Table Of Contents
Table Of Contents

mx.io.ImageRecordUInt8Iter

Description

Iterating on image RecordIO files.

This iterator is identical to ImageRecordIter except for using uint8 as the data type instead of float.

Usage

mx.io.ImageRecordUInt8Iter(...)

Arguments

Argument

Description

path.imglist

string, optional, default=’‘.

Path to the image list (.lst) file. Generally created with tools/im2rec.py. Format (Tab separated): <index of record> <one or more labels> <relative path from root folder>.

path.imgrec

string, optional, default=’‘.

Path to the image RecordIO (.rec) file or a directory path. Created with tools/im2rec.py.

path.imgidx

string, optional, default=’‘.

Path to the image RecordIO index (.idx) file. Created with tools/im2rec.py.

aug.seq

string, optional, default=’aug_default’.

The augmenter names to represent sequence of augmenters to be applied, seperated by comma. Additional keyword parameters will be seen by these augmenters.

label.width

int, optional, default=‘1’.

The number of labels per image.

data.shape

Shape(tuple), required.

The shape of one output image in (channels, height, width) format.

preprocess.threads

int, optional, default=‘4’.

The number of threads to do preprocessing.

verbose

boolean, optional, default=1.

If or not output verbose information.

num.parts

int, optional, default=‘1’.

Virtually partition the data into these many parts.

part.index

int, optional, default=‘0’.

The i-th virtual partition to be read.

shuffle.chunk.size

, optional, default=0.

The data shuffle buffer size in MB. Only valid if shuffle is true.

shuffle.chunk.seed

int, optional, default=‘0’.

The random seed for shuffling

shuffle

boolean, optional, default=0.

Whether to shuffle data randomly or not.

seed

int, optional, default=‘0’.

The random seed.

batch.size

int (non-negative), required.

Batch size.

round.batch

boolean, optional, default=1.

Whether to use round robin to handle overflow batch or not.

prefetch.buffer

, optional, default=4.

Maximum number of batches to prefetch.

dtype

{None, ‘float16’, ‘float32’, ‘float64’, ‘int32’, ‘int64’, ‘uint8’},optional, default=’None’.

Output data type. None means no change.

resize

int, optional, default=’-1’.

Down scale the shorter edge to a new size before applying other augmentations.

rand.crop

boolean, optional, default=0.

If or not randomly crop the image

random.resized.crop

boolean, optional, default=0.

If or not perform random resized cropping on the image, as a standard preprocessing for resnet training on ImageNet data.

max.rotate.angle

int, optional, default=‘0’.

Rotate by a random degree in [-v, v]

max.aspect.ratio

float, optional, default=0.

Change the aspect (namely width/height) to a random value. If min_aspect_ratio is None then the aspect ratio ins sampled from [1 - max_aspect_ratio, 1 + max_aspect_ratio], else it is in [min_aspect_ratio, max_aspect_ratio]

min.aspect.ratio

float or None, optional, default=None.

Change the aspect (namely width/height) to a random value in [min_aspect_ratio, max_aspect_ratio]

max.shear.ratio

float, optional, default=0.

Apply a shear transformation (namely (x,y)->(x+my,y)) with m randomly chose from [-max_shear_ratio, max_shear_ratio]

max.crop.size

int, optional, default=’-1’.

Crop both width and height into a random size in [min_crop_size, max_crop_size].``Ignored if ``random_resized_crop is True.

min.crop.size

int, optional, default=’-1’.

Crop both width and height into a random size in [min_crop_size, max_crop_size].``Ignored if ``random_resized_crop is True.

max.random.scale

float, optional, default=1.

Resize into [width*s, height*s] with s randomly chosen from [min_random_scale, max_random_scale]. Ignored if random_resized_crop is True.

min.random.scale

float, optional, default=1.

Resize into [width*s, height*s] with s randomly chosen from [min_random_scale, max_random_scale]``Ignored if ``random_resized_crop is True.

max.random.area

float, optional, default=1.

Change the area (namely width * height) to a random value in [min_random_area, max_random_area]. Ignored if random_resized_crop is False.

min.random.area

float, optional, default=1.

Change the area (namely width * height) to a random value in [min_random_area, max_random_area]. Ignored if random_resized_crop is False.

max.img.size

float, optional, default=1e+10.

Set the maximal width and height after all resize and rotate argumentation are applied

min.img.size

float, optional, default=0.

Set the minimal width and height after all resize and rotate argumentation are applied

brightness

float, optional, default=0.

Add a random value in [-brightness, brightness] to the brightness of image.

contrast

float, optional, default=0.

Add a random value in [-contrast, contrast] to the contrast of image.

saturation

float, optional, default=0.

Add a random value in [-saturation, saturation] to the saturation of image.

pca.noise

float, optional, default=0.

Add PCA based noise to the image.

random.h

int, optional, default=‘0’.

Add a random value in [-random_h, random_h] to the H channel in HSL color space.

random.s

int, optional, default=‘0’.

Add a random value in [-random_s, random_s] to the S channel in HSL color space.

random.l

int, optional, default=‘0’.

Add a random value in [-random_l, random_l] to the L channel in HSL color space.

rotate

int, optional, default=’-1’.

Rotate by an angle. If set, it overwrites the max_rotate_angle option.

fill.value

int, optional, default=‘255’.

Set the padding pixels value to fill_value.

data.shape

Shape(tuple), required.

The shape of a output image.

inter.method

int, optional, default=‘1’.

The interpolation method: 0-NN 1-bilinear 2-cubic 3-area 4-lanczos4 9-auto 10-rand.

pad

int, optional, default=‘0’.

Change size from [width, height] into [pad + width + pad, pad + height + pad] by padding pixes

seed.aug

int or None, optional, default=’None’.

Random seed for augmentations.