mx.io.ImageRecordUInt8Iter

Description

Iterating on image RecordIO files.

Note

ImageRecordUInt8Iter is deprecated. Use ImageRecordIter(dtype=’uint8’) instead.

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.

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.
| The i-th virtual partition to be read. |
| Setting device_id to valid positive device id will create |
| Context::CPUPinned(device_id). Default is |
| 0. |

shuffle.chunk.size

long (non-negative), 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

seed.aug

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

Random seed for augmentations.

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

long (non-negative), optional, default=4.

Maximum number of batches to prefetch.

ctx

{‘cpu’, ‘gpu’},optional, default=’gpu’.

Context data loader optimized for.

dtype

{None, ‘float16’, ‘float32’, ‘float64’, ‘int32’, ‘int64’, ‘int8’, ‘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