Merge pull request #11 from dgburnette/sigmoid_branch

Sigmoid branch

example-prjs/sigmoid/README.md

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+This is small conv2d 1 layer applying a sigmoid activation function example. To run it:
+1. Run the run_catapult.sh script.
+2. Remove "#define USE_AC_MATH" in nnet_activation.h.
+3. Replace the sigmoid_test.cpp in the my-Catapult-test with the sigmoid_test.cpp a level up (if you would like the testbench to be self-checking).
+4. Move tb_input_features.dat and tb_output_predictions.dat to my-Catapult-test/tb_data (if you want two pre-computed examples).
+5. Compile:
+/wv/hlsb/CATAPULT/TOT/CURRENT/aol/Mgc_home/bin/g++ -g -std=c++11 -DSC_INCLUDE_DYNAMIC_PROCESSES -Wl,-rpath=/wv/hlsb/CATAPULT/TOT/CURRENT/aol/Mgc_home/lib,-rpath=/wv/hlsb/CATAPULT/TOT/CURRENT/aol/Mgc_home/shared/lib ./sigmoid_test.cpp ./firmware/sigmoid.cpp -I/wv/USER/venv/hls4ml/example-prjs/sigmoid/my-Catapult-test -I/wv/hlsb/CATAPULT/TOT/CURRENT/aol/Mgc_home/shared/include -L/wv/hlsb/CATAPULT/TOT/CURRENT/aol/Mgc_home/shared/lib -Wl,-Bstatic -lsystemc -Wl,-Bdynamic -lpthread -o /wv/USER/venv/hls4ml/example-prjs/sigmoid/my-Catapult-test/sigmoid
+
+Note: You can create your own array and get the predictions by editing then running sigmoid.py. 

example-prjs/sigmoid/catapult.py

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+
+import hls4ml
+# import pprint
+import yaml
+import numpy as np
+
+print(hls4ml.__version__)
+
+with open('config.yml', 'r') as ymlfile:
+  config = yaml.safe_load(ymlfile)
+
+# try tweaking the reuse_factor on one layer to get different pipelining
+# config['HLSConfig']['LayerName']['fc1']['ReuseFactor'] = 4
+
+print('NETWORK')
+print(config)
+
+config['OutputDir'] = 'my-Catapult-test'
+config['Backend'] = 'Catapult'
+config['IOType'] = 'io_stream'
+
+config['HLSConfig']['Model']['Strategy'] = 'Latency'
+#config['HLSConfig']['Model']['Strategy'] = 'Resource'
+
+# default threshold is infinity
+config['HLSConfig']['Model']['BramFactor'] = np.inf
+# set to zero to force all weights onto (external function) interface
+config['HLSConfig']['Model']['BramFactor'] = 0
+
+print('CURRENT CONFIGURATION')
+print('Backend='+config['Backend'])
+print('IOType='+config['IOType'])
+print('BramFactor={bf}'.format(bf=config['HLSConfig']['Model']['BramFactor']))
+
+# pprint.pprint(config)
+
+#Convert it to a hls project
+hls_model = hls4ml.converters.keras_to_hls(config)
+
+hls_model.build(vsynth=False)
+
+# URL for this info: https://fastmachinelearning.org/hls4ml/setup/QUICKSTART.html
+

example-prjs/sigmoid/config.yml

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+Backend: Vivado
+KerasJson: sigmoid.json
+KerasH5: sigmoid_weights.h5
+OutputDir: my-Catapult-test
+ProjectName: sigmoid
+XilinxPart: xcku115-flvb2104-2-i
+Part: xcku115-flvb2104-2-i
+ClockPeriod: 5
+
+IOType : io_parallel
+HLSConfig:
+  Model:
+    Precision: ap_fixed<16, 6>
+    ReuseFactor: 1
+    Strategy: Latency

example-prjs/sigmoid/run_catapult.sh

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+#! /bin/bash
+
+# This script runs the Catapult flows to generate the HLS.
+
+VENV=../../../../venv
+
+MGC_HOME=/wv/hlsb/CATAPULT/TOT/CURRENT/aol/Mgc_home
+export MGC_HOME
+
+export PATH=/wv/hlstools/python/python37/bin:$PATH:$XILINX_VIVADO/bin:$MGC_HOME/bin
+export LD_LIBRARY_PATH=/wv/hlstools/python/python37/lib:$XILINX_VIVADO/lib/lnx64.o:$MGC_HOME/lib
+export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
+
+# needed for pytest
+export OSTYPE=linux-gnu
+
+echo "Activating Virtual Environment..."
+#    bash
+source $VENV/bin/activate
+
+rm -rf ./my-Catapult-test*
+
+# to run catapult+vivado_rtl
+sed -e 's/Vivado/Catapult/g' vivado.py >catapult.py
+# to only run catapult
+# sed -e 's/Vivado/Catapult/g' vivado.py | sed -e 's/vsynth=True/vsynth=False/g' >catapult.py
+
+# actually run HLS4ML + Catapult (+ optional vivado RTL)
+python3 catapult.py
+
+# run just the C++ execution
+echo ""
+echo "====================================================="
+echo "====================================================="
+echo "C++ EXECUTION"
+pushd my-Catapult-test; rm -f a.out; $MGC_HOME/bin/g++ -std=c++17 -I. -DWEIGHTS_DIR=\"firmware/weights\" -Ifirmware -I$MGC_HOME/shared/include firmware/sigmoid.cpp sigmoid_test.cpp; a.out; popd
+
+# Using VSCode setup generated by Catapult
+echo ""
+echo "====================================================="
+echo "====================================================="
+echo "To launch VSCode on the C++ generated by hls4ml:"
+echo "setenv LD_LIBRARY_PATH $MGC_HOME/lib:$MGC_HOME/shared/lib"
+echo "pushd my-Catapult-test; /wv/hlstools/vscode/LATEST/code Catapult.code-workspace"

example-prjs/sigmoid/run_vivado.sh

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+#! /bin/bash
+
+# This script runs the Vivado flows to generate the HLS.
+
+VENV=/wv/scratch-baimar9c/venv
+
+MGC_HOME=/wv/hlsb/CATAPULT/TOT/CURRENT/aol/Mgc_home
+export MGC_HOME
+
+export PATH=/wv/hlstools/python/python37/bin:$PATH:$XILINX_VIVADO/bin:$MGC_HOME/bin
+export LD_LIBRARY_PATH=/wv/hlstools/python/python37/lib:$XILINX_VIVADO/lib/lnx64.o:$MGC_HOME/lib
+export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
+
+# needed for pytest
+export OSTYPE=linux-gnu
+
+echo "Activating Virtual Environment..."
+#    bash
+source $VENV/bin/activate
+
+rm -rf ./my-Vivado-test*
+
+mkdir -p tb_data
+
+# to run catapult+vivado_rtl
+sed -e 's/Vivado/Catapult/g' vivado.py >catapult.py
+# to only run catapult
+# sed -e 's/Vivado/Catapult/g' vivado.py | sed -e 's/vsynth=True/vsynth=False/g' >catapult.py
+
+# actually run HLS4ML + Vivado HLS
+python vivado.py
+
+# run just the C++ execution
+echo ""
+echo "====================================================="
+echo "====================================================="
+echo "C++ EXECUTION"
+pushd my-Vivado-test; rm -f a.out; $MGC_HOME/bin/g++ -g -std=c++11 -I. -DWEIGHTS_DIR=\"firmware/weights\" -Ifirmware -Ifirmware/ap_types -I$MGC_HOME/shared/include firmware/sigmoid.cpp sigmoid_test.cpp; a.out; popd
+

example-prjs/sigmoid/sigmoid.json

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+{"class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, 5, 5, 1], "dtype": "float32", "sparse": false, "ragged": false, "name": "conv2d_input"}}, {"class_name": "Conv2D", "config": {"name": "conv2d", "trainable": true, "dtype": "float32", "batch_input_shape": [null, 5, 5, 1], "filters": 1, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "sigmoid", "use_bias": true, "kernel_initializer": "random_kernel", "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "keras_version": "2.11.0", "backend": "tensorflow"}

example-prjs/sigmoid/sigmoid.py

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+import tensorflow as tf
+import numpy as np
+
+# Sources:
+# https://www.geeksforgeeks.org/python-tensorflow-tf-keras-layers-conv2d-function/
+# https://jiafulow.github.io/blog/2021/02/17/simple-fully-connected-nn-firmware-using-hls4ml/
+# https://stackoverflow.com/questions/51930312/how-to-include-a-custom-filter-in-a-keras-based-cnn
+
+# Create custom kernel
+# NOTE: This kernel is random and purely for testing small examples
+def random_kernel(shape=(3,3,1), dtype=None):
+
+    f = np.array([
+            [[[1]], [[-1]], [[1]]],
+            [[[-1]], [[1]], [[-1]]],
+            [[[1]], [[-1]], [[1]]]
+        ])
+    assert f.shape == shape
+    return f
+
+# Create model with one conv2d layer for small example
+def create_model():
+	# Create a model
+	model = tf.keras.Sequential()
+
+	# First layer args.:
+	# filters: Number of output filters.
+	# kernel_size: Convolution window size width and height.
+	# strides: Stride of the convolution.
+	# padding: "same" adds padding if needed to ensure output dimensions are equal to input dimensions. "valid" means no padding.
+	# activation: Non-linear functions (i.e. relu).
+	# use_bias: Boolean or bias vectors.
+	# dilation_rate: Dilation rate for dilated convolutions.
+	# kernel_initializer: Default is glorot_uniform, meaning it initializes acrossed an uniform distribution.
+	# bias_initializer: Initializer for bias vectors.
+	# kernel_constraint: Constraint function for the kernel.
+	# bias_constraint: Constraint function for the bias vectors. 
+
+	# NOTE: Input size indicates a 5x5 pixel image (matrix) with one channel (i.e. just the red channel from RGB).
+	# Image (matrix) size is equal to kernel size since this is a very small example.
+	model.add(tf.keras.layers.Conv2D(1, 3, 1, padding="valid", activation="sigmoid", kernel_initializer=random_kernel, input_shape=(5, 5, 1)))
+
+	return model
+
+# Save model to forms for hls4ml
+def save_model(model, name=None):
+	# Save as model.h5, model_weights.h5, and model.json
+	if name is None:
+		name = model.name
+	model.save(name + '.h5')
+	model.save_weights(name + '_weights.h5')
+	with open(name + '.json', 'w') as outfile:
+		outfile.write(model.to_json())
+	return
+
+if __name__ == '__main__':
+    model = create_model()
+    save_model(model, name='sigmoid')
+
+    # Image Matrix
+    image_mat = np.array([
+	[ [1], [2], [3], [4], [5] ],
+	[ [6], [7], [8], [9], [10] ],
+	[ [11], [12], [13], [14], [15] ],
+        [ [16], [17], [18], [19], [20] ],
+        [ [21], [22], [23], [24], [25] ]
+    ])
+
+    image_mat = image_mat.reshape((1, 5, 5, 1))
+
+    # Get prediction
+    prediction = model.predict(image_mat)
+    print("Image Matrix\n")
+    print(image_mat)
+    print("Prediction\n")
+    print(prediction)
+
+    image_mat2 = np.array([
+	[ [1], [2], [3], [4], [5] ],
+	[ [5], [1], [2], [3], [4] ],
+	[ [4], [5], [1], [2], [3] ],
+        [ [3], [4], [5], [1], [2] ],
+        [ [2], [3], [4], [5], [1] ]
+    ])
+
+    image_mat2 = image_mat2.reshape((1, 5, 5, 1))
+
+    # Get prediction
+    prediction = model.predict(image_mat2)
+    print("Image Matrix\n")
+    print(image_mat2)
+    print("Prediction\n")
+    print(prediction)
+
+    image_mat3 = np.array([
+	[ [-1], [2], [-3], [4], [-5] ],
+	[ [5], [-1], [2], [-3], [4] ],
+	[ [-4], [5], [-1], [2], [-3] ],
+        [ [3], [-4], [5], [-1], [2] ],
+        [ [-2], [3], [-4], [5], [-1] ]
+    ])
+
+    image_mat3 = image_mat3.reshape((1, 5, 5, 1))
+
+    # Get prediction
+    prediction = model.predict(image_mat3)
+    print("Image Matrix\n")
+    print(image_mat3)
+    print("Prediction\n")
+    print(prediction)
+
+