Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub
Figure 7.4 - 1x1 convolutionThe input is an RGB image, and there are two filters; each filter has three 1x1kernels, one for each channel of the input. What are these filters actually doing?Let’s check it out!Figure 7.5 - 1x1 convolutionMaybe it is even more clear if it is presented as a formula:Equation 7.1 - Filter arithmeticA filter using a 1x1 convolution corresponds to a weightedaverage of the input channels.In other words, a 1x1 convolution is a linear combination of theinput channels, computed pixel by pixel.There is another way to get a linear combination of the inputs: a linear layer, also1x1 Convolutions | 525
referred to as a fully connected layer. Performing a 1x1 convolution is akin toapplying a linear layer to each individual pixel over its channels.This is the reason why a 1x1 convolution is said to be equivalentto a fully connected (linear) layer.In the example above, each of the two filters produces a different linearcombination of the RGB channels. Does this ring any bells? In Chapter 6, we sawthat grayscale images can be computed using a linear combination of the red,green, and blue channels of colored images. So, we can convert an image tograyscale using a 1x1 convolution!scissors = Image.open('rps/scissors/scissors01-001.png')image = ToTensor()(scissors)[:3, :, :].view(1, 3, 300, 300)weights = torch.tensor([0.2126, 0.7152, 0.0722]).view(1, 3, 1, 1)convolved = F.conv2d(input=image, weight=weights)converted = ToPILImage()(convolved[0])grayscale = scissors.convert('L')Figure 7.6 - Convolution vs conversionSee? They are the same … or are they? If you have a really sharp eye, maybe you areable to notice a subtle difference between the two shades of gray. It doesn’t haveanything to do with the use of convolutions, though—it turns out, PIL uses slightlydifferent weights for converting RGB into grayscale.526 | Chapter 7: Transfer Learning
- Page 500 and 501: Equation 6.16 - Looking aheadOnce N
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- Page 510 and 511: Mini-Batch SchedulersThese schedule
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- Page 514 and 515: Scheduler PathsBefore trying out a
- Page 516 and 517: After applying each scheduler to SG
- Page 518 and 519: Data Preparation1 # Loads temporary
- Page 520 and 521: Figure 6.31 - LossesEvaluationprint
- Page 522 and 523: [96] http://www.samkass.com/theorie
- Page 524 and 525: ImportsFor the sake of organization
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- Page 528 and 529: remained unchanged.ResNet (MSRA Tea
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- Page 532 and 533: dropout. You’re already familiar
- Page 534 and 535: OutputDownloading: "https://downloa
- Page 536 and 537: Replacing the "Top" of the Model1 a
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- Page 544 and 545: torch.save(train_preproc.tensors, '
- Page 546 and 547: Outputtensor([[109, 124],[124, 124]
- Page 548 and 549: Model Configuration1 optimizer_mode
- Page 552 and 553: The weights used by PIL are 0.299 f
- Page 554 and 555: • reduce the number of output cha
- Page 556 and 557: The constructor method defines the
- Page 558 and 559: Does it sound familiar? That’s wh
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- Page 564 and 565: normed1 = batch_normalizer(batch1[0
- Page 566 and 567: OutputOrderedDict([('running_mean',
- Page 568 and 569: OutputOrderedDict([('running_mean',
- Page 570 and 571: batch_normalizer = nn.BatchNorm2d(n
- Page 572 and 573: torch.manual_seed(23)dummy_points =
- Page 574 and 575: np.concatenate([dummy_points[:5].nu
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referred to as a fully connected layer. Performing a 1x1 convolution is akin to
applying a linear layer to each individual pixel over its channels.
This is the reason why a 1x1 convolution is said to be equivalent
to a fully connected (linear) layer.
In the example above, each of the two filters produces a different linear
combination of the RGB channels. Does this ring any bells? In Chapter 6, we saw
that grayscale images can be computed using a linear combination of the red,
green, and blue channels of colored images. So, we can convert an image to
grayscale using a 1x1 convolution!
scissors = Image.open('rps/scissors/scissors01-001.png')
image = ToTensor()(scissors)[:3, :, :].view(1, 3, 300, 300)
weights = torch.tensor([0.2126, 0.7152, 0.0722]).view(1, 3, 1, 1)
convolved = F.conv2d(input=image, weight=weights)
converted = ToPILImage()(convolved[0])
grayscale = scissors.convert('L')
Figure 7.6 - Convolution vs conversion
See? They are the same … or are they? If you have a really sharp eye, maybe you are
able to notice a subtle difference between the two shades of gray. It doesn’t have
anything to do with the use of convolutions, though—it turns out, PIL uses slightly
different weights for converting RGB into grayscale.
526 | Chapter 7: Transfer Learning
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