From 11f6ff38dcc61348aa4aae8ad2fbbe42b0eab34d Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Thu, 13 Jan 2022 19:26:08 +0800 Subject: [PATCH] add supplementary --- test_tipc/supplementary/__init__.py | 1 + test_tipc/supplementary/config.py | 137 +++++ .../supplementary/custom_op/custom_relu_op.cc | 109 ++++ .../supplementary/custom_op/custom_relu_op.cu | 76 +++ test_tipc/supplementary/custom_op/test.py | 76 +++ test_tipc/supplementary/data.py | 140 +++++ test_tipc/supplementary/data_loader.py | 66 +++ test_tipc/supplementary/load_cifar.py | 40 ++ test_tipc/supplementary/loss.py | 128 +++++ test_tipc/supplementary/metric.py | 56 ++ test_tipc/supplementary/mv3.py | 487 ++++++++++++++++++ test_tipc/supplementary/mv3_distill.yml | 31 ++ test_tipc/supplementary/mv3_large_x0_5.yml | 49 ++ test_tipc/supplementary/optimizer.py | 325 ++++++++++++ test_tipc/supplementary/readme.md | 67 +++ test_tipc/supplementary/requirements.txt | 1 + test_tipc/supplementary/slim/__init__.py | 0 test_tipc/supplementary/slim/slim_fpgm.py | 22 + test_tipc/supplementary/slim/slim_quant.py | 48 ++ .../supplementary/test_tipc/common_func.sh | 65 +++ .../test_tipc/test_train_python.sh | 117 +++++ .../supplementary/test_tipc/tipc_train.png | Bin 0 -> 1068127 bytes .../test_tipc/train_infer_python.txt | 17 + .../test_tipc/train_infer_python_FPGM.txt | 17 + .../test_tipc/train_infer_python_PACT.txt | 17 + test_tipc/supplementary/train.py | 474 +++++++++++++++++ test_tipc/supplementary/train.sh | 5 + test_tipc/supplementary/utils.py | 164 ++++++ 28 files changed, 2735 insertions(+) create mode 100644 test_tipc/supplementary/__init__.py create mode 100644 test_tipc/supplementary/config.py create mode 100644 test_tipc/supplementary/custom_op/custom_relu_op.cc create mode 100644 test_tipc/supplementary/custom_op/custom_relu_op.cu create mode 100644 test_tipc/supplementary/custom_op/test.py create mode 100644 test_tipc/supplementary/data.py create mode 100644 test_tipc/supplementary/data_loader.py create mode 100644 test_tipc/supplementary/load_cifar.py create mode 100644 test_tipc/supplementary/loss.py create mode 100644 test_tipc/supplementary/metric.py create mode 100644 test_tipc/supplementary/mv3.py create mode 100644 test_tipc/supplementary/mv3_distill.yml create mode 100644 test_tipc/supplementary/mv3_large_x0_5.yml create mode 100644 test_tipc/supplementary/optimizer.py create mode 100644 test_tipc/supplementary/readme.md create mode 100644 test_tipc/supplementary/requirements.txt create mode 100644 test_tipc/supplementary/slim/__init__.py create mode 100644 test_tipc/supplementary/slim/slim_fpgm.py create mode 100644 test_tipc/supplementary/slim/slim_quant.py create mode 100644 test_tipc/supplementary/test_tipc/common_func.sh create mode 100644 test_tipc/supplementary/test_tipc/test_train_python.sh create mode 100644 test_tipc/supplementary/test_tipc/tipc_train.png create mode 100644 test_tipc/supplementary/test_tipc/train_infer_python.txt create mode 100644 test_tipc/supplementary/test_tipc/train_infer_python_FPGM.txt create mode 100644 test_tipc/supplementary/test_tipc/train_infer_python_PACT.txt create mode 100644 test_tipc/supplementary/train.py create mode 100644 test_tipc/supplementary/train.sh create mode 100644 test_tipc/supplementary/utils.py diff --git a/test_tipc/supplementary/__init__.py b/test_tipc/supplementary/__init__.py new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/test_tipc/supplementary/__init__.py @@ -0,0 +1 @@ + diff --git a/test_tipc/supplementary/config.py b/test_tipc/supplementary/config.py new file mode 100644 index 00000000..d0dce227 --- /dev/null +++ b/test_tipc/supplementary/config.py @@ -0,0 +1,137 @@ +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) diff --git a/test_tipc/supplementary/custom_op/custom_relu_op.cc b/test_tipc/supplementary/custom_op/custom_relu_op.cc new file mode 100644 index 00000000..97002a91 --- /dev/null +++ b/test_tipc/supplementary/custom_op/custom_relu_op.cc @@ -0,0 +1,109 @@ +// 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 +#include + +#include "paddle/extension.h" + +template +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(0.), x_data[i]); + } +} + +template +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(0) ? 1. : 0.); + } +} + +std::vector 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( + x.data(), out.mutable_data(x.place()), x.size()); + })); + + return {out}; +} + +std::vector 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( + grad_out.data(), + out.data(), + grad_x.mutable_data(x.place()), + out.size()); + })); + + return {grad_x}; +} + +std::vector relu_cuda_forward(const paddle::Tensor& x); +std::vector relu_cuda_backward(const paddle::Tensor& x, + const paddle::Tensor& out, + const paddle::Tensor& grad_out); + +std::vector 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 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 diff --git a/test_tipc/supplementary/custom_op/custom_relu_op.cu b/test_tipc/supplementary/custom_op/custom_relu_op.cu new file mode 100644 index 00000000..9b953a33 --- /dev/null +++ b/test_tipc/supplementary/custom_op/custom_relu_op.cu @@ -0,0 +1,76 @@ +// 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 +__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(0.)); + } +} + +template +__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 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<<>>( + x.data(), out.mutable_data(x.place()), numel); + })); + + return {out}; +} + +std::vector 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<<>>( + grad_out.data(), + out.data(), + grad_x.mutable_data(x.place()), + numel); + })); + + return {grad_x}; +} diff --git a/test_tipc/supplementary/custom_op/test.py b/test_tipc/supplementary/custom_op/test.py new file mode 100644 index 00000000..8b7f303d --- /dev/null +++ b/test_tipc/supplementary/custom_op/test.py @@ -0,0 +1,76 @@ +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() diff --git a/test_tipc/supplementary/data.py b/test_tipc/supplementary/data.py new file mode 100644 index 00000000..2770a9a4 --- /dev/null +++ b/test_tipc/supplementary/data.py @@ -0,0 +1,140 @@ +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 diff --git a/test_tipc/supplementary/data_loader.py b/test_tipc/supplementary/data_loader.py new file mode 100644 index 00000000..049e7b2d --- /dev/null +++ b/test_tipc/supplementary/data_loader.py @@ -0,0 +1,66 @@ +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 diff --git a/test_tipc/supplementary/load_cifar.py b/test_tipc/supplementary/load_cifar.py new file mode 100644 index 00000000..6646dca3 --- /dev/null +++ b/test_tipc/supplementary/load_cifar.py @@ -0,0 +1,40 @@ +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!") diff --git a/test_tipc/supplementary/loss.py b/test_tipc/supplementary/loss.py new file mode 100644 index 00000000..8cb1cd49 --- /dev/null +++ b/test_tipc/supplementary/loss.py @@ -0,0 +1,128 @@ +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 diff --git a/test_tipc/supplementary/metric.py b/test_tipc/supplementary/metric.py new file mode 100644 index 00000000..401cf9b9 --- /dev/null +++ b/test_tipc/supplementary/metric.py @@ -0,0 +1,56 @@ +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 diff --git a/test_tipc/supplementary/mv3.py b/test_tipc/supplementary/mv3.py new file mode 100644 index 00000000..9ffcedac --- /dev/null +++ b/test_tipc/supplementary/mv3.py @@ -0,0 +1,487 @@ +# 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 diff --git a/test_tipc/supplementary/mv3_distill.yml b/test_tipc/supplementary/mv3_distill.yml new file mode 100644 index 00000000..887b1eb1 --- /dev/null +++ b/test_tipc/supplementary/mv3_distill.yml @@ -0,0 +1,31 @@ + +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 + diff --git a/test_tipc/supplementary/mv3_large_x0_5.yml b/test_tipc/supplementary/mv3_large_x0_5.yml new file mode 100644 index 00000000..531c2f0f --- /dev/null +++ b/test_tipc/supplementary/mv3_large_x0_5.yml @@ -0,0 +1,49 @@ + +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 + diff --git a/test_tipc/supplementary/optimizer.py b/test_tipc/supplementary/optimizer.py new file mode 100644 index 00000000..aaa01534 --- /dev/null +++ b/test_tipc/supplementary/optimizer.py @@ -0,0 +1,325 @@ +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 diff --git a/test_tipc/supplementary/readme.md b/test_tipc/supplementary/readme.md new file mode 100644 index 00000000..0d35f945 --- /dev/null +++ b/test_tipc/supplementary/readme.md @@ -0,0 +1,67 @@ + +# 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 ! + +``` diff --git a/test_tipc/supplementary/requirements.txt b/test_tipc/supplementary/requirements.txt new file mode 100644 index 00000000..c55500a7 --- /dev/null +++ b/test_tipc/supplementary/requirements.txt @@ -0,0 +1 @@ +paddleslim==2.2.1 diff --git a/test_tipc/supplementary/slim/__init__.py b/test_tipc/supplementary/slim/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/test_tipc/supplementary/slim/slim_fpgm.py b/test_tipc/supplementary/slim/slim_fpgm.py new file mode 100644 index 00000000..0e762159 --- /dev/null +++ b/test_tipc/supplementary/slim/slim_fpgm.py @@ -0,0 +1,22 @@ +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 diff --git a/test_tipc/supplementary/slim/slim_quant.py b/test_tipc/supplementary/slim/slim_quant.py new file mode 100644 index 00000000..7c201bf5 --- /dev/null +++ b/test_tipc/supplementary/slim/slim_quant.py @@ -0,0 +1,48 @@ +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'], +} diff --git a/test_tipc/supplementary/test_tipc/common_func.sh b/test_tipc/supplementary/test_tipc/common_func.sh new file mode 100644 index 00000000..e2ff5c4d --- /dev/null +++ b/test_tipc/supplementary/test_tipc/common_func.sh @@ -0,0 +1,65 @@ +#!/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 diff --git a/test_tipc/supplementary/test_tipc/test_train_python.sh b/test_tipc/supplementary/test_tipc/test_train_python.sh new file mode 100644 index 00000000..f922b57b --- /dev/null +++ b/test_tipc/supplementary/test_tipc/test_train_python.sh @@ -0,0 +1,117 @@ +#!/bin/bash +source test_tipc/common_func.sh + +FILENAME=$1 +# MODE be one of ['lite_train_lite_infer' 'lite_train_whole_infer'] +MODE=$2 + +dataline=$(awk 'NR==1, NR==51{print}' $FILENAME) + +# parser params +IFS=$'\n' +lines=(${dataline}) + +model_name=$(func_parser_value "${lines[1]}") +python=$(func_parser_value "${lines[2]}") +gpu_list=$(func_parser_value "${lines[3]}") +train_use_gpu_key=$(func_parser_key "${lines[4]}") +train_use_gpu_value=$(func_parser_value "${lines[4]}") +autocast_list=$(func_parser_value "${lines[5]}") +autocast_key=$(func_parser_key "${lines[5]}") +epoch_key=$(func_parser_key "${lines[6]}") +epoch_num=$(func_parser_params "${lines[6]}" "${MODE}") +save_model_key=$(func_parser_key "${lines[7]}") +train_batch_key=$(func_parser_key "${lines[8]}") +train_batch_value=$(func_parser_params "${lines[8]}" "${MODE}") +pretrain_model_key=$(func_parser_key "${lines[9]}") +pretrain_model_value=$(func_parser_value "${lines[9]}") +checkpoints_key=$(func_parser_key "${lines[10]}") +checkpoints_value=$(func_parser_value "${lines[10]}") +use_custom_key=$(func_parser_key "${lines[11]}") +use_custom_list=$(func_parser_value "${lines[11]}") +model_type_key=$(func_parser_key "${lines[12]}") +model_type_list=$(func_parser_value "${lines[12]}") +use_share_conv_key=$(func_parser_key "${lines[13]}") +use_share_conv_list=$(func_parser_value "${lines[13]}") +run_train_py=$(func_parser_value "${lines[14]}") + + +LOG_PATH="./test_tipc/extra_output" +mkdir -p ${LOG_PATH} +status_log="${LOG_PATH}/results_python.log" + +if [ ${MODE} = "lite_train_lite_infer" ] || [ ${MODE} = "whole_train_whole_infer" ]; then + IFS="|" + export Count=0 + USE_GPU_KEY=(${train_use_gpu_value}) + # select cpu\gpu\distribute training + for gpu in ${gpu_list[*]}; do + train_use_gpu=${USE_GPU_KEY[Count]} + Count=$(($Count + 1)) + ips="" + if [ ${gpu} = "-1" ];then + env="" + elif [ ${#gpu} -le 1 ];then + env="export CUDA_VISIBLE_DEVICES=${gpu}" + eval ${env} + elif [ ${#gpu} -le 15 ];then + IFS="," + array=(${gpu}) + env="export CUDA_VISIBLE_DEVICES=${array[0]}" + IFS="|" + else + IFS=";" + array=(${gpu}) + ips=${array[0]} + gpu=${array[1]} + IFS="|" + env=" " + fi + for autocast in ${autocast_list[*]}; do + # set amp + if [ ${autocast} = "amp" ]; then + set_amp_config="AMP.use_amp=True" + else + set_amp_config=" " + fi + + if [ ${run_train_py} = "null" ]; then + continue + fi + + set_autocast=$(func_set_params "${autocast_key}" "${autocast}") + set_epoch=$(func_set_params "${epoch_key}" "${epoch_num}") + set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}") + set_checkpoints=$(func_set_params "${checkpoints_key}" "${checkpoints_value}") + set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_value}") + set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${train_use_gpu}") + + for custom_op in ${use_custom_list[*]}; do + for model_type in ${model_type_list[*]}; do + for share_conv in ${use_share_conv_list[*]}; do + set_use_custom_op=$(func_set_params "${use_custom_key}" "${custom_op}") + set_model_type=$(func_set_params "${model_type_key}" "${model_type}") + set_use_share_conv=$(func_set_params "${use_share_conv_key}" "${share_conv}") + + set_save_model=$(func_set_params "${save_model_key}" "${save_log}") + if [ ${#gpu} -le 2 ];then # train with cpu or single gpu + cmd="${python} ${run_train_py} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_checkpoints} ${set_autocast} ${set_batchsize} ${set_use_custom_op} ${set_model_type} ${set_use_share_conv} ${set_amp_config}" + elif [ ${#ips} -le 26 ];then # train with multi-gpu + cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train_py} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_checkpoints} ${set_autocast} ${set_batchsize} ${set_use_custom_op} ${set_model_type} ${set_use_share_conv} ${set_amp_config}" + fi + + # run train + eval "unset CUDA_VISIBLE_DEVICES" + # echo $cmd + eval $cmd + status_check $? 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z^k61p)W`+nhFCfR$C4^=E2*7uDsMx)w4TL7jcaA@n}NVv&s`W?>yLaO_TCDE&;S2T cX6G&5mMcRWbN@YIyt_;7<=YpPD&|4|2XG0> 0: + pre_str = 'The metric of loaded metric as follows {}'.format(', '.join( + ['{}: {}'.format(k, v) for k, v in pre_best_model_dict.items()])) + logger.info(pre_str) + + # about slim prune and quant + if "quant_train" in config and config['quant_train'] is True: + quanter = QAT(config=quant_config, act_preprocess=PACT) + quanter.quantize(model) + elif "prune_train" in config and config['prune_train'] is True: + model = prune_model(model, [1, 3, 32, 32], 0.1) + else: + pass + + # distribution + model.train() + model = paddle.DataParallel(model) + # build loss function + loss_func = build_loss(config) + + data_num = len(train_loader) + + best_acc = {} + for epoch in range(EPOCH): + st = time.time() + for idx, data in enumerate(train_loader): + img_batch, label = data + img_batch = paddle.transpose(img_batch, [0, 3, 1, 2]) + label = paddle.unsqueeze(label, -1) + + if scaler is not None: + with paddle.amp.auto_cast(): + outs = model(img_batch) + else: + outs = model(img_batch) + + # cal metric + acc = metric_func(outs, label) + + # cal loss + avg_loss = loss_func(outs, label) + + if scaler is None: + # backward + avg_loss.backward() + optimizer.step() + optimizer.clear_grad() + else: + scaled_avg_loss = scaler.scale(avg_loss) + scaled_avg_loss.backward() + scaler.minimize(optimizer, scaled_avg_loss) + + if not isinstance(lr_scheduler, float): + lr_scheduler.step() + + if idx % 10 == 0: + et = time.time() + strs = f"epoch: [{epoch}/{EPOCH}], iter: [{idx}/{data_num}], " + strs += f"loss: {avg_loss.numpy()[0]}" + strs += f", acc_topk1: {acc['top1'].numpy()[0]}, acc_top5: {acc['top5'].numpy()[0]}" + strs += f", batch_time: {round(et-st, 4)} s" + logger.info(strs) + st = time.time() + + if epoch % 10 == 0: + acc = eval(config, model) + if len(best_acc) < 1 or acc['top5'].numpy()[0] > best_acc['top5']: + best_acc = acc + best_acc['epoch'] = epoch + is_best = True + else: + is_best = False + logger.info( + f"The best acc: acc_topk1: {best_acc['top1'].numpy()[0]}, acc_top5: {best_acc['top5'].numpy()[0]}, best_epoch: {best_acc['epoch']}" + ) + save_model( + model, + optimizer, + config['save_model_dir'], + logger, + is_best, + prefix="cls") + + +def train_distill(config, scaler=None): + EPOCH = config['epoch'] + topk = config['topk'] + + batch_size = config['TRAIN']['batch_size'] + num_workers = config['TRAIN']['num_workers'] + train_loader = build_dataloader( + 'train', batch_size=batch_size, num_workers=num_workers) + + # build metric + metric_func = create_metric + + # model = distillmv3_large_x0_5(class_dim=100) + model = build_model(config) + + # pact quant train + if "quant_train" in config and config['quant_train'] is True: + quanter = QAT(config=quant_config, act_preprocess=PACT) + quanter.quantize(model) + elif "prune_train" in config and config['prune_train'] is True: + model = prune_model(model, [1, 3, 32, 32], 0.1) + else: + pass + + # build_optimizer + optimizer, lr_scheduler = create_optimizer( + config, parameter_list=model.parameters()) + + # load model + pre_best_model_dict = load_model(config, model, optimizer) + if len(pre_best_model_dict) > 0: + pre_str = 'The metric of loaded metric as follows {}'.format(', '.join( + ['{}: {}'.format(k, v) for k, v in pre_best_model_dict.items()])) + logger.info(pre_str) + + model.train() + model = paddle.DataParallel(model) + + # build loss function + loss_func_distill = LossDistill(model_name_list=['student', 'student1']) + loss_func_dml = DMLLoss(model_name_pairs=['student', 'student1']) + loss_func_js = KLJSLoss(mode='js') + + data_num = len(train_loader) + + best_acc = {} + for epoch in range(EPOCH): + st = time.time() + for idx, data in enumerate(train_loader): + img_batch, label = data + img_batch = paddle.transpose(img_batch, [0, 3, 1, 2]) + label = paddle.unsqueeze(label, -1) + if scaler is not None: + with paddle.amp.auto_cast(): + outs = model(img_batch) + else: + outs = model(img_batch) + + # cal metric + acc = metric_func(outs['student'], label) + + # cal loss + avg_loss = loss_func_distill(outs, label)['student'] + \ + loss_func_distill(outs, label)['student1'] + \ + loss_func_dml(outs, label)['student_student1'] + + # backward + if scaler is None: + avg_loss.backward() + optimizer.step() + optimizer.clear_grad() + else: + scaled_avg_loss = scaler.scale(avg_loss) + scaled_avg_loss.backward() + scaler.minimize(optimizer, scaled_avg_loss) + + if not isinstance(lr_scheduler, float): + lr_scheduler.step() + + if idx % 10 == 0: + et = time.time() + strs = f"epoch: [{epoch}/{EPOCH}], iter: [{idx}/{data_num}], " + strs += f"loss: {avg_loss.numpy()[0]}" + strs += f", acc_topk1: {acc['top1'].numpy()[0]}, acc_top5: {acc['top5'].numpy()[0]}" + strs += f", batch_time: {round(et-st, 4)} s" + logger.info(strs) + st = time.time() + + if epoch % 10 == 0: + acc = eval(config, model._layers.student) + if len(best_acc) < 1 or acc['top5'].numpy()[0] > best_acc['top5']: + best_acc = acc + best_acc['epoch'] = epoch + is_best = True + else: + is_best = False + logger.info( + f"The best acc: acc_topk1: {best_acc['top1'].numpy()[0]}, acc_top5: {best_acc['top5'].numpy()[0]}, best_epoch: {best_acc['epoch']}" + ) + + save_model( + model, + optimizer, + config['save_model_dir'], + logger, + is_best, + prefix="cls_distill") + + +def train_distill_multiopt(config, scaler=None): + EPOCH = config['epoch'] + topk = config['topk'] + + batch_size = config['TRAIN']['batch_size'] + num_workers = config['TRAIN']['num_workers'] + train_loader = build_dataloader( + 'train', batch_size=batch_size, num_workers=num_workers) + + # build metric + metric_func = create_metric + + # model = distillmv3_large_x0_5(class_dim=100) + model = build_model(config) + + # build_optimizer + optimizer, lr_scheduler = create_optimizer( + config, parameter_list=model.student.parameters()) + optimizer1, lr_scheduler1 = create_optimizer( + config, parameter_list=model.student1.parameters()) + + # load model + pre_best_model_dict = load_model(config, model, optimizer) + if len(pre_best_model_dict) > 0: + pre_str = 'The metric of loaded metric as follows {}'.format(', '.join( + ['{}: {}'.format(k, v) for k, v in pre_best_model_dict.items()])) + logger.info(pre_str) + + # quant train + if "quant_train" in config and config['quant_train'] is True: + quanter = QAT(config=quant_config, act_preprocess=PACT) + quanter.quantize(model) + elif "prune_train" in config and config['prune_train'] is True: + model = prune_model(model, [1, 3, 32, 32], 0.1) + else: + pass + + model.train() + + model = paddle.DataParallel(model) + + # build loss function + loss_func_distill = LossDistill(model_name_list=['student', 'student1']) + loss_func_dml = DMLLoss(model_name_pairs=['student', 'student1']) + loss_func_js = KLJSLoss(mode='js') + + data_num = len(train_loader) + best_acc = {} + for epoch in range(EPOCH): + st = time.time() + for idx, data in enumerate(train_loader): + img_batch, label = data + img_batch = paddle.transpose(img_batch, [0, 3, 1, 2]) + label = paddle.unsqueeze(label, -1) + + if scaler is not None: + with paddle.amp.auto_cast(): + outs = model(img_batch) + else: + outs = model(img_batch) + + # cal metric + acc = metric_func(outs['student'], label) + + # cal loss + avg_loss = loss_func_distill(outs, + label)['student'] + loss_func_dml( + outs, label)['student_student1'] + avg_loss1 = loss_func_distill(outs, + label)['student1'] + loss_func_dml( + outs, label)['student_student1'] + + if scaler is None: + # backward + avg_loss.backward(retain_graph=True) + optimizer.step() + optimizer.clear_grad() + + avg_loss1.backward() + optimizer1.step() + optimizer1.clear_grad() + else: + scaled_avg_loss = scaler.scale(avg_loss) + scaled_avg_loss.backward() + scaler.minimize(optimizer, scaled_avg_loss) + + scaled_avg_loss = scaler.scale(avg_loss1) + scaled_avg_loss.backward() + scaler.minimize(optimizer1, scaled_avg_loss) + + if not isinstance(lr_scheduler, float): + lr_scheduler.step() + if not isinstance(lr_scheduler1, float): + lr_scheduler1.step() + + if idx % 10 == 0: + et = time.time() + strs = f"epoch: [{epoch}/{EPOCH}], iter: [{idx}/{data_num}], " + strs += f"loss: {avg_loss.numpy()[0]}, loss1: {avg_loss1.numpy()[0]}" + strs += f", acc_topk1: {acc['top1'].numpy()[0]}, acc_top5: {acc['top5'].numpy()[0]}" + strs += f", batch_time: {round(et-st, 4)} s" + logger.info(strs) + st = time.time() + + if epoch % 10 == 0: + acc = eval(config, model._layers.student) + if len(best_acc) < 1 or acc['top5'].numpy()[0] > best_acc['top5']: + best_acc = acc + best_acc['epoch'] = epoch + is_best = True + else: + is_best = False + logger.info( + f"The best acc: acc_topk1: {best_acc['top1'].numpy()[0]}, acc_top5: {best_acc['top5'].numpy()[0]}, best_epoch: {best_acc['epoch']}" + ) + save_model( + model, [optimizer, optimizer1], + config['save_model_dir'], + logger, + is_best, + prefix="cls_distill_multiopt") + + +def eval(config, model): + batch_size = config['VALID']['batch_size'] + num_workers = config['VALID']['num_workers'] + valid_loader = build_dataloader( + 'test', batch_size=batch_size, num_workers=num_workers) + + # build metric + metric_func = create_metric + + outs = [] + labels = [] + for idx, data in enumerate(valid_loader): + img_batch, label = data + img_batch = paddle.transpose(img_batch, [0, 3, 1, 2]) + label = paddle.unsqueeze(label, -1) + out = model(img_batch) + + outs.append(out) + labels.append(label) + + outs = paddle.concat(outs, axis=0) + labels = paddle.concat(labels, axis=0) + acc = metric_func(outs, labels) + + strs = f"The metric are as follows: acc_topk1: {acc['top1'].numpy()[0]}, acc_top5: {acc['top5'].numpy()[0]}" + logger.info(strs) + return acc + + +if __name__ == "__main__": + + config, logger = preprocess(is_train=False) + + # AMP scaler + scaler = amp_scaler(config) + + model_type = config['model_type'] + + if model_type == "cls": + train(config) + elif model_type == "cls_distill": + train_distill(config) + elif model_type == "cls_distill_multiopt": + train_distill_multiopt(config) + else: + raise ValueError("model_type should be one of ['']") diff --git a/test_tipc/supplementary/train.sh b/test_tipc/supplementary/train.sh new file mode 100644 index 00000000..a2c7c90c --- /dev/null +++ b/test_tipc/supplementary/train.sh @@ -0,0 +1,5 @@ +# single GPU +python3.7 train.py -c mv3_large_x0_5.yml + +# distribute training +python3.7 -m paddle.distributed.launch --log_dir=./debug/ --gpus '0,1' train.py -c mv3_large_x0_5.yml diff --git a/test_tipc/supplementary/utils.py b/test_tipc/supplementary/utils.py new file mode 100644 index 00000000..ae9ae061 --- /dev/null +++ b/test_tipc/supplementary/utils.py @@ -0,0 +1,164 @@ +# 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. + +import os +import sys +import logging +import functools +import paddle.distributed as dist + +logger_initialized = {} + + +def print_dict(d, logger, delimiter=0): + """ + Recursively visualize a dict and + indenting acrrording by the relationship of keys. + """ + for k, v in sorted(d.items()): + if isinstance(v, dict): + logger.info("{}{} : ".format(delimiter * " ", str(k))) + print_dict(v, logger, delimiter + 4) + elif isinstance(v, list) and len(v) >= 1 and isinstance(v[0], dict): + logger.info("{}{} : ".format(delimiter * " ", str(k))) + for value in v: + print_dict(value, logger, delimiter + 4) + else: + logger.info("{}{} : {}".format(delimiter * " ", k, v)) + + +@functools.lru_cache() +def get_logger(name='root', log_file=None, log_level=logging.DEBUG): + """Initialize and get a logger by name. + If the logger has not been initialized, this method will initialize the + logger by adding one or two handlers, otherwise the initialized logger will + be directly returned. During initialization, a StreamHandler will always be + added. If `log_file` is specified a FileHandler will also be added. + Args: + name (str): Logger name. + log_file (str | None): The log filename. If specified, a FileHandler + will be added to the logger. + log_level (int): The logger level. Note that only the process of + rank 0 is affected, and other processes will set the level to + "Error" thus be silent most of the time. + Returns: + logging.Logger: The expected logger. + """ + logger = logging.getLogger(name) + if name in logger_initialized: + return logger + for logger_name in logger_initialized: + if name.startswith(logger_name): + return logger + + formatter = logging.Formatter( + '[%(asctime)s] %(name)s %(levelname)s: %(message)s', + datefmt="%Y/%m/%d %H:%M:%S") + + stream_handler = logging.StreamHandler(stream=sys.stdout) + stream_handler.setFormatter(formatter) + logger.addHandler(stream_handler) + if log_file is not None and dist.get_rank() == 0: + log_file_folder = os.path.split(log_file)[0] + os.makedirs(log_file_folder, exist_ok=True) + file_handler = logging.FileHandler(log_file, 'a') + file_handler.setFormatter(formatter) + logger.addHandler(file_handler) + if dist.get_rank() == 0: + logger.setLevel(log_level) + else: + logger.setLevel(logging.ERROR) + logger_initialized[name] = True + return logger + + +def load_model(config, model, optimizer=None): + """ + load model from checkpoint or pretrained_model + """ + logger = get_logger() + checkpoints = config.get('checkpoints') + pretrained_model = config.get('pretrained_model') + best_model_dict = {} + if checkpoints: + if checkpoints.endswith('.pdparams'): + checkpoints = checkpoints.replace('.pdparams', '') + assert os.path.exists(checkpoints + ".pdparams"), \ + "The {}.pdparams does not exists!".format(checkpoints) + + # load params from trained model + params = paddle.load(checkpoints + '.pdparams') + state_dict = model.state_dict() + new_state_dict = {} + for key, value in state_dict.items(): + if key not in params: + logger.warning("{} not in loaded params {} !".format( + key, params.keys())) + continue + pre_value = params[key] + if list(value.shape) == list(pre_value.shape): + new_state_dict[key] = pre_value + else: + logger.warning( + "The shape of model params {} {} not matched with loaded params shape {} !". + format(key, value.shape, pre_value.shape)) + model.set_state_dict(new_state_dict) + + if optimizer is not None: + if os.path.exists(checkpoints + '.pdopt'): + optim_dict = paddle.load(checkpoints + '.pdopt') + optimizer.set_state_dict(optim_dict) + else: + logger.warning( + "{}.pdopt is not exists, params of optimizer is not loaded". + format(checkpoints)) + + if os.path.exists(checkpoints + '.states'): + with open(checkpoints + '.states', 'rb') as f: + states_dict = pickle.load(f) if six.PY2 else pickle.load( + f, encoding='latin1') + best_model_dict = states_dict.get('best_model_dict', {}) + if 'epoch' in states_dict: + best_model_dict['start_epoch'] = states_dict['epoch'] + 1 + logger.info("resume from {}".format(checkpoints)) + elif pretrained_model: + load_pretrained_params(model, pretrained_model) + else: + logger.info('train from scratch') + return best_model_dict + + +def load_pretrained_params(model, path): + logger = get_logger() + if path.endswith('.pdparams'): + path = path.replace('.pdparams', '') + assert os.path.exists(path + ".pdparams"), \ + "The {}.pdparams does not exists!".format(path) + + params = paddle.load(path + '.pdparams') + state_dict = model.state_dict() + new_state_dict = {} + for k1 in params.keys(): + if k1 not in state_dict.keys(): + logger.warning("The pretrained params {} not in model".format(k1)) + else: + if list(state_dict[k1].shape) == list(params[k1].shape): + new_state_dict[k1] = params[k1] + else: + logger.warning( + "The shape of model params {} {} not matched with loaded params {} {} !". + format(k1, state_dict[k1].shape, k1, params[k1].shape)) + model.set_state_dict(new_state_dict) + logger.info("load pretrain successful from {}".format(path)) + return model -- GitLab