Add Spectrum Sense Model

gen-demos-max78000.sh

@@ -21,4 +21,6 @@ python izer/add_fake_passthrough.py --input-checkpoint-path trained/ai85-aisegme
 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 $COMMON_ARGS --overlap-data --mlator --no-unload --max-checklines 8192 --new-kernel-loader "$@"
 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 $COMMON_ARGS "$@"
 python ai8xize.py --test-dir $TARGET --prefix facedet_tinierssd --checkpoint-file trained/ai85-facedet-tinierssd-qat8-q.pth.tar --config-file networks/ai85-facedet-tinierssd.yaml --sample-input tests/sample_vggface2_facedetection.npy --fifo  $COMMON_ARGS "$@"
-python ai8xize.py --test-dir $TARGET --prefix kinetics --checkpoint-file trained/ai85-kinetics-qat8-q.pth.tar --config-file networks/ai85-kinetics-actiontcn.yaml --overlap-data --softmax --zero-sram --no-kat $COMMON_ARGS "$@" # note: known-answer test can't be done on MAX78000 due to memory constraints (sample data is too large)
+python ai8xize.py --test-dir $TARGET --prefix kinetics --checkpoint-file trained/ai85-kinetics-qat8-q.pth.tar --config-file networks/ai85-kinetics-actiontcn.yaml --overlap-data --softmax --zero-sram --no-kat $COMMON_ARGS "$@" # note: known-answer test can't be done on MAX78000 due to memory constraints (sample data is too large)
+python izer/add_fake_passthrough.py --input-checkpoint-path trained/ai85-spectrumsense-unet-large-q.pth.tar --output-checkpoint-path trained/ai85-spectrumsense-unet-large-fakept-q.pth.tar --layer-name pt --layer-depth 56 --layer-name-after-pt upconv3 "$@"
+python ai8xize.py --test-dir $TARGET --prefix spectrumsense --checkpoint-file trained/ai85-spectrumsense-unet-large-fakept-q.pth.tar --config-file networks/spectrumsense-unet-large-fakept.yaml --sample-input tests/sample_spectrumsense.npy --energy  $COMMON_ARGS --compact-data --mexpress --overlap-data --mlator --no-unload  "$@"

networks/spectrumsense-unet-large-fakept.yaml

@@ -0,0 +1,180 @@
+---
+# HWC (little data) configuration for Spectrum Sense (UNet)
+# UNet Model
+
+arch: ai85unetlarge
+dataset: SpectrumSense_s352_c2
+
+layers:
+  # Layer 0: prep0
+  - out_offset: 0x0600
+    in_offset: 0x0700
+    processors: 0x0000ffffffffffff
+    output_processors: 0xffffffffffffffff
+    operation: conv2d
+    kernel_size: 1x1
+    pad: 0
+    activate: ReLU
+  # Layer 1: prep1
+  - out_offset: 0x0400
+    processors: 0xffffffffffffffff
+    output_processors: 0xffffffffffffffff
+    operation: conv2d
+    kernel_size: 1x1
+    pad: 0
+    activate: ReLU
+  # Layer 2: prep2
+  - out_offset: 0x0200
+    processors: 0xffffffffffffffff
+    output_processors: 0x00000000ffffffff
+    operation: conv2d
+    kernel_size: 1x1
+    pad: 0
+    activate: ReLU
+  # Layer 3: enc1
+  - out_offset: 0x0000
+    processors: 0x00000000ffffffff
+    output_processors: 0xff00000000000000
+    operation: conv2d
+    kernel_size: 3x3
+    pad: 1
+    activate: ReLU
+  # Layer 4: enc2
+  - out_offset: 0x0000
+    processors: 0xff00000000000000
+    output_processors: 0x00fffffff0000000
+    operation: conv2d
+    kernel_size: 3x3
+    pad: 1
+    max_pool: 2
+    pool_stride: 2
+    activate: ReLU
+  # Layer 5: enc3
+  - out_offset: 0x5000
+    processors: 0x00fffffff0000000
+    output_processors: 0x00ffffffffffffff
+    operation: conv2d
+    kernel_size: 3x3
+    pad: 1
+    max_pool: 2
+    pool_stride: 2
+    activate: ReLU
+  # Layer 6: bneck
+  - out_offset: 0x6000
+    processors: 0x00ffffffffffffff
+    output_processors: 0x00ffffffffffffff
+    operation: conv2d
+    kernel_size: 3x3
+    pad: 1
+    max_pool: 2
+    pool_stride: 2
+    activate: ReLU
+  # Layer 7: pt
+  - in_offset: 0x5000
+    out_offset: 0x4004
+    processors: 0x00ffffffffffffff
+    output_processors: 0x00ffffffffffffff
+    operation: conv2d
+    kernel_size: 1x1
+    pad: 0
+    write_gap: 1
+    in_sequences: [5]
+    activate: None
+  # Layer 8: upconv3
+  - in_offset: 0x6000
+    out_offset: 0x4000
+    processors: 0x00ffffffffffffff
+    output_processors: 0x00ffffffffffffff
+    operation: convtranspose2d
+    kernel_size: 3x3
+    pad: 1
+    activate: None
+    write_gap: 1
+    in_sequences: [6]
+  # Layer 9: dec3
+  - out_offset: 0x2000
+    in_offset: 0x4000
+    processors: 0x00ffffffffffffff
+    output_processors: 0x00ffffffffffffff
+    operation: conv2d
+    kernel_size: 3x3
+    pad: 1
+    activate: ReLU
+    in_sequences: [8, 7]
+  # Layer 10: upconv2
+  - out_offset: 0x0000
+    in_offset: 0x2000
+    processors: 0x00ffffffffffffff
+    output_processors: 0x000000000fffffff
+    operation: convtranspose2d
+    kernel_size: 3x3
+    pad: 1
+    activate: None
+  # Layer 11: dec2
+  - out_offset: 0x2000
+    in_offset: 0x0000
+    processors: 0x00ffffffffffffff
+    output_processors: 0x000000000fffffff
+    operation: conv2d
+    kernel_size: 3x3
+    pad: 1
+    activate: ReLU
+    in_sequences: [10, 4]
+  # Layer 12: upconv1
+  - out_offset: 0x0000
+    processors: 0x000000000fffffff
+    output_processors: 0x00ff000000000000
+    operation: convtranspose2d
+    kernel_size: 3x3
+    pad: 1
+    activate: None
+  # Layer 13: dec1
+  - out_offset: 0x0700
+    in_offset: 0x0000
+    processors: 0xffff000000000000
+    output_processors: 0x0000ffffffffffff
+    operation: conv2d
+    kernel_size: 3x3
+    pad: 1
+    activate: ReLU
+    in_sequences: [12, 3]
+  # Layer 14: dec0
+  - out_offset: 0x0550
+    processors: 0x0000ffffffffffff
+    output_processors: 0xffffffffffffffff
+    operation: conv2d
+    kernel_size: 3x3
+    pad: 1
+    activate: ReLU
+  # Layer 15: conv_p1
+  - out_offset: 0x0400
+    processors: 0xffffffffffffffff
+    output_processors: 0xffffffffffffffff
+    operation: conv2d
+    kernel_size: 1x1
+    pad: 0
+    activate: ReLU
+  # Layer 16: conv_p2
+  - out_offset: 0x0250
+    processors: 0xffffffffffffffff
+    output_processors: 0xffffffffffffffff
+    operation: conv2d
+    kernel_size: 1x1
+    pad: 0
+    activate: ReLU
+  # Layer 17: conv_p3
+  - out_offset: 0x0100
+    processors: 0xffffffffffffffff
+    output_processors: 0xffffffffffffffff
+    operation: conv2d
+    kernel_size: 1x1
+    pad: 0
+    activate: None
+  # Layer 18: conv
+  - out_offset: 0x0000
+    processors: 0xffffffffffffffff
+    output_processors: 0x0000ffffffffffff
+    operation: conv2d
+    kernel_size: 1x1
+    pad: 0
+    activate: None

scripts/gen_spectrumsense_max78000.sh

@@ -0,0 +1,7 @@
+#!/bin/sh
+DEVICE="MAX78000"
+TARGET="sdk/Examples/$DEVICE/CNN"
+COMMON_ARGS="--device $DEVICE --timer 0 --display-checkpoint --verbose"
+
+python ai8xize.py --test-dir $TARGET --prefix spectrumsense --checkpoint-file trained/ai85-spectrumsense-unet-large-fakept-q.pth.tar --config-file networks/spectrumsense-unet-large-fakept.yaml --sample-input tests/sample_spectrumsense_352.npy --energy  $COMMON_ARGS --compact-data --mexpress --overlap-data --mlator --no-unload  "$@"
+

scripts/quantize_spectrumsense_unet.sh

@@ -0,0 +1,3 @@
+#!/bin/sh
+python quantize.py trained/ai85-spectrumsense-unet-large.pth.tar trained/ai85-spectrumsense-unet-large-q.pth.tar --device MAX78000 -v
+python izer/add_fake_passthrough.py --input-checkpoint-path trained/ai85-spectrumsense-unet-large-q.pth.tar --output-checkpoint-path trained/ai85-spectrumsense-unet-large-fakept-q.pth.tar --layer-name pt --layer-depth 56 --layer-name-after-pt upconv3