diff --git a/fluid/PaddleCV/gan/cycle_gan/README.md b/fluid/PaddleCV/gan/cycle_gan/README.md index 6520c123f80f423366287ea53a36a3969d1a73c9..0a9be53c783a557b7c2306f65377e4cafa8cfd90 100644 --- a/fluid/PaddleCV/gan/cycle_gan/README.md +++ b/fluid/PaddleCV/gan/cycle_gan/README.md @@ -74,8 +74,8 @@ env CUDA_VISIBLE_DEVICES=0 python train.py ``` env CUDA_VISIBLE_DEVICE=0 python infer.py \ - --init_model="models/1" --input="./data/inputA/*" \ - --output="./output" + --init_model="checkpoints/1" --input="./data/inputA/*" \ + --input_style A --output="./output" ``` 训练150轮的模型预测效果如图2和图3所示： diff --git a/fluid/PaddleCV/gan/cycle_gan/infer.py b/fluid/PaddleCV/gan/cycle_gan/infer.py index ffc4d1e6193707b6afafcc6eaee8c01d4b0129ce..2282cf6c6aa6d41b1caba41a3cf42c08aea7ecef 100644 --- a/fluid/PaddleCV/gan/cycle_gan/infer.py +++ b/fluid/PaddleCV/gan/cycle_gan/infer.py @@ -26,8 +26,10 @@ def infer(args): data_shape = [-1, 3, 256, 256] input = fluid.layers.data(name='input', shape=data_shape, dtype='float32') if args.input_style == "A": + model_name = 'g_a' fake = build_generator_resnet_9blocks(input, name="g_A") elif args.input_style == "B": + model_name = 'g_b' fake = build_generator_resnet_9blocks(input, name="g_B") else: raise "Input with style [%s] is not supported." % args.input_style @@ -37,7 +39,7 @@ def infer(args): place = fluid.CUDAPlace(0) exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) - fluid.io.load_persistables(exe, args.init_model) + fluid.io.load_persistables(exe, args.init_model + "/" + model_name) if not os.path.exists(args.output): os.makedirs(args.output) diff --git a/fluid/PaddleCV/gan/cycle_gan/layers.py b/fluid/PaddleCV/gan/cycle_gan/layers.py index 0cbd5af6ebccc140104b81a75717fa3555c12d5a..d918946fd659c4b02784c0f9060dd3011cb5840e 100644 --- a/fluid/PaddleCV/gan/cycle_gan/layers.py +++ b/fluid/PaddleCV/gan/cycle_gan/layers.py @@ -3,10 +3,12 @@ import paddle.fluid as fluid import numpy as np import os -use_cudnn = True +# cudnn is not better when batch size is 1. +use_cudnn = False if 'ce_mode' in os.environ: use_cudnn = False + def cal_padding(img_size, stride, filter_size, dilation=1): """Calculate padding size.""" valid_filter_size = dilation * (filter_size - 1) + 1 @@ -18,6 +20,8 @@ def cal_padding(img_size, stride, filter_size, dilation=1): def instance_norm(input, name=None): + # TODO(lvmengsi@baidu.com): Check the accuracy when using fluid.layers.layer_norm. + # return fluid.layers.layer_norm(input, begin_norm_axis=2) helper = fluid.layer_helper.LayerHelper("instance_norm", **locals()) dtype = helper.input_dtype() epsilon = 1e-5 diff --git a/fluid/PaddleCV/gan/cycle_gan/train.py b/fluid/PaddleCV/gan/cycle_gan/train.py index ea7887570f8e4063bb036d67ecf58c45902fd3f2..df7cf57b2a2bd4ee864dafc5d66c953508b05926 100644 --- a/fluid/PaddleCV/gan/cycle_gan/train.py +++ b/fluid/PaddleCV/gan/cycle_gan/train.py @@ -17,7 +17,6 @@ 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 @@ -36,7 +35,7 @@ add_arg('run_ce', bool, False, "Whether to run for model ce.") def train(args): max_images_num = data_reader.max_images_num() - shuffle=True + shuffle = True if args.run_ce: np.random.seed(10) fluid.default_startup_program().random_seed = 90 @@ -66,9 +65,11 @@ def train(args): 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)() + + 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() @@ -119,13 +120,13 @@ def train(args): 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") + exe, out_path + "/g_a", main_program=g_A_trainer.program) fluid.io.save_persistables( - exe, out_path + "/g_b", main_program=g_B_trainer.program, filename="params") + exe, out_path + "/g_b", main_program=g_B_trainer.program) fluid.io.save_persistables( - exe, out_path + "/d_a", main_program=d_A_trainer.program, filename="params") + exe, out_path + "/d_a", main_program=d_A_trainer.program) fluid.io.save_persistables( - exe, out_path + "/d_b", main_program=d_B_trainer.program, filename="params") + exe, out_path + "/d_b", main_program=d_B_trainer.program) print("saved checkpoint to {}".format(out_path)) sys.stdout.flush() @@ -144,8 +145,21 @@ def train(args): if args.init_model: init_model() - losses=[[], []] + losses = [[], []] t_time = 0 + + g_A_trainer_program = fluid.CompiledProgram( + g_A_trainer.program).with_data_parallel( + loss_name=g_A_trainer.g_loss_A.name) + g_B_trainer_program = fluid.CompiledProgram( + g_B_trainer.program).with_data_parallel( + loss_name=g_B_trainer.g_loss_B.name) + d_B_trainer_program = fluid.CompiledProgram( + d_B_trainer.program).with_data_parallel( + loss_name=d_B_trainer.d_loss_B.name) + d_A_trainer_program = fluid.CompiledProgram( + d_A_trainer.program).with_data_parallel( + loss_name=d_A_trainer.d_loss_A.name) for epoch in range(args.epoch): batch_id = 0 for i in range(max_images_num): @@ -158,7 +172,7 @@ def train(args): s_time = time.time() # optimize the g_A network g_A_loss, fake_B_tmp = exe.run( - g_A_trainer.program, + 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}) @@ -167,14 +181,14 @@ def train(args): # optimize the d_B network d_B_loss = exe.run( - d_B_trainer.program, + 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, + 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}) @@ -183,16 +197,16 @@ def train(args): # optimize the d_A network d_A_loss = exe.run( - d_A_trainer.program, + 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)) + 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()