fix scripts (#1626)

Signed-off-by: xavier dupré <[email protected]>

Co-authored-by: xavier dupré <[email protected]>

Author: xadupre

tests/tfhub/_tools.py

@@ -301,7 +301,7 @@ def benchmark(url, dest, onnx_name, opset, imgs, verbose=True, threshold=1e-3,
             "Unable to check discrepencies for res=%r." % res) from e
     except AssertionError as e:
         output_names = [o.name for o in ort.get_outputs()]
-        res = ort.run(None, imgs[0])
+        res = fct_ort(imgs[0])
         for i, r in enumerate(res):
             print("ORT %d: %s: %r: %r" % (i, output_names[i], r.dtype, r.shape))
         raise e

tests/tfhub/tfhub_resnet_v1_101.py

@@ -10,7 +10,7 @@ def main(opset=13):
     name = "resnet_v1_101"
     onnx_name = os.path.join(dest, "%s-%d.onnx" % (name, opset))
 
-    imgs = generate_random_images(shape=(1, 224, 224, 3))
+    imgs = generate_random_images(shape=(1, 224, 224, 3), scale=1.)
 
     benchmark(url, dest, onnx_name, opset, imgs)
 

tests/tfhub/tfhub_resnet_v1_101_keras.py

@@ -0,0 +1,38 @@
+# SPDX-License-Identifier: Apache-2.0
+import os
+import numpy
+import onnxruntime as ort
+import tensorflow as tf
+import tensorflow_hub as hub
+import tf2onnx
+from _tools import generate_random_images, check_discrepencies
+
+imgs = generate_random_images(shape=(1, 224, 224, 3), scale=1.)
+
+model = tf.keras.Sequential([
+    hub.KerasLayer("https://tfhub.dev/google/imagenet/resnet_v1_101/feature_vector/5",
+                   trainable=False)])
+model.build([None, 224, 224, 3])
+
+expected_output = model(imgs[0])
+
+dest = "tf-resnet_v1_101"
+if not os.path.exists(dest):
+    os.makedirs(dest)
+dest_name = os.path.join(dest, "resnet_v1_101-13-keras.onnx")
+if not os.path.exists(dest_name):
+    tf2onnx.convert.from_keras(model, opset=13, output_path=dest_name)
+
+sess = ort.InferenceSession(dest_name)
+print('inputs', [_.name for _ in sess.get_inputs()])
+ort_output = sess.run(None, {"keras_layer_input": imgs[0]})
+
+print("Actual")
+print(ort_output)
+print("Expected")
+print(expected_output)
+
+diff = expected_output.numpy() - ort_output[0]
+max_diff = numpy.abs(diff).max()
+rel_diff = (numpy.abs(diff) / (expected_output.numpy() + 1e-5)).max()
+print(max_diff, rel_diff, [ort_output[0].min(), ort_output[0].max()])

tests/tfhub/tfhub_resnet_v2_101.py

@@ -10,7 +10,7 @@ def main(opset=13):
     name = "resnet_v2_101"
     onnx_name = os.path.join(dest, "%s-%d.onnx" % (name, opset))
 
-    imgs = generate_random_images(shape=(1, 224, 224, 3))
+    imgs = generate_random_images(shape=(1, 224, 224, 3), scale=1.)
 
     benchmark(url, dest, onnx_name, opset, imgs)
 

tests/tfhub/tfhub_resnet_v2_101_classification.py

@@ -10,7 +10,7 @@ def main(opset=13):
     name = "resnet_v2_101_classification"
     onnx_name = os.path.join(dest, "%s-%d.onnx" % (name, opset))
 
-    imgs = generate_random_images(shape=(1, 224, 224, 3))
+    imgs = generate_random_images(shape=(1, 224, 224, 3), scale=1.)
 
     benchmark(url, dest, onnx_name, opset, imgs)