, axis=-1)
else:
cost = F.cross_entropy(input=input, label=target)
if use_pure_fp16:
avg_cost = paddle.sum(cost)
else:
avg_cost = paddle.mean(cost)
return avg_cost
def __call__(self, input, target):
return self._crossentropy(input, target)
def build_loss(config, epsilon=None):
class_dim = config['class_dim']
loss_func = Loss(class_dim=class_dim, epsilon=epsilon)
return loss_func
class LossDistill(Loss):
def __init__(self, model_name_list, class_dim=1000, epsilon=None):
assert class_dim > 1, "class_dim=%d is not larger than 1" % (class_dim)
self._class_dim = class_dim
if epsilon is not None and epsilon >= 0.0 and epsilon <= 1.0:
self._epsilon = epsilon
self._label_smoothing = True
else:
self._epsilon = None
self._label_smoothing = False
self.model_name_list = model_name_list
assert len(self.model_name_list) > 1, "error"
def __call__(self, input, target):
losses = {}
for k in self.model_name_list:
inp = input[k]
losses[k] = self._crossentropy(inp, target)
return losses
class KLJSLoss(object):
def __init__(self, mode='kl'):
assert mode in ['kl', 'js', 'KL', 'JS'
], "mode can only be one of ['kl', 'js', 'KL', 'JS']"
self.mode = mode
def __call__(self, p1, p2, reduction="mean"):
p1 = F.softmax(p1, axis=-1)
p2 = F.softmax(p2, axis=-1)
loss = paddle.multiply(p2, paddle.log((p2 + 1e-5) / (p1 + 1e-5) + 1e-5))
if self.mode.lower() == "js":
loss += paddle.multiply(
p1, paddle.log((p1 + 1e-5) / (p2 + 1e-5) + 1e-5))
loss *= 0.5
if reduction == "mean":
loss = paddle.mean(loss)
elif reduction == "none" or reduction is None:
return loss
else:
loss = paddle.sum(loss)
return loss
class DMLLoss(object):
def __init__(self, model_name_pairs, mode='js'):
self.model_name_pairs = self._check_model_name_pairs(model_name_pairs)
self.kljs_loss = KLJSLoss(mode=mode)
def _check_model_name_pairs(self, model_name_pairs):
if not isinstance(model_name_pairs, list):
return []
elif isinstance(model_name_pairs[0], list) and isinstance(
model_name_pairs[0][0], str):
return model_name_pairs
else:
return [model_name_pairs]
def __call__(self, predicts, target=None):
loss_dict = dict()
for pairs in self.model_name_pairs:
p1 = predicts[pairs[0]]
p2 = predicts[pairs[1]]
loss_dict[pairs[0] + "_" + pairs[1]] = self.kljs_loss(p1, p2)
return loss_dict
# def build_distill_loss(config, epsilon=None):
# class_dim = config['class_dim']
# loss = LossDistill(model_name_list=['student', 'student1'], )
# return loss_func
test_tipc/supplementary/metric.py 0 → 100644
View file @ 9fa209e3
import paddle
import paddle.nn.functional as F
from collections import OrderedDict
def create_metric(out,
label,
architecture=None,
topk=5,
classes_num=1000,
use_distillation=False,
mode="train"):
"""
Create measures of model accuracy, such as top1 and top5
Args:
out(variable): model output variable
feeds(dict): dict of model input variables(included label)
topk(int): usually top5
classes_num(int): num of classes
use_distillation(bool): whether to use distillation training
mode(str): mode, train/valid
Returns:
fetchs(dict): dict of measures
"""
# if architecture["name"] == "GoogLeNet":
# assert len(out) == 3, "GoogLeNet should have 3 outputs"
# out = out[0]
# else:
# # just need student label to get metrics
# if use_distillation:
# out = out[1]
softmax_out = F.softmax(out)
fetchs = OrderedDict()
# set top1 to fetchs
top1 = paddle.metric.accuracy(softmax_out, label=label, k=1)
# set topk to fetchs
k = min(topk, classes_num)
topk = paddle.metric.accuracy(softmax_out, label=label, k=k)
# multi cards' eval
if mode != "train" and paddle.distributed.get_world_size() > 1:
top1 = paddle.distributed.all_reduce(
top1, op=paddle.distributed.ReduceOp.
SUM) / paddle.distributed.get_world_size()
topk = paddle.distributed.all_reduce(
topk, op=paddle.distributed.ReduceOp.
SUM) / paddle.distributed.get_world_size()
fetchs['top1'] = top1
topk_name = 'top{}'.format(k)
fetchs[topk_name] = topk
return fetchs
test_tipc/supplementary/mv3.py 0 → 100644
View file @ 9fa209e3
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn.functional import hardswish, hardsigmoid
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.regularizer import L2Decay
import math
from paddle.utils.cpp_extension import load
# jit compile custom op
custom_ops = load(
name="custom_jit_ops",
sources=["./custom_op/custom_relu_op.cc", "./custom_op/custom_relu_op.cu"])
def make_divisible(v, divisor=8, min_value=None):
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
if new_v < 0.9 * v:
new_v += divisor
return new_v
class MobileNetV3(nn.Layer):
def __init__(self,
scale=1.0,
model_name="small",
dropout_prob=0.2,
class_dim=1000,
use_custom_relu=False):
super(MobileNetV3, self).__init__()
self.use_custom_relu = use_custom_relu
inplanes = 16
if model_name == "large":
self.cfg = [
# k, exp, c, se, nl, s,
[3, 16, 16, False, "relu", 1],
[3, 64, 24, False, "relu", 2],
[3, 72, 24, False, "relu", 1],
[5, 72, 40, True, "relu", 2],
[5, 120, 40, True, "relu", 1],
[5, 120, 40, True, "relu", 1],
[3, 240, 80, False, "hardswish", 2],
[3, 200, 80, False, "hardswish", 1],
[3, 184, 80, False, "hardswish", 1],
[3, 184, 80, False, "hardswish", 1],
[3, 480, 112, True, "hardswish", 1],
[3, 672, 112, True, "hardswish", 1],
[5, 672, 160, True, "hardswish", 2],
[5, 960, 160, True, "hardswish", 1],
[5, 960, 160, True, "hardswish", 1],
]
self.cls_ch_squeeze = 960
self.cls_ch_expand = 1280
elif model_name == "small":
self.cfg = [
# k, exp, c, se, nl, s,
[3, 16, 16, True, "relu", 2],
[3, 72, 24, False, "relu", 2],
[3, 88, 24, False, "relu", 1],
[5, 96, 40, True, "hardswish", 2],
[5, 240, 40, True, "hardswish", 1],
[5, 240, 40, True, "hardswish", 1],
[5, 120, 48, True, "hardswish", 1],
[5, 144, 48, True, "hardswish", 1],
[5, 288, 96, True, "hardswish", 2],
[5, 576, 96, True, "hardswish", 1],
[5, 576, 96, True, "hardswish", 1],
]
self.cls_ch_squeeze = 576
self.cls_ch_expand = 1280
else:
raise NotImplementedError(
"mode[{}_model] is not implemented!".format(model_name))
self.conv1 = ConvBNLayer(
in_c=3,
out_c=make_divisible(inplanes * scale),
filter_size=3,
stride=2,
padding=1,
num_groups=1,
if_act=True,
act="hardswish",
name="conv1",
use_custom_relu=self.use_custom_relu)
self.block_list = []
i = 0
inplanes = make_divisible(inplanes * scale)
for (k, exp, c, se, nl, s) in self.cfg:
block = self.add_sublayer(
"conv" + str(i + 2),
ResidualUnit(
in_c=inplanes,
mid_c=make_divisible(scale * exp),
out_c=make_divisible(scale * c),
filter_size=k,
stride=s,
use_se=se,
act=nl,
name="conv" + str(i + 2),
use_custom_relu=self.use_custom_relu))
self.block_list.append(block)
inplanes = make_divisible(scale * c)
i += 1
self.last_second_conv = ConvBNLayer(
in_c=inplanes,
out_c=make_divisible(scale * self.cls_ch_squeeze),
filter_size=1,
stride=1,
padding=0,
num_groups=1,
if_act=True,
act="hardswish",
name="conv_last",
use_custom_relu=self.use_custom_relu)
self.pool = AdaptiveAvgPool2D(1)
self.last_conv = Conv2D(
in_channels=make_divisible(scale * self.cls_ch_squeeze),
out_channels=self.cls_ch_expand,
kernel_size=1,
stride=1,
padding=0,
weight_attr=ParamAttr(),
bias_attr=False)
self.dropout = Dropout(p=dropout_prob, mode="downscale_in_infer")
self.out = Linear(
self.cls_ch_expand,
class_dim,
weight_attr=ParamAttr(),
bias_attr=ParamAttr())
def forward(self, inputs, label=None):
x = self.conv1(inputs)
for block in self.block_list:
x = block(x)
x = self.last_second_conv(x)
x = self.pool(x)
x = self.last_conv(x)
x = hardswish(x)
x = self.dropout(x)
x = paddle.flatten(x, start_axis=1, stop_axis=-1)
x = self.out(x)
return x
class ConvBNLayer(nn.Layer):
def __init__(self,
in_c,
out_c,
filter_size,
stride,
padding,
num_groups=1,
if_act=True,
act=None,
use_cudnn=True,
name="",
use_custom_relu=False):
super(ConvBNLayer, self).__init__()
self.if_act = if_act
self.act = act
self.conv = Conv2D(
in_channels=in_c,
out_channels=out_c,
kernel_size=filter_size,
stride=stride,
padding=padding,
groups=num_groups,
weight_attr=ParamAttr(),
bias_attr=False)
self.bn = BatchNorm(
num_channels=out_c,
act=None,
param_attr=ParamAttr(regularizer=L2Decay(0.0)),
bias_attr=ParamAttr(regularizer=L2Decay(0.0)))
# moving_mean_name=name + "_bn_mean",
# moving_variance_name=name + "_bn_variance")
self.use_custom_relu = use_custom_relu
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
if self.if_act:
if self.act == "relu":
if self.use_custom_relu:
x = custom_ops.custom_relu(x)
else:
x = F.relu(x)
elif self.act == "hardswish":
x = hardswish(x)
else:
print("The activation function is selected incorrectly.")
exit()
return x
class ResidualUnit(nn.Layer):
def __init__(self,
in_c,
mid_c,
out_c,
filter_size,
stride,
use_se,
act=None,
name='',
use_custom_relu=False):
super(ResidualUnit, self).__init__()
self.if_shortcut = stride == 1 and in_c == out_c
self.if_se = use_se
self.use_custom_relu = use_custom_relu
self.expand_conv = ConvBNLayer(
in_c=in_c,
out_c=mid_c,
filter_size=1,
stride=1,
padding=0,
if_act=True,
act=act,
name=name + "_expand",
use_custom_relu=self.use_custom_relu)
self.bottleneck_conv = ConvBNLayer(
in_c=mid_c,
out_c=mid_c,
filter_size=filter_size,
stride=stride,
padding=int((filter_size - 1) // 2),
num_groups=mid_c,
if_act=True,
act=act,
name=name + "_depthwise",
use_custom_relu=self.use_custom_relu)
if self.if_se:
self.mid_se = SEModule(mid_c, name=name + "_se")
self.linear_conv = ConvBNLayer(
in_c=mid_c,
out_c=out_c,
filter_size=1,
stride=1,
padding=0,
if_act=False,
act=None,
name=name + "_linear",
use_custom_relu=self.use_custom_relu)
def forward(self, inputs):
x = self.expand_conv(inputs)
x = self.bottleneck_conv(x)
if self.if_se:
x = self.mid_se(x)
x = self.linear_conv(x)
if self.if_shortcut:
x = paddle.add(inputs, x)
return x
class SEModule(nn.Layer):
def __init__(self, channel, reduction=4, name=""):
super(SEModule, self).__init__()
self.avg_pool = AdaptiveAvgPool2D(1)
self.conv1 = Conv2D(
in_channels=channel,
out_channels=channel // reduction,
kernel_size=1,
stride=1,
padding=0,
weight_attr=ParamAttr(),
bias_attr=ParamAttr())
self.conv2 = Conv2D(
in_channels=channel // reduction,
out_channels=channel,
kernel_size=1,
stride=1,
padding=0,
weight_attr=ParamAttr(),
bias_attr=ParamAttr())
def forward(self, inputs):
outputs = self.avg_pool(inputs)
outputs = self.conv1(outputs)
outputs = F.relu(outputs)
outputs = self.conv2(outputs)
outputs = hardsigmoid(outputs, slope=0.2, offset=0.5)
return paddle.multiply(x=inputs, y=outputs)
def MobileNetV3_small_x0_35(**args):
model = MobileNetV3(model_name="small", scale=0.35, **args)
return model
def MobileNetV3_small_x0_5(**args):
model = MobileNetV3(model_name="small", scale=0.5, **args)
return model
def MobileNetV3_small_x0_75(**args):
model = MobileNetV3(model_name="small", scale=0.75, **args)
return model
def MobileNetV3_small_x1_0(**args):
model = MobileNetV3(model_name="small", scale=1.0, **args)
return model
def MobileNetV3_small_x1_25(**args):
model = MobileNetV3(model_name="small", scale=1.25, **args)
return model
def MobileNetV3_large_x0_35(**args):
model = MobileNetV3(model_name="large", scale=0.35, **args)
return model
def MobileNetV3_large_x0_5(**args):
model = MobileNetV3(model_name="large", scale=0.5, **args)
return model
def MobileNetV3_large_x0_75(**args):
model = MobileNetV3(model_name="large", scale=0.75, **args)
return model
def MobileNetV3_large_x1_0(**args):
model = MobileNetV3(model_name="large", scale=1.0, **args)
return model
def MobileNetV3_large_x1_25(**args):
model = MobileNetV3(model_name="large", scale=1.25, **args)
return
class DistillMV3(nn.Layer):
def __init__(self,
scale=1.0,
model_name="small",
dropout_prob=0.2,
class_dim=1000,
args=None,
use_custom_relu=False):
super(DistillMV3, self).__init__()
self.student = MobileNetV3(
model_name=model_name,
scale=scale,
class_dim=class_dim,
use_custom_relu=use_custom_relu)
self.student1 = MobileNetV3(
model_name=model_name,
scale=scale,
class_dim=class_dim,
use_custom_relu=use_custom_relu)
def forward(self, inputs, label=None):
predicts = dict()
predicts['student'] = self.student(inputs, label)
predicts['student1'] = self.student1(inputs, label)
return predicts
def distillmv3_large_x0_5(**args):
model = DistillMV3(model_name="large", scale=0.5, **args)
return model
class SiameseMV3(nn.Layer):
def __init__(self,
scale=1.0,
model_name="small",
dropout_prob=0.2,
class_dim=1000,
args=None,
use_custom_relu=False):
super(SiameseMV3, self).__init__()
self.net = MobileNetV3(
model_name=model_name,
scale=scale,
class_dim=class_dim,
use_custom_relu=use_custom_relu)
self.net1 = MobileNetV3(
model_name=model_name,
scale=scale,
class_dim=class_dim,
use_custom_relu=use_custom_relu)
def forward(self, inputs, label=None):
# net
x = self.net.conv1(inputs)
for block in self.net.block_list:
x = block(x)
# net1
x1 = self.net1.conv1(inputs)
for block in self.net1.block_list:
x1 = block(x1)
# add
x = x + x1
x = self.net.last_second_conv(x)
x = self.net.pool(x)
x = self.net.last_conv(x)
x = hardswish(x)
x = self.net.dropout(x)
x = paddle.flatten(x, start_axis=1, stop_axis=-1)
x = self.net.out(x)
return x
def siamese_mv3(class_dim, use_custom_relu):
model = SiameseMV3(
scale=0.5,
model_name="large",
class_dim=class_dim,
use_custom_relu=use_custom_relu)
return model
def build_model(config):
model_type = config['model_type']
if model_type == "cls":
class_dim = config['MODEL']['class_dim']
use_custom_relu = config['MODEL']['use_custom_relu']
if 'siamese' in config['MODEL'] and config['MODEL']['siamese'] is True:
model = siamese_mv3(
class_dim=class_dim, use_custom_relu=use_custom_relu)
else:
model = MobileNetV3_large_x0_5(
class_dim=class_dim, use_custom_relu=use_custom_relu)
elif model_type == "cls_distill":
class_dim = config['MODEL']['class_dim']
use_custom_relu = config['MODEL']['use_custom_relu']
model = distillmv3_large_x0_5(
class_dim=class_dim, use_custom_relu=use_custom_relu)
elif model_type == "cls_distill_multiopt":
class_dim = config['MODEL']['class_dim']
use_custom_relu = config['MODEL']['use_custom_relu']
model = distillmv3_large_x0_5(
class_dim=100, use_custom_relu=use_custom_relu)
else:
raise ValueError("model_type should be one of ['']")
return model
test_tipc/supplementary/mv3_distill.yml 0 → 100644
View file @ 9fa209e3
class_dim: 100
total_images: 50000
epochs: 1000
topk: 5
save_model_dir: ./output/
use_gpu: True
model_type: cls_distill
LEARNING_RATE:
function: 'Cosine'
params:
lr: 0.001
warmup_epoch: 5
OPTIMIZER:
function: 'Momentum'
params:
momentum: 0.9
regularizer:
function: 'L2'
factor: 0.00002
TRAIN:
batch_size: 1280
num_workers: 4
VALID:
batch_size: 64
num_workers: 4
test_tipc/supplementary/mv3_large_x0_5.yml 0 → 100644
View file @ 9fa209e3
class_dim: 100
total_images: 50000
epoch: 1000
topk: 5
save_model_dir: ./output/
use_gpu: True
model_type: cls
use_custom_relu: false
pretrained_model:
checkpoints:
save_model_dir: ./output/cls/
# slim
quant_train: false
prune_train: false
MODEL:
class_dim: 100
use_custom_relu: False
siamese: False
AMP:
use_amp: False
scale_loss: 1024.0
use_dynamic_loss_scale: True
LEARNING_RATE:
function: 'Cosine'
params:
lr: 0.001
warmup_epoch: 5
OPTIMIZER:
function: 'Momentum'
params:
momentum: 0.9
regularizer:
function: 'L2'
factor: 0.00002
TRAIN:
batch_size: 1280
num_workers: 4
VALID:
batch_size: 64
num_workers: 4
test_tipc/supplementary/optimizer.py 0 → 100644
View file @ 9fa209e3
import sys
import math
from paddle.optimizer.lr import LinearWarmup
from paddle.optimizer.lr import PiecewiseDecay
from paddle.optimizer.lr import CosineAnnealingDecay
from paddle.optimizer.lr import ExponentialDecay
import paddle
import paddle.regularizer as regularizer
from copy import deepcopy
class Cosine(CosineAnnealingDecay):
"""
Cosine learning rate decay
lr = 0.05 * (math.cos(epoch * (math.pi / epochs)) + 1)
Args:
lr(float): initial learning rate
step_each_epoch(int): steps each epoch
epochs(int): total training epochs
"""
def __init__(self, lr, step_each_epoch, epochs, **kwargs):
super(Cosine, self).__init__(
learning_rate=lr,
T_max=step_each_epoch * epochs, )
self.update_specified = False
class Piecewise(PiecewiseDecay):
"""
Piecewise learning rate decay
Args:
lr(float): initial learning rate
step_each_epoch(int): steps each epoch
decay_epochs(list): piecewise decay epochs
gamma(float): decay factor
"""
def __init__(self, lr, step_each_epoch, decay_epochs, gamma=0.1, **kwargs):
boundaries = [step_each_epoch * e for e in decay_epochs]
lr_values = [lr * (gamma**i) for i in range(len(boundaries) + 1)]
super(Piecewise, self).__init__(boundaries=boundaries, values=lr_values)
self.update_specified = False
class CosineWarmup(LinearWarmup):
"""
Cosine learning rate decay with warmup
[0, warmup_epoch): linear warmup
[warmup_epoch, epochs): cosine decay
Args:
lr(float): initial learning rate
step_each_epoch(int): steps each epoch
epochs(int): total training epochs
warmup_epoch(int): epoch num of warmup
"""
def __init__(self, lr, step_each_epoch, epochs, warmup_epoch=5, **kwargs):
assert epochs > warmup_epoch, "total epoch({}) should be larger than warmup_epoch({}) in CosineWarmup.".format(
epochs, warmup_epoch)
warmup_step = warmup_epoch * step_each_epoch
start_lr = 0.0
end_lr = lr
lr_sch = Cosine(lr, step_each_epoch, epochs - warmup_epoch)
super(CosineWarmup, self).__init__(
learning_rate=lr_sch,
warmup_steps=warmup_step,
start_lr=start_lr,
end_lr=end_lr)
self.update_specified = False
class ExponentialWarmup(LinearWarmup):
"""
Exponential learning rate decay with warmup
[0, warmup_epoch): linear warmup
[warmup_epoch, epochs): Exponential decay
Args:
lr(float): initial learning rate
step_each_epoch(int): steps each epoch
decay_epochs(float): decay epochs
decay_rate(float): decay rate
warmup_epoch(int): epoch num of warmup
"""
def __init__(self,
lr,
step_each_epoch,
decay_epochs=2.4,
decay_rate=0.97,
warmup_epoch=5,
**kwargs):
warmup_step = warmup_epoch * step_each_epoch
start_lr = 0.0
end_lr = lr
lr_sch = ExponentialDecay(lr, decay_rate)
super(ExponentialWarmup, self).__init__(
learning_rate=lr_sch,
warmup_steps=warmup_step,
start_lr=start_lr,
end_lr=end_lr)
# NOTE: hac method to update exponential lr scheduler
self.update_specified = True
self.update_start_step = warmup_step
self.update_step_interval = int(decay_epochs * step_each_epoch)
self.step_each_epoch = step_each_epoch
class LearningRateBuilder():
"""
Build learning rate variable
https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/layers_cn.html
Args:
function(str): class name of learning rate
params(dict): parameters used for init the class
"""
def __init__(self,
function='Linear',
params={'lr': 0.1,
'steps': 100,
'end_lr': 0.0}):
self.function = function
self.params = params
def __call__(self):
mod = sys.modules[__name__]
lr = getattr(mod, self.function)(**self.params)
return lr
class L1Decay(object):
"""
L1 Weight Decay Regularization, which encourages the weights to be sparse.
Args:
factor(float): regularization coeff. Default:0.0.
"""
def __init__(self, factor=0.0):
super(L1Decay, self).__init__()
self.factor = factor
def __call__(self):
reg = regularizer.L1Decay(self.factor)
return reg
class L2Decay(object):
"""
L2 Weight Decay Regularization, which encourages the weights to be sparse.
Args:
factor(float): regularization coeff. Default:0.0.
"""
def __init__(self, factor=0.0):
super(L2Decay, self).__init__()
self.factor = factor
def __call__(self):
reg = regularizer.L2Decay(self.factor)
return reg
class Momentum(object):
"""
Simple Momentum optimizer with velocity state.
Args:
learning_rate (float|Variable) - The learning rate used to update parameters.
Can be a float value or a Variable with one float value as data element.
momentum (float) - Momentum factor.
regularization (WeightDecayRegularizer, optional) - The strategy of regularization.
"""
def __init__(self,
learning_rate,
momentum,
parameter_list=None,
regularization=None,
**args):
super(Momentum, self).__init__()
self.learning_rate = learning_rate
self.momentum = momentum
self.parameter_list = parameter_list
self.regularization = regularization
def __call__(self):
opt = paddle.optimizer.Momentum(
learning_rate=self.learning_rate,
momentum=self.momentum,
parameters=self.parameter_list,
weight_decay=self.regularization)
return opt
class RMSProp(object):
"""
Root Mean Squared Propagation (RMSProp) is an unpublished, adaptive learning rate method.
Args:
learning_rate (float|Variable) - The learning rate used to update parameters.
Can be a float value or a Variable with one float value as data element.
momentum (float) - Momentum factor.
rho (float) - rho value in equation.
epsilon (float) - avoid division by zero, default is 1e-6.
regularization (WeightDecayRegularizer, optional) - The strategy of regularization.
"""
def __init__(self,
learning_rate,
momentum,
rho=0.95,
epsilon=1e-6,
parameter_list=None,
regularization=None,
**args):
super(RMSProp, self).__init__()
self.learning_rate = learning_rate
self.momentum = momentum
self.rho = rho
self.epsilon = epsilon
self.parameter_list = parameter_list
self.regularization = regularization
def __call__(self):
opt = paddle.optimizer.RMSProp(
learning_rate=self.learning_rate,
momentum=self.momentum,
rho=self.rho,
epsilon=self.epsilon,
parameters=self.parameter_list,
weight_decay=self.regularization)
return opt
class OptimizerBuilder(object):
"""
Build optimizer
Args:
function(str): optimizer name of learning rate
params(dict): parameters used for init the class
regularizer (dict): parameters used for create regularization
"""
def __init__(self,
function='Momentum',
params={'momentum': 0.9},
regularizer=None):
self.function = function
self.params = params
# create regularizer
if regularizer is not None:
mod = sys.modules[__name__]
reg_func = regularizer['function'] + 'Decay'
del regularizer['function']
reg = getattr(mod, reg_func)(**regularizer)()
self.params['regularization'] = reg
def __call__(self, learning_rate, parameter_list=None):
mod = sys.modules[__name__]
opt = getattr(mod, self.function)
return opt(learning_rate=learning_rate,
parameter_list=parameter_list,
**self.params)()
def create_optimizer(config, parameter_list=None):
"""
Create an optimizer using config, usually including
learning rate and regularization.
Args:
config(dict): such as
{
'LEARNING_RATE':
{'function': 'Cosine',
'params': {'lr': 0.1}
},
'OPTIMIZER':
{'function': 'Momentum',
'params':{'momentum': 0.9},
'regularizer':
{'function': 'L2', 'factor': 0.0001}
}
}
Returns:
an optimizer instance
"""
# create learning_rate instance
lr_config = config['LEARNING_RATE']
lr_config['params'].update({
'epochs': config['epoch'],
'step_each_epoch':
config['total_images'] // config['TRAIN']['batch_size'],
})
lr = LearningRateBuilder(**lr_config)()
# create optimizer instance
opt_config = deepcopy(config['OPTIMIZER'])
opt = OptimizerBuilder(**opt_config)
return opt(lr, parameter_list), lr
def create_multi_optimizer(config, parameter_list=None):
"""
"""
# create learning_rate instance
lr_config = config['LEARNING_RATE']
lr_config['params'].update({
'epochs': config['epoch'],
'step_each_epoch':
config['total_images'] // config['TRAIN']['batch_size'],
})
lr = LearningRateBuilder(**lr_config)()
# create optimizer instance
opt_config = deepcopy.copy(config['OPTIMIZER'])
opt = OptimizerBuilder(**opt_config)
return opt(lr, parameter_list), lr
test_tipc/supplementary/readme.md 0 → 100644
View file @ 9fa209e3
# TIPC Linux端补充训练功能测试
Linux端基础训练预测功能测试的主程序为test_train_python.sh，可以测试基于Python的模型训练、评估等基本功能，包括裁剪、量化、蒸馏训练。
![](./tipc_train.png)
测试链条如上图所示，主要测试内容有带共享权重，自定义OP的模型的正常训练和slim相关功能训练流程是否正常。
# 2. 测试流程
本节介绍补充链条的测试流程
## 2.1 安装依赖
- 安装PaddlePaddle >= 2.2
- 安装其他依赖
```
pip3 install -r requirements.txt
```
## 2.2 功能测试
`test_train_python.sh`包含2种运行模式，每种模式的运行数据不同，分别用于测试训练是否正常，分别是：
- 模式1：lite_train_lite_infer，使用少量数据训练，用于快速验证训练到预测的走通流程，不验证精度和速度；
```
bash test_tipc/test_train_python.sh ./test_tipc/ch_ppocr_mobile_v2.0_det/train_infer_python.txt 'lite_train_lite_infer'
```
- 模式2：whole_train_whole_infer，使用全量数据训练，用于快速验证训练到预测的走通流程，验证模型最终训练精度；
```
bash test_tipc/test_train_python.sh ./test_tipc/ch_ppocr_mobile_v2.0_det/train_infer_python.txt 'whole_train_whole_infer'
```
如果是运行量化裁剪等训练方式，需要使用不同的配置文件。量化训练的测试指令如下：
```
bash test_tipc/test_train_python.sh ./test_tipc/ch_ppocr_mobile_v2.0_det/train_infer_python_PACT.txt 'lite_train_lite_infer'
```
同理，FPGM裁剪的运行方式如下：
```
bash test_tipc/test_train_python.sh ./test_tipc/ch_ppocr_mobile_v2.0_det/train_infer_python_FPGM.txt 'lite_train_lite_infer'
```
运行相应指令后，在`test_tipc/output`文件夹下自动会保存运行日志。如'lite_train_lite_infer'模式运行后，在test_tipc/extra_output文件夹有以下文件：
```
test_tipc/output/
|- results_python.log # 运行指令状态的日志
```
其中results_python.log中包含了每条指令的运行状态，如果运行成功会输出：
```
Run successfully with command - python3.7 train.py -c mv3_large_x0_5.yml -o use_gpu=True epoch=20 AMP.use_amp=True TRAIN.batch_size=1280 use_custom_relu=False model_type=cls MODEL.siamese=False !
Run successfully with command - python3.7 train.py -c mv3_large_x0_5.yml -o use_gpu=True epoch=2 AMP.use_amp=True TRAIN.batch_size=1280 use_custom_relu=False model_type=cls MODEL.siamese=False !
Run successfully with command - python3.7 train.py -c mv3_large_x0_5.yml -o use_gpu=True epoch=2 AMP.use_amp=True TRAIN.batch_size=1280 use_custom_relu=False model_type=cls MODEL.siamese=True !
Run successfully with command - python3.7 train.py -c mv3_large_x0_5.yml -o use_gpu=True epoch=2 AMP.use_amp=True TRAIN.batch_size=1280 use_custom_relu=False model_type=cls_distill MODEL.siamese=False !
Run successfully with command - python3.7 train.py -c mv3_large_x0_5.yml -o use_gpu=True epoch=2 AMP.use_amp=True TRAIN.batch_size=1280 use_custom_relu=False model_type=cls_distill MODEL.siamese=True !
Run successfully with command - python3.7 train.py -c mv3_large_x0_5.yml -o use_gpu=True epoch=2 AMP.use_amp=True TRAIN.batch_size=1280 use_custom_relu=False model_type=cls_distill_multiopt MODEL.siamese=False !
```
test_tipc/supplementary/slim/slim_fpgm.py 0 → 100644
View file @ 9fa209e3
import paddleslim
import paddle
import numpy as np
from paddleslim.dygraph import FPGMFilterPruner
def prune_model(model, input_shape, prune_ratio=0.1):
flops = paddle.flops(model, input_shape)
pruner = FPGMFilterPruner(model, input_shape)
params_sensitive = {}
for param in model.parameters():
if 'transpose' not in param.name and 'linear' not in param.name:
# set prune ratio as 10%. The larger the value, the more convolution weights will be cropped
params_sensitive[param.name] = prune_ratio
plan = pruner.prune_vars(params_sensitive, [0])
flops = paddle.flops(model, input_shape)
return model
test_tipc/supplementary/slim/slim_quant.py 0 → 100644
View file @ 9fa209e3
import paddle
import numpy as np
import os
import paddle.nn as nn
import paddleslim
class PACT(paddle.nn.Layer):
def __init__(self):
super(PACT, self).__init__()
alpha_attr = paddle.ParamAttr(
name=self.full_name() + ".pact",
initializer=paddle.nn.initializer.Constant(value=20),
learning_rate=1.0,
regularizer=paddle.regularizer.L2Decay(2e-5))
self.alpha = self.create_parameter(
shape=[1], attr=alpha_attr, dtype='float32')
def forward(self, x):
out_left = paddle.nn.functional.relu(x - self.alpha)
out_right = paddle.nn.functional.relu(-self.alpha - x)
x = x - out_left + out_right
return x
quant_config = {
# weight preprocess type, default is None and no preprocessing is performed.
'weight_preprocess_type': None,
# activation preprocess type, default is None and no preprocessing is performed.
'activation_preprocess_type': None,
# weight quantize type, default is 'channel_wise_abs_max'
'weight_quantize_type': 'channel_wise_abs_max',
# activation quantize type, default is 'moving_average_abs_max'
'activation_quantize_type': 'moving_average_abs_max',
# weight quantize bit num, default is 8
'weight_bits': 8,
# activation quantize bit num, default is 8
'activation_bits': 8,
# data type after quantization, such as 'uint8', 'int8', etc. default is 'int8'
'dtype': 'int8',
# window size for 'range_abs_max' quantization. default is 10000
'window_size': 10000,
# The decay coefficient of moving average, default is 0.9
'moving_rate': 0.9,
# for dygraph quantization, layers of type in quantizable_layer_type will be quantized
'quantizable_layer_type': ['Conv2D', 'Linear'],
}
test_tipc/supplementary/test_tipc/common_func.sh 0 → 100644
View file @ 9fa209e3
#!/bin/bash
function func_parser_key(){
strs=$1
IFS=":"
array=(${strs})
tmp=${array[0]}
echo ${tmp}
}
function func_parser_value(){
strs=$1
IFS=":"
array=(${strs})
tmp=${array[1]}
echo ${tmp}
}
function func_set_params(){
key=$1
value=$2
if [ ${key}x = "null"x ];then
echo " "
elif [[ ${value} = "null" ]] || [[ ${value} = " " ]] || [ ${#value} -le 0 ];then
echo " "
else
echo "${key}=${value}"
fi
}
function func_parser_params(){
strs=$1
MODE=$2
IFS=":"
array=(${strs})
key=${array[0]}
tmp=${array[1]}
IFS="|"
res=""
for _params in ${tmp[*]}; do
IFS="="
array=(${_params})
mode=${array[0]}
value=${array[1]}
if [[ ${mode} = ${MODE} ]]; then
IFS="|"
#echo $(func_set_params "${mode}" "${value}")
echo $value
break
fi
IFS="|"
done
echo ${res}
}
function status_check(){
last_status=$1 # the exit code
run_command=$2
run_log=$3
if [ $last_status -eq 0 ]; then
echo -e "\033[33m Run successfully with command - ${run_command}! \033[0m" | tee -a ${run_log}
else
echo -e "\033[33m Run failed with command - ${run_command}! \033[0m" | tee -a ${run_log}
fi
}
\ No newline at end of file
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