modelsummary (Pytorch Model summary)
Keras style model.summary() in PyTorch, torchsummary
This is Pytorch library for visualization Improved tool of torchsummary and torchsummaryX. I was inspired by torchsummary and I written down code which i referred to. It is not care with number of Input parameter!
import torch
import torch.nn as nn
import torch.nn.functional as F
from modelsummary 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
summary(Net(), torch.zeros((1, 1, 28, 28)), show_input=True)
# show output shape
summary(Net(), torch.zeros((1, 1, 28, 28)), show_input=False)
-----------------------------------------------------------------------
Layer (type) Input Shape Param #
=======================================================================
Conv2d-1 [-1, 1, 28, 28] 260
Conv2d-2 [-1, 10, 12, 12] 5,020
Dropout2d-3 [-1, 20, 8, 8] 0
Linear-4 [-1, 320] 16,050
Linear-5 [-1, 50] 510
=======================================================================
Total params: 21,840
Trainable params: 21,840
Non-trainable params: 0
-----------------------------------------------------------------------
-----------------------------------------------------------------------
Layer (type) Output Shape Param #
=======================================================================
Conv2d-1 [-1, 10, 24, 24] 260
Conv2d-2 [-1, 20, 8, 8] 5,020
Dropout2d-3 [-1, 20, 8, 8] 0
Linear-4 [-1, 50] 16,050
Linear-5 [-1, 10] 510
=======================================================================
Total params: 21,840
Trainable params: 21,840
Non-trainable params: 0
-----------------------------------------------------------------------
Quick Start
Just download with pip modelsummary
pip install modelsummary and from modelsummary import summary
You can use this library like this. If you see more detail, Please see example code.
from modelsummary import summary
model = your_model_name()
# show input shape
summary(model, (input tensor you want), show_input=True)
# show output shape
summary(model, (input tensor you want), show_input=False)
# show hierarchical struct
summary(model, (input tensor you want), show_hierarchical=True)
summary function has this parameter optionsdef summary(model, *inputs, batch_size=-1, show_input=True, show_hierarchical=False)
Options
- model : your model class
- *input : your input tensor datas (Asterisk)
- batch_size :
-1is same with tensorNone - show_input : show input shape data, if this parameter is False, it will show output shape default : True
- show_hierarchical : show hierarchical data structure, default : False
Result
Run example using Transformer Model in Attention is all you need paper(2017)
- showing input shape
# show input shape
summary(model, enc_inputs, dec_inputs, show_input=True)
-----------------------------------------------------------------------
Layer (type) Input Shape Param #
=======================================================================
Encoder-1 [-1, 5] 0
Embedding-2 [-1, 5] 3,072
Embedding-3 [-1, 5] 3,072
EncoderLayer-4 [-1, 5, 512] 0
MultiHeadAttention-5 [-1, 5, 512] 0
Linear-6 [-1, 5, 512] 262,656
Linear-7 [-1, 5, 512] 262,656
Linear-8 [-1, 5, 512] 262,656
PoswiseFeedForwardNet-9 [-1, 5, 512] 0
Conv1d-10 [-1, 512, 5] 1,050,624
Conv1d-11 [-1, 2048, 5] 1,049,088
EncoderLayer-12 [-1, 5, 512] 0
MultiHeadAttention-13 [-1, 5, 512] 0
Linear-14 [-1, 5, 512] 262,656
Linear-15 [-1, 5, 512] 262,656
Linear-16 [-1, 5, 512] 262,656
PoswiseFeedForwardNet-17 [-1, 5, 512] 0
Conv1d-18 [-1, 512, 5] 1,050,624
Conv1d-19 [-1, 2048, 5] 1,049,088
EncoderLayer-20 [-1, 5, 512] 0
MultiHeadAttention-21 [-1, 5, 512] 0
Linear-22 [-1, 5, 512] 262,656
Linear-23 [-1, 5, 512] 262,656
Linear-24 [-1, 5, 512] 262,656
PoswiseFeedForwardNet-25 [-1, 5, 512] 0
Conv1d-26 [-1, 512, 5] 1,050,624
Conv1d-27 [-1, 2048, 5] 1,049,088
EncoderLayer-28 [-1, 5, 512] 0
MultiHeadAttention-29 [-1, 5, 512] 0
Linear-30 [-1, 5, 512] 262,656
Linear-31 [-1, 5, 512] 262,656
Linear-32 [-1, 5, 512] 262,656
PoswiseFeedForwardNet-33 [-1, 5, 512] 0
Conv1d-34 [-1, 512, 5] 1,050,624
Conv1d-35 [-1, 2048, 5] 1,049,088
EncoderLayer-36 [-1, 5, 512] 0
MultiHeadAttention-37 [-1, 5, 512] 0
Linear-38 [-1, 5, 512] 262,656
Linear-39 [-1, 5, 512] 262,656
Linear-40 [-1, 5, 512] 262,656
PoswiseFeedForwardNet-41 [-1, 5, 512] 0
Conv1d-42 [-1, 512, 5] 1,050,624
Conv1d-43 [-1, 2048, 5] 1,049,088
EncoderLayer-44 [-1, 5, 512] 0
MultiHeadAttention-45 [-1, 5, 512] 0
Linear-46 [-1, 5, 512] 262,656
Linear-47 [-1, 5, 512] 262,656
Linear-48 [-1, 5, 512] 262,656
PoswiseFeedForwardNet-49 [-1, 5, 512] 0
Conv1d-50 [-1, 512, 5] 1,050,624
Conv1d-51 [-1, 2048, 5] 1,049,088
Decoder-52 [-1, 5] 0
Embedding-53 [-1, 5] 3,584
Embedding-54 [-1, 5] 3,072
DecoderLayer-55 [-1, 5, 512] 0
MultiHeadAttention-56 [-1, 5, 512] 0
Linear-57 [-1, 5, 512] 262,656
Linear-58 [-1, 5, 512] 262,656
Linear-59 [-1, 5, 512] 262,656
MultiHeadAttention-60 [-1, 5, 512] 0
Linear-61 [-1, 5, 512] 262,656
Linear-62 [-1, 5, 512] 262,656
Linear-63 [-1, 5, 512] 262,656
PoswiseFeedForwardNet-64 [-1, 5, 512] 0
Conv1d-65 [-1, 512, 5] 1,050,624
Conv1d-66 [-1, 2048, 5] 1,049,088
DecoderLayer-67 [-1, 5, 512] 0
MultiHeadAttention-68 [-1, 5, 512] 0
Linear-69 [-1, 5, 512] 262,656
Linear-70 [-1, 5, 512] 262,656
Linear-71 [-1, 5, 512] 262,656
MultiHeadAttention-72 [-1, 5, 512] 0
Linear-73 [-1, 5, 512] 262,656
Linear-74 [-1, 5, 512] 262,656
Linear-75 [-1, 5, 512] 262,656
PoswiseFeedForwardNet-76 [-1, 5, 512] 0
Conv1d-77 [-1, 512, 5] 1,050,624
Conv1d-78 [-1, 2048, 5] 1,049,088
DecoderLayer-79 [-1, 5, 512] 0
MultiHeadAttention-80 [-1, 5, 512] 0
Linear-81 [-1, 5, 512] 262,656
Linear-82 [-1, 5, 512] 262,656
Linear-83 [-1, 5, 512] 262,656
MultiHeadAttention-84 [-1, 5, 512] 0
Linear-85 [-1, 5, 512] 262,656
Linear-86 [-1, 5, 512] 262,656
Linear-87 [-1, 5, 512] 262,656
PoswiseFeedForwardNet-88 [-1, 5, 512] 0
Conv1d-89 [-1, 512, 5] 1,050,624
Conv1d-90 [-1, 2048, 5] 1,049,088
DecoderLayer-91 [-1, 5, 512] 0
MultiHeadAttention-92 [-1, 5, 512] 0
Linear-93 [-1, 5, 512] 262,656
Linear-94 [-1, 5, 512] 262,656
Linear-95 [-1, 5, 512] 262,656
MultiHeadAttention-96 [-1, 5, 512] 0
Linear-97 [-1, 5, 512] 262,656
Linear-98 [-1, 5, 512] 262,656
Linear-99 [-1, 5, 512] 262,656
PoswiseFeedForwardNet-100 [-1, 5, 512] 0
Conv1d-101 [-1, 512, 5] 1,050,624
Conv1d-102 [-1, 2048, 5] 1,049,088
DecoderLayer-103 [-1, 5, 512] 0
MultiHeadAttention-104 [-1, 5, 512] 0
Linear-105 [-1, 5, 512] 262,656
Linear-106 [-1, 5, 512] 262,656
Linear-107 [-1, 5, 512] 262,656
MultiHeadAttention-108 [-1, 5, 512] 0
Linear-109 [-1, 5, 512] 262,656
Linear-110 [-1, 5, 512] 262,656
Linear-111 [-1, 5, 512] 262,656
PoswiseFeedForwardNet-112 [-1, 5, 512] 0
Conv1d-113 [-1, 512, 5] 1,050,624
Conv1d-114 [-1, 2048, 5] 1,049,088
DecoderLayer-115 [-1, 5, 512] 0
MultiHeadAttention-116 [-1, 5, 512] 0
Linear-117 [-1, 5, 512] 262,656
Linear-118 [-1, 5, 512] 262,656
Linear-119 [-1, 5, 512] 262,656
MultiHeadAttention-120 [-1, 5, 512] 0
Linear-121 [-1, 5, 512] 262,656
Linear-122 [-1, 5, 512] 262,656
Linear-123 [-1, 5, 512] 262,656
PoswiseFeedForwardNet-124 [-1, 5, 512] 0
Conv1d-125 [-1, 512, 5] 1,050,624
Conv1d-126 [-1, 2048, 5] 1,049,088
Linear-127 [-1, 5, 512] 3,584
=======================================================================
Total params: 39,396,352
Trainable params: 39,390,208
Non-trainable params: 6,144
- showing output shape
# show output shape
summary(model, enc_inputs, dec_inputs, show_input=False)
-----------------------------------------------------------------------
Layer (type) Output Shape Param #
=======================================================================
Embedding-1 [-1, 5, 512] 3,072
Embedding-2 [-1, 5, 512] 3,072
Linear-3 [-1, 5, 512] 262,656
Linear-4 [-1, 5, 512] 262,656
Linear-5 [-1, 5, 512] 262,656
MultiHeadAttention-6 [-1, 8, 5, 5] 0
Conv1d-7 [-1, 2048, 5] 1,050,624
Conv1d-8 [-1, 512, 5] 1,049,088
PoswiseFeedForwardNet-9 [-1, 5, 512] 0
EncoderLayer-10 [-1, 8, 5, 5] 0
Linear-11 [-1, 5, 512] 262,656
Linear-12 [-1, 5, 512] 262,656
Linear-13 [-1, 5, 512] 262,656
MultiHeadAttention-14 [-1, 8, 5, 5] 0
Conv1d-15 [-1, 2048, 5] 1,050,624
Conv1d-16 [-1, 512, 5] 1,049,088
PoswiseFeedForwardNet-17 [-1, 5, 512] 0
EncoderLayer-18 [-1, 8, 5, 5] 0
Linear-19 [-1, 5, 512] 262,656
Linear-20 [-1, 5, 512] 262,656
Linear-21 [-1, 5, 512] 262,656
MultiHeadAttention-22 [-1, 8, 5, 5] 0
Conv1d-23 [-1, 2048, 5] 1,050,624
Conv1d-24 [-1, 512, 5] 1,049,088
PoswiseFeedForwardNet-25 [-1, 5, 512] 0
EncoderLayer-26 [-1, 8, 5, 5] 0
Linear-27 [-1, 5, 512] 262,656
Linear-28 [-1, 5, 512] 262,656
Linear-29 [-1, 5, 512] 262,656
MultiHeadAttention-30 [-1, 8, 5, 5] 0
Conv1d-31 [-1, 2048, 5] 1,050,624
Conv1d-32 [-1, 512, 5] 1,049,088
PoswiseFeedForwardNet-33 [-1, 5, 512] 0
EncoderLayer-34 [-1, 8, 5, 5] 0
Linear-35 [-1, 5, 512] 262,656
Linear-36 [-1, 5, 512] 262,656
Linear-37 [-1, 5, 512] 262,656
MultiHeadAttention-38 [-1, 8, 5, 5] 0
Conv1d-39 [-1, 2048, 5] 1,050,624
Conv1d-40 [-1, 512, 5] 1,049,088
PoswiseFeedForwardNet-41 [-1, 5, 512] 0
EncoderLayer-42 [-1, 8, 5, 5] 0
Linear-43 [-1, 5, 512] 262,656
Linear-44 [-1, 5, 512] 262,656
Linear-45 [-1, 5, 512] 262,656
MultiHeadAttention-46 [-1, 8, 5, 5] 0
Conv1d-47 [-1, 2048, 5] 1,050,624
Conv1d-48 [-1, 512, 5] 1,049,088
PoswiseFeedForwardNet-49 [-1, 5, 512] 0
EncoderLayer-50 [-1, 8, 5, 5] 0
Encoder-51 [-1, 8, 5, 5] 0
Embedding-52 [-1, 5, 512] 3,584
Embedding-53 [-1, 5, 512] 3,072
Linear-54 [-1, 5, 512] 262,656
Linear-55 [-1, 5, 512] 262,656
Linear-56 [-1, 5, 512] 262,656
MultiHeadAttention-57 [-1, 8, 5, 5] 0
Linear-58 [-1, 5, 512] 262,656
Linear-59 [-1, 5, 512] 262,656
Linear-60 [-1, 5, 512] 262,656
MultiHeadAttention-61 [-1, 8, 5, 5] 0
Conv1d-62 [-1, 2048, 5] 1,050,624
Conv1d-63 [-1, 512, 5] 1,049,088
PoswiseFeedForwardNet-64 [-1, 5, 512] 0
DecoderLayer-65 [-1, 8, 5, 5] 0
Linear-66 [-1, 5, 512] 262,656
Linear-67 [-1, 5, 512] 262,656
Linear-68 [-1, 5, 512] 262,656
MultiHeadAttention-69 [-1, 8, 5, 5] 0
Linear-70 [-1, 5, 512] 262,656
Linear-71 [-1, 5, 512] 262,656
Linear-72 [-1, 5, 512] 262,656
MultiHeadAttention-73 [-1, 8, 5, 5] 0
Conv1d-74 [-1, 2048, 5] 1,050,624
Conv1d-75 [-1, 512, 5] 1,049,088
PoswiseFeedForwardNet-76 [-1, 5, 512] 0
DecoderLayer-77 [-1, 8, 5, 5] 0
Linear-78 [-1, 5, 512] 262,656
Linear-79 [-1, 5, 512] 262,656
Linear-80 [-1, 5, 512] 262,656
MultiHeadAttention-81 [-1, 8, 5, 5] 0
Linear-82 [-1, 5, 512] 262,656
Linear-83 [-1, 5, 512] 262,656
Linear-84 [-1, 5, 512] 262,656
MultiHeadAttention-85 [-1, 8, 5, 5] 0
Conv1d-86 [-1, 2048, 5] 1,050,624
Conv1d-87 [-1, 512, 5] 1,049,088
PoswiseFeedForwardNet-88 [-1, 5, 512] 0
DecoderLayer-89 [-1, 8, 5, 5] 0
Linear-90 [-1, 5, 512] 262,656
Linear-91 [-1, 5, 512] 262,656
Linear-92 [-1, 5, 512] 262,656
MultiHeadAttention-93 [-1, 8, 5, 5] 0
Linear-94 [-1, 5, 512] 262,656
Linear-95 [-1, 5, 512] 262,656
Linear-96 [-1, 5, 512] 262,656
MultiHeadAttention-97 [-1, 8, 5, 5] 0
Conv1d-98 [-1, 2048, 5] 1,050,624
Conv1d-99 [-1, 512, 5] 1,049,088
PoswiseFeedForwardNet-100 [-1, 5, 512] 0
DecoderLayer-101 [-1, 8, 5, 5] 0
Linear-102 [-1, 5, 512] 262,656
Linear-103 [-1, 5, 512] 262,656
Linear-104 [-1, 5, 512] 262,656
MultiHeadAttention-105 [-1, 8, 5, 5] 0
Linear-106 [-1, 5, 512] 262,656
Linear-107 [-1, 5, 512] 262,656
Linear-108 [-1, 5, 512] 262,656
MultiHeadAttention-109 [-1, 8, 5, 5] 0
Conv1d-110 [-1, 2048, 5] 1,050,624
Conv1d-111 [-1, 512, 5] 1,049,088
PoswiseFeedForwardNet-112 [-1, 5, 512] 0
DecoderLayer-113 [-1, 8, 5, 5] 0
Linear-114 [-1, 5, 512] 262,656
Linear-115 [-1, 5, 512] 262,656
Linear-116 [-1, 5, 512] 262,656
MultiHeadAttention-117 [-1, 8, 5, 5] 0
Linear-118 [-1, 5, 512] 262,656
Linear-119 [-1, 5, 512] 262,656
Linear-120 [-1, 5, 512] 262,656
MultiHeadAttention-121 [-1, 8, 5, 5] 0
Conv1d-122 [-1, 2048, 5] 1,050,624
Conv1d-123 [-1, 512, 5] 1,049,088
PoswiseFeedForwardNet-124 [-1, 5, 512] 0
DecoderLayer-125 [-1, 8, 5, 5] 0
Decoder-126 [-1, 8, 5, 5] 0
Linear-127 [-1, 5, 7] 3,584
=======================================================================
Total params: 39,396,352
Trainable params: 39,390,208
Non-trainable params: 6,144
-----------------------------------------------------------------------
- showing 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
Reference
code_reference = { '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 modelsummary 1.1.7
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SHA-256 checksum How to use checksums |
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twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.7.2
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