reorganize the catalog

.pre-commit-config.yaml

@@ -34,7 +34,7 @@
     -   id: convert-markdown-into-html
         name: convert-markdown-into-html
         description: Convert README.md into index.html and README.en.md into index.en.html
-        entry: python pre-commit-hooks/convert_markdown_into_html.py
+        entry: python .pre-commit-hooks/convert_markdown_into_html.py
         language: system
         files: .+README(\.en)?\.md$
 

.travis.yml

@@ -19,7 +19,7 @@ before_install:
   -  pip install -U virtualenv pre-commit pip
   -  GOPATH=/tmp/go go get -u github.com/wangkuiyi/ipynb/markdown-to-ipynb
 script:
-  -  PATH=/tmp/go/bin:$PATH travis/precommit.sh
+  -  PATH=/tmp/go/bin:$PATH .travis/precommit.sh
 notifications:
   email:
     on_success: change

image_classification/deprecated/classify.py

-# Copyright (c) 2016 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.
-
-import os, sys
-import cPickle
-import numpy as np
-from PIL import Image
-from optparse import OptionParser
-
-import paddle.utils.image_util as image_util
-from py_paddle import swig_paddle, DataProviderConverter
-from paddle.trainer.PyDataProvider2 import dense_vector
-from paddle.trainer.config_parser import parse_config
-
-import logging
-logging.basicConfig(
-    format='[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s')
-logging.getLogger().setLevel(logging.INFO)
-
-
-def vis_square(data, fname):
-    import matplotlib
-    matplotlib.use('Agg')
-    import matplotlib.pyplot as plt
-    """Take an array of shape (n, height, width) or (n, height, width, 3)
-       and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)"""
-    # normalize data for display
-    data = (data - data.min()) / (data.max() - data.min())
-    # force the number of filters to be square
-    n = int(np.ceil(np.sqrt(data.shape[0])))
-    padding = (
-        ((0, n**2 - data.shape[0]), (0, 1),
-         (0, 1))  # add some space between filters
-        + ((0, 0), ) *
-        (data.ndim - 3))  # don't pad the last dimension (if there is one)
-    data = np.pad(
-        data, padding, mode='constant',
-        constant_values=1)  # pad with ones (white)
-    # tile the filters into an image
-    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(
-        range(4, data.ndim + 1)))
-    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])
-    plt.imshow(data, cmap='gray')
-    plt.savefig(fname)
-    plt.axis('off')
-
-
-class ImageClassifier():
-    def __init__(self,
-                 train_conf,
-                 resize_dim,
-                 crop_dim,
-                 model_dir=None,
-                 use_gpu=True,
-                 mean_file=None,
-                 oversample=False,
-                 is_color=True):
-        self.train_conf = train_conf
-        self.model_dir = model_dir
-        if model_dir is None:
-            self.model_dir = os.path.dirname(train_conf)
-
-        self.resize_dim = resize_dim
-        self.crop_dims = [crop_dim, crop_dim]
-        self.oversample = oversample
-        self.is_color = is_color
-
-        self.transformer = image_util.ImageTransformer(is_color=is_color)
-        self.transformer.set_transpose((2, 0, 1))
-        self.transformer.set_channel_swap((2, 1, 0))
-
-        self.mean_file = mean_file
-        if self.mean_file is not None:
-            mean = np.load(self.mean_file)['mean']
-            mean = mean.reshape(3, self.crop_dims[0], self.crop_dims[1])
-            self.transformer.set_mean(mean)  # mean pixel
-        else:
-            # if you use three mean value, set like:
-            # this three mean value is calculated from ImageNet.
-            self.transformer.set_mean(np.array([103.939, 116.779, 123.68]))
-
-        conf_args = "use_gpu=%d,is_predict=1" % (int(use_gpu))
-        conf = parse_config(train_conf, conf_args)
-        swig_paddle.initPaddle("--use_gpu=%d" % (int(use_gpu)))
-        self.network = swig_paddle.GradientMachine.createFromConfigProto(
-            conf.model_config)
-        assert isinstance(self.network, swig_paddle.GradientMachine)
-        self.network.loadParameters(self.model_dir)
-
-        dim = 3 * self.crop_dims[0] * self.crop_dims[1]
-        slots = [dense_vector(dim)]
-        self.converter = DataProviderConverter(slots)
-
-    def get_data(self, img_path):
-        """
-        1. load image from img_path.
-        2. resize or oversampling.
-        3. transformer data: transpose, channel swap, sub mean.
-        return K x H x W ndarray.
-
-        img_path: image path.
-        """
-        image = image_util.load_image(img_path, self.is_color)
-        # Another way to extract oversampled features is that
-        # cropping and averaging from large feature map which is
-        # calculated by large size of image.
-        # This way reduces the computation.
-        if self.oversample:
-            image = image_util.resize_image(image, self.resize_dim)
-            image = np.array(image)
-            input = np.zeros(
-                (1, image.shape[0], image.shape[1], 3), dtype=np.float32)
-            input[0] = image.astype(np.float32)
-            input = image_util.oversample(input, self.crop_dims)
-        else:
-            image = image.resize(self.crop_dims, Image.ANTIALIAS)
-            input = np.zeros(
-                (1, self.crop_dims[0], self.crop_dims[1], 3), dtype=np.float32)
-            input[0] = np.array(image).astype(np.float32)
-
-        data_in = []
-        for img in input:
-            img = self.transformer.transformer(img).flatten()
-            data_in.append([img.tolist()])
-        return data_in
-
-    def forward(self, input_data):
-        in_arg = self.converter(input_data)
-        return self.network.forwardTest(in_arg)
-
-    def forward(self, data, output_layer):
-        input = self.converter(data)
-        self.network.forwardTest(input)
-        output = self.network.getLayerOutputs(output_layer)
-        res = {}
-        if isinstance(output_layer, basestring):
-            output_layer = [output_layer]
-        for name in output_layer:
-            # For oversampling, average predictions across crops.
-            # If not, the shape of output[name]: (1, class_number),
-            # the mean is also applicable.
-            res[name] = output[name].mean(0)
-        return res
-
-
-def option_parser():
-    usage = "%prog -c config -i data_list -w model_dir [options]"
-    parser = OptionParser(usage="usage: %s" % usage)
-    parser.add_option(
-        "--job",
-        action="store",
-        dest="job_type",
-        choices=[
-            'predict',
-            'extract',
-        ],
-        default='predict',
-        help="The job type. \
-                            predict: predicting,\
-                            extract: extract features")
-    parser.add_option(
-        "--conf",
-        action="store",
-        dest="train_conf",
-        default='models/vgg.py',
-        help="network config")
-    parser.add_option(
-        "--data",
-        action="store",
-        dest="data_file",
-        default='image/dog.png',
-        help="image list")
-    parser.add_option(
-        "--model",
-        action="store",
-        dest="model_path",
-        default=None,
-        help="model path")
-    parser.add_option(
-        "-c", dest="cpu_gpu", action="store_false", help="Use cpu mode.")
-    parser.add_option(
-        "-g",
-        dest="cpu_gpu",
-        default=True,
-        action="store_true",
-        help="Use gpu mode.")
-    parser.add_option(
-        "--mean",
-        action="store",
-        dest="mean",
-        default='data/mean.meta',
-        help="The mean file.")
-    parser.add_option(
-        "--multi_crop",
-        action="store_true",
-        dest="multi_crop",
-        default=False,
-        help="Wether to use multiple crops on image.")
-    return parser.parse_args()
-
-
-def main():
-    options, args = option_parser()
-    mean = 'data/mean.meta' if not options.mean else options.mean
-    conf = 'models/vgg.py' if not options.train_conf else options.train_conf
-    obj = ImageClassifier(
-        conf,
-        32,
-        32,
-        options.model_path,
-        use_gpu=options.cpu_gpu,
-        mean_file=mean,
-        oversample=options.multi_crop)
-    image_path = options.data_file
-    if options.job_type == 'predict':
-        output_layer = '__fc_layer_2__'
-        data = obj.get_data(image_path)
-        prob = obj.forward(data, output_layer)
-        lab = np.argsort(-prob[output_layer])
-        logging.info("Label of %s is: %d", image_path, lab[0])
-
-    elif options.job_type == "extract":
-        output_layer = '__conv_0__'
-        data = obj.get_data(options.data_file)
-        features = obj.forward(data, output_layer)
-        dshape = (64, 32, 32)
-        fea = features[output_layer].reshape(dshape)
-        vis_square(fea, 'fea_conv0.png')
-
-
-if __name__ == '__main__':
-    main()

image_classification/deprecated/data/cifar10.py

-# Copyright (c) 2016 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.
-
-import os
-import numpy as np
-import cPickle
-
-DATA = "cifar-10-batches-py"
-CHANNEL = 3
-HEIGHT = 32
-WIDTH = 32
-
-
-def create_mean(dataset):
-    if not os.path.isfile("mean.meta"):
-        mean = np.zeros(CHANNEL * HEIGHT * WIDTH)
-        num = 0
-        for f in dataset:
-            batch = np.load(f)
-            mean += batch['data'].sum(0)
-            num += len(batch['data'])
-        mean /= num
-        print mean.size
-        data = {"mean": mean, "size": mean.size}
-        cPickle.dump(
-            data, open("mean.meta", 'w'), protocol=cPickle.HIGHEST_PROTOCOL)
-
-
-def create_data():
-    train_set = [DATA + "/data_batch_%d" % (i + 1) for i in xrange(0, 5)]
-    test_set = [DATA + "/test_batch"]
-
-    # create mean values
-    create_mean(train_set)
-
-    # create dataset lists
-    if not os.path.isfile("train.txt"):
-        train = ["data/" + i for i in train_set]
-        open("train.txt", "w").write("\n".join(train))
-        open("train.list", "w").write("\n".join(["data/train.txt"]))
-
-    if not os.path.isfile("text.txt"):
-        test = ["data/" + i for i in test_set]
-        open("test.txt", "w").write("\n".join(test))
-        open("test.list", "w").write("\n".join(["data/test.txt"]))
-
-
-if __name__ == '__main__':
-    create_data()

image_classification/deprecated/data/get_data.sh

-# Copyright (c) 2016 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.
-set -e
-
-wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
-tar zxf cifar-10-python.tar.gz
-rm cifar-10-python.tar.gz
-
-python cifar10.py

image_classification/deprecated/dataprovider.py

-# Copyright (c) 2016 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.
-
-import numpy as np
-import cPickle
-from paddle.trainer.PyDataProvider2 import *
-
-
-def initializer(settings, mean_path, is_train, **kwargs):
-    settings.is_train = is_train
-    settings.input_size = 3 * 32 * 32
-    settings.mean = np.load(mean_path)['mean']
-    settings.input_types = {
-        'image': dense_vector(settings.input_size),
-        'label': integer_value(10)
-    }
-
-
-@provider(init_hook=initializer, pool_size=50000)
-def process(settings, file_list):
-    with open(file_list, 'r') as fdata:
-        for fname in fdata:
-            fo = open(fname.strip(), 'rb')
-            batch = cPickle.load(fo)
-            fo.close()
-            images = batch['data']
-            labels = batch['labels']
-            for im, lab in zip(images, labels):
-                if settings.is_train and np.random.randint(2):
-                    im = im.reshape(3, 32, 32)
-                    im = im[:, :, ::-1]
-                    im = im.flatten()
-                im = im - settings.mean
-                yield {'image': im.astype('float32'), 'label': int(lab)}

image_classification/deprecated/extract.sh

-#!/bin/bash
-# Copyright (c) 2016 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.
-set -e
-
-python classify.py --job=extract --model=output/pass-00299 --data=image/dog.png # -c

image_classification/deprecated/models/resnet.py

-# Copyright (c) 2016 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 paddle.trainer_config_helpers import *
-
-is_predict = get_config_arg("is_predict", bool, False)
-if not is_predict:
-    args = {'meta': 'data/mean.meta'}
-    define_py_data_sources2(
-        train_list='data/train.list',
-        test_list='data/test.list',
-        module='dataprovider',
-        obj='process',
-        args={'mean_path': 'data/mean.meta'})
-
-settings(
-    batch_size=128,
-    learning_rate=0.1 / 128.0,
-    learning_rate_decay_a=0.1,
-    learning_rate_decay_b=50000 * 140,
-    learning_rate_schedule='discexp',
-    learning_method=MomentumOptimizer(0.9),
-    regularization=L2Regularization(0.0002 * 128))
-
-
-def conv_bn_layer(input,
-                  ch_out,
-                  filter_size,
-                  stride,
-                  padding,
-                  active_type=ReluActivation(),
-                  ch_in=None):
-    tmp = img_conv_layer(
-        input=input,
-        filter_size=filter_size,
-        num_channels=ch_in,
-        num_filters=ch_out,
-        stride=stride,
-        padding=padding,
-        act=LinearActivation(),
-        bias_attr=False)
-    return batch_norm_layer(input=tmp, act=active_type)
-
-
-def shortcut(ipt, n_in, n_out, stride):
-    if n_in != n_out:
-        print("n_in != n_out")
-        return conv_bn_layer(ipt, n_out, 1, stride, 0, LinearActivation())
-    else:
-        return ipt
-
-
-def basicblock(ipt, ch_out, stride):
-    ch_in = ipt.num_filters
-    tmp = conv_bn_layer(ipt, ch_out, 3, stride, 1)
-    tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1, LinearActivation())
-    short = shortcut(ipt, ch_in, ch_out, stride)
-    return addto_layer(input=[tmp, short], act=ReluActivation())
-
-
-def bottleneck(ipt, ch_out, stride):
-    ch_in = ipt.num_filter
-    tmp = conv_bn_layer(ipt, ch_out, 1, stride, 0)
-    tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1)
-    tmp = conv_bn_layer(tmp, ch_out * 4, 1, 1, 0, LinearActivation())
-    short = shortcut(ipt, ch_in, ch_out * 4, stride)
-    return addto_layer(input=[tmp, short], act=ReluActivation())
-
-
-def layer_warp(block_func, ipt, features, count, stride):
-    tmp = block_func(ipt, features, stride)
-    for i in range(1, count):
-        tmp = block_func(tmp, features, 1)
-    return tmp
-
-
-def resnet_imagenet(ipt, depth=50):
-    cfg = {
-        18: ([2, 2, 2, 1], basicblock),
-        34: ([3, 4, 6, 3], basicblock),
-        50: ([3, 4, 6, 3], bottleneck),
-        101: ([3, 4, 23, 3], bottleneck),
-        152: ([3, 8, 36, 3], bottleneck)
-    }
-    stages, block_func = cfg[depth]
-    tmp = conv_bn_layer(
-        ipt, ch_in=3, ch_out=64, filter_size=7, stride=2, padding=3)
-    tmp = img_pool_layer(input=tmp, pool_size=3, stride=2)
-    tmp = layer_warp(block_func, tmp, 64, stages[0], 1)
-    tmp = layer_warp(block_func, tmp, 128, stages[1], 2)
-    tmp = layer_warp(block_func, tmp, 256, stages[2], 2)
-    tmp = layer_warp(block_func, tmp, 512, stages[3], 2)
-    tmp = img_pool_layer(
-        input=tmp, pool_size=7, stride=1, pool_type=AvgPooling())
-
-    tmp = fc_layer(input=tmp, size=1000, act=SoftmaxActivation())
-    return tmp
-
-
-def resnet_cifar10(ipt, depth=32):
-    #depth should be one of 20, 32, 44, 56, 110, 1202
-    assert (depth - 2) % 6 == 0
-    n = (depth - 2) / 6
-    nStages = {16, 64, 128}
-    conv1 = conv_bn_layer(
-        ipt, ch_in=3, ch_out=16, filter_size=3, stride=1, padding=1)
-    res1 = layer_warp(basicblock, conv1, 16, n, 1)
-    res2 = layer_warp(basicblock, res1, 32, n, 2)
-    res3 = layer_warp(basicblock, res2, 64, n, 2)
-    pool = img_pool_layer(
-        input=res3, pool_size=8, stride=1, pool_type=AvgPooling())
-    return pool
-
-
-datadim = 3 * 32 * 32
-classdim = 10
-data = data_layer(name='image', size=datadim)
-net = resnet_cifar10(data, depth=32)
-out = fc_layer(input=net, size=10, act=SoftmaxActivation())
-if not is_predict:
-    lbl = data_layer(name="label", size=classdim)
-    outputs(classification_cost(input=out, label=lbl))
-else:
-    outputs(out)

image_classification/deprecated/models/vgg.py

-# Copyright (c) 2016 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 paddle.trainer_config_helpers import *
-
-is_predict = get_config_arg("is_predict", bool, False)
-if not is_predict:
-    define_py_data_sources2(
-        train_list='data/train.list',
-        test_list='data/test.list',
-        module='dataprovider',
-        obj='process',
-        args={'mean_path': 'data/mean.meta'})
-
-settings(
-    batch_size=128,
-    learning_rate=0.1 / 128.0,
-    learning_rate_decay_a=0.1,
-    learning_rate_decay_b=50000 * 100,
-    learning_rate_schedule='discexp',
-    learning_method=MomentumOptimizer(0.9),
-    regularization=L2Regularization(0.0005 * 128), )
-
-
-def vgg_bn_drop(input):
-    def conv_block(ipt, num_filter, groups, dropouts, num_channels=None):
-        return img_conv_group(
-            input=ipt,
-            num_channels=num_channels,
-            pool_size=2,
-            pool_stride=2,
-            conv_num_filter=[num_filter] * groups,
-            conv_filter_size=3,
-            conv_act=ReluActivation(),
-            conv_with_batchnorm=True,
-            conv_batchnorm_drop_rate=dropouts,
-            pool_type=MaxPooling())
-
-    conv1 = conv_block(input, 64, 2, [0.3, 0], 3)
-    conv2 = conv_block(conv1, 128, 2, [0.4, 0])
-    conv3 = conv_block(conv2, 256, 3, [0.4, 0.4, 0])
-    conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])
-    conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
-
-    drop = dropout_layer(input=conv5, dropout_rate=0.5)
-    fc1 = fc_layer(input=drop, size=512, act=LinearActivation())
-    bn = batch_norm_layer(
-        input=fc1, act=ReluActivation(), layer_attr=ExtraAttr(drop_rate=0.5))
-    fc2 = fc_layer(input=bn, size=512, act=LinearActivation())
-    return fc2
-
-
-datadim = 3 * 32 * 32
-classdim = 10
-data = data_layer(name='image', size=datadim)
-net = vgg_bn_drop(data)
-out = fc_layer(input=net, size=classdim, act=SoftmaxActivation())
-if not is_predict:
-    lbl = data_layer(name="label", size=classdim)
-    cost = classification_cost(input=out, label=lbl)
-    outputs(cost)
-else:
-    outputs(out)

image_classification/deprecated/predict.sh

-#!/bin/bash
-# Copyright (c) 2016 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.
-set -e
-
-python classify.py --job=predict --model=output/pass-00299 --data=image/dog.png # -c

image_classification/deprecated/train.sh

-#!/bin/bash
-# Copyright (c) 2016 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.
-set -e
-
-#cfg=models/resnet.py
-cfg=models/vgg.py
-output=output
-log=train.log
-
-paddle train \
-    --config=$cfg \
-    --use_gpu=true \
-    --trainer_count=1 \
-    --log_period=100 \
-    --num_passes=300 \
-    --save_dir=$output \
-    2>&1 | tee $log