random crop function

[DAVID-Hown]

Describe the question.

eii = ExternalInputIterator(batch_size) pipe = Pipeline(batch_size=batch_size, num_threads=2, device_id=3) with pipe: jpegs = fn.external_source(source=eii, num_outputs=1, dtype=types.UINT8) decode = fn.decoders.image(jpegs, device="mixed", output_type=types.RGB) pipe.set_outputs(crop_patch)

how to add random crop to 224x224, between decode and output

Check for duplicates

• I have searched the open bugs/issues and have found no duplicates for this bug report

[JanuszL]

Hi @DAVID-Hown,

Thank you for reaching out.
You can just:

crop_patch= fn.crop_mirror_normalize(decode,
                                     crop_h=224,
                                     crop_w=224,
                                     crop_pos_x=fn.random.uniform(range=(0., 1.)),
                                     crop_pos_y=fn.random.uniform(range=(0., 1.)))

[DAVID-Hown]

@JanuszL Thank you, But I get a error TypeError: Invalid shape (3, 224, 224) for image data

img = batch_cpu.at(0)
print(img.shape)  # (3, 224, 224)
plt.imshow(img)
pylab.show()

maybe format is not right. this is my code:

import types
import collections
import numpy as np
from random import shuffle
from nvidia.dali.pipeline import Pipeline
import nvidia.dali.fn as fn
import nvidia.dali.types as types
import matplotlib.pyplot as plt
import pylab

batch_size = 16


class ExternalInputIterator(object):
    def __init__(self, batch_size, image_path):
        self.image_path = image_path
        self.batch_size = batch_size

    def __iter__(self):
        self.i = 0
        return self

    def __next__(self):
        batch = []
        for _ in range(self.batch_size):
            f = open(self.image_path, 'rb')
            batch.append(np.frombuffer(f.read(), dtype=np.uint8))
            self.i = (self.i + 1) % self.batch_size
        return (batch,)


img_path = "P22040562823834_mark_0.jpg"
eii = ExternalInputIterator(batch_size, img_path)
pipe = Pipeline(batch_size=batch_size, num_threads=2, device_id=3)
with pipe:
    jpegs = fn.external_source(source=eii, num_outputs=1, dtype=types.UINT8)
    decode = fn.decoders.image(jpegs, device="mixed", output_type=types.RGB)
    crop_patch = fn.crop_mirror_normalize(decode,
                                          crop_h=224,
                                          crop_w=224,
                                          crop_pos_x=fn.random.uniform(range=(0., 1.)),
                                          crop_pos_y=fn.random.uniform(range=(0., 1.)))
    pipe.set_outputs(crop_patch)

pipe.build()
pipe_out = pipe.run()
batch_cpu = pipe_out[0].as_cpu()

img = batch_cpu.at(0)

I plan to input an image, then use DALI library to accelerate IO speed, and directly output batch_size=16 tensor, do you have any suggestions

[JanuszL]

If you want to show it you need to change layout to HWC. You can do it:

crop_patch= fn.crop_mirror_normalize(decode,
                                     crop_h=224,
                                     crop_w=224,
                                     crop_pos_x=fn.random.uniform(range=(0., 1.)),
                                     crop_pos_y=fn.random.uniform(range=(0., 1.)),
                                     output_layout="HWC")

[DAVID-Hown]

@JanuszL
My code is the one above; In the model inference phase, I want to input an image and output a result; In order to ensure the accuracy of the model, I need to input a picture, cut 16 patches of 224x224 size at random, and then feed into the model, the model outputs the score of each patch, and finally take the average of all the results。I am not familiar with using this library, so I would like to ask you

[DAVID-Hown]

@JanuszL hi，batch size=16, obtain 16 dimensions tensorgpulist, how to stack into 16X224X224X3 pytorch supported dimensions

[JanuszL]

Hi,

You can call crop/slice operator 16 times and stack the results using cat/stack operators.

[DAVID-Hown]

@JanuszLHow to convert pipe_out directly to 16X224X224X3 parallelly

[JanuszL]

How about:

decode = fn.decoders.image(jpegs, device="mixed", output_type=types.RGB)
  crop_patch = []
  for _ in range(16):
    crop_patch.append(fn.crop_mirror_normalize(decode,
                                        crop_h=224,
                                        crop_w=224,
                                        crop_pos_x=fn.random.uniform(range=(0., 1.)),
                                        crop_pos_y=fn.random.uniform(range=(0., 1.)),
                                        output_layout="HWC"))
    crop_patchs = fn.stack(*crop_patch)
  pipe.set_outputs(crop_patchs)

[DAVID-Hown]

@JanuszL

crop_patch = fn.crop_mirror_normalize(decode,
                                          device="gpu",
                                          crop_h=224, crop_w=224,
                                          crop_pos_x=fn.random.uniform(range=(0., 1.)),
                                          crop_pos_y=fn.random.uniform(range=(0., 1.)),
                                          dtype=types.FLOAT,
                                          output_layout="CHW",
                                          mean=[0.485, 0.456, 0.406],
                                          std=[0.229, 0.224, 0.225])

I found that the function did not normalize my input

[JanuszL]

If you want to normalize it to 0-1 range please:

        mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
        std=[0.229 * 255, 0.224 * 255, 0.225 * 255],