PyTorch の動作確認をしてみた（１１）

1. 環境は、Window 10 Home (64bit) 上で行った。

2. Anaconda3 (64bit) – Spyder上で、動作確認を行った。

3. python のバージョンは、python 3.7.0 である。

4. pytorch のバージョンは、pytorch 0.4.1 である。

5. GPU は, NVIDIA社の GeForce GTX 1050 である。

6. CPU は, Intel社の Core(TM) i7-7700HQ である。

今回確認した内容は、現場で使える! PyTorch開発入門深層学習モデルの作成とアプリケーションへの実装 (AI & TECHNOLOGY) の 4.4 CNN回帰モデルによる画像の高解像化(P.083 – P.088) である。

※1. プログラムの詳細は、書籍を参考(P.083 – P.088)にして下さい。
※2. ConvTranspose2d が出てきたので, 改めて本家のサイトを確認し, プログラム上に, 計算式等をメモした.

■CNN回帰モデル(書籍CNNモデルを抜粋・加筆).

# -*- coding: utf-8 -*-
# 1. library import.
from __future__ import print_function
import torch
from torch import nn, optim
from torchvision.datasets import ImageFolder
from torchvision import transforms
from torch.utils.data import DataLoader
import os, time

～(略)～

# 4. declare network.
# torch.nn
# https://pytorch.org/docs/stable/nn.html#torch.nn.ConvTranspose2d
# ex.
# Input:  torch.Size([1, 3, 128, 128])
# Output: torch.Size([1, 3, 512, 512])
net = nn.Sequential(
    # 次元の計算について, 公式サイト通りとする.
    # ex. 
    # kernel_size = (2, 3) ならば, kernel_size[0] = 2, kernel_size[1] = 3
    # kernel_size = 5      ならば, kernel_size[0] = 5, kernel_size[1] = 5
    # padding = (3, 5)     ならば, padding[0] = 3, padding[1] = 5
    # padding = 2          ならば, padding[0] = 2, padding[1] = 2
    # stride = (4, 5)      ならば, stride[0] = 4, stride[1] = 5
    # stride = 3           ならば, stride[0] = 3, stride[1] = 3
    # dilation は, ここでは, 初期値 (1, 1) を 使用するため, dilation[0] = 1, dilation[1] = 1
    # output_padding は, ここでは, 初期値 0 を 使用するため, output_padding[0] = 0, output_padding[1] = 0
    
    # Conv2d
    # Input:  (N, Cin, Hin, Win)
    # Output: (N, Cout, Hout, Wout)
    # Hout = (Hin + 2 × padding[0] − dilation[0] × (kernel_size[0] − 1) − 1) / stride[0] + 1
    # Wout = (Win + 2 × padding[1] − dilation[1] × (kernel_size[1] − 1) − 1) / stride[1] + 1

    # ConvTranspose2d
    # Input:  (N, Cin, Hin, Win)
    # Output: (N, Cout, Hout, Wout)
    # Hout = (Hin − 1) × stride[0] − 2 × padding[0] + kernel_size[0] + output_padding[0]
    # Wout = (Win − 1) × stride[1] − 2 × padding[1] + kernel_size[1] + output_padding[1]
    
    # 1. Conv2d
    # Input:  (1, 3, 128, 128)
    # Hout =  (128 + 2 × 1 − 1 × (4 − 1) − 1) / 2 + 1 = 64
    # Wout =  (128 + 2 × 1 − 1 × (4 − 1) − 1) / 2 + 1 = 64
    # Output: (1, 256, 64, 64)
    nn.Conv2d(3, 256, 4, stride = 2, padding = 1),
    nn.ReLU(),

    # 1. BatchNorm2d
    # same shape as input.
    # Input:  (1, 256, 64, 64)
    # Output: (1, 256, 64, 64)
    nn.BatchNorm2d(256),

    # 2. Conv2d
    # Input:  (1, 256, 64, 64)
    # Hout =  (64 + 2 × 1 − 1 × (4 − 1) − 1) / 2 + 1 = 32
    # Wout =  (64 + 2 × 1 − 1 × (4 − 1) − 1) / 2 + 1 = 32
    # Output: (1, 512, 32, 32)
    nn.Conv2d(256, 512, 4, stride = 2, padding = 1),
    nn.ReLU(),
    
    # 2. BatchNorm2d
    # same shape as input.
    # Input:  (1, 512, 32, 32)
    # Output: (1, 512, 32, 32)
    nn.BatchNorm2d(512),

    # 1. ConvTranspose2d
    # Input:  (1, 512, 32, 32)
    # Hout =  (32 - 1) × 2 - 2 × 1 + 4 + 0 = 64
    # Wout =  (32 - 1) × 2 - 2 × 1 + 4 + 0 = 64
    # Output: (1, 256, 64, 64)
    nn.ConvTranspose2d(512, 256, 4, stride = 2, padding = 1),
    nn.ReLU(),
    
    # 1. BatchNorm2d
    # same shape as input.
    # Input:  (1, 256, 64, 64)
    # Output: (1, 256, 64, 64)
    nn.BatchNorm2d(256),
    
    # 2. ConvTranspose2d
    # Input:  (1, 256, 64, 64)
    # Hout =  (64 - 1) × 2 - 2 × 1 + 4 + 0 = 128
    # Wout =  (64 - 1) × 2 - 2 × 1 + 4 + 0 = 128
    # Output: (1, 128, 128, 128)
    nn.ConvTranspose2d(256, 128, 4, stride = 2, padding = 1),
    nn.ReLU(),
    
    # 2. BatchNorm2d
    # same shape as input.
    # Input:  (1, 128, 128, 128)
    # Output: (1, 128, 128, 128)
    nn.BatchNorm2d(128),

    # 3. ConvTranspose2d
    # Input:  (1, 128, 128, 128)
    # Hout =  (128 - 1) × 2 - 2 × 1 + 4 + 0 = 256
    # Wout =  (128 - 1) × 2 - 2 × 1 + 4 + 0 = 256
    # Output: (1, 64, 256, 256)
    nn.ConvTranspose2d(128, 64, 4, stride = 2, padding = 1),
    nn.ReLU(),
    
    # 3. BatchNorm2d
    # same shape as input.
    # Input:  (1, 64, 256, 256)
    # Output: (1, 64, 256, 256)
    nn.BatchNorm2d(64),

    # 4. ConvTranspose2d
    # Input:  (1, 64, 256, 256)
    # Hout =  (256 - 1) × 2 - 2 × 1 + 4 + 0 = 512
    # Wout =  (256 - 1) × 2 - 2 × 1 + 4 + 0 = 512
    # Output: (1, 3, 512, 512)
    nn.ConvTranspose2d(64, 3, 4, stride = 2, padding = 1),
    
)

# 5. dummy data.
test_input = torch.ones(1, 3, 128, 128)
conv_output_size = net(test_input).size()
print('-input size --------------------------------------')
print(test_input.size())
print('-net----------------------------------------------')
print(net)
print('-output size--------------------------------------')
print(conv_output_size)

# 6. display processing time.
end = time.time()
print('--------------------------------------------------')
print('Elapsed Time: ' + str(end - start) + "[sec]")

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# -*- coding: utf-8 -*-

# 1. library import.

from __future__ import print_function

import torch

from torch import nn, optim

from torchvision.datasets import ImageFolder

from torchvision import transforms

from torch.utils.data import DataLoader

import os, time

～(略)～

# 4. declare network.

# torch.nn

# https://pytorch.org/docs/stable/nn.html#torch.nn.ConvTranspose2d

# ex.

# Input: torch.Size([1, 3, 128, 128])

# Output: torch.Size([1, 3, 512, 512])

net = nn.Sequential(

# 次元の計算について, 公式サイト通りとする.

# ex.

# kernel_size = (2, 3) ならば, kernel_size[0] = 2, kernel_size[1] = 3

# kernel_size = 5 ならば, kernel_size[0] = 5, kernel_size[1] = 5

# padding = (3, 5) ならば, padding[0] = 3, padding[1] = 5

# padding = 2 ならば, padding[0] = 2, padding[1] = 2

# stride = (4, 5) ならば, stride[0] = 4, stride[1] = 5

# stride = 3 ならば, stride[0] = 3, stride[1] = 3

# dilation は, ここでは, 初期値 (1, 1) を使用するため, dilation[0] = 1, dilation[1] = 1

# output_padding は, ここでは, 初期値 0 を使用するため, output_padding[0] = 0, output_padding[1] = 0

# Conv2d

# Input: (N, Cin, Hin, Win)

# Output: (N, Cout, Hout, Wout)

# Hout = (Hin + 2 × padding[0] − dilation[0] × (kernel_size[0] − 1) − 1) / stride[0] + 1

# Wout = (Win + 2 × padding[1] − dilation[1] × (kernel_size[1] − 1) − 1) / stride[1] + 1

# ConvTranspose2d

# Input: (N, Cin, Hin, Win)

# Output: (N, Cout, Hout, Wout)

# Hout = (Hin − 1) × stride[0] − 2 × padding[0] + kernel_size[0] + output_padding[0]

# Wout = (Win − 1) × stride[1] − 2 × padding[1] + kernel_size[1] + output_padding[1]

# 1. Conv2d

# Input: (1, 3, 128, 128)

# Hout = (128 + 2 × 1 − 1 × (4 − 1) − 1) / 2 + 1 = 64

# Wout = (128 + 2 × 1 − 1 × (4 − 1) − 1) / 2 + 1 = 64

# Output: (1, 256, 64, 64)

nn.Conv2d(3, 256, 4, stride = 2, padding = 1),

nn.ReLU(),

# 1. BatchNorm2d

# same shape as input.

# Input: (1, 256, 64, 64)

# Output: (1, 256, 64, 64)

nn.BatchNorm2d(256),

# 2. Conv2d

# Input: (1, 256, 64, 64)

# Hout = (64 + 2 × 1 − 1 × (4 − 1) − 1) / 2 + 1 = 32

# Wout = (64 + 2 × 1 − 1 × (4 − 1) − 1) / 2 + 1 = 32

# Output: (1, 512, 32, 32)

nn.Conv2d(256, 512, 4, stride = 2, padding = 1),

nn.ReLU(),

# 2. BatchNorm2d

# same shape as input.

# Input: (1, 512, 32, 32)

# Output: (1, 512, 32, 32)

nn.BatchNorm2d(512),

# 1. ConvTranspose2d

# Input: (1, 512, 32, 32)

# Hout = (32 - 1) × 2 - 2 × 1 + 4 + 0 = 64

# Wout = (32 - 1) × 2 - 2 × 1 + 4 + 0 = 64

# Output: (1, 256, 64, 64)

nn.ConvTranspose2d(512, 256, 4, stride = 2, padding = 1),

nn.ReLU(),

# 1. BatchNorm2d

# same shape as input.

# Input: (1, 256, 64, 64)

# Output: (1, 256, 64, 64)

nn.BatchNorm2d(256),

# 2. ConvTranspose2d

# Input: (1, 256, 64, 64)

# Hout = (64 - 1) × 2 - 2 × 1 + 4 + 0 = 128

# Wout = (64 - 1) × 2 - 2 × 1 + 4 + 0 = 128

# Output: (1, 128, 128, 128)

nn.ConvTranspose2d(256, 128, 4, stride = 2, padding = 1),

nn.ReLU(),

# 2. BatchNorm2d

# same shape as input.

# Input: (1, 128, 128, 128)

# Output: (1, 128, 128, 128)

nn.BatchNorm2d(128),

# 3. ConvTranspose2d

# Input: (1, 128, 128, 128)

# Hout = (128 - 1) × 2 - 2 × 1 + 4 + 0 = 256

# Wout = (128 - 1) × 2 - 2 × 1 + 4 + 0 = 256

# Output: (1, 64, 256, 256)

nn.ConvTranspose2d(128, 64, 4, stride = 2, padding = 1),

nn.ReLU(),

# 3. BatchNorm2d

# same shape as input.

# Input: (1, 64, 256, 256)

# Output: (1, 64, 256, 256)

nn.BatchNorm2d(64),

# 4. ConvTranspose2d

# Input: (1, 64, 256, 256)

# Hout = (256 - 1) × 2 - 2 × 1 + 4 + 0 = 512

# Wout = (256 - 1) × 2 - 2 × 1 + 4 + 0 = 512

# Output: (1, 3, 512, 512)

nn.ConvTranspose2d(64, 3, 4, stride = 2, padding = 1),

)

# 5. dummy data.

test_input = torch.ones(1, 3, 128, 128)

conv_output_size = net(test_input).size()

print('-input size --------------------------------------')

print(test_input.size())

print('-net----------------------------------------------')

print(net)

print('-output size--------------------------------------')

print(conv_output_size)

# 6. display processing time.

end = time.time()

print('--------------------------------------------------')

print('Elapsed Time: ' + str(end - start) + "[sec]")

■実行結果(input size = torch.Size([1, 3, 128, 128]) の場合).

-input size --------------------------------------
torch.Size([1, 3, 128, 128])
-net----------------------------------------------
Sequential(
  (0): Conv2d(3, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
  (1): ReLU()
  (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (3): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
  (4): ReLU()
  (5): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (6): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
  (7): ReLU()
  (8): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (9): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
  (10): ReLU()
  (11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (12): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
  (13): ReLU()
  (14): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (15): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
-output size--------------------------------------
torch.Size([1, 3, 512, 512])
--------------------------------------------------
Elapsed Time: 1.847071886062622[sec]

-input size --------------------------------------

torch.Size([1, 3, 128, 128])

-net----------------------------------------------

Sequential(

(0): Conv2d(3, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))

(1): ReLU()

(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(3): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))

(4): ReLU()

(5): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(6): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))

(7): ReLU()

(8): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(9): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))

(10): ReLU()

(11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(12): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))

(13): ReLU()

(14): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(15): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))

)

-output size--------------------------------------

torch.Size([1, 3, 512, 512])

--------------------------------------------------

Elapsed Time: 1.847071886062622[sec]

■実行結果(input size = torch.Size([1, 3, 32, 32]) の場合).

-input size --------------------------------------
torch.Size([1, 3, 32, 32])
-net----------------------------------------------
Sequential(
  (0): Conv2d(3, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
  (1): ReLU()
  (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (3): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
  (4): ReLU()
  (5): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (6): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
  (7): ReLU()
  (8): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (9): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
  (10): ReLU()
  (11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (12): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
  (13): ReLU()
  (14): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (15): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
-output size--------------------------------------
torch.Size([1, 3, 128, 128])
--------------------------------------------------
Elapsed Time: 1.452575922012329[sec]

-input size --------------------------------------

torch.Size([1, 3, 32, 32])

-net----------------------------------------------

Sequential(

(0): Conv2d(3, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))

(1): ReLU()

(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(3): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))

(4): ReLU()

(5): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(6): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))

(7): ReLU()

(8): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(9): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))

(10): ReLU()

(11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(12): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))

(13): ReLU()

(14): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(15): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))

)

-output size--------------------------------------

torch.Size([1, 3, 128, 128])

--------------------------------------------------

Elapsed Time: 1.452575922012329[sec]

■以上の実行結果から, 以下のことが分かった.

以下の内容の通り, 入力画像 の 縦・横サイズ が, それぞれ4倍に拡大されることを確認できた.
① 入力サイズ torch.Size([1, 3, 128, 128]) に対応する 出力サイズ は, torch.Size([1, 3, 512, 512]) となること.
※画像: 1枚 × (RGBのチャンネル)3種類 × (高)512px × (幅)512px に変換されるイメージ.

② 入力サイズ torch.Size([1, 3, 32, 32]) に対応する 出力サイズ は, torch.Size([1, 3, 128, 128]) となること.
※画像: 1枚 × (RGBのチャンネル)3種類 × (高)128px × (幅)128px に変換されるイメージ.

以下の内容の通り, 入力画像の縦・横サイズが, それぞれ4倍に拡大されることを確認できた.

① 入力サイズ torch.Size([1, 3, 128, 128]) に対応する出力サイズは, torch.Size([1, 3, 512, 512]) となること.

※画像: 1枚 × (RGBのチャンネル)3種類 × (高)512px × (幅)512px に変換されるイメージ.

② 入力サイズ torch.Size([1, 3, 32, 32]) に対応する出力サイズは, torch.Size([1, 3, 128, 128]) となること.

※画像: 1枚 × (RGBのチャンネル)3種類 × (高)128px × (幅)128px に変換されるイメージ.

■参照サイト
【参照URL①】torch.nn.Conv2d
【参照URL②】torch.nn.BatchNorm2d
【参照URL③】torch.nn.ConvTranspose2d

■参考書籍
現場で使える! PyTorch開発入門深層学習モデルの作成とアプリケーションへの実装 (AI & TECHNOLOGY)

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