Merge pull request #79 from Microsoft/caffe_emitter

Caffe emitter

mmdnn/conversion/caffe/caffe_emitter.py

@@ -41,7 +41,6 @@ def header_code(self):
 from caffe import to_proto
 from six import text_type as _text_type
 
-n = caffe.NetSpec()
 
 __weights_dict = dict()
 
@@ -58,14 +57,15 @@ def load_weights(weight_file):
 
 
 def KitModel(weight_file = None):
-
+    n = caffe.NetSpec()
 """
 
     @property
     def end_code(self):
-        return """
+        return """    return n
+
 def make_net(prototxt):
-    KitModel()
+    n = KitModel()
     with open(prototxt, 'w') as fpb:
         print(n.to_proto(), file=fpb)
 
@@ -88,6 +88,7 @@ def gen_weight(weight_file, model, prototxt):
         if 'bias' in __weights_dict[key]:
             net.params[key][1].data.flat = __weights_dict[key]['bias']
     net.save(model)
+    return net
 
 
 

mmdnn/conversion/examples/caffe/imagenet_test.py

@@ -0,0 +1,77 @@
+#----------------------------------------------------------------------------------------------
+#  Copyright (c) Microsoft Corporation. All rights reserved.
+#  Licensed under the MIT License. See License.txt in the project root for license information.
+#----------------------------------------------------------------------------------------------
+
+import argparse
+import numpy as np
+import sys
+import os
+from six import text_type as _text_type
+from mmdnn.conversion.examples.imagenet_test import TestKit
+import caffe
+
+
+class TestCaffe(TestKit):
+
+    def __init__(self):
+        super(TestCaffe, self).__init__()
+
+        self.truth['caffe']['alexnet'] = [(657, 0.41121054), (744, 0.20789708), (847, 0.086725503), (821, 0.05908291), (595, 0.058017164)]
+
+        if self.args.dump:
+            self.dump_net = self.args.dump + '.prototxt'
+            self.dump_weight = self.args.dump + '.caffemodel'
+        else:
+            self.dump_net = 'tmp.prototxt'
+            self.dump_weight = 'tmp.caffemodel'
+
+        self.MainModel.make_net(self.dump_net)
+        self.MainModel.gen_weight(self.args.w, self.dump_weight, self.dump_net)
+        self.model = caffe.Net(self.dump_net, self.dump_weight, caffe.TEST)
+
+    def preprocess(self, image_path):
+        x = super(TestCaffe, self).preprocess(image_path)
+        # caffe uses NCHW
+        x = np.transpose(x, [2, 0, 1])
+        self.data = np.expand_dims(x, 0)
+
+
+    def print_result(self):
+        self.model.blobs['data'].data[...] = self.data
+        predict = self.model.forward()['prob'][0]
+        super(TestCaffe, self).print_result(predict)
+
+
+    def print_intermediate_result(self, layer_name, if_transpose = False):
+        intermediate_output = self.model.blobs[layer_name].data[0]
+        super(TestCaffe, self).print_intermediate_result(intermediate_output, if_transpose)
+
+
+    def inference(self, image_path):
+        self.preprocess(image_path)
+
+        # self.print_intermediate_result('', False)
+
+        self.print_result()
+
+        self.test_truth()
+
+        # delete tmp model files
+        if os.path.isfile(self.dump_net):
+            os.remove(self.dump_net)
+        if os.path.isfile(self.dump_weight):
+            os.remove(self.dump_weight)
+
+
+    def dump(self):
+        print ('Caffe model files are saved as [{}] and [{}], generated by [{}.py] and [{}].'.format(
+            self.dump_net, self.dump_weight, self.args.n, self.args.w))
+
+
+if __name__=='__main__':
+    tester = TestCaffe()
+    if tester.args.dump:
+        tester.dump()
+    else:
+        tester.inference(tester.args.image)

mmdnn/conversion/examples/imagenet_test.py

@@ -15,6 +15,7 @@ class TestKit(object):
     truth = {
         'caffe' : {
             'alexnet'        : [(821, 0.25088307), (657, 0.20857951), (744, 0.096812263), (595, 0.066312768), (847, 0.053720973)],
+            #'alexnet'        : [(657, 0.41121086), (744, 0.20789686), (847, 0.086725488), (821, 0.059082959), (595, 0.058017101)],
             'vgg19'          : [(21, 0.37522122), (144, 0.28500062), (23, 0.099720284), (134, 0.036305398), (22, 0.033559237)],
             'inception_v1'   : [(21, 0.93591732), (23, 0.037170019), (22, 0.014315935), (128, 0.005050648), (749, 0.001965977)],
             'resnet152'      : [(144, 0.93159181), (23, 0.033074539), (21, 0.028599562), (99, 0.001878676), (146, 0.001557963)],