resnetの中身を見るようのテストを追加

python/test/utils/test_conversion.py

@@ -20,6 +20,7 @@
 import onnx
 import numpy as np
 import pdb
+from collections import OrderedDict
 import onnx_caffe2.backend
 from nnabla.utils.converter.nnabla import NnpReader, NnpExporter
 from nnabla.utils.converter.onnx import OnnxReader, OnnxExporter, onnx_model_to_nnp_protobuf
@@ -295,3 +296,61 @@ def test_nnp_onnx_conversion_squeezenet(tmpdir, nnp_fixture):
     if show_output:
         print(c2, nnout)
     assert np.allclose(c2, nnout)
+
+def test_onnx_nnp_conversion_resnet50(tmpdir, nnp_fixture):
+    onnx_dir = TEST_DATA_DIR
+    onnx_name = "resnet50.onnx"
+    nnp_name = "resnet50.nnp"
+    out_name = "softmaxout_1"
+    exec_name = "exec_0"
+    in_name = "data_0"
+    show_onnx = False
+    show_nnp = False
+    show_output = False
+    path = os.path.join(onnx_dir, onnx_name)
+    # Process onnx with caffe2 backend
+    #ops = OrderedDict()
+    model = onnx.load(path)
+    for n in model.graph.node:
+        #ops[n.op_type] = n.op_type
+        print(n)
+    #for k in ops:
+    #    print(k)
+    #if show_onnx:
+    #    print(model)
+    #img = np.random.rand(1,3,224,224).astype(np.float32)
+    #c2out = onnx_caffe2.backend.run_model(model, [img])
+    ## Process onnx with naabla
+    #nnp = onnx_model_to_nnp_protobuf(model)
+    #assert nnp is not None
+    #assert len(nnp.other_files) == 0
+    #assert nnp.protobuf is not None
+    #if show_nnp:
+    #    print(nnp.protobuf)
+
+    #nnpex = NnpExporter(nnp, batch_size=0)
+    #nnpdir = tmpdir.mkdir("nnp")
+    #p = os.path.join(str(nnpdir), nnp_name)
+    #nnpex.export_nnp(p)
+    ## read exported nnp and run network
+    #nn_net = nnload.load([p])
+    ##pdb.set_trace()
+    ## set input data and run inference
+    #net = nn_net.executors[exec_name].network
+    #in_data = net.variables[in_name]
+    #in_data.variable_instance.d = img
+    #net = run_executor(nn_net, exec_name)
+    ##in_data = exe.variables["in_data_0"]
+    ##print(in_data.variable_instance.d)
+    #nnout = net.variables[out_name].variable_instance.d
+    ##print(nnout.variable_instance.d)
+
+    ## Print all the intermediate buffer shape in order
+    ##for k, v in net.functions.items():
+    ##    out = v.outputs[0]
+    ##    print(out.name, net.variables[out.name].variable_instance.shape)
+    ## Compare both naabla and caffe2 results
+    #c2 = c2out[out_name]
+    #if show_output:
+    #    print(c2, nnout)
+    #assert np.allclose(c2, nnout)