From 5c47580ac44bb56bb41e136c8d09cd3006176557 Mon Sep 17 00:00:00 2001 From: Dun Date: Tue, 18 Sep 2018 16:21:44 +0800 Subject: [PATCH] add Deeplabv3+ model (#1252) * add Deeplabv3+ model --- fluid/deeplabv3+/README.md | 107 +++++++++++ fluid/deeplabv3+/eval.py | 124 +++++++++++++ fluid/deeplabv3+/imgs/model.png | Bin 0 -> 275682 bytes fluid/deeplabv3+/models.py | 310 ++++++++++++++++++++++++++++++++ fluid/deeplabv3+/reader.py | 132 ++++++++++++++ fluid/deeplabv3+/train.py | 157 ++++++++++++++++ 6 files changed, 830 insertions(+) create mode 100644 fluid/deeplabv3+/README.md create mode 100644 fluid/deeplabv3+/eval.py create mode 100644 fluid/deeplabv3+/imgs/model.png create mode 100644 fluid/deeplabv3+/models.py create mode 100644 fluid/deeplabv3+/reader.py create mode 100644 fluid/deeplabv3+/train.py diff --git a/fluid/deeplabv3+/README.md b/fluid/deeplabv3+/README.md new file mode 100644 index 00000000..ccb38689 --- /dev/null +++ b/fluid/deeplabv3+/README.md @@ -0,0 +1,107 @@ +DeepLab运行本目录下的程序示例需要使用PaddlePaddle develop最新版本。如果您的PaddlePaddle安装版本低于此要求,请按照[安装文档](http://www.paddlepaddle.org/docs/develop/documentation/zh/build_and_install/pip_install_cn.html)中的说明更新PaddlePaddle安装版本。 + + +## 代码结构 +``` +├── models.py # 网络结构定义脚本 +├── train.py # 训练任务脚本 +├── eval.py # 评估脚本 +└── reader.py # 定义通用的函数以及数据预处理脚本 +``` + +## 简介 + +DeepLabv3+ 是DeepLab语义分割系列网络的最新作,其前作有 DeepLabv1,DeepLabv2, DeepLabv3, +在最新作中,DeepLab的作者通过encoder-decoder进行多尺度信息的融合,同时保留了原来的空洞卷积和ASSP层, +其骨干网络使用了Xception模型,提高了语义分割的健壮性和运行速率,在 PASCAL VOC 2012 dataset取得新的state-of-art performance,89.0mIOU。 + +![](./imgs/model.png) + + +## 数据准备 + + + +本文采用Cityscape数据集,请前往[Cityscape官网](https://www.cityscapes-dataset.com)注册下载。 +下载以后的数据目录结构如下 +``` +data/cityscape/ +|-- gtFine +| |-- test +| |-- train +| `-- val +|-- leftImg8bit + |-- test + |-- train + `-- val +``` + +# 预训练模型准备 + +如果需要从头开始训练模型,用户需要下载我们的初始化模型 +``` +wget http://paddlemodels.cdn.bcebos.com/deeplab/deeplabv3plus_xception65_initialize.tar.gz +``` +如果需要最终训练模型进行fine tune或者直接用于预测,请下载我们的最终模型 +``` +wget http://paddlemodels.cdn.bcebos.com/deeplab/deeplabv3plus.tar.gz +``` + + +## 模型训练与预测 + +### 训练 +执行以下命令进行训练,同时指定weights的保存路径,初始化路径,以及数据存放位置: +``` +python ./train.py \ + --batch_size=1 \ + --train_crop_size=769 \ + --total_step=50 \ + --init_weights_path=$INIT_WEIGHTS_PATH \ + --save_weights_path=$SAVE_WEIGHTS_PATH \ + --dataset_path=$DATASET_PATH +``` +使用以下命令获得更多使用说明: +``` +python train.py --help +``` +以上命令用于测试训练过程是否正常,仅仅迭代了50次并且使用了1的batch size,如果需要复现 +原论文的实验,请使用以下设置: +``` +python ./train.py \ + --batch_size=8 \ + --parallel=true + --train_crop_size=769 \ + --total_step=90000 \ + --init_weights_path=$INIT_WEIGHTS_PATH \ + --save_weights_path=$SAVE_WEIGHTS_PATH \ + --dataset_path=$DATASET_PATH +``` + +### 测试 +执行以下命令在`Cityscape`测试数据集上进行测试: +``` +python ./eval.py \ + --init_weights_path=$INIT_WEIGHTS_PATH \ + --dataset_path=$DATASET_PATH +``` +需要通过选项`--model_path`指定模型文件。 +测试脚本的输出的评估指标为[mean IoU]()。 + + +## 实验结果 +训练完成以后,使用`eval.py`在验证集上进行测试,得到以下结果: +``` +load from: ../models/deeplabv3p +total number 500 +step: 500, mIoU: 0.7873 +``` + +## 其他信息 +|数据集 | pretrained model | trained model | mean IoU +|---|---|---|---| +|CityScape | [deeplabv3plus_xception65_initialize.tar.gz](http://paddlemodels.cdn.bcebos.com/deeplab/deeplabv3plus_xception65_initialize.tar.gz) | [deeplabv3plus.tar.gz](http://paddlemodels.cdn.bcebos.com/deeplab/deeplabv3plus.tar.gz) | 0.7873 | + +## 参考 + +- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1802.02611) diff --git a/fluid/deeplabv3+/eval.py b/fluid/deeplabv3+/eval.py new file mode 100644 index 00000000..2227a618 --- /dev/null +++ b/fluid/deeplabv3+/eval.py @@ -0,0 +1,124 @@ +import os +os.environ['FLAGS_fraction_of_gpu_memory_to_use'] = '0.98' + +import paddle +import paddle.fluid as fluid +import numpy as np +import argparse +from reader import CityscapeDataset +import reader +import models +import sys + + +def add_argument(name, type, default, help): + parser.add_argument('--' + name, default=default, type=type, help=help) + + +def add_arguments(): + add_argument('total_step', int, -1, + "Number of the step to be evaluated, -1 for full evaluation.") + add_argument('init_weights_path', str, None, + "Path of the weights to evaluate.") + add_argument('dataset_path', str, None, "Cityscape dataset path.") + add_argument('verbose', bool, False, "Print mIoU for each step if verbose.") + add_argument('use_gpu', bool, True, "Whether use GPU or CPU.") + + +def mean_iou(pred, label): + label = fluid.layers.elementwise_min( + label, fluid.layers.assign(np.array( + [num_classes], dtype=np.int32))) + label_ignore = (label == num_classes).astype('int32') + label_nignore = (label != num_classes).astype('int32') + + pred = pred * label_nignore + label_ignore * num_classes + + miou, wrong, correct = fluid.layers.mean_iou(pred, label, num_classes + 1) + return miou, wrong, correct + + +def load_model(): + if args.init_weights_path.endswith('/'): + fluid.io.load_params( + exe, dirname=args.init_weights_path, main_program=tp) + else: + fluid.io.load_params( + exe, dirname="", filename=args.init_weights_path, main_program=tp) + + +CityscapeDataset = reader.CityscapeDataset + +parser = argparse.ArgumentParser() +add_arguments() + +args = parser.parse_args() + +models.clean() +models.is_train = False +deeplabv3p = models.deeplabv3p + +image_shape = [1025, 2049] +eval_shape = [1024, 2048] + +sp = fluid.Program() +tp = fluid.Program() +batch_size = 1 +reader.default_config['crop_size'] = -1 +reader.default_config['shuffle'] = False +num_classes = 19 + +with fluid.program_guard(tp, sp): + img = fluid.layers.data(name='img', shape=[3, 0, 0], dtype='float32') + label = fluid.layers.data(name='label', shape=eval_shape, dtype='int32') + img = fluid.layers.resize_bilinear(img, image_shape) + logit = deeplabv3p(img) + logit = fluid.layers.resize_bilinear(logit, eval_shape) + pred = fluid.layers.argmax(logit, axis=1).astype('int32') + miou, out_wrong, out_correct = mean_iou(pred, label) + +tp = tp.clone(True) +fluid.memory_optimize( + tp, + print_log=False, + skip_opt_set=[pred.name, miou, out_wrong, out_correct], + level=1) + +place = fluid.CPUPlace() +if args.use_gpu: + place = fluid.CUDAPlace(0) +exe = fluid.Executor(place) +exe.run(sp) + +if args.init_weights_path: + print "load from:", args.init_weights_path + load_model() + +dataset = CityscapeDataset(args.dataset_path, 'val') +if args.total_step == -1: + total_step = len(dataset.label_files) +else: + total_step = args.total_step + +batches = dataset.get_batch_generator(batch_size, total_step) + +sum_iou = 0 +all_correct = np.array([0], dtype=np.int64) +all_wrong = np.array([0], dtype=np.int64) + +for i, imgs, labels, names in batches: + result = exe.run(tp, + feed={'img': imgs, + 'label': labels}, + fetch_list=[pred, miou, out_wrong, out_correct]) + wrong = result[2][:-1] + all_wrong + right = result[3][:-1] + all_correct + all_wrong = wrong.copy() + all_correct = 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+default_group_number = 32 + + +@contextlib.contextmanager +def scope(name): + global name_scope + bk = name_scope + name_scope = name_scope + name + '/' + yield + name_scope = bk + + +def check(data, number): + if type(data) == int: + return [data] * number + assert len(data) == number + return data + + +def clean(): + global op_results + op_results = {} + + +def append_op_result(result, name): + global op_results + op_index = len(op_results) + name = name_scope + name + str(op_index) + op_results[name] = result + return result + + +def conv(*args, **kargs): + kargs['param_attr'] = name_scope + 'weights' + if kargs.has_key('bias_attr') and kargs['bias_attr']: + kargs['bias_attr'] = name_scope + 'biases' + else: + kargs['bias_attr'] = False + return append_op_result(fluid.layers.conv2d(*args, **kargs), 'conv') + + +def group_norm(input, G, eps=1e-5, param_attr=None, bias_attr=None): + helper = fluid.layer_helper.LayerHelper('group_norm', **locals()) + + N, C, H, W = input.shape + if C % G != 0: + print "group can not divide channle:", C, G + for d in range(10): + for t in [d, -d]: + if G + t <= 0: continue + if C % (G + t) == 0: + G = G + t + break + if C % G == 0: + print "use group size:", G + break + assert C % G == 0 + param_shape = (G, ) + x = input + x = fluid.layers.reshape(x, [N, G, C // G * H * W]) + mean = fluid.layers.reduce_mean(x, dim=2, keep_dim=True) + x = x - mean + var = fluid.layers.reduce_mean(fluid.layers.square(x), dim=2, keep_dim=True) + x = x / fluid.layers.sqrt(var + eps) + + scale = helper.create_parameter( + attr=helper.param_attr, + shape=param_shape, + dtype='float32', + default_initializer=fluid.initializer.Constant(1.0)) + + bias = helper.create_parameter( + attr=helper.bias_attr, shape=param_shape, dtype='float32', is_bias=True) + x = fluid.layers.elementwise_add( + fluid.layers.elementwise_mul( + x, scale, axis=1), bias, axis=1) + return fluid.layers.reshape(x, input.shape) + + +def bn(*args, **kargs): + if default_norm_type == 'bn': + with scope('BatchNorm'): + return append_op_result( + fluid.layers.batch_norm( + *args, + epsilon=default_epsilon, + momentum=bn_momentum, + param_attr=name_scope + 'gamma', + bias_attr=name_scope + 'beta', + moving_mean_name=name_scope + 'moving_mean', + moving_variance_name=name_scope + 'moving_variance', + **kargs), + 'bn') + elif default_norm_type == 'gn': + with scope('GroupNorm'): + return append_op_result( + group_norm( + args[0], + default_group_number, + eps=default_epsilon, + param_attr=name_scope + 'gamma', + bias_attr=name_scope + 'beta'), + 'gn') + else: + raise "Unsupport norm type:" + default_norm_type + + +def bn_relu(data): + return append_op_result(fluid.layers.relu(bn(data)), 'relu') + + +def relu(data): + return append_op_result(fluid.layers.relu(data), 'relu') + + +def seq_conv(input, channel, stride, filter, dilation=1, act=None): + with scope('depthwise'): + input = conv( + input, + input.shape[1], + filter, + stride, + groups=input.shape[1], + padding=(filter / 2) * dilation, + dilation=dilation) + input = bn(input) + if act: input = act(input) + with scope('pointwise'): + input = conv(input, channel, 1, 1, groups=1, padding=0) + input = bn(input) + if act: input = act(input) + return input + + +def xception_block(input, + channels, + strides=1, + filters=3, + dilation=1, + skip_conv=True, + has_skip=True, + activation_fn_in_separable_conv=False): + repeat_number = 3 + channels = check(channels, repeat_number) + filters = check(filters, repeat_number) + strides = check(strides, repeat_number) + data = input + datum = [] + for i in range(repeat_number): + with scope('separable_conv' + str(i + 1)): + if not activation_fn_in_separable_conv: + data = relu(data) + data = seq_conv( + data, + channels[i], + strides[i], + filters[i], + dilation=dilation) + else: + data = seq_conv( + data, + channels[i], + strides[i], + filters[i], + dilation=dilation, + act=relu) + datum.append(data) + if not has_skip: + return append_op_result(data, 'xception_block'), datum + if skip_conv: + with scope('shortcut'): + skip = bn( + conv( + input, channels[-1], 1, strides[-1], groups=1, padding=0)) + else: + skip = input + return append_op_result(data + skip, 'xception_block'), datum + + +def entry_flow(data): + with scope("entry_flow"): + with scope("conv1"): + data = conv(data, 32, 3, stride=2, padding=1) + data = bn_relu(data) + with scope("conv2"): + data = conv(data, 64, 3, stride=1, padding=1) + data = bn_relu(data) + with scope("block1"): + data, _ = xception_block(data, 128, [1, 1, 2]) + with scope("block2"): + data, datum = xception_block(data, 256, [1, 1, 2]) + with scope("block3"): + data, _ = xception_block(data, 728, [1, 1, 2]) + return data, datum[1] + + +def middle_flow(data): + with scope("middle_flow"): + for i in range(16): + with scope("block" + str(i + 1)): + data, _ = xception_block(data, 728, [1, 1, 1], skip_conv=False) + return data + + +def exit_flow(data): + with scope("exit_flow"): + with scope('block1'): + data, _ = xception_block(data, [728, 1024, 1024], [1, 1, 1]) + with scope('block2'): + data, _ = xception_block( + data, [1536, 1536, 2048], [1, 1, 1], + dilation=2, + has_skip=False, + activation_fn_in_separable_conv=True) + return data + + +def dropout(x, keep_rate): + if is_train: + return fluid.layers.dropout(x, 1 - keep_rate) / keep_rate + else: + return x + + +def encoder(input): + with scope('encoder'): + channel = 256 + with scope("image_pool"): + image_avg = fluid.layers.reduce_mean(input, [2, 3], keep_dim=True) + append_op_result(image_avg, 'reduce_mean') + image_avg = bn_relu( + conv( + image_avg, channel, 1, 1, groups=1, padding=0)) + image_avg = fluid.layers.resize_bilinear(image_avg, input.shape[2:]) + + with scope("aspp0"): + aspp0 = bn_relu(conv(input, channel, 1, 1, groups=1, padding=0)) + with scope("aspp1"): + aspp1 = seq_conv(input, channel, 1, 3, dilation=6, act=relu) + with scope("aspp2"): + aspp2 = seq_conv(input, channel, 1, 3, dilation=12, act=relu) + with scope("aspp3"): + aspp3 = seq_conv(input, channel, 1, 3, dilation=18, act=relu) + with scope("concat"): + data = append_op_result( + fluid.layers.concat( + [image_avg, aspp0, aspp1, aspp2, aspp3], axis=1), + 'concat') + data = bn_relu(conv(data, channel, 1, 1, groups=1, padding=0)) + data = dropout(data, dropout_keep_prop) + return data + + +def decoder(encode_data, decode_shortcut): + with scope('decoder'): + with scope('concat'): + decode_shortcut = bn_relu( + conv( + decode_shortcut, decode_channel, 1, 1, groups=1, padding=0)) + encode_data = fluid.layers.resize_bilinear( + encode_data, decode_shortcut.shape[2:]) + encode_data = fluid.layers.concat( + [encode_data, decode_shortcut], axis=1) + append_op_result(encode_data, 'concat') + with scope("separable_conv1"): + encode_data = seq_conv( + encode_data, encode_channel, 1, 3, dilation=1, act=relu) + with scope("separable_conv2"): + encode_data = seq_conv( + encode_data, encode_channel, 1, 3, dilation=1, act=relu) + return encode_data + + +def deeplabv3p(img): + global default_epsilon + append_op_result(img, 'img') + with scope('xception_65'): + default_epsilon = 1e-3 + # Entry flow + data, decode_shortcut = entry_flow(img) + # Middle flow + data = middle_flow(data) + # Exit flow + data = exit_flow(data) + default_epsilon = 1e-5 + encode_data = encoder(data) + encode_data = decoder(encode_data, decode_shortcut) + with scope('logit'): + logit = conv( + encode_data, label_number, 1, stride=1, padding=0, bias_attr=True) + logit = fluid.layers.resize_bilinear(logit, img.shape[2:]) + return logit diff --git a/fluid/deeplabv3+/reader.py b/fluid/deeplabv3+/reader.py new file mode 100644 index 00000000..f4141cbb --- /dev/null +++ b/fluid/deeplabv3+/reader.py @@ -0,0 +1,132 @@ +import cv2 +import numpy as np + +default_config = { + "shuffle": True, + "min_resize": 0.5, + "max_resize": 2, + "crop_size": 769, +} + + +def slice_with_pad(a, s, value=0): + pads = [] + slices = [] + for i in range(len(a.shape)): + if i >= len(s): + pads.append([0, 0]) + slices.append([0, a.shape[i]]) + else: + l, r = s[i] + if l < 0: + pl = -l + l = 0 + else: + pl = 0 + if r > a.shape[i]: + pr = r - a.shape[i] + r = a.shape[i] + else: + pr = 0 + pads.append([pl, pr]) + slices.append([l, r]) + slices = map(lambda x: slice(x[0], x[1], 1), slices) + a = a[slices] + a = np.pad(a, pad_width=pads, mode='constant', constant_values=value) + return a + + +class CityscapeDataset: + def __init__(self, dataset_dir, subset='train', config=default_config): + import commands + label_dirname = dataset_dir + 'gtFine/' + subset + label_files = commands.getoutput( + "find %s -type f | grep labelTrainIds | sort" % + label_dirname).splitlines() + self.label_files = label_files + self.label_dirname = label_dirname + self.index = 0 + self.subset = subset + self.dataset_dir = dataset_dir + self.config = config + self.reset() + print "total number", len(label_files) + + def reset(self, shuffle=False): + self.index = 0 + if self.config["shuffle"]: + np.random.shuffle(self.label_files) + + def next_img(self): + self.index += 1 + if self.index >= len(self.label_files): + self.reset() + + def get_img(self): + shape = self.config["crop_size"] + while True: + ln = self.label_files[self.index] + img_name = self.dataset_dir + 'leftImg8bit/' + self.subset + ln[len( + self.label_dirname):] + img_name = img_name.replace('gtFine_labelTrainIds', 'leftImg8bit') + label = cv2.imread(ln) + img = cv2.imread(img_name) + if img is None: + print "load img failed:", img_name + self.next_img() + else: + break + if shape == -1: + return img, label, ln + random_scale = np.random.rand(1) * (self.config['max_resize'] - + self.config['min_resize'] + ) + self.config['min_resize'] + crop_size = int(shape / random_scale) + bb = crop_size // 2 + + def _randint(low, high): + return int(np.random.rand(1) * (high - low) + low) + + offset_x = np.random.randint(bb, max(bb + 1, img.shape[0] - + bb)) - crop_size // 2 + offset_y = np.random.randint(bb, max(bb + 1, img.shape[1] - + bb)) - crop_size // 2 + img_crop = slice_with_pad(img, [[offset_x, offset_x + crop_size], + [offset_y, offset_y + crop_size]], 128) + img = cv2.resize(img_crop, (shape, shape)) + label_crop = slice_with_pad(label, [[offset_x, offset_x + crop_size], + [offset_y, offset_y + crop_size]], + 255) + label = cv2.resize( + label_crop, (shape, shape), interpolation=cv2.INTER_NEAREST) + return img, label, ln + str( + (offset_x, offset_y, crop_size, random_scale)) + + def get_batch(self, batch_size=1): + imgs = [] + labels = [] + names = [] + while len(imgs) < batch_size: + img, label, ln = self.get_img() + imgs.append(img) + labels.append(label) + names.append(ln) + self.next_img() + return np.array(imgs), np.array(labels), names + + def get_batch_generator(self, batch_size, total_step): + def do_get_batch(): + for i in range(total_step): + imgs, labels, names = self.get_batch(batch_size) + labels = labels.astype(np.int32)[:, :, :, 0] + imgs = imgs[:, :, :, ::-1].transpose( + 0, 3, 1, 2).astype(np.float32) / (255.0 / 2) - 1 + yield i, imgs, labels, names + + batches = do_get_batch() + try: + from prefetch_generator import BackgroundGenerator + batches = BackgroundGenerator(batches, 100) + except: + print "You can install 'prefetch_generator' for acceleration of data reading." + return batches diff --git a/fluid/deeplabv3+/train.py b/fluid/deeplabv3+/train.py new file mode 100644 index 00000000..d5dd2cff --- /dev/null +++ b/fluid/deeplabv3+/train.py @@ -0,0 +1,157 @@ +import os +os.environ['FLAGS_fraction_of_gpu_memory_to_use'] = '0.98' + +import paddle +import paddle.fluid as fluid +import numpy as np +import argparse +from reader import CityscapeDataset +import reader +import models + + +def add_argument(name, type, default, help): + parser.add_argument('--' + name, default=default, type=type, help=help) + + +def add_arguments(): + add_argument('batch_size', int, 2, + "The number of images in each batch during training.") + add_argument('train_crop_size', int, 769, + "'Image crop size during training.") + add_argument('base_lr', float, 0.0001, + "The base learning rate for model training.") + add_argument('total_step', int, 90000, "Number of the training step.") + add_argument('init_weights_path', str, None, + "Path of the initial weights in paddlepaddle format.") + add_argument('save_weights_path', str, None, + "Path of the saved weights during training.") + add_argument('dataset_path', str, None, "Cityscape dataset path.") + add_argument('parallel', bool, False, "using ParallelExecutor.") + add_argument('use_gpu', bool, True, "Whether use GPU or CPU.") + + +def load_model(): + if args.init_weights_path.endswith('/'): + fluid.io.load_params( + exe, dirname=args.init_weights_path, main_program=tp) + else: + fluid.io.load_params( + exe, dirname="", filename=args.init_weights_path, main_program=tp) + + +def save_model(): + if args.save_weights_path.endswith('/'): + fluid.io.save_params( + exe, dirname=args.save_weights_path, main_program=tp) + else: + fluid.io.save_params( + exe, dirname="", filename=args.save_weights_path, main_program=tp) + + +def loss(logit, label): + label_nignore = (label < num_classes).astype('float32') + label = fluid.layers.elementwise_min( + label, + fluid.layers.assign(np.array( + [num_classes - 1], dtype=np.int32))) + logit = fluid.layers.transpose(logit, [0, 2, 3, 1]) + logit = fluid.layers.reshape(logit, [-1, num_classes]) + label = fluid.layers.reshape(label, [-1, 1]) + label = fluid.layers.cast(label, 'int64') + label_nignore = fluid.layers.reshape(label_nignore, [-1, 1]) + loss = fluid.layers.softmax_with_cross_entropy(logit, label) + loss = loss * label_nignore + no_grad_set.add(label_nignore.name) + no_grad_set.add(label.name) + return loss, label_nignore + + +CityscapeDataset = reader.CityscapeDataset +parser = argparse.ArgumentParser() + +add_arguments() + +args = parser.parse_args() + +models.clean() +models.bn_momentum = 0.9997 +models.dropout_keep_prop = 0.9 +deeplabv3p = models.deeplabv3p + +sp = fluid.Program() +tp = fluid.Program() +crop_size = args.train_crop_size +batch_size = args.batch_size +image_shape = [crop_size, crop_size] +reader.default_config['crop_size'] = crop_size +reader.default_config['shuffle'] = True +num_classes = 19 +weight_decay = 0.00004 + +base_lr = args.base_lr +total_step = args.total_step + +no_grad_set = set() + +with fluid.program_guard(tp, sp): + img = fluid.layers.data( + name='img', shape=[3] + image_shape, dtype='float32') + label = fluid.layers.data(name='label', shape=image_shape, dtype='int32') + logit = deeplabv3p(img) + pred = fluid.layers.argmax(logit, axis=1).astype('int32') + loss, mask = loss(logit, label) + lr = fluid.layers.polynomial_decay( + base_lr, total_step, end_learning_rate=0, power=0.9) + area = fluid.layers.elementwise_max( + fluid.layers.reduce_mean(mask), + fluid.layers.assign(np.array( + [0.1], dtype=np.float32))) + loss_mean = fluid.layers.reduce_mean(loss) / area + + opt = fluid.optimizer.Momentum( + lr, + momentum=0.9, + regularization=fluid.regularizer.L2DecayRegularizer( + regularization_coeff=weight_decay), ) + retv = opt.minimize(loss_mean, startup_program=sp, no_grad_set=no_grad_set) + +fluid.memory_optimize( + tp, print_log=False, skip_opt_set=[pred.name, loss_mean.name], level=1) + +place = fluid.CPUPlace() +if args.use_gpu: + place = fluid.CUDAPlace(0) +exe = fluid.Executor(place) +exe.run(sp) + +if args.init_weights_path: + print "load from:", args.init_weights_path + load_model() + +dataset = CityscapeDataset(args.dataset_path, 'train') + +if args.parallel: + print "Using ParallelExecutor." + exe_p = fluid.ParallelExecutor( + use_cuda=True, loss_name=loss_mean.name, main_program=tp) + +batches = dataset.get_batch_generator(batch_size, total_step) + +for i, imgs, labels, names in batches: + if args.parallel: + retv = exe_p.run(fetch_list=[pred.name, loss_mean.name], + feed={'img': imgs, + 'label': labels}) + else: + retv = exe.run(tp, + feed={'img': imgs, + 'label': labels}, + fetch_list=[pred, loss_mean]) + if i % 100 == 0: + print "Model is saved to", args.save_weights_path + save_model() + print "step %s, loss: %s" % (i, np.mean(retv[1])) + +print "Training done. Model is saved to", args.save_weights_path +save_model() -- GitLab