diff --git a/fluid/image_classification/caffe2fluid/README.md b/fluid/image_classification/caffe2fluid/README.md
index 6aba34b9cafbd87b3474575fcbcee65819769c2f..64f6b9cf901337fced0ac4e7eb625012dfcf6d2c 100644
--- a/fluid/image_classification/caffe2fluid/README.md
+++ b/fluid/image_classification/caffe2fluid/README.md
@@ -2,20 +2,31 @@
This tool is used to convert a Caffe model to Fluid model
### Howto
-1, Prepare caffepb.py in ./proto if your python has no 'pycaffe' module, two options provided here:
+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
- 1) generate it from caffe.proto using protoc
- bash ./proto/compile.sh
+- download one from github directly
+ cd proto/ && wget https://github.com/ethereon/caffe-tensorflow/blob/master/kaffe/caffe/caffepb.py
+
- 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 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
+
-2, Convert the caffe model using 'convert.py' which will generate a python script and a weight(in .npy) file
+- save weights as fluid model file
+ python alexnet.py alexnet.npy ./fluid_model
+
-3, Use the converted model to predict
-
- see more detail info in 'examples/xxx'
+3. Use the converted model to infer
+- see more details in '*examples/imagenet/run.sh*'
+4. compare the inference results with caffe
+- see more details in '*examples/imagenet/diff.sh*'
### Tested models
- Lenet
@@ -33,4 +44,4 @@ This tool is used to convert a Caffe model to Fluid model
[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
+Some of this code come from here: [caffe-tensorflow](https://github.com/ethereon/caffe-tensorflow)
diff --git a/fluid/image_classification/caffe2fluid/examples/imagenet/README.md b/fluid/image_classification/caffe2fluid/examples/imagenet/README.md
index b82050859239be8804ddec8e2054edc38c4ac052..b9cf1941d29428c84c34df2a9ec00d7ae8e79014 100644
--- a/fluid/image_classification/caffe2fluid/examples/imagenet/README.md
+++ b/fluid/image_classification/caffe2fluid/examples/imagenet/README.md
@@ -1,10 +1,37 @@
-a demo to show converting caffe models on 'imagenet' using caffe2fluid
+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
+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_model
+
+
+4. Do inference
+ python infer.py infer ./fluid_mode data/65.jpeg
+
+
+5. convert model and do inference together
+ bash ./run.sh alexnet ./models.caffe/alexnet ./models/alexnet
+
+ The Caffe model is stored in './models.caffe/alexnet/alexnet.prototxt|caffemodel'
+ and the Fluid model will be save in './models/alexnet/alexnet.py|npy'
+
+6. test the difference with caffe's results(need pycaffe installed)
+ bash ./diff.sh resnet
+
+Make sure your caffemodel stored in './models.caffe/resnet'.
+The results will be stored in './results/resnet.paddle|caffe'
diff --git a/fluid/image_classification/caffe2fluid/examples/imagenet/diff.sh b/fluid/image_classification/caffe2fluid/examples/imagenet/diff.sh
old mode 100644
new mode 100755
diff --git a/fluid/image_classification/caffe2fluid/examples/imagenet/infer.py b/fluid/image_classification/caffe2fluid/examples/imagenet/infer.py
index bb75caa9e7364465042c5c88f471e8f6f5137237..099c0abb2e7773ca7ba5ccd8b90726d11613cc60 100644
--- a/fluid/image_classification/caffe2fluid/examples/imagenet/infer.py
+++ b/fluid/image_classification/caffe2fluid/examples/imagenet/infer.py
@@ -59,12 +59,12 @@ def build_model(net_file, net_name):
inputs_dict = MyNet.input_shapes()
input_name = inputs_dict.keys()[0]
input_shape = inputs_dict[input_name]
- images = fluid.layers.data(name='image', shape=input_shape, dtype='float32')
+ 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})
- input_shape = MyNet.input_shapes()[input_name]
- return net, input_shape
+ return net, inputs_dict
def dump_results(results, names, root):
@@ -78,26 +78,27 @@ def dump_results(results, names, root):
np.save(filename + '.npy', res)
-def infer(net_file, net_name, model_file, imgfile, debug=True):
- """ do inference using a model which consist 'xxx.py' and 'xxx.npy'
+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_shape = build_model(net_file, net_name)
+ 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
- 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)
+ #place = fluid.CPUPlace()
+ if net_weight.find('.npy') > 0:
+ net.load(data_path=net_weight, exe=exe, place=place)
else:
- net.load(data_path=model_file, exe=exe)
+ raise ValueError('not found weight file')
#3, test this model
test_program = fluid.default_main_program().clone()
@@ -111,10 +112,75 @@ def infer(net_file, net_name, model_file, imgfile, debug=True):
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
+ }
+
+
+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:
+ pass
+ 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']
+
+ input_name = feed_names[0]
+ input_shape = feed_shapes[0]
+
np_images = load_data(imgfile, input_shape)
- results = exe.run(program=test_program,
- feed={'image': np_images},
- fetch_list=fetch_list_var)
+ results = exe.run(program=program,
+ feed={input_name: np_images},
+ fetch_list=fetch_targets)
if debug is True:
dump_path = 'results.paddle'
@@ -122,7 +188,7 @@ def infer(net_file, net_name, model_file, imgfile, debug=True):
print('all result of layers dumped to [%s]' % (dump_path))
else:
result = results[0]
- print('predicted class:', np.argmax(result))
+ print('succeed infer with results[class:%d]' % (np.argmax(result)))
return 0
@@ -167,9 +233,12 @@ if __name__ == "__main__":
weight_file = 'models/resnet50/resnet50.npy'
datafile = 'data/65.jpeg'
net_name = 'ResNet50'
+ model_file = 'models/resnet50/fluid'
- argc = len(sys.argv)
- if sys.argv[1] == 'caffe':
+ 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]' %
@@ -178,18 +247,34 @@ if __name__ == "__main__":
prototxt = sys.argv[2]
caffemodel = sys.argv[3]
datafile = sys.argv[4]
- sys.exit(caffe_infer(prototxt, caffemodel, datafile))
- elif argc == 5:
- net_file = sys.argv[1]
- weight_file = sys.argv[2]
+ 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]
- net_name = sys.argv[4]
- elif argc > 1:
+ 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 dump %s %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('\tpython %s [net_file] [weight_file] [datafile] [net_name]' %
- (sys.argv[0]))
- print('\teg:python %s %s %s %s %s' % (sys.argv[0], net_file,
- weight_file, datafile, net_name))
+ 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)
- infer(net_file, net_name, weight_file, datafile)
+ sys.exit(ret)
diff --git a/fluid/image_classification/caffe2fluid/examples/imagenet/run.sh b/fluid/image_classification/caffe2fluid/examples/imagenet/run.sh
old mode 100644
new mode 100755
index ff3cc4ac44a8ccaeb0b33f1bcdbc46886fb7d7e9..2f0a0ba01d6161c031cbc6b452b826cbbb6002b3
--- a/fluid/image_classification/caffe2fluid/examples/imagenet/run.sh
+++ b/fluid/image_classification/caffe2fluid/examples/imagenet/run.sh
@@ -71,7 +71,7 @@ if [[ -z $only_convert ]];then
if [[ -z $net_name ]];then
net_name="MyNet"
fi
- $PYTHON ./infer.py $net_file $weight_file $imgfile $net_name
+ $PYTHON ./infer.py dump $net_file $weight_file $imgfile $net_name
ret=$?
fi
exit $ret
diff --git a/fluid/image_classification/caffe2fluid/examples/mnist/run.sh b/fluid/image_classification/caffe2fluid/examples/mnist/run.sh
old mode 100644
new mode 100755
diff --git a/fluid/image_classification/caffe2fluid/kaffe/paddle/transformer.py b/fluid/image_classification/caffe2fluid/kaffe/paddle/transformer.py
index 3697529971fa6ca01d1703375243d16f0a0c1edd..20155e992f1bb804b823880a20600d31675f029a 100644
--- a/fluid/image_classification/caffe2fluid/kaffe/paddle/transformer.py
+++ b/fluid/image_classification/caffe2fluid/kaffe/paddle/transformer.py
@@ -216,7 +216,10 @@ class TensorFlowEmitter(object):
def emit_convert_def(self, input_nodes):
codes = []
inputs = {}
+ #codes.append('shapes = cls.input_shapes()')
codes.append('shapes = cls.input_shapes()')
+ codes.append('input_name = shapes.keys()[0]')
+ codes.append('input_shape = shapes[input_name]')
for n in input_nodes:
name = n.name
layer_var = name + '_layer'
@@ -235,8 +238,14 @@ class TensorFlowEmitter(object):
codes.append("exe = fluid.Executor(place)")
codes.append("exe.run(fluid.default_startup_program())")
codes.append("net.load(data_path=npy_model, exe=exe, place=place)")
+ codes.append("output_vars = [net.get_output()]")
+ codes.append("fluid.io.save_inference_model(" \
+ "fluid_path, [input_name],output_vars," \
+ "exe, main_program=None, model_filename='model'," \
+ "params_filename='params')")
codes.append(
- "fluid.io.save_persistables(executor=exe, dirname=fluid_path)")
+ "print('save fluid model as [model] and [params] in directory [%s]' % (fluid_path))"
+ )
self.outdent()
func_def = self.statement('@classmethod')
@@ -254,8 +263,17 @@ class TensorFlowEmitter(object):
self.prefix = ''
main_def = self.statement('if __name__ == "__main__":')
self.indent()
- main_def += self.statement("#usage: python xxxnet.py xxx.npy ./model\n")
+ main_def += self.statement(
+ "#usage: save as an inference model for online service\n")
main_def += self.statement("import sys")
+ main_def += self.statement("if len(sys.argv) != 3:")
+ self.indent()
+ main_def += self.statement("print('usage:')")
+ main_def += self.statement(
+ "print('\tpython %s [xxxnet.npy] [save_dir]' % (sys.argv[0]))")
+ main_def += self.statement("exit(1)")
+
+ self.outdent()
main_def += self.statement("npy_weight = sys.argv[1]")
main_def += self.statement("fluid_model = sys.argv[2]")
main_def += self.statement("%s.convert(npy_weight, fluid_model)" %
diff --git a/fluid/image_classification/caffe2fluid/proto/compile.sh b/fluid/image_classification/caffe2fluid/proto/compile.sh
old mode 100644
new mode 100755