From 11050fdae41c9aceca7254c8f36409d323b341b1 Mon Sep 17 00:00:00 2001
From: Alexander Pivovarov <apivovarov@gmail.com>
Date: Wed, 10 Oct 2018 17:28:23 -0700
Subject: [PATCH] Use new onnx API to load model from file (#1874)
---
nnvm/tests/python/frontend/onnx/test_forward.py | 2 +-
nnvm/tests/python/frontend/onnx/test_graph.py | 2 +-
tutorials/nnvm/from_onnx.py | 2 +-
3 files changed, 3 insertions(+), 3 deletions(-)
diff --git a/nnvm/tests/python/frontend/onnx/test_forward.py b/nnvm/tests/python/frontend/onnx/test_forward.py
index 187e6c175..7ca520a88 100644
--- a/nnvm/tests/python/frontend/onnx/test_forward.py
+++ b/nnvm/tests/python/frontend/onnx/test_forward.py
@@ -66,7 +66,7 @@ def get_caffe2_output(model, x, dtype='float32'):
def verify_onnx_forward_impl(graph_file, data_shape, out_shape):
dtype = 'float32'
x = np.random.uniform(size=data_shape)
- model = onnx.load(graph_file)
+ model = onnx.load_model(graph_file)
c2_out = get_caffe2_output(model, x, dtype)
for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, x, target, ctx, out_shape, dtype)
diff --git a/nnvm/tests/python/frontend/onnx/test_graph.py b/nnvm/tests/python/frontend/onnx/test_graph.py
index 0aad9d22f..b3961c1a3 100755
--- a/nnvm/tests/python/frontend/onnx/test_graph.py
+++ b/nnvm/tests/python/frontend/onnx/test_graph.py
@@ -6,7 +6,7 @@ from model_zoo import super_resolution, super_resolution_sym
from model_zoo import squeezenet as squeezenet
def compare_graph(onnx_file, nnvm_sym, ishape):
- onnx_model = onnx.load(onnx_file)
+ onnx_model = onnx.load_model(onnx_file)
onnx_sym, params = nnvm.frontend.from_onnx(onnx_model)
g1 = nnvm.graph.create(onnx_sym)
g2 = nnvm.graph.create(nnvm_sym)
diff --git a/tutorials/nnvm/from_onnx.py b/tutorials/nnvm/from_onnx.py
index df8dee827..0fdef8afa 100644
--- a/tutorials/nnvm/from_onnx.py
+++ b/tutorials/nnvm/from_onnx.py
@@ -46,7 +46,7 @@ model_url = ''.join(['https://gist.github.com/zhreshold/',
'super_resolution_0.2.onnx'])
download(model_url, 'super_resolution.onnx', True)
# now you have super_resolution.onnx on disk
-onnx_model = onnx.load('super_resolution.onnx')
+onnx_model = onnx.load_model('super_resolution.onnx')
# we can load the graph as NNVM compatible model
sym, params = nnvm.frontend.from_onnx(onnx_model)
--
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