This section shows you how DALI arithmetic expressions can be used to achieve conditional-like application of augmentations and be used for some of the masking operations.

## Conditional Results¶

1. Create a pipeline that will use DALI arithmetic expressions to conditionally augment images.

2. Since DALI does not support conditional or partial execution, we have to emulate this behavior by multiplexing.

For example, all transforms are applied to all inputs, but only the result of one of them is propagated to the output and others are rejected based on some condition.

Note: All possible inputs to our multiplexing operation will still be calculated by DALI.

### Imports¶

Here are the necessary imports.

[1]:

from nvidia.dali.pipeline import Pipeline
import nvidia.dali.ops as ops
import nvidia.dali.fn as fn
import nvidia.dali.types as types
from nvidia.dali.types import Constant


### Explicitly Used Operators¶

• The pipeline will use readers.file to provide the input images. We also need the decoders.image to decode the loaded images.

• We will use coin_flip as a source for the random conditions and cast the result to bool, so it will work with the type promotion rules.

• As an example augmentation, we will apply the brightness_contrast operator. The select parameters are extreme, so it will clearly show in the output.

### Graph with Custom Augmentation¶

2. Apply the augmentation and keep the handles to both tensors, not augmented imgs and augmented imgs_adjusted.

3. Cast the condition, output of coin_flip, to bool.

## The Multiplexing Operation¶

1. Calculate the output out that is an equivalent to the following:

for idx in range(batch_size):
if condition[idx]:
else:
out[idx] = imgs[idx]

1. Transform the condition to an arithmetic expression:

out = condition * imgs_adjusted + (not condition) * imgs

• When the condition is True we multiply the imgs_adjusted by the True value (thus keeping it).

• When it is False the multiplication yields 0.

• Multiplying some numerical type by boolean keeps the numerical type.

To implement the else branch, we need to negate the condition and perform a similar multiplication operation, and add them together.

As the result of to Python operator limitations, negating the boolean condition is implemented as a bitwise xor operation with boolean constant True.

1. To visualize the results, we return the output of the multiplexing operation, the original images, and coin_flip values.

[2]:

pipe = Pipeline(batch_size=5, num_threads=1, device_id=0)
with pipe:
device="cpu", file_root="../../data/images", file_list="../../data/images/file_list.txt")
imgs = fn.decoders.image(input_buf, device="cpu", output_type=types.RGB)
condition = fn.random.coin_flip(dtype=types.DALIDataType.BOOL)
neg_condition = condition ^ True
out = condition * imgs_adjusted + neg_condition * imgs

pipe.set_outputs(out, imgs, condition)


### Multiplexing as a Helper Function¶

To clean things up, we can wrap the multiplexing operation in the mux helper function.

Note: The inputs to mux need to allow for the specified element-wise expression. In our case, the condition is a batch of tensors that represent scalars, and that the corresponding elements of the True and False cases have matching shapes.

[3]:

def mux(condition, true_case, false_case):
neg_condition = condition ^ True
return condition * true_case + neg_condition * false_case

with pipe:
device="cpu", file_root="../../data/images", file_list="../../data/images/file_list.txt")
imgs = fn.decoders.image(input_buf, device="cpu", output_type=types.RGB)
condition = fn.random.coin_flip(dtype=types.DALIDataType.BOOL)

pipe.set_outputs(out, imgs, condition)


### Running the Pipeline¶

1. Create an instance of the pipeline and build it.

A batch_size = 5 was used, so we can observe that some of the output images are augmented and some are not.

[4]:

pipe.build()

1. We will use a simple helper function to show the images.

It takes the three outputs from the pipeline, the output of multiplexing is placed in left column, and the original images in the right, and it assigns proper captions.

[5]:

import matplotlib.pyplot as plt
import numpy as np

def display(augmented, reference, flip_value = None, cpu = True):
data_idx = 0
fig, axes = plt.subplots(len(augmented), 2, figsize=(15, 15))
for i in range(len(augmented)):
img = augmented.at(i) if cpu else augmented.as_cpu().at(i)
ref = reference.at(i) if cpu else reference.as_cpu().at(i)
if flip_value:
val = flip_value.at(i) if cpu else flip_value.as_cpu().at(i)
else:
val = True
axes[i, 0].imshow(np.squeeze(img))
axes[i, 1].imshow(np.squeeze(ref))
axes[i, 0].axis('off')
axes[i, 1].axis('off')
axes[i, 0].set_title("Image was augmented" if val else "Image was not augmented")
axes[i, 1].set_title("Original image")

1. Run and display the results.

You can play this cell several times to see the result for different images.

[6]:

(output, reference, flip_val) = pipe.run()
display(output, reference, flip_val)


## Generating Masks with Comparisons and Bitwise Operations¶

We can extend the pipeline by using some more complex logical conditions. We will use comparison operators to build masks that represent regions where the image has low and high pixel intensities.

We will use bitwise OR operation to build a mask that represents the union of this regions. The values in the mask are boolean, so the bitwise |, & ^ operations can be used like their logical counterparts.

DALI arithmetic expressions are elementwise and specific channel values can vary. We will calculate the masks on gray images, so we will get one value per pixel and duplicate the information to a 3-channel mask, by using the Cat operator, to ensure that the shape of image and mask match. We need the ColorSpaceConversion operator to handle RGB->Gray conversion.

We will apply brightening and darkening to specified regions by using the similar approach as before with multiplexing.

## Comparison Operators¶

DALI allows you to directly use all Python comparison operators. The tensors that will be obtained from comparison contain boolean values.

Creating 1-channel masks for low and high intensities is the same as writing imgs_gray < 30 and imgs_gray > 230.

[7]:

def not_(mask):

with pipe:
device="cpu", file_root="../../data/images", file_list="../../data/images/file_list.txt")
imgs = fn.decoders.image(input_buf, device="cpu", output_type=types.RGB)

imgs_gray = fn.color_space_conversion(imgs, image_type=types.RGB, output_type=types.GRAY)
imgs_bright = fn.brightness_contrast(imgs, brightness=3)
imgs_dark = fn.brightness_contrast(imgs, brightness=0.75)


[8]:

mask_pipe.build()


We will adjust our display function so in addition to original and augmented images we can also see the masks that we obtained.

[9]:

def display2(augmented, reference, mask, cpu = True):
data_idx = 0
fig, axes = plt.subplots(len(augmented), 3, figsize=(15, 15))
for i in range(len(augmented)):
img = augmented.at(i) if cpu else augmented.as_cpu().at(i)
ref = reference.at(i) if cpu else reference.as_cpu().at(i)
axes[i, 0].imshow(np.squeeze(img))
axes[i, 1].imshow(np.squeeze(ref))
axes[i, 2].imshow(np.squeeze(m))
axes[i, 0].axis('off')
axes[i, 1].axis('off')
axes[i, 2].axis('off')
axes[i, 0].set_title("Augmented image")
axes[i, 1].set_title("Reference decoded image")

[10]:

(output, reference, mask) = mask_pipe.run()

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