# copyright (c) 2019 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 os import numpy as np import time import functools import paddle import paddle.fluid as fluid import argparse import subprocess import sys sys.path.append('../..') sys.path.append('.') import imagenet_reader as reader import models from utility import add_arguments, print_arguments from utility import save_persistable_nodes, load_persistable_nodes import quant parser = argparse.ArgumentParser(description=__doc__) add_arg = functools.partial(add_arguments, argparser=parser) # yapf: disable add_arg('batch_size', int, 256, "Minibatch size.") add_arg('use_gpu', bool, True, "Whether to use GPU or not.") add_arg('total_images', int, 1281167, "Training image number.") add_arg('num_epochs', int, 120, "number of epochs.") add_arg('class_dim', int, 1000, "Class number.") add_arg('image_shape', str, "3,224,224", "input image size") add_arg('model_save_dir', str, "output", "model save directory") add_arg('pretrained_fp32_model', str, None, "Whether to use the pretrained float32 model to initialize the weights.") add_arg('checkpoint', str, None, "Whether to resume the training process from the checkpoint.") add_arg('lr', float, 0.1, "set learning rate.") add_arg('lr_strategy', str, "piecewise_decay", "Set the learning rate decay strategy.") add_arg('model', str, "SE_ResNeXt50_32x4d", "Set the network to use.") add_arg('data_dir', str, "./data/ILSVRC2012", "The ImageNet dataset root dir.") add_arg('act_quant_type', str, "abs_max", "quantization type for activation, valid value:'abs_max','range_abs_max', 'moving_average_abs_max'" ) add_arg('wt_quant_type', str, "abs_max", "quantization type for weight, valid value:'abs_max','channel_wise_abs_max'" ) # yapf: enabl def optimizer_setting(params): ls = params["learning_strategy"] if ls["name"] == "piecewise_decay": if "total_images" not in params: total_images = 1281167 else: total_images = params["total_images"] batch_size = ls["batch_size"] step = int(total_images / batch_size + 1) bd = [step * e for e in ls["epochs"]] print("decay list:{}".format(bd)) base_lr = params["lr"] lr = [] lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)] optimizer = fluid.optimizer.Momentum( learning_rate=fluid.layers.piecewise_decay( boundaries=bd, values=lr), momentum=0.9, regularization=fluid.regularizer.L2Decay(1e-4)) elif ls["name"] == "cosine_decay": if "total_images" not in params: total_images = 1281167 else: total_images = params["total_images"] batch_size = ls["batch_size"] step = int(total_images / batch_size + 1) lr = params["lr"] num_epochs = params["num_epochs"] optimizer = fluid.optimizer.Momentum( learning_rate=fluid.layers.cosine_decay( learning_rate=lr, step_each_epoch=step, epochs=num_epochs), momentum=0.9, regularization=fluid.regularizer.L2Decay(4e-5)) elif ls["name"] == "exponential_decay": if "total_images" not in params: total_images = 1281167 else: total_images = params["total_images"] batch_size = ls["batch_size"] step = int(total_images / batch_size +1) lr = params["lr"] num_epochs = params["num_epochs"] learning_decay_rate_factor=ls["learning_decay_rate_factor"] num_epochs_per_decay = ls["num_epochs_per_decay"] NUM_GPUS = 1 optimizer = fluid.optimizer.Momentum( learning_rate=fluid.layers.exponential_decay( learning_rate = lr * NUM_GPUS, decay_steps = step * num_epochs_per_decay / NUM_GPUS, decay_rate = learning_decay_rate_factor), momentum=0.9, regularization = fluid.regularizer.L2Decay(4e-5)) else: lr = params["lr"] optimizer = fluid.optimizer.Momentum( learning_rate=lr, momentum=0.9, regularization=fluid.regularizer.L2Decay(1e-4)) return optimizer def net_config(image, label, model, args): model_list = [m for m in dir(models) if "__" not in m] assert args.model in model_list,"{} is not lists: {}".format( args.model, model_list) class_dim = args.class_dim model_name = args.model if model_name == "GoogleNet": out0, out1, out2 = model.net(input=image, class_dim=class_dim) cost0 = fluid.layers.cross_entropy(input=out0, label=label) cost1 = fluid.layers.cross_entropy(input=out1, label=label) cost2 = fluid.layers.cross_entropy(input=out2, label=label) avg_cost0 = fluid.layers.mean(x=cost0) avg_cost1 = fluid.layers.mean(x=cost1) avg_cost2 = fluid.layers.mean(x=cost2) avg_cost = avg_cost0 + 0.3 * avg_cost1 + 0.3 * avg_cost2 acc_top1 = fluid.layers.accuracy(input=out0, label=label, k=1) acc_top5 = fluid.layers.accuracy(input=out0, label=label, k=5) out = out0 else: out = model.net(input=image, class_dim=class_dim) cost = fluid.layers.cross_entropy(input=out, label=label) avg_cost = fluid.layers.mean(x=cost) acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1) acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5) return out, avg_cost, acc_top1, acc_top5 def build_program(is_train, main_prog, startup_prog, args): image_shape = [int(m) for m in args.image_shape.split(",")] model_name = args.model model_list = [m for m in dir(models) if "__" not in m] assert model_name in model_list, "{} is not in lists: {}".format(args.model, model_list) model = models.__dict__[model_name]() with fluid.program_guard(main_prog, startup_prog): py_reader = fluid.layers.py_reader( capacity=16, shapes=[[-1] + image_shape, [-1, 1]], lod_levels=[0, 0], dtypes=["float32", "int64"], use_double_buffer=True) with fluid.unique_name.guard(): image, label = fluid.layers.read_file(py_reader) out, avg_cost, acc_top1, acc_top5 = net_config(image, label, model, args) avg_cost.persistable = True acc_top1.persistable = True acc_top5.persistable = True if is_train: params = model.params params["total_images"] = args.total_images params["lr"] = args.lr params["num_epochs"] = args.num_epochs params["learning_strategy"]["batch_size"] = args.batch_size params["learning_strategy"]["name"] = args.lr_strategy optimizer = optimizer_setting(params) optimizer.minimize(avg_cost) global_lr = optimizer._global_learning_rate() if is_train: return image, out, py_reader, avg_cost, acc_top1, acc_top5, global_lr else: return image, out, py_reader, avg_cost, acc_top1, acc_top5 def train(args): ############################################################################################################ # 1. quantization configs ############################################################################################################ quant_config = { # weight quantize type, default is 'abs_max' 'weight_quantize_type': 'abs_max', # activation quantize type, default is 'abs_max' 'activation_quantize_type': 'abs_max', # weight quantize bit num, default is 8 'weight_bits': 8, # activation quantize bit num, default is 8 'activation_bits': 8, # op of name_scope in not_quant_pattern list, will not quantized 'not_quant_pattern': ['skip_quant'], # op of types in quantize_op_types, will quantized 'quantize_op_types': ['conv2d', 'depthwise_conv2d', 'mul'], # data type after quantization, default is 'int8' 'dtype': 'int8', # window size for 'range_abs_max' quantization. defaulf is 10000 'window_size': 10000, # The decay coefficient of moving average, default is 0.9 'moving_rate': 0.9, # if set quant_weight_only True, then only quantize parameters of layers which need quantization, # and insert anti-quantization op for parameters of these layers. 'quant_weight_only': False } # parameters from arguments model_name = args.model pretrained_fp32_model = args.pretrained_fp32_model checkpoint = args.checkpoint model_save_dir = args.model_save_dir data_dir = args.data_dir activation_quant_type = args.act_quant_type weight_quant_type = args.wt_quant_type print("Using %s as the actiavtion quantize type." % activation_quant_type) print("Using %s as the weight quantize type." % weight_quant_type) startup_prog = fluid.Program() train_prog = fluid.Program() test_prog = fluid.Program() _, _, train_py_reader, train_cost, train_acc1, train_acc5, global_lr = build_program( is_train=True, main_prog=train_prog, startup_prog=startup_prog, args=args) image, out, test_py_reader, test_cost, test_acc1, test_acc5 = build_program( is_train=False, main_prog=test_prog, startup_prog=startup_prog, args=args) test_prog = test_prog.clone(for_test=True) place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace() scope = fluid.global_scope() exe = fluid.Executor(place) # load checkpoint todo exe.run(startup_prog) if pretrained_fp32_model: def if_exist(var): return os.path.exists(os.path.join(pretrained_fp32_model, var.name)) fluid.io.load_vars( exe, pretrained_fp32_model, main_program=train_prog, predicate=if_exist) if args.use_gpu: visible_device = os.getenv('CUDA_VISIBLE_DEVICES') if visible_device: device_num = len(visible_device.split(',')) else: device_num = subprocess.check_output( ['nvidia-smi', '-L']).decode().count('\n') else: device_num = 1 train_batch_size = args.batch_size / device_num test_batch_size = 1 if activation_quant_type == 'abs_max' else 8 train_reader = paddle.batch( reader.train(data_dir=data_dir), batch_size=train_batch_size, drop_last=True) test_reader = paddle.batch(reader.val(data_dir=data_dir), batch_size=test_batch_size) train_py_reader.decorate_paddle_reader(train_reader) test_py_reader.decorate_paddle_reader(test_reader) train_fetch_list = [train_cost.name, train_acc1.name, train_acc5.name, global_lr.name] test_fetch_list = [test_cost.name, test_acc1.name, test_acc5.name] ############################################################################################################ # 2. quantization transform programs (training aware) # Make some quantization transforms in the graph before training and testing. # According to the weight and activation quantization type, the graph will be added # some fake quantize operators and fake dequantize operators. ############################################################################################################ train_prog = quant.quanter.quant_aware(train_prog, scope, place, quant_config, for_test = False) test_prog = quant.quanter.quant_aware(test_prog, scope, place, quant_config, for_test=True) build_strategy = fluid.BuildStrategy() build_strategy.memory_optimize = False build_strategy.enable_inplace = False params = models.__dict__[args.model]().params for pass_id in range(params["num_epochs"]): train_py_reader.start() train_info = [[], [], []] test_info = [[], [], []] train_time = [] batch_id = 0 try: while True: t1 = time.time() loss, acc1, acc5, lr = exe.run(train_prog, fetch_list=train_fetch_list) t2 = time.time() period = t2 - t1 loss = np.mean(np.array(loss)) acc1 = np.mean(np.array(acc1)) acc5 = np.mean(np.array(acc5)) train_info[0].append(loss) train_info[1].append(acc1) train_info[2].append(acc5) lr = np.mean(np.array(lr)) train_time.append(period) if batch_id % 10 == 0: print("Pass {0}, trainbatch {1}, loss {2}, \ acc1 {3}, acc5 {4}, lr {5}, time {6}" .format(pass_id, batch_id, loss, acc1, acc5, "%.6f" % lr, "%2.2f sec" % period)) sys.stdout.flush() batch_id += 1 except fluid.core.EOFException: train_py_reader.reset() train_loss = np.array(train_info[0]).mean() train_acc1 = np.array(train_info[1]).mean() train_acc5 = np.array(train_info[2]).mean() test_py_reader.start() test_batch_id = 0 try: while True: t1 = time.time() loss, acc1, acc5 = exe.run(program=test_prog, fetch_list=test_fetch_list) t2 = time.time() period = t2 - t1 loss = np.mean(loss) acc1 = np.mean(acc1) acc5 = np.mean(acc5) test_info[0].append(loss) test_info[1].append(acc1) test_info[2].append(acc5) if test_batch_id % 10 == 0: print("Pass {0},testbatch {1},loss {2}, \ acc1 {3},acc5 {4},time {5}" .format(pass_id, test_batch_id, loss, acc1, acc5, "%2.2f sec" % period)) sys.stdout.flush() test_batch_id += 1 except fluid.core.EOFException: test_py_reader.reset() test_loss = np.array(test_info[0]).mean() test_acc1 = np.array(test_info[1]).mean() test_acc5 = np.array(test_info[2]).mean() print("End pass {0}, train_loss {1}, train_acc1 {2}, train_acc5 {3}, " "test_loss {4}, test_acc1 {5}, test_acc5 {6}".format( pass_id, train_loss, train_acc1, train_acc5, test_loss, test_acc1, test_acc5)) sys.stdout.flush() # save checkpoints todo # save_checkpoint_path = os.path.join(model_save_dir, model_name, str(pass_id)) # if not os.path.isdir(save_checkpoint_path): # os.makedirs(save_checkpoint_path) # save_persistable_nodes(exe, save_checkpoint_path, main_graph) ############################################################################################################ # 3. Freeze the graph after training by adjusting the quantize # operators' order for the inference. # The dtype of float_program's weights is float32, but in int8 range. ############################################################################################################ float_program, int8_program = quant.quanter.convert(test_prog, scope, place, quant_config, save_int8=True) ############################################################################################################ # 4. Save inference model ############################################################################################################ model_path = os.path.join(model_save_dir, model_name, args.act_quant_type) float_path = os.path.join(model_path, 'float') int8_path = os.path.join(model_path, 'int8') if not os.path.isdir(model_path): os.makedirs(model_path) fluid.io.save_inference_model( dirname=float_path, feeded_var_names=[image.name], target_vars=[out], executor=exe, main_program=float_program) fluid.io.save_inference_model( dirname=int8_path, feeded_var_names=[image.name], target_vars=[out], executor=exe, main_program=int8_program) def main(): args = parser.parse_args() print_arguments(args) train(args) if __name__ == '__main__': main()