Difference between revisions of "ML-TN-001 - AI at the edge: comparison of different embedded platforms - Part 3"
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Revision as of 15:33, 5 October 2020
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Contents
History[edit | edit source]
| Version | Date | Notes |
|---|---|---|
| 1.0.0 | September 2020 | First public release |
Introduction[edit | edit source]
This Technical Note (TN for short) belongs to the series introduced here. Specifically, it illustrates the execution of an inference application (fruit classifier) that makes use of the model described in this section when executed on the Xilinx Zynq UltraScale+ MPSoC ZCU104 Evaluation Kit. The results achieved are also compared to the ones produced by the platforms that were considered in the previous articles of this series.
Building the application[edit | edit source]
Training the model[edit | edit source]
Pruning the model[edit | edit source]
conv2d_1/kernel:0 -- Param: 864 -- Zeros: 00.00% conv2d_1/bias:0 -- Param: 32 -- Zeros: 00.00% conv2d_2/kernel:0 -- Param: 9216 -- Zeros: 00.00% conv2d_2/bias:0 -- Param: 32 -- Zeros: 00.00% conv2d_3/kernel:0 -- Param: 18432 -- Zeros: 00.00% conv2d_3/bias:0 -- Param: 64 -- Zeros: 00.00% conv2d_4/kernel:0 -- Param: 73728 -- Zeros: 00.00% conv2d_4/bias:0 -- Param: 128 -- Zeros: 00.00% dense_1/kernel:0 -- Param: 4718592 -- Zeros: 00.00% dense_1/bias:0 -- Param: 256 -- Zeros: 00.39% predictions/kernel:0 -- Param: 1536 -- Zeros: 00.00% predictions/bias:0 -- Param: 6 -- Zeros: 00.00%
Size of gzipped loaded model: 17801431.00 bytes
Test set 1/1 [==============================] - 0s 214ms/step - loss: 1.3166 - acc: 0.7083
conv2d_1/kernel:0 -- Param: 864 -- Zeros: 00.00% conv2d_1/bias:0 -- Param: 32 -- Zeros: 00.00% conv2d_2/kernel:0 -- Param: 9216 -- Zeros: 00.00% conv2d_2/bias:0 -- Param: 32 -- Zeros: 00.00% conv2d_3/kernel:0 -- Param: 18432 -- Zeros: 00.00% conv2d_3/bias:0 -- Param: 64 -- Zeros: 00.00% conv2d_4/kernel:0 -- Param: 73728 -- Zeros: 00.00% conv2d_4/bias:0 -- Param: 128 -- Zeros: 00.00% dense_1/kernel:0 -- Param: 4718592 -- Zeros: 80.00% dense_1/bias:0 -- Param: 256 -- Zeros: 00.00% predictions/kernel:0 -- Param: 1536 -- Zeros: 80.01% predictions/bias:0 -- Param: 6 -- Zeros: 00.00%
Size of gzipped loaded model: 5795289.00 bytes
Test set 1/1 [==============================] - 0s 29ms/step - loss: 1.4578 - acc: 0.6667
Freezing the computational graph[edit | edit source]
Baseline model
INFO:tensorflow:Froze 12 variables. I1002 09:08:49.716494 140705992206144 graph_util_impl.py:334] Froze 12 variables. INFO:tensorflow:Converted 12 variables to const ops. I1002 09:08:49.776397 140705992206144 graph_util_impl.py:394] Converted 12 variables to const ops.
Transform the computational graph[edit | edit source]
Applied transformations
transformations_list = ['remove_nodes(op=Identity, op=CheckNumerics)',
'merge_duplicate_nodes',
'strip_unused_nodes',
'fold_constants(ignore_errors=true)',
'fold_batch_norms']
Baseline model
describe : frozen_graph.pb input feature nodes : ['images_in'] unused nodes : [] output nodes : ['predictions/kernel', 'predictions/bias', 'predictions/MatMul/ReadVariableOp', 'predictions/MatMul', 'predictions/BiasAdd/ReadVariableOp', 'predictions/BiasAdd', 'predictions/Softmax'] quantization nodes : [] constant count : 16 variable count : 0 identity count : 13 total nodes : 56
Op: Placeholder -- Name: images_in Op: Const -- Name: conv2d_1/kernel Op: Const -- Name: conv2d_1/bias Op: Identity -- Name: conv2d_1/Conv2D/ReadVariableOp Op: Conv2D -- Name: conv2d_1/Conv2D Op: Identity -- Name: conv2d_1/BiasAdd/ReadVariableOp Op: BiasAdd -- Name: conv2d_1/BiasAdd Op: Relu -- Name: conv2d_1/Relu Op: MaxPool -- Name: maxpool_1/MaxPool Op: Const -- Name: conv2d_2/kernel Op: Const -- Name: conv2d_2/bias Op: Identity -- Name: conv2d_2/Conv2D/ReadVariableOp Op: Conv2D -- Name: conv2d_2/Conv2D Op: Identity -- Name: conv2d_2/BiasAdd/ReadVariableOp Op: BiasAdd -- Name: conv2d_2/BiasAdd Op: Relu -- Name: conv2d_2/Relu Op: MaxPool -- Name: maxpool_2/MaxPool Op: Const -- Name: conv2d_3/kernel Op: Const -- Name: conv2d_3/bias Op: Identity -- Name: conv2d_3/Conv2D/ReadVariableOp Op: Conv2D -- Name: conv2d_3/Conv2D Op: Identity -- Name: conv2d_3/BiasAdd/ReadVariableOp Op: BiasAdd -- Name: conv2d_3/BiasAdd Op: Relu -- Name: conv2d_3/Relu Op: MaxPool -- Name: maxpool_3/MaxPool Op: Const -- Name: conv2d_4/kernel Op: Const -- Name: conv2d_4/bias Op: Identity -- Name: conv2d_4/Conv2D/ReadVariableOp Op: Conv2D -- Name: conv2d_4/Conv2D Op: Identity -- Name: conv2d_4/BiasAdd/ReadVariableOp Op: BiasAdd -- Name: conv2d_4/BiasAdd Op: Relu -- Name: conv2d_4/Relu Op: MaxPool -- Name: maxpool_4/MaxPool Op: Shape -- Name: flatten/Shape Op: Const -- Name: flatten/strided_slice/stack Op: Const -- Name: flatten/strided_slice/stack_1 Op: Const -- Name: flatten/strided_slice/stack_2 Op: StridedSlice -- Name: flatten/strided_slice Op: Const -- Name: flatten/Reshape/shape/1 Op: Pack -- Name: flatten/Reshape/shape Op: Reshape -- Name: flatten/Reshape Op: Const -- Name: dense_1/kernel Op: Const -- Name: dense_1/bias Op: Identity -- Name: dense_1/MatMul/ReadVariableOp Op: MatMul -- Name: dense_1/MatMul Op: Identity -- Name: dense_1/BiasAdd/ReadVariableOp Op: BiasAdd -- Name: dense_1/BiasAdd Op: Relu -- Name: dense_1/Relu Op: Identity -- Name: dropout_1/Identity Op: Const -- Name: predictions/kernel Op: Const -- Name: predictions/bias Op: Identity -- Name: predictions/MatMul/ReadVariableOp Op: MatMul -- Name: predictions/MatMul Op: Identity -- Name: predictions/BiasAdd/ReadVariableOp Op: BiasAdd -- Name: predictions/BiasAdd Op: Softmax -- Name: predictions/Softmax
describe : baseline_transf_graph.pb input feature nodes : ['images_in'] unused nodes : [] output nodes : ['predictions/MatMul', 'predictions/kernel', 'predictions/bias', 'predictions/Softmax', 'predictions/BiasAdd'] quantization nodes : [] constant count : 15 variable count : 0 identity count : 0 total nodes : 42
Op: Conv2D -- Name: conv2d_1/Conv2D Op: BiasAdd -- Name: conv2d_2/BiasAdd Op: Relu -- Name: conv2d_4/Relu Op: Conv2D -- Name: conv2d_3/Conv2D Op: Const -- Name: conv2d_2/kernel Op: MaxPool -- Name: maxpool_4/MaxPool Op: Const -- Name: conv2d_1/kernel Op: Const -- Name: conv2d_3/kernel Op: Placeholder -- Name: images_in Op: Pack -- Name: flatten/Reshape/shape Op: Const -- Name: conv2d_3/bias Op: Const -- Name: conv2d_4/kernel Op: Reshape -- Name: flatten/Reshape Op: Shape -- Name: flatten/Shape Op: Conv2D -- Name: conv2d_4/Conv2D Op: Const -- Name: conv2d_2/bias Op: MaxPool -- Name: maxpool_2/MaxPool Op: Relu -- Name: conv2d_1/Relu Op: MatMul -- Name: predictions/MatMul Op: BiasAdd -- Name: dense_1/BiasAdd Op: MaxPool -- Name: maxpool_1/MaxPool Op: Const -- Name: flatten/strided_slice/stack Op: Const -- Name: dense_1/kernel Op: BiasAdd -- Name: conv2d_1/BiasAdd Op: Const -- Name: flatten/Reshape/shape/1 Op: Const -- Name: predictions/kernel Op: BiasAdd -- Name: conv2d_4/BiasAdd Op: Const -- Name: conv2d_1/bias Op: Relu -- Name: conv2d_2/Relu Op: Const -- Name: flatten/strided_slice/stack_1 Op: Const -- Name: dense_1/bias Op: Const -- Name: predictions/bias Op: Conv2D -- Name: conv2d_2/Conv2D Op: MaxPool -- Name: maxpool_3/MaxPool Op: Const -- Name: conv2d_4/bias Op: Relu -- Name: dense_1/Relu Op: Relu -- Name: conv2d_3/Relu Op: Softmax -- Name: predictions/Softmax Op: BiasAdd -- Name: conv2d_3/BiasAdd Op: MatMul -- Name: dense_1/MatMul Op: StridedSlice -- Name: flatten/strided_slice Op: BiasAdd -- Name: predictions/BiasAdd
Graph accuracy with test dataset: 0.7083
Graph accuracy with test dataset: 0.6667
Quantize the computational graph[edit | edit source]
Baseline model
graph accuracy with test dataset: 0.7083
Pruned model
graph accuracy with test dataset: 0.7083
Compiling the model[edit | edit source]
Baseline model
Kernel topology "custom_cnn_kernel_graph.jpg" for network "custom_cnn"
kernel list info for network "custom_cnn"
Kernel ID : Name
0 : custom_cnn_0
1 : custom_cnn_1
Kernel Name : custom_cnn_0
--------------------------------------------------------------------------------
Kernel Type : DPUKernel
Code Size : 0.02MB
Param Size : 4.60MB
Workload MACs : 498.21MOPS
IO Memory Space : 0.52MB
Mean Value : 0, 0, 0,
Total Tensor Count : 7
Boundary Input Tensor(s) (H*W*C)
images_in:0(0) : 224*224*3
Boundary Output Tensor(s) (H*W*C)
predictions_MatMul:0(0) : 1*1*6
Total Node Count : 6
Input Node(s) (H*W*C)
conv2d_1_Conv2D(0) : 224*224*3
Output Node(s) (H*W*C)
predictions_MatMul(0) : 1*1*6
Kernel Name : custom_cnn_1
--------------------------------------------------------------------------------
Kernel Type : CPUKernel
Boundary Input Tensor(s) (H*W*C)
predictions_Softmax:0(0) : 1*1*6
Boundary Output Tensor(s) (H*W*C)
predictions_Softmax:0(0) : 1*1*6
Input Node(s) (H*W*C)
predictions_Softmax : 1*1*6
Output Node(s) (H*W*C)
predictions_Softmax : 1*1*6
Pruned model
Kernel topology "pruned_custom_cnn_kernel_graph.jpg" for network "pruned_custom_cnn"
kernel list info for network "pruned_custom_cnn"
Kernel ID : Name
0 : pruned_custom_cnn_0
1 : pruned_custom_cnn_1
Kernel Name : pruned_custom_cnn_0
--------------------------------------------------------------------------------
Kernel Type : DPUKernel
Code Size : 0.02MB
Param Size : 4.60MB
Workload MACs : 498.21MOPS
IO Memory Space : 0.52MB
Mean Value : 0, 0, 0,
Total Tensor Count : 7
Boundary Input Tensor(s) (H*W*C)
images_in:0(0) : 224*224*3
Boundary Output Tensor(s) (H*W*C)
predictions_MatMul:0(0) : 1*1*6
Total Node Count : 6
Input Node(s) (H*W*C)
conv2d_1_Conv2D(0) : 224*224*3
Output Node(s) (H*W*C)
predictions_MatMul(0) : 1*1*6
Kernel Name : pruned_custom_cnn_1
--------------------------------------------------------------------------------
Kernel Type : CPUKernel
Boundary Input Tensor(s) (H*W*C)
predictions_Softmax:0(0) : 1*1*6
Boundary Output Tensor(s) (H*W*C)
predictions_Softmax:0(0) : 1*1*6
Input Node(s) (H*W*C)
predictions_Softmax : 1*1*6
Output Node(s) (H*W*C)
predictions_Softmax : 1*1*6
