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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/10/17 08:39:15 UTC
[GitHub] pengzhao-intel edited a comment on issue #12808: MKL-DNN
Quantization Examples and README
pengzhao-intel edited a comment on issue #12808: MKL-DNN Quantization Examples and README
URL: https://github.com/apache/incubator-mxnet/pull/12808#issuecomment-430537478
@aaronmarkham I asked another member in our team to reproduce all results only following the README.
And all results are reproducible smoothly :) The log as below, a little long.
Could you help merge this PR? Thanks :)
> ResNet50-V1 FP32
>
> INFO:logger:batch size = 64 for inference
> INFO:logger:rgb_mean = 123.68,116.779,103.939
> INFO:logger:rgb_std = 58.393,57.12,57.375
> INFO:logger:label_name = softmax_label
> INFO:logger:Input data shape = (3, 224, 224)
> INFO:logger:Dataset for inference: ./data/val_256_q90.rec
> [11:00:02] src/io/iter_image_recordio_2.cc:172: ImageRecordIOParser2: ./data/val_256_q90.rec, use 1 threads for decoding..
> INFO:logger:Loading symbol from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/resnet50_v1-symbol.json
> INFO:logger:Loading params from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/resnet50_v1-0000.params
> INFO:logger:Skipping the first 50 batches
> INFO:logger:Running model ./model/resnet50_v1-symbol.json for inference
> [11:00:07] src/operator/subgraph/mkldnn/mkldnn_conv_property.cc:138: Start to execute MKLDNN Convolution optimization pass.
> [11:00:07] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 147456 bytes with malloc directly
> [11:00:07] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 589824 bytes with malloc directly
> [11:00:08] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 2359296 bytes with malloc directly
> [11:00:08] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 9437184 bytes with malloc directly
> INFO:logger:Finished inference with 32000 images
> INFO:logger:Finished with 95.636832 images per second
> INFO:logger:('accuracy', 0.76490625)
> INFO:logger:('top_k_accuracy_5', 0.93184375)
>
> ResNet50-V1 INT8
>
> INFO:logger:batch size = 64 for inference
> INFO:logger:rgb_mean = 123.68,116.779,103.939
> INFO:logger:rgb_std = 58.393,57.12,57.375
> INFO:logger:label_name = softmax_label
> INFO:logger:Input data shape = (3, 224, 224)
> INFO:logger:Dataset for inference: ./data/val_256_q90.rec
> [11:06:44] src/io/iter_image_recordio_2.cc:172: ImageRecordIOParser2: ./data/val_256_q90.rec, use 1 threads for decoding..
> INFO:logger:Loading symbol from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/resnet50_v1-quantized-5batches-naive-symbol.json
> INFO:logger:Loading params from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/resnet50_v1-quantized-0000.params
> INFO:logger:Skipping the first 50 batches
> INFO:logger:Running model ./model/resnet50_v1-quantized-5batches-naive-symbol.json for inference
> [11:06:49] src/operator/subgraph/mkldnn/mkldnn_conv_property.cc:138: Start to execute MKLDNN Convolution optimization pass.
> [11:06:50] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 147456 bytes with malloc directly
> [11:06:50] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 589824 bytes with malloc directly
> [11:06:51] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 2359296 bytes with malloc directly
> INFO:logger:Finished inference with 32000 images
> INFO:logger:Finished with 47.259650 images per second
> INFO:logger:('accuracy', 0.76096875)
> INFO:logger:('top_k_accuracy_5', 0.92978125)
>
>
>
> ResNet101-V1 FP32
>
> INFO:logger:batch size = 64 for inference
> INFO:logger:rgb_mean = 123.68,116.779,103.939
> INFO:logger:rgb_std = 58.393,57.12,57.375
> INFO:logger:label_name = softmax_label
> INFO:logger:Input data shape = (3, 224, 224)
> INFO:logger:Dataset for inference: ./data/val_256_q90.rec
> [13:19:48] src/io/iter_image_recordio_2.cc:172: ImageRecordIOParser2: ./data/val_256_q90.rec, use 1 threads for decoding..
> INFO:logger:Loading symbol from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/resnet101_v1-symbol.json
> INFO:logger:Loading params from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/resnet101_v1-0000.params
> INFO:logger:Skipping the first 50 batches
> INFO:logger:Running model ./model/resnet101_v1-symbol.json for inference
> [13:19:53] src/operator/subgraph/mkldnn/mkldnn_conv_property.cc:138: Start to execute MKLDNN Convolution optimization pass.
> [13:19:53] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 147456 bytes with malloc directly
> [13:19:53] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 589824 bytes with malloc directly
> [13:19:53] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 2359296 bytes with malloc directly
> [13:19:54] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 9437184 bytes with malloc directly
> INFO:logger:Finished inference with 32000 images
> INFO:logger:Finished with 188.447936 images per second
> INFO:logger:('accuracy', 0.773)
> INFO:logger:('top_k_accuracy_5', 0.9358125)
>
>
> ResNet101-V1 INT8
>
> INFO:logger:batch size = 64 for inference
> INFO:logger:rgb_mean = 123.68,116.779,103.939
> INFO:logger:rgb_std = 58.393,57.12,57.375
> INFO:logger:label_name = softmax_label
> INFO:logger:Input data shape = (3, 224, 224)
> INFO:logger:Dataset for inference: ./data/val_256_q90.rec
> [13:22:45] src/io/iter_image_recordio_2.cc:172: ImageRecordIOParser2: ./data/val_256_q90.rec, use 1 threads for decoding..
> INFO:logger:Loading symbol from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/resnet101_v1-quantized-5batches-naive-symbol.json
> INFO:logger:Loading params from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/resnet101_v1-quantized-0000.params
> INFO:logger:Skipping the first 50 batches
> INFO:logger:Running model ./model/resnet101_v1-quantized-5batches-naive-symbol.json for inference
> [13:22:50] src/operator/subgraph/mkldnn/mkldnn_conv_property.cc:138: Start to execute MKLDNN Convolution optimization pass.
> [13:22:50] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 147456 bytes with malloc directly
> [13:22:50] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 589824 bytes with malloc directly
> [13:22:51] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 2359296 bytes with malloc directly
> INFO:logger:Finished inference with 32000 images
> INFO:logger:Finished with 265.491600 images per second
> INFO:logger:('accuracy', 0.77096875)
> INFO:logger:('top_k_accuracy_5', 0.93415625)
>
>
> SqueezeNet 1.0 FP32
>
> INFO:logger:batch size = 64 for inference
> INFO:logger:rgb_mean = 123.68,116.779,103.939
> INFO:logger:rgb_std = 58.393,57.12,57.375
> INFO:logger:label_name = softmax_label
> INFO:logger:Input data shape = (3, 224, 224)
> INFO:logger:Dataset for inference: ./data/val_256_q90.rec
> [13:33:53] src/io/iter_image_recordio_2.cc:172: ImageRecordIOParser2: ./data/val_256_q90.rec, use 1 threads for decoding..
> INFO:logger:Loading symbol from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/squeezenet1.0-symbol.json
> INFO:logger:Loading params from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/squeezenet1.0-0000.params
> INFO:logger:Skipping the first 50 batches
> INFO:logger:Running model ./model/squeezenet1.0-symbol.json for inference
> [13:33:57] src/operator/subgraph/mkldnn/mkldnn_conv_property.cc:138: Start to execute MKLDNN Convolution optimization pass.
> [13:33:58] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 71663616 bytes with malloc directly
> INFO:logger:Finished inference with 32000 images
> INFO:logger:Finished with 118.483729 images per second
> INFO:logger:('accuracy', 0.57003125)
> INFO:logger:('top_k_accuracy_5', 0.79559375)
>
>
> SqueezeNet 1.0 INT8
>
> INFO:logger:batch size = 64 for inference
> INFO:logger:rgb_mean = 123.68,116.779,103.939
> INFO:logger:rgb_std = 58.393,57.12,57.375
> INFO:logger:label_name = softmax_label
> INFO:logger:Input data shape = (3, 224, 224)
> INFO:logger:Dataset for inference: ./data/val_256_q90.rec
> [13:38:30] src/io/iter_image_recordio_2.cc:172: ImageRecordIOParser2: ./data/val_256_q90.rec, use 1 threads for decoding..
> INFO:logger:Loading symbol from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/squeezenet1.0-quantized-5batches-naive-symbol.json
> INFO:logger:Loading params from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/squeezenet1.0-quantized-0000.params
> INFO:logger:Skipping the first 50 batches
> INFO:logger:Running model ./model/squeezenet1.0-quantized-5batches-naive-symbol.json for inference
> [13:38:34] src/operator/subgraph/mkldnn/mkldnn_conv_property.cc:138: Start to execute MKLDNN Convolution optimization pass.
> [13:38:35] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 512000 bytes with malloc directly
> INFO:logger:Finished inference with 32000 images
> INFO:logger:Finished with 107.084923 images per second
> INFO:logger:('accuracy', 0.5671875)
> INFO:logger:('top_k_accuracy_5', 0.79346875)
>
>
> MobileNet 1.0 FP32
>
> INFO:logger:batch size = 64 for inference
> INFO:logger:rgb_mean = 123.68,116.779,103.939
> INFO:logger:rgb_std = 58.393,57.12,57.375
> INFO:logger:label_name = softmax_label
> INFO:logger:Input data shape = (3, 224, 224)
> INFO:logger:Dataset for inference: ./data/val_256_q90.rec
> [13:55:42] src/io/iter_image_recordio_2.cc:172: ImageRecordIOParser2: ./data/val_256_q90.rec, use 1 threads for decoding..
> INFO:logger:Loading symbol from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/mobilenet1.0-symbol.json
> INFO:logger:Loading params from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/mobilenet1.0-0000.params
> INFO:logger:Skipping the first 50 batches
> INFO:logger:Running model ./model/mobilenet1.0-symbol.json for inference
> [13:55:46] src/operator/subgraph/mkldnn/mkldnn_conv_property.cc:138: Start to execute MKLDNN Convolution optimization pass.
> [13:55:46] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 8192 bytes with malloc directly
> [13:55:46] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 32768 bytes with malloc directly
> INFO:logger:Finished inference with 32000 images
> INFO:logger:Finished with 621.256745 images per second
> INFO:logger:('accuracy', 0.72234375)
> INFO:logger:('top_k_accuracy_5', 0.90734375)
>
>
> MobileNet 1.0 INT8
>
> INFO:logger:batch size = 64 for inference
> INFO:logger:rgb_mean = 123.68,116.779,103.939
> INFO:logger:rgb_std = 58.393,57.12,57.375
> INFO:logger:label_name = softmax_label
> INFO:logger:Input data shape = (3, 224, 224)
> INFO:logger:Dataset for inference: ./data/val_256_q90.rec
> [13:56:39] src/io/iter_image_recordio_2.cc:172: ImageRecordIOParser2: ./data/val_256_q90.rec, use 1 threads for decoding..
> INFO:logger:Loading symbol from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/mobilenet1.0-quantized-5batches-naive-symbol.json
> INFO:logger:Loading params from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/mobilenet1.0-quantized-0000.params
> INFO:logger:Skipping the first 50 batches
> INFO:logger:Running model ./model/mobilenet1.0-quantized-5batches-naive-symbol.json for inference
> [13:56:43] src/operator/subgraph/mkldnn/mkldnn_conv_property.cc:138: Start to execute MKLDNN Convolution optimization pass.
> [13:56:44] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 8192 bytes with malloc directly
> INFO:logger:Finished inference with 32000 images
> INFO:logger:Finished with 177.447123 images per second
> INFO:logger:('accuracy', 0.71921875)
> INFO:logger:('top_k_accuracy_5', 0.90553125)
>
> Inception-V3 FP32
>
> INFO:logger:batch size = 64 for inference
> INFO:logger:rgb_mean = 123.68,116.779,103.939
> INFO:logger:rgb_std = 58.393,57.12,57.375
> INFO:logger:label_name = softmax_label
> INFO:logger:Input data shape = (3, 299, 299)
> INFO:logger:Dataset for inference: ./data/val_256_q90.rec
> [14:07:11] src/io/iter_image_recordio_2.cc:172: ImageRecordIOParser2: ./data/val_256_q90.rec, use 1 threads for decoding..
> INFO:logger:Loading symbol from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/inceptionv3-symbol.json
> INFO:logger:Loading params from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/inceptionv3-0000.params
> INFO:logger:Skipping the first 50 batches
> INFO:logger:Running model ./model/inceptionv3-symbol.json for inference
> [14:07:17] src/operator/subgraph/mkldnn/mkldnn_conv_property.cc:138: Start to execute MKLDNN Convolution optimization pass.
> [14:07:18] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 36864 bytes with malloc directly
> [14:07:18] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 552960 bytes with malloc directly
> [14:07:18] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 3981312 bytes with malloc directly
> INFO:logger:Finished inference with 32000 images
> INFO:logger:Finished with 194.935475 images per second
> INFO:logger:('accuracy', 0.77984375)
> INFO:logger:('top_k_accuracy_5', 0.93928125)
>
> Inception-V3 INT8
>
> INFO:logger:batch size = 64 for inference
> INFO:logger:rgb_mean = 123.68,116.779,103.939
> INFO:logger:rgb_std = 58.393,57.12,57.375
> INFO:logger:label_name = softmax_label
> INFO:logger:Input data shape = (3, 299, 299)
> INFO:logger:Dataset for inference: ./data/val_256_q90.rec
> [14:10:04] src/io/iter_image_recordio_2.cc:172: ImageRecordIOParser2: ./data/val_256_q90.rec, use 1 threads for decoding..
> INFO:logger:Loading symbol from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/inceptionv3-quantized-5batches-naive-symbol.json
> INFO:logger:Loading params from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/inceptionv3-quantized-0000.params
> INFO:logger:Skipping the first 50 batches
> INFO:logger:Running model ./model/inceptionv3-quantized-5batches-naive-symbol.json for inference
> [14:10:10] src/operator/subgraph/mkldnn/mkldnn_conv_property.cc:138: Start to execute MKLDNN Convolution optimization pass.
> [14:10:11] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 9216 bytes with malloc directly
> [14:10:11] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 138240 bytes with malloc directly
> [14:10:11] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 995328 bytes with malloc directly
> INFO:logger:Finished inference with 32000 images
> INFO:logger:Finished with 102.014292 images per second
> INFO:logger:('accuracy', 0.781125)
> INFO:logger:('top_k_accuracy_5', 0.93915625)
>
> ResNet152-V2 FP32
>
> 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
> [15:27:18] src/io/iter_image_recordio_2.cc:172: ImageRecordIOParser2: ./data/val_256_q90.rec, use 1 threads for decoding..
> INFO:logger:Loading symbol from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/imagenet1k-resnet-152-symbol.json
> [15:27:19] src/nnvm/legacy_json_util.cc:209: Loading symbol saved by previous version v0.8.0. Attempting to upgrade...
> [15:27:19] src/nnvm/legacy_json_util.cc:217: Symbol successfully upgraded!
> INFO:logger:Loading params from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/imagenet1k-resnet-152-0000.params
> INFO:logger:Skipping the first 50 batches
> INFO:logger:Running model ./model/imagenet1k-resnet-152-symbol.json for inference
> [15:27:23] src/operator/subgraph/mkldnn/mkldnn_conv_property.cc:138: Start to execute MKLDNN Convolution optimization pass.
> [15:27:24] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 147456 bytes with malloc directly
> [15:27:24] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 589824 bytes with malloc directly
> [15:27:24] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 2359296 bytes with malloc directly
> [15:27:25] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 9437184 bytes with malloc directly
> INFO:logger:Finished inference with 32000 images
> INFO:logger:Finished with 107.342105 images per second
> INFO:logger:('accuracy', 0.7676875)
> INFO:logger:('top_k_accuracy_5', 0.93034375)
>
>
> ResNet152-V2 INT8
>
> 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
> [15:32:25] src/io/iter_image_recordio_2.cc:172: ImageRecordIOParser2: ./data/val_256_q90.rec, use 1 threads for decoding..
> INFO:logger:Loading symbol from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/imagenet1k-resnet-152-quantized-5batches-naive-symbol.json
> INFO:logger:Loading params from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/imagenet1k-resnet-152-quantized-0000.params
> INFO:logger:Skipping the first 50 batches
> INFO:logger:Running model ./model/imagenet1k-resnet-152-quantized-5batches-naive-symbol.json for inference
> [15:32:31] src/operator/subgraph/mkldnn/mkldnn_conv_property.cc:138: Start to execute MKLDNN Convolution optimization pass.
> [15:32:32] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 147456 bytes with malloc directly
> [15:32:32] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 589824 bytes with malloc directly
> [15:32:33] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 2359296 bytes with malloc directly
> INFO:logger:Finished inference with 32000 images
> INFO:logger:Finished with 49.981990 images per second
> INFO:logger:('accuracy', 0.764875)
> INFO:logger:('top_k_accuracy_5', 0.929625)
>
>
>
> Inception-BN FP32
>
>
> INFO:logger:batch size = 64 for inference
> INFO:logger:rgb_mean = 123.68,116.779,103.939
> 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
> [14:35:54] src/io/iter_image_recordio_2.cc:172: ImageRecordIOParser2: ./data/val_256_q90.rec, use 1 threads for decoding..
> INFO:logger:Loading symbol from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/imagenet1k-inception-bn-symbol.json
> [14:35:55] src/nnvm/legacy_json_util.cc:209: Loading symbol saved by previous version v0.8.0. Attempting to upgrade...
> [14:35:55] src/nnvm/legacy_json_util.cc:217: Symbol successfully upgraded!
> INFO:logger:Loading params from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/imagenet1k-inception-bn-0000.params
> INFO:logger:Skipping the first 50 batches
> INFO:logger:Running model ./model/imagenet1k-inception-bn-symbol.json for inference
> [14:35:58] src/operator/subgraph/mkldnn/mkldnn_conv_property.cc:138: Start to execute MKLDNN Convolution optimization pass.
> [14:35:58] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 442368 bytes with malloc directly
> INFO:logger:Finished inference with 32000 images
> INFO:logger:Finished with 445.455068 images per second
> INFO:logger:('accuracy', 0.72096875)
> INFO:logger:('top_k_accuracy_5', 0.9060625)
> INFO:logger:batch size = 64 for inference
>
>
> Inception-BN INT8
>
> INFO:logger:rgb_mean = 123.68,116.779,103.939
> 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
> [14:37:13] src/io/iter_image_recordio_2.cc:172: ImageRecordIOParser2: ./data/val_256_q90.rec, use 1 threads for decoding..
> INFO:logger:Loading symbol from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/imagenet1k-inception-bn-quantized-5batches-naive-symbol.json
> INFO:logger:Loading params from file /home/junmingc/xinyu-pr/incubator-mxnet/example/quantization/./model/imagenet1k-inception-bn-quantized-0000.params
> INFO:logger:Skipping the first 50 batches
> INFO:logger:Running model ./model/imagenet1k-inception-bn-quantized-5batches-naive-symbol.json for inference
> [14:37:17] src/operator/subgraph/mkldnn/mkldnn_conv_property.cc:138: Start to execute MKLDNN Convolution optimization pass.
> [14:37:17] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 110592 bytes with malloc directly
> INFO:logger:Finished inference with 32000 images
> INFO:logger:Finished with 504.734005 images per second
> INFO:logger:('accuracy', 0.72)
> INFO:logger:('top_k_accuracy_5', 0.90534375)
>
>
>
> SSD-VGG FP32
>
>
>
> INFO:root:Finished inference with 2240 images
> INFO:root:Finished with 38.558065 images per second
> aeroplane: 0.9057971014492755
> bicycle: 0.8745079058420262
> bird: 0.8407998824780214
> boat: 0.8172609700362614
> bottle: 0.5668336378418553
> bus: 0.8078869544773997
> car: 0.8794030962023759
> cat: 0.9079754601226996
> chair: 0.7888907332306704
> cow: 0.8945315505963835
> diningtable: 0.7868957871396894
> dog: 0.9082568807339451
> horse: 0.9072605247101893
> motorbike: 0.8943175964203456
> person: 0.8056045508318882
> pottedplant: 0.6446982570250784
> sheep: 0.7651375306023592
> sofa: 0.9021571648690294
> train: 0.9090909090909093
> tvmonitor: 0.8749246403233354
> mAP: 0.8341115567011869
>
>
> SSD-VGG INT8
>
>
> [15:05:39] src/io/iter_image_det_recordio.cc:283: ImageDetRecordIOParser: /home/junmingc/xinyu-pr/incubator-mxnet/example/ssd/data/val.rec, use 32 threads for decoding..
> [15:05:39] src/io/iter_image_det_recordio.cc:340: ImageDetRecordIOParser: /home/junmingc/xinyu-pr/incubator-mxnet/example/ssd/data/val.rec, label padding width: 254
> [15:05:42] src/operator/subgraph/mkldnn/mkldnn_conv_property.cc:138: Start to execute MKLDNN Convolution optimization pass.
> [15:05:44] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 36864 bytes with malloc directly
> [15:05:52] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 297271296 bytes with malloc directly
> [15:05:53] src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate 628031488 bytes with malloc directly
> INFO:root:Finished inference with 2240 images
> INFO:root:Finished with 49.863403 images per second
> aeroplane: 0.9077238550922763
> bicycle: 0.8582335743673779
> bird: 0.8019871500955977
> boat: 0.8060330180036129
> bottle: 0.5698642432266028
> bus: 0.8507464458004145
> car: 0.8452099241062656
> cat: 0.9085262563523435
> chair: 0.7528436223937933
> cow: 0.8954234786153293
> diningtable: 0.7521532992180973
> dog: 0.9086700336700338
> horse: 0.9011363636363638
> motorbike: 0.8766282736870973
> person: 0.7914271904834184
> pottedplant: 0.6103364637135992
> sheep: 0.7632195285761423
> sofa: 0.9040495867768596
> train: 0.9090909090909093
> tvmonitor: 0.8664679683062037
> mAP: 0.823988559260617
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