from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import random import sys import paddle import argparse import functools import time import numpy as np from scipy.misc import imsave import paddle.fluid as fluid import paddle.fluid.profiler as profiler from paddle.fluid import core import data_reader from utility import add_arguments, print_arguments, ImagePool from trainer import * parser = argparse.ArgumentParser(description=__doc__) add_arg = functools.partial(add_arguments, argparser=parser) # yapf: disable add_arg('batch_size', int, 1, "Minibatch size.") add_arg('epoch', int, 2, "The number of epoched to be trained.") add_arg('output', str, "./output_0", "The directory the model and the test result to be saved to.") add_arg('init_model', str, None, "The init model file of directory.") add_arg('save_checkpoints', bool, True, "Whether to save checkpoints.") add_arg('run_test', bool, True, "Whether to run test.") add_arg('use_gpu', bool, True, "Whether to use GPU to train.") add_arg('profile', bool, False, "Whether to profile.") add_arg('run_ce', bool, False, "Whether to run for model ce.") # yapf: enable def train(args): max_images_num = data_reader.max_images_num() shuffle=True if args.run_ce: np.random.seed(10) fluid.default_startup_program().random_seed = 90 max_images_num = 1 shuffle = False data_shape = [-1] + data_reader.image_shape() input_A = fluid.layers.data( name='input_A', shape=data_shape, dtype='float32') input_B = fluid.layers.data( name='input_B', shape=data_shape, dtype='float32') fake_pool_A = fluid.layers.data( name='fake_pool_A', shape=data_shape, dtype='float32') fake_pool_B = fluid.layers.data( name='fake_pool_B', shape=data_shape, dtype='float32') g_A_trainer = GATrainer(input_A, input_B) g_B_trainer = GBTrainer(input_A, input_B) d_A_trainer = DATrainer(input_A, fake_pool_A) d_B_trainer = DBTrainer(input_B, fake_pool_B) # prepare environment place = fluid.CPUPlace() if args.use_gpu: place = fluid.CUDAPlace(0) exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) A_pool = ImagePool() B_pool = ImagePool() A_reader = paddle.batch(data_reader.a_reader(shuffle=shuffle), args.batch_size)() B_reader = paddle.batch(data_reader.b_reader(shuffle=shuffle), args.batch_size)() if not args.run_ce: A_test_reader = data_reader.a_test_reader() B_test_reader = data_reader.b_test_reader() def test(epoch): out_path = args.output + "/test" if not os.path.exists(out_path): os.makedirs(out_path) i = 0 for data_A, data_B in zip(A_test_reader(), B_test_reader()): A_name = data_A[1] B_name = data_B[1] tensor_A = core.LoDTensor() tensor_B = core.LoDTensor() tensor_A.set(data_A[0], place) tensor_B.set(data_B[0], place) fake_A_temp, fake_B_temp, cyc_A_temp, cyc_B_temp = exe.run( g_A_trainer.infer_program, fetch_list=[ g_A_trainer.fake_A, g_A_trainer.fake_B, g_A_trainer.cyc_A, g_A_trainer.cyc_B ], feed={"input_A": tensor_A, "input_B": tensor_B}) fake_A_temp = np.squeeze(fake_A_temp[0]).transpose([1, 2, 0]) fake_B_temp = np.squeeze(fake_B_temp[0]).transpose([1, 2, 0]) cyc_A_temp = np.squeeze(cyc_A_temp[0]).transpose([1, 2, 0]) cyc_B_temp = np.squeeze(cyc_B_temp[0]).transpose([1, 2, 0]) input_A_temp = np.squeeze(data_A[0]).transpose([1, 2, 0]) input_B_temp = np.squeeze(data_B[0]).transpose([1, 2, 0]) imsave(out_path + "/fakeB_" + str(epoch) + "_" + A_name, ( (fake_B_temp + 1) * 127.5).astype(np.uint8)) imsave(out_path + "/fakeA_" + str(epoch) + "_" + B_name, ( (fake_A_temp + 1) * 127.5).astype(np.uint8)) imsave(out_path + "/cycA_" + str(epoch) + "_" + A_name, ( (cyc_A_temp + 1) * 127.5).astype(np.uint8)) imsave(out_path + "/cycB_" + str(epoch) + "_" + B_name, ( (cyc_B_temp + 1) * 127.5).astype(np.uint8)) imsave(out_path + "/inputA_" + str(epoch) + "_" + A_name, ( (input_A_temp + 1) * 127.5).astype(np.uint8)) imsave(out_path + "/inputB_" + str(epoch) + "_" + B_name, ( (input_B_temp + 1) * 127.5).astype(np.uint8)) i += 1 def checkpoints(epoch): out_path = args.output + "/checkpoints/" + str(epoch) if not os.path.exists(out_path): os.makedirs(out_path) fluid.io.save_persistables( exe, out_path + "/g_a", main_program=g_A_trainer.program, filename="params") fluid.io.save_persistables( exe, out_path + "/g_b", main_program=g_B_trainer.program, filename="params") fluid.io.save_persistables( exe, out_path + "/d_a", main_program=d_A_trainer.program, filename="params") fluid.io.save_persistables( exe, out_path + "/d_b", main_program=d_B_trainer.program, filename="params") print("saved checkpoint to {}".format(out_path)) sys.stdout.flush() def init_model(): assert os.path.exists( args.init_model), "[%s] cann't be found." % args.init_mode fluid.io.load_persistables( exe, args.init_model + "/g_a", main_program=g_A_trainer.program) fluid.io.load_persistables( exe, args.init_model + "/g_b", main_program=g_B_trainer.program) fluid.io.load_persistables( exe, args.init_model + "/d_a", main_program=d_A_trainer.program) fluid.io.load_persistables( exe, args.init_model + "/d_b", main_program=d_B_trainer.program) print("Load model from {}".format(args.init_model)) if args.init_model: init_model() losses=[[], []] t_time = 0 for epoch in range(args.epoch): batch_id = 0 for i in range(max_images_num): data_A = next(A_reader) data_B = next(B_reader) tensor_A = core.LoDTensor() tensor_B = core.LoDTensor() tensor_A.set(data_A, place) tensor_B.set(data_B, place) s_time = time.time() # optimize the g_A network g_A_loss, fake_B_tmp = exe.run( g_A_trainer.program, fetch_list=[g_A_trainer.g_loss_A, g_A_trainer.fake_B], feed={"input_A": tensor_A, "input_B": tensor_B}) fake_pool_B = B_pool.pool_image(fake_B_tmp) # optimize the d_B network d_B_loss = exe.run( d_B_trainer.program, fetch_list=[d_B_trainer.d_loss_B], feed={"input_B": tensor_B, "fake_pool_B": fake_pool_B})[0] # optimize the g_B network g_B_loss, fake_A_tmp = exe.run( g_B_trainer.program, fetch_list=[g_B_trainer.g_loss_B, g_B_trainer.fake_A], feed={"input_A": tensor_A, "input_B": tensor_B}) fake_pool_A = A_pool.pool_image(fake_A_tmp) # optimize the d_A network d_A_loss = exe.run( d_A_trainer.program, fetch_list=[d_A_trainer.d_loss_A], feed={"input_A": tensor_A, "fake_pool_A": fake_pool_A})[0] batch_time = time.time() - s_time t_time += batch_time print("epoch{}; batch{}; g_A_loss: {}; d_B_loss: {}; g_B_loss: {}; d_A_loss: {}; " "Batch_time_cost: {:.2f}".format( epoch, batch_id, g_A_loss[0], d_B_loss[0], g_B_loss[0], d_A_loss[0], batch_time)) losses[0].append(g_A_loss[0]) losses[1].append(d_A_loss[0]) sys.stdout.flush() batch_id += 1 if args.run_test and not args.run_ce: test(epoch) if args.save_checkpoints and not args.run_ce: checkpoints(epoch) if args.run_ce: print("kpis,g_train_cost,{}".format(np.mean(losses[0]))) print("kpis,d_train_cost,{}".format(np.mean(losses[1]))) print("kpis,duration,{}".format(t_time / args.epoch)) if __name__ == "__main__": args = parser.parse_args() print_arguments(args) if args.profile: if args.use_gpu: with profiler.cuda_profiler("cuda_profiler.txt", 'csv') as nvprof: train(args) else: with profiler.profiler("CPU", sorted_key='total') as cpuprof: train(args) else: train(args)