add-caffe-test (#462)

tensorboardX/caffe2_graph.py

@@ -689,7 +689,7 @@ def _operators_to_graph_def(
     if show_simplified:  # use_tensorflow_naming
         _rename_tensorflow_style(shapes, blob_name_tracker, ops)
     producing_ops = {}
-    blobs = set()
+    blobs = []
     input_blobs, inter_blobs, _ = _compute_in_out(ops)
     current_graph = GraphDef()
     seen = set(input_blobs)
@@ -699,9 +699,9 @@ def _operators_to_graph_def(
             [_operator_to_node(shapes, op)]  # .extend() expects an iterable
         current_graph.node.extend(nodes_from_op)
         for input_blob in op.input:
-            blobs.add(input_blob)
+            blobs.append(input_blob)
         for i, output_blob in enumerate(op.output):
-            blobs.add(output_blob)
+            blobs.append(output_blob)
             producing_ops.setdefault(output_blob, []).append((op, i))
 
     if show_simplified:

tests/expect/test_caffe2.test_simple_cnnmodel.expect

@@ -0,0 +1,319 @@
+node {
+  name: "conv1/XavierFill"
+  op: "XavierFill"
+  attr {
+    key: "_output_shapes"
+    value {
+      list {
+        shape {
+          dim {
+            size: 96
+          }
+          dim {
+            size: 3
+          }
+          dim {
+            size: 11
+          }
+          dim {
+            size: 11
+          }
+        }
+      }
+    }
+  }
+}
+node {
+  name: "conv1/ConstantFill"
+  op: "ConstantFill"
+  attr {
+    key: "_output_shapes"
+    value {
+      list {
+        shape {
+          dim {
+            size: 96
+          }
+        }
+      }
+    }
+  }
+}
+node {
+  name: "classifier/XavierFill"
+  op: "XavierFill"
+  attr {
+    key: "_output_shapes"
+    value {
+      list {
+        shape {
+          dim {
+            size: 1000
+          }
+          dim {
+            size: 4096
+          }
+        }
+      }
+    }
+  }
+}
+node {
+  name: "classifier/ConstantFill"
+  op: "ConstantFill"
+  attr {
+    key: "_output_shapes"
+    value {
+      list {
+        shape {
+          dim {
+            size: 1000
+          }
+        }
+      }
+    }
+  }
+}
+node {
+  name: "conv1/Conv"
+  op: "Conv"
+  input: "conv1/data"
+  input: "conv1/conv1_w"
+  input: "conv1/conv1_b"
+  attr {
+    key: "exhaustive_search"
+    value {
+      i: 0
+    }
+  }
+  attr {
+    key: "kernel"
+    value {
+      i: 11
+    }
+  }
+  attr {
+    key: "order"
+    value {
+      s: "NCHW"
+    }
+  }
+  attr {
+    key: "stride"
+    value {
+      i: 4
+    }
+  }
+}
+node {
+  name: "conv1/Relu"
+  op: "Relu"
+  input: "conv1/conv1"
+  attr {
+    key: "cudnn_exhaustive_search"
+    value {
+      i: 0
+    }
+  }
+  attr {
+    key: "order"
+    value {
+      s: "NCHW"
+    }
+  }
+}
+node {
+  name: "conv1/MaxPool"
+  op: "MaxPool"
+  input: "conv1/conv1_1"
+  attr {
+    key: "cudnn_exhaustive_search"
+    value {
+      i: 0
+    }
+  }
+  attr {
+    key: "kernel"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "order"
+    value {
+      s: "NCHW"
+    }
+  }
+  attr {
+    key: "stride"
+    value {
+      i: 2
+    }
+  }
+}
+node {
+  name: "classifier/FC"
+  op: "FC"
+  input: "conv1/pool1"
+  input: "classifier/fc_w"
+  input: "classifier/fc_b"
+  attr {
+    key: "cudnn_exhaustive_search"
+    value {
+      i: 0
+    }
+  }
+  attr {
+    key: "order"
+    value {
+      s: "NCHW"
+    }
+  }
+  attr {
+    key: "use_cudnn"
+    value {
+      i: 1
+    }
+  }
+}
+node {
+  name: "classifier/Softmax"
+  op: "Softmax"
+  input: "classifier/fc"
+  attr {
+    key: "cudnn_exhaustive_search"
+    value {
+      i: 0
+    }
+  }
+  attr {
+    key: "order"
+    value {
+      s: "NCHW"
+    }
+  }
+}
+node {
+  name: "classifier/LabelCrossEntropy"
+  op: "LabelCrossEntropy"
+  input: "classifier/pred"
+  input: "classifier/label"
+}
+node {
+  name: "classifier/AveragedLoss"
+  op: "AveragedLoss"
+  input: "classifier/xent"
+}
+node {
+  name: "conv1/conv1_w"
+  op: "Blob"
+  input: "conv1/XavierFill:0"
+}
+node {
+  name: "conv1/conv1_b"
+  op: "Blob"
+  input: "conv1/ConstantFill:0"
+}
+node {
+  name: "classifier/fc_w"
+  op: "Blob"
+  input: "classifier/XavierFill:0"
+}
+node {
+  name: "classifier/fc_b"
+  op: "Blob"
+  input: "classifier/ConstantFill:0"
+}
+node {
+  name: "conv1/data"
+  op: "Placeholder"
+}
+node {
+  name: "conv1/conv1_w"
+  op: "Blob"
+  input: "conv1/XavierFill:0"
+}
+node {
+  name: "conv1/conv1_b"
+  op: "Blob"
+  input: "conv1/ConstantFill:0"
+}
+node {
+  name: "conv1/conv1"
+  op: "Blob"
+  input: "conv1/Conv:0"
+}
+node {
+  name: "conv1/conv1"
+  op: "Blob"
+  input: "conv1/Conv:0"
+}
+node {
+  name: "conv1/conv1_1"
+  op: "Blob"
+  input: "conv1/Relu:0"
+}
+node {
+  name: "conv1/conv1_1"
+  op: "Blob"
+  input: "conv1/Relu:0"
+}
+node {
+  name: "conv1/pool1"
+  op: "Blob"
+  input: "conv1/MaxPool:0"
+}
+node {
+  name: "conv1/pool1"
+  op: "Blob"
+  input: "conv1/MaxPool:0"
+}
+node {
+  name: "classifier/fc_w"
+  op: "Blob"
+  input: "classifier/XavierFill:0"
+}
+node {
+  name: "classifier/fc_b"
+  op: "Blob"
+  input: "classifier/ConstantFill:0"
+}
+node {
+  name: "classifier/fc"
+  op: "Blob"
+  input: "classifier/FC:0"
+}
+node {
+  name: "classifier/fc"
+  op: "Blob"
+  input: "classifier/FC:0"
+}
+node {
+  name: "classifier/pred"
+  op: "Blob"
+  input: "classifier/Softmax:0"
+}
+node {
+  name: "classifier/pred"
+  op: "Blob"
+  input: "classifier/Softmax:0"
+}
+node {
+  name: "classifier/label"
+  op: "Placeholder"
+}
+node {
+  name: "classifier/xent"
+  op: "Blob"
+  input: "classifier/LabelCrossEntropy:0"
+}
+node {
+  name: "classifier/xent"
+  op: "Blob"
+  input: "classifier/LabelCrossEntropy:0"
+}
+node {
+  name: "classifier/loss"
+  op: "Blob"
+  input: "classifier/AveragedLoss:0"
+}