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updated and checked CNN architectures still works with latest pytorch

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Aladdin Persson
2022-12-20 12:13:12 +01:00
parent 28a6abea27
commit b6985eccc9
5 changed files with 100 additions and 77 deletions
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27
ML/Pytorch/CNN_architectures/lenet5_pytorch.py
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@@ -1,12 +1,9 @@
"""
An implementation of LeNet CNN architecture.
Video explanation: https://youtu.be/fcOW-Zyb5Bo
Got any questions leave a comment on youtube :)
Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
* 2020-04-05 Initial coding
* 2022-12-20 Update comments, code revision, checked still works with latest PyTorch version
"""
import torch
@@ -17,27 +14,27 @@ class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__()
self.relu = nn.ReLU()
self.pool = nn.AvgPool2d(kernel_size=(2, 2), stride=(2, 2))
self.pool = nn.AvgPool2d(kernel_size=2, stride=2)
self.conv1 = nn.Conv2d(
in_channels=1,
out_channels=6,
kernel_size=(5, 5),
stride=(1, 1),
padding=(0, 0),
kernel_size=5,
stride=1,
padding=0,
)
self.conv2 = nn.Conv2d(
in_channels=6,
out_channels=16,
kernel_size=(5, 5),
stride=(1, 1),
padding=(0, 0),
kernel_size=5,
stride=1,
padding=0,
)
self.conv3 = nn.Conv2d(
in_channels=16,
out_channels=120,
kernel_size=(5, 5),
stride=(1, 1),
padding=(0, 0),
kernel_size=5,
stride=1,
padding=0,
)
self.linear1 = nn.Linear(120, 84)
self.linear2 = nn.Linear(84, 10)
@@ -64,4 +61,4 @@ def test_lenet():
if __name__ == "__main__":
out = test_lenet()
print(out.shape)
print(out.shape)

69
ML/Pytorch/CNN_architectures/pytorch_efficientnet.py
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@@ -1,3 +1,12 @@
"""
An implementation of EfficientNet CNN architecture.
Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
* 2021-02-05 Initial coding
* 2022-12-20 Update comments, code revision, checked still works with latest PyTorch version
"""
import torch
import torch.nn as nn
from math import ceil
@@ -25,9 +34,10 @@ phi_values = {
"b7": (6, 600, 0.5),
}
class CNNBlock(nn.Module):
def __init__(
self, in_channels, out_channels, kernel_size, stride, padding, groups=1
self, in_channels, out_channels, kernel_size, stride, padding, groups=1
):
super(CNNBlock, self).__init__()
self.cnn = nn.Conv2d(
@@ -40,16 +50,17 @@ class CNNBlock(nn.Module):
bias=False,
)
self.bn = nn.BatchNorm2d(out_channels)
self.silu = nn.SiLU() # SiLU <-> Swish
self.silu = nn.SiLU() # SiLU <-> Swish
def forward(self, x):
return self.silu(self.bn(self.cnn(x)))
class SqueezeExcitation(nn.Module):
def __init__(self, in_channels, reduced_dim):
super(SqueezeExcitation, self).__init__()
self.se = nn.Sequential(
nn.AdaptiveAvgPool2d(1), # C x H x W -> C x 1 x 1
nn.AdaptiveAvgPool2d(1), # C x H x W -> C x 1 x 1
nn.Conv2d(in_channels, reduced_dim, 1),
nn.SiLU(),
nn.Conv2d(reduced_dim, in_channels, 1),
@@ -59,17 +70,18 @@ class SqueezeExcitation(nn.Module):
def forward(self, x):
return x * self.se(x)
class InvertedResidualBlock(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride,
padding,
expand_ratio,
reduction=4, # squeeze excitation
survival_prob=0.8, # for stochastic depth
self,
in_channels,
out_channels,
kernel_size,
stride,
padding,
expand_ratio,
reduction=4, # squeeze excitation
survival_prob=0.8, # for stochastic depth
):
super(InvertedResidualBlock, self).__init__()
self.survival_prob = 0.8
@@ -80,12 +92,21 @@ class InvertedResidualBlock(nn.Module):
if self.expand:
self.expand_conv = CNNBlock(
in_channels, hidden_dim, kernel_size=3, stride=1, padding=1,
in_channels,
hidden_dim,
kernel_size=3,
stride=1,
padding=1,
)
self.conv = nn.Sequential(
CNNBlock(
hidden_dim, hidden_dim, kernel_size, stride, padding, groups=hidden_dim,
hidden_dim,
hidden_dim,
kernel_size,
stride,
padding,
groups=hidden_dim,
),
SqueezeExcitation(hidden_dim, reduced_dim),
nn.Conv2d(hidden_dim, out_channels, 1, bias=False),
@@ -96,7 +117,9 @@ class InvertedResidualBlock(nn.Module):
if not self.training:
return x
binary_tensor = torch.rand(x.shape[0], 1, 1, 1, device=x.device) < self.survival_prob
binary_tensor = (
torch.rand(x.shape[0], 1, 1, 1, device=x.device) < self.survival_prob
)
return torch.div(x, self.survival_prob) * binary_tensor
def forward(self, inputs):
@@ -122,8 +145,8 @@ class EfficientNet(nn.Module):
def calculate_factors(self, version, alpha=1.2, beta=1.1):
phi, res, drop_rate = phi_values[version]
depth_factor = alpha ** phi
width_factor = beta ** phi
depth_factor = alpha**phi
width_factor = beta**phi
return width_factor, depth_factor, drop_rate
def create_features(self, width_factor, depth_factor, last_channels):
@@ -132,7 +155,7 @@ class EfficientNet(nn.Module):
in_channels = channels
for expand_ratio, channels, repeats, stride, kernel_size in base_model:
out_channels = 4*ceil(int(channels*width_factor) / 4)
out_channels = 4 * ceil(int(channels * width_factor) / 4)
layers_repeats = ceil(repeats * depth_factor)
for layer in range(layers_repeats):
@@ -141,9 +164,9 @@ class EfficientNet(nn.Module):
in_channels,
out_channels,
expand_ratio=expand_ratio,
stride = stride if layer == 0 else 1,
stride=stride if layer == 0 else 1,
kernel_size=kernel_size,
padding=kernel_size//2, # if k=1:pad=0, k=3:pad=1, k=5:pad=2
padding=kernel_size // 2, # if k=1:pad=0, k=3:pad=1, k=5:pad=2
)
)
in_channels = out_channels
@@ -170,6 +193,8 @@ def test():
num_classes=num_classes,
).to(device)
print(model(x).shape) # (num_examples, num_classes)
print(model(x).shape) # (num_examples, num_classes)
test()
if __name__ == "__main__":
test()

35
ML/Pytorch/CNN_architectures/pytorch_inceptionet.py
View File

@@ -1,15 +1,11 @@
"""
An implementation of GoogLeNet / InceptionNet from scratch.
Video explanation: https://youtu.be/uQc4Fs7yx5I
Got any questions leave a comment on youtube :)
Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
* 2020-04-07 Initial coding
* 2022-12-20 Update comments, code revision, checked still works with latest PyTorch version
"""
# Imports
import torch
from torch import nn
@@ -25,9 +21,9 @@ class GoogLeNet(nn.Module):
self.conv1 = conv_block(
in_channels=3,
out_channels=64,
kernel_size=(7, 7),
stride=(2, 2),
padding=(3, 3),
kernel_size=7,
stride=2,
padding=3,
)
self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
@@ -37,7 +33,7 @@ class GoogLeNet(nn.Module):
# In this order: in_channels, out_1x1, red_3x3, out_3x3, red_5x5, out_5x5, out_1x1pool
self.inception3a = Inception_block(192, 64, 96, 128, 16, 32, 32)
self.inception3b = Inception_block(256, 128, 128, 192, 32, 96, 64)
self.maxpool3 = nn.MaxPool2d(kernel_size=(3, 3), stride=2, padding=1)
self.maxpool3 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.inception4a = Inception_block(480, 192, 96, 208, 16, 48, 64)
self.inception4b = Inception_block(512, 160, 112, 224, 24, 64, 64)
@@ -63,7 +59,6 @@ class GoogLeNet(nn.Module):
x = self.conv1(x)
x = self.maxpool1(x)
x = self.conv2(x)
# x = self.conv3(x)
x = self.maxpool2(x)
x = self.inception3a(x)
@@ -104,21 +99,21 @@ class Inception_block(nn.Module):
self, in_channels, out_1x1, red_3x3, out_3x3, red_5x5, out_5x5, out_1x1pool
):
super(Inception_block, self).__init__()
self.branch1 = conv_block(in_channels, out_1x1, kernel_size=(1, 1))
self.branch1 = conv_block(in_channels, out_1x1, kernel_size=1)
self.branch2 = nn.Sequential(
conv_block(in_channels, red_3x3, kernel_size=(1, 1)),
conv_block(red_3x3, out_3x3, kernel_size=(3, 3), padding=(1, 1)),
conv_block(in_channels, red_3x3, kernel_size=1),
conv_block(red_3x3, out_3x3, kernel_size=(3, 3), padding=1),
)
self.branch3 = nn.Sequential(
conv_block(in_channels, red_5x5, kernel_size=(1, 1)),
conv_block(red_5x5, out_5x5, kernel_size=(5, 5), padding=(2, 2)),
conv_block(in_channels, red_5x5, kernel_size=1),
conv_block(red_5x5, out_5x5, kernel_size=5, padding=2),
)
self.branch4 = nn.Sequential(
nn.MaxPool2d(kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
conv_block(in_channels, out_1x1pool, kernel_size=(1, 1)),
nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
conv_block(in_channels, out_1x1pool, kernel_size=1),
)
def forward(self, x):
@@ -144,7 +139,6 @@ class InceptionAux(nn.Module):
x = self.relu(self.fc1(x))
x = self.dropout(x)
x = self.fc2(x)
return x
@@ -160,7 +154,8 @@ class conv_block(nn.Module):
if __name__ == "__main__":
# N = 3 (Mini batch size)
x = torch.randn(3, 3, 224, 224)
BATCH_SIZE = 5
x = torch.randn(BATCH_SIZE, 3, 224, 224)
model = GoogLeNet(aux_logits=True, num_classes=1000)
print(model(x)[2].shape)
assert model(x)[2].shape == torch.Size([BATCH_SIZE, 1000])

33
ML/Pytorch/CNN_architectures/pytorch_resnet.py
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@@ -5,11 +5,9 @@ The intuition for ResNet is simple and clear, but to code
it didn't feel super clear at first, even when reading Pytorch own
implementation.
Video explanation:
Got any questions leave a comment on youtube :)
Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
* 2020-04-12 Initial coding
* 2022-12-20 Update comments, code revision, checked still works with latest PyTorch version
"""
import torch
@@ -20,10 +18,15 @@ class block(nn.Module):
def __init__(
self, in_channels, intermediate_channels, identity_downsample=None, stride=1
):
super(block, self).__init__()
super().__init__()
self.expansion = 4
self.conv1 = nn.Conv2d(
in_channels, intermediate_channels, kernel_size=1, stride=1, padding=0, bias=False
in_channels,
intermediate_channels,
kernel_size=1,
stride=1,
padding=0,
bias=False,
)
self.bn1 = nn.BatchNorm2d(intermediate_channels)
self.conv2 = nn.Conv2d(
@@ -32,7 +35,7 @@ class block(nn.Module):
kernel_size=3,
stride=stride,
padding=1,
bias=False
bias=False,
)
self.bn2 = nn.BatchNorm2d(intermediate_channels)
self.conv3 = nn.Conv2d(
@@ -41,7 +44,7 @@ class block(nn.Module):
kernel_size=1,
stride=1,
padding=0,
bias=False
bias=False,
)
self.bn3 = nn.BatchNorm2d(intermediate_channels * self.expansion)
self.relu = nn.ReLU()
@@ -72,7 +75,9 @@ class ResNet(nn.Module):
def __init__(self, block, layers, image_channels, num_classes):
super(ResNet, self).__init__()
self.in_channels = 64
self.conv1 = nn.Conv2d(image_channels, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.conv1 = nn.Conv2d(
image_channels, 64, kernel_size=7, stride=2, padding=3, bias=False
)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
@@ -124,7 +129,7 @@ class ResNet(nn.Module):
intermediate_channels * 4,
kernel_size=1,
stride=stride,
bias=False
bias=False,
),
nn.BatchNorm2d(intermediate_channels * 4),
)
@@ -158,9 +163,13 @@ def ResNet152(img_channel=3, num_classes=1000):
def test():
net = ResNet101(img_channel=3, num_classes=1000)
y = net(torch.randn(4, 3, 224, 224)).to("cuda")
BATCH_SIZE = 4
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = ResNet101(img_channel=3, num_classes=1000).to(device)
y = net(torch.randn(BATCH_SIZE, 3, 224, 224)).to(device)
assert y.size() == torch.Size([BATCH_SIZE, 1000])
print(y.size())
test()
if __name__ == "__main__":
test()

13
ML/Pytorch/CNN_architectures/pytorch_vgg_implementation.py
View File

@@ -1,12 +1,9 @@
"""
A from scratch implementation of the VGG architecture.
Video explanation: https://youtu.be/ACmuBbuXn20
Got any questions leave a comment on youtube :)
Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
* 2020-04-05 Initial coding
* 2022-12-20 Update comments, code revision, checked still works with latest PyTorch version
"""
# Imports
@@ -113,7 +110,7 @@ class VGG_net(nn.Module):
if __name__ == "__main__":
device = "cuda" if torch.cuda.is_available() else "cpu"
model = VGG_net(in_channels=3, num_classes=1000).to(device)
print(model)
## N = 3 (Mini batch size)
# x = torch.randn(3, 3, 224, 224).to(device)
# print(model(x).shape)
BATCH_SIZE = 3
x = torch.randn(3, 3, 224, 224).to(device)
assert model(x).shape == torch.Size([BATCH_SIZE, 1000])
print(model(x).shape)