update inputshape readme and multiple input

mmdnn/conversion/_script/convertToIR.py

@@ -5,12 +5,15 @@ from six import text_type as _text_type
 
 def _convert(args):
     if args.inputShape != None:
-        inputshape = [int(x) for x in args.inputShape]
+        inputshape = []
+        for x in args.inputShape:
+            shape = x.split(',')
+            inputshape.append([int(x) for x in shape])
     else:
         inputshape = None
     if args.srcFramework == 'caffe':
         from mmdnn.conversion.caffe.transformer import CaffeTransformer
-        transformer = CaffeTransformer(args.network, args.weights, "tensorflow", inputshape, phase = args.caffePhase)
+        transformer = CaffeTransformer(args.network, args.weights, "tensorflow", inputshape[0], phase = args.caffePhase)
         graph = transformer.transform_graph()
         data = transformer.transform_data()
 
@@ -48,25 +51,27 @@ def _convert(args):
 
         # assert args.network or args.frozen_pb
         if args.frozen_pb:
+            if args.inNodeName is None:
+                raise ValueError("Need to provide the input node of Tensorflow model.")
             from mmdnn.conversion.tensorflow.tensorflow_frozenparser import TensorflowParser2
             parser = TensorflowParser2(args.frozen_pb, inputshape, args.inNodeName, args.dstNodeName)
         else:
             from mmdnn.conversion.tensorflow.tensorflow_parser import TensorflowParser
-            if args.inNodeName and inputshape:
+            if args.inNodeName and inputshape[0]:
-                parser = TensorflowParser(args.network, args.weights, args.dstNodeName, inputshape, args.inNodeName)
+                parser = TensorflowParser(args.network, args.weights, args.dstNodeName, inputshape[0], args.inNodeName)
             else:
                 parser = TensorflowParser(args.network, args.weights, args.dstNodeName)
 
     elif args.srcFramework == 'mxnet':
         assert inputshape != None
         if args.weights == None:
-            model = (args.network, inputshape)
+            model = (args.network, inputshape[0])
         else:
             import re
             if re.search('.', args.weights):
                 args.weights = args.weights[:-7]
             prefix, epoch = args.weights.rsplit('-', 1)
-            model = (args.network, prefix, epoch, inputshape)
+            model = (args.network, prefix, epoch, inputshape[0])
 
         from mmdnn.conversion.mxnet.mxnet_parser import MXNetParser
         parser = MXNetParser(model)
@@ -79,13 +84,13 @@ def _convert(args):
     elif args.srcFramework == 'pytorch':
         assert inputshape != None
         from mmdnn.conversion.pytorch.pytorch_parser import PytorchParser
-        parser = PytorchParser(args.network, inputshape)
+        parser = PytorchParser(args.network, inputshape[0])
 
     elif args.srcFramework == 'torch' or args.srcFramework == 'torch7':
         from mmdnn.conversion.torch.torch_parser import TorchParser
         model = args.network or args.weights
         assert model != None
-        parser = TorchParser(model, inputshape)
+        parser = TorchParser(model, inputshape[0])
 
     elif args.srcFramework == 'onnx':
         from mmdnn.conversion.onnx.onnx_parser import ONNXParser
@@ -138,12 +143,14 @@ def _get_parser():
 
     parser.add_argument(
         '--inNodeName', '-inode',
+        nargs='+',
         type=_text_type,
-        default='input',
+        default=None,
         help="[Tensorflow] Input nodes' name of the graph.")
 
     parser.add_argument(
         '--dstNodeName', '-node',
+        nargs='+',
         type=_text_type,
         default=None,
         help="[Tensorflow] Output nodes' name of the graph.")
@@ -160,7 +167,7 @@ def _get_parser():
         nargs='+',
         type=_text_type,
         default=None,
-        help='[MXNet/Caffe2/Torch7] Input shape of model (channel, height, width)')
+        help='[Tensorflow/MXNet/Caffe2/Torch7] Input shape of model (channel, height, width)')
 
 
     # Caffe

mmdnn/conversion/caffe/README.md

@@ -57,6 +57,7 @@ CNTK model file is saved as [caffe_resnet152.dnn], generated by [069867aa7f674b7
 
 Then you get the CNTK original model *caffe_resnet152.dnn* converted from Caffe. Temporal files are removed automatically.
 
+If you want to assume a fixed inputshape, you can use "--inputShape 224,224,3"
 ---
 
 ## Step-by-step conversion (for debugging)

mmdnn/conversion/tensorflow/README.md

@@ -89,26 +89,48 @@ TensorBoard 0.4.0rc3 at http://kit-station:6006 (Press CTRL+C to quit)
 
 ## One-step conversion
 
-Above MMdnn@0.1.4, we provide one command to achieve the conversion
+Above MMdnn@0.1.4, we provide one command to achieve the conversion.
+For checkpoint format:
 
 ```bash
-$  mmconvert -sf tensorflow -in imagenet_inception_v1.ckpt.meta -iw inception_v1.ckpt -df cntk -om tf_inception_v1.dnn --inputShape 3 224 224 --dstNodeName MMdnn_Output
+$  mmconvert -sf tensorflow -in ./model.ckpt.meta -iw ./model.ckpt -df caffe --inputShape 224,224,3 --dstNodeName FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_128/Relu6 --inNodeName Preprocessor/sub  -om mobilenet_v1_caffe
 .
 .
 .
-Parse file [imagenet_inception_v1.ckpt.meta] with binary format successfully.
+Parse file [./model.ckpt.meta] with binary format successfully.
-Tensorflow model file [imagenet_inception_v1.ckpt.meta] loaded successfully.
+Tensorflow model file [./model.ckpt.meta] loaded successfully.
-Tensorflow checkpoint file [inception_v1.ckpt] loaded successfully. [231] variables loaded.
+Tensorflow checkpoint file [./model.ckpt] loaded successfully. [200] variables loaded.
-IR network structure is saved as [b136dfb62a964486bb2bc2e27cece6e8.json].
+IR network structure is saved as [61ecc03803a747429a9d4ff6dc346c21.json].
-IR network structure is saved as [b136dfb62a964486bb2bc2e27cece6e8.pb].
+IR network structure is saved as [61ecc03803a747429a9d4ff6dc346c21.pb].
-IR weights are saved as [b136dfb62a964486bb2bc2e27cece6e8.npy].
+IR weights are saved as [61ecc03803a747429a9d4ff6dc346c21.npy].
-Parse file [b136dfb62a964486bb2bc2e27cece6e8.pb] with binary format successfully.
+Parse file [61ecc03803a747429a9d4ff6dc346c21.pb] with binary format successfully.
-Target network code snippet is saved as [b136dfb62a964486bb2bc2e27cece6e8.py].
+Target network code snippet is saved as [61ecc03803a747429a9d4ff6dc346c21.py].
-CNTK model file is saved as [tf_inception_v1.dnn], generated by [b136dfb62a964486bb2bc2e27cece6e8.py] and [b136dfb62a964486bb2bc2e27cece6e8.npy].
+Target weights are saved as [61ecc03803a747429a9d4ff6dc346c21.npy].
+Caffe model files are saved as [mobilenet_v1_caffe.prototxt] and [mobilenet_v1_caffe.caffemodel], generated by [61ecc03803a747429a9d4ff6dc346c21.py] and [61ecc03803a747429a9d4ff6dc346c21.npy].
 
 ```
 
-Then you get the CNTK original model *tf_inception_v1.dnn* converted from Tensorflow. Temporal files are removed automatically.
+Then you get the Caffe original model *mobilenet_v1_caffe.prototxt* and *mobilenet_v1_caffe.caffemodel* converted from Tensorflow. Temporal files are removed automatically.
+
+For frozen protobuf format:
+
+
+```bash
+$  mmconvert -sf tensorflow --frozen_pb entropy.pb -df caffe --inputShape 108,140,1 --dstNodeName Dense2/fc5/BiasAdd --inNodeName X -om entropy_caffe
+.
+.
+.
+IR network structure is saved as [2217c0216dd445cca7e44255d989c6c3.json].
+IR network structure is saved as [2217c0216dd445cca7e44255d989c6c3.pb].
+IR weights are saved as [2217c0216dd445cca7e44255d989c6c3.npy].
+Parse file [2217c0216dd445cca7e44255d989c6c3.pb] with binary format successfully.
+Target network code snippet is saved as [2217c0216dd445cca7e44255d989c6c3.py].
+Target weights are saved as [2217c0216dd445cca7e44255d989c6c3.npy].
+Caffe model files are saved as [entropy_caffe.prototxt] and [entropy_caffe.caffemodel], generated by [2217c0216dd445cca7e44255d989c6c3.py] and [2217c0216dd445cca7e44255d989c6c3.npy].
+
+```
+
+if there are more than one input nodes, you can use space to seperate them, eg.(--inputShape 224,224,3 4  --inNodeName image style)
 
 ---
 
@@ -118,15 +140,16 @@ Then you get the CNTK original model *tf_inception_v1.dnn* converted from Tensor
 
 You can convert only network structure to IR for visualization or training in other frameworks.
 
-We use resnet_v2_152 model as an example.
+We use MobilenetV1 model as an example.
 
 ```bash
-$ mmtoir -f tensorflow -d resnet152 -n imagenet_resnet_v2_152.ckpt.meta --dstNodeName MMdnn_Output
 
-Parse file [imagenet_resnet_v2_152.ckpt.meta] with binary format successfully.
+$  mmtoir -f tensorflow -n ./model.ckpt.meta --dstNodeName FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_128/Relu6 --inputShape 224,224,3 --inNodeName Preprocessor/sub -d mobilenet_v1
-Tensorflow model file [imagenet_resnet_v2_152.ckpt.meta] loaded successfully.
+
-IR network structure is saved as [resnet152.json].
+Parse file [./model.ckpt.meta] with binary format successfully.
-IR network structure is saved as [resnet152.pb].
+Tensorflow model file [./model.ckpt.meta] loaded successfully.
+IR network structure is saved as [mobilenet_v1.json].
+IR network structure is saved as [mobilenet_v1.pb].
 Warning: weights are not loaded.
 ```
 
@@ -135,28 +158,29 @@ Warning: weights are not loaded.
 You can use following bash command to convert the checkpoint files to IR architecture file [*resnet152.pb*], [*resnet152.json*] and IR weights file [*resnet152.npy*]
 
 ```bash
-$ mmtoir -f tensorflow -d resnet152 -n imagenet_resnet_v2_152.ckpt.meta -w imagenet_resnet_v2_152.ckpt  --dstNodeName MMdnn_Output
+$ mmtoir -f tensorflow -d resnet152  -n ./model.ckpt.meta -w ./model.ckpt -d mobilenet_v1_tf --inputShape 224,224,3 --dstNodeName FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_128/Relu6 --inNodeName Preprocessor/sub
 
-Parse file [imagenet_resnet_v2_152.ckpt.meta] with binary format successfully.
+
-Tensorflow model file [imagenet_resnet_v2_152.ckpt.meta] loaded successfully.
+Parse file [./model.ckpt.meta] with binary format successfully.
-Tensorflow checkpoint file [imagenet_resnet_v2_152.ckpt] loaded successfully. [816] variables loaded.
+Tensorflow model file [./model.ckpt.meta] loaded successfully.
-IR network structure is saved as [resnet152.json].
+Tensorflow checkpoint file [./model.ckpt] loaded successfully. [200] variables loaded.
-IR network structure is saved as [resnet152.pb].
+IR network structure is saved as [mobilenet_v1_tf.json].
-IR weights are saved as [resnet152.npy].
+IR network structure is saved as [mobilenet_v1_tf.pb].
+IR weights are saved as [mobilenet_v1_tf.npy].
 ```
 
-### Convert frozen protobuf model file from Tensorflow to IR
+### Convert frozen protobuf model file(.pb) from Tensorflow to IR
 
 You can convert frozen protobuf model file to IR for visualization or training in other frameworks.
 
-We use resnet_v2_152 model as an example.
+We use inception_v1 as an example.
 
 ```bash
-$ mmtoir -f tensorflow --frozen_pb inception_v1_2016_08_28_frozen.pb -d inception_v1 --dstNodeName InceptionV1/Logits/Predictions/Reshape_1 --inputShape 224 224 3
+$ mmtoir -f tensorflow --frozen_pb ./tests/cache/inception_v1_2016_08_28_frozen.pb -d inception_v1_part --dstNodeName InceptionV1/Logits/Predictions/Reshape_1 --inputShape 28 28 192 --inNodeName InceptionV1/InceptionV1/MaxPool_3a_3x3/MaxPool
 
-IR network structure is saved as [inception_v1.json].
+IR network structure is saved as [inception_v1_part.json].
-IR network structure is saved as [inception_v1.pb].
+IR network structure is saved as [inception_v1_part.pb].
-IR weights are saved as [inception_v1.npy].
+IR weights are saved as [inception_v1_part.npy].
 ```
 
 ### Convert models from IR to Tensorflow code snippet

mmdnn/conversion/tensorflow/tensorflow_frozenparser.py

@@ -95,16 +95,19 @@ class TensorflowParser2(Parser):
         original_gdef = tensorflow.GraphDef()
 
         original_gdef.ParseFromString(serialized)
-        # model = original_gdef
+
+        in_type_list = {}
+        for n in original_gdef.node:
+            if n.name in in_nodes:
+                in_type_list[n.name] = n.attr['dtype'].type
+
         from tensorflow.python.tools import strip_unused_lib
         from tensorflow.python.framework import dtypes
         from tensorflow.python.platform import gfile
-        input_node_names = in_nodes.split(',')
-        output_node_names = dest_nodes.split(',')
         original_gdef = strip_unused_lib.strip_unused(
                 input_graph_def = original_gdef,
-                input_node_names = input_node_names,
+                input_node_names = in_nodes,
-                output_node_names = output_node_names,
+                output_node_names = dest_nodes,
                 placeholder_type_enum = dtypes.float32.as_datatype_enum)
         # Save it to an output file
         frozen_model_file = './frozen.pb'
@@ -118,14 +121,24 @@ class TensorflowParser2(Parser):
 
         output_shape_map = dict()
         input_shape_map = dict()
+
         with tensorflow.Graph().as_default() as g:
-            x = tensorflow.placeholder(tensorflow.float32, shape = [None] + inputshape)
+            input_map = {}
-            tensorflow.import_graph_def(model, name='', input_map={in_nodes + ':0' : x})
+            for i in range(len(inputshape)):
+                if in_type_list[in_nodes[i]] == 1:
+                    dtype = tensorflow.float32
+                elif in_type_list[in_nodes[i]] == 3:
+                    dtype = tensorflow.int32
+                x = tensorflow.placeholder(dtype, shape = [None] + inputshape[i])
+                input_map[in_nodes[i] + ':0'] = x
+
+            tensorflow.import_graph_def(model, name='', input_map=input_map)
 
         with tensorflow.Session(graph = g) as sess:
             meta_graph_def = tensorflow.train.export_meta_graph(filename='./my-model.meta')
             model = meta_graph_def.graph_def
 
+
         self.tf_graph = TensorflowGraph(model)
         self.tf_graph.build()
 
@@ -510,12 +523,11 @@ class TensorflowParser2(Parser):
         self.set_weight(source_node.name, 'mean', mean)
 
     def rename_Placeholder(self, source_node):
-        # print(source_node.layer.attr["shape"].shape)
+        # print(source_node.layer)
         if source_node.layer.attr["shape"].shape.unknown_rank == True:
             return
         IR_node = self._convert_identity_operation(source_node, new_op='DataInput')
         TensorflowParser2._copy_shape(source_node, IR_node)
-
         IR_node.attr['shape'].shape.dim[0].size = -1
         IR_node.attr['_output_shapes'].list.shape[0].dim[0].size = -1
 

mmdnn/conversion/tensorflow/tensorflow_parser.py

@@ -197,12 +197,10 @@ class TensorflowParser(Parser):
             from tensorflow.python.tools import strip_unused_lib
             from tensorflow.python.framework import dtypes
             from tensorflow.python.platform import gfile
-            input_node_names = in_nodes.split(',')
-            output_node_names = dest_nodes.split(',')
             model = strip_unused_lib.strip_unused(
                     input_graph_def = model,
-                    input_node_names = input_node_names,
+                    input_node_names = in_nodes,
-                    output_node_names = output_node_names,
+                    output_node_names = dest_nodes,
                     placeholder_type_enum = dtypes.float32.as_datatype_enum)
 
             input_list = [None]
@@ -212,7 +210,7 @@ class TensorflowParser(Parser):
             # Build network graph
             self.tf_graph = TensorflowGraph(model)
             for node in self.tf_graph.model.node:
-                if node.name in input_node_names:
+                if node.name in in_nodes:
                     node.attr['shape'].list.shape.extend([tensor_input.as_proto()])
                     node.attr['_output_shapes'].list.shape.pop()  #unknown_rank pop
                     node.attr['_output_shapes'].list.shape.extend([tensor_input.as_proto()])

tests/test_conversion_imagenet.py

@@ -131,7 +131,7 @@ class TestModels(CorrectnessTest):
         # original to IR
         IR_file = TestModels.tmpdir + 'tensorflow_frozen_' + architecture_name + "_converted"
         parser = TensorflowParser2(
-            TestModels.cachedir + para[0], para[1], para[2].split(':')[0], para[3].split(':')[0])
+            TestModels.cachedir + para[0], [para[1]], [para[2].split(':')[0]], [para[3].split(':')[0]])
         parser.run(IR_file)
         del parser
         del TensorflowParser2