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Scripts and model checkpoints for KAT of target detection on SVHN dataset (#246)
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‎gen-demos-max78000.sh

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@@ -15,3 +15,4 @@ python ai8xize.py --test-dir $TARGET --prefix faceid --checkpoint-file trained/a
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python ai8xize.py --test-dir $TARGET --prefix cats-dogs --checkpoint-file trained/ai85-catsdogs-qat8-q.pth.tar --config-file networks/cats-dogs-hwc.yaml --fifo --softmax $COMMON_ARGS "$@"
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python ai8xize.py --test-dir $TARGET --prefix camvid_unet --checkpoint-file trained/ai85-camvid-unet-large-fakept-q.pth.tar --config-file networks/camvid-unet-large-fakept.yaml $COMMON_ARGS --overlap-data --mlator --no-unload --max-checklines 8192 --new-kernel-loader "$@"
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python ai8xize.py --test-dir $TARGET --prefix aisegment_unet --checkpoint-file trained/ai85-aisegment-unet-large-fakept-q.pth.tar --config-file networks/aisegment-unet-large-fakept.yaml $COMMON_ARGS --overlap-data --mlator --no-unload --max-checklines 8192 --new-kernel-loader "$@"
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python ai8xize.py --test-dir $TARGET --prefix svhn_tinierssd --checkpoint-file trained/ai85-svhn-tinierssd-qat8-q.pth.tar --config-file networks/svhn-tinierssd.yaml --overlap-data $COMMON_ARGS "$@"

‎gen-demos-max78002.sh

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@@ -16,6 +16,7 @@ python ai8xize.py --test-dir $TARGET --prefix faceid --checkpoint-file trained/a
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python ai8xize.py --test-dir $TARGET --prefix cats-dogs --checkpoint-file trained/ai85-catsdogs-qat8-q.pth.tar --config-file networks/cats-dogs-hwc-no-fifo.yaml --softmax $COMMON_ARGS "$@"
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python ai8xize.py --test-dir $TARGET --prefix camvid_unet --checkpoint-file trained/ai85-camvid-unet-large-fakept-q.pth.tar --config-file networks/camvid-unet-large-fakept.yaml $COMMON_ARGS --overlap-data --mlator --no-unload --max-checklines 8192 "$@"
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python ai8xize.py --test-dir $TARGET --prefix aisegment_unet --checkpoint-file trained/ai85-aisegment-unet-large-fakept-q.pth.tar --config-file networks/aisegment-unet-large-fakept.yaml $COMMON_ARGS --overlap-data --mlator --no-unload --max-checklines 8192 "$@"
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python ai8xize.py --test-dir $TARGET --prefix svhn_tinierssd --checkpoint-file trained/ai85-svhn-tinierssd-qat8-q.pth.tar --config-file networks/svhn-tinierssd.yaml --overlap-data $COMMON_ARGS "$@"
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python ai8xize.py --test-dir $TARGET --prefix cifar-100-effnet2 --checkpoint-file trained/ai87-cifar100-effnet2-qat8-q.pth.tar --config-file networks/ai87-cifar100-effnet2.yaml --softmax $COMMON_ARGS "$@"
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python ai8xize.py --test-dir $TARGET --prefix cifar-100-mobilenet-v2-0.75 --checkpoint-file trained/ai87-cifar100-mobilenet-v2-0.75-qat8-q.pth.tar --config-file networks/ai87-cifar100-mobilenet-v2-0.75.yaml --softmax $COMMON_ARGS "$@"
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python ai8xize.py --test-dir $TARGET --prefix imagenet --checkpoint-file trained/ai87-imagenet-effnet2-q.pth.tar --config-file networks/ai87-imagenet-effnet2.yaml $COMMON_ARGS "$@"

‎networks/svhn-tinierssd.yaml

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---
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# HWC (little data) configuration for SVHN
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# Parallel Model
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arch: ai85tinierssd
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dataset: svhn_74
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layers:
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# Layer 0: backbone_conv1
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- out_offset: 0x2000
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in_offset: 0x2000
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processors: 0x0000000000000007
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output_processors: 0xffffffff00000000
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operation: conv2d
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kernel_size: 3x3
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pad: 1
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activate: ReLU
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# Layer 1: backbone_conv2
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- out_offset: 0x2000
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in_offset: 0x2000
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processors: 0xffffffff00000000
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output_processors: 0x00000000ffffffff
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operation: conv2d
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kernel_size: 3x3
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pad: 1
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activate: ReLU
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# Layer 2: backbone_conv3
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- out_offset: 0x1000
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in_offset: 0x2000
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processors: 0x00000000ffffffff
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output_processors: 0xffffffffffffffff
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operation: conv2d
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kernel_size: 3x3
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pad: 1
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activate: ReLU
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max_pool: 2
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pool_stride: 2
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# Layer 3: backbone_conv4
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- out_offset: 0x0000 #1600
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in_offset: 0x1000
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processors: 0xffffffffffffffff
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output_processors: 0xffffffffffffffff
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operation: conv2d
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kernel_size: 3x3
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pad: 1
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activate: ReLU
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# Layer 4: backbone_conv5
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- out_offset: 0x1600 #1600+600 = 1C00
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in_offset: 0x0000
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processors: 0xffffffffffffffff
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output_processors: 0xffffffffffffffff
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operation: conv2d
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kernel_size: 3x3
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pad: 1
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activate: ReLU
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max_pool: 3
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pool_stride: 2
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# Layer 5: backbone_conv6
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- out_offset: 0x2000 #1600
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in_offset: 0x1600
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processors: 0xffffffffffffffff
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output_processors: 0xffffffffffffffff
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operation: conv2d
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kernel_size: 3x3
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pad: 1
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activate: ReLU
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# Layer 6: backbone_conv7
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- out_offset: 0x4000 #1600
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in_offset: 0x2000
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processors: 0xffffffffffffffff
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output_processors: 0xffffffffffffffff
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operation: conv2d
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kernel_size: 3x3
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pad: 1
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activate: ReLU
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# Layer 7: backbone_conv8
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- name: backbone_conv8
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out_offset: 0x1600 #1C00
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in_offset: 0x4000
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processors: 0xffffffffffffffff
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output_processors: 0x00000000ffffffff
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operation: conv2d
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kernel_size: 3x3
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pad: 1
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activate: ReLU
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# Layer 8: backbone_conv9
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- name: backbone_conv9
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out_offset: 0x1C00 #1E00
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in_offset: 0x1600
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processors: 0x00000000ffffffff
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output_processors: 0xffffffff00000000
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operation: conv2d
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kernel_size: 3x3
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pad: 1
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activate: ReLU
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max_pool: 2
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pool_stride: 2
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# Layer 9: backbone_conv10
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- name: backbone_conv10
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out_offset: 0x1E00 #1F00
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in_offset: 0x1C00
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processors: 0xffffffff00000000
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output_processors: 0x00000000ffffffff
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operation: conv2d
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kernel_size: 3x3
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pad: 1
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activate: ReLU
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max_pool: 3
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pool_stride: 2
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# Layer 10: conv12_1
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- name: conv12_1
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out_offset: 0x1F00
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in_offset: 0x1E00
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processors: 0x00000000ffffffff
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output_processors: 0x000000000000ffff
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operation: conv2d
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kernel_size: 3x3
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pad: 1
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activate: ReLU
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# Layer 11: conv12_2
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- name: conv12_2
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out_offset: 0x2000
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in_offset: 0x1F00
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processors: 0x000000000000ffff
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output_processors: 0x00000000ffff0000
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operation: conv2d
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kernel_size: 3x3
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pad: 1
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activate: ReLU
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max_pool: 2
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pool_stride: 2
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# Layer 12: loc_conv8
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- out_offset: 0x3000 #510
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in_offset: 0x1600
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processors: 0x00000000ffffffff
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output_processors: 0x000000000000ffff
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operation: conv2d
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kernel_size: 3x3
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pad: 1
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in_sequences: backbone_conv8
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output: true
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# Layer 13: loc_conv9
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- out_offset: 0x3510 #144 = 2654
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in_offset: 0x1C00
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processors: 0xffffffff00000000
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output_processors: 0x000000000000ffff
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operation: conv2d
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kernel_size: 3x3
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pad: 1
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in_sequences: backbone_conv9
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output: true
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# Layer 14: loc_conv10
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- out_offset: 0x3654
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in_offset: 0x1E00
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processors: 0x00000000ffffffff
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output_processors: 0x000000000000ffff
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operation: conv2d
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kernel_size: 3x3
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pad: 1
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in_sequences: backbone_conv10
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output: true
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# Layer 15: loc_conv12_2
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- out_offset: 0x3694
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in_offset: 0x2000
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processors: 0x00000000ffff0000
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output_processors: 0x000000000000ffff
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operation: conv2d
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kernel_size: 3x3
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pad: 1
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in_sequences: conv12_2
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output: true
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# Layer 16: cl_conv8
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- out_offset: 0x3000 #510
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in_offset: 0x1600
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processors: 0x00000000ffffffff
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output_processors: 0xfffffffffff00000
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operation: conv2d
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kernel_size: 3x3
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pad: 1
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in_sequences: backbone_conv8
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output: true
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# Layer 17: cl_conv9
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- out_offset: 0x3510 #144 = 2654
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in_offset: 0x1C00
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processors: 0xffffffff00000000
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output_processors: 0xfffffffffff00000
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operation: conv2d
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kernel_size: 3x3
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pad: 1
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in_sequences: backbone_conv9
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output: true
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# Layer 18: cl_conv10
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- out_offset: 0x3654
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in_offset: 0x1E00
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processors: 0x00000000ffffffff
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output_processors: 0xfffffffffff00000
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operation: conv2d
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kernel_size: 3x3
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pad: 1
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in_sequences: backbone_conv10
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output: true
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# Layer 19: cl_conv12_2
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- out_offset: 0x3694
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in_offset: 0x2000
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processors: 0x00000000ffff0000
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output_processors: 0xfffffffffff00000
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operation: conv2d
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kernel_size: 3x3
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pad: 1
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in_sequences: conv12_2
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output: true

‎scripts/quantize_svhn_tinierssd.sh

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#!/bin/sh
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python quantize.py trained/ai85-svhn-tinierssd-qat8.pth.tar trained/ai85-svhn-tinierssd-qat8-q.pth.tar --device MAX78000 -v

‎tests/sample_svhn_74.npy

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‎trained/ai85-svhn-tinierssd-qat8-q.pth.tar

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