torchinfo
v1.8.0
（以前是火炬 - 薩默里）
Torchinfo提供了pytorch中print(your_model)提供的信息，類似於Tensorflow的model.summary() API，以查看模型的可視化，在調試網絡時，這很有幫助。在此項目中，我們在Pytorch中實現了類似的功能，並創建了一個乾淨，簡單的接口以在您的項目中使用。
這是 @sksq96和@nmhkahn的原始Torchsummary和Torchsummaryx Projects的完全重寫版本。該項目通過引入全新的API來解決原始項目中留下的所有問題，並提出了拉的請求。
支持Pytorch版本1.4.0+。
pip install torchinfo
或者，通過conda：
conda install -c conda-forge torchinfo
from torchinfo import summary
model = ConvNet ()
batch_size = 16
summary ( model , input_size = ( batch_size , 1 , 28 , 28 )) ================================================================================================================
Layer (type:depth-idx) Input Shape Output Shape Param # Mult-Adds
================================================================================================================
SingleInputNet [7, 1, 28, 28] [7, 10] -- --
├─Conv2d: 1-1 [7, 1, 28, 28] [7, 10, 24, 24] 260 1,048,320
├─Conv2d: 1-2 [7, 10, 12, 12] [7, 20, 8, 8] 5,020 2,248,960
├─Dropout2d: 1-3 [7, 20, 8, 8] [7, 20, 8, 8] -- --
├─Linear: 1-4 [7, 320] [7, 50] 16,050 112,350
├─Linear: 1-5 [7, 50] [7, 10] 510 3,570
================================================================================================================
Total params: 21,840
Trainable params: 21,840
Non-trainable params: 0
Total mult-adds (M): 3.41
================================================================================================================
Input size (MB): 0.02
Forward/backward pass size (MB): 0.40
Params size (MB): 0.09
Estimated Total Size (MB): 0.51
================================================================================================================
注意：如果您使用的是Jupyter筆記本或Google Colab， summary(model, ...)必須是單元格的返回值。如果不是這樣，則應將摘要包裝在打印（），例如print(summary(model, ...))中。有關示例，請參見tests/jupyter_test.ipynb 。
此版本現在支持：
其他新功能：
社區貢獻：
def summary (
model : nn . Module ,
input_size : Optional [ INPUT_SIZE_TYPE ] = None ,
input_data : Optional [ INPUT_DATA_TYPE ] = None ,
batch_dim : Optional [ int ] = None ,
cache_forward_pass : Optional [ bool ] = None ,
col_names : Optional [ Iterable [ str ]] = None ,
col_width : int = 25 ,
depth : int = 3 ,
device : Optional [ torch . device ] = None ,
dtypes : Optional [ List [ torch . dtype ]] = None ,
mode : str = "same" ,
row_settings : Optional [ Iterable [ str ]] = None ,
verbose : int = 1 ,
** kwargs : Any ,
) -> ModelStatistics :
"""
Summarize the given PyTorch model. Summarized information includes:
1) Layer names,
2) input/output shapes,
3) kernel shape,
4) # of parameters,
5) # of operations (Mult-Adds),
6) whether layer is trainable
NOTE: If neither input_data or input_size are provided, no forward pass through the
network is performed, and the provided model information is limited to layer names.
Args:
model (nn.Module):
PyTorch model to summarize. The model should be fully in either train()
or eval() mode. If layers are not all in the same mode, running summary
may have side effects on batchnorm or dropout statistics. If you
encounter an issue with this, please open a GitHub issue.
input_size (Sequence of Sizes):
Shape of input data as a List/Tuple/torch.Size
(dtypes must match model input, default is FloatTensors).
You should include batch size in the tuple.
Default: None
input_data (Sequence of Tensors):
Arguments for the model's forward pass (dtypes inferred).
If the forward() function takes several parameters, pass in a list of
args or a dict of kwargs (if your forward() function takes in a dict
as its only argument, wrap it in a list).
Default: None
batch_dim (int):
Batch_dimension of input data. If batch_dim is None, assume
input_data / input_size contains the batch dimension, which is used
in all calculations. Else, expand all tensors to contain the batch_dim.
Specifying batch_dim can be an runtime optimization, since if batch_dim
is specified, torchinfo uses a batch size of 1 for the forward pass.
Default: None
cache_forward_pass (bool):
If True, cache the run of the forward() function using the model
class name as the key. If the forward pass is an expensive operation,
this can make it easier to modify the formatting of your model
summary, e.g. changing the depth or enabled column types, especially
in Jupyter Notebooks.
WARNING: Modifying the model architecture or input data/input size when
this feature is enabled does not invalidate the cache or re-run the
forward pass, and can cause incorrect summaries as a result.
Default: False
col_names (Iterable[str]):
Specify which columns to show in the output. Currently supported: (
"input_size",
"output_size",
"num_params",
"params_percent",
"kernel_size",
"groups",
"mult_adds",
"trainable",
)
Default: ("output_size", "num_params")
If input_data / input_size are not provided, only "num_params" is used.
col_width (int):
Width of each column.
Default: 25
depth (int):
Depth of nested layers to display (e.g. Sequentials).
Nested layers below this depth will not be displayed in the summary.
Default: 3
device (torch.Device):
Uses this torch device for model and input_data.
If not specified, uses the dtype of input_data if given, or the
parameters of the model. Otherwise, uses the result of
torch.cuda.is_available().
Default: None
dtypes (List[torch.dtype]):
If you use input_size, torchinfo assumes your input uses FloatTensors.
If your model use a different data type, specify that dtype.
For multiple inputs, specify the size of both inputs, and
also specify the types of each parameter here.
Default: None
mode (str)
Either "train", "eval" or "same", which determines whether we call
model.train() or model.eval() before calling summary(). In any case,
original model mode is restored at the end.
Default: "same".
row_settings (Iterable[str]):
Specify which features to show in a row. Currently supported: (
"ascii_only",
"depth",
"var_names",
)
Default: ("depth",)
verbose (int):
0 (quiet): No output
1 (default): Print model summary
2 (verbose): Show weight and bias layers in full detail
Default: 1
If using a Juypter Notebook or Google Colab, the default is 0.
**kwargs:
Other arguments used in `model.forward` function. Passing *args is no
longer supported.
Return:
ModelStatistics object
See torchinfo/model_statistics.py for more information.
""" from torchinfo import summary
model_stats = summary ( your_model , ( 1 , 3 , 28 , 28 ), verbose = 0 )
summary_str = str ( model_stats )
# summary_str contains the string representation of the summary! class LSTMNet ( nn . Module ):
def __init__ ( self , vocab_size = 20 , embed_dim = 300 , hidden_dim = 512 , num_layers = 2 ):
super (). __init__ ()
self . hidden_dim = hidden_dim
self . embedding = nn . Embedding ( vocab_size , embed_dim )
self . encoder = nn . LSTM ( embed_dim , hidden_dim , num_layers = num_layers , batch_first = True )
self . decoder = nn . Linear ( hidden_dim , vocab_size )
def forward ( self , x ):
embed = self . embedding ( x )
out , hidden = self . encoder ( embed )
out = self . decoder ( out )
out = out . view ( - 1 , out . size ( 2 ))
return out , hidden
summary (
LSTMNet (),
( 1 , 100 ),
dtypes = [ torch . long ],
verbose = 2 ,
col_width = 16 ,
col_names = [ "kernel_size" , "output_size" , "num_params" , "mult_adds" ],
row_settings = [ "var_names" ],
) ========================================================================================================================
Layer (type (var_name)) Kernel Shape Output Shape Param # Mult-Adds
========================================================================================================================
LSTMNet (LSTMNet) -- [100, 20] -- --
├─Embedding (embedding) -- [1, 100, 300] 6,000 6,000
│ └─weight [300, 20] └─6,000
├─LSTM (encoder) -- [1, 100, 512] 3,768,320 376,832,000
│ └─weight_ih_l0 [2048, 300] ├─614,400
│ └─weight_hh_l0 [2048, 512] ├─1,048,576
│ └─bias_ih_l0 [2048] ├─2,048
│ └─bias_hh_l0 [2048] ├─2,048
│ └─weight_ih_l1 [2048, 512] ├─1,048,576
│ └─weight_hh_l1 [2048, 512] ├─1,048,576
│ └─bias_ih_l1 [2048] ├─2,048
│ └─bias_hh_l1 [2048] └─2,048
├─Linear (decoder) -- [1, 100, 20] 10,260 10,260
│ └─weight [512, 20] ├─10,240
│ └─bias [20] └─20
========================================================================================================================
Total params: 3,784,580
Trainable params: 3,784,580
Non-trainable params: 0
Total mult-adds (M): 376.85
========================================================================================================================
Input size (MB): 0.00
Forward/backward pass size (MB): 0.67
Params size (MB): 15.14
Estimated Total Size (MB): 15.80
========================================================================================================================
import torchvision
model = torchvision . models . resnet152 ()
summary ( model , ( 1 , 3 , 224 , 224 ), depth = 3 ) ==========================================================================================
Layer (type:depth-idx) Output Shape Param #
==========================================================================================
ResNet [1, 1000] --
├─Conv2d: 1-1 [1, 64, 112, 112] 9,408
├─BatchNorm2d: 1-2 [1, 64, 112, 112] 128
├─ReLU: 1-3 [1, 64, 112, 112] --
├─MaxPool2d: 1-4 [1, 64, 56, 56] --
├─Sequential: 1-5 [1, 256, 56, 56] --
│ └─Bottleneck: 2-1 [1, 256, 56, 56] --
│ │ └─Conv2d: 3-1 [1, 64, 56, 56] 4,096
│ │ └─BatchNorm2d: 3-2 [1, 64, 56, 56] 128
│ │ └─ReLU: 3-3 [1, 64, 56, 56] --
│ │ └─Conv2d: 3-4 [1, 64, 56, 56] 36,864
│ │ └─BatchNorm2d: 3-5 [1, 64, 56, 56] 128
│ │ └─ReLU: 3-6 [1, 64, 56, 56] --
│ │ └─Conv2d: 3-7 [1, 256, 56, 56] 16,384
│ │ └─BatchNorm2d: 3-8 [1, 256, 56, 56] 512
│ │ └─Sequential: 3-9 [1, 256, 56, 56] 16,896
│ │ └─ReLU: 3-10 [1, 256, 56, 56] --
│ └─Bottleneck: 2-2 [1, 256, 56, 56] --
...
...
...
├─AdaptiveAvgPool2d: 1-9 [1, 2048, 1, 1] --
├─Linear: 1-10 [1, 1000] 2,049,000
==========================================================================================
Total params: 60,192,808
Trainable params: 60,192,808
Non-trainable params: 0
Total mult-adds (G): 11.51
==========================================================================================
Input size (MB): 0.60
Forward/backward pass size (MB): 360.87
Params size (MB): 240.77
Estimated Total Size (MB): 602.25
==========================================================================================
class MultipleInputNetDifferentDtypes ( nn . Module ):
def __init__ ( self ):
super (). __init__ ()
self . fc1a = nn . Linear ( 300 , 50 )
self . fc1b = nn . Linear ( 50 , 10 )
self . fc2a = nn . Linear ( 300 , 50 )
self . fc2b = nn . Linear ( 50 , 10 )
def forward ( self , x1 , x2 ):
x1 = F . relu ( self . fc1a ( x1 ))
x1 = self . fc1b ( x1 )
x2 = x2 . type ( torch . float )
x2 = F . relu ( self . fc2a ( x2 ))
x2 = self . fc2b ( x2 )
x = torch . cat (( x1 , x2 ), 0 )
return F . log_softmax ( x , dim = 1 )
summary ( model , [( 1 , 300 ), ( 1 , 300 )], dtypes = [ torch . float , torch . long ])另外，您也可以傳遞Input_Data本身，Torchinfo將自動推斷數據類型。
input_data = torch . randn ( 1 , 300 )
other_input_data = torch . randn ( 1 , 300 ). long ()
model = MultipleInputNetDifferentDtypes ()
summary ( model , input_data = [ input_data , other_input_data , ...]) class ContainerModule ( nn . Module ):
def __init__ ( self ):
super (). __init__ ()
self . _layers = nn . ModuleList ()
self . _layers . append ( nn . Linear ( 5 , 5 ))
self . _layers . append ( ContainerChildModule ())
self . _layers . append ( nn . Linear ( 5 , 5 ))
def forward ( self , x ):
for layer in self . _layers :
x = layer ( x )
return x
class ContainerChildModule ( nn . Module ):
def __init__ ( self ):
super (). __init__ ()
self . _sequential = nn . Sequential ( nn . Linear ( 5 , 5 ), nn . Linear ( 5 , 5 ))
self . _between = nn . Linear ( 5 , 5 )
def forward ( self , x ):
out = self . _sequential ( x )
out = self . _between ( out )
for l in self . _sequential :
out = l ( out )
out = self . _sequential ( x )
for l in self . _sequential :
out = l ( out )
return out
summary ( ContainerModule (), ( 1 , 5 )) ==========================================================================================
Layer (type:depth-idx) Output Shape Param #
==========================================================================================
ContainerModule [1, 5] --
├─ModuleList: 1-1 -- --
│ └─Linear: 2-1 [1, 5] 30
│ └─ContainerChildModule: 2-2 [1, 5] --
│ │ └─Sequential: 3-1 [1, 5] --
│ │ │ └─Linear: 4-1 [1, 5] 30
│ │ │ └─Linear: 4-2 [1, 5] 30
│ │ └─Linear: 3-2 [1, 5] 30
│ │ └─Sequential: 3-3 -- (recursive)
│ │ │ └─Linear: 4-3 [1, 5] (recursive)
│ │ │ └─Linear: 4-4 [1, 5] (recursive)
│ │ └─Sequential: 3-4 [1, 5] (recursive)
│ │ │ └─Linear: 4-5 [1, 5] (recursive)
│ │ │ └─Linear: 4-6 [1, 5] (recursive)
│ │ │ └─Linear: 4-7 [1, 5] (recursive)
│ │ │ └─Linear: 4-8 [1, 5] (recursive)
│ └─Linear: 2-3 [1, 5] 30
==========================================================================================
Total params: 150
Trainable params: 150
Non-trainable params: 0
Total mult-adds (M): 0.00
==========================================================================================
Input size (MB): 0.00
Forward/backward pass size (MB): 0.00
Params size (MB): 0.00
Estimated Total Size (MB): 0.00
==========================================================================================
所有問題和拉的請求都非常感謝！如果您想知道如何構建項目：
pip install -r requirements-dev.txt 。我們使用所有開發軟件包的最新版本。pre-commit install 。pre-commit run -a 。pytest 。pytest --overwrite 。pytest --no-output
