重命名 pt2tf 为 pt2pb

pt2pb/onnx-tensorflow/example/onnx_to_tf.py

@@ -0,0 +1,7 @@
+import onnx
+
+from onnx_tf.backend import prepare
+
+onnx_model = onnx.load("input_path")  # load onnx model
+tf_rep = prepare(onnx_model)  # prepare tf representation
+tf_rep.export_graph("output_path")  # export the model

pt2pb/onnx-tensorflow/example/relu.py

@@ -0,0 +1,11 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+from onnx_tf.backend import run_node, prepare
+from onnx import helper
+
+node_def = helper.make_node("Relu", ["X"], ["Y"])
+output = run_node(node_def, [[-0.1, 0.1]])
+print(output["Y"])

pt2pb/onnx-tensorflow/example/test_model_large_stepping.py

@@ -0,0 +1,68 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import unittest
+import numpy as np
+
+import caffe2.python.onnx.backend as c2
+import onnx
+import onnx_tf.backend as tf
+from onnx import helper
+from onnx import TensorProto
+
+
+def find_between(s, first, last):
+  try:
+    start = s.index(first) + len(first)
+    end = s.index(last, start)
+    return s[start:end]
+  except ValueError:
+    return ""
+
+
+class TestLargeModel(unittest.TestCase):
+  MODEL_PATH = "../../onnx_models/"
+
+  def test(self):
+    _model = onnx.load(self.MODEL_PATH + "shufflenet/model.onnx")
+    node_count = len(_model.graph.node)
+    more_outputs = []
+    output_to_check = []
+    for node in _model.graph.node:
+      more_outputs.append(
+          helper.make_tensor_value_info(node.output[0], TensorProto.FLOAT,
+                                        (100, 100)))
+      output_to_check.append(node.output[0])
+    _model.graph.output.extend(more_outputs)
+
+    tf_rep = tf.prepare(_model)
+    cf_rep = c2.prepare(_model)
+
+    sample = np.load(
+        self.MODEL_PATH + "shufflenet/test_data_{}.npz".format(str(1)),
+        encoding='bytes')
+    inputs = list(sample['inputs'])
+    outputs = list(sample['outputs'])
+
+    my_out = tf_rep.run(inputs)
+    cf_out = cf_rep.run(inputs)
+
+    for op in output_to_check:
+      try:
+        np.savetxt(
+            op.replace("/", "__") + ".cf", cf_out[op].flatten(), delimiter='\t')
+        np.savetxt(
+            op.replace("/", "__") + ".tf", my_out[op].flatten(), delimiter='\t')
+        np.testing.assert_allclose(my_out[op], cf_out[op], rtol=1e-2)
+        print(op, "results of this layer are correct within tolerence.")
+      except Exception as e:
+        np.set_printoptions(threshold=np.inf)
+        mismatch_percent = (find_between(str(e), "(mismatch", "%)"))
+        print(op, "mismatch with percentage {} %".format(mismatch_percent))
+
+
+if __name__ == '__main__':
+  unittest.main()
+  pass