update warn infos and add interfaces for prompt infos and get ms api name

mindinsight/mindconverter/config.py

@@ -327,6 +327,48 @@ def load_json_file(file_path):
     return info
 
 
+def get_corresponding_ms_name(pt_name):
+    """
+    Get corresponding MindSpore op name for PyTorch name according to the mappings in mindconverter.
+
+    Args:
+        pt_name: PyTorch op name, whether shortened form or full name is available.
+
+    Returns:
+        str, full MindSpore op name, None if the op is not supported in mindconverter.
+
+    Raises:
+        ValueError， if get shortened form of MindSpore name not starts with `P` or 'nn', which means it is wrong in
+        the mappings file.
+    """
+    helper = ALL_MAPPING.get(pt_name)
+    if helper is None:
+        return None
+    ms_name = helper.ms_api.name
+    if ms_name.startswith('nn.'):
+        full_ms_name = 'mindspore.' + ms_name
+    elif ms_name.startswith('P.'):
+        full_ms_name = 'mindspore.ops.operations.' + ms_name[len('P.'):]
+    else:
+        raise ValueError('check your mapping infos, the corresponding mindspore op name may wrong for torch op : '
+                         '{}'.format(pt_name))
+    return full_ms_name
+
+
+def get_prompt_info(pt_name):
+    """
+    Get prompt info for PyTorch op name.
+
+    Args:
+        pt_name: PyTorch op name, whether shortened form or full name is available.
+
+    Returns:
+        str, prompt info on the op, None if no prompt info for the op.
+    """
+    prompt_dict = {**UNSUPPORTED_WARN_INFOS, **SUPPORTED_WARN_INFOS}
+    return prompt_dict.get(pt_name)
+
+
 # ---------------------------- mappings ----------------------------
 NN_MAPPING_PATH = os.path.realpath(os.path.join(os.path.dirname(__file__), 'mappings/nn_mappings.json'))
 NN_MAPPING = get_mapping_from_file(NN_MAPPING_PATH)
@@ -390,72 +432,98 @@ ALL_UNSUPPORTED = NN_UNSUPPORTED + F_UNSUPPORTED + TORCH_DOT_UNSUPPORTED + TENSO
 
 
 UNSUPPORTED_WARN_INFOS = {
-    "nn.AdaptiveAvgPool2d": "maybe could convert to P.ReduceMean",
-    "nn.AvgPool1d": "maybe could convert to nn.AvgPool1d",
-    "nn.ConvTranspose2d": "maybe could convert to nn.Conv2dTranspose",
-    "nn.CrossEntropyLoss": "maybe could convert to nn.SoftmaxCrossEntropyWithLogits",
-    "nn.Embedding": "maybe could convert to nn.Embedding",
-    "nn.GroupNorm": "maybe could convert to nn.GroupNorm",
-    "nn.MSELoss": "maybe could convert to nn.MSELoss",
-    "nn.LSTM": "maybe could convert to nn.LSTM",
-    "nn.LSTMCell": "maybe could convert to nn.LSTMCell",
-    "nn.ModuleList": "maybe could convert to nn.CellList",
-    "nn.SmoothL1Loss": "maybe could convert to nn.SmoothL1Loss",
-    "nn.Tanh": "maybe could convert to nn.Tanh",
-    "nn.Upsample": "maybe could convert to P.ResizeBilinear",
-    "nn.L1Loss": "maybe could convert to nn.L1Loss",
-    "nn.Parameter": "maybe could convert to mindspore.Parameter",
-    "nn.ParameterList": "maybe could convert to mindspore.ParameterTuple",
-    "nn.Unfold": "maybe could convert to nn.Unfold",
-    "nn.PixelShuffle": "maybe could convert to P.DepthToSpace",
-    "F.adaptive_avg_pool2d": "maybe could convert to P.ReduceMean",
-    "F.conv2d": "maybe could convert to mindspore.ops.operations.Conv2D",
-    "F.dropout": "please use nn.Dropout in __init__()",
-    "F.interpolate": "maybe could convert to P.ResizeBilinear",
-    "torch.bmm": "maybe could convert to P.BatchMatMul",
-    "torch.cumsum": "maybe could convert to P.CumSum",
-    "F.relu": "maybe could convert to P.ReLU",
-    "F.pad": "maybe could convert to P.Pad",
-    "F.softmax": "maybe could convert to P.Softmax",
-    "torch.clamp": "maybe could convert to mindspore.ops.composite.clip_by_value",
-    "torch.eq": "maybe could convert to P.Equal",
-    "torch.load": "maybe could convert to mindspore.train.serialization.load_checkpoint",
-    "torch.matmul": "maybe could convert to P.MatMul",
-    "torch.max": "try to use P.ArgMaxWithValue, notice that two values are returned by P.ArgMaxWithValue",
-    "torch.mean": "maybe could convert to P.ReduceMean",
-    "torch.min": "try to use P.ArgMinWithValue, notice that two values are returned by P.ArgMinWithValue",
-    "torch.mm": "maybe could convert to P.MatMul",
-    "torch.mul": "maybe could convert to P.Mul",
-    "torch.norm": "maybe could convert to nn.Norm",
-    "torch.numel": "maybe could convert to P.Size",
-    "F.one_hot": "maybe could convert to P.OneHot",
-    "torch.ones_like": "maybe could convert to P.OnesLike",
-    "torch.randn": "maybe could convert to P.TruncatedNormal",
-    "torch.round": "maybe could convert to P.Round",
-    "torch.save": "maybe could convert to mindspore.train.serialization.save_checkpoint",
-    "torch.sigmoid": "maybe could convert to P.Sigmoid",
-    "torch.split": "maybe could convert to P.Split",
-    "torch.squeeze": "maybe could convert to P.Squeeze",
-    "torch.stack": "maybe could convert to P.Pack",
-    "torch.sum": "maybe could convert to mindspore.ops.operations.ReduceSum",
-    "torch.tanh": "maybe could convert to mindspore.ops.operations.Tanh",
-    "torch.tensor": "maybe could convert to mindspore.Tensor",
-    "torch.transpose": "maybe could convert to P.Transpose",
-    "torch.unsqueeze": "maybe could convert to P.ExpandDims",
-    "torch.zeros_like": "maybe could convert to P.ZerosLike",
-    ".chunk": "maybe could convert to P.Split",
-    ".fill_": "maybe could convert to P.Fill",
-    ".float": "maybe could convert to P.Cast",
-    ".mm": "maybe could convert to P.MatMul",
-    "mul": "maybe could convert to P.Mul",
-    ".pow": "maybe could convert to P.Pow",
-    ".round": "maybe could convert to P.Round",
-    ".scatter": "maybe could convert to P.ScatterNd",
-    "sigmoid": "maybe could convert to nn.Sigmoid",
-    ".sign": "maybe could convert to P.Sign",
-    ".sqrt": "maybe could convert to P.Sqrt",
-    ".sub": "maybe could convert to P.Sub",
-    ".transpose": "maybe could convert to P.Transpose",
-    ".unsqueeze": "maybe could convert to P.ExpandDims",
-    ".zero_": "maybe could convert to P.ZerosLike",
+    "nn.AdaptiveAvgPool2d": "Maybe could convert to mindspore.ops.operations.ReduceMean.",
+    "nn.AvgPool1d": "Maybe could convert to mindspore.nn.AvgPool1d.",
+    "nn.ConvTranspose2d": "Maybe could convert to mindspore.nn.Conv2dTranspose.",
+    "nn.CrossEntropyLoss": "Maybe could convert to mindspore.nn.SoftmaxCrossEntropyWithLogits.",
+    "nn.Embedding": "Maybe could convert to mindspore.nn.Embedding.",
+    "nn.GroupNorm": "Maybe could convert to mindspore.nn.GroupNorm.",
+    "nn.MSELoss": "Maybe could convert to mindspore.nn.MSELoss.",
+    "nn.LSTM": "Maybe could convert to mindspore.nn.LSTM.",
+    "nn.LSTMCell": "Maybe could convert to mindspore.nn.LSTMCell.",
+    "nn.ModuleList": "Maybe could convert to mindspore.nn.CellList.",
+    "nn.SmoothL1Loss": "Maybe could convert to mindspore.nn.SmoothL1Loss.",
+    "nn.Tanh": "Maybe could convert to mindspore.nn.Tanh.",
+    "nn.Upsample": "Maybe could convert to mindspore.ops.operations.ResizeBilinear.",
+    "nn.L1Loss": "Maybe could convert to mindspore.nn.L1Loss.",
+    "nn.Parameter": "Maybe could convert to mindspore.Parameter.",
+    "nn.ParameterList": "Maybe could convert to mindspore.ParameterTuple.",
+    "nn.Unfold": "Maybe could convert to mindspore.nn.Unfold.",
+    "nn.PixelShuffle": "Maybe could convert to mindspore.ops.operations.DepthToSpace.",
+    "F.adaptive_avg_pool2d": "Maybe could convert to mindspore.ops.operations.ReduceMean.",
+    "F.conv2d": "Maybe could convert to mindspore.ops.operations.Conv2D.",
+    "F.dropout": "please use mindspore.nn.Dropout in __init__().",
+    "F.interpolate": "Maybe could convert to mindspore.ops.operations.ResizeBilinear.",
+    "F.one_hot": "Maybe could convert to mindspore.ops.operations.OneHot.",
+    "torch.bmm": "Maybe could convert to mindspore.ops.operations.BatchMatMul.",
+    "torch.cumsum": "Maybe could convert to mindspore.ops.operations.CumSum.",
+    "F.relu": "Maybe could convert to mindspore.ops.operations.ReLU.",
+    "F.pad": "Maybe could convert to mindspore.ops.operations.Pad.",
+    "F.softmax": "Maybe could convert to mindspore.ops.operations.Softmax.",
+    "torch.clamp": "Maybe could convert to mindspore.ops.composite.clip_by_value.",
+    "torch.eq": "Maybe could convert to mindspore.ops.operations.Equal.",
+    "torch.load": "Maybe could convert to mindspore.train.serialization.load_checkpoint.",
+    "torch.matmul": "Maybe could convert to mindspore.ops.operations.MatMul.",
+    "torch.max": "try to use P.ArgMaxWithValue, notice that two values are returned by mindspore.ops.operations."
+                 "ArgMaxWithValue.",
+    "torch.mean": "Maybe could convert to mindspore.ops.operations.ReduceMean.",
+    "torch.min": "try to use P.ArgMinWithValue, notice that two values are returned by mindspore.ops.operations."
+                 "ArgMinWithValue.",
+    "torch.mm": "Maybe could convert to mindspore.ops.operations.MatMul.",
+    "torch.mul": "Maybe could convert to mindspore.ops.operations.Mul.",
+    "torch.norm": "Maybe could convert to mindspore.nn.Norm.",
+    "torch.numel": "Maybe could convert to mindspore.ops.operations.Size.",
+    "torch.ones_like": "Maybe could convert to mindspore.ops.operations.OnesLike.",
+    "torch.randn": "Maybe could convert to mindspore.ops.operations.TruncatedNormal.",
+    "torch.round": "Maybe could convert to mindspore.ops.operations.Round.",
+    "torch.save": "Maybe could convert to mindspore.train.serialization.save_checkpoint.",
+    "torch.sigmoid": "Maybe could convert to mindspore.ops.operations.Sigmoid.",
+    "torch.split": "Maybe could convert to mindspore.ops.operations.Split.",
+    "torch.squeeze": "Maybe could convert to mindspore.ops.operations.Squeeze.",
+    "torch.stack": "Maybe could convert to mindspore.ops.operations.Pack.",
+    "torch.sum": "Maybe could convert to mindspore.ops.operations.ReduceSum.",
+    "torch.tanh": "Maybe could convert to mindspore.ops.operations.Tanh.",
+    "torch.tensor": "Maybe could convert to mindspore.Tensor.",
+    "torch.transpose": "Maybe could convert to mindspore.ops.operations.Transpose.",
+    "torch.unsqueeze": "Maybe could convert to mindspore.ops.operations.ExpandDims.",
+    "torch.zeros_like": "Maybe could convert to mindspore.ops.operations.ZerosLike.",
+    ".chunk": "Maybe could convert to mindspore.ops.operations.Split.",
+    ".fill_": "Maybe could convert to mindspore.ops.operations.Fill.",
+    ".float": "Maybe could convert to mindspore.ops.operations.Cast.",
+    ".mm": "Maybe could convert to mindspore.ops.operations.MatMul.",
+    ".mul": "Maybe could convert to mindspore.ops.operations.Mul.",
+    ".pow": "Maybe could convert to mindspore.ops.operations.Pow.",
+    ".round": "Maybe could convert to mindspore.ops.operations.Round.",
+    ".scatter": "Maybe could convert to mindspore.ops.operations.ScatterNd.",
+    ".sigmoid": "Maybe could convert to mindspore.nn.Sigmoid.",
+    ".sign": "Maybe could convert to mindspore.ops.operations.Sign.",
+    ".sqrt": "Maybe could convert to mindspore.ops.operations.Sqrt.",
+    ".sub": "Maybe could convert to mindspore.ops.operations.Sub.",
+    ".transpose": "Maybe could convert to mindspore.ops.operations.Transpose.",
+    ".unsqueeze": "Maybe could convert to mindspore.ops.operations.ExpandDims.",
+    ".zero_": "Maybe could convert to mindspore.ops.operations.ZerosLike.",
+}
+
+NN_UNSUPPORTED_INFOS = {k: v for k, v in UNSUPPORTED_WARN_INFOS.items() if k.startswith('nn.')}
+TORCH_NN_UNSUPPORTED_INFOS = {('torch.' + k): v for k, v in NN_UNSUPPORTED_INFOS.items()}
+
+F_UNSUPPORTED_INFOS = {k: v for k, v in UNSUPPORTED_WARN_INFOS.items() if k.startswith('F.')}
+NN_FUNCTIONAL_UNSUPPORTED_INFOS = {'nn.functional.' + k[len('F.'):]: v for k, v in F_UNSUPPORTED_INFOS.items()}
+TORCH_NN_FUNCTIONAL_UNSUPPORTED_INFOS = {'torch.nn.functional.' + k[len('F.'):]: v for k, v in
+                                         F_UNSUPPORTED_INFOS.items()}
+
+UNSUPPORTED_WARN_INFOS.update(TORCH_NN_UNSUPPORTED_INFOS)
+UNSUPPORTED_WARN_INFOS.update(NN_FUNCTIONAL_UNSUPPORTED_INFOS)
+UNSUPPORTED_WARN_INFOS.update(TORCH_NN_FUNCTIONAL_UNSUPPORTED_INFOS)
+
+SUPPORTED_WARN_INFOS = {
+    "torch.eye": "Pay attention to use right mindspore data type.",
+    "nn.Linear": "Pay attention to reshape the input to 2 dims if it is 3 dims before, because MindSpore.nn.Dense only "
+                 "support 2-dim input.",
+    ".view": "Only float Tensor is supported in mindspore.ops.operations.Reshape.",
+    ".reshape": "Only float Tensor is supported in mindspore.ops.operations.Reshape."
 }
+
+NN_SUPPORTED_INFOS = {k: v for k, v in SUPPORTED_WARN_INFOS.items() if k.startswith('nn.')}
+TORCH_NN_SUPPORTED_INFOS = {('torch.' + k): v for k, v in NN_SUPPORTED_INFOS.items()}
+SUPPORTED_WARN_INFOS.update(TORCH_NN_SUPPORTED_INFOS)