From b969c14df73d5edd610f7de57957f24c4dd5a25a Mon Sep 17 00:00:00 2001 From: qingqing01 Date: Thu, 2 Apr 2020 04:16:37 +0000 Subject: [PATCH] Add CycleGAN --- cyclegan/__init__.py | 0 cyclegan/cyclegan.py | 232 +++++++++++++++++++++++++++++++++ cyclegan/data.py | 142 ++++++++++++++++++++ cyclegan/image/A2B.png | Bin 0 -> 158236 bytes cyclegan/image/B2A.png | Bin 0 -> 146685 bytes cyclegan/image/net.png | Bin 0 -> 156149 bytes cyclegan/image/testA/123_A.jpg | Bin 0 -> 34221 bytes cyclegan/image/testB/78_B.jpg | Bin 0 -> 25393 bytes cyclegan/infer.py | 105 +++++++++++++++ cyclegan/layers.py | 140 ++++++++++++++++++++ cyclegan/test.py | 100 ++++++++++++++ cyclegan/train.py | 154 ++++++++++++++++++++++ model.py | 6 +- 13 files changed, 875 insertions(+), 4 deletions(-) create mode 100644 cyclegan/__init__.py create mode 100644 cyclegan/cyclegan.py create mode 100644 cyclegan/data.py create mode 100644 cyclegan/image/A2B.png create mode 100644 cyclegan/image/B2A.png create mode 100644 cyclegan/image/net.png create mode 100644 cyclegan/image/testA/123_A.jpg create mode 100644 cyclegan/image/testB/78_B.jpg create mode 100644 cyclegan/infer.py create mode 100644 cyclegan/layers.py create mode 100644 cyclegan/test.py create mode 100644 cyclegan/train.py diff --git a/cyclegan/__init__.py b/cyclegan/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/cyclegan/cyclegan.py b/cyclegan/cyclegan.py new file mode 100644 index 0000000..6fdd21c --- /dev/null +++ b/cyclegan/cyclegan.py @@ -0,0 +1,232 @@ +# Copyright (c) 2020 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. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from layers import ConvBN, DeConvBN +import paddle.fluid as fluid +from model import Model, Loss + + +class ResnetBlock(fluid.dygraph.Layer): + def __init__(self, dim, dropout=False): + super(ResnetBlock, self).__init__() + self.dropout = dropout + self.conv0 = ConvBN(dim, dim, 3, 1) + self.conv1 = ConvBN(dim, dim, 3, 1, act=None) + + def forward(self, inputs): + out_res = fluid.layers.pad2d(inputs, [1, 1, 1, 1], mode="reflect") + out_res = self.conv0(out_res) + if self.dropout: + out_res = fluid.layers.dropout(out_res, dropout_prob=0.5) + out_res = fluid.layers.pad2d(out_res, [1, 1, 1, 1], mode="reflect") + out_res = self.conv1(out_res) + return out_res + inputs + + +class ResnetGenerator(fluid.dygraph.Layer): + def __init__(self, input_channel, n_blocks=9, dropout=False): + super(ResnetGenerator, self).__init__() + + self.conv0 = ConvBN(input_channel, 32, 7, 1) + self.conv1 = ConvBN(32, 64, 3, 2, padding=1) + self.conv2 = ConvBN(64, 128, 3, 2, padding=1) + + dim = 128 + self.resnet_blocks = [] + for i in range(n_blocks): + block = self.add_sublayer("generator_%d" % (i + 1), + ResnetBlock(dim, dropout)) + self.resnet_blocks.append(block) + + self.deconv0 = DeConvBN( + dim, 32 * 2, 3, 2, padding=[1, 1], outpadding=[0, 1, 0, 1]) + self.deconv1 = DeConvBN( + 32 * 2, 32, 3, 2, padding=[1, 1], outpadding=[0, 1, 0, 1]) + + self.conv3 = ConvBN( + 32, input_channel, 7, 1, norm=False, act=False, use_bias=True) + + def forward(self, inputs): + pad_input = fluid.layers.pad2d(inputs, [3, 3, 3, 3], mode="reflect") + y = self.conv0(pad_input) + y = self.conv1(y) + y = self.conv2(y) + for resnet_block in self.resnet_blocks: + y = resnet_block(y) + y = self.deconv0(y) + y = self.deconv1(y) + y = fluid.layers.pad2d(y, [3, 3, 3, 3], mode="reflect") + y = self.conv3(y) + y = fluid.layers.tanh(y) + return y + + +class NLayerDiscriminator(fluid.dygraph.Layer): + def __init__(self, input_channel, d_dims=64, d_nlayers=3): + super(NLayerDiscriminator, self).__init__() + self.conv0 = ConvBN( + input_channel, + d_dims, + 4, + 2, + 1, + norm=False, + use_bias=True, + relufactor=0.2) + + nf_mult, nf_mult_prev = 1, 1 + self.conv_layers = [] + for n in range(1, d_nlayers): + nf_mult_prev = nf_mult + nf_mult = min(2**n, 8) + conv = self.add_sublayer( + 'discriminator_%d' % (n), + ConvBN( + d_dims * nf_mult_prev, + d_dims * nf_mult, + 4, + 2, + 1, + relufactor=0.2)) + self.conv_layers.append(conv) + + nf_mult_prev = nf_mult + nf_mult = min(2**d_nlayers, 8) + self.conv4 = ConvBN( + d_dims * nf_mult_prev, d_dims * nf_mult, 4, 1, 1, relufactor=0.2) + self.conv5 = ConvBN( + d_dims * nf_mult, + 1, + 4, + 1, + 1, + norm=False, + act=None, + use_bias=True, + relufactor=0.2) + + def forward(self, inputs): + y = self.conv0(inputs) + for conv in self.conv_layers: + y = conv(y) + y = self.conv4(y) + y = self.conv5(y) + return y + + +class Generator(Model): + def __init__(self, input_channel=3): + super(Generator, self).__init__() + self.g = ResnetGenerator(input_channel) + + def forward(self, input): + fake = self.g(input) + return fake + + +class GeneratorCombine(Model): + def __init__(self, g_AB=None, g_BA=None, d_A=None, d_B=None, + is_train=True): + super(GeneratorCombine, self).__init__() + self.g_AB = g_AB + self.g_BA = g_BA + self.is_train = is_train + if self.is_train: + self.d_A = d_A + self.d_B = d_B + + def forward(self, input_A, input_B): + # Translate images to the other domain + fake_B = self.g_AB(input_A) + fake_A = self.g_BA(input_B) + + # Translate images back to original domain + cyc_A = self.g_BA(fake_B) + cyc_B = self.g_AB(fake_A) + if not self.is_train: + return fake_A, fake_B, cyc_A, cyc_B + + # Identity mapping of images + idt_A = self.g_AB(input_B) + idt_B = self.g_BA(input_A) + + # Discriminators determines validity of translated images + # d_A(g_AB(A)) + valid_A = self.d_A.d(fake_B) + # d_B(g_BA(A)) + valid_B = self.d_B.d(fake_A) + return input_A, input_B, fake_A, fake_B, cyc_A, cyc_B, idt_A, idt_B, valid_A, valid_B + + +class GLoss(Loss): + def __init__(self, lambda_A=10., lambda_B=10., lambda_identity=0.5): + super(GLoss, self).__init__() + self.lambda_A = lambda_A + self.lambda_B = lambda_B + self.lambda_identity = lambda_identity + + def forward(self, outputs, labels=None): + input_A, input_B, fake_A, fake_B, cyc_A, cyc_B, idt_A, idt_B, valid_A, valid_B = outputs + + def mse(a, b): + return fluid.layers.reduce_mean(fluid.layers.square(a - b)) + + def mae(a, b): # L1Loss + return fluid.layers.reduce_mean(fluid.layers.abs(a - b)) + + g_A_loss = mse(valid_A, 1.) + g_B_loss = mse(valid_B, 1.) + g_loss = g_A_loss + g_B_loss + + cyc_A_loss = mae(input_A, cyc_A) * self.lambda_A + cyc_B_loss = mae(input_B, cyc_B) * self.lambda_B + cyc_loss = cyc_A_loss + cyc_B_loss + + idt_loss_A = mae(input_B, idt_A) * (self.lambda_B * + self.lambda_identity) + idt_loss_B = mae(input_A, idt_B) * (self.lambda_A * + self.lambda_identity) + idt_loss = idt_loss_A + idt_loss_B + + loss = cyc_loss + g_loss + idt_loss + return loss + + +class Discriminator(Model): + def __init__(self, input_channel=3): + super(Discriminator, self).__init__() + self.d = NLayerDiscriminator(input_channel) + + def forward(self, real, fake): + pred_real = self.d(real) + pred_fake = self.d(fake) + return pred_real, pred_fake + + +class DLoss(Loss): + def __init__(self): + super(DLoss, self).__init__() + + def forward(self, inputs, labels=None): + pred_real, pred_fake = inputs + loss = fluid.layers.square(pred_fake) + fluid.layers.square(pred_real - + 1.) + loss = fluid.layers.reduce_mean(loss / 2.0) + return loss diff --git a/cyclegan/data.py b/cyclegan/data.py new file mode 100644 index 0000000..4b8c4ae --- /dev/null +++ b/cyclegan/data.py @@ -0,0 +1,142 @@ +# Copyright (c) 2019 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. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +import os +import random +import numpy as np +from PIL import Image, ImageOps + +DATASET = "cityscapes" +A_LIST_FILE = "./data/" + DATASET + "/trainA.txt" +B_LIST_FILE = "./data/" + DATASET + "/trainB.txt" +A_TEST_LIST_FILE = "./data/" + DATASET + "/testA.txt" +B_TEST_LIST_FILE = "./data/" + DATASET + "/testB.txt" +IMAGES_ROOT = "./data/" + DATASET + "/" + +import paddle.fluid as fluid + + +class Cityscapes(fluid.io.Dataset): + def __init__(self, root_path, file_path, mode='train', return_name=False): + self.root_path = root_path + self.file_path = file_path + self.mode = mode + self.return_name = return_name + self.images = [root_path + l for l in open(file_path, 'r').readlines()] + + def _train(self, image): + ## Resize + image = image.resize((286, 286), Image.BICUBIC) + ## RandomCrop + i = np.random.randint(0, 30) + j = np.random.randint(0, 30) + image = image.crop((i, j, i + 256, j + 256)) + # RandomHorizontalFlip + if np.random.rand() > 0.5: + image = ImageOps.mirror(image) + return image + + def __getitem__(self, idx): + f = self.images[idx].strip("\n\r\t ") + image = Image.open(f) + if self.mode == 'train': + image = self._train(image) + else: + image = image.resize((256, 256), Image.BICUBIC) + # ToTensor + image = np.array(image).transpose([2, 0, 1]).astype('float32') + image = image / 255.0 + # Normalize, mean=[0.5,0.5,0.5], std=[0.5,0.5,0.5] + image = (image - 0.5) / 0.5 + if self.return_name: + return [image], os.path.basename(f) + else: + return [image] + + def __len__(self): + return len(self.images) + + +def DataA(root=IMAGES_ROOT, fpath=A_LIST_FILE): + """ + Reader of images with A style for training. + """ + return Cityscapes(root, fpath) + + +def DataB(root=IMAGES_ROOT, fpath=B_LIST_FILE): + """ + Reader of images with B style for training. + """ + return Cityscapes(root, fpath) + + +def TestDataA(root=IMAGES_ROOT, fpath=A_TEST_LIST_FILE): + """ + Reader of images with A style for training. + """ + return Cityscapes(root, fpath, mode='test', return_name=True) + + +def TestDataB(root=IMAGES_ROOT, fpath=B_TEST_LIST_FILE): + """ + Reader of images with B style for training. + """ + return Cityscapes(root, fpath, mode='test', return_name=True) + + +class ImagePool(object): + def __init__(self, pool_size=50): + self.pool = [] + self.count = 0 + self.pool_size = pool_size + + def get(self, image): + if self.count < self.pool_size: + self.pool.append(image) + self.count += 1 + return image + else: + p = random.random() + if p > 0.5: + random_id = random.randint(0, self.pool_size - 1) + temp = self.pool[random_id] + self.pool[random_id] = image + return temp + else: + return image + + +if __name__ == '__main__': + place = fluid.CUDAPlace(0) + #fluid.enable_dygraph(place) + dataset = DataA(shuffle=False) + a_loader = fluid.io.DataLoader( + dataset, + feed_list=[ + fluid.data( + name='im', shape=[ + None, + 2, + 2, + ], dtype='float32') + ], + places=place, + return_list=False, + batch_size=2) + for data in a_loader: + print(data) diff --git a/cyclegan/image/A2B.png b/cyclegan/image/A2B.png new file mode 100644 index 0000000000000000000000000000000000000000..b67466da9bdf04344ac6a8f417169414641be664 GIT binary patch literal 158236 zcmeFXMN}L>*EZOVyM*BG?(XjHZXtMZ_XdIm4ek)!-CcqN4el92LOO#2yp*a?)q*2%_!8il9Foj 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mode 100644 index 0000000..308e65b --- /dev/null +++ b/cyclegan/infer.py @@ -0,0 +1,105 @@ +# Copyright (c) 2019 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. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import glob +import numpy as np +import argparse + +from PIL import Image +from scipy.misc import imsave + +import paddle.fluid as fluid + +from model import Model, Input, set_device +from cyclegan import Generator, GeneratorCombine + + +def main(): + place = set_device(FLAGS.device) + fluid.enable_dygraph(place) if FLAGS.dynamic else None + + # Generators + g_AB = Generator() + g_BA = Generator() + g = GeneratorCombine(g_AB, g_BA, is_train=False) + + im_shape = [-1, 3, 256, 256] + input_A = Input(im_shape, 'float32', 'input_A') + input_B = Input(im_shape, 'float32', 'input_B') + g.prepare(inputs=[input_A, input_B]) + g.load(FLAGS.init_model, skip_mismatch=True, reset_optimizer=True) + + out_path = FLAGS.output + "/single" + if not os.path.exists(out_path): + os.makedirs(out_path) + for f in glob.glob(FLAGS.input): + image_name = os.path.basename(f) + image = Image.open(f).convert('RGB') + image = image.resize((256, 256), Image.BICUBIC) + image = np.array(image) / 127.5 - 1 + + image = image[:, :, 0:3].astype("float32") + data = image.transpose([2, 0, 1])[np.newaxis, :] + + if FLAGS.input_style == "A": + _, fake, _, _ = g.test([data, data]) + + if FLAGS.input_style == "B": + fake, _, _, _ = g.test([data, data]) + + fake = np.squeeze(fake[0]).transpose([1, 2, 0]) + + opath = "{}/fake{}{}".format(out_path, FLAGS.input_style, image_name) + imsave(opath, ((fake + 1) * 127.5).astype(np.uint8)) + print("transfer {} to {}".format(f, opath)) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser("CycleGAN inference") + parser.add_argument( + "-d", "--dynamic", action='store_false', help="Enable dygraph mode") + parser.add_argument( + "-p", + "--device", + type=str, + default='gpu', + help="device to use, gpu or cpu") + parser.add_argument( + "-i", + "--input", + type=str, + default='./image/testA/123_A.jpg', + help="input image") + parser.add_argument( + "-o", + '--output', + type=str, + default='output', + help="The test result to be saved to.") + parser.add_argument( + "-m", + "--init_model", + type=str, + default='checkpoint/194', + help="The init model file of directory.") + parser.add_argument( + "-s", "--input_style", type=str, default='A', help="A or B") + FLAGS = parser.parse_args() + print(FLAGS) + main() diff --git a/cyclegan/layers.py b/cyclegan/layers.py new file mode 100644 index 0000000..8c79ef5 --- /dev/null +++ b/cyclegan/layers.py @@ -0,0 +1,140 @@ +# Copyright (c) 2019 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. + +from __future__ import division +import paddle.fluid as fluid +from paddle.fluid.dygraph.nn import Conv2D, Conv2DTranspose, BatchNorm + +# cudnn is not better when batch size is 1. +use_cudnn = False +import numpy as np + + +class ConvBN(fluid.dygraph.Layer): + """docstring for Conv2D""" + + def __init__(self, + num_channels, + num_filters, + filter_size, + stride=1, + padding=0, + stddev=0.02, + norm=True, + is_test=False, + act='leaky_relu', + relufactor=0.0, + use_bias=False): + super(ConvBN, self).__init__() + + pattr = fluid.ParamAttr( + initializer=fluid.initializer.NormalInitializer( + loc=0.0, scale=stddev)) + self.conv = Conv2D( + num_channels=num_channels, + num_filters=num_filters, + filter_size=filter_size, + stride=stride, + padding=padding, + use_cudnn=use_cudnn, + param_attr=pattr, + bias_attr=use_bias) + if norm: + self.bn = BatchNorm( + num_filters, + param_attr=fluid.ParamAttr( + initializer=fluid.initializer.NormalInitializer(1.0, + 0.02)), + bias_attr=fluid.ParamAttr( + initializer=fluid.initializer.Constant(0.0)), + is_test=False, + trainable_statistics=True) + self.relufactor = relufactor + self.norm = norm + self.act = act + + def forward(self, inputs): + conv = self.conv(inputs) + if self.norm: + conv = self.bn(conv) + + if self.act == 'leaky_relu': + conv = fluid.layers.leaky_relu(conv, alpha=self.relufactor) + elif self.act == 'relu': + conv = fluid.layers.relu(conv) + else: + conv = conv + + return conv + + +class DeConvBN(fluid.dygraph.Layer): + def __init__(self, + num_channels, + num_filters, + filter_size, + stride=1, + padding=[0, 0], + outpadding=[0, 0, 0, 0], + stddev=0.02, + act='leaky_relu', + norm=True, + is_test=False, + relufactor=0.0, + use_bias=False): + super(DeConvBN, self).__init__() + + pattr = fluid.ParamAttr( + initializer=fluid.initializer.NormalInitializer( + loc=0.0, scale=stddev)) + self._deconv = Conv2DTranspose( + num_channels, + num_filters, + filter_size=filter_size, + stride=stride, + padding=padding, + param_attr=pattr, + bias_attr=use_bias) + if norm: + self.bn = BatchNorm( + num_filters, + param_attr=fluid.ParamAttr( + initializer=fluid.initializer.NormalInitializer(1.0, + 0.02)), + bias_attr=fluid.ParamAttr( + initializer=fluid.initializer.Constant(0.0)), + is_test=False, + trainable_statistics=True) + self.outpadding = outpadding + self.relufactor = relufactor + self.use_bias = use_bias + self.norm = norm + self.act = act + + def forward(self, inputs): + conv = self._deconv(inputs) + conv = fluid.layers.pad2d( + conv, paddings=self.outpadding, mode='constant', pad_value=0.0) + + if self.norm: + conv = self.bn(conv) + + if self.act == 'leaky_relu': + conv = fluid.layers.leaky_relu(conv, alpha=self.relufactor) + elif self.act == 'relu': + conv = fluid.layers.relu(conv) + else: + conv = conv + + return conv diff --git a/cyclegan/test.py b/cyclegan/test.py new file mode 100644 index 0000000..69a8260 --- /dev/null +++ b/cyclegan/test.py @@ -0,0 +1,100 @@ +# Copyright (c) 2019 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. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import argparse +import numpy as np +from scipy.misc import imsave + +import paddle.fluid as fluid + +from model import Model, Input, set_device +from cyclegan import Generator, GeneratorCombine +import data as data + + +def main(): + place = set_device(FLAGS.device) + fluid.enable_dygraph(place) if FLAGS.dynamic else None + + # Generators + g_AB = Generator() + g_BA = Generator() + g = GeneratorCombine(g_AB, g_BA, is_train=False) + + im_shape = [-1, 3, 256, 256] + input_A = Input(im_shape, 'float32', 'input_A') + input_B = Input(im_shape, 'float32', 'input_B') + g.prepare(inputs=[input_A, input_B]) + g.load(FLAGS.init_model, skip_mismatch=True, reset_optimizer=True) + + if not os.path.exists(FLAGS.output): + os.makedirs(FLAGS.output) + + test_data_A = data.TestDataA() + test_data_B = data.TestDataB() + + for i in range(len(test_data_A)): + data_A, A_name = test_data_A[i] + data_B, B_name = test_data_B[i] + data_A = np.array(data_A).astype("float32") + data_B = np.array(data_B).astype("float32") + + fake_A, fake_B, cyc_A, cyc_B = g.test([data_A, data_B]) + + datas = [fake_A, fake_B, cyc_A, cyc_B, data_A, data_B] + odatas = [] + for o in datas: + d = np.squeeze(o[0]).transpose([1, 2, 0]) + im = ((d + 1) * 127.5).astype(np.uint8) + odatas.append(im) + imsave(FLAGS.output + "/fakeA_" + B_name, odatas[0]) + imsave(FLAGS.output + "/fakeB_" + A_name, odatas[1]) + imsave(FLAGS.output + "/cycA_" + A_name, odatas[2]) + imsave(FLAGS.output + "/cycB_" + B_name, odatas[3]) + imsave(FLAGS.output + "/inputA_" + A_name, odatas[4]) + imsave(FLAGS.output + "/inputB_" + B_name, odatas[5]) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser("CycleGAN test") + parser.add_argument( + "-d", "--dynamic", action='store_false', help="Enable dygraph mode") + parser.add_argument( + "-p", + "--device", + type=str, + default='gpu', + help="device to use, gpu or cpu") + parser.add_argument( + "-b", "--batch_size", default=1, type=int, help="batch size") + parser.add_argument( + "-o", + '--output', + type=str, + default='output/eval', + help="The test result to be saved to.") + parser.add_argument( + "-m", + "--init_model", + type=str, + default='checkpoint/199', + help="The init model file of directory.") + FLAGS = parser.parse_args() + print(FLAGS) + main() diff --git a/cyclegan/train.py b/cyclegan/train.py new file mode 100644 index 0000000..b098be9 --- /dev/null +++ b/cyclegan/train.py @@ -0,0 +1,154 @@ +# Copyright (c) 2019 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. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import random +import argparse +import contextlib +import time + +import paddle +import paddle.fluid as fluid + +from model import Model, Input, set_device + +import reader as reader +import data as data +from cyclegan import Generator, Discriminator, GeneratorCombine, GLoss, DLoss + +step_per_epoch = 2974 + + +def opt(parameters): + lr_base = 0.0002 + bounds = [100, 120, 140, 160, 180] + lr = [1., 0.8, 0.6, 0.4, 0.2, 0.1] + bounds = [i * step_per_epoch for i in bounds] + lr = [i * lr_base for i in lr] + optimizer = fluid.optimizer.Adam( + learning_rate=fluid.layers.piecewise_decay( + boundaries=bounds, values=lr), + parameter_list=parameters, + beta1=0.5) + return optimizer + + +def main(): + place = set_device(FLAGS.device) + fluid.enable_dygraph(place) if FLAGS.dynamic else None + + # Generators + g_AB = Generator() + g_BA = Generator() + + # Discriminators + d_A = Discriminator() + d_B = Discriminator() + + g = GeneratorCombine(g_AB, g_BA, d_A, d_B) + + da_params = d_A.parameters() + db_params = d_B.parameters() + g_params = g_AB.parameters() + g_BA.parameters() + + da_optimizer = opt(da_params) + db_optimizer = opt(db_params) + g_optimizer = opt(g_params) + + im_shape = [None, 3, 256, 256] + input_A = Input(im_shape, 'float32', 'input_A') + input_B = Input(im_shape, 'float32', 'input_B') + fake_A = Input(im_shape, 'float32', 'fake_A') + fake_B = Input(im_shape, 'float32', 'fake_B') + + g_AB.prepare(inputs=[input_A]) + g_BA.prepare(inputs=[input_B]) + + g.prepare(g_optimizer, GLoss(), inputs=[input_A, input_B]) + d_A.prepare(da_optimizer, DLoss(), inputs=[input_B, fake_B]) + d_B.prepare(db_optimizer, DLoss(), inputs=[input_A, fake_A]) + + loader_A = fluid.io.DataLoader( + data.DataA(), + feed_list=[x.forward() for x in [input_A]] + if not FLAGS.dynamic else None, + places=place, + shuffle=True, + return_list=True, + use_buffer_reader=True, + batch_size=FLAGS.batch_size) + loader_B = fluid.io.DataLoader( + data.DataB(), + feed_list=[x.forward() for x in [input_B]] + if not FLAGS.dynamic else None, + places=place, + shuffle=True, + return_list=True, + use_buffer_reader=True, + batch_size=FLAGS.batch_size) + + A_pool = data.ImagePool() + B_pool = data.ImagePool() + + for epoch in range(FLAGS.epoch): + for i, (data_A, data_B) in enumerate(zip(loader_A, loader_B)): + data_A = data_A[0][0] if not FLAGS.dynamic else data_A[0] + data_B = data_B[0][0] if not FLAGS.dynamic else data_B[0] + start = time.time() + + fake_B = g_AB.test(data_A)[0] + fake_A = g_BA.test(data_B)[0] + g_loss = g.train([data_A, data_B])[0] + fake_pb = B_pool.get(fake_B) + da_loss = d_A.train([data_B, fake_pb])[0] + + fake_pa = A_pool.get(fake_A) + db_loss = d_B.train([data_A, fake_pa])[0] + + t = time.time() - start + if i % 20 == 0: + print("epoch: {} | step: {:3d} | g_loss: {:.4f} | " + + "da_loss: {:.4f} | db_loss: {:.4f} | s/step {:.4f}". + format(epoch, i, g_loss[0], da_loss[0], db_loss[0], t)) + g.save('{}/{}'.format(FLAGS.checkpoint_path, epoch)) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser("CycleGAN Training on Cityscapes") + parser.add_argument( + "-d", "--dynamic", action='store_false', help="Enable dygraph mode") + parser.add_argument( + "--device", type=str, default='gpu', help="device to use, gpu or cpu") + parser.add_argument( + "-e", "--epoch", default=200, type=int, help="Epoch number") + parser.add_argument( + "-b", "--batch_size", default=1, type=int, help="batch size") + parser.add_argument( + "-o", + "--checkpoint_path", + type=str, + default='checkpoint', + help="path to save checkpoint") + parser.add_argument( + "-r", + "--resume", + default=None, + type=str, + help="checkpoint path to resume") + FLAGS = parser.parse_args() + main() diff --git a/model.py b/model.py index 9134048..419afaf 100644 --- a/model.py +++ b/model.py @@ -112,9 +112,9 @@ class Loss(object): def forward(self, outputs, labels): raise NotImplementedError() - def __call__(self, outputs, labels): + def __call__(self, outputs, labels=None): labels = to_list(labels) - if in_dygraph_mode(): + if in_dygraph_mode() and labels: labels = [to_variable(l) for l in labels] losses = to_list(self.forward(to_list(outputs), labels)) if self.average: @@ -870,8 +870,6 @@ class Model(fluid.dygraph.Layer): if not isinstance(inputs, (list, dict, Input)): raise TypeError( "'inputs' must be list or dict in static graph mode") - if loss_function and not isinstance(labels, (list, Input)): - raise TypeError("'labels' must be list in static graph mode") metrics = metrics or [] for metric in to_list(metrics): -- GitLab