Add flops and params counter

docs/en_US/Compressor/CompressionReference.md

@@ -31,3 +31,10 @@
     :members:
 
 ```
+
+## Model FLOPs/Parameters Counter
+
+```eval_rst
+..  autofunction:: nni.compression.torch.utils.counter.count_flops_params
+
+```

docs/en_US/Compressor/CompressionUtils.md

@@ -118,4 +118,16 @@ When the masks of different layers in a model have conflict (for example, assign
 ```
 from nni.compression.torch.utils.mask_conflict import fix_mask_conflict
 fixed_mask = fix_mask_conflict('./resnet18_mask', net, data)
+```
+
+### Model FLOPs/Parameters Counter
+We provide a model counter for calculating the model FLOPs and parameters. This counter supports calculating FLOPs/parameters of a normal model without masks, it can also calculates FLOPs/parameters of a model with mask wrappers, which helps users easily check model complexity during model compression on NNI. Note that, for sturctured pruning, we only identify the remained filters according to its mask, which not taking the pruned input channels into consideration, so the calculated FLOPs will be larger than real number (i.e., the number calculated after Model Speedup).
+
+### Usage
+```
+from nni.compression.torch.utils.counter import count_flops_params
+
+# Given input size (1, 1, 28, 28)
+flops, params = count_flops_params(model, (1, 1, 28, 28))
+print(f'FLOPs: {flops/1e6:.3f}M,  Params: {params/1e6:.3f}M)
 ```

src/sdk/pynni/nni/compression/torch/utils/counter.py

@@ -0,0 +1,127 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+import logging
+import torch
+import torch.nn as nn
+from nni.compression.torch.compressor import PrunerModuleWrapper
+
+
+_logger = logging.getLogger(__name__)
+
+try:
+    from thop import profile
+except ImportError:
+    _logger.warning('Please install thop using command: pip install thop')
+
+
+def count_flops_params(model: nn.Module, input_size, verbose=True):
+    """
+    Count FLOPs and Params of the given model.
+    This function would identify the mask on the module
+    and take the pruned shape into consideration.
+    Note that, for sturctured pruning, we only identify
+    the remained filters according to its mask, which
+    not taking the pruned input channels into consideration,
+    so the calculated FLOPs will be larger than real number.
+
+    Parameters
+    ---------
+    model : nn.Module
+        target model.
+    input_size: list, tuple
+        the input shape of data
+
+
+    Returns
+    -------
+    flops: float
+        total flops of the model
+    params:
+        total params of the model
+    """
+
+    assert input_size is not None
+
+    device = next(model.parameters()).device
+    inputs = torch.randn(input_size).to(device)
+
+    hook_module_list = []
+    prev_m = None
+    for m in model.modules():
+        weight_mask = None
+        m_type = type(m)
+        if m_type in custom_ops:
+            if isinstance(prev_m, PrunerModuleWrapper):
+                weight_mask = prev_m.weight_mask
+
+            m.register_buffer('weight_mask', weight_mask)
+            hook_module_list.append(m)
+        prev_m = m
+
+    flops, params = profile(model, inputs=(inputs, ), custom_ops=custom_ops, verbose=verbose)
+
+    for m in hook_module_list:
+        m._buffers.pop("weight_mask")
+
+    return flops, params
+
+def count_convNd_mask(m, x, y):
+    """
+    The forward hook to count FLOPs and Parameters of convolution operation.
+
+    Parameters
+    ----------
+    m : torch.nn.Module
+        convolution module to calculate the FLOPs and Parameters
+    x : torch.Tensor
+        input data
+    y : torch.Tensor
+        output data
+    """
+    output_channel = y.size()[1]
+    output_size = torch.zeros(y.size()[2:]).numel()
+    kernel_size = torch.zeros(m.weight.size()[2:]).numel()
+
+    bias_flops = 1 if m.bias is not None else 0
+
+    if m.weight_mask is not None:
+        output_channel = m.weight_mask.sum() // (m.in_channels * kernel_size)
+
+    total_ops = output_channel * output_size * (m.in_channels // m.groups * kernel_size + bias_flops)
+
+    m.total_ops += torch.DoubleTensor([int(total_ops)])
+
+
+def count_linear_mask(m, x, y):
+    """
+    The forward hook to count FLOPs and Parameters of linear transformation.
+
+    Parameters
+    ----------
+    m : torch.nn.Module
+        linear to calculate the FLOPs and Parameters
+    x : torch.Tensor
+        input data
+    y : torch.Tensor
+        output data
+    """
+    output_channel = y.size()[1]
+    output_size = torch.zeros(y.size()[2:]).numel()
+
+    bias_flops = 1 if m.bias is not None else 0
+
+    if m.weight_mask is not None:
+        output_channel = m.weight_mask.sum() // m.in_features
+
+    total_ops = output_channel * output_size * (m.in_features + bias_flops)
+
+    m.total_ops += torch.DoubleTensor([int(total_ops)])
+
+
+custom_ops = {
+    nn.Conv1d: count_convNd_mask,
+    nn.Conv2d: count_convNd_mask,
+    nn.Conv3d: count_convNd_mask,
+    nn.Linear: count_linear_mask,
+}