Merge pull request #761 from walloollaw/add-examples-for-caffe2fluid

Add examples for caffe2fluid

fluid/image_classification/caffe2fluid/README.md

@@ -2,7 +2,8 @@
 This tool is used to convert a Caffe model to Fluid model
 
 ### Howto
-1, Prepare caffepb.py in ./proto, two options provided
+1, Prepare caffepb.py in ./proto if your python has no 'pycaffe' module, two options provided here:
+
     1) generate it from caffe.proto using protoc
         bash ./proto/compile.sh
 
@@ -12,14 +13,24 @@ This tool is used to convert a Caffe model to Fluid model
 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'
+
+    see more detail info in 'examples/xxx'
 
 
-### Supported models
+### Tested 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)
+    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: https://github.com/ethereon/caffe-tensorflow

fluid/image_classification/caffe2fluid/convert.py

@@ -4,8 +4,8 @@
 import sys
 import numpy as np
 import argparse
-from kaffe import KaffeError, print_stderr
 
+from kaffe import KaffeError, print_stderr
 from kaffe.paddle import Transformer
 
 
@@ -47,6 +47,8 @@ def convert(def_path, caffemodel_path, data_output_path, code_output_path,
     except KaffeError as err:
         fatal_error('Error encountered: {}'.format(err))
 
+    return 0
+
 
 def main():
     """ main
@@ -64,9 +66,10 @@ def main():
         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)
+    return convert(args.def_path, args.caffemodel, args.data_output_path,
+                   args.code_output_path, args.phase)
 
 
 if __name__ == '__main__':
-    main()
+    ret = main()
+    sys.exit(ret)

fluid/image_classification/caffe2fluid/examples/imagenet/README.md

@@ -0,0 +1,10 @@
+a demo to show converting caffe models 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. run the tool
+    eg: bash ./run.sh resnet50 ./models.caffe/resnet50 ./models/resnet50

fluid/image_classification/caffe2fluid/examples/imagenet/infer.py

@@ -0,0 +1,142 @@
+#!/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
+import paddle.v2 as paddle
+import paddle.v2.fluid as 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.basename(net_file).rstrip('.py')
+    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]' % (module_name))
+        print(e)
+        return None
+
+    input_name = 'data'
+    input_shape = MyNet.input_shapes()[input_name]
+    images = fluid.layers.data(name='image', shape=input_shape, dtype='float32')
+    #label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+
+    net = MyNet({input_name: images})
+    input_shape = MyNet.input_shapes()[input_name]
+    return net, input_shape
+
+
+def dump_results(results, names, root):
+    if os.path.exists(root) is False:
+        os.path.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 infer(net_file, net_name, model_file, imgfile, debug=False):
+    """ do inference using a model which consist 'xxx.py' and 'xxx.npy'
+    """
+    #1, build model
+    net, input_shape = build_model(net_file, net_name)
+    prediction = net.get_output()
+
+    #2, load weights for this model
+    place = fluid.CPUPlace()
+    exe = fluid.Executor(place)
+    startup_program = fluid.default_startup_program()
+    exe.run(startup_program)
+
+    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()
+
+    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)
+
+    np_images = load_data(imgfile, input_shape)
+    results = exe.run(program=test_program,
+                      feed={'image': np_images},
+                      fetch_list=fetch_list_var)
+
+    if debug is True:
+        dump_path = 'results.layers'
+        dump_results(results, fetch_list_name, dump_path)
+        print('all results dumped to [%s]' % (dump_path))
+    else:
+        result = results[0]
+        print('predicted class:', np.argmax(result))
+
+
+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'
+    imgfile = 'data/65.jpeg'
+    net_name = 'ResNet50'
+
+    argc = len(sys.argv)
+    if argc == 5:
+        net_file = sys.argv[1]
+        weight_file = sys.argv[2]
+        imgfile = sys.argv[3]
+        net_name = sys.argv[4]
+    elif argc > 1:
+        print('usage:')
+        print('\tpython %s [net_file] [weight_file] [imgfile] [net_name]' %
+              (sys.argv[0]))
+        print('\teg:python %s %s %s %s %s' % (sys.argv[0], net_file,
+                                              weight_file, imgfile, net_name))
+        sys.exit(1)
+
+    infer(net_file, net_name, weight_file, imgfile)

fluid/image_classification/caffe2fluid/examples/imagenet/run.sh

@@ -0,0 +1,72 @@
+#!/bin/bash
+
+#function:
+#   a tool used to:
+#       1, convert a caffe model
+#       2, do inference using this model
+#
+#usage:
+#   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"
+    net_name=`grep "name" $proto_file | head -n1 | perl -ne 'if(/\"([^\"]+)\"/){ print $1."\n";}'`
+    $PYTHON ./infer.py $net_file $weight_file $imgfile $net_name
+    ret=$?
+fi
+exit $ret

fluid/image_classification/caffe2fluid/examples/mnist/README.md

@@ -0,0 +1,10 @@
+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

fluid/image_classification/caffe2fluid/tests/lenet/predict.py → fluid/image_classification/caffe2fluid/examples/mnist/evaluate.py

@@ -4,12 +4,12 @@
 #   demo to show how to use converted model using caffe2fluid
 #
 
+import sys
+import os
 import numpy as np
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
 
-from lenet import LeNet as MyNet
-
 
 def test_model(exe, test_program, fetch_list, test_reader, feeder):
     acc_set = []
@@ -24,10 +24,15 @@ def test_model(exe, test_program, fetch_list, test_reader, feeder):
     return float(acc_val)
 
 
-def main(model_path):
+def evaluate(net_file, model_file):
     """ main
     """
-    print('load fluid model in %s' % (model_path))
+    #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
 
     with_gpu = False
     paddle.init(use_gpu=with_gpu)
@@ -45,10 +50,10 @@ def main(model_path):
     exe.run(fluid.default_startup_program())
 
     #2, load weights
-    if model_path.find('.npy') > 0:
-        net.load(data_path=model_path, exe=exe, place=place)
+    if model_file.find('.npy') > 0:
+        net.load(data_path=model_file, exe=exe, place=place)
     else:
-        net.load(data_path=model_path, exe=exe)
+        net.load(data_path=model_file, exe=exe)
 
     #3, test this model
     test_program = fluid.default_main_program().clone()
@@ -65,10 +70,17 @@ def main(model_path):
 
 
 if __name__ == "__main__":
-    import sys
-    if len(sys.argv) == 2:
-        fluid_model_path = sys.argv[1]
-    else:
-        fluid_model_path = './model.fluid'
-
-    main(fluid_model_path)
+    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)