Caffe2fluid

fluid/image_classification/caffe2fluid/README.md

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+### Caffe2Fluid
+This tool is used to convert a Caffe model to Fluid model
+
+### Howto
+1, Prepare caffepb.py in ./proto, two options provided
+    1) generate it from caffe.proto using protoc
+        bash ./proto/compile.sh
+
+    2) download one from github directly
+        cd proto/ && wget https://github.com/ethereon/caffe-tensorflow/blob/master/kaffe/caffe/caffepb.py
+
+2, Convert the caffe model using 'convert.py' which will generate a python script and a weight(in .npy) file
+
+3, Use the converted model to predict
+    see more detail info in 'tests/lenet/README.md'
+
+
+### Supported models
+- Lenet on mnist dataset
+
+- ResNets:(ResNet-50, ResNet-101, ResNet-152)
+    model addrs:(https://onedrive.live.com/?authkey=%21AAFW2-FVoxeVRck&id=4006CBB8476FF777%2117887&cid=4006CBB8476FF777)
+
+### Notes
+Some of this code come from here: https://github.com/ethereon/caffe-tensorflow

fluid/image_classification/caffe2fluid/convert.py

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+#!/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('Done.')
+    except KaffeError as err:
+        fatal_error('Error encountered: {}'.format(err))
+
+
+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)
+    convert(args.def_path, args.caffemodel, args.data_output_path,
+            args.code_output_path, args.phase)
+
+
+if __name__ == '__main__':
+    main()

fluid/image_classification/caffe2fluid/kaffe/__init__.py

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

fluid/image_classification/caffe2fluid/kaffe/caffe/__init__.py

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+from .resolver import get_caffe_resolver, has_pycaffe

fluid/image_classification/caffe2fluid/kaffe/caffe/resolver.py

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+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 caffepb
+    return caffepb
+
+
+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.
+    * This backend is UNTESTED!
+------------------------------------------------------------
+
+'''
+    sys.stderr.write(msg)

fluid/image_classification/caffe2fluid/kaffe/errors.py

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+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