Feature/caffe importer improvements (#3397)

* add mean image to ndarray converter

* special handling for input layer type

* added note about inconsistency of pooling with Caffe

* add support for TanH and Sigmoid activations

tools/caffe_converter/README.md

@@ -35,3 +35,6 @@ For example: `python convert_model.py VGG_ILSVRC_16_layers_deploy.prototxt VGG_I
 * We have verified the results of VGG_16/VGG_19 model and BVLC_googlenet results from Caffe model zoo.
 * The tool only supports single input and single output network.
 * The tool can only work with the L2LayerParameter in Caffe.
+* Caffe uses a convention for multi-strided pooling output shape inconsistent with MXNet
+    * This importer doesn't handle this problem properly yet
+    * And example of this failure is importing bvlc_Googlenet. The user needs to add padding to stride-2 pooling to make this work right now.

tools/caffe_converter/convert_symbol.py

@@ -77,6 +77,12 @@ def proto2script(proto_file):
         input_dim = proto.input_dim
     elif len(proto.input_shape) > 0: 
         input_dim = proto.input_shape[0].dim
+    elif (layer[0].type == "Input"):
+        input_dim = layer[0].input_param.shape._values[0].dim
+        layer.pop(0)
+    else:
+        raise Exception('Invalid proto file.')   
+
     # We assume the first bottom blob of first layer is the output from data layer
     input_name = layer[0].bottom[0]
     output_name = ""
@@ -116,6 +122,14 @@ def proto2script(proto_file):
             type_string = 'mx.symbol.Activation'
             param_string = "act_type='relu'"
             need_flatten[name] = need_flatten[mapping[layer[i].bottom[0]]]
+        if layer[i].type == 'TanH' or layer[i].type == 23:
+            type_string = 'mx.symbol.Activation'
+            param_string = "act_type='tanh'"
+            need_flatten[name] = need_flatten[mapping[layer[i].bottom[0]]]
+        if layer[i].type == 'Sigmoid' or layer[i].type == 19:
+            type_string = 'mx.symbol.Activation'
+            param_string = "act_type='sigmoid'"
+            need_flatten[name] = need_flatten[mapping[layer[i].bottom[0]]]
         if layer[i].type == 'LRN' or layer[i].type == 15:
             type_string = 'mx.symbol.LRN'
             param = layer[i].lrn_param

tools/caffe_converter/mean_image.py

@@ -0,0 +1,48 @@
+import mxnet as mx
+import numpy as np
+import argparse
+
+caffe_flag = True
+try:
+    import caffe
+    from caffe.proto import caffe_pb2
+except ImportError:
+    caffe_flag = False
+    import caffe_parse.caffe_pb2
+
+def protoBlobFileToND(protofile):
+    data = ''
+    file = open(protofile, "r")
+    if not file:
+        raise self.ProcessException("ERROR (" + protofile + ")!")
+    data = file.read()
+    file.close()
+
+    if caffe_flag:
+        mean_blob = caffe.proto.caffe_pb2.BlobProto()
+    else:
+        mean_blob = caffe_parse.caffe_pb2.BlobProto()
+
+    mean_blob.ParseFromString(data)
+    img_mean_np = np.array(mean_blob.data)
+    img_mean_np = img_mean_np.reshape(
+        mean_blob.channels, mean_blob.height, mean_blob.width
+    )
+    # swap channels from Caffe BGR to RGB
+    img_mean_np2 = img_mean_np
+    img_mean_np[0] = img_mean_np2[2]
+    img_mean_np[2] = img_mean_np2[0]
+    return mx.nd.array(img_mean_np)
+
+def main():
+    parser = argparse.ArgumentParser(description='Caffe prototxt to mxnet model parameter converter.\
+                    Note that only basic functions are implemented. You are welcomed to contribute to this file.')
+    parser.add_argument('mean_image_proto', help='The protobuf file in Caffe format')
+    parser.add_argument('save_name', help='The name of the output file prefix')
+    args = parser.parse_args()
+    nd = protoBlobFileToND(args.mean_image_proto)
+    mx.nd.save(args.save_name + ".nd", {"mean_image": nd})
+
+
+if __name__ == '__main__':
+    main()