Comments (13)
JanuszL
commented on May 15, 2024
2
I just prepared some example based on your experience, #58.
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cliffwoolley
commented on May 15, 2024
1
What's the next step to improve this one? Do we need to document the example better, improve it generally, ... ?
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JanuszL
commented on May 15, 2024
1
I think we need to provide example with different readers doing the same at the end.
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ptrendx
commented on May 15, 2024
Hmm, that's strange - @Kh4L could you look at it?
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JanuszL
commented on May 15, 2024
Hi,
For me following code works (corrected some errors from your example):
class FileReadPipeline(Pipeline):
def __init__(self,batch_size, num_threads, device_id):
super(FileReadPipeline, self).__init__(batch_size, num_threads, device_id, seed = 12)
self.input = ops.FileReader(file_root = image_dir, random_shuffle = True, initial_fill = 21)
self.decode = ops.nvJPEGDecoder(device = "mixed", output_type = types.RGB)
self.resize = ops.Resize(device = "gpu", random_resize = True,
resize_a = 256, resize_b = 480,
image_type = types.RGB,
interp_type = types.INTERP_LINEAR)
self.cmn = ops.CropMirrorNormalize(device = "gpu",
output_dtype = types.FLOAT,
crop = (227, 227),
image_type = types.RGB,
mean = [128., 128., 128.],
std = [1., 1., 1.])
self.uniform = ops.Uniform(range = (0.0, 1.0))
def define_graph(self):
jpegs, labels = self.input()
images = self.decode(jpegs)
resized_images = self.resize(images)
output = self.cmn(resized_images, crop_pos_x = self.uniform(),
crop_pos_y = self.uniform())
# images are on the GPU
return (output, labels.gpu())
I will check TFRecordPipeline too later.
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jxmelody
commented on May 15, 2024
Hi @JanuszL ,
Thanks for your reply.
I checked my code and I found it's somewhere confusing cause I changed it before I raise this issue... so I modified it.
Actually, the most confused question of me is the serialize part ... What's the diffrence between serialized pipeline and non-serialized pipeline ？must serialized pipeline when using tensorflow ？
At last, I noticed that your code above are different with mine in last line:
return (output, labels,.gpu())
#return (output,labels)
So, is the .gpu() neccessary?
(sorry, I cant run the code right now,but I will check if the change can work tommrow (9 hours later... ), thanks again ;))
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Kh4L
commented on May 15, 2024
Hi @jxmelody ,
For TFRecordPipeline you have to define the graph by overriding def define_graph(self):.
As @JanuszL wrote, in FileReadPipeline you are returning images that is images = self.decode(jpegs). You actually want to return output (from CropMirrorNormalize).
Yes, .gpu() is necassary because FileReader returns CPU Tensors and tensorflow-gpu expects only GPU Tensors as Output.
So your define_graph's should return
return (output, labels.gpu())
The serialized pipeline is a string representing your pipeline in protobuf format: it contains parameters and the graph defined in Python. Our DALI-Tensorflow op needs this serialized pipeline to build and run internally the actual DALI Pipeline.
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jxmelody
commented on May 15, 2024
Hi @Kh4L ,
Sorry ... I forgot to paste the define_graph function of TFRecordPipeline , but I actually wrote this function and that not work... Anyway, I will add it to my code above, and you can check this.
For FileReaderPipeline, by correcting the errors as @JanuszL wrote, it can work now, It's my mistake. Thank both of you!
But TFRecordPipeline still can not work ,even though I have return .gpu() as you said .
Thank you for explaining the serialization . So as far as I understand, it's necessary to serialize the pipline first before use sess.run([image, label]), isn't it?
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JanuszL
commented on May 15, 2024
Hi,
Yes, indeed you need to serialize the pipeline first, then use it to initialize daliop. Please follow TensorFlow-ResNet50 example.
TFRecordPipeline works for me. You need to use image/class/label as labels, image/class/text is rather human-readable representation not meant for training.
class TFRecordPipeline(Pipeline):
def __init__(self, batch_size, num_threads, device_id):
super(TFRecordPipeline, self).__init__(batch_size,
num_threads,
device_id)
self.input = ops.TFRecordReader(path = tfrecord,
index_path = tfrecord_idx,
features = {"image/encoded" : tfrec.FixedLenFeature((), tfrec.string, ""),
'image/class/label': tfrec.FixedLenFeature([1], tfrec.int64, -1)
})
self.decode = ops.nvJPEGDecoder(device = "mixed", output_type = types.RGB)
self.resize = ops.Resize(device = "gpu", resize_a = 256, resize_b = 256)
self.cmnp = ops.CropMirrorNormalize(device = "gpu",
output_dtype = types.FLOAT,
crop = (224, 224),
image_type = types.RGB,
mean = [0., 0., 0.],
std = [1., 1., 1.])
self.uniform = ops.Uniform(range = (0.0, 1.0))
def define_graph(self):
inputs = self.input()
images = self.decode(inputs["image/encoded"])
resized_images = self.resize(images)
output = self.cmnp(resized_images, crop_pos_x = self.uniform(),
crop_pos_y = self.uniform())
return (output, inputs["image/class/label"].gpu())
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JanuszL
commented on May 15, 2024
Tracked internally in DALI-133
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jxmelody
commented on May 15, 2024
@JanuszL thanks, you are right . I changed texts to label, then it works!
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JanuszL
commented on May 15, 2024
Just merged #58.
It will be included in next minor release.
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biswajitcsecu
commented on May 15, 2024
it is working.
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Related Issues (20)
- random_resized_crop does not support different image sizes HOT 13
- How exactly does distributing the dataset with DALI/TensorFlow work? HOT 3
- Video Reader performance and alternative considerations HOT 4
- `experimental_fetch_to_device` makes no difference since TF 2.14 HOT 2
- external_source doesn't return last incomplete batch HOT 5
- Video Reader sequence start and end HOT 3
- Is it possible to modify the labels according to the random rotation of images? HOT 5
- Create a pytorch dataloader with 2 data sources HOT 5
- Resize on CPU with output shape coming from an external source operator HOT 3
- webdataset error HOT 2
- AWS DALI video_loader error HOT 3
- why multi-gpu training slower than single gpu HOT 1
- How to manage empty items in a batch? HOT 5
- nvidia.dali.fn.python_function() failed with TypeError HOT 5
- Can I install DALI on CPU node without GPU/CUDA HOT 3
- fn.lookup_table giving wrong result for gpu HOT 5
- Need argmax/argmin operator HOT 1
- How to destory a pipeline? HOT 1
- random crop function HOT 10
- cv2.resize(interpolation=cv2.INTER_LINEAR) different with fn.resize(img, antialias=True, subpixel_scale=True, interp_type=types.DALIInterpType.INTERP_LINEAR, resize_y=target_img_height, max_size=max_width) HOT 2
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