Improved visualization tool of torchsummary. Here, it visualizes kernel size, output shape, # params, and Mult-Adds. Also the torchsummaryX can handle RNN, Recursive NN, or model with multiple inputs.
pip install torchsummaryX and
from torchsummaryX import summary
summary(your_model, torch.zeros((1, 3, 224, 224)))Args:
model(Module): Model to summarizex(Tensor): Input tensor of the model with [N, C, H, W] shape dtype and device have to match to the modelargs, kwargs: Other arguments used inmodel.forwardfunction
CNN for MNIST
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x, dim=1)
summary(Net(), torch.zeros((1, 1, 28, 28)))----------------------------------------------------------------------------------------------------
Layer Kernel Shape Output Shape # Params (K) # Mult-Adds (M)
====================================================================================================
0_Conv2d [1, 10, 5, 5] [1, 10, 24, 24] 0.26 0.14
1_Conv2d [10, 20, 5, 5] [1, 20, 8, 8] 5.02 0.32
2_Dropout2d - [1, 20, 8, 8] - -
3_Linear [320, 50] [1, 50] 16.05 0.02
4_Linear [50, 10] [1, 10] 0.51 0.00
====================================================================================================
# Params: 21.84K
# Mult-Adds: 0.48M
----------------------------------------------------------------------------------------------------
RNN
class Net(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)
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
inputs = torch.zeros((100, 1), dtype=torch.long) # [length, batch_size]
summary(Net(), inputs)----------------------------------------------------------------------------------------------------
Layer Kernel Shape Output Shape # Params (K) # Mult-Adds (M)
====================================================================================================
0_Embedding [300, 20] [100, 1, 300] 6.00 0.01
1_LSTM - [100, 1, 512] 3,768.32 3.76
weight_ih_l0 [2048, 300]
weight_hh_l0 [2048, 512]
weight_ih_l1 [2048, 512]
weight_hh_l1 [2048, 512]
2_Linear [512, 20] [100, 1, 20] 10.26 0.01
====================================================================================================
# Params: 3,784.58K
# Mult-Adds: 3.78M
----------------------------------------------------------------------------------------------------
Recursive NN
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(64, 64, 3, 1, 1)
def forward(self, x):
out = self.conv1(x)
out = self.conv1(out)
return out
summary(Net(), torch.zeros((1, 64, 28, 28)))----------------------------------------------------------------------------------------------------
Layer Kernel Shape Output Shape # Params (K) # Mult-Adds (M)
====================================================================================================
0_Conv2d [64, 64, 3, 3] [1, 64, 28, 28] 36.93 28.90
1_Conv2d [64, 64, 3, 3] [1, 64, 28, 28] (recursive) 28.90
====================================================================================================
# Params: 36.93K
# Mult-Adds: 57.80M
----------------------------------------------------------------------------------------------------
Multiple arguments
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(64, 64, 3, 1, 1)
def forward(self, x, args1, args2):
out = self.conv1(x)
out = self.conv1(out)
return out
summary(Net(), torch.zeros((1, 64, 28, 28)), "args1", args2="args2")