Generating int8 Calibration File for ResNet-18 Caffe Model about tensorrt-utils HOT 16 OPEN

Hi @Hassan313 ,

If you change the ONNX parser code to use Caffe parser instead:

• tensorrt-utils/classification/imagenet/onnx_to_tensorrt.py

  Line 79 in 8dcd18c

  with trt.Builder(TRT_LOGGER) as builder, builder.create_network(network_flags) as network, trt.OnnxParser(network, TRT_LOGGER) as parser:

• tensorrt-utils/classification/imagenet/onnx_to_tensorrt.py

  Lines 116 to 122 in 8dcd18c

  	with open(args.onnx, "rb") as f:
  	parsed = parser.parse(f.read())
  	# Exit if parsing fails
  	if not parsed:
  	logger.error("Failed to parse model.")
  	sys.exit(1)

I believe you should be able to use the INT8 code as is. I'm assuming Resnet18 has the same input/output shapes as other imagenet models.

If you have any specific problems or errors, please share them.

from tensorrt-utils.

Hi @Hassan313 ,

Per the error, it looks like you're still using the ONNX parser. Did you change the code to use the Caffe parser as suggested above?

from tensorrt-utils.

If you're actually using an ONNX model and are using TensorRT >= 7.0, you'll need to add the --explicit-batch flag when running the script.

The README instructions are a little outdated. (based on the 19.10 container which was TensorRT 6 and did not have this restriction).

from tensorrt-utils.

This is just a python syntax error, not specific to TensorRT.

Looks like you're missing a parentheses at the end of network.mark_output(model_tensors.find("prob"))

from tensorrt-utils.

I believe the Caffe parser uses a different syntax than the ONNX parser used here: https://www.github.com/rmccorm4/tensorrt-utils/tree/8dcd18c5c88f35bdb04e42e46b46862d81c36230/classification%2Fimagenet%2Fonnx_to_tensorrt.py

Although I notice you have some Caffe parser code in your snippet above, so perhaps you're calling the parser a second time incorrectly in the code block I linked.

Please refer to a Caffe python sample for how to use the Caffe parser.

from tensorrt-utils.

I'm not sure if there is an automatic way to fix that (i.e. let parser correctly name all layers). I'm not sure why that happens. It's probably when there's no 1:1 mapping from Caffe layer to TensorRT op, so TensorRT breaks the layer out into several ops or something like that.

You could try to compare the layers with your Caffe layers (programmatically), and set the names of each TensorRT INetworkDefinition layer manually.

Pseudocode:

for i in network.num_layers:
    layer = network.get_layer(i)
    layer.name = ...

from tensorrt-utils.

Hi @Hassan313 ,

Not too sure. You'll probably have to do a bit of investigation to see how the 2 files correlate. Maybe try creating a few caches, see if it's always the same ordering. Try comparing with the layer names in the network object. Verify that the Caffe layers are actually in order or random, etc.

from tensorrt-utils.

Hi @rmccorm4 ,

Thank you for your response.

I am trying to the part 3 as below:

When I try to run the command, I get the below error:

ERROR: Failed to parse the ONNX file: resnet50/model.onnx
In node -1 (importModel): INVALID_VALUE: Assertion failed: !_importer_ctx.network()->hasImplicitBatchDimension() && "This version of the ONNX parser only supports TensorRT INetworkDefinitions with an explicit batch dimension. Please ensure the network was created using the EXPLICIT_BATCH NetworkDefinitionCreationFlag."

What should I do to eliminate the problem?

Thank you very much.

from tensorrt-utils.

Hi @rmccorm4 ,

I am first trying to see if I can run the original code. I have not changed the ONNX parser to Caffe parser.

Can you kindly help with this error first? Or you suggest I directly go to changing the parser?

Thank you very much.

from tensorrt-utils.

@rmccorm4 That solved the problem. I can successfully run the below:

from tensorrt-utils.

FYI that infer_tensorrt script is definitely out of date for TensorRT 7 ONNX models. Since the EXPLICIT_BATCH flag is used, the batch size dimension of the original ONNX model will be used. It will likely provide garbage data for batch sizes different than the one in the ONNX model.

However, if you move forward with a Caffe model (which doesn't support explicit batch, and instead uses implicit batch), then that infer_tensorrt script will likely work as intended for various batch sizes.

from tensorrt-utils.

@rmccorm4 Hi Ryan,

I have changed the ONNX parser to Caffe parser. The below code is the part of the code that I have changed:

# Building engine
with trt.Builder(TRT_LOGGER) as builder, \
     builder.create_network(network_flags) as network, \
     builder.create_builder_config() as config, \
     trt.CaffeParser() as parser:
        
    config.max_workspace_size = 2**30 # 1GiB
    
    model_tensors = parser.parse(deploy="/home/hassan/tensorrt-utils/classification/imagenet/resnet18/deploy.txt", model="/home/hassan/tensorrt-utils/classification/imagenet/resnet18/resnet-18.caffemodel", network=network, dtype=trt.float32)
    network.mark_output(model_tensors.find("prob")
    # Set Builder Config Flags
    for flag in builder_flag_map:
        if getattr(args, flag):
            logger.info("Setting {}".format(builder_flag_map[flag]))
            config.set_flag(builder_flag_map[flag])

I am getting the below error:

File "onnx_to_tensorrt.py", line 169
for flag in builder_flag_map:
^
SyntaxError: invalid syntax

Can you kindly help?

Thank you very much.

from tensorrt-utils.

@rmccorm4 Thank you for your reply. Sorry, it was my syntax mistake.

Now I am able to run the code, however I am getting the below logs:

And the calibration file is not getting created.

Can you kindly help?

Thank you very much.

from tensorrt-utils.

@rmccorm4 Thank you for your reply.

I am now able to run the code with the Caffe model of ResNet-18. However, there are many "unnamed layers" in the file. How can I fix this problem?

Below is what I am getting:

TRT-7000-EntropyCalibration2
data: 3caa54fc
(Unnamed Layer* 0) [Convolution]_output: 3d6969df
(Unnamed Layer* 1) [Scale]_output: 3ac9c5e4
(Unnamed Layer* 2) [Scale]_output: 3bb23f5c
conv1: 3bb2132a
pool1: 3bb2132a
(Unnamed Layer* 5) [Convolution]_output: 3b0f3403
(Unnamed Layer* 6) [Scale]_output: 3c19f4a8
res2a_branch1: 3b39bf9e
(Unnamed Layer* 8) [Convolution]_output: 3b64b387
(Unnamed Layer* 9) [Scale]_output: 3c34ac95
(Unnamed Layer* 10) [Scale]_output: 3b309597
res2a_branch2a: 3b21cb45
(Unnamed Layer* 12) [Convolution]_output: 3acc6b2e
(Unnamed Layer* 13) [Scale]_output: 3cb1a784
res2a_branch2b: 3b5eba4f
(Unnamed Layer* 15) [ElementWise]_output: 3ba75a96
res2a: 3b73bec7
(Unnamed Layer* 17) [Convolution]_output: 3b8658ae
(Unnamed Layer* 18) [Scale]_output: 3cce329f
(Unnamed Layer* 19) [Scale]_output: 3b9b34cf
res2b_branch2a: 3b6795f7
(Unnamed Layer* 21) [Convolution]_output: 3b1fb3bc
(Unnamed Layer* 22) [Scale]_output: 3d1fdc85
res2b_branch2b: 3c0d6bd4
(Unnamed Layer* 24) [ElementWise]_output: 3ba9e0a1
res2b: 3ba9e0a1
(Unnamed Layer* 26) [Convolution]_output: 3b16170a
(Unnamed Layer* 27) [Scale]_output: 3cf6451c
res3a_branch1: 3b263b66
(Unnamed Layer* 29) [Convolution]_output: 3ba9a8f5
(Unnamed Layer* 30) [Scale]_output: 3cc67d8a
(Unnamed Layer* 31) [Scale]_output: 3ba7f0eb
res3a_branch2a: 3b86b13f
(Unnamed Layer* 33) [Convolution]_output: 3ae5e952
(Unnamed Layer* 34) [Scale]_output: 3cb6efb3
res3a_branch2b: 3ba33981
(Unnamed Layer* 36) [ElementWise]_output: 3bc24e1f
res3a: 3ba9509c
(Unnamed Layer* 38) [Convolution]_output: 3b66809e
(Unnamed Layer* 39) [Scale]_output: 3ce33610
(Unnamed Layer* 40) [Scale]_output: 3b777039
res3b_branch2a: 3b75afc6
(Unnamed Layer* 42) [Convolution]_output: 3afb4c74
(Unnamed Layer* 43) [Scale]_output: 3d124362
res3b_branch2b: 3baee2bd
(Unnamed Layer* 45) [ElementWise]_output: 3bda3b92
res3b: 3c232aa1
(Unnamed Layer* 47) [Convolution]_output: 3acb6310
(Unnamed Layer* 48) [Scale]_output: 3cd0b5d2
res4a_branch1: 3b0e18b5
(Unnamed Layer* 50) [Convolution]_output: 3b8f205a
(Unnamed Layer* 51) [Scale]_output: 3ceb05ce
(Unnamed Layer* 52) [Scale]_output: 3b92b025
res4a_branch2a: 3b7460fa
(Unnamed Layer* 54) [Convolution]_output: 3b08f7e1
(Unnamed Layer* 55) [Scale]_output: 3cc4e16c
res4a_branch2b: 3b7876f9
(Unnamed Layer* 57) [ElementWise]_output: 3ba01f39
res4a: 3b9b7d97
(Unnamed Layer* 59) [Convolution]_output: 3b621e55
(Unnamed Layer* 60) [Scale]_output: 3d01f964
(Unnamed Layer* 61) [Scale]_output: 3b8113fa
res4b_branch2a: 3b75dd91
(Unnamed Layer* 63) [Convolution]_output: 3ad95424
(Unnamed Layer* 64) [Scale]_output: 3ce10797
res4b_branch2b: 3b9ab9fb
(Unnamed Layer* 66) [ElementWise]_output: 3b9f849e
res4b: 3b9e4632
(Unnamed Layer* 68) [Convolution]_output: 3a41c535
(Unnamed Layer* 69) [Scale]_output: 3ccc7fd8
res5a_branch1: 3b3127c4
(Unnamed Layer* 71) [Convolution]_output: 3b236e54
(Unnamed Layer* 72) [Scale]_output: 3cd32a30
(Unnamed Layer* 73) [Scale]_output: 3b91769c
res5a_branch2a: 3b365c95
(Unnamed Layer* 75) [Convolution]_output: 3b00c450
(Unnamed Layer* 76) [Scale]_output: 3c90df16
res5a_branch2b: 3bae948a
(Unnamed Layer* 78) [ElementWise]_output: 3bd0697d
res5a: 3ba310bb
(Unnamed Layer* 80) [Convolution]_output: 3b0316c2
(Unnamed Layer* 81) [Scale]_output: 3cbd74e6
(Unnamed Layer* 82) [Scale]_output: 3b425e32
res5b_branch2a: 3b04391a
(Unnamed Layer* 84) [Convolution]_output: 3a8278a9
(Unnamed Layer* 85) [Scale]_output: 3d572bce
res5b_branch2b: 3d8a603c
(Unnamed Layer* 87) [ElementWise]_output: 3d80bed8
res5b: 3da2229e
pool5: 3da2229e
fc1000: 3d3e8d71
prob: 392964a1

Moreover, here is the JSON version of the calibration file:

{
"data": {
"scale": 0.02079247683286667,
"min": 0,
"max": 0,
"offset": 0
},
"(Unnamed Layer* 0) [Convolution]_output": {
"scale": 0.05698573216795921,
"min": 0,
"max": 0,
"offset": 0
},
"(Unnamed Layer* 1) [Scale]_output": {
"scale": 0.0015394059009850025,
"min": 0,
"max": 0,
"offset": 0
},
"(Unnamed Layer* 2) [Scale]_output": {
"scale": 0.0054396819323301315,
"min": 0,
"max": 0,
"offset": 0
},
"conv1": {
"scale": 0.005434413440525532,
"min": 0,
"max": 0,
"offset": 0
},
"pool1": {
"scale": 0.005434413440525532,
"min": 0,
"max": 0,
"offset": 0
},
"(Unnamed Layer* 5) [Convolution]_output": {
"scale": 0.0021851069759577513,
"min": 0,
"max": 0,
"offset": 0
},
"(Unnamed Layer* 6) [Scale]_output": {
"scale": 0.009396709501743317,
"min": 0,
"max": 0,
"offset": 0
},
"res2a_branch1": {
"scale": 0.002834297250956297,
"min": 0,
"max": 0,
"offset": 0
},
"(Unnamed Layer* 8) [Convolution]_output": {
"scale": 0.003489704569801688,
"min": 0,
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"offset": 0
},
"(Unnamed Layer* 9) [Scale]_output": {
"scale": 0.011027474887669086,
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},
"(Unnamed Layer* 10) [Scale]_output": {
"scale": 0.002694463124498725,
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},
"res2a_branch2a": {
"scale": 0.0024687808472663164,
"min": 0,
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"offset": 0
},
"(Unnamed Layer* 12) [Convolution]_output": {
"scale": 0.0015595906879752874,
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},
"(Unnamed Layer* 13) [Scale]_output": {
"scale": 0.02168632298707962,
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},
"res2a_branch2b": {
"scale": 0.0033985560294240713,
"min": 0,
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"offset": 0
},
"(Unnamed Layer* 15) [ElementWise]_output": {
"scale": 0.0051072342321276665,
"min": 0,
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"offset": 0
},
"res2a": {
"scale": 0.0037192569579929113,
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},
"(Unnamed Layer* 17) [Convolution]_output": {
"scale": 0.00409992691129446,
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},
"(Unnamed Layer* 18) [Scale]_output": {
"scale": 0.025170622393488884,
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"(Unnamed Layer* 19) [Scale]_output": {
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},
"(Unnamed Layer* 21) [Convolution]_output": {
"scale": 0.002436860464513302,
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},
"(Unnamed Layer* 22) [Scale]_output": {
"scale": 0.03902866318821907,
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"(Unnamed Layer* 24) [ElementWise]_output": {
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"(Unnamed Layer* 26) [Convolution]_output": {
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"(Unnamed Layer* 29) [Convolution]_output": {
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},
"(Unnamed Layer* 78) [ElementWise]_output": {
"scale": 0.0063602314330637455,
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},
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"(Unnamed Layer* 80) [Convolution]_output": {
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"(Unnamed Layer* 82) [Scale]_output": {
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"pool5": {
"scale": 0.07916758954524994,
"min": 0,
"max": 0,
"offset": 0
},
"fc1000": {
"scale": 0.04652160778641701,
"min": 0,
"max": 0,
"offset": 0
},
"prob": {
"scale": 0.0001615458313608542,
"min": 0,
"max": 0,
"offset": 0
}
}

Can you kindly help?

Thank you very much.

from tensorrt-utils.

@rmccorm4 Hi Ryan,

Thank you very much for your reply. I will try the solution you mentioned. Thank you very much for your prompt replies. I really appreciate your help and support.

from tensorrt-utils.

I'm not sure if there is an automatic way to fix that (i.e. let parser correctly name all layers). I'm not sure why that happens. It's probably when there's no 1:1 mapping from Caffe layer to TensorRT op, so TensorRT breaks the layer out into several ops or something like that.

You could try to compare the layers with your Caffe layers (programmatically), and set the names of each TensorRT INetworkDefinition layer manually.

Pseudocode:

for i in network.num_layers:
    layer = network.get_layer(i)
    layer.name = ...

You could try to compare the layers with your Caffe layers (programmatically), and set the names of each TensorRT INetworkDefinition layer manually.

The layers sequence in the calibration file is not in the order of the deploy file of the Caffe model. How can I figure out the sequence of layers in the calibration file?

from tensorrt-utils.