[GitHub] [incubator-mxnet] zhhoper commented on issue #17231: cannot quantization example

2020-01-17 Thread GitBox 
zhhoper commented on issue #17231: cannot quantization example
URL: 
https://github.com/apache/incubator-mxnet/issues/17231#issuecomment-575823751
 
 
   Hi, the mxnet build from source does not seem to work. I install the mxnet 
with pip, it can compress the network but the run time is super slow.  The 
mxnet version is 1.6.0. 


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[GitHub] [incubator-mxnet] zhhoper commented on issue #17231: cannot quantization example

2020-01-15 Thread GitBox 
zhhoper commented on issue #17231: cannot quantization example
URL: 
https://github.com/apache/incubator-mxnet/issues/17231#issuecomment-574543735
 
 
   @wuxun-zhang @ZhennanQin I run the example using mxnet 1.6.0, it seems to 
work ok. However, the run time of quantized model is much slower (more than 10 
times) than the original one. Is there anything I need to set up in order to 
speed up the quantized model?
   I test resnet152 
   For float32:
   command:
   python imagenet_inference.py 
--symbol-file=./model/imagenet1k-resnet-152-symbol.json 
--param-file=./model/imagenet1k-resnet-152-.params --num-skipped-batches=50 
--batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec 
--ctx=cpu
   Output:
   INFO:logger:batch size = 64 for inference
   INFO:logger:rgb_mean = 0,0,0
   INFO:logger:rgb_std = 1,1,1
   INFO:logger:label_name = softmax_label
   INFO:logger:Input data shape = (3, 224, 224)
   INFO:logger:Dataset for inference: ./data/val_256_q90.rec
   [07:03:16] ../src/io/iter_image_recordio_2.cc:831: Create ImageRecordIter2 
optimized for CPU backend.Use omp threads instead of preprocess_threads.
   [07:03:16] ../src/io/iter_image_recordio_2.cc:178: ImageRecordIOParser2: 
./data/val_256_q90.rec, use 16 threads for decoding..
   [07:03:16] ../src/base.cc:84: Upgrade advisory: this mxnet has been built 
against cuDNN lib version 7401, which is older than the oldest version tested 
by CI (7600).  Set MXNET_CUDNN_LIB_CHECKING=0 to quiet this warning.
   INFO:logger:Loading symbol from file 
/home/ubuntu/software/incubator-mxnet/example/quantization/./model/imagenet1k-resnet-152-symbol.json
   [07:03:18] ../src/nnvm/legacy_json_util.cc:209: Loading symbol saved by 
previous version v0.8.0. Attempting to upgrade...
   [07:03:18] ../src/nnvm/legacy_json_util.cc:217: Symbol successfully upgraded!
   INFO:logger:Loading params from file 
/home/ubuntu/software/incubator-mxnet/example/quantization/./model/imagenet1k-resnet-152-.params
   INFO:logger:Skipping the first 50 batches
   INFO:logger:Running model ./model/imagenet1k-resnet-152-symbol.json for 
inference
   [07:03:19] ../src/executor/graph_executor.cc:1982: Subgraph backend MKLDNN 
is activated.
   INFO:logger:Finished inference with 32000 images
   **INFO:logger:Finished with 22.124158 images per second**
   WARNING:logger:Note: GPU performance is expected to be slower than CPU. 
Please refer quantization/README.md for details
   INFO:logger:('accuracy', 0.7676875)
   INFO:logger:('top_k_accuracy_5', 0.93034375)
   
   For quantized model
   command:
   python imagenet_inference.py 
--symbol-file=./model/imagenet1k-resnet-152-quantized-5batches-naive-symbol.json
 --param-file=./model/imagenet1k-resnet-152-quantized-.params 
--num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 
--dataset=./data/val_256_q90.rec --ctx=cpu
   output:
   INFO:logger:batch size = 64 for inference
   INFO:logger:rgb_mean = 0,0,0
   INFO:logger:rgb_std = 1,1,1
   INFO:logger:label_name = softmax_label
   INFO:logger:Input data shape = (3, 224, 224)
   INFO:logger:Dataset for inference: ./data/val_256_q90.rec
   [00:37:40] ../src/io/iter_image_recordio_2.cc:831: Create ImageRecordIter2 
optimized for CPU backend.Use omp threads instead of preprocess_threads.
   [00:37:40] ../src/io/iter_image_recordio_2.cc:178: ImageRecordIOParser2: 
./data/val_256_q90.rec, use 16 threads for decoding..
   [00:37:40] ../src/base.cc:84: Upgrade advisory: this mxnet has been built 
against cuDNN lib version 7401, which is older than the oldest version tested 
by CI (7600). Set MXNET_CUDNN_LIB_CHECKING=0 to quiet this warning.
   INFO:logger:Loading symbol from file 
/home/ubuntu/software/incubator-mxnet/example/quantization/./model/imagenet1k-resnet-152-quantized-5batches-naive-symbol.json
   INFO:logger:Loading params from file 
/home/ubuntu/software/incubator-mxnet/example/quantization/./model/imagenet1k-resnet-152-quantized-.params
   INFO:logger:Skipping the first 50 batches
   INFO:logger:Running model 
./model/imagenet1k-resnet-152-quantized-5batches-naive-symbol.json for inference
   [00:37:43] ../src/executor/graph_executor.cc:1982: Subgraph backend MKLDNN 
is activated.
   INFO:logger:Finished inference with 32000 images
   **INFO:logger:Finished with 1.495486 images per second**
   WARNING:logger:Note: GPU performance is expected to be slower than CPU. 
Please refer quantization/README.md for details
   INFO:logger:('accuracy', 0.76328125)
   INFO:logger:('top_k_accuracy_5', 0.92859375)


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[GitHub] [incubator-mxnet] zhhoper commented on issue #17231: cannot quantization example

2020-01-14 Thread GitBox 
zhhoper commented on issue #17231: cannot quantization example
URL: 
https://github.com/apache/incubator-mxnet/issues/17231#issuecomment-574399327
 
 
   @wuxun-zhang Sorry that I haven't been able to touch that after reporting 
the bug. Will take a look at that and let you know if the bug is still there.


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[GitHub] [incubator-mxnet] zhhoper commented on issue #17231: cannot quantization example

2020-01-06 Thread GitBox 
zhhoper commented on issue #17231: cannot quantization example
URL: 
https://github.com/apache/incubator-mxnet/issues/17231#issuecomment-571373823
 
 
   @ZhennanQin I tried to set calib-mode to 'naive', met the same error. Error 
message as follows
   
   INFO:logger:Namespace(batch_size=32, calib_dataset='data/val_256_q90.rec', 
calib_mode='naive', data_nthreads=60, enable_calib_quantize=True, epoch=0, 
exclude_first_conv=False, image_shape='3,224,224', label_name='softmax_label', 
model='resnet50_v1', no_pretrained=False, num_calib_batches=10, 
quantized_dtype='auto', quiet=False, shuffle_chunk_seed=3982304, 
shuffle_dataset=True, shuffle_seed=48564309)
   INFO:logger:shuffle_dataset=True
   INFO:logger:calibration mode set to naive
   INFO:logger:Get pre-trained model from MXNet or Gluoncv modelzoo.
   INFO:logger:If you want to use custom model, please set --no-pretrained.
   INFO:logger:model resnet50_v1 is converted from GluonCV
   INFO:logger:Converting model from Gluon-CV ModelZoo resnet50_v1... into path 
/home/ubuntu/software/incubator-mxnet/example/quantization/model
   
/home/ubuntu/anaconda3/envs/mxnet_0.15/lib/python3.6/site-packages/mxnet-1.6.0-py3.6.egg/mxnet/module/base_module.py:67:
 UserWarning: Data provided by label_shapes don't match names specified by 
label_names ([] vs. ['softmax_label'])
 warnings.warn(msg)
   [00:17:25] ../src/executor/graph_executor.cc:1982: Subgraph backend MKLDNN 
is activated.
   INFO:logger:batch size = 32 for calibration
   INFO:logger:number of batches = 10 for calibration
   INFO:logger:These layers have been excluded []
   INFO:logger:label_name = softmax_label
   INFO:logger:Input data shape = (3, 224, 224)
   INFO:logger:rgb_mean = 123.68,116.779,103.939
   INFO:logger:rgb_std = 58.393, 57.12, 57.375
   INFO:logger:Creating ImageRecordIter for reading calibration dataset
   [00:17:26] ../src/io/iter_image_recordio_2.cc:178: ImageRecordIOParser2: 
data/val_256_q90.rec, use 16 threads for decoding..
   
   Segmentation fault: 11


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