Fix issue with model esrgan-tf2_1

examples/benchmark_tfmodel_ort.py

@@ -0,0 +1,47 @@
+"""
+The following code compares the speed of tensorflow against onnxruntime
+with a model downloaded from Tensorflow Hub.
+"""
+import time
+import numpy
+from tqdm import tqdm
+import tensorflow_hub as hub
+import onnxruntime as ort
+
+
+def generate_random_images(shape=(100, 100), n=10):
+    imgs = []
+    for i in range(n):
+        sh = (1,) + shape + (3,)
+        img = numpy.clip(numpy.abs(numpy.random.randn(*sh)), 0, 1) * 255
+        img = img.astype(numpy.float32)
+        imgs.append(img)
+    return imgs
+
+
+def measure_time(fct, imgs):
+    results = []
+    times = []
+    for img in tqdm(imgs):
+        begin = time.perf_counter()
+        result = fct(img)
+        end = time.perf_counter()
+        results.append(result)
+        times.append(end - begin)
+    return results, times
+
+
+imgs = generate_random_images()
+
+# Download model from https://tfhub.dev/captain-pool/esrgan-tf2/1
+# python -m tf2onnx.convert --saved-model esrgan --output "esrgan-tf2.onnx" --opset 12
+ort = ort.InferenceSession('esrgan-tf2.onnx')
+fct_ort = lambda img: ort.run(None, {'input_0:0': img})
+results_ort, duration_ort = measure_time(fct_ort, imgs)
+print(len(imgs), duration_ort)
+
+model = hub.load("https://tfhub.dev/captain-pool/esrgan-tf2/1")
+results_tf, duration_tf = measure_time(model, imgs)
+print(len(imgs), duration_tf)
+
+print("ratio ORT / TF", sum(duration_ort) / sum(duration_tf))

tests/run_pretrained_models.py

@@ -246,7 +246,13 @@ def run_test(self, name, backend="caffe2", onnx_file=None, opset=None, extra_ops
         if self.model_type in ["checkpoint"]:
             graph_def, input_names, outputs = tf_loader.from_checkpoint(model_path, input_names, outputs)
         elif self.model_type in ["saved_model"]:
-            graph_def, input_names, outputs = tf_loader.from_saved_model(model_path, input_names, outputs, self.tag)
+            try:
+                res = tf_loader.from_saved_model(model_path, input_names, outputs, self.tag)
+            except OSError:
+                model_path = dir_name
+                logger.info("Load model(2) from %r", model_path)
+                res = tf_loader.from_saved_model(model_path, input_names, outputs, self.tag)
+            graph_def, input_names, outputs = res[:3]
         elif self.model_type in ["keras"]:
             graph_def, input_names, outputs = tf_loader.from_keras(model_path, input_names, outputs)
         else:

tests/run_pretrained_models.yaml

@@ -115,6 +115,24 @@ benchtf-gru:
 ##
 ## standard image nets
 ##
+
+esrgan-tf2:
+  # url: https://tfhub.dev/captain-pool/esrgan-tf2/1/esrgan-tf2_1.tar.gz
+  url: https://github.com/captain-pool/GSOC/releases/download/1.0.0/esrgan.tar.gz
+  model: ersgan
+  model_type: saved_model
+  input_get: get_beach
+  opset_constraints:
+    "onnx":
+      "min": 10
+  inputs:
+    "input_0:0": [1, 50, 50, 3]
+  outputs:
+    - Identity:0
+  rtol: 0.02
+  atol: 0.0005
+  tf_min_version: 2.1
+
 inception_v3_slim:
   url: https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz
   model: inception_v3_2016_08_28_frozen.pb

tf2onnx/optimizer/transpose_optimizer.py

@@ -648,7 +648,10 @@ def _reducemean_handler(self, trans, node):
     def _slice_handler(self, trans, node):
         axes = None
         if self._g.opset < 10:
-            axes = node.get_attr("axes").ints
+            axes_values = node.get_attr("axes")
+            if not axes_values:
+                return False
+            axes = axes_values.ints
             if axes == [0, 1, 2, 3]:
                 node.set_attr("axes", NCHW_TO_NHWC)
                 return self._switch_transpose_and_node(node, trans)

tf2onnx/tf_loader.py

@@ -168,7 +168,16 @@ def from_graphdef(model_path, input_names, output_names):
     with tf_session() as sess:
         graph_def = tf_graphdef()
         with tf_gfile.GFile(model_path, 'rb') as f:
-            graph_def.ParseFromString(f.read())
+            try:
+                content = f.read()
+            except Exception as e:
+                raise OSError(
+                    "Unable to load file '{}'.".format(model_path)) from e
+            try:
+                graph_def.ParseFromString(content)
+            except Exception as e:
+                raise RuntimeError(
+                    "Unable to parse file '{}'.".format(model_path)) from e
             tf.import_graph_def(graph_def, name='')
         input_names = inputs_without_resource(sess, input_names)
         frozen_graph = freeze_session(sess, input_names=input_names, output_names=output_names)