Resumen del modelo en pytorch
¿Hay alguna manera, puedo imprimir el resumen de un modelo en PyTorch como lo hace el método model.summary()
en Keras de la siguiente manera?
Model Summary:
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_1 (InputLayer) (None, 1, 15, 27) 0
____________________________________________________________________________________________________
convolution2d_1 (Convolution2D) (None, 8, 15, 27) 872 input_1[0][0]
____________________________________________________________________________________________________
maxpooling2d_1 (MaxPooling2D) (None, 8, 7, 27) 0 convolution2d_1[0][0]
____________________________________________________________________________________________________
flatten_1 (Flatten) (None, 1512) 0 maxpooling2d_1[0][0]
____________________________________________________________________________________________________
dense_1 (Dense) (None, 1) 1513 flatten_1[0][0]
====================================================================================================
Total params: 2,385
Trainable params: 2,385
Non-trainable params: 0
5 answers
Mientras que no obtendrá información tan detallada sobre el modelo como en el modelo de Keras.resumen, simplemente imprimiendo el modelo le dará alguna idea sobre las diferentes capas involucradas y sus especificaciones.
Por ejemplo:
from torchvision import models
model = models.vgg16()
print(model)
La salida en este caso sería algo como sigue:
VGG (
(features): Sequential (
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU (inplace)
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): ReLU (inplace)
(4): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))
(5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(6): ReLU (inplace)
(7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(8): ReLU (inplace)
(9): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))
(10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU (inplace)
(12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(13): ReLU (inplace)
(14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(15): ReLU (inplace)
(16): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))
(17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(18): ReLU (inplace)
(19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(20): ReLU (inplace)
(21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(22): ReLU (inplace)
(23): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))
(24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(25): ReLU (inplace)
(26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(27): ReLU (inplace)
(28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(29): ReLU (inplace)
(30): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))
)
(classifier): Sequential (
(0): Dropout (p = 0.5)
(1): Linear (25088 -> 4096)
(2): ReLU (inplace)
(3): Dropout (p = 0.5)
(4): Linear (4096 -> 4096)
(5): ReLU (inplace)
(6): Linear (4096 -> 1000)
)
)
Ahora podría, como se menciona en Kashyap, usar el método state_dict
para obtener los pesos de las diferentes capas. Pero el uso de esta lista de las capas tal vez proporcionar más dirección es la creación de una función de ayuda para obtener que Keras como resumen del modelo! Espero que esto ayude!
Warning: date(): Invalid date.timezone value 'Europe/Kyiv', we selected the timezone 'UTC' for now. in /var/www/agent_stack/data/www/ajaxhispano.com/template/agent.layouts/content.php on line 61
2017-05-23 12:02:02
Esto mostrará los pesos y parámetros de un modelo (pero no la forma de salida).
from torch.nn.modules.module import _addindent
import torch
import numpy as np
def torch_summarize(model, show_weights=True, show_parameters=True):
"""Summarizes torch model by showing trainable parameters and weights."""
tmpstr = model.__class__.__name__ + ' (\n'
for key, module in model._modules.items():
# if it contains layers let call it recursively to get params and weights
if type(module) in [
torch.nn.modules.container.Container,
torch.nn.modules.container.Sequential
]:
modstr = torch_summarize(module)
else:
modstr = module.__repr__()
modstr = _addindent(modstr, 2)
params = sum([np.prod(p.size()) for p in module.parameters()])
weights = tuple([tuple(p.size()) for p in module.parameters()])
tmpstr += ' (' + key + '): ' + modstr
if show_weights:
tmpstr += ', weights={}'.format(weights)
if show_parameters:
tmpstr += ', parameters={}'.format(params)
tmpstr += '\n'
tmpstr = tmpstr + ')'
return tmpstr
# Test
import torchvision.models as models
model = models.alexnet()
print(torch_summarize(model))
# # Output
# AlexNet (
# (features): Sequential (
# (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2)), weights=((64, 3, 11, 11), (64,)), parameters=23296
# (1): ReLU (inplace), weights=(), parameters=0
# (2): MaxPool2d (size=(3, 3), stride=(2, 2), dilation=(1, 1)), weights=(), parameters=0
# (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2)), weights=((192, 64, 5, 5), (192,)), parameters=307392
# (4): ReLU (inplace), weights=(), parameters=0
# (5): MaxPool2d (size=(3, 3), stride=(2, 2), dilation=(1, 1)), weights=(), parameters=0
# (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), weights=((384, 192, 3, 3), (384,)), parameters=663936
# (7): ReLU (inplace), weights=(), parameters=0
# (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), weights=((256, 384, 3, 3), (256,)), parameters=884992
# (9): ReLU (inplace), weights=(), parameters=0
# (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), weights=((256, 256, 3, 3), (256,)), parameters=590080
# (11): ReLU (inplace), weights=(), parameters=0
# (12): MaxPool2d (size=(3, 3), stride=(2, 2), dilation=(1, 1)), weights=(), parameters=0
# ), weights=((64, 3, 11, 11), (64,), (192, 64, 5, 5), (192,), (384, 192, 3, 3), (384,), (256, 384, 3, 3), (256,), (256, 256, 3, 3), (256,)), parameters=2469696
# (classifier): Sequential (
# (0): Dropout (p = 0.5), weights=(), parameters=0
# (1): Linear (9216 -> 4096), weights=((4096, 9216), (4096,)), parameters=37752832
# (2): ReLU (inplace), weights=(), parameters=0
# (3): Dropout (p = 0.5), weights=(), parameters=0
# (4): Linear (4096 -> 4096), weights=((4096, 4096), (4096,)), parameters=16781312
# (5): ReLU (inplace), weights=(), parameters=0
# (6): Linear (4096 -> 1000), weights=((1000, 4096), (1000,)), parameters=4097000
# ), weights=((4096, 9216), (4096,), (4096, 4096), (4096,), (1000, 4096), (1000,)), parameters=58631144
# )
Editar: isaykatsman tiene un PR pytorch para agregar un model.summary()
que es exactamente como keras https://github.com/pytorch/pytorch/pull/3043/files
Warning: date(): Invalid date.timezone value 'Europe/Kyiv', we selected the timezone 'UTC' for now. in /var/www/agent_stack/data/www/ajaxhispano.com/template/agent.layouts/content.php on line 61
2018-02-15 02:21:28
Sí, puede obtener una representación exacta de Keras, usando el paquete pytorch-summary.
Ejemplo para VGG16
from torchvision import models
from torchsummary import summary
vgg = models.vgg16()
summary(vgg, (3, 224, 224))
----------------------------------------------------------------
Layer (type) Output Shpae Param #
================================================================
Conv2d-1 [-1, 64, 224, 224] 1792
ReLU-2 [-1, 64, 224, 224] 0
Conv2d-3 [-1, 64, 224, 224] 36928
ReLU-4 [-1, 64, 224, 224] 0
MaxPool2d-5 [-1, 64, 112, 112] 0
Conv2d-6 [-1, 128, 112, 112] 73856
ReLU-7 [-1, 128, 112, 112] 0
Conv2d-8 [-1, 128, 112, 112] 147584
ReLU-9 [-1, 128, 112, 112] 0
MaxPool2d-10 [-1, 128, 56, 56] 0
Conv2d-11 [-1, 256, 56, 56] 295168
ReLU-12 [-1, 256, 56, 56] 0
Conv2d-13 [-1, 256, 56, 56] 590080
ReLU-14 [-1, 256, 56, 56] 0
Conv2d-15 [-1, 256, 56, 56] 590080
ReLU-16 [-1, 256, 56, 56] 0
MaxPool2d-17 [-1, 256, 28, 28] 0
Conv2d-18 [-1, 512, 28, 28] 1180160
ReLU-19 [-1, 512, 28, 28] 0
Conv2d-20 [-1, 512, 28, 28] 2359808
ReLU-21 [-1, 512, 28, 28] 0
Conv2d-22 [-1, 512, 28, 28] 2359808
ReLU-23 [-1, 512, 28, 28] 0
MaxPool2d-24 [-1, 512, 14, 14] 0
Conv2d-25 [-1, 512, 14, 14] 2359808
ReLU-26 [-1, 512, 14, 14] 0
Conv2d-27 [-1, 512, 14, 14] 2359808
ReLU-28 [-1, 512, 14, 14] 0
Conv2d-29 [-1, 512, 14, 14] 2359808
ReLU-30 [-1, 512, 14, 14] 0
MaxPool2d-31 [-1, 512, 7, 7] 0
Linear-32 [-1, 4096] 102764544
ReLU-33 [-1, 4096] 0
Dropout-34 [-1, 4096] 0
Linear-35 [-1, 4096] 16781312
ReLU-36 [-1, 4096] 0
Dropout-37 [-1, 4096] 0
Linear-38 [-1, 1000] 4097000
================================================================
Total params: 138357544
Trainable params: 138357544
Non-trainable params: 0
----------------------------------------------------------------
Warning: date(): Invalid date.timezone value 'Europe/Kyiv', we selected the timezone 'UTC' for now. in /var/www/agent_stack/data/www/ajaxhispano.com/template/agent.layouts/content.php on line 61
2018-07-25 22:18:13
AFAK no hay modelo.summary () como equivalente en pytorch
Mientras tanto, puedes referirte a script de szagoruyko, que da una buena visualización como en resnet18-example
Salud
Warning: date(): Invalid date.timezone value 'Europe/Kyiv', we selected the timezone 'UTC' for now. in /var/www/agent_stack/data/www/ajaxhispano.com/template/agent.layouts/content.php on line 61
2017-03-05 19:09:50
Más simple de recordar (no tan bonito como Keras):
print(model)
Esto también funciona:
repr(model)
Si solo desea el número de parámetros:
sum([param.nelement() for param in model.parameters()])
De: Hay una función de pytorch similar como modelo.summary() como keras? (forum.PyTorch.org)
Warning: date(): Invalid date.timezone value 'Europe/Kyiv', we selected the timezone 'UTC' for now. in /var/www/agent_stack/data/www/ajaxhispano.com/template/agent.layouts/content.php on line 61
2018-08-03 19:30:15