Update CoreML_Converter_SSDMobileNetV1_From_Saved_Model.ipynb

d22c2273 · Ufuk Kaan Tugay · 9aeaac7b · d22c2273
Commit d22c2273 authored 3 years ago by Ufuk Kaan Tugay
--- a/CoreMLConverters/CoreML_Converter_SSDMobileNetV1_From_Saved_Model.ipynb
+++ b/CoreMLConverters/CoreML_Converter_SSDMobileNetV1_From_Saved_Model.ipynb
@@ -37,8 +37,7 @@
        "\n",
        "path = \"/content/gdrive/My Drive/V1Mobilenet/saved_model\"\n",
        "\n",
-        "# Load the protobuf file from the disk and parse it to retrieve the\n",
-        "# graph_def\n",
+        
        "with tf.io.gfile.GFile(path, \"rb\") as f:\n",
        "    graph_def = tf.compat.v1.GraphDef()\n",
        "    graph_def.ParseFromString(f.read())\n",
@@ -49,7 +48,6 @@
        "\n",
        "ops = graph.get_operations()\n",
        "N = len(ops)\n",
-        "# print all the placeholder ops, these would be the inputs\n",
        "print(\"Inputs ops: \")\n",
        "for op in ops:\n",
        "    if op.type == \"Placeholder\":\n",
@@ -57,9 +55,6 @@
        "              format(op.name, op.outputs[0].get_shape()))\n",
        "\n",
        "\n",
-        "# print all the tensors that are the first output of an op\n",
-        "# and do not feed into any other op\n",
-        "# these are prospective outputs\n",
        "print(\"\\nProspective output tensor(s): \", )\n",
        "sink_ops = []\n",
        "input_tensors = set()\n",
@@ -86,7 +81,7 @@
        "mlmodel = ct.convert(graph,\n",
        "                     inputs=[ct.TensorType(shape=x.shape)])\n",
        "\n",
-        "# Core ML model prediction\n",
+
        "coreml_out_dict = mlmodel.predict({\"input\" : x}, useCPUOnly=True)\n",
        "coreml_out = list(coreml_out_dict.values())[0]\n",
        "np.testing.assert_allclose(tf_out, coreml_out, rtol=1e-3, atol=1e-2)"
@@ -160,4 +155,4 @@
      ]
    }
  ]
-}
\ No newline at end of file
+}
 %% Cell type:code id: tags:

 ``` 
 !pip install tfcoreml
 !pip install coremltools
 !pip install tensorflow==1.15.0
 import tfcoreml
 import coremltools as ct
 import tensorflow as tf
 from google.colab import drive
 drive.mount('/content/gdrive')

 path = "/content/gdrive/My Drive/V1Mobilenet/saved_model"

-# Load the protobuf file from the disk and parse it to retrieve the
-# graph_def
 with tf.io.gfile.GFile(path, "rb") as f:
    graph_def = tf.compat.v1.GraphDef()
    graph_def.ParseFromString(f.read())

 # Import the graph_def into a new Graph
 with tf.Graph().as_default() as graph:
    tf.import_graph_def(graph_def, name="")

 ops = graph.get_operations()
 N = len(ops)
-# print all the placeholder ops, these would be the inputs
 print("Inputs ops: ")
 for op in ops:
    if op.type == "Placeholder":
        print("op name: {}, output shape : {}".
              format(op.name, op.outputs[0].get_shape()))


-# print all the tensors that are the first output of an op
-# and do not feed into any other op
-# these are prospective outputs
 print("\nProspective output tensor(s): ", )
 sink_ops = []
 input_tensors = set()
 for op in ops:
    for x in op.inputs:
        if x.name not in input_tensors:
            input_tensors.add(x.name)
 for op in ops:
    if len(op.outputs) > 0:
        x = op.outputs[0]
        if x.name not in input_tensors:
            print("tensor name: {}, tensor shape : {}, parent op type: {}"
                  .format(x.name, x.get_shape(), op.type))

 import numpy as np

 x = np.random.rand(1, 224, 224, 3)

 with tf.Session(graph = graph) as sess:
    tf_out = sess.run('MobilenetV2/Predictions/Reshape_1:0',
                      feed_dict={'input:0': x})


 mlmodel = ct.convert(graph,
                     inputs=[ct.TensorType(shape=x.shape)])

-# Core ML model prediction
 coreml_out_dict = mlmodel.predict({"input" : x}, useCPUOnly=True)
 coreml_out = list(coreml_out_dict.values())[0]
 np.testing.assert_allclose(tf_out, coreml_out, rtol=1e-3, atol=1e-2)
 ```

 %% Output

    Requirement already satisfied: tfcoreml in /usr/local/lib/python3.7/dist-packages (2.0)
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    [31mERROR: Operation cancelled by user[0m
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    Mounted at /content/gdrive

    ---------------------------------------------------------------------------
    InvalidArgumentError                      Traceback (most recent call last)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/importer.py in import_graph_def(graph_def, input_map, return_elements, name, op_dict, producer_op_list)
    InvalidArgumentError: NodeDef mentions attr 'incompatible_shape_error' not in Op<name=Equal; signature=x:T, y:T -> z:bool; attr=T:type,allowed=[DT_BFLOAT16, DT_HALF, DT_FLOAT, DT_DOUBLE, DT_UINT8, ..., DT_QINT8, DT_QINT32, DT_STRING, DT_BOOL, DT_COMPLEX128]; is_commutative=true>; NodeDef: {{node MultipleGridAnchorGenerator/assert_equal_1/Equal}}. (Check whether your GraphDef-interpreting binary is up to date with your GraphDef-generating binary.).

 During handling of the above exception, another exception occurred:
    ValueError                                Traceback (most recent call last)
    <ipython-input-4-47ad54076222> in <module>()
         17 # Import the graph_def into a new Graph
         18 with tf.Graph().as_default() as graph:
    ---> 19     tf.import_graph_def(graph_def, name="")
         20
         21 ops = graph.get_operations()
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/deprecation.py in new_func(*args, **kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/importer.py in import_graph_def(graph_def, input_map, return_elements, name, op_dict, producer_op_list)
    ValueError: NodeDef mentions attr 'incompatible_shape_error' not in Op<name=Equal; signature=x:T, y:T -> z:bool; attr=T:type,allowed=[DT_BFLOAT16, DT_HALF, DT_FLOAT, DT_DOUBLE, DT_UINT8, ..., DT_QINT8, DT_QINT32, DT_STRING, DT_BOOL, DT_COMPLEX128]; is_commutative=true>; NodeDef: {{node MultipleGridAnchorGenerator/assert_equal_1/Equal}}. (Check whether your GraphDef-interpreting binary is up to date with your GraphDef-generating binary.).