.. _sec_fcn:
Tam Evrişimli Ağlar
===================
:numref:`sec_semantic_segmentation` içinde tartışıldığı gibi, anlamsal
bölümleme imgeleri piksel düzeyinde sınıflandırır. Bir tam evrişimli ağ
(FCN), imge piksellerini :cite:`Long.Shelhamer.Darrell.2015` piksel
sınıflarına dönüştürmek için bir evrişimli sinir ağı kullanır. İmge
sınıflandırması veya nesne algılama için daha önce karşılaştığımız
CNN'lerden farklı olarak, tam evrişimli bir ağ, ara öznitelik
haritalarının yüksekliğini ve genişliğini girdi imgesindekine geri
dönüştürür: bu, :numref:`sec_transposed_conv` içinde tanıtılan devrik
evrişimli katman ile elde edilir. Sonuç olarak, sınıflandırma çıktısı ve
girdi imgesi, piksel düzeyinde bire bir karşılığa sahiptir: Herhangi bir
çıktı pikselindeki kanal boyutu, girdi pikseli için sınıflandırma
sonuçlarını aynı uzamsal konumda tutar.
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%matplotlib inline
from d2l import mxnet as d2l
from mxnet import gluon, image, init, np, npx
from mxnet.gluon import nn
npx.set_np()
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%matplotlib inline
import torch
import torchvision
from torch import nn
from torch.nn import functional as F
from d2l import torch as d2l
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pretrained_net = gluon.model_zoo.vision.resnet18_v2(pretrained=True)
pretrained_net.features[-3:], pretrained_net.output
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:class: output
(HybridSequential(
(0): Activation(relu)
(1): GlobalAvgPool2D(size=(1, 1), stride=(1, 1), padding=(0, 0), ceil_mode=True, global_pool=True, pool_type=avg, layout=NCHW)
(2): Flatten
),
Dense(512 -> 1000, linear))
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pretrained_net = torchvision.models.resnet18(pretrained=True)
list(pretrained_net.children())[-3:]
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/home/d2l-worker/miniconda3/envs/d2l-tr-release-0/lib/python3.9/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.
warnings.warn(
/home/d2l-worker/miniconda3/envs/d2l-tr-release-0/lib/python3.9/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
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:class: output
[Sequential(
(0): BasicBlock(
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
),
AdaptiveAvgPool2d(output_size=(1, 1)),
Linear(in_features=512, out_features=1000, bias=True)]
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net = nn.HybridSequential()
for layer in pretrained_net.features[:-2]:
net.add(layer)
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net = nn.Sequential(*list(pretrained_net.children())[:-2])
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X = np.random.uniform(size=(1, 3, 320, 480))
net(X).shape
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(1, 512, 10, 15)
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X = torch.rand(size=(1, 3, 320, 480))
net(X).shape
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torch.Size([1, 512, 10, 15])
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num_classes = 21
net.add(nn.Conv2D(num_classes, kernel_size=1),
nn.Conv2DTranspose(
num_classes, kernel_size=64, padding=16, strides=32))
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num_classes = 21
net.add_module('final_conv', nn.Conv2d(512, num_classes, kernel_size=1))
net.add_module('transpose_conv', nn.ConvTranspose2d(num_classes, num_classes,
kernel_size=64, padding=16, stride=32))
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def bilinear_kernel(in_channels, out_channels, kernel_size):
factor = (kernel_size + 1) // 2
if kernel_size % 2 == 1:
center = factor - 1
else:
center = factor - 0.5
og = (np.arange(kernel_size).reshape(-1, 1),
np.arange(kernel_size).reshape(1, -1))
filt = (1 - np.abs(og[0] - center) / factor) * \
(1 - np.abs(og[1] - center) / factor)
weight = np.zeros((in_channels, out_channels, kernel_size, kernel_size))
weight[range(in_channels), range(out_channels), :, :] = filt
return np.array(weight)
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def bilinear_kernel(in_channels, out_channels, kernel_size):
factor = (kernel_size + 1) // 2
if kernel_size % 2 == 1:
center = factor - 1
else:
center = factor - 0.5
og = (torch.arange(kernel_size).reshape(-1, 1),
torch.arange(kernel_size).reshape(1, -1))
filt = (1 - torch.abs(og[0] - center) / factor) * \
(1 - torch.abs(og[1] - center) / factor)
weight = torch.zeros((in_channels, out_channels,
kernel_size, kernel_size))
weight[range(in_channels), range(out_channels), :, :] = filt
return weight
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conv_trans = nn.Conv2DTranspose(3, kernel_size=4, padding=1, strides=2)
conv_trans.initialize(init.Constant(bilinear_kernel(3, 3, 4)))
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conv_trans = nn.ConvTranspose2d(3, 3, kernel_size=4, padding=1, stride=2,
bias=False)
conv_trans.weight.data.copy_(bilinear_kernel(3, 3, 4));
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img = image.imread('../img/catdog.jpg')
X = np.expand_dims(img.astype('float32').transpose(2, 0, 1), axis=0) / 255
Y = conv_trans(X)
out_img = Y[0].transpose(1, 2, 0)
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img = torchvision.transforms.ToTensor()(d2l.Image.open('../img/catdog.jpg'))
X = img.unsqueeze(0)
Y = conv_trans(X)
out_img = Y[0].permute(1, 2, 0).detach()
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d2l.set_figsize()
print('input image shape:', img.shape)
d2l.plt.imshow(img.asnumpy());
print('output image shape:', out_img.shape)
d2l.plt.imshow(out_img.asnumpy());
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input image shape: (561, 728, 3)
output image shape: (1122, 1456, 3)
.. figure:: output_fcn_ce3435_75_1.svg
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d2l.set_figsize()
print('input image shape:', img.permute(1, 2, 0).shape)
d2l.plt.imshow(img.permute(1, 2, 0));
print('output image shape:', out_img.shape)
d2l.plt.imshow(out_img);
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input image shape: torch.Size([561, 728, 3])
output image shape: torch.Size([1122, 1456, 3])
.. figure:: output_fcn_ce3435_78_1.svg
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W = bilinear_kernel(num_classes, num_classes, 64)
net[-1].initialize(init.Constant(W))
net[-2].initialize(init=init.Xavier())
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W = bilinear_kernel(num_classes, num_classes, 64)
net.transpose_conv.weight.data.copy_(W);
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batch_size, crop_size = 32, (320, 480)
train_iter, test_iter = d2l.load_data_voc(batch_size, crop_size)
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read 1114 examples
read 1078 examples
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batch_size, crop_size = 32, (320, 480)
train_iter, test_iter = d2l.load_data_voc(batch_size, crop_size)
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read 1114 examples
read 1078 examples
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num_epochs, lr, wd, devices = 5, 0.1, 1e-3, d2l.try_all_gpus()
loss = gluon.loss.SoftmaxCrossEntropyLoss(axis=1)
net.collect_params().reset_ctx(devices)
trainer = gluon.Trainer(net.collect_params(), 'sgd',
{'learning_rate': lr, 'wd': wd})
d2l.train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs, devices)
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loss 0.339, train acc 0.889, test acc 0.843
193.4 examples/sec on [gpu(0), gpu(1)]
.. figure:: output_fcn_ce3435_102_1.svg
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def loss(inputs, targets):
return F.cross_entropy(inputs, targets, reduction='none').mean(1).mean(1)
num_epochs, lr, wd, devices = 5, 0.001, 1e-3, d2l.try_all_gpus()
trainer = torch.optim.SGD(net.parameters(), lr=lr, weight_decay=wd)
d2l.train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs, devices)
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loss 0.445, train acc 0.862, test acc 0.828
251.3 examples/sec on [device(type='cuda', index=0), device(type='cuda', index=1)]
.. figure:: output_fcn_ce3435_105_1.svg
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def predict(img):
X = test_iter._dataset.normalize_image(img)
X = np.expand_dims(X.transpose(2, 0, 1), axis=0)
pred = net(X.as_in_ctx(devices[0])).argmax(axis=1)
return pred.reshape(pred.shape[1], pred.shape[2])
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def predict(img):
X = test_iter.dataset.normalize_image(img).unsqueeze(0)
pred = net(X.to(devices[0])).argmax(dim=1)
return pred.reshape(pred.shape[1], pred.shape[2])
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def label2image(pred):
colormap = np.array(d2l.VOC_COLORMAP, ctx=devices[0], dtype='uint8')
X = pred.astype('int32')
return colormap[X, :]
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def label2image(pred):
colormap = torch.tensor(d2l.VOC_COLORMAP, device=devices[0])
X = pred.long()
return colormap[X, :]
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voc_dir = d2l.download_extract('voc2012', 'VOCdevkit/VOC2012')
test_images, test_labels = d2l.read_voc_images(voc_dir, False)
n, imgs = 4, []
for i in range(n):
crop_rect = (0, 0, 480, 320)
X = image.fixed_crop(test_images[i], *crop_rect)
pred = label2image(predict(X))
imgs += [X, pred, image.fixed_crop(test_labels[i], *crop_rect)]
d2l.show_images(imgs[::3] + imgs[1::3] + imgs[2::3], 3, n, scale=2);
.. figure:: output_fcn_ce3435_129_0.svg
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voc_dir = d2l.download_extract('voc2012', 'VOCdevkit/VOC2012')
test_images, test_labels = d2l.read_voc_images(voc_dir, False)
n, imgs = 4, []
for i in range(n):
crop_rect = (0, 0, 320, 480)
X = torchvision.transforms.functional.crop(test_images[i], *crop_rect)
pred = label2image(predict(X))
imgs += [X.permute(1,2,0), pred.cpu(),
torchvision.transforms.functional.crop(
test_labels[i], *crop_rect).permute(1,2,0)]
d2l.show_images(imgs[::3] + imgs[1::3] + imgs[2::3], 3, n, scale=2);
.. figure:: output_fcn_ce3435_132_0.svg
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`Tartışmalar `__
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`Tartışmalar `__
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