Merge pull request #1 from Renwb1991/caffe2fluid

X2Paddle: add caffe2fluid

caffe2fluid/.gitignore

+proto/caffepb.py
+proto/caffe_pb2.py

caffe2fluid/README.md

+### Caffe2Fluid
+This tool is used to convert a Caffe model to a Fluid model
+
+### Key Features
+1. Convert caffe model to fluid model with codes of defining a network(useful for re-training)
+
+2. Pycaffe is not necessary when just want convert model without do caffe-inference
+
+3. Caffe's customized layers convertion also be supported by extending this tool
+
+4. A bunch of tools in `examples/imagenet/tools` are provided to compare the difference
+
+### HowTo
+1. Prepare `caffepb.py` in `./proto` if your python has no `pycaffe` module, two options provided here:
+    - Generate pycaffe from caffe.proto
+        ```
+        bash ./proto/compile.sh
+        ```
+
+    - Download one from github directly
+        ```
+        cd proto/ && wget https://raw.githubusercontent.com/ethereon/caffe-tensorflow/master/kaffe/caffe/caffepb.py
+        ```
+
+2. Convert the Caffe model to Fluid model
+   - Generate fluid code and weight file
+       ```
+       python convert.py alexnet.prototxt \
+               --caffemodel alexnet.caffemodel \
+               --data-output-path alexnet.npy \
+               --code-output-path alexnet.py
+       ```
+
+   - Save weights as fluid model file
+       ```
+       # only infer the last layer's result
+       python alexnet.py alexnet.npy ./fluid
+       # infer these 2 layer's result
+       python alexnet.py alexnet.npy ./fluid fc8,prob
+       ```
+
+3. Use the converted model to infer
+    - See more details in `examples/imagenet/tools/run.sh`
+
+4. Compare the inference results with caffe
+    - See more details in `examples/imagenet/tools/diff.sh`
+
+### How to convert custom layer
+1. Implement your custom layer in a file under `kaffe/custom_layers`, eg: mylayer.py
+    - Implement ```shape_func(input_shape, [other_caffe_params])``` to calculate the output shape
+    - Implement ```layer_func(inputs, name, [other_caffe_params])``` to construct a fluid layer
+    - Register these two functions ```register(kind='MyType', shape=shape_func, layer=layer_func)```
+    - Notes: more examples can be found in `kaffe/custom_layers`
+
+2. Add ```import mylayer``` to  `kaffe/custom_layers/\_\_init__.py`
+
+3. Prepare your pycaffe as your customized version(same as previous env prepare)
+    - (option1) replace `proto/caffe.proto` with your own caffe.proto and compile it
+    - (option2) change your `pycaffe` to the customized version
+
+4. Convert the Caffe model to Fluid model
+
+5. Set env $CAFFE2FLUID_CUSTOM_LAYERS to the parent directory of 'custom_layers'
+   ```
+   export CAFFE2FLUID_CUSTOM_LAYERS=/path/to/caffe2fluid/kaffe
+   ```
+
+6. Use the converted model when loading model in `xxxnet.py` and `xxxnet.npy`(no need if model is already in `fluid/model` and `fluid/params`)
+
+### Tested models
+- Lenet:
+[model addr](https://github.com/ethereon/caffe-tensorflow/blob/master/examples/mnist)
+
+- ResNets:(ResNet-50, ResNet-101, ResNet-152)
+[model addr](https://onedrive.live.com/?authkey=%21AAFW2-FVoxeVRck&id=4006CBB8476FF777%2117887&cid=4006CBB8476FF777)
+
+- GoogleNet:
+[model addr](https://gist.github.com/jimmie33/7ea9f8ac0da259866b854460f4526034)
+
+- VGG:
+[model addr](https://gist.github.com/ksimonyan/211839e770f7b538e2d8)
+
+- AlexNet:
+[model addr](https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet)
+
+### Notes
+Some of this code come from here: [caffe-tensorflow](https://github.com/ethereon/caffe-tensorflow)

caffe2fluid/convert.py

+#!/usr/bin/env python
+
+import os
+import sys
+import numpy as np
+import argparse
+
+from kaffe import KaffeError, print_stderr
+from kaffe.paddle import Transformer
+
+
+def fatal_error(msg):
+    """ fatal error encounted
+    """
+    print_stderr(msg)
+    exit(-1)
+
+
+def validate_arguments(args):
+    """ validate args
+    """
+    if (args.data_output_path is not None) and (args.caffemodel is None):
+        fatal_error('No input data path provided.')
+    if (args.caffemodel is not None) and (args.data_output_path is None):
+        fatal_error('No output data path provided.')
+    if (args.code_output_path is None) and (args.data_output_path is None):
+        fatal_error('No output path specified.')
+
+
+def convert(def_path, caffemodel_path, data_output_path, code_output_path,
+            phase):
+    """ convert caffe model to tf/paddle models
+    """
+    try:
+        transformer = Transformer(def_path, caffemodel_path, phase=phase)
+        print_stderr('Converting data...')
+        if caffemodel_path is not None:
+            data = transformer.transform_data()
+            print_stderr('Saving data...')
+            with open(data_output_path, 'wb') as data_out:
+                np.save(data_out, data)
+        if code_output_path:
+            print_stderr('Saving source...')
+            with open(code_output_path, 'wb') as src_out:
+                src_out.write(transformer.transform_source())
+        print_stderr('set env variable before using converted model '\
+                'if used custom_layers:')
+        custom_pk_path = os.path.dirname(os.path.abspath(__file__))
+        custom_pk_path = os.path.join(custom_pk_path, 'kaffe')
+        print_stderr('export CAFFE2FLUID_CUSTOM_LAYERS=%s' % (custom_pk_path))
+        print_stderr('Done.')
+        return 0
+    except KaffeError as err:
+        fatal_error('Error encountered: {}'.format(err))
+
+    return 1
+
+
+def main():
+    """ main
+    """
+    parser = argparse.ArgumentParser()
+    parser.add_argument('def_path', help='Model definition (.prototxt) path')
+    parser.add_argument('--caffemodel', help='Model data (.caffemodel) path')
+    parser.add_argument('--data-output-path', help='Converted data output path')
+    parser.add_argument(
+        '--code-output-path', help='Save generated source to this path')
+    parser.add_argument(
+        '-p',
+        '--phase',
+        default='test',
+        help='The phase to convert: test (default) or train')
+    args = parser.parse_args()
+    validate_arguments(args)
+    return convert(args.def_path, args.caffemodel, args.data_output_path,
+                   args.code_output_path, args.phase)
+
+
+if __name__ == '__main__':
+    ret = main()
+    sys.exit(ret)

caffe2fluid/examples/imagenet/README.md

+A demo to show converting caffe models trained on 'imagenet' using caffe2fluid
+
+---
+
+# How to use
+
+1. Prepare python environment
+
+2. Download caffe model to "models.caffe/xxx" which contains "xxx.caffemodel" and "xxx.prototxt"
+
+3. Convert the Caffe model to Fluid model
+    - generate fluid code and weight file
+        ```python convert.py alexnet.prototxt \
+        --caffemodel alexnet.caffemodel \
+        --data-output-path alexnet.npy \
+        --code-output-path alexnet.py
+        ```
+
+    - save weights as fluid model file
+        ```
+        python alexnet.py alexnet.npy ./fluid
+        ```
+
+4. Do inference
+    ```
+    python infer.py infer ./fluid data/65.jpeg
+    ```
+
+5. convert model and do inference together
+   ```
+    bash ./tools/run.sh alexnet ./models.caffe/alexnet ./models/alexnet
+    ```
+    * Assume the Caffe model is stored in '*./models.caffe/alexnet/alexnet.prototxt|caffemodel*'
+    * converted model will be stored as '*./models/alexnet/alexnet.py|npy*'
+
+6. test the difference with caffe's results(need pycaffe installed)
+   ```
+    bash ./tools/diff.sh resnet
+    ```
+    * Make sure your caffemodel stored in '*./models.caffe/resnet*'
+    * The results will be stored in '*./results/resnet.paddle|caffe*'

caffe2fluid/examples/imagenet/compare.py

+#!/usr/bin/python
+
+#
+#a tool to compare tensors in two files or two directories
+#
+
+import sys
+import os
+
+
+def walk_dir(rootdir):
+    for subdir, dirs, files in os.walk(rootdir):
+        for file in files:
+            yield file
+
+
+def calc_diff(f1, f2):
+    import numpy as np
+
+    d1 = np.load(f1)
+    d2 = np.load(f2)
+
+    #print d1.shape
+    #print d2.shape
+    #print d1[0, 0, 0:10, 0:10]
+    #print d2[0, 0, 0:10, 0:10]
+
+    d1 = d1.flatten()
+    d2 = d2.flatten()
+
+    d1_num = reduce(lambda x, y: x * y, d1.shape)
+    d2_num = reduce(lambda x, y: x * y, d2.shape)
+    if d1_num != d2_num:
+        print d1.shape
+        print d2.shape
+        assert (d1_num == d2_num), "their shape is not consistent"
+
+    try:
+        mask = np.abs(d1) >= np.abs(d2)
+        mask = mask.astype('int32')
+
+        df = np.abs(d1 - d2)
+        df = df / (1.0e-10 + np.abs(d1) * mask + np.abs(d2) * (1 - mask))
+        max_df = np.max(df)
+        sq_df = np.mean(df * df)
+        return max_df, sq_df
+    except Exception as e:
+        return 1.0, 1.0
+
+
+def compare(path1, path2, no_exception):
+    def diff(f1, f2):
+        max_df, sq_df = calc_diff(f1, f2)
+        print('[max_df:%.4e, sq_df:%.4e] when compare %s <=> %s' %
+              (max_df, sq_df, os.path.basename(f1), os.path.basename(f2)))
+        if no_exception is False:
+            assert (max_df < 1e-5), \
+                    'max_df is too large with value[%.6e]' % (max_df)
+            assert (sq_df < 1e-10), \
+                    'sq_df is too large with value[%.6e]' % (sq_df)
+
+    if os.path.exists(path1) is False:
+        print('not found %s' % (path1))
+        return 1
+    elif os.path.exists(path2) is False:
+        print('not found %s' % (path2))
+        return 1
+
+    if path1.find('.npy') > 0 and path2.find('.npy') > 0:
+        diff(path1, path2)
+        return
+
+    for f in walk_dir(path2):
+        if f.find('.npy') < 0:
+            continue
+
+        f1 = os.path.join(path1, f)
+        f2 = os.path.join(path2, f)
+        diff(f1, f2)
+
+    print('all checking succeed to pass')
+    return 0
+
+
+if __name__ == "__main__":
+    if len(sys.argv) == 1:
+        path1 = 'lenet.tf/results'
+        path2 = 'lenet.paddle/results'
+    elif len(sys.argv) >= 3:
+        path1 = sys.argv[1]
+        path2 = sys.argv[2]
+        if len(sys.argv) == 4:
+            no_exception = True
+        else:
+            no_exception = False
+    else:
+        print('usage:')
+        print(' %s [path1] [path2]' % (sys.argv[0]))
+        exit(1)
+
+    #print('compare inner result in %s %s' % (path1, path2))
+    exit(compare(path1, path2, no_exception))

caffe2fluid/examples/imagenet/infer.py

+#!/bin/env python
+
+#function:
+#   a demo to show how to use the converted model genereated by caffe2fluid
+#   
+#notes:
+#   only support imagenet data
+
+import os
+import sys
+import inspect
+import numpy as np
+
+
+def import_fluid():
+    import paddle.fluid as fluid
+    return fluid
+
+
+def load_data(imgfile, shape):
+    h, w = shape[1:]
+    from PIL import Image
+    im = Image.open(imgfile)
+
+    # The storage order of the loaded image is W(widht),
+    # H(height), C(channel). PaddlePaddle requires
+    # the CHW order, so transpose them.
+    im = im.resize((w, h), Image.ANTIALIAS)
+    im = np.array(im).astype(np.float32)
+    im = im.transpose((2, 0, 1))  # CHW
+    im = im[(2, 1, 0), :, :]  # BGR
+
+    # The mean to be subtracted from each image.
+    # By default, the per-channel ImageNet mean.
+    mean = np.array([104., 117., 124.], dtype=np.float32)
+    mean = mean.reshape([3, 1, 1])
+    im = im - mean
+    return im.reshape([1] + shape)
+
+
+def build_model(net_file, net_name):
+    print('build model with net_file[%s] and net_name[%s]' %
+          (net_file, net_name))
+
+    net_path = os.path.dirname(net_file)
+    module_name = os.path.splitext(os.path.basename(net_file))[0]
+    if net_path not in sys.path:
+        sys.path.insert(0, net_path)
+
+    try:
+        m = __import__(module_name, fromlist=[net_name])
+        MyNet = getattr(m, net_name)
+    except Exception as e:
+        print('failed to load module[%s.%s]' % (module_name, net_name))
+        print(e)
+        return None
+
+    fluid = import_fluid()
+    inputs_dict = MyNet.input_shapes()
+    input_name = inputs_dict.keys()[0]
+    input_shape = inputs_dict[input_name]
+    images = fluid.layers.data(
+        name=input_name, shape=input_shape, dtype='float32')
+    #label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+
+    net = MyNet({input_name: images})
+    return net, inputs_dict
+
+
+def dump_results(results, names, root):
+    if os.path.exists(root) is False:
+        os.mkdir(root)
+
+    for i in range(len(names)):
+        n = names[i]
+        res = results[i]
+        filename = os.path.join(root, n)
+        np.save(filename + '.npy', res)
+
+
+def normalize_name(name_map):
+    return {
+        k.replace('/', '_'): v.replace('/', '_')
+        for k, v in name_map.items()
+    }
+
+
+def rename_layer_name(names, net):
+    """ because the names of output layers from caffe maybe changed for 'INPLACE' operation,
+        and paddle's layers maybe fused, so we need to re-mapping their relationship for comparing
+    """
+    #build a mapping from paddle's name to caffe's name
+    trace = getattr(net, 'name_trace', None)
+    cf_trace = trace['caffe']
+    real2cf = normalize_name(cf_trace['real2chg'])
+
+    pd_trace = trace['paddle']
+    pd2real = normalize_name(pd_trace['chg2real'])
+    pd_deleted = normalize_name(pd_trace['deleted'])
+
+    pd2cf_name = {}
+    for pd_name, real_name in pd2real.items():
+        if real_name in real2cf:
+            pd2cf_name[pd_name] = '%s.%s.%s.both_changed' \
+                    % (real2cf[real_name], real_name, pd_name)
+        else:
+            pd2cf_name[pd_name] = '%s.%s.pd_changed' % (real_name, pd_name)
+
+    for pd_name, trace in pd_deleted.items():
+        assert pd_name not in pd2cf_name, "this name[%s] has already exist" % (
+            pd_name)
+        pd2cf_name[pd_name] = '%s.pd_deleted' % (pd_name)
+
+    for real_name, cf_name in real2cf.items():
+        if cf_name not in pd2cf_name:
+            pd2cf_name[cf_name] = '%s.cf_deleted' % (cf_name)
+
+        if real_name not in pd2cf_name:
+            pd2cf_name[real_name] = '%s.%s.cf_changed' % (cf_name, real_name)
+
+    ret = []
+    for name in names:
+        new_name = pd2cf_name[name] if name in pd2cf_name else name
+        print('remap paddle name[%s] to output name[%s]' % (name, new_name))
+        ret.append(new_name)
+    return ret
+
+
+def load_model(exe, place, net_file, net_name, net_weight, debug):
+    """ load model using xxxnet.py and xxxnet.npy
+    """
+    fluid = import_fluid()
+
+    #1, build model
+    net, input_map = build_model(net_file, net_name)
+    feed_names = input_map.keys()
+    feed_shapes = [v for k, v in input_map.items()]
+
+    prediction = net.get_output()
+
+    #2, load weights for this model
+    startup_program = fluid.default_startup_program()
+    exe.run(startup_program)
+
+    #place = fluid.CPUPlace()
+    if net_weight.find('.npy') > 0:
+        net.load(data_path=net_weight, exe=exe, place=place)
+    else:
+        raise ValueError('not found weight file')
+
+    #3, test this model
+    test_program = fluid.default_main_program().clone()
+
+    fetch_list_var = []
+    fetch_list_name = []
+    if debug is False:
+        fetch_list_var.append(prediction)
+    else:
+        for k, v in net.layers.items():
+            fetch_list_var.append(v)
+            fetch_list_name.append(k)
+
+    return {
+        'program': test_program,
+        'feed_names': feed_names,
+        'fetch_vars': fetch_list_var,
+        'fetch_names': fetch_list_name,
+        'feed_shapes': feed_shapes,
+        'net': net
+    }
+
+
+def get_shape(fluid, program, name):
+    for var in program.list_vars():
+        if var.name == 'data':
+            return list(var.shape[1:])
+
+    raise ValueError('not found shape for input layer[%s], '
+                     'you can specify by yourself' % (name))
+
+
+def load_inference_model(dirname, exe):
+    """ load fluid's inference model
+    """
+    fluid = import_fluid()
+    model_fn = 'model'
+    params_fn = 'params'
+    if os.path.exists(os.path.join(dirname, model_fn)) \
+            and os.path.exists(os.path.join(dirname, params_fn)):
+        program, feed_names, fetch_targets = fluid.io.load_inference_model(\
+                dirname, exe, model_fn, params_fn)
+    else:
+        raise ValueError('not found model files in direcotry[%s]' % (dirname))
+
+    #print fluid.global_scope().find_var(feed_names[0])
+    input_shape = get_shape(fluid, program, feed_names[0])
+    feed_shapes = [input_shape]
+
+    return program, feed_names, fetch_targets, feed_shapes
+
+
+def infer(model_path, imgfile, net_file=None, net_name=None, debug=True):
+    """ do inference using a model which consist 'xxx.py' and 'xxx.npy'
+    """
+    fluid = import_fluid()
+
+    place = fluid.CPUPlace()
+    exe = fluid.Executor(place)
+    try:
+        ret = load_inference_model(model_path, exe)
+        program, feed_names, fetch_targets, feed_shapes = ret
+        debug = False
+        print('found a inference model for fluid')
+    except ValueError as e:
+        print('try to load model using net file and weight file')
+        net_weight = model_path
+        ret = load_model(exe, place, net_file, net_name, net_weight, debug)
+        program = ret['program']
+        feed_names = ret['feed_names']
+        fetch_targets = ret['fetch_vars']
+        fetch_list_name = ret['fetch_names']
+        feed_shapes = ret['feed_shapes']
+        net = ret['net']
+
+    input_name = feed_names[0]
+    input_shape = feed_shapes[0]
+
+    np_images = load_data(imgfile, input_shape)
+    results = exe.run(program=program,
+                      feed={input_name: np_images},
+                      fetch_list=fetch_targets)
+
+    if debug is True:
+        dump_path = 'results.paddle'
+        dump_names = rename_layer_name(fetch_list_name, net)
+        dump_results(results, dump_names, dump_path)
+        print('all result of layers dumped to [%s]' % (dump_path))
+    else:
+        result = results[0]
+        print('succeed infer with results[class:%d]' % (np.argmax(result)))
+
+    return 0
+
+
+def caffe_infer(prototxt, caffemodel, datafile):
+    """ do inference using pycaffe for debug,
+        all intermediate results will be dumpped to 'results.caffe'
+    """
+    import caffe
+
+    net = caffe.Net(prototxt, caffemodel, caffe.TEST)
+    input_layer = net.blobs.keys()[0]
+    print('got name of input layer is:%s' % (input_layer))
+    input_shape = list(net.blobs[input_layer].data.shape[1:])
+
+    if '.npy' in datafile:
+        np_images = np.load(datafile)
+    else:
+        np_images = load_data(datafile, input_shape)
+
+    inputs = {input_layer: np_images}
+    net.forward_all(**inputs)
+
+    results = []
+    names = []
+    for k, v in net.blobs.items():
+        k = k.replace('/', '_')
+        names.append(k)
+        results.append(v.data.copy())
+
+    dump_path = 'results.caffe'
+    dump_results(results, names, dump_path)
+    print('all result of layers dumped to [%s]' % (dump_path))
+    return 0
+
+
+if __name__ == "__main__":
+    """ maybe more convenient to use 'run.sh' to call this tool
+    """
+    net_file = 'models/resnet50/resnet50.py'
+    weight_file = 'models/resnet50/resnet50.npy'
+    datafile = 'data/65.jpeg'
+    net_name = 'ResNet50'
+    model_file = 'models/resnet50/fluid'
+
+    ret = None
+    if len(sys.argv) <= 2:
+        pass
+    elif sys.argv[1] == 'caffe':
+        if len(sys.argv) != 5:
+            print('usage:')
+            print('\tpython %s caffe [prototxt] [caffemodel] [datafile]' %
+                  (sys.argv[0]))
+            sys.exit(1)
+        prototxt = sys.argv[2]
+        caffemodel = sys.argv[3]
+        datafile = sys.argv[4]
+        ret = caffe_infer(prototxt, caffemodel, datafile)
+    elif sys.argv[1] == 'infer':
+        if len(sys.argv) != 4:
+            print('usage:')
+            print('\tpython %s infer [fluid_model] [datafile]' % (sys.argv[0]))
+            sys.exit(1)
+        model_path = sys.argv[2]
+        datafile = sys.argv[3]
+        ret = infer(model_path, datafile)
+    elif sys.argv[1] == 'dump':
+        if len(sys.argv) != 6:
+            print('usage:')
+            print('\tpython %s dump [net_file] [weight_file] [datafile] [net_name]' \
+                    % (sys.argv[0]))
+            print('\teg:python %s dump %s %s %s %s' % (sys.argv[0],\
+                net_file, weight_file, datafile, net_name))
+            sys.exit(1)
+
+        net_file = sys.argv[2]
+        weight_file = sys.argv[3]
+        datafile = sys.argv[4]
+        net_name = sys.argv[5]
+        ret = infer(weight_file, datafile, net_file, net_name)
+
+    if ret is None:
+        print('usage:')
+        print(' python %s [infer] [fluid_model] [imgfile]' % (sys.argv[0]))
+        print(' eg:python %s infer %s %s' % (sys.argv[0], model_file, datafile))
+        sys.exit(1)
+
+    sys.exit(ret)

caffe2fluid/examples/imagenet/tools/cmp.sh

+#!/bin/bash
+
+#
+#function:
+#   a tool used to compare the results produced by paddle and caffe
+#
+
+if [[ $# -lt 2 ]];then
+    echo "usage:"
+    echo "  bash $0 [model_name] [param_name] [caffe_name]"
+    exit 1
+fi
+
+model_name=$1
+param_name=$2
+paddle_file="./results/${model_name}.paddle/${param_name}.npy"
+if [[ $# -eq 3 ]];then
+    caffe_file="./results/${model_name}.caffe/${3}.npy"
+else
+    caffe_file="./results/${model_name}.caffe/${2}.npy"
+fi
+cmd="python ./compare.py $paddle_file $caffe_file"
+echo $cmd
+eval $cmd

caffe2fluid/examples/imagenet/tools/cmp_layers.sh

+#!/bin/bash
+
+#function:
+#   a tool used to compare all layers' results
+#
+#set -x
+if [[ $# -ne 1 ]];then
+    echo "usage:"
+    echo "  bash $0 [model_name]"
+    echo "  eg:bash $0 alexnet"
+    exit 1
+fi
+
+model_name=$1
+prototxt="models.caffe/$model_name/${model_name}.prototxt"
+cat $prototxt | grep name | perl -ne 'if(/^\s*name\s*:\s+\"([^\"]+)/){ print $1."\n";}' >.layer_names
+
+final_layer=$(cat $prototxt | perl -ne 'if(/^\s*top\s*:\s+\"([^\"]+)/){ print $1."\n";}' | tail -n1)
+ret=$(grep "^$final_layer$" .layer_names | wc -l)
+if [[ $ret -eq 0 ]];then
+    echo $final_layer >>.layer_names
+fi
+
+for i in $(cat .layer_names);do
+    i=${i//\//_}
+    cf_npy="results/${model_name}.caffe/${i}.npy"
+    #pd_npy="results/${model_name}.paddle/${i}.npy"
+    #pd_npy=$(find results/${model_name}.paddle -iname "${i}*.npy" | head -n1)
+    pd_npy=$(find results/${model_name}.paddle -iname "${i}.*npy" | grep deleted -v | head -n1)
+
+    if [[ ! -e $cf_npy ]];then
+        echo "caffe's result not exist[$cf_npy]"
+        continue
+    fi
+
+    if [[ ! -e $pd_npy ]];then
+        echo "paddle's result not exist[$pd_npy]"
+        continue
+    fi
+
+    python compare.py $cf_npy $pd_npy no_exception
+    if [[ $? -eq 0 ]];then
+        echo "succeed to compare layer[$i]"
+    else
+        echo "failed to compare layer[$i]"
+    fi
+
+done

caffe2fluid/examples/imagenet/tools/diff.sh

+#!/bin/bash
+
+#
+#function:
+#   a tool used to check the difference of models' results generated by caffe model and paddle model
+#
+#howto:
+#   bash diff.sh resnet50 #when this has been finished, you can get the difference in precision
+#
+#notes:
+#   0, in order to infer using caffe, we need pycaffe installed
+#   1, prepare your caffe model in 'models.caffe/', eg: 'model.caffe/resnet101/resnet101.[prototxt|caffemodel]'
+#   2, converted paddle model will be in 'models'
+#   3, results of layers will be stored in 'results/${model_name}.[paddle|caffe]'
+#   4, only the last layer will be checked by default
+
+model_name="resnet50"
+results_root="results/"
+
+if [[ -n $1 ]];then
+    if [ $1 = "-h" ];then
+        echo "usage:"
+        echo "  bash $0 [model_name]"
+        echo "  eg:bash $0 resnet50"
+        exit 0
+    fi
+    model_name=$1
+fi
+
+mkdir -p $results_root
+
+prototxt="models.caffe/$model_name/${model_name}.prototxt"
+caffemodel="models.caffe/${model_name}/${model_name}.caffemodel"
+
+#1, dump layers' results from paddle
+paddle_results="$results_root/${model_name}.paddle"
+rm -rf $paddle_results
+rm -rf "results.paddle"
+bash ./tools/run.sh $model_name ./models.caffe/$model_name ./models/$model_name
+if [[ $? -ne 0 ]] || [[ ! -e "results.paddle" ]];then
+    echo "not found paddle's results, maybe failed to convert"
+    exit 1
+fi
+mv results.paddle $paddle_results
+
+#2, dump layers' results from caffe
+caffe_results="$results_root/${model_name}.caffe"
+rm -rf $caffe_results
+rm -rf "results.caffe"
+PYTHON=`which cfpython`
+if [[ -z $PYTHON ]];then
+    PYTHON=`which python`
+fi
+$PYTHON ./infer.py caffe $prototxt $caffemodel $paddle_results/data.npy
+if [[ $? -ne 0 ]] || [[ ! -e "results.caffe" ]];then
+    echo "not found caffe's results, maybe failed to do inference with caffe"
+    exit 1
+fi
+mv results.caffe $caffe_results
+
+#3, extract layer names
+cat $prototxt | grep name | perl -ne 'if(/^\s*name\s*:\s+\"([^\"]+)/){ print $1."\n";}' >.layer_names
+
+final_layer=$(cat $prototxt | perl -ne 'if(/^\s*top\s*:\s+\"([^\"]+)/){ print $1."\n";}' | tail -n1)
+ret=$(grep "^$final_layer$" .layer_names | wc -l)
+if [[ $ret -eq 0 ]];then
+    echo $final_layer >>.layer_names
+fi
+
+#4, compare one by one
+#for i in $(cat .layer_names);do
+for i in $(cat .layer_names | tail -n1);do
+    i=${i//\//_}
+    echo "process $i"
+    pd_npy=$(find $paddle_results/ -iname "${i}.*npy" | grep deleted -v | head -n1)
+    #pd_npy="$paddle_results/${i}.npy"
+    if [[ -f $pd_npy ]];then
+        $PYTHON compare.py $caffe_results/${i}.npy $pd_npy
+    else
+        echo "not found npy file[${i}.*npy] for layer[$i]"
+        exit 1
+    fi
+done

caffe2fluid/examples/imagenet/tools/run.sh

+#!/bin/bash
+
+#function:
+#   a tool used to:
+#       1, convert a caffe model
+#       2, do inference(only in fluid) using this model
+#
+#usage:
+#   cd caffe2fluid/examples/imagenet && bash run.sh resnet50 ./models.caffe/resnet50 ./models/resnet50
+#
+
+#set -x
+if [[ $# -lt 3 ]];then
+    echo "usage:"
+    echo "  bash $0 [model_name] [cf_model_path] [pd_model_path] [only_convert]"
+    echo "  eg: bash $0 resnet50 ./models.caffe/resnet50 ./models/resnet50"
+    exit 1
+else
+    model_name=$1
+    cf_model_path=$2
+    pd_model_path=$3
+    only_convert=$4
+fi
+
+proto_file=$cf_model_path/${model_name}.prototxt
+caffemodel_file=$cf_model_path/${model_name}.caffemodel
+weight_file=$pd_model_path/${model_name}.npy
+net_file=$pd_model_path/${model_name}.py
+
+if [[ ! -e $proto_file ]];then
+    echo "not found prototxt[$proto_file]"
+    exit 1
+fi
+
+if [[ ! -e $caffemodel_file ]];then
+    echo "not found caffemodel[$caffemodel_file]"
+    exit 1
+fi
+
+if [[ ! -e $pd_model_path ]];then
+    mkdir $pd_model_path
+fi
+
+PYTHON=`which cfpython`
+if [[ -z $PYTHON ]];then
+    PYTHON=`which python`
+fi
+$PYTHON ../../convert.py \
+        $proto_file \
+        --caffemodel $caffemodel_file \
+        --data-output-path $weight_file\
+        --code-output-path $net_file
+
+ret=$?
+if [[ $ret -ne 0 ]];then
+    echo "failed to convert caffe model[$cf_model_path]"
+    exit $ret
+else
+    echo "succeed to convert caffe model[$cf_model_path] to fluid model[$pd_model_path]"
+fi
+
+if [[ -z $only_convert ]];then
+    PYTHON=`which pdpython`
+    if [[ -z $PYTHON ]];then
+        PYTHON=`which python`
+    fi
+    imgfile="data/65.jpeg"
+    #FIX ME:
+    #   only look the first line in prototxt file for the name of this network, maybe not correct
+    net_name=`grep "name" $proto_file | head -n1 | perl -ne 'if(/^name\s*:\s*\"([^\"]+)\"/){ print $1."\n";}'`
+    if [[ -z $net_name ]];then
+        net_name="MyNet"
+    fi
+    cmd="$PYTHON ./infer.py dump $net_file $weight_file $imgfile $net_name"
+    echo $cmd
+    eval $cmd
+    ret=$?
+fi
+exit $ret

caffe2fluid/examples/imagenet/tools/test.sh

+#!/bin/bash
+
+#
+#script to test all models
+#
+
+models="alexnet vgg16 googlenet resnet152 resnet101 resnet50"
+for i in $models;do
+    echo "begin to process $i"
+    bash ./tools/diff.sh $i 2>&1
+    echo "finished to process $i with ret[$?]"
+done

caffe2fluid/examples/mnist/README.md

+a demo to show converting caffe model on 'mnist' using caffe2fluid
+
+---
+
+# How to use
+
+1. prepare python environment
+2. download caffe model to "models.caffe/lenet" which contains "lenet.caffemodel" and "lenet.prototxt"
+3. run the tool
+    eg: bash ./run.sh lenet ./models.caffe/lenet ./models/lenet

caffe2fluid/examples/mnist/evaluate.py

+#!/bin/env python
+
+#function:
+#   demo to show how to use converted model using caffe2fluid
+#
+
+import sys
+import os
+import numpy as np
+import paddle.fluid as fluid
+import paddle
+
+
+def test_model(exe, test_program, fetch_list, test_reader, feeder):
+    acc_set = []
+
+    for data in test_reader():
+        acc_np, pred = exe.run(program=test_program,
+                               feed=feeder.feed(data),
+                               fetch_list=fetch_list)
+        acc_set.append(float(acc_np))
+
+    acc_val = np.array(acc_set).mean()
+    return float(acc_val)
+
+
+def evaluate(net_file, model_file):
+    """ main
+    """
+    #1, build model
+    net_path = os.path.dirname(net_file)
+    if net_path not in sys.path:
+        sys.path.insert(0, net_path)
+
+    from lenet import LeNet as MyNet
+
+    #1, define network topology
+    images = fluid.layers.data(name='image', shape=[1, 28, 28], dtype='float32')
+    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+
+    net = MyNet({'data': images})
+    prediction = net.layers['prob']
+    acc = fluid.layers.accuracy(input=prediction, label=label)
+
+    place = fluid.CPUPlace()
+    exe = fluid.Executor(place)
+    exe.run(fluid.default_startup_program())
+
+    #2, load weights
+    if model_file.find('.npy') > 0:
+        net.load(data_path=model_file, exe=exe, place=place)
+    else:
+        net.load(data_path=model_file, exe=exe)
+
+    #3, test this model
+    test_program = fluid.default_main_program().clone()
+    test_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=128)
+
+    feeder = fluid.DataFeeder(feed_list=[images, label], place=place)
+    fetch_list = [acc, prediction]
+
+    print('go to test model using test set')
+    acc_val = test_model(exe, test_program, \
+            fetch_list, test_reader, feeder)
+
+    print('test accuracy is [%.4f], expected value[0.919]' % (acc_val))
+
+
+if __name__ == "__main__":
+    net_file = 'models/lenet/lenet.py'
+    weight_file = 'models/lenet/lenet.npy'
+
+    argc = len(sys.argv)
+    if argc == 3:
+        net_file = sys.argv[1]
+        weight_file = sys.argv[2]
+    elif argc > 1:
+        print('usage:')
+        print('\tpython %s [net_file] [weight_file]' % (sys.argv[0]))
+        print('\teg:python %s %s %s %s' % (sys.argv[0], net_file, weight_file))
+        sys.exit(1)
+
+    evaluate(net_file, weight_file)

caffe2fluid/examples/mnist/run.sh

+#!/bin/bash
+
+#function:
+#   a tool used to:
+#       1, convert a caffe model
+#       2, do inference using this model
+#
+#usage:
+#   bash run.sh lenet ./models.caffe/lenet ./models/lenet
+#
+
+#set -x
+if [[ $# -lt 3 ]];then
+    echo "usage:"
+    echo "  bash $0 [model_name] [cf_model_path] [pd_model_path] [only_convert]"
+    echo "  eg: bash $0 lenet ./models.caffe/lenet ./models/lenet"
+    exit 1
+else
+    model_name=$1
+    cf_model_path=$2
+    pd_model_path=$3
+    no_eval=$4
+fi
+
+proto_file=$cf_model_path/${model_name}.prototxt
+caffemodel_file=$cf_model_path/${model_name}.caffemodel
+weight_file=$pd_model_path/${model_name}.npy
+net_file=$pd_model_path/${model_name}.py
+
+if [[ ! -e $proto_file ]];then
+    echo "not found prototxt[$proto_file]"
+    exit 1
+fi
+
+if [[ ! -e $caffemodel_file ]];then
+    echo "not found caffemodel[$caffemodel_file]"
+    exit 1
+fi
+
+if [[ ! -e $pd_model_path ]];then
+    mkdir $pd_model_path
+fi
+
+PYTHON=`which cfpython`
+if [[ -z $PYTHON ]];then
+    PYTHON=`which python`
+fi
+$PYTHON ../../convert.py \
+        $proto_file \
+        --caffemodel $caffemodel_file \
+        --data-output-path $weight_file\
+        --code-output-path $net_file
+
+ret=$?
+if [[ $ret -ne 0 ]];then
+    echo "failed to convert caffe model[$cf_model_path]"
+    exit $ret
+else
+    echo "succeed to convert caffe model[$cf_model_path] to fluid model[$pd_model_path]"
+fi
+
+if [[ -z $only_convert ]];then
+    PYTHON=`which pdpython`
+    if [[ -z $PYTHON ]];then
+        PYTHON=`which python`
+    fi
+    net_name=`grep "name" $proto_file | head -n1 | perl -ne 'if(/\"([^\"]+)\"/){ print $1."\n";}'`
+    if [[ $net_name != "LeNet" ]];then
+        echo "only support LeNet"
+        exit 1
+    fi
+    $PYTHON ./evaluate.py $net_file $weight_file
+    ret=$?
+fi
+exit $ret

caffe2fluid/kaffe/__init__.py

+from .graph import GraphBuilder, NodeMapper
+from .errors import KaffeError, print_stderr
+
+import os
+from . import paddle

caffe2fluid/kaffe/caffe/__init__.py

+from .resolver import get_caffe_resolver, has_pycaffe

caffe2fluid/kaffe/caffe/resolver.py

+import os
+import sys
+
+SHARED_CAFFE_RESOLVER = None
+
+
+def import_caffepb():
+    p = os.path.realpath(__file__)
+    p = os.path.dirname(p)
+    p = os.path.join(p, '../../proto')
+    sys.path.insert(0, p)
+    import caffe_pb2
+    return caffe_pb2
+
+
+class CaffeResolver(object):
+    def __init__(self):
+        self.import_caffe()
+
+    def import_caffe(self):
+        self.caffe = None
+        try:
+            # Try to import PyCaffe first
+            import caffe
+            self.caffe = caffe
+        except ImportError:
+            # Fall back to the protobuf implementation
+            self.caffepb = import_caffepb()
+            show_fallback_warning()
+        if self.caffe:
+            # Use the protobuf code from the imported distribution.
+            # This way, Caffe variants with custom layers will work.
+            self.caffepb = self.caffe.proto.caffe_pb2
+        self.NetParameter = self.caffepb.NetParameter
+
+    def has_pycaffe(self):
+        return self.caffe is not None
+
+
+def get_caffe_resolver():
+    global SHARED_CAFFE_RESOLVER
+    if SHARED_CAFFE_RESOLVER is None:
+        SHARED_CAFFE_RESOLVER = CaffeResolver()
+    return SHARED_CAFFE_RESOLVER
+
+
+def has_pycaffe():
+    return get_caffe_resolver().has_pycaffe()
+
+
+def show_fallback_warning():
+    msg = '''
+------------------------------------------------------------
+    WARNING: PyCaffe not found!
+    Falling back to a pure protocol buffer implementation.
+    * Conversions will be drastically slower.
+------------------------------------------------------------
+
+'''
+    sys.stderr.write(msg)

caffe2fluid/kaffe/custom_layers/__init__.py

+"""
+"""
+
+from .register import get_registered_layers
+#custom layer import begins
+
+import axpy
+import flatten
+import argmax
+import reshape
+import roipooling
+import priorbox
+import permute
+import detection_out
+import normalize
+import select
+import crop
+import power
+import reduction
+
+#custom layer import ends
+
+custom_layers = get_registered_layers()
+
+
+def set_args(f, params, node=None):
+    """ set args for function 'f' using the parameters in node.layer.parameters
+
+    Args:
+        f (function): a python function object
+        params (object): a object contains attributes needed by f's arguments
+
+    Returns:
+        arg_names (list): a list of argument names
+        kwargs (dict): a dict contains needed arguments
+    """
+    from ..protobuf_to_dict import protobuf_to_dict
+
+    argc = f.__code__.co_argcount
+    arg_list = f.__code__.co_varnames[0:argc]
+
+    kwargs = {}
+    for arg_name in arg_list:
+        if arg_name in params:
+            kwargs[arg_name] = params[arg_name]
+
+    if node is not None and len(node.metadata):
+        kwargs.update(node.metadata)
+
+    return arg_list, kwargs
+
+
+def has_layer(kind):
+    """ test whether this layer exists in custom layer
+    """
+    return kind in custom_layers
+
+
+def compute_output_shape(kind, node):
+    assert kind in custom_layers, "layer[%s] not exist in custom layers" % (
+        kind)
+    shape_func = custom_layers[kind]['shape']
+
+    parents = node.parents
+    inputs = [list(p.output_shape) for p in parents]
+    arg_names, kwargs = set_args(shape_func, node.params)
+
+    if len(inputs) == 1:
+        inputs = inputs[0]
+
+    return shape_func(inputs, **kwargs)
+
+
+def make_node(template, kind, node):
+    """ make a PaddleNode for custom layer which means construct
+        a piece of code to define a layer implemented in 'custom_layers'
+
+    Args:
+        @template (PaddleNode): a factory to new a instance of PaddleNode
+        @kind (str): type of custom layer
+        @node (graph.Node): a layer in the net
+
+    Returns:
+        instance of PaddleNode
+    """
+    assert kind in custom_layers, "layer[%s] not exist in custom layers" % (
+        kind)
+
+    layer_func = custom_layers[kind]['layer']
+
+    #construct arguments needed by custom layer function from node's parameters
+    arg_names, kwargs = set_args(layer_func, node.params, node)
+
+    return template('custom_layer', kind, **kwargs)
+
+
+def make_custom_layer(kind, inputs, name, *args, **kwargs):
+    """ execute a custom layer which is implemented by users
+
+    Args:
+        @kind (str): type name of this layer
+        @inputs (vars): variable list created by fluid
+        @namme (str): name for this layer
+        @args (tuple): other positional arguments
+        @kwargs (dict): other kv arguments
+
+    Returns:
+        output (var): output variable for this layer
+    """
+    assert kind in custom_layers, "layer[%s] not exist in custom layers" % (
+        kind)
+
+    layer_func = custom_layers[kind]['layer']
+    return layer_func(inputs, name, *args, **kwargs)

caffe2fluid/kaffe/custom_layers/argmax.py

+""" a custom layer for 'argmax', maybe we should implement this in standard way.
+    more info can be found here: http://caffe.berkeleyvision.org/tutorial/layers/argmax.html
+"""
+from .register import register
+
+
+def import_fluid():
+    import paddle.fluid as fluid
+    return fluid
+
+
+def argmax_shape(input_shape, out_max_val=False, top_k=1, axis=-1):
+    """ calculate the output shape of this layer using input shape
+
+    Args:
+        @input_shape (list of num): a list of number which represents the input shape
+        @out_max_val (bool): parameter from caffe's ArgMax layer
+        @top_k (int): parameter from caffe's ArgMax layer
+        @axis (int): parameter from caffe's ArgMax layer
+
+    Returns:
+        @output_shape (list of num): a list of numbers represent the output shape
+    """
+    input_shape = list(input_shape)
+
+    if axis < 0:
+        axis += len(input_shape)
+
+    assert (axis + 1 == len(input_shape)
+            ), 'only can be applied on the last dimension[axis:%d, %s] now,'\
+                    'make sure you have set axis param in xxx.prototxt file' \
+                    % (axis, str(input_shape))
+
+    output_shape = input_shape
+    output_shape[-1] = top_k
+    if out_max_val is True:
+        output_shape[-1] *= 2
+
+    return output_shape
+
+
+def argmax_layer(input, name, out_max_val=False, top_k=1, axis=-1):
+    """ build a layer of type 'ArgMax' using fluid
+
+    Args:
+        @input (variable): input fluid variable for this layer
+        @name (str): name for this layer
+        @out_max_val (bool): parameter from caffe's ArgMax layer
+        @top_k (int): parameter from caffe's ArgMax layer
+        @axis (int): parameter from caffe's ArgMax layer
+
+    Returns:
+        output (variable): output variable for this layer
+    """
+
+    fluid = import_fluid()
+
+    if axis < 0:
+        axis += len(input.shape)
+
+    if out_max_val is True:
+        topk_var, index_var = fluid.layers.topk(input=input, k=top_k)
+        index_var = fluid.layers.cast(index_var, dtype=topk_var.dtype)
+        output = fluid.layers.concat(
+            [index_var, topk_var], axis=axis, name=name)
+    else:
+        topk_var, index_var = fluid.layers.topk(input=input, k=top_k, name=name)
+        output = index_var
+
+    return output
+
+
+register(kind='ArgMax', shape=argmax_shape, layer=argmax_layer)

caffe2fluid/kaffe/custom_layers/axpy.py

+""" A custom layer for 'axpy' which receives 3 tensors and output 1 tensor.
+    the function performed is:(the mupltiplication and add are elementewise)
+        output = inputs[0] * inputs[1] + inputs[2]
+"""
+
+from .register import register
+
+
+def axpy_shape(input_shapes):
+    """ calculate the output shape of this layer using input shapes
+
+    Args:
+        @input_shapes (list of tuples): a list of input shapes
+
+    Returns:
+        @output_shape (list of num): a list of numbers represent the output shape
+    """
+    assert len(input_shapes) == 3, "not valid input shape for axpy layer"
+    assert len(input_shapes[0]) == len(input_shapes[1]), 'should have same dims'
+
+    output_shape = input_shapes[1]
+    assert (input_shapes[2] == output_shape),\
+            "shape not consistent for axpy[%s <--> %s]" \
+            % (str(output_shape), str(input_shapes[2]))
+
+    return output_shape
+
+
+def axpy_layer(inputs, name):
+    """ build a layer of type 'Axpy' using fluid
+
+    Args:
+        @inputs (list of variables): input fluid variables for this layer
+        @name (str): name for this layer
+
+    Returns:
+        output (variable): output variable for this layer
+    """
+    import paddle.fluid as fluid
+
+    assert len(inputs) == 3, "invalid inputs for axpy[%s]" % (name)
+    alpha = inputs[0]
+    x = inputs[1]
+    y = inputs[2]
+    output = fluid.layers.elementwise_mul(x, alpha, axis=0)
+    output = fluid.layers.elementwise_add(output, y, name=name)
+
+    return output
+
+
+register(kind='Axpy', shape=axpy_shape, layer=axpy_layer)

caffe2fluid/kaffe/custom_layers/crop.py

+""" a custom layer for 'crop', maybe we should implement this in standard way.
+    more info can be found here: http://caffe.berkeleyvision.org/tutorial/layers/crop.html
+"""
+from .register import register
+
+
+def crop_shape(input_shape, shape=None):
+    """ calculate the output shape of this layer using input shape
+
+    Args:
+        @input_shape (num | list of num): a list of number or num which represents the input shape
+        @shape (list of integer): the shape of output
+
+    Returns:
+        @output_shape (list of num): a list of numbers represent the output shape
+    """
+    if isinstance(input_shape, list):
+        assert len(input_shape) == 2, "the number of crop's inputs must be 2"
+        return input_shape[1]
+    elif not shape is None:
+        assert len(shape) == len(
+            input_shape.shape), "input_shape is diff with output_shape"
+        return shape
+    else:
+        raise Exception, "crop_shape input error"
+        return None
+
+
+def crop_layer(input, name, shape=None, axis=2, offset=None):
+    """ build a layer of type 'Crop' using fluid
+
+    Args:
+        @input (variables | list of variables): input fluid variable for this layer
+        @shape (list of integer): the shape of output
+        @name (str): name for this layer
+        @axis (integer): parameter from caffe's Crop layer
+        @offset (Variable|list/tuple of integer|None): parameter from caffe's Crop layer
+
+    Returns:
+        output (variable): output variable for this layer
+    """
+    input_shape = None
+    output_shape = None
+    input_tensor = None
+    if isinstance(input, list):
+        assert len(input) == 2, "the number of crop's inputs must be 2"
+        input_shape = input[0].shape
+        output_shape = input[1].shape
+        input_tensor = input[0]
+    elif not shape is None:
+        assert len(shape) == len(
+            input.shape), "input_shape is diff with output_shape"
+        input_shape = input.shape
+        output_shape = shape
+        input_tensor = input
+    else:
+        raise Exception, "crop_layer input error"
+
+    assert len(output_shape) == len(
+        input_shape), "input_shape is diff with output_shape"
+
+    if axis < 0:
+        axis += len(input_shape)
+
+    if offset is not None:
+        assert (len(input_shape) - axis
+                ) == len(offset), "invalid offset[%s] in crop layer" % (
+                    str(offset))
+        offset = [0] * axis + offset
+    import paddle.fluid as fluid
+    output = fluid.layers.crop(
+        input_tensor, shape=output_shape, offsets=offset, name=name)
+
+    return output
+
+
+register(kind='Crop', shape=crop_shape, layer=crop_layer)

caffe2fluid/kaffe/custom_layers/detection_out.py

+""" A custom layer for 'detectionout' used in 'SSD' model to produce outputs
+    Note: Since Paddle's implementation of 'detectionout' applied 'flatten' and 'softmax' ops on the input of 'conf', 
+    while Caffe's implementation do not.
+"""
+
+from .register import register
+
+
+def detectionoutput_shape(input_shape):
+    """ the output shape of this layer is dynamic and not determined by 'input_shape'
+
+    Args:
+        @input_shape (list of int): input shape
+
+    Returns:
+        @output_shape (list of num): a list of numbers represent the output shape
+    """
+    output_shape = [-1, 6]
+    return output_shape
+
+
+def detectionoutput_layer(inputs,
+                          name,
+                          background_label=0,
+                          share_location=True,
+                          nms_param=None,
+                          keep_top_k=100,
+                          confidence_threshold=0.1):
+    """ build a layer of type 'detectionout' using fluid
+
+    Args:
+        @inputs (list of variables): input fluid variables for this layer
+        @name (str): name for this layer
+
+    Returns:
+        output (variable): output variable for this layer
+    """
+    import paddle.fluid as fluid
+
+    if nms_param is None:
+        nms_param = {"nms_threshold": 0.3, "top_k": 10, "eta": 1.0}
+
+    mbox_conf_flatten = inputs[1]
+    mbox_priorbox = inputs[2]
+    mbox_priorbox_list = fluid.layers.split(mbox_priorbox, 2, dim=1)
+    pb = mbox_priorbox_list[0]
+    pbv = mbox_priorbox_list[1]
+    pb = fluid.layers.reshape(x=pb, shape=[-1, 4])
+    pbv = fluid.layers.reshape(x=pbv, shape=[-1, 4])
+    mbox_loc = inputs[0]
+    mbox_loc = fluid.layers.reshape(
+        x=mbox_loc, shape=[-1, mbox_conf_flatten.shape[1], 4])
+
+    default = {"nms_threshold": 0.3, "top_k": 10, "eta": 1.0}
+    fields = ['eta', 'top_k', 'nms_threshold']
+
+    for f in default.keys():
+        if not nms_param.has_key(f):
+            nms_param[f] = default[f]
+
+    nmsed_outs = fluid.layers.detection_output(
+        scores=mbox_conf_flatten,
+        loc=mbox_loc,
+        prior_box=pb,
+        prior_box_var=pbv,
+        background_label=background_label,
+        nms_threshold=nms_param["nms_threshold"],
+        nms_top_k=nms_param["top_k"],
+        keep_top_k=keep_top_k,
+        score_threshold=confidence_threshold,
+        nms_eta=nms_param["eta"])
+
+    return nmsed_outs
+
+
+register(
+    kind='DetectionOutput',
+    shape=detectionoutput_shape,
+    layer=detectionoutput_layer)

caffe2fluid/kaffe/custom_layers/flatten.py

+""" a custom layer for 'flatten', maybe we should implement this in standard way.
+    more info can be found here: http://caffe.berkeleyvision.org/tutorial/layers/flatten.html
+"""
+from .register import register
+
+
+def flatten_shape(input_shape, axis=1, end_axis=-1):
+    """ calculate the output shape of this layer using input shape
+
+    Args:
+        @input_shape (list of num): a list of number which represents the input shape
+        @axis (int): parameter from caffe's Flatten layer
+        @end_axis (int): parameter from caffe's Flatten layer
+
+    Returns:
+        @output_shape (list of num): a list of numbers represent the output shape
+    """
+
+    start_axis = axis
+    end_axis = end_axis
+    input_shape = list(input_shape)
+    if start_axis < 0:
+        start_axis += len(input_shape)
+
+    if end_axis < 0:
+        end_axis += len(input_shape) + 1
+
+    assert start_axis <= end_axis, 'invalid axis[%d] or end_axis[%d] params'\
+            % (start_axis, end_axis)
+    output_shape = input_shape[0:start_axis]
+    flat_sz = reduce(lambda a, b: a * b, input_shape[start_axis:end_axis])
+    output_shape += [flat_sz]
+    output_shape += input_shape[end_axis:-1]
+
+    return output_shape
+
+
+def flatten_layer(input, name, axis=1, end_axis=-1):
+    """ build a layer of type 'Flatten' using fluid
+
+    Args:
+        @input (variable): input fluid variable for this layer
+        @name (str): name for this layer
+        @axis (int): parameter from caffe's Flatten layer
+        @end_axis (int): parameter from caffe's Flatten layer
+
+    Returns:
+        output (variable): output variable for this layer
+    """
+    import paddle.fluid as fluid
+
+    input_shape = list(input.shape)
+
+    if input_shape[0] == -1:
+        input_shape[0] = 1
+        output_shape = flatten_shape(input_shape, axis=axis, end_axis=end_axis)
+        output_shape[0] = -1
+    else:
+        output_shape = flatten_shape(input_shape, axis=axis, end_axis=end_axis)
+
+    output = fluid.layers.reshape(input, shape=output_shape, name=name)
+
+    return output
+
+
+register(kind='Flatten', shape=flatten_shape, layer=flatten_layer)

caffe2fluid/kaffe/custom_layers/normalize.py

+""" A custom layer for 'normalize' op
+"""
+
+from .register import register
+
+
+def normalize_shape(input_shape,
+                    across_spatial=True,
+                    scale_filler=True,
+                    eps=1e-10):
+    """ calculate the output shape of this layer using input shapes
+
+    Args:
+        @input_shape (list of tuples): input shape
+
+    Returns:
+        @output_shape (list of num): a list of numbers represent the output shape
+    """
+    output_shape = input_shape
+    return output_shape
+
+
+def normalize_layer(input,
+                    name,
+                    across_spatial=True,
+                    scale_filler=True,
+                    channel_shared=False,
+                    eps=1e-10):
+    """ build a layer of type 'normalize' using fluid
+
+    Args:
+        @inputs (list of variables): input fluid variables for this layer
+        @name (str): name for this layer
+
+    Returns:
+        output (variable): output variable for this layer
+    """
+    import paddle.fluid as fluid
+
+    param_prefix = name.split('.')[0]
+
+    assert across_spatial == False, "Only support across_spatial == False for Normalize[%s]" % (
+        name)
+    l2_norm = fluid.layers.l2_normalize(input, axis=1)  # l2 norm along channel
+
+    shape = [1] if channel_shared else [input.shape[1]]
+    scale_attr = fluid.ParamAttr(name=param_prefix + '_scale')
+    scale_param = fluid.layers.create_parameter(
+        shape=shape, dtype=input.dtype, name=name, attr=scale_attr)
+
+    out = fluid.layers.elementwise_mul(
+        x=l2_norm, y=scale_param, axis=-1 if channel_shared else 1)
+    return out
+
+
+register(kind='Normalize', shape=normalize_shape, layer=normalize_layer)

caffe2fluid/kaffe/custom_layers/permute.py

+""" A custom layer for 'Permute' which is equivalent to transpose in paddle
+"""
+
+from .register import register
+
+
+def permute_shape(input_shape, order):
+    """ calculate the output shape of this layer using input shapes
+
+    Args:
+        @input_shape (list of numbers): input shape
+
+    Returns:
+        @output_shape (list of num): a list of numbers represent the output shape
+    """
+    output_shape = []
+    for ii in order:
+        assert ii < len(input_shape), "invalid order for permute[%s]" % (name)
+        output_shape.append(input_shape[ii])
+    return output_shape
+
+
+def permute_layer(input, name, order):
+    """ build a layer of type 'permute' using fluid
+
+    Args:
+        @input (input variable): input fluid variables for this layer
+        @name (str): name for this layer
+        @order (list of int): order to permute the dims
+
+    Returns:
+        output (variable): output variable for this layer
+    """
+    import paddle.fluid as fluid
+    output = fluid.layers.transpose(input, order, name=name)
+
+    return output
+
+
+register(kind='Permute', shape=permute_shape, layer=permute_layer)

caffe2fluid/kaffe/custom_layers/power.py

+""" a custom layer for 'power', maybe we should implement this in standard way.
+    more info can be found here: http://caffe.berkeleyvision.org/tutorial/layers/power.html
+"""
+from .register import register
+
+
+def power_shape(input_shape, shape=None):
+    """ calculate the output shape of this layer using input shape
+
+    Args:
+        @input_shape (list of num): a list of number which represents the input shape
+
+    Returns:
+        @output_shape (list of num): a list of numbers represent the output shape
+    """
+    return input_shape
+
+
+def power_layer(input, name, power=1.0, scale=1.0, shift=0.0):
+    """ build a layer of type 'Power' using fluid
+
+    Args:
+        @input (variables): input fluid variable for this layer
+        @name (str): name for this layer
+        @power (float): parameter from caffe's Power layer
+	@scale (float): parameter from caffe's Power layer
+        @shift (float): parameter from caffe's Power layer
+
+    Returns:
+        output (variable): output variable for this layer
+    """
+    import paddle.fluid as fluid
+    scale_out = fluid.layers.scale(
+        input, scale=scale, bias=shift, bias_after_scale=True)
+    output = fluid.layers.pow(scale_out, factor=power)
+
+    return output
+
+
+register(kind='Power', shape=power_shape, layer=power_layer)

caffe2fluid/kaffe/custom_layers/priorbox.py

+""" A custom layer for 'priorbox' which is used in ssd to generate prior box info
+    Since the order of prior box is different between caffe and paddle,
+    we use 'slice' and 'concate' ops to align them.
+"""
+
+from .register import register
+
+
+def priorbox_shape(input_shapes, min_size, max_size=None, aspect_ratio=None):
+    """ calculate the output shape of this layer using input shapes
+
+    Args:
+        @input_shapes (list of tuples): a list of input shapes
+
+    Returns:
+        @output_shape (list of num): a list of numbers represent the output shape
+    """
+    assert len(input_shapes) == 2, "invalid inputs for Priorbox[%s]" % (name)
+    fc_shape = input_shapes[0]
+    N = 1
+    if not max_size == None:
+        N += 1
+    if not aspect_ratio == None:
+        N += 2 * len(aspect_ratio)
+
+    N_bbx = fc_shape[2] * fc_shape[3] * N
+    output_shape = [1, 2, 4 * N_bbx]
+    return output_shape
+
+
+def priorbox_layer(inputs,
+                   name,
+                   min_size,
+                   max_size=None,
+                   aspect_ratio=None,
+                   variance=[0.1, 0.1, 0.2, 0.2],
+                   flip=False,
+                   clip=False,
+                   step=0.0,
+                   offset=0.5):
+    """ build a layer of type 'Priorbox' using fluid
+
+    Args:
+        @inputs (list of variables): input fluid variables for this layer
+        @name (str): name for this layer
+
+    Returns:
+        output (variable): output variable for this layer
+    """
+    import paddle.fluid as fluid
+
+    assert len(inputs) == 2, "invalid inputs for Priorbox[%s]" % (name)
+    input = inputs[0]
+    image = inputs[1]
+    steps = tuple(step) if type(step) is list or type(step) is tuple else (step,
+                                                                           step)
+    box, variance_ = fluid.layers.prior_box(
+        input,
+        image,
+        min_size,
+        max_size,
+        aspect_ratio,
+        variance,
+        flip,
+        clip,
+        steps,
+        offset,
+        min_max_aspect_ratios_order=True)
+    """
+    #adjust layout when the output is not consistent with caffe's
+
+    feat_shape = list(input.shape)
+    H = feat_shape[2]
+    W = feat_shape[3]
+    box_tmp = fluid.layers.reshape(box, [H, W, -1, 4])
+    nb_prior_bbx = int(box_tmp.shape[2])
+    tensor_list = fluid.layers.split(box_tmp, nb_prior_bbx, 2)
+
+    #TODO:
+    #   current implementation for this layer is not efficient
+    #   and we should fix this bug in future when Paddle support the same prior-box layout with Caffe
+    index_list = [0]
+    index_list = index_list * nb_prior_bbx
+    index_offset = 0
+    if max_size is not None:
+        index_list[1] = -1
+        index_offset = 1
+    for ii in xrange(2 * len(aspect_ratio)):
+        index_list[ii + 1 + index_offset] = ii + 1
+
+    tensor_list_gathered = [tensor_list[ii] for ii in index_list]
+    caffe_prior_bbx = fluid.layers.concat(tensor_list_gathered, axis=2)
+    box = fluid.layers.reshape(caffe_prior_bbx, [1, 1, -1])
+    """
+
+    box = fluid.layers.reshape(box, [1, 1, -1])
+    variance_ = fluid.layers.reshape(variance_, [1, 1, -1])
+    output = fluid.layers.concat([box, variance_], axis=1)
+
+    return output
+
+
+register(kind='PriorBox', shape=priorbox_shape, layer=priorbox_layer)

caffe2fluid/kaffe/custom_layers/reduction.py

+""" a custom layer for 'crop', maybe we should implement this in standard way.
+    more info can be found here: http://caffe.berkeleyvision.org/tutorial/layers/reduction.html
+"""
+from .register import register
+
+
+def reduction_shape(input_shape, axis=0):
+    """ calculate the output shape of this layer using input shape
+
+    Args:
+        @input_shape (list of num): a list of number which represents the input shape
+        @axis (int): parameter from caffe's reduction layer
+
+    Returns:
+        @output_shape (list of num): a list of numbers represent the output shape
+    """
+    if axis < 0:
+        axis += len(input_shape) + 1
+
+    assert axis <= len(input_shape), 'invalid axis[%d] error' % (axis)
+
+    return input_shape[0:axis]
+
+
+def reduction_layer(input, name, axis=0, operation=1, coeff=1.0):
+    """ build a layer of type 'Crop' using fluid
+
+    Args:
+        @input (variable): input fluid variable for this layer
+        @name (str): name for this layer
+        @axis (int): parameter from caffe's reduction layer
+        @operation (int): parameter from caffe's reduction layer
+        @coeff (float): parameter from caffe's reduction layer
+
+    Returns:
+        output (variable): output variable for this layer
+    """
+    assert operation >= 1 and operation <= 4, "reduction reduction [%s] error" % (
+        operation)
+
+    input_len = len(input.shape)
+    if axis < 0:
+        axis += input_len + 1
+
+    dim = range(input_len)
+
+    import paddle.fluid as fluid
+    if operation == 1:  ## operation = SUM
+        output = fluid.layers.reduce_sum(
+            input, dim=dim[axis:], keep_dim=False, name=name)
+    elif operation == 2:  ## operation = ASUM
+        absout = fluid.layers.abs(input)
+        output = fluid.layers.reduce_sum(
+            absout, dim=dim[axis:], keep_dim=False, name=name)
+    elif operation == 3:  ## operation = SUMSQ
+        powout = fluid.layers.pow(x=input, factor=2.0)
+        output = fluid.layers.reduce_sum(
+            powout, dim=dim[axis:], keep_dim=False, name=name)
+    else:  ## operation = MEAN
+        output = fluid.layers.reduce_mean(
+            input, dim=dim[axis:], keep_dim=False, name=name)
+
+    mulout = fluid.layers.scale(x=output, scale=coeff)
+    return mulout
+
+
+register(kind='Reduction', shape=reduction_shape, layer=reduction_layer)

caffe2fluid/kaffe/custom_layers/register.py

+""" this module provides 'register' for registering customized layers
+"""
+
+g_custom_layers = {}
+
+
+def register(kind, shape, layer):
+    """ register a custom layer or a list of custom layers
+
+    Args:
+        @kind (str or list): type name of the layer
+        @shape (function): a function to generate the shape of layer's output
+        @layer (function): a function to generate the shape of layer's output
+
+    Returns:
+        None
+    """
+    assert type(shape).__name__ == 'function', 'shape should be a function'
+    assert type(layer).__name__ == 'function', 'layer should be a function'
+
+    if type(kind) is str:
+        kind = [kind]
+    else:
+        assert type(
+            kind) is list, 'invalid param "kind" for register, not a list or str'
+
+    for k in kind:
+        assert type(
+            k) is str, 'invalid param "kind" for register, not a list of str'
+        assert k not in g_custom_layers, 'this type[%s] has already been registered' % (
+            k)
+        print('register layer[%s]' % (k))
+        g_custom_layers[k] = {'shape': shape, 'layer': layer}
+
+
+def get_registered_layers():
+    return g_custom_layers

caffe2fluid/kaffe/custom_layers/reshape.py

+""" a custom layer for 'reshape', maybe we should implement this in standard way.
+    more info can be found here: http://caffe.berkeleyvision.org/tutorial/layers/reshape.html
+"""
+from .register import register
+
+
+def import_fluid():
+    import paddle.fluid as fluid
+    return fluid
+
+
+def reshape_shape(input_sp, shape, axis=0, num_axes=-1):
+    """ calculate the output shape of this layer using input shape
+
+    Args:
+        @input_shape (list of num): a list of number which represents the input shape
+        @shape (object): parameter from caffe's Reshape layer
+        @axis (int): parameter from caffe's Reshape layer
+        @num_axes(int): parameter from caffe's Reshape layer
+
+    Returns:
+        @output_shape (list of num): a list of numbers represent the output shape
+    """
+
+    def count(num_list):
+        return reduce(lambda a, b: a * b, num_list)
+
+    input_shape = list(input_sp)
+    input_count = count(input_shape)
+
+    input_num_axes = len(input_shape)
+
+    input_start_axis = axis
+    start_axis = input_start_axis if input_start_axis >= 0 \
+            else input_num_axes + input_start_axis + 1
+
+    assert start_axis >= 0, "[Reshape]axis %d out of range" % (input_start_axis)
+    assert start_axis <= input_num_axes, "[Reshape]axis %d out of range for %d-D input data"\
+            % (input_start_axis, input_num_axes)
+
+    assert num_axes >= -1, "[Reshape]num_axes must be >= 0, or -1 for all"
+
+    end_axis = input_num_axes if num_axes == -1 else start_axis + num_axes
+    assert end_axis <= input_num_axes, "end_axis[%d] = axis[%d] + num_axes[%d] is out of range"\
+            % (end_axis, start_axis, num_axes)
+
+    num_axes_replaced = end_axis - start_axis
+    num_axes_retained = input_num_axes - num_axes_replaced
+    num_new_axes = len(shape['dim'])
+    output_shape = []
+
+    for i in range(start_axis):
+        output_shape.append(input_shape[i])
+
+    for i in range(num_new_axes):
+        output_shape.append(shape['dim'][i])
+
+    for i in range(end_axis, input_num_axes):
+        output_shape.append(input_shape[i])
+
+    assert len(output_shape) == num_axes_retained + num_new_axes,\
+            "[Reshape]invalid dims of output shape[%s]" % (str(output_shape))
+
+    inferred_axis = -1
+    copy_axes = []
+    constant_count = 1
+    for i in range(num_new_axes):
+        top_dim = shape['dim'][i]
+        if top_dim == 0:
+            copy_axes.append(i)
+            copy_axis_index = start_axis + i
+            output_shape[copy_axis_index] = input_shape[copy_axis_index]
+        elif top_dim == -1:
+            assert inferred_axis == -1, "[Reshape]new shape contains multiple -1 dims"
+            inferred_axis = i
+        else:
+            constant_count *= top_dim
+
+    if inferred_axis >= 0:
+        explicit_count = constant_count
+        l = input_shape[0:start_axis]
+        if len(l) > 0:
+            explicit_count *= count(l)
+
+        l = input_shape[end_axis:]
+        if len(l) > 0:
+            explicit_count *= count(l)
+
+        for i in range(len(copy_axes)):
+            explicit_count *= output_shape[start_axis + copy_axes[i]]
+
+        assert input_count % explicit_count == 0, "[Reshape]botom count[%d] "\
+                "must be divisible by product of the specified dimensions[%d] "\
+                % (input_count, explicit_count)
+        output_shape[start_axis + inferred_axis] = input_count / explicit_count
+
+    output_count = count(output_shape)
+    assert output_count == input_count, "[Reshape]output count[%d] must match input count[%d]" % (
+        output_count, input_count)
+
+    return output_shape
+
+
+def reshape_layer(input, name, shape, axis=0, num_axes=-1):
+    """ build a layer of type 'Flatten' using fluid
+
+    Args:
+        @input (variable): input fluid variable for this layer
+        @name (str): name for this layer
+        @shape (object): parameter from caffe's Reshape layer
+        @axis (int): parameter from caffe's Reshape layer
+        @num_axes(int): parameter from caffe's Reshape layer
+
+    Returns:
+        output (variable): output variable for this layer
+    """
+    fluid = import_fluid()
+
+    input_shape = list(input.shape)
+
+    if input_shape[0] == -1:
+        input_shape[0] = 1
+        output_shape = reshape_shape(input_shape, shape, axis, num_axes)
+        output_shape[0] = -1
+    else:
+        output_shape = reshape_shape(input_shape, shape, axis, num_axes)
+
+    output = fluid.layers.reshape(input, shape=output_shape, name=name)
+
+    return output
+
+
+register(kind='Reshape', shape=reshape_shape, layer=reshape_layer)

caffe2fluid/kaffe/custom_layers/roipooling.py

+""" a custom layer for 'ROIPooling', maybe we should implement this in standard way.
+    more info can be found here: http://caffe.berkeleyvision.org/tutorial/layers/ROIPooling.html
+"""
+from .register import register
+
+
+def roipooling_shape(input_shapes, pooled_h, pooled_w, spatial_scale):
+    """ calculate the output shape of this layer using input shape
+
+    Args:
+        @input_shape (list of num): a list of number which represents the input shape
+        @out_max_val (bool): parameter from caffe's ROIPooling layer
+        @top_k (int): parameter from caffe's ROIPooling layer
+        @axis (int): parameter from caffe's ROIPooling layer
+
+    Returns:
+        @output_shape (list of num): a list of numbers represent the output shape
+    """
+    assert len(input_shapes) == 2, "not valid input shape for roipooling layer"
+    base_fea_shape = input_shapes[0]
+    rois_shape = input_shapes[1]
+    output_shape = base_fea_shape
+    output_shape[0] = rois_shape[0]
+    output_shape[2] = pooled_h
+    output_shape[3] = pooled_w
+    return output_shape
+
+
+def roipooling_layer(inputs, name, pooled_h, pooled_w, spatial_scale):
+    """ build a layer of type 'ROIPooling' using fluid
+
+    Args:
+        @input (variable): input fluid variable for this layer
+        @name (str): name for this layer
+        @out_max_val (bool): parameter from caffe's ROIPooling layer
+        @top_k (int): parameter from caffe's ROIPooling layer
+        @axis (int): parameter from caffe's ROIPooling layer
+
+    Returns:
+        output (variable): output variable for this layer
+    """
+
+    import paddle.fluid as fluid
+    assert len(inputs) == 2, "not valid input shape for roipooling layer"
+    base_fea = inputs[0]
+    rois = inputs[1][:, 1:5]
+    rois_fea = fluid.layers.roi_pool(base_fea, rois, pooled_h, pooled_w,
+                                     spatial_scale)
+
+    return rois_fea
+
+
+register(kind='ROIPooling', shape=roipooling_shape, layer=roipooling_layer)

caffe2fluid/kaffe/custom_layers/select.py

+""" a custom layer for 'select' which is used to replace standard 'Slice' layer 
+    for converting layer with multiple different output tensors
+"""
+from .register import register
+
+
+def select_shape(input_shape, slice_point, axis=1):
+    """ calculate the output shape of this layer using input shape
+
+    Args:
+        @input_shape (list of num): a list of number which represents the input shape
+        @slice_point (list): parameter from caffe's Slice layer
+        @axis (int): parameter from caffe's Slice layer
+
+    Returns:
+        @output_shape (list of num): a list of numbers represent the output shape
+    """
+
+    input_shape = list(input_shape)
+    start = slice_point[0]
+    if len(slice_point) == 2:
+        end = slice_point[1]
+    else:
+        end = input_shape[axis]
+
+    assert end > start, "invalid slice_point with [start:%d, end:%d]"\
+             % (start, end)
+    output_shape = input_shape
+    output_shape[axis] = end - start
+    return output_shape
+
+
+def select_layer(input, name, slice_point, axis=1):
+    """ build a layer of type 'Slice' using fluid
+
+    Args:
+        @input (variable): input fluid variable for this layer
+        @name (str): name for this layer
+        @slice_point (list): parameter from caffe's Slice layer
+        @axis (int): parameter from caffe's Slice layer
+
+    Returns:
+        output (variable): output variable for this layer
+    """
+    import paddle.fluid as fluid
+    input_shape = list(input.shape)
+
+    start = slice_point[0]
+    if len(slice_point) == 2:
+        end = slice_point[1]
+    else:
+        end = input_shape[axis]
+
+    sections = []
+    if start > 0:
+        sections.append(start)
+
+    pos = len(sections)
+    sections.append(end - start)
+    if end != input_shape[axis]:
+        sections.append(input_shape[axis] - end)
+
+    outputs = fluid.layers.split(input, sections, dim=axis, name=name)
+    return outputs[pos]
+
+
+register(kind='Select', shape=select_shape, layer=select_layer)

caffe2fluid/kaffe/errors.py

+import sys
+
+#debug level, can be 'warn', 'verbose'
+log_level = 'warn'
+
+
+class KaffeError(Exception):
+    pass
+
+
+def print_stderr(msg):
+    sys.stderr.write('%s\n' % msg)
+
+
+def debug(msg):
+    if log_level == 'verbose':
+        print_stderr('[DEBUG]' + msg)
+
+
+def notice(msg):
+    print_stderr('[NOTICE]' + msg)
+
+
+def warn(msg):
+    print_stderr('[WARNING]' + msg)
+
+
+def set_loglevel(level):
+    global log_level
+
+    if 'warn' != level and 'verbose' != level:
+        raise Exception('not supported log level[%s]' % (level))
+
+    log_level = level

caffe2fluid/kaffe/graph.py

+from google.protobuf import text_format
+
+from .caffe import get_caffe_resolver
+from .errors import KaffeError, print_stderr
+from .layers import LayerAdapter, LayerType, NodeKind, NodeDispatch
+from .shapes import make_tensor
+
+
+class Node(object):
+    def __init__(self, name, kind, layer=None):
+        self.name = name
+        self.kind = kind
+        self.layer = LayerAdapter(layer, kind) if layer else None
+        self.parents = []
+        self.children = []
+        self.data = None  #parameters of this node
+        self.output_shape = None  #output shape of this node
+        self.metadata = {}
+
+    def add_parent(self, parent_node):
+        assert parent_node not in self.parents
+        self.parents.append(parent_node)
+        if self not in parent_node.children:
+            parent_node.children.append(self)
+
+    def add_child(self, child_node):
+        assert child_node not in self.children
+        self.children.append(child_node)
+        if self not in child_node.parents:
+            child_node.parents.append(self)
+
+    def get_only_parent(self):
+        if len(self.parents) != 1:
+            raise KaffeError('Node (%s) expected to have 1 parent. Found %s.' %
+                             (self, len(self.parents)))
+        return self.parents[0]
+
+    @property
+    def parameters(self):
+        """ get parameters stored in a protobuf object
+        """
+        if self.layer is not None:
+            return self.layer.parameters
+        return None
+
+    @property
+    def params(self):
+        """ get parameters stored in a dict
+        """
+        from .protobuf_to_dict import protobuf_to_dict
+
+        p = self.parameters
+        if p is not None:
+            return protobuf_to_dict(p)
+        else:
+            return None
+
+    def __str__(self):
+        return '[%s] %s' % (self.kind, self.name)
+
+    def __repr__(self):
+        return '%s (0x%x)' % (self.name, id(self))
+
+
+class Graph(object):
+    def __init__(self, nodes=None, name=None, trace={}):
+        self.nodes = nodes or []
+        self.node_lut = {node.name: node for node in self.nodes}
+        self.output_trace = trace
+        if name is None or name == '':
+            self.name = 'MyNet'
+        else:
+            self.name = name
+
+    def add_node(self, node):
+        self.nodes.append(node)
+        self.node_lut[node.name] = node
+
+    def get_node(self, name):
+        try:
+            return self.node_lut[name]
+        except KeyError:
+            raise KaffeError('Layer not found: %s' % name)
+
+    def add_name_trace(self, trace, which='caffe'):
+        self.output_trace[which] = trace
+
+    def get_name_trace(self, which=None):
+        if which is not None:
+            return self.output_trace[which]
+        else:
+            return self.output_trace
+
+    def get_input_nodes(self):
+        return [node for node in self.nodes if len(node.parents) == 0]
+
+    def get_output_nodes(self):
+        return [node for node in self.nodes if len(node.children) == 0]
+
+    def topologically_sorted(self):
+        sorted_nodes = []
+        unsorted_nodes = list(self.nodes)
+        temp_marked = set()
+        perm_marked = set()
+
+        def visit(node):
+            if node in temp_marked:
+                raise KaffeError('Graph is not a DAG.')
+            if node in perm_marked:
+                return
+            temp_marked.add(node)
+            for child in node.children:
+                visit(child)
+            perm_marked.add(node)
+            temp_marked.remove(node)
+            sorted_nodes.insert(0, node)
+
+        while len(unsorted_nodes):
+            visit(unsorted_nodes.pop())
+        return sorted_nodes
+
+    def compute_output_shapes(self):
+        sorted_nodes = self.topologically_sorted()
+        for node in sorted_nodes:
+            node.output_shape = make_tensor(
+                *NodeKind.compute_output_shape(node))
+
+    def replaced(self, new_nodes):
+        return Graph(nodes=new_nodes, name=self.name, trace=self.output_trace)
+
+    def transformed(self, transformers):
+        graph = self
+        for transformer in transformers:
+            graph = transformer(graph)
+            if graph is None:
+                raise KaffeError('Transformer failed: {}'.format(transformer))
+            assert isinstance(graph, Graph)
+
+        return graph
+
+    def __contains__(self, key):
+        return key in self.node_lut
+
+    def __str__(self):
+        hdr = '{:<20} {:<30} {:>20} {:>20}'.format('Type', 'Name', 'Param',
+                                                   'Output')
+        s = [hdr, '-' * 94]
+        for node in self.topologically_sorted():
+            # If the node has learned parameters, display the first one's shape.
+            # In case of convolutions, this corresponds to the weights.
+            if node.data is None:
+                data_shape = '--'
+                out_shape = node.output_shape or '--'
+                s.append('{:<20} {:<30} {:>20} {:>20}'.format(
+                    node.kind, node.name, data_shape, tuple(out_shape)))
+            else:
+                for d in node.data:
+                    #data_shape = node.data[0].shape if node.data else '--'
+                    data_shape = d.shape
+                    out_shape = node.output_shape or '--'
+                    s.append('{:<20} {:<30} {:>20} {:>20}'.format(
+                        node.kind, node.name, data_shape, tuple(out_shape)))
+        return '\n'.join(s)
+
+
+class GraphBuilder(object):
+    '''Constructs a model graph from a Caffe protocol buffer definition.'''
+
+    def __init__(self, def_path, phase='test'):
+        '''
+        def_path: Path to the model definition (.prototxt)
+        data_path: Path to the model data (.caffemodel)
+        phase: Either 'test' or 'train'. Used for filtering phase-specific nodes.
+        '''
+        self.def_path = def_path
+        self.phase = phase
+        self.load()
+
+    def load(self):
+        '''Load the layer definitions from the prototxt.'''
+        self.params = get_caffe_resolver().NetParameter()
+        with open(self.def_path, 'rb') as def_file:
+            text_format.Merge(def_file.read(), self.params)
+
+    def filter_layers(self, layers):
+        '''Filter out layers based on the current phase.'''
+        phase_map = {0: 'train', 1: 'test'}
+        filtered_layer_names = set()
+        filtered_layers = []
+        for layer in layers:
+            phase = self.phase
+            if len(layer.include):
+                phase = phase_map[layer.include[0].phase]
+            if len(layer.exclude):
+                phase = phase_map[1 - layer.include[0].phase]
+            exclude = (phase != self.phase)
+            # Dropout layers appear in a fair number of Caffe
+            # test-time networks. These are just ignored. We'll
+            # filter them out here.
+            if (not exclude) and (phase == 'test'):
+                exclude = (layer.type == LayerType.Dropout)
+            if not exclude:
+                filtered_layers.append(layer)
+                # Guard against dupes.
+                assert layer.name not in filtered_layer_names
+                filtered_layer_names.add(layer.name)
+        return filtered_layers
+
+    def make_node(self, layer):
+        '''Create a graph node for the given layer.'''
+        kind = NodeKind.map_raw_kind(layer.type)
+        if kind is None:
+            raise KaffeError('Unknown layer type encountered: %s' % layer.type)
+
+        # We want to use the layer's top names (the "output" names), rather than the
+        # name attribute, which is more of readability thing than a functional one.
+        # Other layers will refer to a node by its "top name".
+        return Node(layer.name, kind, layer=layer)
+
+    def make_input_nodes(self):
+        '''
+        Create data input nodes.
+
+        This method is for old-style inputs, where the input specification
+        was not treated as a first-class layer in the prototext.
+        Newer models use the "Input layer" type.
+        '''
+        nodes = [Node(name, NodeKind.Data) for name in self.params.input]
+        inputs_num = len(nodes)
+        if inputs_num > 0:
+            input_dims_num = len(self.params.input_dim)
+            if input_dims_num > 0 and input_dims_num != inputs_num * 4:
+                raise KaffeError('invalid input_dim[%d] param in prototxt' %
+                                 (input_dims_num))
+
+            input_dims = [[]] * inputs_num
+            for i in range(input_dims_num):
+                dim = self.params.input_dim[i]
+                which = int(i / 4)
+                input_dims[which].append(int(dim))
+
+            for i in range(inputs_num):
+                if len(self.params.input_shape) == inputs_num:
+                    input_dim = map(int, self.params.input_shape[i].dim)
+                    input_dims[i] = input_dim
+
+                nodes[i].output_shape = tuple(input_dims[i])
+        return nodes
+
+    def build(self):
+        '''
+        Builds the graph from the Caffe layer definitions.
+        '''
+        # Get the layers
+        layers = self.params.layers or self.params.layer
+        # Filter out phase-excluded layers
+        layers = self.filter_layers(layers)
+        # Get any separately-specified input layers
+        nodes = self.make_input_nodes()
+        nodes += [self.make_node(layer) for layer in layers]
+        # Initialize the graph
+        graph = Graph(nodes=nodes, name=self.params.name)
+        # Connect the nodes
+        #
+        # A note on layers and outputs:
+        # In Caffe, each layer can produce multiple outputs ("tops") from a set of inputs
+        # ("bottoms"). The bottoms refer to other layers' tops. The top can rewrite a bottom
+        # (in case of in-place operations). Note that the layer's name is not used for establishing
+        # any connectivity. It's only used for data association. By convention, a layer with a
+        # single top will often use the same name (although this is not required).
+        #
+        # The current implementation only supports single-output nodes (note that a node can still
+        # have multiple children, since multiple child nodes can refer to the single top's name).
+        node_outputs = {}
+        output_trace = {}
+        for layer in layers:
+            node = graph.get_node(layer.name)
+            for input_name in layer.bottom:
+                assert input_name != layer.name
+                parent_node = node_outputs.get(input_name)
+                if (parent_node is None) or (parent_node == node):
+                    parent_node = graph.get_node(input_name)
+                node.add_parent(parent_node)
+
+            if len(layer.top) > 1:
+                raise KaffeError('Multiple top nodes are not supported.')
+
+            for output_name in layer.top:
+                if output_name == layer.name:
+                    # Output is named the same as the node. No further action required.
+                    continue
+                # There are two possibilities here:
+                #
+                # Case 1: output_name refers to another node in the graph.
+                # This is an "in-place operation" that overwrites an existing node.
+                # This would create a cycle in the graph. We'll undo the in-placing
+                # by substituting this node wherever the overwritten node is referenced.
+                #
+                # Case 2: output_name violates the convention layer.name == output_name.
+                # Since we are working in the single-output regime, we will can rename it to
+                # match the layer name.
+                #
+                # For both cases, future references to this top re-routes to this node.
+                node_outputs[output_name] = node
+                if output_name in output_trace:
+                    output_trace[output_name].append(node.name)
+                else:
+                    output_trace[output_name] = [output_name, node.name]
+
+        #build a mapping from real-name to changed-name(for caffe's INPLACE inference)
+        real2chg = {}
+        deleted = {}
+        for k, v in output_trace.items():
+            real2chg[v[-1]] = k
+            for n in v:
+                if n in real2chg:
+                    continue
+                if n not in deleted:
+                    deleted[n] = '%s.%s' % (k, v[-1])
+
+        graph.add_name_trace({
+            'real2chg': real2chg,
+            'deleted': deleted
+        }, 'caffe')
+        graph.compute_output_shapes()
+        return graph
+
+
+class NodeMapper(NodeDispatch):
+    def __init__(self, graph):
+        self.graph = graph
+
+    def map(self):
+        nodes = self.graph.topologically_sorted()
+        # Remove input nodes - we'll handle them separately.
+        input_nodes = self.graph.get_input_nodes()
+        nodes = [t for t in nodes if t not in input_nodes]
+        # Decompose DAG into chains.
+        chains = []
+        for node in nodes:
+            attach_to_chain = None
+            if len(node.parents) == 1:
+                parent = node.get_only_parent()
+                for chain in chains:
+                    if chain[-1] == parent:
+                        # Node is part of an existing chain.
+                        attach_to_chain = chain
+                        break
+            if attach_to_chain is None:
+                # Start a new chain for this node.
+                attach_to_chain = []
+                chains.append(attach_to_chain)
+            attach_to_chain.append(node)
+        # Map each chain.
+        mapped_chains = []
+        for chain in chains:
+            mapped_chains.append(self.map_chain(chain))
+        return self.commit(mapped_chains)
+
+    def map_chain(self, chain):
+        return [self.map_node(node) for node in chain]
+
+    def map_node(self, node):
+        map_func = self.get_handler(node.kind, 'map')
+        mapped_node = map_func(node)
+        assert mapped_node is not None
+        mapped_node.node = node
+        return mapped_node
+
+    def commit(self, mapped_chains):
+        raise NotImplementedError('Must be implemented by subclass.')

caffe2fluid/kaffe/layers.py

+import re
+import numbers
+from collections import namedtuple
+
+import custom_layers
+from .shapes import *
+
+LAYER_DESCRIPTORS = {
+
+    # Caffe Types
+    'AbsVal': shape_identity,
+    'Accuracy': shape_scalar,
+    'ArgMax': shape_not_implemented,
+    'BatchNorm': shape_identity,
+    'BNLL': shape_not_implemented,
+    'Concat': shape_concat,
+    'ContrastiveLoss': shape_scalar,
+    'Convolution': shape_convolution,
+    'Deconvolution': shape_deconvolution,
+    'Data': shape_data,
+    'Dropout': shape_identity,
+    'DummyData': shape_data,
+    'Crop': shape_crop,
+    'EuclideanLoss': shape_scalar,
+    'Eltwise': shape_identity,
+    'Exp': shape_identity,
+    'Flatten': shape_not_implemented,
+    'HDF5Data': shape_data,
+    'HDF5Output': shape_identity,
+    'HingeLoss': shape_scalar,
+    'Im2col': shape_not_implemented,
+    'ImageData': shape_data,
+    'InfogainLoss': shape_scalar,
+    'InnerProduct': shape_inner_product,
+    'Input': shape_data,
+    'LRN': shape_identity,
+    'MemoryData': shape_mem_data,
+    'MultinomialLogisticLoss': shape_scalar,
+    'MVN': shape_not_implemented,
+    'Pooling': shape_pool,
+    'Power': shape_power,
+    'ReLU': shape_identity,
+    'PReLU': shape_identity,
+    'Scale': shape_identity,
+    'Sigmoid': shape_identity,
+    'SigmoidCrossEntropyLoss': shape_scalar,
+    'Silence': shape_not_implemented,
+    'Softmax': shape_identity,
+    'SoftmaxWithLoss': shape_scalar,
+    'Split': shape_not_implemented,
+    'Slice': shape_not_implemented,
+    'TanH': shape_identity,
+    'WindowData': shape_not_implemented,
+    'Threshold': shape_identity,
+}
+
+# layer types in 'V1LayerParameter'
+# (v1layertype name, enum value, mapped to layer type)
+v1_layertypes = [
+    ('ABSVAL', 35),
+    ('ACCURACY', 1),
+    ('ARGMAX', 30),
+    ('BNLL', 2),
+    ('CONCAT', 3),
+    ('CONVOLUTION', 4),
+    ('DATA', 5),
+    ('DECONVOLUTION', 39),
+    ('DROPOUT', 6),
+    ('ELTWISE', 25),
+    ('EXP', 38),
+    ('FLATTEN', 8),
+    ('IM2COL', 11),
+    ('INNERPRODUCT', 14),
+    ('LRN', 15),
+    ('MEMORYDATA', 29),
+    ('MULTINOMIALLOGISTICLOSS', 16),
+    ('MVN', 34),
+    ('POOLING', 17),
+    ('POWER', 26),
+    ('RELU', 18),
+    ('SIGMOID', 19),
+    ('SIGMOIDCROSSENTROPYLOSS', 27),
+    ('SILENCE', 36),
+    ('SOFTMAX', 20),
+    ('SPLIT', 22),
+    ('SLICE', 33),
+    ('TANH', 23),
+    ('WINDOWDATA', 24),
+    ('THRESHOLD', 31),
+]
+
+LAYER_TYPES = LAYER_DESCRIPTORS.keys()
+LayerType = type('LayerType', (), {t: t for t in LAYER_TYPES})
+
+#map the layer name in V1 to standard name
+V1_LAYER_MAP = {'_not_init_': True}
+
+
+def get_v1_layer_map():
+    global V1_LAYER_MAP
+    if '_not_init_' not in V1_LAYER_MAP:
+        return V1_LAYER_MAP
+    else:
+        del V1_LAYER_MAP['_not_init_']
+
+    name2layer = {}
+    for n in LAYER_TYPES:
+        name2layer[n.upper()] = n
+
+    for l in v1_layertypes:
+        n, v = l
+        if n in name2layer and v not in V1_LAYER_MAP:
+            V1_LAYER_MAP[v] = name2layer[n]
+        else:
+            raise KaffeError('not found v1 layer type %s' % n)
+    return V1_LAYER_MAP
+
+
+class NodeKind(LayerType):
+    @staticmethod
+    def map_raw_kind(kind):
+        if custom_layers.has_layer(kind):
+            return kind
+
+        if kind in LAYER_TYPES:
+            return kind
+
+        v1_layers = get_v1_layer_map()
+        if kind in v1_layers:
+            return v1_layers[kind]
+        else:
+            return None
+
+    @staticmethod
+    def compute_output_shape(node):
+        if custom_layers.has_layer(node.kind):
+            return custom_layers.compute_output_shape(node.kind, node)
+
+        try:
+            val = LAYER_DESCRIPTORS[node.kind](node)
+            return val
+        except NotImplementedError:
+            raise KaffeError(
+                'Output shape computation not implemented for type: %s' %
+                node.kind)
+
+
+class NodeDispatchError(KaffeError):
+    pass
+
+
+class NodeDispatch(object):
+    @staticmethod
+    def get_handler_name(node_kind):
+        if len(node_kind) <= 6:
+            # A catch-all for things like ReLU and tanh
+            return node_kind.lower()
+        # Convert from CamelCase to under_scored
+        name = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', node_kind)
+        return re.sub('([a-z0-9])([A-Z])', r'\1_\2', name).lower()
+
+    def get_handler(self, node_kind, prefix):
+        if custom_layers.has_layer(node_kind):
+            return getattr(self, 'map_custom')
+
+        name = self.get_handler_name(node_kind)
+        name = '_'.join((prefix, name))
+        try:
+            return getattr(self, name)
+        except AttributeError:
+            raise NodeDispatchError(
+                'No handler found for node kind: %s (expected: %s)' %
+                (node_kind, name))
+
+
+class LayerAdapter(object):
+    def __init__(self, layer, kind):
+        self.layer = layer
+        self.kind = kind
+
+    @property
+    def parameters(self):
+        name = NodeDispatch.get_handler_name(self.kind)
+        if self.kind.lower() == "normalize":
+            name = "norm"
+        elif self.kind.lower() == "deconvolution":
+            name = "convolution"
+
+        name = '_'.join((name, 'param'))
+        try:
+            return getattr(self.layer, name)
+        except AttributeError:
+            print(dir(self.layer))
+            raise NodeDispatchError(
+                'Caffe parameters not found attr[%s] for layer kind[%s]' %
+                (name, self.kind))
+
+    @staticmethod
+    def get_kernel_value(scalar, repeated, idx, default=None):
+        if scalar:
+            return scalar
+        if repeated:
+            if isinstance(repeated, numbers.Number):
+                return repeated
+            if len(repeated) == 1:
+                # Same value applies to all spatial dimensions
+                return int(repeated[0])
+            assert idx < len(repeated)
+            # Extract the value for the given spatial dimension
+            return repeated[idx]
+        if default is None:
+            raise ValueError('Unable to determine kernel parameter!')
+        return default
+
+    @property
+    def kernel_parameters(self):
+        assert self.kind in (NodeKind.Convolution, NodeKind.Pooling,\
+                    NodeKind.Deconvolution)
+
+        params = self.parameters
+        k_h = self.get_kernel_value(params.kernel_h, params.kernel_size, 0)
+        k_w = self.get_kernel_value(params.kernel_w, params.kernel_size, 1)
+        s_h = self.get_kernel_value(
+            params.stride_h, params.stride, 0, default=1)
+        s_w = self.get_kernel_value(
+            params.stride_w, params.stride, 1, default=1)
+        p_h = self.get_kernel_value(params.pad_h, params.pad, 0, default=0)
+        p_w = self.get_kernel_value(params.pad_w, params.pad, 1, default=0)
+
+        dila_h = dila_w = 1
+        if self.kind in (NodeKind.Convolution, NodeKind.Deconvolution):
+            dila_len = len(params.dilation)
+            if dila_len == 2:
+                dila_h = params.dilation[0]
+                dila_w = params.dilation[1]
+            elif dila_len == 1:
+                dila_h = dila_w = params.dilation[0]
+            else:
+                assert dila_len == 0, "invalid length[%s] of dilation in convolution" % (
+                    dila_len)
+
+        return KernelParameters(k_h, k_w, s_h, s_w, p_h, p_w, dila_h, dila_w)
+
+
+KernelParameters = namedtuple(
+    'KernelParameters',
+    [
+        'kernel_h', 'kernel_w', 'stride_h', 'stride_w', 'pad_h', 'pad_w',
+        'dila_h', 'dila_w'
+    ], )

caffe2fluid/kaffe/net_template.py

+""" this module is used as a template for generating sub class of Network
+"""
+
+
+class MyNet(object):
+    ### automatically generated by caffe2fluid ###
+    inputs_info = "INPUTS_INFO"
+    custom_layers_path = "_CAFFE2FLUID_CUSTOM_LAYERS_"
+
+    def custom_layer_factory(self):
+        import os
+
+        pk_paths = []
+        default = os.path.dirname(os.path.abspath(__file__))
+        location = os.environ.get('CAFFE2FLUID_CUSTOM_LAYERS', default)
+        pk_name = 'custom_layers'
+        pk_dir = os.path.join(location, pk_name)
+        pk_paths.append((location, pk_dir))
+
+        location = MyNet.custom_layers_path
+        pk_dir = os.path.join(MyNet.custom_layers_path, pk_name)
+        pk_paths.append((location, pk_dir))
+
+        for loc, pk_dir in pk_paths:
+            if os.path.exists(pk_dir):
+                if loc not in sys.path:
+                    sys.path.insert(0, loc)
+                    break
+
+        try:
+            from custom_layers import make_custom_layer
+            return make_custom_layer
+        except Exception as e:
+            print('maybe you should set $CAFFE2FLUID_CUSTOM_LAYERS first')
+            raise e
+
+    @classmethod
+    def input_shapes(cls):
+        return cls.inputs_info
+
+    @classmethod
+    def convert(cls, npy_model, fluid_path, outputs=None):
+        fluid = import_fluid()
+        shapes = cls.input_shapes()
+        input_name = shapes.keys()[0]
+        feed_data = {}
+        for name, shape in shapes.items():
+            data_layer = fluid.layers.data(
+                name=name, shape=shape, dtype="float32")
+            feed_data[name] = data_layer
+
+        net = cls(feed_data)
+        place = fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        exe.run(fluid.default_startup_program())
+        net.load(data_path=npy_model, exe=exe, place=place)
+        output_vars = []
+
+        model_filename = 'model'
+        params_filename = 'params'
+        if outputs is None:
+            output_vars.append(net.get_output())
+        else:
+            if outputs[0] == 'dump_all':
+                model_filename = None
+                params_filename = None
+                output_vars.append(net.get_output())
+            else:
+                if type(outputs) is list:
+                    for n in outputs:
+                        assert n in net.layers, 'not found layer with this name[%s]' % (
+                            n)
+                        output_vars.append(net.layers[n])
+
+        fluid.io.save_inference_model(
+            fluid_path, [input_name],
+            output_vars,
+            exe,
+            main_program=None,
+            model_filename=model_filename,
+            params_filename=params_filename)
+        return 0
+
+
+def main():
+    """ a tool used to convert caffe model to fluid
+    """
+
+    import sys
+    import os
+    filename = os.path.splitext(os.path.basename(sys.argv[0]))[0]
+    if len(sys.argv) < 3:
+        print('usage:')
+        print('	python %s %s.npy [save_dir] [layer names seperated by comma]' \
+                % (sys.argv[0], filename))
+        print('	eg: python %s %s.npy ./fluid' % (sys.argv[0], filename))
+        print('	eg: python %s %s.npy ./fluid layer_name1,layer_name2' \
+                % (sys.argv[0], filename))
+        return 1
+
+    npy_weight = sys.argv[1]
+    fluid_model = sys.argv[2]
+    outputs = None
+    if len(sys.argv) >= 4:
+        outputs = sys.argv[3].split(',')
+
+    ret = MyNet.convert(npy_weight, fluid_model, outputs)
+    if ret == 0:
+        outputs = 'last output layer' if outputs is None else outputs
+        print('succeed to convert to fluid format with output layers[%s]'
+              ' in directory[%s]' % (outputs, fluid_model))
+    else:
+        print('failed to convert model to fluid format')
+
+    return ret
+
+
+def generate_net_code(net_name, inputs_info):
+    """ generate framework of a custom net code which represent a subclass of Network
+
+    Args:
+        @net_name (str): class name for this net
+        @inputs_info (str): a str which represents a dict,  eg: '{"data": [3, 32, 32]}'
+    Returns:
+        net_codes (str): codes for this subclass
+    """
+    import os
+    import inspect
+
+    net_codes = str(inspect.getsource(MyNet))
+    net_codes = net_codes.replace('MyNet(object)', '%s(Network)' % net_name)
+    net_codes = net_codes.replace('MyNet', net_name)
+    net_codes = net_codes.replace('"INPUTS_INFO"', inputs_info)
+
+    custom_layer_dir = os.path.dirname(os.path.abspath(__file__))
+    net_codes = net_codes.replace('_CAFFE2FLUID_CUSTOM_LAYERS_',
+                                  custom_layer_dir)
+    return net_codes
+
+
+def generate_main_code(net_name):
+    """ generate a piece of code for 'main' function
+
+    Args:
+        @net_name (str): class name for this net
+
+    Returns:
+        main_codes (str): codes for this main function
+    """
+    import inspect
+
+    main_codes = str(inspect.getsource(main))
+    main_codes = main_codes.replace('MyNet', net_name)
+    return main_codes
+
+
+if __name__ == "__main__":
+    """ just for testing
+    """
+    print generate_net_code('Attribute', "{'data': [3, 277, 277]}")
+    print generate_main_code('Attribute')

caffe2fluid/kaffe/paddle/__init__.py

+from .transformer import Transformer
+from .network import Network

caffe2fluid/kaffe/paddle/network.py

+import sys
+import os
+import math
+import numpy as np
+
+
+def import_fluid():
+    import paddle.fluid as fluid
+    return fluid
+
+
+def layer(op):
+    '''Decorator for composable network layers.'''
+
+    def layer_decorated(self, *args, **kwargs):
+        # Automatically set a name if not provided.
+        name = kwargs.setdefault('name', self.get_unique_name(op.__name__))
+        # Figure out the layer inputs.
+        if len(self.terminals) == 0:
+            raise RuntimeError('No input variables found for layer %s.' % name)
+        elif len(self.terminals) == 1:
+            layer_input = self.terminals[0]
+        else:
+            layer_input = list(self.terminals)
+
+        self.layer_reverse_trace[name] = layer_input
+        # Perform the operation and get the output.
+        layer_output = op(self, layer_input, *args, **kwargs)
+        # Add to layer LUT.
+        self.layers[name] = layer_output
+        self.var2name[layer_output.name] = (name, layer_output)
+
+        # This output is now the input for the next layer.
+        self.feed(layer_output)
+        # Return self for chained calls.
+        return self
+
+    return layer_decorated
+
+
+class Network(object):
+    def __init__(self, inputs, trainable=True):
+        # The input nodes for this network
+        self.inputs = inputs
+        # The current list of terminal nodes
+        self.terminals = []
+        # Mapping from layer names to layers
+        self.layers = dict(inputs)
+        # If true, the resulting variables are set as trainable
+        self.trainable = trainable
+        # Switch variable for dropout
+        self.paddle_env = None
+        self.output_names = []
+        self.name_trace = None
+
+        self.layer_reverse_trace = {}
+        self.var2name = {}
+        self.setup()
+
+    def setup(self):
+        '''Construct the network. '''
+        raise NotImplementedError('Must be implemented by the subclass.')
+
+    def locate_ancestor(self, v, which=[0], ancestor_level=1):
+        """ find a ancestor for a node 'v' which is a fluid variable
+        """
+        ancestor = None
+        which = which * ancestor_level
+        name = self.var2name[v.name][0]
+
+        for i in range(ancestor_level):
+            v = self.layer_reverse_trace[name]
+            if type(v) is list:
+                ancestor = self.var2name[v[which[i]].name]
+            else:
+                ancestor = self.var2name[v.name]
+            name = ancestor[0]
+        return ancestor
+
+    def load(self, data_path, exe=None, place=None, ignore_missing=False):
+        '''Load network weights.
+        data_path: The path to the numpy-serialized network weights
+        ignore_missing: If true, serialized weights for missing layers are ignored.
+        '''
+        fluid = import_fluid()
+        #load fluid mode directly
+        if os.path.isdir(data_path):
+            assert (exe is not None), \
+                'must provide a executor to load fluid model'
+            fluid.io.load_persistables(executor=exe, dirname=data_path)
+            return True
+
+        #load model from a npy file
+        if exe is None or place is None:
+            if self.paddle_env is None:
+                place = fluid.CPUPlace()
+                exe = fluid.Executor(place)
+                self.paddle_env = {'place': place, 'exe': exe}
+                exe = exe.run(fluid.default_startup_program())
+            else:
+                place = self.paddle_env['place']
+                exe = self.paddle_env['exe']
+
+        data_dict = np.load(data_path).item()
+        for op_name in data_dict:
+            if op_name == 'caffe2fluid_name_trace':
+                self.name_trace = data_dict[op_name]
+                continue
+
+            layer = self.layers[op_name]
+            for param_name, data in data_dict[op_name].iteritems():
+                try:
+                    name = '%s_%s' % (op_name, param_name)
+                    v = fluid.global_scope().find_var(name)
+                    w = v.get_tensor()
+                    w.set(data.reshape(w.shape()), place)
+                except ValueError:
+                    if not ignore_missing:
+                        raise
+        return True
+
+    def feed(self, *args):
+        '''Set the input(s) for the next operation by replacing the terminal nodes.
+        The arguments can be either layer names or the actual layers.
+        '''
+        assert len(args) != 0
+        self.terminals = []
+        for fed_layer in args:
+            if isinstance(fed_layer, basestring):
+                try:
+                    fed_layer = self.layers[fed_layer]
+                except KeyError:
+                    raise KeyError('Unknown layer name fed: %s' % fed_layer)
+            self.terminals.append(fed_layer)
+        return self
+
+    def get_output(self):
+        '''Returns the current network output.'''
+        return self.terminals[-1]
+
+    def get_unique_name(self, prefix):
+        '''Returns an index-suffixed unique name for the given prefix.
+        This is used for auto-generating layer names based on the type-prefix.
+        '''
+        ident = sum(t.startswith(prefix) for t, _ in self.layers.items()) + 1
+        return '%s_%d' % (prefix, ident)
+
+    def get_unique_output_name(self, prefix, layertype):
+        '''Returns an index-suffixed unique name for the given prefix.
+            This is used for auto-generating layer names based on the type-prefix.
+        '''
+        ident = sum(t.startswith(prefix) for t in self.output_names) + 1
+        unique_name = '%s.%s.output.%d' % (prefix, layertype, ident)
+        self.output_names.append(unique_name)
+        return unique_name
+
+    @layer
+    def conv(self,
+             input,
+             k_h,
+             k_w,
+             c_o,
+             s_h,
+             s_w,
+             name,
+             relu=True,
+             relu_negative_slope=0.0,
+             padding=None,
+             dilation=1,
+             group=1,
+             biased=True):
+        if padding is None:
+            padding = [0, 0]
+
+        # Get the number of channels in the input
+        c_i, h_i, w_i = input.shape[1:]
+
+        # Verify that the grouping parameter is valid
+        assert c_i % group == 0
+        assert c_o % group == 0
+
+        fluid = import_fluid()
+        prefix = name + '_'
+        leaky_relu = False
+        act = 'relu'
+        if relu is False:
+            act = None
+        elif relu_negative_slope != 0.0:
+            leaky_relu = True
+            act = None
+
+        output = fluid.layers.conv2d(
+            name=self.get_unique_output_name(name, 'conv2d'),
+            input=input,
+            filter_size=[k_h, k_w],
+            num_filters=c_o,
+            stride=[s_h, s_w],
+            padding=padding,
+            dilation=dilation,
+            groups=group,
+            param_attr=fluid.ParamAttr(name=prefix + "weights"),
+            bias_attr=fluid.ParamAttr(name=prefix + "biases"),
+            act=act)
+
+        if leaky_relu:
+            output = fluid.layers.leaky_relu(output, alpha=relu_negative_slope)
+
+        return output
+
+    @layer
+    def deconv(self,
+               input,
+               k_h,
+               k_w,
+               c_o,
+               s_h,
+               s_w,
+               name,
+               relu=True,
+               relu_negative_slope=0.0,
+               padding=None,
+               dilation=1,
+               biased=True):
+        if padding is None:
+            padding = [0, 0]
+
+        # Get the number of channels in the input
+        c_i, h_i, w_i = input.shape[1:]
+
+        fluid = import_fluid()
+        prefix = name + '_'
+        leaky_relu = False
+        act = 'relu'
+        if relu is False:
+            act = None
+        elif relu_negative_slope != 0.0:
+            leaky_relu = True
+            act = None
+
+        p_h = padding[0]
+        p_w = padding[1]
+        h_o = (h_i - 1) * s_h - 2 * p_h + dilation * (k_h - 1) + 1
+        w_o = (w_i - 1) * s_w - 2 * p_w + dilation * (k_w - 1) + 1
+        output = fluid.layers.conv2d_transpose(
+            name=self.get_unique_output_name(name, 'conv2d_transpose'),
+            input=input,
+            num_filters=c_o,
+            output_size=[h_o, w_o],
+            filter_size=[k_h, k_w],
+            padding=padding,
+            stride=[s_h, s_w],
+            dilation=dilation,
+            param_attr=fluid.ParamAttr(name=prefix + "weights"),
+            bias_attr=fluid.ParamAttr(name=prefix + "biases"),
+            act=act)
+
+        if leaky_relu:
+            output = fluid.layers.leaky_relu(output, alpha=relu_negative_slope)
+
+        return output
+
+    @layer
+    def relu(self, input, name):
+        fluid = import_fluid()
+        output = fluid.layers.relu(input)
+        return output
+
+    @layer
+    def prelu(self, input, channel_shared, name):
+        fluid = import_fluid()
+        if channel_shared:
+            mode = 'all'
+        else:
+            mode = 'channel'
+
+        prefix = name + '_'
+        output = fluid.layers.prelu(
+            input,
+            mode=mode,
+            param_attr=fluid.ParamAttr(name=prefix + 'negslope'))
+        return output
+
+    def pool(self,
+             pool_type,
+             input,
+             k_h,
+             k_w,
+             s_h,
+             s_w,
+             ceil_mode,
+             padding,
+             name,
+             exclusive=True):
+        # Get the number of channels in the input
+        in_hw = input.shape[2:]
+        k_hw = [k_h, k_w]
+        s_hw = [s_h, s_w]
+
+        fluid = import_fluid()
+        output = fluid.layers.pool2d(
+            name=name,
+            input=input,
+            pool_size=k_hw,
+            pool_stride=s_hw,
+            pool_padding=padding,
+            ceil_mode=ceil_mode,
+            pool_type=pool_type,
+            exclusive=exclusive)
+        return output
+
+    @layer
+    def max_pool(self,
+                 input,
+                 k_h,
+                 k_w,
+                 s_h,
+                 s_w,
+                 ceil_mode,
+                 padding=[0, 0],
+                 name=None):
+        return self.pool(
+            'max',
+            input,
+            k_h,
+            k_w,
+            s_h,
+            s_w,
+            ceil_mode,
+            padding,
+            name=self.get_unique_output_name(name, 'max_pool'))
+
+    @layer
+    def avg_pool(self,
+                 input,
+                 k_h,
+                 k_w,
+                 s_h,
+                 s_w,
+                 ceil_mode,
+                 padding=[0, 0],
+                 name=None):
+        return self.pool(
+            'avg',
+            input,
+            k_h,
+            k_w,
+            s_h,
+            s_w,
+            ceil_mode,
+            padding,
+            name=self.get_unique_output_name(name, 'avg_pool'),
+            exclusive=False)
+
+    @layer
+    def sigmoid(self, input, name):
+        fluid = import_fluid()
+        return fluid.layers.sigmoid(
+            input, name=self.get_unique_output_name(name, 'sigmoid'))
+
+    @layer
+    def tanh(self, input, name):
+        fluid = import_fluid()
+        return fluid.layers.tanh(
+            input, name=self.get_unique_output_name(name, 'tanh'))
+
+    @layer
+    def lrn(self, input, radius, alpha, beta, name, bias=1.0):
+        fluid = import_fluid()
+        output = fluid.layers.lrn(input=input,
+                                  n=radius,
+                                  k=bias,
+                                  alpha=alpha,
+                                  beta=beta,
+                                  name=self.get_unique_output_name(name, 'lrn'))
+        return output
+
+    @layer
+    def concat(self, inputs, axis, name):
+        fluid = import_fluid()
+        output = fluid.layers.concat(
+            input=inputs,
+            axis=axis,
+            name=self.get_unique_output_name(name, 'concat'))
+        return output
+
+    @layer
+    def add(self, inputs, name):
+        fluid = import_fluid()
+        output = inputs[0]
+        for i in inputs[1:]:
+            output = fluid.layers.elementwise_add(
+                x=output, y=i, name=self.get_unique_output_name(name, 'add'))
+        return output
+
+    @layer
+    def max(self, inputs, name):
+        fluid = import_fluid()
+        output = inputs[0]
+        for i in inputs[1:]:
+            output = fluid.layers.elementwise_max(
+                x=output, y=i, name=self.get_unique_output_name(name, 'max'))
+        return output
+
+    @layer
+    def multiply(self, inputs, name):
+        fluid = import_fluid()
+        output = inputs[0]
+        for i in inputs[1:]:
+            output = fluid.layers.elementwise_mul(
+                x=output, y=i, name=self.get_unique_output_name(name, 'mul'))
+        return output
+
+    @layer
+    def fc(self, input, num_out, name, relu=True, act=None):
+        fluid = import_fluid()
+
+        if act is None:
+            act = 'relu' if relu is True else None
+
+        prefix = name + '_'
+        output = fluid.layers.fc(
+            name=self.get_unique_output_name(name, 'fc'),
+            input=input,
+            size=num_out,
+            act=act,
+            param_attr=fluid.ParamAttr(name=prefix + 'weights'),
+            bias_attr=fluid.ParamAttr(name=prefix + 'biases'))
+        return output
+
+    @layer
+    def softmax(self, input, axis=2, name=None):
+        fluid = import_fluid()
+        shape = input.shape
+        dims = len(shape)
+        axis = axis + dims if axis < 0 else axis
+
+        need_transpose = False
+        if axis + 1 != dims:
+            need_transpose = True
+
+        if need_transpose:
+            order = range(dims)
+            order.remove(axis)
+            order.append(axis)
+            input = fluid.layers.transpose(
+                input,
+                perm=order,
+                name=self.get_unique_output_name(name, 'transpose'))
+
+        output = fluid.layers.softmax(
+            input, name=self.get_unique_output_name(name, 'softmax'))
+
+        if need_transpose:
+            order = range(len(shape))
+            order[axis] = dims - 1
+            order[-1] = axis
+            output = fluid.layers.transpose(
+                output,
+                perm=order,
+                name=self.get_unique_output_name(name, 'transpose'))
+        return output
+
+    @layer
+    def batch_normalization(self,
+                            input,
+                            name,
+                            scale_offset=True,
+                            eps=1e-5,
+                            relu=False,
+                            relu_negative_slope=0.0):
+        # NOTE: Currently, only inference is supported
+        fluid = import_fluid()
+        prefix = name + '_'
+        param_attr = None if scale_offset is False else fluid.ParamAttr(
+            name=prefix + 'scale')
+        bias_attr = None if scale_offset is False else fluid.ParamAttr(
+            name=prefix + 'offset')
+        mean_name = prefix + 'mean'
+        variance_name = prefix + 'variance'
+
+        leaky_relu = False
+        act = 'relu'
+        if relu is False:
+            act = None
+        elif relu_negative_slope != 0.0:
+            leaky_relu = True
+            act = None
+
+        output = fluid.layers.batch_norm(
+            name=self.get_unique_output_name(name, 'batch_norm'),
+            input=input,
+            is_test=True,
+            param_attr=param_attr,
+            bias_attr=bias_attr,
+            moving_mean_name=mean_name,
+            moving_variance_name=variance_name,
+            epsilon=eps,
+            act=act)
+
+        if leaky_relu:
+            output = fluid.layers.leaky_relu(output, alpha=relu_negative_slope)
+
+        return output
+
+    @layer
+    def dropout(self, input, drop_prob, name, is_test=True):
+        fluid = import_fluid()
+        if is_test:
+            output = input
+        else:
+            output = fluid.layers.dropout(
+                input,
+                dropout_prob=drop_prob,
+                is_test=is_test,
+                name=self.get_unique_output_name(name, 'dropout'))
+        return output
+
+    @layer
+    def scale(self, input, axis=1, num_axes=1, name=None):
+        fluid = import_fluid()
+
+        assert num_axes == 1, "layer scale not support this num_axes[%d] now" % (
+            num_axes)
+
+        prefix = name + '_'
+        scale_shape = input.shape[axis:axis + num_axes]
+        param_attr = fluid.ParamAttr(name=prefix + 'scale')
+        scale_param = fluid.layers.create_parameter(
+            shape=scale_shape,
+            dtype=input.dtype,
+            name=name,
+            attr=param_attr,
+            is_bias=True,
+            default_initializer=fluid.initializer.Constant(value=1.0))
+
+        offset_attr = fluid.ParamAttr(name=prefix + 'offset')
+        offset_param = fluid.layers.create_parameter(
+            shape=scale_shape,
+            dtype=input.dtype,
+            name=name,
+            attr=offset_attr,
+            is_bias=True,
+            default_initializer=fluid.initializer.Constant(value=0.0))
+
+        output = fluid.layers.elementwise_mul(
+            input,
+            scale_param,
+            axis=axis,
+            name=self.get_unique_output_name(name, 'scale_mul'))
+        output = fluid.layers.elementwise_add(
+            output,
+            offset_param,
+            axis=axis,
+            name=self.get_unique_output_name(name, 'scale_add'))
+        return output
+
+    def custom_layer_factory(self):
+        """ get a custom layer maker provided by subclass
+        """
+        raise NotImplementedError(
+            '[custom_layer_factory] must be implemented by the subclass.')
+
+    @layer
+    def custom_layer(self, inputs, kind, name, *args, **kwargs):
+        """ make custom layer
+        """
+        #FIX ME:
+        #   there is a trick for different API between caffe and paddle
+        if kind == "DetectionOutput":
+            conf_var = inputs[1]
+            real_conf_var = self.locate_ancestor(conf_var, ancestor_level=2)
+            inputs[1] = real_conf_var[1]
+
+        name = self.get_unique_output_name(name, kind)
+        layer_factory = self.custom_layer_factory()
+        return layer_factory(kind, inputs, name, *args, **kwargs)

caffe2fluid/kaffe/paddle/transformer.py

+import numpy as np
+
+from ..errors import KaffeError, print_stderr
+from ..graph import GraphBuilder, NodeMapper
+from ..layers import NodeKind
+from ..transformers import (DataInjector, DataReshaper, NodeRenamer,
+                            SubNodeFuser, ReLUFuser, BatchNormScaleBiasFuser,
+                            BatchNormPreprocessor, ParameterNamer, CropFuser)
+from . import network
+
+
+class PaddleNode(object):
+    '''An intermediate representation for Paddle operations.'''
+
+    def __init__(self, op, *args, **kwargs):
+        # A string corresponding to the Paddle operation
+        self.op = op
+        # Positional arguments for the operation
+        self.args = args
+        # Keyword arguments for the operation
+        self.kwargs = list(kwargs.items())
+        # The source Caffe node
+        self.node = None
+
+    def format(self, arg):
+        '''Returns a string representation for the given value.'''
+        return "'%s'" % arg if isinstance(arg, basestring) else str(arg)
+
+    def pair(self, key, value):
+        '''Returns key=formatted(value).'''
+        return '%s=%s' % (key, self.format(value))
+
+    def emit(self):
+        '''Emits the Python source for this node.'''
+        # Format positional arguments
+        args = map(self.format, self.args)
+        # Format any keyword arguments
+        if self.kwargs:
+            args += [self.pair(k, v) for k, v in self.kwargs]
+        # Set the node name
+        args.append(self.pair('name', self.node.name))
+        args = ', '.join(args)
+        return '%s(%s)' % (self.op, args)
+
+
+class MaybeActivated(object):
+    def __init__(self, node, default=True):
+        self.inject_kwargs = {}
+        if node.metadata.get('relu', False) != default:
+            self.inject_kwargs['relu'] = not default
+
+        default_slope = 0.0
+        slope = node.metadata.get('relu_negative_slope', default_slope)
+        if slope != default_slope:
+            self.inject_kwargs['relu_negative_slope'] = slope
+
+    def __call__(self, *args, **kwargs):
+        kwargs.update(self.inject_kwargs)
+        return PaddleNode(*args, **kwargs)
+
+
+class PaddleMapper(NodeMapper):
+    def get_kernel_params(self, node):
+        kernel_params = node.layer.kernel_parameters
+        input_shape = node.get_only_parent().output_shape
+        padding = [kernel_params.pad_h, kernel_params.pad_w]
+        if padding[0] == 0 and padding[1] == 0:
+            padding = {}
+        else:
+            padding = {'padding': padding}
+        return (kernel_params, padding)
+
+    def map_convolution(self, node):
+        (kernel_params, kwargs) = self.get_kernel_params(node)
+        h = kernel_params.kernel_h
+        w = kernel_params.kernel_w
+        c_o = node.output_shape[1]
+        c_i = node.parents[0].output_shape[1]
+        group = node.parameters.group
+        if group != 1:
+            kwargs['group'] = group
+        if not node.parameters.bias_term:
+            kwargs['biased'] = False
+
+        if kernel_params.dila_h != 1 or kernel_params.dila_w != 1:
+            kwargs['dilation'] = (kernel_params.dila_h, kernel_params.dila_w)
+
+        assert kernel_params.kernel_h == h
+        assert kernel_params.kernel_w == w
+        return MaybeActivated(node)(
+            'conv', kernel_params.kernel_h, kernel_params.kernel_w, c_o,
+            kernel_params.stride_h, kernel_params.stride_w, **kwargs)
+
+    def map_deconvolution(self, node):
+        (kernel_params, kwargs) = self.get_kernel_params(node)
+        h = kernel_params.kernel_h
+        w = kernel_params.kernel_w
+        c_o = node.output_shape[1]
+        c_i = node.parents[0].output_shape[1]
+        if not node.parameters.bias_term:
+            kwargs['biased'] = False
+
+        if kernel_params.dila_h != 1 or kernel_params.dila_w != 1:
+            kwargs['dilation'] = (kernel_params.dila_h, kernel_params.dila_w)
+
+        assert kernel_params.kernel_h == h
+        assert kernel_params.kernel_w == w
+        return MaybeActivated(node)(
+            'deconv', kernel_params.kernel_h, kernel_params.kernel_w, c_o,
+            kernel_params.stride_h, kernel_params.stride_w, **kwargs)
+
+    def map_relu(self, node):
+        return PaddleNode('relu')
+
+    def map_prelu(self, node):
+        channel_shared = getattr(node.parameters, 'channel_shared', False)
+        return PaddleNode('prelu', channel_shared)
+
+    def map_tanh(self, node):
+        return PaddleNode('tanh')
+
+    def map_pooling(self, node):
+        pool_type = node.parameters.pool
+        if pool_type == 0:
+            pool_op = 'max_pool'
+        elif pool_type == 1:
+            pool_op = 'avg_pool'
+        else:
+            # Stochastic pooling, for instance.
+            raise KaffeError('Unsupported pooling type.')
+
+        ceil_mode = getattr(node.layer.parameters, 'ceil_mode', True)
+        global_pool = getattr(node.layer.parameters, 'global_pooling', False)
+        if global_pool:
+            input_shape = node.get_only_parent().output_shape
+            return PaddleNode(pool_op, input_shape.height, input_shape.width, 1,
+                              1, ceil_mode)
+        else:
+            (kernel_params, padding) = self.get_kernel_params(node)
+            return PaddleNode(pool_op, kernel_params.kernel_h,
+                              kernel_params.kernel_w, kernel_params.stride_h,
+                              kernel_params.stride_w, ceil_mode, **padding)
+
+    def map_sigmoid(self, node):
+        return PaddleNode('sigmoid')
+
+    def map_custom(self, node):
+        from .. import custom_layers
+        return custom_layers.make_node(PaddleNode, node.kind, node)
+
+    def map_inner_product(self, node):
+        #TODO: Axis
+        assert node.parameters.axis == 1
+        #TODO: Unbiased
+        assert node.parameters.bias_term == True
+        return MaybeActivated(node)('fc', node.parameters.num_output)
+
+    def map_softmax(self, node):
+        return PaddleNode('softmax', node.parameters.axis)
+
+    def map_lrn(self, node):
+        params = node.parameters
+        # The window size must be an odd value. For a window
+        # size of (2*n+1), Paddle defines depth_radius = n.
+        assert params.local_size % 2 == 1
+        # Caffe scales by (alpha/(2*n+1)), whereas Paddle
+        # just scales by alpha (as does Krizhevsky's paper).
+        # We'll account for that here.
+        alpha = params.alpha / float(params.local_size)
+        return PaddleNode('lrn', params.local_size, alpha, params.beta)
+
+    def map_concat(self, node):
+        return PaddleNode('concat', node.parameters.axis)
+
+    def map_dropout(self, node):
+        return PaddleNode('dropout', node.parameters.dropout_ratio)
+
+    def map_batch_norm(self, node):
+        scale_offset = len(node.data) == 4
+
+        #this default value comes from caffe's param in batch_norm
+        default_eps = 1e-5
+        kwargs = {'scale_offset': scale_offset}
+        if node.parameters.eps != default_eps:
+            kwargs['eps'] = node.parameters.eps
+
+        return MaybeActivated(
+            node, default=False)('batch_normalization', **kwargs)
+
+    def map_eltwise(self, node):
+        operations = {0: 'multiply', 1: 'add', 2: 'max'}
+        op_code = node.parameters.operation
+        try:
+            return PaddleNode(operations[op_code])
+        except KeyError:
+            raise KaffeError('Unknown elementwise operation: {}'.format(
+                op_code))
+
+    def map_scale(self, node):
+        params = node.parameters
+        return PaddleNode('scale', axis=params.axis, num_axes=params.num_axes)
+
+    def commit(self, chains):
+        return chains
+
+
+class PaddleEmitter(object):
+    def __init__(self, tab=None):
+        self.tab = tab or ' ' * 4
+        self.prefix = ''
+        self.net_name = ''
+
+    def indent(self):
+        self.prefix += self.tab
+
+    def outdent(self):
+        self.prefix = self.prefix[:-len(self.tab)]
+
+    def statement(self, s):
+        return self.prefix + s + '\n'
+
+    def emit_imports(self):
+        import inspect
+        codes = []
+        codes.append(
+            '### generated by caffe2fluid, your net is in class "%s" ###\n' %
+            (self.net_name))
+        network_source = inspect.getsource(network)
+        codes.append(network_source + '\n')
+        return self.statement('\n'.join(codes))
+
+    def emit_setup_def(self):
+        return self.statement('def setup(self):')
+
+    def get_inputs_info(self, input_nodes):
+        input_shapes = {}
+        for n in input_nodes:
+            name = n.name
+            output_shape = n.output_shape
+            shape = [str(s) for s in output_shape[1:]]
+            input_shapes[name] = ', '.join(shape)
+        input_shapes = ['"%s": [%s]' % (n, l) for n, l in input_shapes.items()]
+        shape_str = ','.join(input_shapes)
+        return '{%s}' % (shape_str)
+
+    def emit_main_def(self, name):
+        if name is None:
+            return ''
+
+        self.prefix = ''
+        main_def = self.statement('if __name__ == "__main__":')
+        self.indent()
+        main_def += self.statement('exit(main())')
+        return '\n\n' + main_def
+
+    def emit_parents(self, chain):
+        assert len(chain)
+        s = 'self.feed('
+        sep = ', \n' + self.prefix + (' ' * len(s))
+        s += sep.join(
+            ["'%s'" % parent.name for parent in chain[0].node.parents])
+        return self.statement(s + ')')
+
+    def emit_node(self, node):
+        return self.statement('self.' + node.emit())
+
+    def emit(self, name, chains, input_nodes=None):
+        from ..net_template import generate_net_code
+        from ..net_template import generate_main_code
+
+        self.net_name = name
+        inputs_info = self.get_inputs_info(input_nodes)
+
+        s = self.emit_imports()
+        s += generate_net_code(name, inputs_info) + '\n'
+        self.indent()
+
+        # define the net using api
+        s += self.emit_setup_def()
+        self.indent()
+        blocks = []
+        for chain in chains:
+            b = ''
+            b += self.emit_parents(chain)
+            for node in chain:
+                b += self.emit_node(node)
+            blocks.append(b[:-1])
+        s = s + '\n\n'.join(blocks)
+
+        # define the main function
+        s += '\n\n\n' + generate_main_code(name)
+        s += self.emit_main_def(name)
+        return s
+
+
+class Transformer(object):
+    def __init__(self, def_path, data_path, verbose=True, phase='test'):
+        self.verbose = verbose
+        self.phase = phase
+        self.load(def_path, data_path, phase)
+        self.params = None
+        self.source = None
+
+    def load(self, def_path, data_path, phase):
+        # Build the graph
+        graph = GraphBuilder(def_path, phase).build()
+
+        if data_path is not None:
+            # Load and associate learned parameters
+            graph = DataInjector(def_path, data_path)(graph)
+
+        # Transform the graph
+        transformers = [
+            # Fuse split batch normalization layers
+            BatchNormScaleBiasFuser(),
+
+            # Fuse ReLUs
+            # TODO: Move non-linearity application to layer wrapper, allowing
+            # any arbitrary operation to be optionally activated.
+            ReLUFuser(allowed_parent_types=[
+                NodeKind.Convolution, NodeKind.InnerProduct, NodeKind.BatchNorm
+            ]),
+
+            # Rename nodes
+            # Slashes are used for scoping in Paddle. Replace slashes
+            # in node names with underscores.
+            # (Caffe's GoogLeNet implementation uses slashes)
+            NodeRenamer(lambda node: node.name.replace('/', '_')),
+
+            # Fuse Crop
+            # Crop is to return a scalar output Blob for an input Blob of arbitrary size.
+            # When one of the input Blob is "input" or "DummyData", we can remove this input Blob
+            # and put the shape into the reduction layer.
+            CropFuser()
+        ]
+
+        self.graph = graph.transformed(transformers)
+
+        #for the purpose of recording name mapping because of fused nodes
+        trace = SubNodeFuser.traced_names()
+        chg2real = {}
+        deleted = {}
+        for k, v in trace.items():
+            chg2real[k] = v[-1]  #mapping from changed-name to real-name
+            for n in v:
+                if n in chg2real:
+                    continue
+                if n not in deleted:
+                    deleted[n] = '%s.%s' % (k, v[-1])
+
+        self.graph.add_name_trace({
+            'chg2real': chg2real,
+            'deleted': deleted
+        }, 'paddle')
+
+        # Display the graph
+        if self.verbose:
+            print_stderr(self.graph)
+
+    def transform_data(self):
+        if self.params is None:
+            transformers = [
+                # Reshape the parameters to Paddle's ordering
+                DataReshaper({
+                    # (c_o, c_i) -> (c_i, c_o)
+                    NodeKind.InnerProduct: (1, 0)
+                }),
+
+                # Pre-process batch normalization data
+                BatchNormPreprocessor(),
+
+                # Convert parameters to dictionaries
+                ParameterNamer(),
+            ]
+            self.graph = self.graph.transformed(transformers)
+            self.params = {
+                node.name: node.data
+                for node in self.graph.nodes if node.data
+            }
+            self.params['caffe2fluid_name_trace'] = self.graph.get_name_trace()
+
+        return self.params
+
+    def transform_source(self):
+        if self.source is None:
+            mapper = PaddleMapper(self.graph)
+            chains = mapper.map()
+            emitter = PaddleEmitter()
+            input_nodes = self.graph.get_input_nodes()
+            self.source = emitter.emit(self.graph.name, chains, input_nodes)
+        return self.source

caffe2fluid/kaffe/protobuf_to_dict.py

+"""a util for convert protobuf to dict
+"""
+
+from google.protobuf.message import Message
+from google.protobuf.descriptor import FieldDescriptor
+
+__all__ = [
+    "protobuf_to_dict", "TYPE_CALLABLE_MAP", "dict_to_protobuf",
+    "REVERSE_TYPE_CALLABLE_MAP"
+]
+
+EXTENSION_CONTAINER = '___X'
+
+TYPE_CALLABLE_MAP = {
+    FieldDescriptor.TYPE_DOUBLE: float,
+    FieldDescriptor.TYPE_FLOAT: float,
+    FieldDescriptor.TYPE_INT32: int,
+    FieldDescriptor.TYPE_INT64: long,
+    FieldDescriptor.TYPE_UINT32: int,
+    FieldDescriptor.TYPE_UINT64: long,
+    FieldDescriptor.TYPE_SINT32: int,
+    FieldDescriptor.TYPE_SINT64: long,
+    FieldDescriptor.TYPE_FIXED32: int,
+    FieldDescriptor.TYPE_FIXED64: long,
+    FieldDescriptor.TYPE_SFIXED32: int,
+    FieldDescriptor.TYPE_SFIXED64: long,
+    FieldDescriptor.TYPE_BOOL: bool,
+    FieldDescriptor.TYPE_STRING: unicode,
+    FieldDescriptor.TYPE_BYTES: lambda b: b.encode("base64"),
+    FieldDescriptor.TYPE_ENUM: int,
+}
+
+
+def repeated(type_callable):
+    return lambda value_list: [type_callable(value) for value in value_list]
+
+
+def enum_label_name(field, value):
+    return field.enum_type.values_by_number[int(value)].name
+
+
+def protobuf_to_dict(pb,
+                     type_callable_map=TYPE_CALLABLE_MAP,
+                     use_enum_labels=False):
+    result_dict = {}
+    extensions = {}
+    for field, value in pb.ListFields():
+        type_callable = _get_field_value_adaptor(pb, field, type_callable_map,
+                                                 use_enum_labels)
+        if field.label == FieldDescriptor.LABEL_REPEATED:
+            type_callable = repeated(type_callable)
+
+        if field.is_extension:
+            extensions[str(field.number)] = type_callable(value)
+            continue
+
+        result_dict[field.name] = type_callable(value)
+
+    if extensions:
+        result_dict[EXTENSION_CONTAINER] = extensions
+    return result_dict
+
+
+def _get_field_value_adaptor(pb,
+                             field,
+                             type_callable_map=TYPE_CALLABLE_MAP,
+                             use_enum_labels=False):
+    if field.type == FieldDescriptor.TYPE_MESSAGE:
+        # recursively encode protobuf sub-message
+        return lambda pb: protobuf_to_dict(pb,
+            type_callable_map=type_callable_map,
+            use_enum_labels=use_enum_labels)
+
+    if use_enum_labels and field.type == FieldDescriptor.TYPE_ENUM:
+        return lambda value: enum_label_name(field, value)
+
+    if field.type in type_callable_map:
+        return type_callable_map[field.type]
+
+    raise TypeError("Field %s.%s has unrecognised type id %d" %
+                    (pb.__class__.__name__, field.name, field.type))
+
+
+def get_bytes(value):
+    return value.decode('base64')
+
+
+REVERSE_TYPE_CALLABLE_MAP = {FieldDescriptor.TYPE_BYTES: get_bytes, }
+
+
+def dict_to_protobuf(pb_klass_or_instance,
+                     values,
+                     type_callable_map=REVERSE_TYPE_CALLABLE_MAP,
+                     strict=True):
+    """Populates a protobuf model from a dictionary.
+
+    :param pb_klass_or_instance: a protobuf message class, or an protobuf instance
+    :type pb_klass_or_instance: a type or instance of a subclass of google.protobuf.message.Message
+    :param dict values: a dictionary of values. Repeated and nested values are 
+       fully supported.
+    :param dict type_callable_map: a mapping of protobuf types to callables for setting
+       values on the target instance.
+    :param bool strict: complain if keys in the map are not fields on the message.
+    """
+    if isinstance(pb_klass_or_instance, Message):
+        instance = pb_klass_or_instance
+    else:
+        instance = pb_klass_or_instance()
+    return _dict_to_protobuf(instance, values, type_callable_map, strict)
+
+
+def _get_field_mapping(pb, dict_value, strict):
+    field_mapping = []
+    for key, value in dict_value.items():
+        if key == EXTENSION_CONTAINER:
+            continue
+        if key not in pb.DESCRIPTOR.fields_by_name:
+            if strict:
+                raise KeyError("%s does not have a field called %s" % (pb, key))
+            continue
+        field_mapping.append(
+            (pb.DESCRIPTOR.fields_by_name[key], value, getattr(pb, key, None)))
+
+    for ext_num, ext_val in dict_value.get(EXTENSION_CONTAINER, {}).items():
+        try:
+            ext_num = int(ext_num)
+        except ValueError:
+            raise ValueError("Extension keys must be integers.")
+        if ext_num not in pb._extensions_by_number:
+            if strict:
+                raise KeyError(
+                    "%s does not have a extension with number %s. Perhaps you forgot to import it?"
+                    % (pb, key))
+            continue
+        ext_field = pb._extensions_by_number[ext_num]
+        pb_val = None
+        pb_val = pb.Extensions[ext_field]
+        field_mapping.append((ext_field, ext_val, pb_val))
+
+    return field_mapping
+
+
+def _dict_to_protobuf(pb, value, type_callable_map, strict):
+    fields = _get_field_mapping(pb, value, strict)
+
+    for field, input_value, pb_value in fields:
+        if field.label == FieldDescriptor.LABEL_REPEATED:
+            for item in input_value:
+                if field.type == FieldDescriptor.TYPE_MESSAGE:
+                    m = pb_value.add()
+                    _dict_to_protobuf(m, item, type_callable_map, strict)
+                elif field.type == FieldDescriptor.TYPE_ENUM and isinstance(
+                        item, basestring):
+                    pb_value.append(_string_to_enum(field, item))
+                else:
+                    pb_value.append(item)
+            continue
+        if field.type == FieldDescriptor.TYPE_MESSAGE:
+            _dict_to_protobuf(pb_value, input_value, type_callable_map, strict)
+            continue
+
+        if field.type in type_callable_map:
+            input_value = type_callable_map[field.type](input_value)
+
+        if field.is_extension:
+            pb.Extensions[field] = input_value
+            continue
+
+        if field.type == FieldDescriptor.TYPE_ENUM and isinstance(input_value,
+                                                                  basestring):
+            input_value = _string_to_enum(field, input_value)
+
+        setattr(pb, field.name, input_value)
+
+    return pb
+
+
+def _string_to_enum(field, input_value):
+    enum_dict = field.enum_type.values_by_name
+    try:
+        input_value = enum_dict[input_value].number
+    except KeyError:
+        raise KeyError("`%s` is not a valid value for field `%s`" %
+                       (input_value, field.name))
+    return input_value

caffe2fluid/kaffe/shapes.py

+import math
+from collections import namedtuple
+
+from .errors import KaffeError
+
+Tensor4DShape = namedtuple('Tensor4DShape',
+                           ['batch_size', 'channels', 'height', 'width'])
+
+Tensor3DShape = namedtuple('Tensor3DShape', ['batch_size', 'data1', 'data2'])
+
+Tensor2DShape = namedtuple('Tensor2DShape', ['batch_size', 'data'])
+
+ScalarShape = namedtuple('ScalarShape', ['batch_size'])
+
+
+def make_tensor(batch_size, d1=None, d2=None, d3=None):
+    if d3 is not None:
+        return Tensor4DShape(batch_size, d1, d2, d3)
+    elif d1 is not None and d2 is not None:
+        return Tensor3DShape(batch_size, d1, d2)
+    elif d1 is not None and d2 is None:
+        return Tensor2DShape(batch_size, d1)
+    elif d1 is None and d2 is None and d3 is None:
+        return ScalarShape(batch_size)
+    else:
+        raise NotImplementedError('invalid params for make_tensor %s' \
+                % (str((batch_size, d1, d2, d3))))
+
+
+def get_filter_output_shape(i_h, i_w, params, round_func):
+    dila_h = getattr(params, 'dila_h', 1)
+    dila_w = getattr(params, 'dila_w', 1)
+
+    o_h = (i_h + 2 * params.pad_h -
+           (dila_h * (params.kernel_h - 1) + 1)) / float(params.stride_h) + 1
+    o_w = (i_w + 2 * params.pad_w -
+           (dila_w * (params.kernel_w - 1) + 1)) / float(params.stride_w) + 1
+
+    return (int(round_func(o_h)), int(round_func(o_w)))
+
+
+def get_strided_kernel_output_shape(node, round_func):
+    assert node.layer is not None
+    input_shape = node.get_only_parent().output_shape
+    o_h, o_w = get_filter_output_shape(input_shape.height, input_shape.width,
+                                       node.layer.kernel_parameters, round_func)
+    params = node.layer.parameters
+    has_c_o = hasattr(params, 'num_output')
+    c = params.num_output if has_c_o else input_shape.channels
+    return make_tensor(input_shape.batch_size, c, o_h, o_w)
+
+
+def shape_not_implemented(node):
+    raise NotImplementedError
+
+
+def shape_identity(node):
+    assert len(node.parents) > 0
+    return node.parents[0].output_shape
+
+
+def shape_scalar(node):
+    return make_tensor(1, 1, 1, 1)
+
+
+def shape_crop(node):
+    raise KaffeError('crop function had been defined in customer_layers')
+
+
+def shape_power(node):
+    raise KaffeError('power function had been defined in customer_layers')
+
+
+def shape_data(node):
+    if node.output_shape:
+        # Old-style input specification
+        shape = node.output_shape
+    else:
+        try:
+            # New-style input specification
+            shape = map(int, node.parameters.shape[0].dim)
+        except:
+            # We most likely have a data layer on our hands. The problem is,
+            # Caffe infers the dimensions of the data from the source (eg: LMDB).
+            # We want to avoid reading datasets here. Fail for now.
+            # This can be temporarily fixed by transforming the data layer to
+            # Caffe's "input" layer (as is usually used in the "deploy" version).
+            # TODO: Find a better solution for this.
+            raise KaffeError(
+                'Cannot determine dimensions of data layer.\n'
+                'See comments in function shape_data for more info.')
+    return shape
+
+
+def shape_mem_data(node):
+    params = node.parameters
+    return make_tensor(params.batch_size, params.channels, params.height,
+                       params.width)
+
+
+def shape_concat(node):
+    axis = node.layer.parameters.axis
+    output_shape = None
+    for parent in node.parents:
+        if output_shape is None:
+            output_shape = list(parent.output_shape)
+        else:
+            output_shape[axis] += parent.output_shape[axis]
+    return tuple(output_shape)
+
+
+def shape_convolution(node):
+    return get_strided_kernel_output_shape(node, math.floor)
+
+
+def shape_deconvolution(node):
+    assert node.layer is not None
+    input_shape = node.get_only_parent().output_shape
+    h_i = input_shape.height
+    w_i = input_shape.width
+
+    params = node.layer.kernel_parameters
+    p_h = params.pad_h
+    p_w = params.pad_w
+
+    dila_h = params.dila_h
+    dila_w = params.dila_w
+
+    k_h = params.kernel_h
+    k_w = params.kernel_w
+
+    s_h = params.stride_h
+    s_w = params.stride_w
+
+    h_o = (h_i - 1) * s_h - 2 * p_h + dila_h * (k_h - 1) + 1
+    w_o = (w_i - 1) * s_w - 2 * p_w + dila_w * (k_w - 1) + 1
+
+    params = node.layer.parameters
+    has_c_o = hasattr(params, 'num_output')
+    c = params.num_output if has_c_o else input_shape.channels
+    return make_tensor(input_shape.batch_size, c, h_o, w_o)
+
+
+def shape_pool(node):
+    global_pool = getattr(node.layer.parameters, 'global_pooling', False)
+    if global_pool:
+        input_shape = node.get_only_parent().output_shape
+        return make_tensor(input_shape.batch_size, input_shape.channels, 1, 1)
+
+    ceil_mode = getattr(node.layer.parameters, 'ceil_mode', True)
+    if ceil_mode is True:
+        method = math.ceil
+    else:
+        method = math.floor
+    return get_strided_kernel_output_shape(node, method)
+
+
+def shape_inner_product(node):
+    input_shape = node.get_only_parent().output_shape
+    return make_tensor(input_shape.batch_size, node.layer.parameters.num_output)

caffe2fluid/kaffe/transformers.py

+'''
+A collection of graph transforms.
+
+A transformer is a callable that accepts a graph and returns a transformed version.
+'''
+import os
+import numpy as np
+
+from .caffe import get_caffe_resolver, has_pycaffe
+from .errors import KaffeError, debug, notice, warn
+from .layers import NodeKind
+
+
+class DataInjector(object):
+    '''
+    Associates parameters loaded from a .caffemodel file with their corresponding nodes.
+    '''
+
+    def __init__(self, def_path, data_path):
+        # The .prototxt file defining the graph
+        self.def_path = def_path
+        # The .caffemodel file containing the learned parameters
+        self.data_path = data_path
+        # Set to true if the fallback protocol-buffer based backend was used
+        self.did_use_pb = False
+        # A list containing (layer name, parameters) tuples
+        self.params = None
+        # Load the parameters
+        self.load()
+
+    def load(self):
+        if has_pycaffe():
+            self.load_using_caffe()
+        else:
+            self.load_using_pb()
+
+    def load_using_caffe(self):
+        caffe = get_caffe_resolver().caffe
+        net = caffe.Net(self.def_path, self.data_path, caffe.TEST)
+        data = lambda blob: blob.data
+        self.params = [(k, map(data, v)) for k, v in net.params.items()]
+
+    def load_using_pb(self):
+        data = get_caffe_resolver().NetParameter()
+        data.MergeFromString(open(self.data_path, 'rb').read())
+        pair = lambda layer: (layer.name, self.normalize_pb_data(layer))
+        layers = data.layers or data.layer
+        self.params = [pair(layer) for layer in layers if layer.blobs]
+        self.did_use_pb = True
+
+    def normalize_pb_data(self, layer):
+        transformed = []
+        for blob in layer.blobs:
+            if len(blob.shape.dim):
+                dims = blob.shape.dim
+                c_o, c_i, h, w = map(int, [1] * (4 - len(dims)) + list(dims))
+            else:
+                c_o = blob.num
+                c_i = blob.channels
+                h = blob.height
+                w = blob.width
+            data = np.array(blob.data, dtype=np.float32).reshape(c_o, c_i, h, w)
+            transformed.append(data)
+        return transformed
+
+    def adjust_parameters(self, node, data):
+        if not self.did_use_pb:
+            return data
+
+        # When using the protobuf-backend, each parameter initially has four dimensions.
+        # In certain cases (like FC layers), we want to eliminate the singleton dimensions.
+        # This implementation takes care of the common cases. However, it does leave the
+        # potential for future issues.
+        # The Caffe-backend does not suffer from this problem.
+        data = list(data)
+
+        squeeze_indices = [1]  # Squeeze biases.
+        if node.kind == NodeKind.InnerProduct:
+            squeeze_indices.append(0)  # Squeeze FC.
+
+        for idx in squeeze_indices:
+            if idx >= len(data):
+                continue
+
+            d = data[idx]
+            assert len(
+                d.shape
+            ) == 4, 'invalid shape[%s] from caffe when adjust_parameters' % (
+                str(d.shape))
+
+            shape_old = d.shape
+            sq_axis = None
+            if idx == 0:
+                sq_axis = (0, 1)
+            elif idx == 1:
+                sq_axis = (0, 1, 2)
+            else:
+                continue
+
+            data[idx] = np.squeeze(d, axis=sq_axis)
+            shape_new = data[idx].shape
+            if len(shape_old) != shape_new:
+                debug('squeeze idx:%d, with kind:%s,name:%s' % \
+                        (idx, node.kind, node.name))
+        return data
+
+    def __call__(self, graph):
+        for layer_name, data in self.params:
+            if layer_name in graph:
+                node = graph.get_node(layer_name)
+                node.data = self.adjust_parameters(node, data)
+            else:
+                notice('Ignoring parameters for non-existent layer: %s' % \
+                        layer_name)
+        return graph
+
+
+class DataReshaper(object):
+    def __init__(self, mapping, replace=True):
+        # A dictionary mapping NodeKind to the transposed order.
+        self.mapping = mapping
+        # The node kinds eligible for reshaping
+        self.reshaped_node_types = self.mapping.keys()
+        # If true, the reshaped data will replace the old one.
+        # Otherwise, it's set to the reshaped_data attribute.
+        self.replace = replace
+
+    def has_spatial_parent(self, node):
+        try:
+            parent = node.get_only_parent()
+            s = parent.output_shape
+            if len(s) == 4:
+                return s.height > 1 or s.width > 1
+            else:
+                return False
+        except KaffeError:
+            return False
+
+    def map(self, node_kind):
+        try:
+            return self.mapping[node_kind]
+        except KeyError:
+            raise KaffeError('Ordering not found for node kind: {}'.format(
+                node_kind))
+
+    def __call__(self, graph):
+        for node in graph.nodes:
+            if node.data is None:
+                continue
+
+            if node.kind not in self.reshaped_node_types:
+                # Check for 2+ dimensional data
+                #if any(len(tensor.shape) > 1 for tensor in node.data):
+                #    notice('parmaters not reshaped for node: {}'.format(node))
+                continue
+
+            transpose_order = self.map(node.kind)
+            weights = node.data[0]
+            if node.kind == NodeKind.InnerProduct:
+                # The FC layer connected to the spatial layer needs to be
+                # re-wired to match the new spatial ordering.
+                #in_shape = node.get_only_parent().output_shape
+                fc_shape = weights.shape
+                output_channels = fc_shape[0]
+                weights = weights.reshape((output_channels, -1))
+                weights = weights.transpose(transpose_order)
+                node.reshaped_data = weights
+            else:
+                node.reshaped_data = weights.transpose(transpose_order)
+
+        if self.replace:
+            for node in graph.nodes:
+                if hasattr(node, 'reshaped_data'):
+                    # Set the weights
+                    node.data[0] = node.reshaped_data
+                    del node.reshaped_data
+        return graph
+
+
+class CropFuser(object):
+    '''
+    Crop is to return a scalar output Blob for an input Blob of arbitrary size.
+    When one of the input Blob is "input" or "DummyData", we can remove the input Blob
+    and put the shape into the reduction layer.
+    '''
+    _traced_names = {}
+
+    @classmethod
+    def traced_names(cls):
+        return cls._traced_names
+
+    @classmethod
+    def trace(cls, fname, tname):
+        """ recording the names mapping,
+            the value of 'fname' will be replaced by value of 'tname'
+        """
+        if fname not in cls._traced_names:
+            cls._traced_names[fname] = []
+        cls._traced_names[fname].append(tname)
+
+    def __init__(self,
+                 allowed_parent_types=[NodeKind.Input, NodeKind.DummyData]):
+        self.allowed_parent_types = allowed_parent_types
+
+    def __call__(self, graph):
+        nodes = graph.nodes
+        fused_nodes = []
+        for node in nodes:
+            if len(node.parents) != 2:
+                # reduction layer must has two parent layers.
+                continue
+            parent = node.parents[1]
+            if not self.is_eligible_pair(parent, node):
+                continue
+            # Change the graph structure.
+            parent.children.remove(node)
+            node.parents.remove(parent)
+            # Let the sub-class merge the fused node in any arbitrary way.
+            if not len(parent.children):
+                fused_nodes.append(parent)
+            #fused_nodes.append(parent)
+            self.merge(parent, node)
+        # rebuild the graph
+        transformed_nodes = [node for node in nodes if node not in fused_nodes]
+        return graph.replaced(transformed_nodes)
+
+    def is_eligible_pair(self, parent, child):
+        '''Returns true if this parent/child pair is eligible for fusion.'''
+        return child.kind == NodeKind.Crop
+        #return (self.allowed_parent_types is not None and \
+        #        len(parent.children) == 1 and \
+        #        parent.kind in self.allowed_parent_types and \
+        #        child.kind == NodeKind.Crop)
+
+    def merge(self, parent, child):
+        '''Merge the parent node into the child.'''
+        child.metadata['shape'] = [
+            parent.output_shape.batch_size, parent.output_shape.channels,
+            parent.output_shape.height, parent.output_shape.width
+        ]
+
+
+class SubNodeFuser(object):
+    '''
+    An abstract helper for merging a single-child with its single-parent.
+    '''
+    _traced_names = {}
+
+    @classmethod
+    def traced_names(cls):
+        return cls._traced_names
+
+    @classmethod
+    def trace(cls, fname, tname):
+        """ recording the names mapping,
+            the value of 'fname' will be replaced by value of 'tname'
+        """
+        if fname not in cls._traced_names:
+            cls._traced_names[fname] = []
+        cls._traced_names[fname].append(tname)
+
+    def __call__(self, graph):
+        nodes = graph.nodes
+        fused_nodes = []
+        for node in nodes:
+            if len(node.parents) != 1:
+                # We're only fusing nodes with single parents
+                continue
+            parent = node.get_only_parent()
+            if len(parent.children) != 1:
+                # We can only fuse a node if its parent's
+                # value isn't used by any other node.
+                continue
+            if not self.is_eligible_pair(parent, node):
+                continue
+            # Rewrite the fused node's children to its parent.
+            for child in node.children:
+                pos = child.parents.index(node)
+                child.parents[pos] = parent
+                parent.add_child(child)
+            # Disconnect the fused node from the graph.
+            parent.children.remove(node)
+            fused_nodes.append(node)
+            # Let the sub-class merge the fused node in any arbitrary way.
+            self.merge(parent, node)
+        transformed_nodes = [node for node in nodes if node not in fused_nodes]
+        return graph.replaced(transformed_nodes)
+
+    def is_eligible_pair(self, parent, child):
+        '''Returns true if this parent/child pair is eligible for fusion.'''
+        raise NotImplementedError('Must be implemented by subclass.')
+
+    def merge(self, parent, child):
+        '''Merge the child node into the parent.'''
+        raise NotImplementedError('Must be implemented by subclass')
+
+
+class ReLUFuser(SubNodeFuser):
+    '''
+    Fuses rectified linear units with their parent nodes.
+    '''
+
+    def __init__(self, allowed_parent_types=None):
+        # Fuse ReLUs when the parent node is one of the given types.
+        # If None, all node types are eligible.
+        self.allowed_parent_types = allowed_parent_types
+
+    def is_eligible_pair(self, parent, child):
+        return ((self.allowed_parent_types is None or \
+                parent.kind in self.allowed_parent_types) and \
+                child.kind == NodeKind.ReLU)
+
+    def merge(self, parent, child):
+        SubNodeFuser.trace(parent.name, child.name)
+        parent.metadata['relu'] = True
+        parent.metadata['relu_negative_slope'] = child.parameters.negative_slope
+
+
+class BatchNormScaleBiasFuser(SubNodeFuser):
+    '''
+    The original batch normalization paper includes two learned
+    parameters: a scaling factor \gamma and a bias \beta.
+    Caffe's implementation does not include these two. However, it is commonly
+    replicated by adding a scaling+bias layer immidiately after the batch norm.
+
+    This fuser merges the scaling+bias layer with the batch norm.
+    '''
+
+    def is_eligible_pair(self, parent, child):
+        return (parent.kind == NodeKind.BatchNorm and \
+                child.kind == NodeKind.Scale and \
+                child.parameters.axis == 1 and \
+                child.parameters.bias_term == True)
+
+    def merge(self, parent, child):
+        SubNodeFuser.trace(parent.name, child.name)
+        parent.scale_bias_node = child
+
+
+class BatchNormPreprocessor(object):
+    '''
+    Prescale batch normalization parameters.
+    Concatenate gamma (scale) and beta (bias) terms if set.
+    '''
+
+    def __call__(self, graph):
+        for node in graph.nodes:
+            if node.kind != NodeKind.BatchNorm:
+                continue
+            assert node.data is not None
+            assert len(node.data) == 3
+            node.data = [np.squeeze(i) for i in node.data]
+            mean, variance, scale = node.data
+            # Prescale the stats
+            scaling_factor = 1.0 / scale if scale != 0 else 0
+            mean *= scaling_factor
+            variance *= scaling_factor
+            # Replace with the updated values
+            node.data = [mean, variance]
+            if hasattr(node, 'scale_bias_node'):
+                # Include the scale and bias terms
+                gamma, beta = node.scale_bias_node.data
+                node.data += [np.squeeze(i) for i in [gamma, beta]]
+        return graph
+
+
+class NodeRenamer(object):
+    '''
+    Renames nodes in the graph using a given unary function that
+    accepts a node and returns its new name.
+    '''
+
+    def __init__(self, renamer):
+        self.renamer = renamer
+
+    def __call__(self, graph):
+        for node in graph.nodes:
+            node.name = self.renamer(node)
+        return graph
+
+
+class ParameterNamer(object):
+    '''
+    Convert layer data arrays to a dictionary mapping parameter names to their values.
+    '''
+
+    def __call__(self, graph):
+        for node in graph.nodes:
+            if node.data is None:
+                continue
+            if node.kind in (NodeKind.Convolution, NodeKind.InnerProduct,\
+                    NodeKind.Deconvolution):
+                names = ('weights', )
+                if node.parameters.bias_term:
+                    names += ('biases', )
+            elif node.kind == NodeKind.BatchNorm:
+                names = ('mean', 'variance')
+                if len(node.data) == 4:
+                    names += ('scale', 'offset')
+            elif node.kind == NodeKind.Scale:
+                names = ('scale', )
+                if getattr(node.parameters, 'bias_term', False):
+                    names = ('scale', 'offset')
+            elif node.kind == NodeKind.PReLU:
+                names = ('negslope', )
+            elif node.kind == "Normalize":
+                names = ('scale', )
+            else:
+                warn('Unhandled parameters when naming this it[%s]' %
+                     (node.kind))
+                continue
+            assert len(names) == len(node.data)
+            node.data = dict(zip(names, node.data))
+        return graph

caffe2fluid/proto/caffe.proto

+syntax = "proto2";
+
+package caffe;
+
+// Specifies the shape (dimensions) of a Blob.
+message BlobShape { repeated int64 dim = 1 [ packed = true ]; }
+
+message BlobProto {
+  optional BlobShape shape = 7;
+  repeated float data = 5 [ packed = true ];
+  repeated float diff = 6 [ packed = true ];
+  repeated double double_data = 8 [ packed = true ];
+  repeated double double_diff = 9 [ packed = true ];
+
+  // 4D dimensions -- deprecated.  Use "shape" instead.
+  optional int32 num = 1 [ default = 0 ];
+  optional int32 channels = 2 [ default = 0 ];
+  optional int32 height = 3 [ default = 0 ];
+  optional int32 width = 4 [ default = 0 ];
+}
+
+// The BlobProtoVector is simply a way to pass multiple blobproto instances
+// around.
+message BlobProtoVector { repeated BlobProto blobs = 1; }
+
+message Datum {
+  optional int32 channels = 1;
+  optional int32 height = 2;
+  optional int32 width = 3;
+  // the actual image data, in bytes
+  optional bytes data = 4;
+  optional int32 label = 5;
+  // Optionally, the datum could also hold float data.
+  repeated float float_data = 6;
+  // If true data contains an encoded image that need to be decoded
+  optional bool encoded = 7 [ default = false ];
+}
+
+message FillerParameter {
+  // The filler type.
+  optional string type = 1 [ default = 'constant' ];
+  optional float value = 2 [ default = 0 ]; // the value in constant filler
+  optional float min = 3 [ default = 0 ];   // the min value in uniform filler
+  optional float max = 4 [ default = 1 ];   // the max value in uniform filler
+  optional float mean = 5 [ default = 0 ];  // the mean value in Gaussian filler
+  optional float std = 6 [ default = 1 ];   // the std value in Gaussian filler
+  // The expected number of non-zero output weights for a given input in
+  // Gaussian filler -- the default -1 means don't perform sparsification.
+  optional int32 sparse = 7 [ default = -1 ];
+  // Normalize the filler variance by fan_in, fan_out, or their average.
+  // Applies to 'xavier' and 'msra' fillers.
+  enum VarianceNorm {
+    FAN_IN = 0;
+    FAN_OUT = 1;
+    AVERAGE = 2;
+  }
+  optional VarianceNorm variance_norm = 8 [ default = FAN_IN ];
+}
+
+message NetParameter {
+  optional string name = 1; // consider giving the network a name
+  // DEPRECATED. See InputParameter. The input blobs to the network.
+  repeated string input = 3;
+  // DEPRECATED. See InputParameter. The shape of the input blobs.
+  repeated BlobShape input_shape = 8;
+
+  // 4D input dimensions -- deprecated.  Use "input_shape" instead.
+  // If specified, for each input blob there should be four
+  // values specifying the num, channels, height and width of the input blob.
+  // Thus, there should be a total of (4 * #input) numbers.
+  repeated int32 input_dim = 4;
+
+  // Whether the network will force every layer to carry out backward operation.
+  // If set False, then whether to carry out backward is determined
+  // automatically according to the net structure and learning rates.
+  optional bool force_backward = 5 [ default = false ];
+  // The current "state" of the network, including the phase, level, and stage.
+  // Some layers may be included/excluded depending on this state and the states
+  // specified in the layers' include and exclude fields.
+  optional NetState state = 6;
+
+  // Print debugging information about results while running Net::Forward,
+  // Net::Backward, and Net::Update.
+  optional bool debug_info = 7 [ default = false ];
+
+  // The layers that make up the net.  Each of their configurations, including
+  // connectivity and behavior, is specified as a LayerParameter.
+  repeated LayerParameter layer = 100; // ID 100 so layers are printed last.
+
+  // DEPRECATED: use 'layer' instead.
+  repeated V1LayerParameter layers = 2;
+}
+
+// NOTE
+// Update the next available ID when you add a new SolverParameter field.
+//
+// SolverParameter next available ID: 42 (last added: layer_wise_reduce)
+message SolverParameter {
+  //////////////////////////////////////////////////////////////////////////////
+  // Specifying the train and test networks
+  //
+  // Exactly one train net must be specified using one of the following fields:
+  //     train_net_param, train_net, net_param, net
+  // One or more test nets may be specified using any of the following fields:
+  //     test_net_param, test_net, net_param, net
+  // If more than one test net field is specified (e.g., both net and
+  // test_net are specified), they will be evaluated in the field order given
+  // above: (1) test_net_param, (2) test_net, (3) net_param/net.
+  // A test_iter must be specified for each test_net.
+  // A test_level and/or a test_stage may also be specified for each test_net.
+  //////////////////////////////////////////////////////////////////////////////
+
+  // Proto filename for the train net, possibly combined with one or more
+  // test nets.
+  optional string net = 24;
+  // Inline train net param, possibly combined with one or more test nets.
+  optional NetParameter net_param = 25;
+
+  optional string train_net = 1; // Proto filename for the train net.
+  repeated string test_net = 2;  // Proto filenames for the test nets.
+  optional NetParameter train_net_param = 21; // Inline train net params.
+  repeated NetParameter test_net_param = 22;  // Inline test net params.
+
+  // The states for the train/test nets. Must be unspecified or
+  // specified once per net.
+  //
+  // By default, train_state will have phase = TRAIN,
+  // and all test_state's will have phase = TEST.
+  // Other defaults are set according to the NetState defaults.
+  optional NetState train_state = 26;
+  repeated NetState test_state = 27;
+
+  // The number of iterations for each test net.
+  repeated int32 test_iter = 3;
+
+  // The number of iterations between two testing phases.
+  optional int32 test_interval = 4 [ default = 0 ];
+  optional bool test_compute_loss = 19 [ default = false ];
+  // If true, run an initial test pass before the first iteration,
+  // ensuring memory availability and printing the starting value of the loss.
+  optional bool test_initialization = 32 [ default = true ];
+  optional float base_lr = 5; // The base learning rate
+  // the number of iterations between displaying info. If display = 0, no info
+  // will be displayed.
+  optional int32 display = 6;
+  // Display the loss averaged over the last average_loss iterations
+  optional int32 average_loss = 33 [ default = 1 ];
+  optional int32 max_iter = 7; // the maximum number of iterations
+  // accumulate gradients over `iter_size` x `batch_size` instances
+  optional int32 iter_size = 36 [ default = 1 ];
+
+  // The learning rate decay policy. The currently implemented learning rate
+  // policies are as follows:
+  //    - fixed: always return base_lr.
+  //    - step: return base_lr * gamma ^ (floor(iter / step))
+  //    - exp: return base_lr * gamma ^ iter
+  //    - inv: return base_lr * (1 + gamma * iter) ^ (- power)
+  //    - multistep: similar to step but it allows non uniform steps defined by
+  //      stepvalue
+  //    - poly: the effective learning rate follows a polynomial decay, to be
+  //      zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power)
+  //    - sigmoid: the effective learning rate follows a sigmod decay
+  //      return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize))))
+  //
+  // where base_lr, max_iter, gamma, step, stepvalue and power are defined
+  // in the solver parameter protocol buffer, and iter is the current iteration.
+  optional string lr_policy = 8;
+  optional float gamma = 9;     // The parameter to compute the learning rate.
+  optional float power = 10;    // The parameter to compute the learning rate.
+  optional float momentum = 11; // The momentum value.
+  optional float weight_decay = 12; // The weight decay.
+  // regularization types supported: L1 and L2
+  // controlled by weight_decay
+  optional string regularization_type = 29 [ default = "L2" ];
+  // the stepsize for learning rate policy "step"
+  optional int32 stepsize = 13;
+  // the stepsize for learning rate policy "multistep"
+  repeated int32 stepvalue = 34;
+
+  // Set clip_gradients to >= 0 to clip parameter gradients to that L2 norm,
+  // whenever their actual L2 norm is larger.
+  optional float clip_gradients = 35 [ default = -1 ];
+
+  optional int32 snapshot = 14 [ default = 0 ]; // The snapshot interval
+  optional string snapshot_prefix = 15;         // The prefix for the snapshot.
+  // whether to snapshot diff in the results or not. Snapshotting diff will help
+  // debugging but the final protocol buffer size will be much larger.
+  optional bool snapshot_diff = 16 [ default = false ];
+  enum SnapshotFormat {
+    HDF5 = 0;
+    BINARYPROTO = 1;
+  }
+  optional SnapshotFormat snapshot_format = 37 [ default = BINARYPROTO ];
+  // the mode solver will use: 0 for CPU and 1 for GPU. Use GPU in default.
+  enum SolverMode {
+    CPU = 0;
+    GPU = 1;
+  }
+  optional SolverMode solver_mode = 17 [ default = GPU ];
+  // the device_id will that be used in GPU mode. Use device_id = 0 in default.
+  optional int32 device_id = 18 [ default = 0 ];
+  // If non-negative, the seed with which the Solver will initialize the Caffe
+  // random number generator -- useful for reproducible results. Otherwise,
+  // (and by default) initialize using a seed derived from the system clock.
+  optional int64 random_seed = 20 [ default = -1 ];
+
+  // type of the solver
+  optional string type = 40 [ default = "SGD" ];
+
+  // numerical stability for RMSProp, AdaGrad and AdaDelta and Adam
+  optional float delta = 31 [ default = 1e-8 ];
+  // parameters for the Adam solver
+  optional float momentum2 = 39 [ default = 0.999 ];
+
+  // RMSProp decay value
+  // MeanSquare(t) = rms_decay*MeanSquare(t-1) + (1-rms_decay)*SquareGradient(t)
+  optional float rms_decay = 38 [ default = 0.99 ];
+
+  // If true, print information about the state of the net that may help with
+  // debugging learning problems.
+  optional bool debug_info = 23 [ default = false ];
+
+  // If false, don't save a snapshot after training finishes.
+  optional bool snapshot_after_train = 28 [ default = true ];
+
+  // DEPRECATED: old solver enum types, use string instead
+  enum SolverType {
+    SGD = 0;
+    NESTEROV = 1;
+    ADAGRAD = 2;
+    RMSPROP = 3;
+    ADADELTA = 4;
+    ADAM = 5;
+  }
+  // DEPRECATED: use type instead of solver_type
+  optional SolverType solver_type = 30 [ default = SGD ];
+
+  // Overlap compute and communication for data parallel training
+  optional bool layer_wise_reduce = 41 [ default = true ];
+}
+
+// A message that stores the solver snapshots
+message SolverState {
+  optional int32 iter = 1;         // The current iteration
+  optional string learned_net = 2; // The file that stores the learned net.
+  repeated BlobProto history = 3;  // The history for sgd solvers
+  optional int32 current_step = 4
+      [ default = 0 ]; // The current step for learning rate
+}
+
+enum Phase {
+  TRAIN = 0;
+  TEST = 1;
+}
+
+message NetState {
+  optional Phase phase = 1 [ default = TEST ];
+  optional int32 level = 2 [ default = 0 ];
+  repeated string stage = 3;
+}
+
+message NetStateRule {
+  // Set phase to require the NetState have a particular phase (TRAIN or TEST)
+  // to meet this rule.
+  optional Phase phase = 1;
+
+  // Set the minimum and/or maximum levels in which the layer should be used.
+  // Leave undefined to meet the rule regardless of level.
+  optional int32 min_level = 2;
+  optional int32 max_level = 3;
+
+  // Customizable sets of stages to include or exclude.
+  // The net must have ALL of the specified stages and NONE of the specified
+  // "not_stage"s to meet the rule.
+  // (Use multiple NetStateRules to specify conjunctions of stages.)
+  repeated string stage = 4;
+  repeated string not_stage = 5;
+}
+
+// Specifies training parameters (multipliers on global learning constants,
+// and the name and other settings used for weight sharing).
+message ParamSpec {
+  // The names of the parameter blobs -- useful for sharing parameters among
+  // layers, but never required otherwise.  To share a parameter between two
+  // layers, give it a (non-empty) name.
+  optional string name = 1;
+
+  // Whether to require shared weights to have the same shape, or just the same
+  // count -- defaults to STRICT if unspecified.
+  optional DimCheckMode share_mode = 2;
+  enum DimCheckMode {
+    // STRICT (default) requires that num, channels, height, width each match.
+    STRICT = 0;
+    // PERMISSIVE requires only the count (num*channels*height*width) to match.
+    PERMISSIVE = 1;
+  }
+
+  // The multiplier on the global learning rate for this parameter.
+  optional float lr_mult = 3 [ default = 1.0 ];
+
+  // The multiplier on the global weight decay for this parameter.
+  optional float decay_mult = 4 [ default = 1.0 ];
+}
+
+// NOTE
+// Update the next available ID when you add a new LayerParameter field.
+//
+// LayerParameter next available layer-specific ID: 147 (last added:
+// recurrent_param)
+message LayerParameter {
+  optional string name = 1;   // the layer name
+  optional string type = 2;   // the layer type
+  repeated string bottom = 3; // the name of each bottom blob
+  repeated string top = 4;    // the name of each top blob
+
+  // The train / test phase for computation.
+  optional Phase phase = 10;
+
+  // The amount of weight to assign each top blob in the objective.
+  // Each layer assigns a default value, usually of either 0 or 1,
+  // to each top blob.
+  repeated float loss_weight = 5;
+
+  // Specifies training parameters (multipliers on global learning constants,
+  // and the name and other settings used for weight sharing).
+  repeated ParamSpec param = 6;
+
+  // The blobs containing the numeric parameters of the layer.
+  repeated BlobProto blobs = 7;
+
+  // Specifies whether to backpropagate to each bottom. If unspecified,
+  // Caffe will automatically infer whether each input needs backpropagation
+  // to compute parameter gradients. If set to true for some inputs,
+  // backpropagation to those inputs is forced; if set false for some inputs,
+  // backpropagation to those inputs is skipped.
+  //
+  // The size must be either 0 or equal to the number of bottoms.
+  repeated bool propagate_down = 11;
+
+  // Rules controlling whether and when a layer is included in the network,
+  // based on the current NetState.  You may specify a non-zero number of rules
+  // to include OR exclude, but not both.  If no include or exclude rules are
+  // specified, the layer is always included.  If the current NetState meets
+  // ANY (i.e., one or more) of the specified rules, the layer is
+  // included/excluded.
+  repeated NetStateRule include = 8;
+  repeated NetStateRule exclude = 9;
+
+  // Parameters for data pre-processing.
+  optional TransformationParameter transform_param = 100;
+
+  // Parameters shared by loss layers.
+  optional LossParameter loss_param = 101;
+
+  // Layer type-specific parameters.
+  //
+  // Note: certain layers may have more than one computational engine
+  // for their implementation. These layers include an Engine type and
+  // engine parameter for selecting the implementation.
+  // The default for the engine is set by the ENGINE switch at compile-time.
+  optional AccuracyParameter accuracy_param = 102;
+  optional ArgMaxParameter argmax_param = 103;
+  optional BatchNormParameter batch_norm_param = 139;
+  optional BiasParameter bias_param = 141;
+  optional ConcatParameter concat_param = 104;
+  optional ContrastiveLossParameter contrastive_loss_param = 105;
+  optional ConvolutionParameter convolution_param = 106;
+  optional CropParameter crop_param = 144;
+  optional DataParameter data_param = 107;
+  optional DropoutParameter dropout_param = 108;
+  optional DummyDataParameter dummy_data_param = 109;
+  optional EltwiseParameter eltwise_param = 110;
+  optional ELUParameter elu_param = 140;
+  optional EmbedParameter embed_param = 137;
+  optional ExpParameter exp_param = 111;
+  optional FlattenParameter flatten_param = 135;
+  optional HDF5DataParameter hdf5_data_param = 112;
+  optional HDF5OutputParameter hdf5_output_param = 113;
+  optional HingeLossParameter hinge_loss_param = 114;
+  optional ImageDataParameter image_data_param = 115;
+  optional InfogainLossParameter infogain_loss_param = 116;
+  optional InnerProductParameter inner_product_param = 117;
+  optional InputParameter input_param = 143;
+  optional LogParameter log_param = 134;
+  optional LRNParameter lrn_param = 118;
+  optional MemoryDataParameter memory_data_param = 119;
+  optional MVNParameter mvn_param = 120;
+  optional ParameterParameter parameter_param = 145;
+  optional PoolingParameter pooling_param = 121;
+  optional PowerParameter power_param = 122;
+  optional PReLUParameter prelu_param = 131;
+  optional PythonParameter python_param = 130;
+  optional RecurrentParameter recurrent_param = 146;
+  optional ReductionParameter reduction_param = 136;
+  optional ReLUParameter relu_param = 123;
+  optional ReshapeParameter reshape_param = 133;
+  optional ScaleParameter scale_param = 142;
+  optional SigmoidParameter sigmoid_param = 124;
+  optional SoftmaxParameter softmax_param = 125;
+  optional SPPParameter spp_param = 132;
+  optional SliceParameter slice_param = 126;
+  optional TanHParameter tanh_param = 127;
+  optional ThresholdParameter threshold_param = 128;
+  optional TileParameter tile_param = 138;
+  optional WindowDataParameter window_data_param = 129;
+}
+
+// Message that stores parameters used to apply transformation
+// to the data layer's data
+message TransformationParameter {
+  // For data pre-processing, we can do simple scaling and subtracting the
+  // data mean, if provided. Note that the mean subtraction is always carried
+  // out before scaling.
+  optional float scale = 1 [ default = 1 ];
+  // Specify if we want to randomly mirror data.
+  optional bool mirror = 2 [ default = false ];
+  // Specify if we would like to randomly crop an image.
+  optional uint32 crop_size = 3 [ default = 0 ];
+  // mean_file and mean_value cannot be specified at the same time
+  optional string mean_file = 4;
+  // if specified can be repeated once (would subtract it from all the channels)
+  // or can be repeated the same number of times as channels
+  // (would subtract them from the corresponding channel)
+  repeated float mean_value = 5;
+  // Force the decoded image to have 3 color channels.
+  optional bool force_color = 6 [ default = false ];
+  // Force the decoded image to have 1 color channels.
+  optional bool force_gray = 7 [ default = false ];
+}
+
+// Message that stores parameters shared by loss layers
+message LossParameter {
+  // If specified, ignore instances with the given label.
+  optional int32 ignore_label = 1;
+  // How to normalize the loss for loss layers that aggregate across batches,
+  // spatial dimensions, or other dimensions.  Currently only implemented in
+  // SoftmaxWithLoss and SigmoidCrossEntropyLoss layers.
+  enum NormalizationMode {
+    // Divide by the number of examples in the batch times spatial dimensions.
+    // Outputs that receive the ignore label will NOT be ignored in computing
+    // the normalization factor.
+    FULL = 0;
+    // Divide by the total number of output locations that do not take the
+    // ignore_label.  If ignore_label is not set, this behaves like FULL.
+    VALID = 1;
+    // Divide by the batch size.
+    BATCH_SIZE = 2;
+    // Do not normalize the loss.
+    NONE = 3;
+  }
+  // For historical reasons, the default normalization for
+  // SigmoidCrossEntropyLoss is BATCH_SIZE and *not* VALID.
+  optional NormalizationMode normalization = 3 [ default = VALID ];
+  // Deprecated.  Ignored if normalization is specified.  If normalization
+  // is not specified, then setting this to false will be equivalent to
+  // normalization = BATCH_SIZE to be consistent with previous behavior.
+  optional bool normalize = 2;
+}
+
+// Messages that store parameters used by individual layer types follow, in
+// alphabetical order.
+
+message AccuracyParameter {
+  // When computing accuracy, count as correct by comparing the true label to
+  // the top k scoring classes.  By default, only compare to the top scoring
+  // class (i.e. argmax).
+  optional uint32 top_k = 1 [ default = 1 ];
+
+  // The "label" axis of the prediction blob, whose argmax corresponds to the
+  // predicted label -- may be negative to index from the end (e.g., -1 for the
+  // last axis).  For example, if axis == 1 and the predictions are
+  // (N x C x H x W), the label blob is expected to contain N*H*W ground truth
+  // labels with integer values in {0, 1, ..., C-1}.
+  optional int32 axis = 2 [ default = 1 ];
+
+  // If specified, ignore instances with the given label.
+  optional int32 ignore_label = 3;
+}
+
+message ArgMaxParameter {
+  // If true produce pairs (argmax, maxval)
+  optional bool out_max_val = 1 [ default = false ];
+  optional uint32 top_k = 2 [ default = 1 ];
+  // The axis along which to maximise -- may be negative to index from the
+  // end (e.g., -1 for the last axis).
+  // By default ArgMaxLayer maximizes over the flattened trailing dimensions
+  // for each index of the first / num dimension.
+  optional int32 axis = 3;
+}
+
+message ConcatParameter {
+  // The axis along which to concatenate -- may be negative to index from the
+  // end (e.g., -1 for the last axis).  Other axes must have the
+  // same dimension for all the bottom blobs.
+  // By default, ConcatLayer concatenates blobs along the "channels" axis (1).
+  optional int32 axis = 2 [ default = 1 ];
+
+  // DEPRECATED: alias for "axis" -- does not support negative indexing.
+  optional uint32 concat_dim = 1 [ default = 1 ];
+}
+
+message BatchNormParameter {
+  // If false, normalization is performed over the current mini-batch
+  // and global statistics are accumulated (but not yet used) by a moving
+  // average.
+  // If true, those accumulated mean and variance values are used for the
+  // normalization.
+  // By default, it is set to false when the network is in the training
+  // phase and true when the network is in the testing phase.
+  optional bool use_global_stats = 1;
+  // What fraction of the moving average remains each iteration?
+  // Smaller values make the moving average decay faster, giving more
+  // weight to the recent values.
+  // Each iteration updates the moving average @f$S_{t-1}@f$ with the
+  // current mean @f$ Y_t @f$ by
+  // @f$ S_t = (1-\beta)Y_t + \beta \cdot S_{t-1} @f$, where @f$ \beta @f$
+  // is the moving_average_fraction parameter.
+  optional float moving_average_fraction = 2 [ default = .999 ];
+  // Small value to add to the variance estimate so that we don't divide by
+  // zero.
+  optional float eps = 3 [ default = 1e-5 ];
+}
+
+message BiasParameter {
+  // The first axis of bottom[0] (the first input Blob) along which to apply
+  // bottom[1] (the second input Blob).  May be negative to index from the end
+  // (e.g., -1 for the last axis).
+  //
+  // For example, if bottom[0] is 4D with shape 100x3x40x60, the output
+  // top[0] will have the same shape, and bottom[1] may have any of the
+  // following shapes (for the given value of axis):
+  //    (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60
+  //    (axis == 1 == -3)          3;     3x40;     3x40x60
+  //    (axis == 2 == -2)                   40;       40x60
+  //    (axis == 3 == -1)                                60
+  // Furthermore, bottom[1] may have the empty shape (regardless of the value of
+  // "axis") -- a scalar bias.
+  optional int32 axis = 1 [ default = 1 ];
+
+  // (num_axes is ignored unless just one bottom is given and the bias is
+  // a learned parameter of the layer.  Otherwise, num_axes is determined by the
+  // number of axes by the second bottom.)
+  // The number of axes of the input (bottom[0]) covered by the bias
+  // parameter, or -1 to cover all axes of bottom[0] starting from `axis`.
+  // Set num_axes := 0, to add a zero-axis Blob: a scalar.
+  optional int32 num_axes = 2 [ default = 1 ];
+
+  // (filler is ignored unless just one bottom is given and the bias is
+  // a learned parameter of the layer.)
+  // The initialization for the learned bias parameter.
+  // Default is the zero (0) initialization, resulting in the BiasLayer
+  // initially performing the identity operation.
+  optional FillerParameter filler = 3;
+}
+
+message ContrastiveLossParameter {
+  // margin for dissimilar pair
+  optional float margin = 1 [ default = 1.0 ];
+  // The first implementation of this cost did not exactly match the cost of
+  // Hadsell et al 2006 -- using (margin - d^2) instead of (margin - d)^2.
+  // legacy_version = false (the default) uses (margin - d)^2 as proposed in the
+  // Hadsell paper. New models should probably use this version.
+  // legacy_version = true uses (margin - d^2). This is kept to support /
+  // reproduce existing models and results
+  optional bool legacy_version = 2 [ default = false ];
+}
+
+message ConvolutionParameter {
+  optional uint32 num_output = 1; // The number of outputs for the layer
+  optional bool bias_term = 2 [ default = true ]; // whether to have bias terms
+
+  // Pad, kernel size, and stride are all given as a single value for equal
+  // dimensions in all spatial dimensions, or once per spatial dimension.
+  repeated uint32 pad = 3;         // The padding size; defaults to 0
+  repeated uint32 kernel_size = 4; // The kernel size
+  repeated uint32 stride = 6;      // The stride; defaults to 1
+  // Factor used to dilate the kernel, (implicitly) zero-filling the resulting
+  // holes. (Kernel dilation is sometimes referred to by its use in the
+  // algorithme à trous from Holschneider et al. 1987.)
+  repeated uint32 dilation = 18; // The dilation; defaults to 1
+
+  // For 2D convolution only, the *_h and *_w versions may also be used to
+  // specify both spatial dimensions.
+  optional uint32 pad_h = 9 [ default = 0 ];  // The padding height (2D only)
+  optional uint32 pad_w = 10 [ default = 0 ]; // The padding width (2D only)
+  optional uint32 kernel_h = 11;              // The kernel height (2D only)
+  optional uint32 kernel_w = 12;              // The kernel width (2D only)
+  optional uint32 stride_h = 13;              // The stride height (2D only)
+  optional uint32 stride_w = 14;              // The stride width (2D only)
+
+  optional uint32 group = 5 [ default = 1 ]; // The group size for group conv
+
+  optional FillerParameter weight_filler = 7; // The filler for the weight
+  optional FillerParameter bias_filler = 8;   // The filler for the bias
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 15 [ default = DEFAULT ];
+
+  // The axis to interpret as "channels" when performing convolution.
+  // Preceding dimensions are treated as independent inputs;
+  // succeeding dimensions are treated as "spatial".
+  // With (N, C, H, W) inputs, and axis == 1 (the default), we perform
+  // N independent 2D convolutions, sliding C-channel (or (C/g)-channels, for
+  // groups g>1) filters across the spatial axes (H, W) of the input.
+  // With (N, C, D, H, W) inputs, and axis == 1, we perform
+  // N independent 3D convolutions, sliding (C/g)-channels
+  // filters across the spatial axes (D, H, W) of the input.
+  optional int32 axis = 16 [ default = 1 ];
+
+  // Whether to force use of the general ND convolution, even if a specific
+  // implementation for blobs of the appropriate number of spatial dimensions
+  // is available. (Currently, there is only a 2D-specific convolution
+  // implementation; for input blobs with num_axes != 2, this option is
+  // ignored and the ND implementation will be used.)
+  optional bool force_nd_im2col = 17 [ default = false ];
+}
+
+message CropParameter {
+  // To crop, elements of the first bottom are selected to fit the dimensions
+  // of the second, reference bottom. The crop is configured by
+  // - the crop `axis` to pick the dimensions for cropping
+  // - the crop `offset` to set the shift for all/each dimension
+  // to align the cropped bottom with the reference bottom.
+  // All dimensions up to but excluding `axis` are preserved, while
+  // the dimensions including and trailing `axis` are cropped.
+  // If only one `offset` is set, then all dimensions are offset by this amount.
+  // Otherwise, the number of offsets must equal the number of cropped axes to
+  // shift the crop in each dimension accordingly.
+  // Note: standard dimensions are N,C,H,W so the default is a spatial crop,
+  // and `axis` may be negative to index from the end (e.g., -1 for the last
+  // axis).
+  optional int32 axis = 1 [ default = 2 ];
+  repeated uint32 offset = 2;
+}
+
+message DataParameter {
+  enum DB {
+    LEVELDB = 0;
+    LMDB = 1;
+  }
+  // Specify the data source.
+  optional string source = 1;
+  // Specify the batch size.
+  optional uint32 batch_size = 4;
+  // The rand_skip variable is for the data layer to skip a few data points
+  // to avoid all asynchronous sgd clients to start at the same point. The skip
+  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
+  // be larger than the number of keys in the database.
+  // DEPRECATED. Each solver accesses a different subset of the database.
+  optional uint32 rand_skip = 7 [ default = 0 ];
+  optional DB backend = 8 [ default = LEVELDB ];
+  // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
+  // simple scaling and subtracting the data mean, if provided. Note that the
+  // mean subtraction is always carried out before scaling.
+  optional float scale = 2 [ default = 1 ];
+  optional string mean_file = 3;
+  // DEPRECATED. See TransformationParameter. Specify if we would like to
+  // randomly
+  // crop an image.
+  optional uint32 crop_size = 5 [ default = 0 ];
+  // DEPRECATED. See TransformationParameter. Specify if we want to randomly
+  // mirror
+  // data.
+  optional bool mirror = 6 [ default = false ];
+  // Force the encoded image to have 3 color channels
+  optional bool force_encoded_color = 9 [ default = false ];
+  // Prefetch queue (Increase if data feeding bandwidth varies, within the
+  // limit of device memory for GPU training)
+  optional uint32 prefetch = 10 [ default = 4 ];
+}
+
+message DropoutParameter {
+  optional float dropout_ratio = 1 [ default = 0.5 ]; // dropout ratio
+}
+
+// DummyDataLayer fills any number of arbitrarily shaped blobs with random
+// (or constant) data generated by "Fillers" (see "message FillerParameter").
+message DummyDataParameter {
+  // This layer produces N >= 1 top blobs.  DummyDataParameter must specify 1 or
+  // N
+  // shape fields, and 0, 1 or N data_fillers.
+  //
+  // If 0 data_fillers are specified, ConstantFiller with a value of 0 is used.
+  // If 1 data_filler is specified, it is applied to all top blobs.  If N are
+  // specified, the ith is applied to the ith top blob.
+  repeated FillerParameter data_filler = 1;
+  repeated BlobShape shape = 6;
+
+  // 4D dimensions -- deprecated.  Use "shape" instead.
+  repeated uint32 num = 2;
+  repeated uint32 channels = 3;
+  repeated uint32 height = 4;
+  repeated uint32 width = 5;
+}
+
+message EltwiseParameter {
+  enum EltwiseOp {
+    PROD = 0;
+    SUM = 1;
+    MAX = 2;
+  }
+  optional EltwiseOp operation = 1 [ default = SUM ]; // element-wise operation
+  repeated float coeff = 2; // blob-wise coefficient for SUM operation
+
+  // Whether to use an asymptotically slower (for >2 inputs) but stabler method
+  // of computing the gradient for the PROD operation. (No effect for SUM op.)
+  optional bool stable_prod_grad = 3 [ default = true ];
+}
+
+// Message that stores parameters used by ELULayer
+message ELUParameter {
+  // Described in:
+  // Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate
+  // Deep Network Learning by Exponential Linear Units (ELUs). arXiv
+  optional float alpha = 1 [ default = 1 ];
+}
+
+// Message that stores parameters used by EmbedLayer
+message EmbedParameter {
+  optional uint32 num_output = 1; // The number of outputs for the layer
+  // The input is given as integers to be interpreted as one-hot
+  // vector indices with dimension num_input.  Hence num_input should be
+  // 1 greater than the maximum possible input value.
+  optional uint32 input_dim = 2;
+
+  optional bool bias_term = 3 [ default = true ]; // Whether to use a bias term
+  optional FillerParameter weight_filler = 4;     // The filler for the weight
+  optional FillerParameter bias_filler = 5;       // The filler for the bias
+}
+
+// Message that stores parameters used by ExpLayer
+message ExpParameter {
+  // ExpLayer computes outputs y = base ^ (shift + scale * x), for base > 0.
+  // Or if base is set to the default (-1), base is set to e,
+  // so y = exp(shift + scale * x).
+  optional float base = 1 [ default = -1.0 ];
+  optional float scale = 2 [ default = 1.0 ];
+  optional float shift = 3 [ default = 0.0 ];
+}
+
+/// Message that stores parameters used by FlattenLayer
+message FlattenParameter {
+  // The first axis to flatten: all preceding axes are retained in the output.
+  // May be negative to index from the end (e.g., -1 for the last axis).
+  optional int32 axis = 1 [ default = 1 ];
+
+  // The last axis to flatten: all following axes are retained in the output.
+  // May be negative to index from the end (e.g., the default -1 for the last
+  // axis).
+  optional int32 end_axis = 2 [ default = -1 ];
+}
+
+// Message that stores parameters used by HDF5DataLayer
+message HDF5DataParameter {
+  // Specify the data source.
+  optional string source = 1;
+  // Specify the batch size.
+  optional uint32 batch_size = 2;
+
+  // Specify whether to shuffle the data.
+  // If shuffle == true, the ordering of the HDF5 files is shuffled,
+  // and the ordering of data within any given HDF5 file is shuffled,
+  // but data between different files are not interleaved; all of a file's
+  // data are output (in a random order) before moving onto another file.
+  optional bool shuffle = 3 [ default = false ];
+}
+
+message HDF5OutputParameter { optional string file_name = 1; }
+
+message HingeLossParameter {
+  enum Norm {
+    L1 = 1;
+    L2 = 2;
+  }
+  // Specify the Norm to use L1 or L2
+  optional Norm norm = 1 [ default = L1 ];
+}
+
+message ImageDataParameter {
+  // Specify the data source.
+  optional string source = 1;
+  // Specify the batch size.
+  optional uint32 batch_size = 4 [ default = 1 ];
+  // The rand_skip variable is for the data layer to skip a few data points
+  // to avoid all asynchronous sgd clients to start at the same point. The skip
+  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
+  // be larger than the number of keys in the database.
+  optional uint32 rand_skip = 7 [ default = 0 ];
+  // Whether or not ImageLayer should shuffle the list of files at every epoch.
+  optional bool shuffle = 8 [ default = false ];
+  // It will also resize images if new_height or new_width are not zero.
+  optional uint32 new_height = 9 [ default = 0 ];
+  optional uint32 new_width = 10 [ default = 0 ];
+  // Specify if the images are color or gray
+  optional bool is_color = 11 [ default = true ];
+  // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
+  // simple scaling and subtracting the data mean, if provided. Note that the
+  // mean subtraction is always carried out before scaling.
+  optional float scale = 2 [ default = 1 ];
+  optional string mean_file = 3;
+  // DEPRECATED. See TransformationParameter. Specify if we would like to
+  // randomly
+  // crop an image.
+  optional uint32 crop_size = 5 [ default = 0 ];
+  // DEPRECATED. See TransformationParameter. Specify if we want to randomly
+  // mirror
+  // data.
+  optional bool mirror = 6 [ default = false ];
+  optional string root_folder = 12 [ default = "" ];
+}
+
+message InfogainLossParameter {
+  // Specify the infogain matrix source.
+  optional string source = 1;
+  optional int32 axis = 2 [ default = 1 ]; // axis of prob
+}
+
+message InnerProductParameter {
+  optional uint32 num_output = 1; // The number of outputs for the layer
+  optional bool bias_term = 2 [ default = true ]; // whether to have bias terms
+  optional FillerParameter weight_filler = 3;     // The filler for the weight
+  optional FillerParameter bias_filler = 4;       // The filler for the bias
+
+  // The first axis to be lumped into a single inner product computation;
+  // all preceding axes are retained in the output.
+  // May be negative to index from the end (e.g., -1 for the last axis).
+  optional int32 axis = 5 [ default = 1 ];
+  // Specify whether to transpose the weight matrix or not.
+  // If transpose == true, any operations will be performed on the transpose
+  // of the weight matrix. The weight matrix itself is not going to be
+  // transposed
+  // but rather the transfer flag of operations will be toggled accordingly.
+  optional bool transpose = 6 [ default = false ];
+}
+
+message InputParameter {
+  // This layer produces N >= 1 top blob(s) to be assigned manually.
+  // Define N shapes to set a shape for each top.
+  // Define 1 shape to set the same shape for every top.
+  // Define no shape to defer to reshaping manually.
+  repeated BlobShape shape = 1;
+}
+
+// Message that stores parameters used by LogLayer
+message LogParameter {
+  // LogLayer computes outputs y = log_base(shift + scale * x), for base > 0.
+  // Or if base is set to the default (-1), base is set to e,
+  // so y = ln(shift + scale * x) = log_e(shift + scale * x)
+  optional float base = 1 [ default = -1.0 ];
+  optional float scale = 2 [ default = 1.0 ];
+  optional float shift = 3 [ default = 0.0 ];
+}
+
+// Message that stores parameters used by LRNLayer
+message LRNParameter {
+  optional uint32 local_size = 1 [ default = 5 ];
+  optional float alpha = 2 [ default = 1. ];
+  optional float beta = 3 [ default = 0.75 ];
+  enum NormRegion {
+    ACROSS_CHANNELS = 0;
+    WITHIN_CHANNEL = 1;
+  }
+  optional NormRegion norm_region = 4 [ default = ACROSS_CHANNELS ];
+  optional float k = 5 [ default = 1. ];
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 6 [ default = DEFAULT ];
+}
+
+message MemoryDataParameter {
+  optional uint32 batch_size = 1;
+  optional uint32 channels = 2;
+  optional uint32 height = 3;
+  optional uint32 width = 4;
+}
+
+message MVNParameter {
+  // This parameter can be set to false to normalize mean only
+  optional bool normalize_variance = 1 [ default = true ];
+
+  // This parameter can be set to true to perform DNN-like MVN
+  optional bool across_channels = 2 [ default = false ];
+
+  // Epsilon for not dividing by zero while normalizing variance
+  optional float eps = 3 [ default = 1e-9 ];
+}
+
+message ParameterParameter { optional BlobShape shape = 1; }
+
+message PoolingParameter {
+  enum PoolMethod {
+    MAX = 0;
+    AVE = 1;
+    STOCHASTIC = 2;
+  }
+  optional PoolMethod pool = 1 [ default = MAX ]; // The pooling method
+  // Pad, kernel size, and stride are all given as a single value for equal
+  // dimensions in height and width or as Y, X pairs.
+  optional uint32 pad = 4 [ default = 0 ];   // The padding size (equal in Y, X)
+  optional uint32 pad_h = 9 [ default = 0 ]; // The padding height
+  optional uint32 pad_w = 10 [ default = 0 ]; // The padding width
+  optional uint32 kernel_size = 2;            // The kernel size (square)
+  optional uint32 kernel_h = 5;               // The kernel height
+  optional uint32 kernel_w = 6;               // The kernel width
+  optional uint32 stride = 3 [ default = 1 ]; // The stride (equal in Y, X)
+  optional uint32 stride_h = 7;               // The stride height
+  optional uint32 stride_w = 8;               // The stride width
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 11 [ default = DEFAULT ];
+  // If global_pooling then it will pool over the size of the bottom by doing
+  // kernel_h = bottom->height and kernel_w = bottom->width
+  optional bool global_pooling = 12 [ default = false ];
+}
+
+message PowerParameter {
+  // PowerLayer computes outputs y = (shift + scale * x) ^ power.
+  optional float power = 1 [ default = 1.0 ];
+  optional float scale = 2 [ default = 1.0 ];
+  optional float shift = 3 [ default = 0.0 ];
+}
+
+message PythonParameter {
+  optional string module = 1;
+  optional string layer = 2;
+  // This value is set to the attribute `param_str` of the `PythonLayer` object
+  // in Python before calling the `setup()` method. This could be a number,
+  // string, dictionary in Python dict format, JSON, etc. You may parse this
+  // string in `setup` method and use it in `forward` and `backward`.
+  optional string param_str = 3 [ default = ''];
+  // DEPRECATED
+  optional bool share_in_parallel = 4 [ default = false ];
+}
+
+// Message that stores parameters used by RecurrentLayer
+message RecurrentParameter {
+  // The dimension of the output (and usually hidden state) representation --
+  // must be explicitly set to non-zero.
+  optional uint32 num_output = 1 [ default = 0 ];
+
+  optional FillerParameter weight_filler = 2; // The filler for the weight
+  optional FillerParameter bias_filler = 3;   // The filler for the bias
+
+  // Whether to enable displaying debug_info in the unrolled recurrent net.
+  optional bool debug_info = 4 [ default = false ];
+
+  // Whether to add as additional inputs (bottoms) the initial hidden state
+  // blobs, and add as additional outputs (tops) the final timestep hidden state
+  // blobs.  The number of additional bottom/top blobs required depends on the
+  // recurrent architecture -- e.g., 1 for RNNs, 2 for LSTMs.
+  optional bool expose_hidden = 5 [ default = false ];
+}
+
+// Message that stores parameters used by ReductionLayer
+message ReductionParameter {
+  enum ReductionOp {
+    SUM = 1;
+    ASUM = 2;
+    SUMSQ = 3;
+    MEAN = 4;
+  }
+
+  optional ReductionOp operation = 1 [ default = SUM ]; // reduction operation
+
+  // The first axis to reduce to a scalar -- may be negative to index from the
+  // end (e.g., -1 for the last axis).
+  // (Currently, only reduction along ALL "tail" axes is supported; reduction
+  // of axis M through N, where N < num_axes - 1, is unsupported.)
+  // Suppose we have an n-axis bottom Blob with shape:
+  //     (d0, d1, d2, ..., d(m-1), dm, d(m+1), ..., d(n-1)).
+  // If axis == m, the output Blob will have shape
+  //     (d0, d1, d2, ..., d(m-1)),
+  // and the ReductionOp operation is performed (d0 * d1 * d2 * ... * d(m-1))
+  // times, each including (dm * d(m+1) * ... * d(n-1)) individual data.
+  // If axis == 0 (the default), the output Blob always has the empty shape
+  // (count 1), performing reduction across the entire input --
+  // often useful for creating new loss functions.
+  optional int32 axis = 2 [ default = 0 ];
+
+  optional float coeff = 3 [ default = 1.0 ]; // coefficient for output
+}
+
+// Message that stores parameters used by ReLULayer
+message ReLUParameter {
+  // Allow non-zero slope for negative inputs to speed up optimization
+  // Described in:
+  // Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013). Rectifier nonlinearities
+  // improve neural network acoustic models. In ICML Workshop on Deep Learning
+  // for Audio, Speech, and Language Processing.
+  optional float negative_slope = 1 [ default = 0 ];
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 2 [ default = DEFAULT ];
+}
+
+message ReshapeParameter {
+  // Specify the output dimensions. If some of the dimensions are set to 0,
+  // the corresponding dimension from the bottom layer is used (unchanged).
+  // Exactly one dimension may be set to -1, in which case its value is
+  // inferred from the count of the bottom blob and the remaining dimensions.
+  // For example, suppose we want to reshape a 2D blob "input" with shape 2 x 8:
+  //
+  //   layer {
+  //     type: "Reshape" bottom: "input" top: "output"
+  //     reshape_param { ... }
+  //   }
+  //
+  // If "input" is 2D with shape 2 x 8, then the following reshape_param
+  // specifications are all equivalent, producing a 3D blob "output" with shape
+  // 2 x 2 x 4:
+  //
+  //   reshape_param { shape { dim:  2  dim: 2  dim:  4 } }
+  //   reshape_param { shape { dim:  0  dim: 2  dim:  4 } }
+  //   reshape_param { shape { dim:  0  dim: 2  dim: -1 } }
+  //   reshape_param { shape { dim:  0  dim:-1  dim:  4 } }
+  //
+  optional BlobShape shape = 1;
+
+  // axis and num_axes control the portion of the bottom blob's shape that are
+  // replaced by (included in) the reshape. By default (axis == 0 and
+  // num_axes == -1), the entire bottom blob shape is included in the reshape,
+  // and hence the shape field must specify the entire output shape.
+  //
+  // axis may be non-zero to retain some portion of the beginning of the input
+  // shape (and may be negative to index from the end; e.g., -1 to begin the
+  // reshape after the last axis, including nothing in the reshape,
+  // -2 to include only the last axis, etc.).
+  //
+  // For example, suppose "input" is a 2D blob with shape 2 x 8.
+  // Then the following ReshapeLayer specifications are all equivalent,
+  // producing a blob "output" with shape 2 x 2 x 4:
+  //
+  //   reshape_param { shape { dim: 2  dim: 2  dim: 4 } }
+  //   reshape_param { shape { dim: 2  dim: 4 } axis:  1 }
+  //   reshape_param { shape { dim: 2  dim: 4 } axis: -3 }
+  //
+  // num_axes specifies the extent of the reshape.
+  // If num_axes >= 0 (and axis >= 0), the reshape will be performed only on
+  // input axes in the range [axis, axis+num_axes].
+  // num_axes may also be -1, the default, to include all remaining axes
+  // (starting from axis).
+  //
+  // For example, suppose "input" is a 2D blob with shape 2 x 8.
+  // Then the following ReshapeLayer specifications are equivalent,
+  // producing a blob "output" with shape 1 x 2 x 8.
+  //
+  //   reshape_param { shape { dim:  1  dim: 2  dim:  8 } }
+  //   reshape_param { shape { dim:  1  dim: 2  }  num_axes: 1 }
+  //   reshape_param { shape { dim:  1  }  num_axes: 0 }
+  //
+  // On the other hand, these would produce output blob shape 2 x 1 x 8:
+  //
+  //   reshape_param { shape { dim: 2  dim: 1  dim: 8  }  }
+  //   reshape_param { shape { dim: 1 }  axis: 1  num_axes: 0 }
+  //
+  optional int32 axis = 2 [ default = 0 ];
+  optional int32 num_axes = 3 [ default = -1 ];
+}
+
+message ScaleParameter {
+  // The first axis of bottom[0] (the first input Blob) along which to apply
+  // bottom[1] (the second input Blob).  May be negative to index from the end
+  // (e.g., -1 for the last axis).
+  //
+  // For example, if bottom[0] is 4D with shape 100x3x40x60, the output
+  // top[0] will have the same shape, and bottom[1] may have any of the
+  // following shapes (for the given value of axis):
+  //    (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60
+  //    (axis == 1 == -3)          3;     3x40;     3x40x60
+  //    (axis == 2 == -2)                   40;       40x60
+  //    (axis == 3 == -1)                                60
+  // Furthermore, bottom[1] may have the empty shape (regardless of the value of
+  // "axis") -- a scalar multiplier.
+  optional int32 axis = 1 [ default = 1 ];
+
+  // (num_axes is ignored unless just one bottom is given and the scale is
+  // a learned parameter of the layer.  Otherwise, num_axes is determined by the
+  // number of axes by the second bottom.)
+  // The number of axes of the input (bottom[0]) covered by the scale
+  // parameter, or -1 to cover all axes of bottom[0] starting from `axis`.
+  // Set num_axes := 0, to multiply with a zero-axis Blob: a scalar.
+  optional int32 num_axes = 2 [ default = 1 ];
+
+  // (filler is ignored unless just one bottom is given and the scale is
+  // a learned parameter of the layer.)
+  // The initialization for the learned scale parameter.
+  // Default is the unit (1) initialization, resulting in the ScaleLayer
+  // initially performing the identity operation.
+  optional FillerParameter filler = 3;
+
+  // Whether to also learn a bias (equivalent to a ScaleLayer+BiasLayer, but
+  // may be more efficient).  Initialized with bias_filler (defaults to 0).
+  optional bool bias_term = 4 [ default = false ];
+  optional FillerParameter bias_filler = 5;
+}
+
+message SigmoidParameter {
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 1 [ default = DEFAULT ];
+}
+
+message SliceParameter {
+  // The axis along which to slice -- may be negative to index from the end
+  // (e.g., -1 for the last axis).
+  // By default, SliceLayer concatenates blobs along the "channels" axis (1).
+  optional int32 axis = 3 [ default = 1 ];
+  repeated uint32 slice_point = 2;
+
+  // DEPRECATED: alias for "axis" -- does not support negative indexing.
+  optional uint32 slice_dim = 1 [ default = 1 ];
+}
+
+// Message that stores parameters used by SoftmaxLayer, SoftmaxWithLossLayer
+message SoftmaxParameter {
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 1 [ default = DEFAULT ];
+
+  // The axis along which to perform the softmax -- may be negative to index
+  // from the end (e.g., -1 for the last axis).
+  // Any other axes will be evaluated as independent softmaxes.
+  optional int32 axis = 2 [ default = 1 ];
+}
+
+message TanHParameter {
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 1 [ default = DEFAULT ];
+}
+
+// Message that stores parameters used by TileLayer
+message TileParameter {
+  // The index of the axis to tile.
+  optional int32 axis = 1 [ default = 1 ];
+
+  // The number of copies (tiles) of the blob to output.
+  optional int32 tiles = 2;
+}
+
+// Message that stores parameters used by ThresholdLayer
+message ThresholdParameter {
+  optional float threshold = 1 [ default = 0 ]; // Strictly positive values
+}
+
+message WindowDataParameter {
+  // Specify the data source.
+  optional string source = 1;
+  // For data pre-processing, we can do simple scaling and subtracting the
+  // data mean, if provided. Note that the mean subtraction is always carried
+  // out before scaling.
+  optional float scale = 2 [ default = 1 ];
+  optional string mean_file = 3;
+  // Specify the batch size.
+  optional uint32 batch_size = 4;
+  // Specify if we would like to randomly crop an image.
+  optional uint32 crop_size = 5 [ default = 0 ];
+  // Specify if we want to randomly mirror data.
+  optional bool mirror = 6 [ default = false ];
+  // Foreground (object) overlap threshold
+  optional float fg_threshold = 7 [ default = 0.5 ];
+  // Background (non-object) overlap threshold
+  optional float bg_threshold = 8 [ default = 0.5 ];
+  // Fraction of batch that should be foreground objects
+  optional float fg_fraction = 9 [ default = 0.25 ];
+  // Amount of contextual padding to add around a window
+  // (used only by the window_data_layer)
+  optional uint32 context_pad = 10 [ default = 0 ];
+  // Mode for cropping out a detection window
+  // warp: cropped window is warped to a fixed size and aspect ratio
+  // square: the tightest square around the window is cropped
+  optional string crop_mode = 11 [ default = "warp" ];
+  // cache_images: will load all images in memory for faster access
+  optional bool cache_images = 12 [ default = false ];
+  // append root_folder to locate images
+  optional string root_folder = 13 [ default = "" ];
+}
+
+message SPPParameter {
+  enum PoolMethod {
+    MAX = 0;
+    AVE = 1;
+    STOCHASTIC = 2;
+  }
+  optional uint32 pyramid_height = 1;
+  optional PoolMethod pool = 2 [ default = MAX ]; // The pooling method
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 6 [ default = DEFAULT ];
+}
+
+// DEPRECATED: use LayerParameter.
+message V1LayerParameter {
+  repeated string bottom = 2;
+  repeated string top = 3;
+  optional string name = 4;
+  repeated NetStateRule include = 32;
+  repeated NetStateRule exclude = 33;
+  enum LayerType {
+    NONE = 0;
+    ABSVAL = 35;
+    ACCURACY = 1;
+    ARGMAX = 30;
+    BNLL = 2;
+    CONCAT = 3;
+    CONTRASTIVE_LOSS = 37;
+    CONVOLUTION = 4;
+    DATA = 5;
+    DECONVOLUTION = 39;
+    DROPOUT = 6;
+    DUMMY_DATA = 32;
+    EUCLIDEAN_LOSS = 7;
+    ELTWISE = 25;
+    EXP = 38;
+    FLATTEN = 8;
+    HDF5_DATA = 9;
+    HDF5_OUTPUT = 10;
+    HINGE_LOSS = 28;
+    IM2COL = 11;
+    IMAGE_DATA = 12;
+    INFOGAIN_LOSS = 13;
+    INNER_PRODUCT = 14;
+    LRN = 15;
+    MEMORY_DATA = 29;
+    MULTINOMIAL_LOGISTIC_LOSS = 16;
+    MVN = 34;
+    POOLING = 17;
+    POWER = 26;
+    RELU = 18;
+    SIGMOID = 19;
+    SIGMOID_CROSS_ENTROPY_LOSS = 27;
+    SILENCE = 36;
+    SOFTMAX = 20;
+    SOFTMAX_LOSS = 21;
+    SPLIT = 22;
+    SLICE = 33;
+    TANH = 23;
+    WINDOW_DATA = 24;
+    THRESHOLD = 31;
+  }
+  optional LayerType type = 5;
+  repeated BlobProto blobs = 6;
+  repeated string param = 1001;
+  repeated DimCheckMode blob_share_mode = 1002;
+  enum DimCheckMode {
+    STRICT = 0;
+    PERMISSIVE = 1;
+  }
+  repeated float blobs_lr = 7;
+  repeated float weight_decay = 8;
+  repeated float loss_weight = 35;
+  optional AccuracyParameter accuracy_param = 27;
+  optional ArgMaxParameter argmax_param = 23;
+  optional ConcatParameter concat_param = 9;
+  optional ContrastiveLossParameter contrastive_loss_param = 40;
+  optional ConvolutionParameter convolution_param = 10;
+  optional DataParameter data_param = 11;
+  optional DropoutParameter dropout_param = 12;
+  optional DummyDataParameter dummy_data_param = 26;
+  optional EltwiseParameter eltwise_param = 24;
+  optional ExpParameter exp_param = 41;
+  optional HDF5DataParameter hdf5_data_param = 13;
+  optional HDF5OutputParameter hdf5_output_param = 14;
+  optional HingeLossParameter hinge_loss_param = 29;
+  optional ImageDataParameter image_data_param = 15;
+  optional InfogainLossParameter infogain_loss_param = 16;
+  optional InnerProductParameter inner_product_param = 17;
+  optional LRNParameter lrn_param = 18;
+  optional MemoryDataParameter memory_data_param = 22;
+  optional MVNParameter mvn_param = 34;
+  optional PoolingParameter pooling_param = 19;
+  optional PowerParameter power_param = 21;
+  optional ReLUParameter relu_param = 30;
+  optional SigmoidParameter sigmoid_param = 38;
+  optional SoftmaxParameter softmax_param = 39;
+  optional SliceParameter slice_param = 31;
+  optional TanHParameter tanh_param = 37;
+  optional ThresholdParameter threshold_param = 25;
+  optional WindowDataParameter window_data_param = 20;
+  optional TransformationParameter transform_param = 36;
+  optional LossParameter loss_param = 42;
+  optional V0LayerParameter layer = 1;
+}
+
+// DEPRECATED: V0LayerParameter is the old way of specifying layer parameters
+// in Caffe.  We keep this message type around for legacy support.
+message V0LayerParameter {
+  optional string name = 1; // the layer name
+  optional string type = 2; // the string to specify the layer type
+
+  // Parameters to specify layers with inner products.
+  optional uint32 num_output = 3; // The number of outputs for the layer
+  optional bool biasterm = 4 [ default = true ]; // whether to have bias terms
+  optional FillerParameter weight_filler = 5;    // The filler for the weight
+  optional FillerParameter bias_filler = 6;      // The filler for the bias
+
+  optional uint32 pad = 7 [ default = 0 ];     // The padding size
+  optional uint32 kernelsize = 8;              // The kernel size
+  optional uint32 group = 9 [ default = 1 ];   // The group size for group conv
+  optional uint32 stride = 10 [ default = 1 ]; // The stride
+  enum PoolMethod {
+    MAX = 0;
+    AVE = 1;
+    STOCHASTIC = 2;
+  }
+  optional PoolMethod pool = 11 [ default = MAX ];     // The pooling method
+  optional float dropout_ratio = 12 [ default = 0.5 ]; // dropout ratio
+
+  optional uint32 local_size = 13 [ default = 5 ]; // for local response norm
+  optional float alpha = 14 [ default = 1. ];      // for local response norm
+  optional float beta = 15 [ default = 0.75 ];     // for local response norm
+  optional float k = 22 [ default = 1. ];
+
+  // For data layers, specify the data source
+  optional string source = 16;
+  // For data pre-processing, we can do simple scaling and subtracting the
+  // data mean, if provided. Note that the mean subtraction is always carried
+  // out before scaling.
+  optional float scale = 17 [ default = 1 ];
+  optional string meanfile = 18;
+  // For data layers, specify the batch size.
+  optional uint32 batchsize = 19;
+  // For data layers, specify if we would like to randomly crop an image.
+  optional uint32 cropsize = 20 [ default = 0 ];
+  // For data layers, specify if we want to randomly mirror data.
+  optional bool mirror = 21 [ default = false ];
+
+  // The blobs containing the numeric parameters of the layer
+  repeated BlobProto blobs = 50;
+  // The ratio that is multiplied on the global learning rate. If you want to
+  // set the learning ratio for one blob, you need to set it for all blobs.
+  repeated float blobs_lr = 51;
+  // The weight decay that is multiplied on the global weight decay.
+  repeated float weight_decay = 52;
+
+  // The rand_skip variable is for the data layer to skip a few data points
+  // to avoid all asynchronous sgd clients to start at the same point. The skip
+  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
+  // be larger than the number of keys in the database.
+  optional uint32 rand_skip = 53 [ default = 0 ];
+
+  // Fields related to detection (det_*)
+  // foreground (object) overlap threshold
+  optional float det_fg_threshold = 54 [ default = 0.5 ];
+  // background (non-object) overlap threshold
+  optional float det_bg_threshold = 55 [ default = 0.5 ];
+  // Fraction of batch that should be foreground objects
+  optional float det_fg_fraction = 56 [ default = 0.25 ];
+
+  // optional bool OBSOLETE_can_clobber = 57 [default = true];
+
+  // Amount of contextual padding to add around a window
+  // (used only by the window_data_layer)
+  optional uint32 det_context_pad = 58 [ default = 0 ];
+
+  // Mode for cropping out a detection window
+  // warp: cropped window is warped to a fixed size and aspect ratio
+  // square: the tightest square around the window is cropped
+  optional string det_crop_mode = 59 [ default = "warp" ];
+
+  // For ReshapeLayer, one needs to specify the new dimensions.
+  optional int32 new_num = 60 [ default = 0 ];
+  optional int32 new_channels = 61 [ default = 0 ];
+  optional int32 new_height = 62 [ default = 0 ];
+  optional int32 new_width = 63 [ default = 0 ];
+
+  // Whether or not ImageLayer should shuffle the list of files at every epoch.
+  // It will also resize images if new_height or new_width are not zero.
+  optional bool shuffle_images = 64 [ default = false ];
+
+  // For ConcatLayer, one needs to specify the dimension for concatenation, and
+  // the other dimensions must be the same for all the bottom blobs.
+  // By default it will concatenate blobs along the channels dimension.
+  optional uint32 concat_dim = 65 [ default = 1 ];
+
+  optional HDF5OutputParameter hdf5_output_param = 1001;
+}
+
+message PReLUParameter {
+  // Parametric ReLU described in K. He et al, Delving Deep into Rectifiers:
+  // Surpassing Human-Level Performance on ImageNet Classification, 2015.
+
+  // Initial value of a_i. Default is a_i=0.25 for all i.
+  optional FillerParameter filler = 1;
+  // Whether or not slope parameters are shared across channels.
+  optional bool channel_shared = 2 [ default = false ];
+}

caffe2fluid/proto/compile.sh

+#!/bin/bash
+
+#function:
+#   script used to generate caffepb.py from caffe.proto using protoc
+#
+
+PROTOC=`which protoc`
+if [[ -z $PROTOC ]];then
+    echo "not found protoc, you should first install it following this[https://github.com/google/protobuf/releases]"
+    exit 1
+fi
+
+WORK_ROOT=$(dirname `readlink -f "$BASH_SOURCE[0]"`)
+PY_NAME="$WORK_ROOT/caffe_pb2.py"
+$PROTOC --proto_path=$WORK_ROOT --python_out=$WORK_ROOT $WORK_ROOT/caffe.proto
+ret=$?
+
+if [ -e "$PY_NAME" ];then
+    echo "succeed to generate [$PY_NAME]"
+    exit 0
+else
+    echo "failed to generate [$PY_NAME]"
+fi
+exit $ret