Pytorch Model Summary -- Keras style model.summary() for PyTorch
It is a Keras style model.summary() implementation for PyTorch
This is an Improved PyTorch library of modelsummary. Like in modelsummary, It does not care with number of Input parameter!
Improvements:
- For user defined pytorch layers, now
summarycan show layers inside it- some assumptions: when is an user defined layer, if any weight/params/bias is trainable, then it is assumed that this layer is trainable (but only trainable params are counted in Tr. Params #)
- Adding column counting only trainable parameters (it makes sense when there are user defined layers)
- Showing all input/output shapes, instead of showing only the first one
- example: LSTM layer return a Tensor and a tuple (Tensor, Tensor), then output_shape has three set of values
- Printing: table width defined dynamically
- Adding option to add hierarchical summary in output
- Adding batch_size value (when provided) in table footer
- fix bugs
Parameters
Default values have keras behavior
summary(model, *inputs, batch_size=-1, show_input=False, show_hierarchical=False,
print_summary=False, max_depth=1, show_parent_layers=False):
model: pytorch model object*inputs: ...batch_size: if provided, it is printed in summary tableshow_input: show input shape. Otherwise, output shape for each layer. (Default: False)show_hierarchical: in addition of summary table, return hierarchical view of the model (Default: False)print_summary: when true, is not required to use print function outsidesummarymethod (Default: False)max_depth: it specifies how many times it can go inside user defined layers to show them (Default: 1)show_parent_layer: it adds a column to show parent layers path until reaching current layer in depth. (Default: False)
import torch
import torch.nn as nn
import torch.nn.functional as F
from pytorch_model_summary import summary
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)
# show input shape
print(summary(Net(), torch.zeros((1, 1, 28, 28)), show_input=True))
# show output shape
print(summary(Net(), torch.zeros((1, 1, 28, 28)), show_input=False))
# show output shape and hierarchical view of net
print(summary(Net(), torch.zeros((1, 1, 28, 28)), show_input=False, show_hierarchical=True))
-----------------------------------------------------------------------
Layer (type) Input Shape Param # Tr. Param #
=======================================================================
Conv2d-1 [1, 1, 28, 28] 260 260
Conv2d-2 [1, 10, 12, 12] 5,020 5,020
Dropout2d-3 [1, 20, 8, 8] 0 0
Linear-4 [1, 320] 16,050 16,050
Linear-5 [1, 50] 510 510
=======================================================================
Total params: 21,840
Trainable params: 21,840
Non-trainable params: 0
-----------------------------------------------------------------------
-----------------------------------------------------------------------
Layer (type) Output Shape Param # Tr. Param #
=======================================================================
Conv2d-1 [1, 10, 24, 24] 260 260
Conv2d-2 [1, 20, 8, 8] 5,020 5,020
Dropout2d-3 [1, 20, 8, 8] 0 0
Linear-4 [1, 50] 16,050 16,050
Linear-5 [1, 10] 510 510
=======================================================================
Total params: 21,840
Trainable params: 21,840
Non-trainable params: 0
-----------------------------------------------------------------------
-----------------------------------------------------------------------
Layer (type) Output Shape Param # Tr. Param #
=======================================================================
Conv2d-1 [1, 10, 24, 24] 260 260
Conv2d-2 [1, 20, 8, 8] 5,020 5,020
Dropout2d-3 [1, 20, 8, 8] 0 0
Linear-4 [1, 50] 16,050 16,050
Linear-5 [1, 10] 510 510
=======================================================================
Total params: 21,840
Trainable params: 21,840
Non-trainable params: 0
-----------------------------------------------------------------------
=========================== Hierarchical Summary ===========================
Net(
(conv1): Conv2d(1, 10, kernel_size=(5, 5), stride=(1, 1)), 260 params
(conv2): Conv2d(10, 20, kernel_size=(5, 5), stride=(1, 1)), 5,020 params
(conv2_drop): Dropout2d(p=0.5), 0 params
(fc1): Linear(in_features=320, out_features=50, bias=True), 16,050 params
(fc2): Linear(in_features=50, out_features=10, bias=True), 510 params
), 21,840 params
============================================================================
Quick Start
Just download with pip
pip install pytorch-model-summary and
from pytorch_model_summary import summary
or
import pytorch_model_summary as pms
pms.summary([params])
to avoid reference conflicts with other methods in your code
You can use this library like this. If you want to see more detail, Please see examples below.
Examples using different set of parameters
Run example using Transformer Model in Attention is all you need paper(2017)
- showing input shape
# show input shape
pms.summary(model, enc_inputs, dec_inputs, show_input=True, print_summary=True)
-----------------------------------------------------------------------------------
Layer (type) Input Shape Param # Tr. Param #
===================================================================================
Encoder-1 [1, 5] 17,332,224 17,329,152
Decoder-2 [1, 5], [1, 5], [1, 5, 512] 22,060,544 22,057,472
Linear-3 [1, 5, 512] 3,584 3,584
===================================================================================
Total params: 39,396,352
Trainable params: 39,390,208
Non-trainable params: 6,144
-----------------------------------------------------------------------------------
- showing output shape and batch_size in table. In addition, also hierarchical summary version
# show output shape and batch_size in table. In addition, also hierarchical summary version
pms.summary(model, enc_inputs, dec_inputs, batch_size=1, show_hierarchical=True, print_summary=True)
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Layer (type) Output Shape Param # Tr. Param #
===========================================================================================================================================================================================================================================
Encoder-1 [1, 5, 512], [1, 8, 5, 5], [1, 8, 5, 5], [1, 8, 5, 5], [1, 8, 5, 5], [1, 8, 5, 5], [1, 8, 5, 5] 17,332,224 17,329,152
Decoder-2 [1, 5, 512], [1, 8, 5, 5], [1, 8, 5, 5], [1, 8, 5, 5], [1, 8, 5, 5], [1, 8, 5, 5], [1, 8, 5, 5], [1, 8, 5, 5], [1, 8, 5, 5], [1, 8, 5, 5], [1, 8, 5, 5], [1, 8, 5, 5], [1, 8, 5, 5] 22,060,544 22,057,472
Linear-3 [1, 5, 7] 3,584 3,584
===========================================================================================================================================================================================================================================
Total params: 39,396,352
Trainable params: 39,390,208
Non-trainable params: 6,144
Batch size: 1
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
================================ Hierarchical Summary ================================
Transformer(
(encoder): Encoder(
(src_emb): Embedding(6, 512), 3,072 params
(pos_emb): Embedding(6, 512), 3,072 params
(layers): ModuleList(
(0): EncoderLayer(
(enc_self_attn): MultiHeadAttention(
(W_Q): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_K): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_V): Linear(in_features=512, out_features=512, bias=True), 262,656 params
), 787,968 params
(pos_ffn): PoswiseFeedForwardNet(
(conv1): Conv1d(512, 2048, kernel_size=(1,), stride=(1,)), 1,050,624 params
(conv2): Conv1d(2048, 512, kernel_size=(1,), stride=(1,)), 1,049,088 params
), 2,099,712 params
), 2,887,680 params
(1): EncoderLayer(
(enc_self_attn): MultiHeadAttention(
(W_Q): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_K): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_V): Linear(in_features=512, out_features=512, bias=True), 262,656 params
), 787,968 params
(pos_ffn): PoswiseFeedForwardNet(
(conv1): Conv1d(512, 2048, kernel_size=(1,), stride=(1,)), 1,050,624 params
(conv2): Conv1d(2048, 512, kernel_size=(1,), stride=(1,)), 1,049,088 params
), 2,099,712 params
), 2,887,680 params
(2): EncoderLayer(
(enc_self_attn): MultiHeadAttention(
(W_Q): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_K): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_V): Linear(in_features=512, out_features=512, bias=True), 262,656 params
), 787,968 params
(pos_ffn): PoswiseFeedForwardNet(
(conv1): Conv1d(512, 2048, kernel_size=(1,), stride=(1,)), 1,050,624 params
(conv2): Conv1d(2048, 512, kernel_size=(1,), stride=(1,)), 1,049,088 params
), 2,099,712 params
), 2,887,680 params
(3): EncoderLayer(
(enc_self_attn): MultiHeadAttention(
(W_Q): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_K): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_V): Linear(in_features=512, out_features=512, bias=True), 262,656 params
), 787,968 params
(pos_ffn): PoswiseFeedForwardNet(
(conv1): Conv1d(512, 2048, kernel_size=(1,), stride=(1,)), 1,050,624 params
(conv2): Conv1d(2048, 512, kernel_size=(1,), stride=(1,)), 1,049,088 params
), 2,099,712 params
), 2,887,680 params
(4): EncoderLayer(
(enc_self_attn): MultiHeadAttention(
(W_Q): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_K): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_V): Linear(in_features=512, out_features=512, bias=True), 262,656 params
), 787,968 params
(pos_ffn): PoswiseFeedForwardNet(
(conv1): Conv1d(512, 2048, kernel_size=(1,), stride=(1,)), 1,050,624 params
(conv2): Conv1d(2048, 512, kernel_size=(1,), stride=(1,)), 1,049,088 params
), 2,099,712 params
), 2,887,680 params
(5): EncoderLayer(
(enc_self_attn): MultiHeadAttention(
(W_Q): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_K): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_V): Linear(in_features=512, out_features=512, bias=True), 262,656 params
), 787,968 params
(pos_ffn): PoswiseFeedForwardNet(
(conv1): Conv1d(512, 2048, kernel_size=(1,), stride=(1,)), 1,050,624 params
(conv2): Conv1d(2048, 512, kernel_size=(1,), stride=(1,)), 1,049,088 params
), 2,099,712 params
), 2,887,680 params
), 17,326,080 params
), 17,332,224 params
(decoder): Decoder(
(tgt_emb): Embedding(7, 512), 3,584 params
(pos_emb): Embedding(6, 512), 3,072 params
(layers): ModuleList(
(0): DecoderLayer(
(dec_self_attn): MultiHeadAttention(
(W_Q): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_K): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_V): Linear(in_features=512, out_features=512, bias=True), 262,656 params
), 787,968 params
(dec_enc_attn): MultiHeadAttention(
(W_Q): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_K): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_V): Linear(in_features=512, out_features=512, bias=True), 262,656 params
), 787,968 params
(pos_ffn): PoswiseFeedForwardNet(
(conv1): Conv1d(512, 2048, kernel_size=(1,), stride=(1,)), 1,050,624 params
(conv2): Conv1d(2048, 512, kernel_size=(1,), stride=(1,)), 1,049,088 params
), 2,099,712 params
), 3,675,648 params
(1): DecoderLayer(
(dec_self_attn): MultiHeadAttention(
(W_Q): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_K): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_V): Linear(in_features=512, out_features=512, bias=True), 262,656 params
), 787,968 params
(dec_enc_attn): MultiHeadAttention(
(W_Q): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_K): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_V): Linear(in_features=512, out_features=512, bias=True), 262,656 params
), 787,968 params
(pos_ffn): PoswiseFeedForwardNet(
(conv1): Conv1d(512, 2048, kernel_size=(1,), stride=(1,)), 1,050,624 params
(conv2): Conv1d(2048, 512, kernel_size=(1,), stride=(1,)), 1,049,088 params
), 2,099,712 params
), 3,675,648 params
(2): DecoderLayer(
(dec_self_attn): MultiHeadAttention(
(W_Q): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_K): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_V): Linear(in_features=512, out_features=512, bias=True), 262,656 params
), 787,968 params
(dec_enc_attn): MultiHeadAttention(
(W_Q): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_K): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_V): Linear(in_features=512, out_features=512, bias=True), 262,656 params
), 787,968 params
(pos_ffn): PoswiseFeedForwardNet(
(conv1): Conv1d(512, 2048, kernel_size=(1,), stride=(1,)), 1,050,624 params
(conv2): Conv1d(2048, 512, kernel_size=(1,), stride=(1,)), 1,049,088 params
), 2,099,712 params
), 3,675,648 params
(3): DecoderLayer(
(dec_self_attn): MultiHeadAttention(
(W_Q): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_K): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_V): Linear(in_features=512, out_features=512, bias=True), 262,656 params
), 787,968 params
(dec_enc_attn): MultiHeadAttention(
(W_Q): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_K): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_V): Linear(in_features=512, out_features=512, bias=True), 262,656 params
), 787,968 params
(pos_ffn): PoswiseFeedForwardNet(
(conv1): Conv1d(512, 2048, kernel_size=(1,), stride=(1,)), 1,050,624 params
(conv2): Conv1d(2048, 512, kernel_size=(1,), stride=(1,)), 1,049,088 params
), 2,099,712 params
), 3,675,648 params
(4): DecoderLayer(
(dec_self_attn): MultiHeadAttention(
(W_Q): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_K): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_V): Linear(in_features=512, out_features=512, bias=True), 262,656 params
), 787,968 params
(dec_enc_attn): MultiHeadAttention(
(W_Q): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_K): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_V): Linear(in_features=512, out_features=512, bias=True), 262,656 params
), 787,968 params
(pos_ffn): PoswiseFeedForwardNet(
(conv1): Conv1d(512, 2048, kernel_size=(1,), stride=(1,)), 1,050,624 params
(conv2): Conv1d(2048, 512, kernel_size=(1,), stride=(1,)), 1,049,088 params
), 2,099,712 params
), 3,675,648 params
(5): DecoderLayer(
(dec_self_attn): MultiHeadAttention(
(W_Q): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_K): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_V): Linear(in_features=512, out_features=512, bias=True), 262,656 params
), 787,968 params
(dec_enc_attn): MultiHeadAttention(
(W_Q): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_K): Linear(in_features=512, out_features=512, bias=True), 262,656 params
(W_V): Linear(in_features=512, out_features=512, bias=True), 262,656 params
), 787,968 params
(pos_ffn): PoswiseFeedForwardNet(
(conv1): Conv1d(512, 2048, kernel_size=(1,), stride=(1,)), 1,050,624 params
(conv2): Conv1d(2048, 512, kernel_size=(1,), stride=(1,)), 1,049,088 params
), 2,099,712 params
), 3,675,648 params
), 22,053,888 params
), 22,060,544 params
(projection): Linear(in_features=512, out_features=7, bias=False), 3,584 params
), 39,396,352 params
======================================================================================
- showing layers until depth 2
# show layers until depth 2
pms.summary(model, enc_inputs, dec_inputs, max_depth=2, print_summary=True)
-----------------------------------------------------------------------------------------------
Layer (type) Output Shape Param # Tr. Param #
===============================================================================================
Embedding-1 [1, 5, 512] 3,072 3,072
Embedding-2 [1, 5, 512] 3,072 0
EncoderLayer-3 [1, 5, 512], [1, 8, 5, 5] 2,887,680 2,887,680
EncoderLayer-4 [1, 5, 512], [1, 8, 5, 5] 2,887,680 2,887,680
EncoderLayer-5 [1, 5, 512], [1, 8, 5, 5] 2,887,680 2,887,680
EncoderLayer-6 [1, 5, 512], [1, 8, 5, 5] 2,887,680 2,887,680
EncoderLayer-7 [1, 5, 512], [1, 8, 5, 5] 2,887,680 2,887,680
EncoderLayer-8 [1, 5, 512], [1, 8, 5, 5] 2,887,680 2,887,680
Embedding-9 [1, 5, 512] 3,584 3,584
Embedding-10 [1, 5, 512] 3,072 0
DecoderLayer-11 [1, 5, 512], [1, 8, 5, 5], [1, 8, 5, 5] 3,675,648 3,675,648
DecoderLayer-12 [1, 5, 512], [1, 8, 5, 5], [1, 8, 5, 5] 3,675,648 3,675,648
DecoderLayer-13 [1, 5, 512], [1, 8, 5, 5], [1, 8, 5, 5] 3,675,648 3,675,648
DecoderLayer-14 [1, 5, 512], [1, 8, 5, 5], [1, 8, 5, 5] 3,675,648 3,675,648
DecoderLayer-15 [1, 5, 512], [1, 8, 5, 5], [1, 8, 5, 5] 3,675,648 3,675,648
DecoderLayer-16 [1, 5, 512], [1, 8, 5, 5], [1, 8, 5, 5] 3,675,648 3,675,648
Linear-17 [1, 5, 7] 3,584 3,584
===============================================================================================
Total params: 39,396,352
Trainable params: 39,390,208
Non-trainable params: 6,144
-----------------------------------------------------------------------------------------------
- showing deepest layers
# show deepest layers
pms.summary(model, enc_inputs, dec_inputs, max_depth=None, print_summary=True)
-----------------------------------------------------------------------
Layer (type) Output Shape Param # Tr. Param #
=======================================================================
Embedding-1 [1, 5, 512] 3,072 3,072
Embedding-2 [1, 5, 512] 3,072 0
Linear-3 [1, 5, 512] 262,656 262,656
Linear-4 [1, 5, 512] 262,656 262,656
Linear-5 [1, 5, 512] 262,656 262,656
Conv1d-6 [1, 2048, 5] 1,050,624 1,050,624
Conv1d-7 [1, 512, 5] 1,049,088 1,049,088
Linear-8 [1, 5, 512] 262,656 262,656
Linear-9 [1, 5, 512] 262,656 262,656
Linear-10 [1, 5, 512] 262,656 262,656
Conv1d-11 [1, 2048, 5] 1,050,624 1,050,624
Conv1d-12 [1, 512, 5] 1,049,088 1,049,088
Linear-13 [1, 5, 512] 262,656 262,656
Linear-14 [1, 5, 512] 262,656 262,656
Linear-15 [1, 5, 512] 262,656 262,656
Conv1d-16 [1, 2048, 5] 1,050,624 1,050,624
Conv1d-17 [1, 512, 5] 1,049,088 1,049,088
Linear-18 [1, 5, 512] 262,656 262,656
Linear-19 [1, 5, 512] 262,656 262,656
Linear-20 [1, 5, 512] 262,656 262,656
Conv1d-21 [1, 2048, 5] 1,050,624 1,050,624
Conv1d-22 [1, 512, 5] 1,049,088 1,049,088
Linear-23 [1, 5, 512] 262,656 262,656
Linear-24 [1, 5, 512] 262,656 262,656
Linear-25 [1, 5, 512] 262,656 262,656
Conv1d-26 [1, 2048, 5] 1,050,624 1,050,624
Conv1d-27 [1, 512, 5] 1,049,088 1,049,088
Linear-28 [1, 5, 512] 262,656 262,656
Linear-29 [1, 5, 512] 262,656 262,656
Linear-30 [1, 5, 512] 262,656 262,656
Conv1d-31 [1, 2048, 5] 1,050,624 1,050,624
Conv1d-32 [1, 512, 5] 1,049,088 1,049,088
Embedding-33 [1, 5, 512] 3,584 3,584
Embedding-34 [1, 5, 512] 3,072 0
Linear-35 [1, 5, 512] 262,656 262,656
Linear-36 [1, 5, 512] 262,656 262,656
Linear-37 [1, 5, 512] 262,656 262,656
Linear-38 [1, 5, 512] 262,656 262,656
Linear-39 [1, 5, 512] 262,656 262,656
Linear-40 [1, 5, 512] 262,656 262,656
Conv1d-41 [1, 2048, 5] 1,050,624 1,050,624
Conv1d-42 [1, 512, 5] 1,049,088 1,049,088
Linear-43 [1, 5, 512] 262,656 262,656
Linear-44 [1, 5, 512] 262,656 262,656
Linear-45 [1, 5, 512] 262,656 262,656
Linear-46 [1, 5, 512] 262,656 262,656
Linear-47 [1, 5, 512] 262,656 262,656
Linear-48 [1, 5, 512] 262,656 262,656
Conv1d-49 [1, 2048, 5] 1,050,624 1,050,624
Conv1d-50 [1, 512, 5] 1,049,088 1,049,088
Linear-51 [1, 5, 512] 262,656 262,656
Linear-52 [1, 5, 512] 262,656 262,656
Linear-53 [1, 5, 512] 262,656 262,656
Linear-54 [1, 5, 512] 262,656 262,656
Linear-55 [1, 5, 512] 262,656 262,656
Linear-56 [1, 5, 512] 262,656 262,656
Conv1d-57 [1, 2048, 5] 1,050,624 1,050,624
Conv1d-58 [1, 512, 5] 1,049,088 1,049,088
Linear-59 [1, 5, 512] 262,656 262,656
Linear-60 [1, 5, 512] 262,656 262,656
Linear-61 [1, 5, 512] 262,656 262,656
Linear-62 [1, 5, 512] 262,656 262,656
Linear-63 [1, 5, 512] 262,656 262,656
Linear-64 [1, 5, 512] 262,656 262,656
Conv1d-65 [1, 2048, 5] 1,050,624 1,050,624
Conv1d-66 [1, 512, 5] 1,049,088 1,049,088
Linear-67 [1, 5, 512] 262,656 262,656
Linear-68 [1, 5, 512] 262,656 262,656
Linear-69 [1, 5, 512] 262,656 262,656
Linear-70 [1, 5, 512] 262,656 262,656
Linear-71 [1, 5, 512] 262,656 262,656
Linear-72 [1, 5, 512] 262,656 262,656
Conv1d-73 [1, 2048, 5] 1,050,624 1,050,624
Conv1d-74 [1, 512, 5] 1,049,088 1,049,088
Linear-75 [1, 5, 512] 262,656 262,656
Linear-76 [1, 5, 512] 262,656 262,656
Linear-77 [1, 5, 512] 262,656 262,656
Linear-78 [1, 5, 512] 262,656 262,656
Linear-79 [1, 5, 512] 262,656 262,656
Linear-80 [1, 5, 512] 262,656 262,656
Conv1d-81 [1, 2048, 5] 1,050,624 1,050,624
Conv1d-82 [1, 512, 5] 1,049,088 1,049,088
Linear-83 [1, 5, 7] 3,584 3,584
=======================================================================
Total params: 39,396,352
Trainable params: 39,390,208
Non-trainable params: 6,144
-----------------------------------------------------------------------
- showing layers until depth 3 and adding column with parent layers
# show layers until depth 3 and add column with parent layers
pms.summary(model, enc_inputs, dec_inputs, max_depth=3, show_parent_layers=True, print_summary=True)
-----------------------------------------------------------------------------------------------------------------------------
Parent Layers Layer (type) Output Shape Param # Tr. Param #
=============================================================================================================================
Transformer/Encoder Embedding-1 [1, 5, 512] 3,072 3,072
Transformer/Encoder Embedding-2 [1, 5, 512] 3,072 0
Transformer/Encoder/EncoderLayer MultiHeadAttention-3 [1, 5, 512], [1, 8, 5, 5] 787,968 787,968
Transformer/Encoder/EncoderLayer PoswiseFeedForwardNet-4 [1, 5, 512] 2,099,712 2,099,712
Transformer/Encoder/EncoderLayer MultiHeadAttention-5 [1, 5, 512], [1, 8, 5, 5] 787,968 787,968
Transformer/Encoder/EncoderLayer PoswiseFeedForwardNet-6 [1, 5, 512] 2,099,712 2,099,712
Transformer/Encoder/EncoderLayer MultiHeadAttention-7 [1, 5, 512], [1, 8, 5, 5] 787,968 787,968
Transformer/Encoder/EncoderLayer PoswiseFeedForwardNet-8 [1, 5, 512] 2,099,712 2,099,712
Transformer/Encoder/EncoderLayer MultiHeadAttention-9 [1, 5, 512], [1, 8, 5, 5] 787,968 787,968
Transformer/Encoder/EncoderLayer PoswiseFeedForwardNet-10 [1, 5, 512] 2,099,712 2,099,712
Transformer/Encoder/EncoderLayer MultiHeadAttention-11 [1, 5, 512], [1, 8, 5, 5] 787,968 787,968
Transformer/Encoder/EncoderLayer PoswiseFeedForwardNet-12 [1, 5, 512] 2,099,712 2,099,712
Transformer/Encoder/EncoderLayer MultiHeadAttention-13 [1, 5, 512], [1, 8, 5, 5] 787,968 787,968
Transformer/Encoder/EncoderLayer PoswiseFeedForwardNet-14 [1, 5, 512] 2,099,712 2,099,712
Transformer/Decoder Embedding-15 [1, 5, 512] 3,584 3,584
Transformer/Decoder Embedding-16 [1, 5, 512] 3,072 0
Transformer/Decoder/DecoderLayer MultiHeadAttention-17 [1, 5, 512], [1, 8, 5, 5] 787,968 787,968
Transformer/Decoder/DecoderLayer MultiHeadAttention-18 [1, 5, 512], [1, 8, 5, 5] 787,968 787,968
Transformer/Decoder/DecoderLayer PoswiseFeedForwardNet-19 [1, 5, 512] 2,099,712 2,099,712
Transformer/Decoder/DecoderLayer MultiHeadAttention-20 [1, 5, 512], [1, 8, 5, 5] 787,968 787,968
Transformer/Decoder/DecoderLayer MultiHeadAttention-21 [1, 5, 512], [1, 8, 5, 5] 787,968 787,968
Transformer/Decoder/DecoderLayer PoswiseFeedForwardNet-22 [1, 5, 512] 2,099,712 2,099,712
Transformer/Decoder/DecoderLayer MultiHeadAttention-23 [1, 5, 512], [1, 8, 5, 5] 787,968 787,968
Transformer/Decoder/DecoderLayer MultiHeadAttention-24 [1, 5, 512], [1, 8, 5, 5] 787,968 787,968
Transformer/Decoder/DecoderLayer PoswiseFeedForwardNet-25 [1, 5, 512] 2,099,712 2,099,712
Transformer/Decoder/DecoderLayer MultiHeadAttention-26 [1, 5, 512], [1, 8, 5, 5] 787,968 787,968
Transformer/Decoder/DecoderLayer MultiHeadAttention-27 [1, 5, 512], [1, 8, 5, 5] 787,968 787,968
Transformer/Decoder/DecoderLayer PoswiseFeedForwardNet-28 [1, 5, 512] 2,099,712 2,099,712
Transformer/Decoder/DecoderLayer MultiHeadAttention-29 [1, 5, 512], [1, 8, 5, 5] 787,968 787,968
Transformer/Decoder/DecoderLayer MultiHeadAttention-30 [1, 5, 512], [1, 8, 5, 5] 787,968 787,968
Transformer/Decoder/DecoderLayer PoswiseFeedForwardNet-31 [1, 5, 512] 2,099,712 2,099,712
Transformer/Decoder/DecoderLayer MultiHeadAttention-32 [1, 5, 512], [1, 8, 5, 5] 787,968 787,968
Transformer/Decoder/DecoderLayer MultiHeadAttention-33 [1, 5, 512], [1, 8, 5, 5] 787,968 787,968
Transformer/Decoder/DecoderLayer PoswiseFeedForwardNet-34 [1, 5, 512] 2,099,712 2,099,712
Transformer Linear-35 [1, 5, 7] 3,584 3,584
=============================================================================================================================
Total params: 39,396,352
Trainable params: 39,390,208
Non-trainable params: 6,144
-----------------------------------------------------------------------------------------------------------------------------
Reference
code_reference = { 'https://github.com/graykode/modelsummary',
'https://github.com/pytorch/pytorch/issues/2001',
'https://gist.github.com/HTLife/b6640af9d6e7d765411f8aa9aa94b837',
'https://github.com/sksq96/pytorch-summary',
'Inspired by https://github.com/sksq96/pytorch-summary'}
1 maintainer
Metadata
1 maintainer
Release files for pytorch-model-summary 0.1.2
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|---|---|---|---|---|
| pytorch_model_summary-0.1.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 24.2 kB
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