[Feature] get_model_complexity_info() supports multiple inputs (#1065)

mmengine/analysis/print_helper.py

@@ -12,6 +12,7 @@
 from rich.table import Table
 from torch import nn
 
+from mmengine.utils import is_tuple_of
 from .complexity_analysis import (ActivationAnalyzer, FlopAnalyzer,
                                   parameter_count)
 
@@ -675,19 +676,38 @@ def complexity_stats_table(
 
 def get_model_complexity_info(
     model: nn.Module,
-    input_shape: Optional[tuple] = None,
-    inputs: Union[torch.Tensor, Tuple[torch.Tensor, ...], None] = None,
+    input_shape: Union[Tuple[int, ...], Tuple[Tuple[int, ...], ...],
+                       None] = None,
+    inputs: Union[torch.Tensor, Tuple[torch.Tensor, ...], Tuple[Any, ...],
+                  None] = None,
     show_table: bool = True,
     show_arch: bool = True,
 ):
     """Interface to get the complexity of a model.
 
+    The parameter `inputs` are fed to the forward method of model.
+    If `inputs` is not specified, the `input_shape` is required and
+    it will be used to construct the dummy input fed to model.
+    If the forward of model requires two or more inputs, the `inputs`
+    should be a tuple of tensor or the `input_shape` should be a tuple
+    of tuple which each element will be constructed into a dumpy input.
+
+    Examples:
+        >>> # the forward of model accepts only one input
+        >>> input_shape = (3, 224, 224)
+        >>> get_model_complexity_info(model, input_shape=input_shape)
+        >>> # the forward of model accepts two or more inputs
+        >>> input_shape = ((3, 224, 224), (3, 10))
+        >>> get_model_complexity_info(model, input_shape=input_shape)
+
     Args:
         model (nn.Module): The model to analyze.
-        input_shape (tuple, optional): The input shape of the model.
-            If inputs is not specified, the input_shape should be set.
+        input_shape (Union[Tuple[int, ...], Tuple[Tuple[int, ...]], None]):
+            The input shape of the model.
+            If "inputs" is not specified, the "input_shape" should be set.
             Defaults to None.
-        inputs (torch.Tensor or tuple[torch.Tensor, ...], optional]):
+        inputs (torch.Tensor, tuple[torch.Tensor, ...] or Tuple[Any, ...],\
+            optional]):
             The input tensor(s) of the model. If not given the input tensor
             will be generated automatically with the given input_shape.
             Defaults to None.
@@ -705,7 +725,21 @@ def get_model_complexity_info(
         raise ValueError('"input_shape" and "inputs" cannot be both set.')
 
     if inputs is None:
-        inputs = (torch.randn(1, *input_shape), )
+        if is_tuple_of(input_shape, int):  # tuple of int, construct one tensor
+            inputs = (torch.randn(1, *input_shape), )
+        elif is_tuple_of(input_shape, tuple) and all([
+                is_tuple_of(one_input_shape, int)
+                for one_input_shape in input_shape  # type: ignore
+        ]):  # tuple of tuple of int, construct multiple tensors
+            inputs = tuple([
+                torch.randn(1, *one_input_shape)
+                for one_input_shape in input_shape  # type: ignore
+            ])
+        else:
+            raise ValueError(
+                '"input_shape" should be either a `tuple of int` (to construct'
+                'one input tensor) or a `tuple of tuple of int` (to construct'
+                'multiple input tensors).')
 
     flop_handler = FlopAnalyzer(model, inputs)
     activation_handler = ActivationAnalyzer(model, inputs)

tests/test_analysis/test_print_helper.py

@@ -0,0 +1,108 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+
+import pytest
+import torch
+import torch.nn as nn
+
+from mmengine.analysis.complexity_analysis import FlopAnalyzer, parameter_count
+from mmengine.analysis.print_helper import get_model_complexity_info
+from mmengine.utils import digit_version
+from mmengine.utils.dl_utils import TORCH_VERSION
+
+
+class NetAcceptOneTensor(nn.Module):
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.l1 = nn.Linear(in_features=5, out_features=6)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        out = self.l1(x)
+        return out
+
+
+class NetAcceptTwoTensors(nn.Module):
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.l1 = nn.Linear(in_features=5, out_features=6)
+        self.l2 = nn.Linear(in_features=7, out_features=6)
+
+    def forward(self, x1: torch.Tensor, x2: torch.Tensor) -> torch.Tensor:
+        out = self.l1(x1) + self.l2(x2)
+        return out
+
+
+class NetAcceptOneTensorAndOneScalar(nn.Module):
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.l1 = nn.Linear(in_features=5, out_features=6)
+        self.l2 = nn.Linear(in_features=5, out_features=6)
+
+    def forward(self, x1: torch.Tensor, r) -> torch.Tensor:
+        out = r * self.l1(x1) + (1 - r) * self.l2(x1)
+        return out
+
+
+def test_get_model_complexity_info():
+    input1 = torch.randn(1, 9, 5)
+    input_shape1 = (9, 5)
+    input2 = torch.randn(1, 9, 7)
+    input_shape2 = (9, 7)
+    scalar = 0.3
+
+    # test a network that accepts one tensor as input
+    model = NetAcceptOneTensor()
+    complexity_info = get_model_complexity_info(model=model, inputs=input1)
+    flops = FlopAnalyzer(model=model, inputs=input1).total()
+    params = parameter_count(model=model)['']
+    assert complexity_info['flops'] == flops
+    assert complexity_info['params'] == params
+
+    complexity_info = get_model_complexity_info(
+        model=model, input_shape=input_shape1)
+    flops = FlopAnalyzer(
+        model=model, inputs=(torch.randn(1, *input_shape1), )).total()
+    assert complexity_info['flops'] == flops
+
+    # test a network that accepts two tensors as input
+    model = NetAcceptTwoTensors()
+    complexity_info = get_model_complexity_info(
+        model=model, inputs=(input1, input2))
+    flops = FlopAnalyzer(model=model, inputs=(input1, input2)).total()
+    params = parameter_count(model=model)['']
+    assert complexity_info['flops'] == flops
+    assert complexity_info['params'] == params
+
+    complexity_info = get_model_complexity_info(
+        model=model, input_shape=(input_shape1, input_shape2))
+    inputs = (torch.randn(1, *input_shape1), torch.randn(1, *input_shape2))
+    flops = FlopAnalyzer(model=model, inputs=inputs).total()
+    assert complexity_info['flops'] == flops
+
+    # test a network that accepts one tensor and one scalar as input
+    model = NetAcceptOneTensorAndOneScalar()
+    # For pytorch<1.9, a scalar input is not acceptable for torch.jit,
+    # wrap it to `torch.tensor`. See https://github.com/pytorch/pytorch/blob/cd9dd653e98534b5d3a9f2576df2feda40916f1d/torch/csrc/jit/python/python_arg_flatten.cpp#L90. # noqa: E501
+    scalar = torch.tensor([
+        scalar
+    ]) if digit_version(TORCH_VERSION) < digit_version('1.9.0') else scalar
+    complexity_info = get_model_complexity_info(
+        model=model, inputs=(input1, scalar))
+    flops = FlopAnalyzer(model=model, inputs=(input1, scalar)).total()
+    params = parameter_count(model=model)['']
+    assert complexity_info['flops'] == flops
+    assert complexity_info['params'] == params
+
+    # `get_model_complexity_info()` should throw `ValueError`
+    # when neithor `inputs` nor `input_shape` is specified
+    with pytest.raises(ValueError, match='should be set'):
+        get_model_complexity_info(model)
+
+    # `get_model_complexity_info()` should throw `ValueError`
+    # when both `inputs` and `input_shape` are specified
+    model = NetAcceptOneTensor()
+    with pytest.raises(ValueError, match='cannot be both set'):
+        get_model_complexity_info(
+            model, inputs=input1, input_shape=input_shape1)