pr299_test.py

import numpy as np
import random as rnd
import hls4ml
from tensorflow.keras.layers import Conv2D, MaxPooling2D, BatchNormalization
from qkeras.qconvolutional import QConv2D
from qkeras.quantizers import quantized_bits, quantized_relu
from tensorflow.keras.models import Sequential
from qkeras import QActivation

height = 8
width = 8
chan = 3

input_shape = (height,width,chan)
num_classes = 32

keras_model = Sequential()
keras_model.add(QConv2D(4, kernel_size=(3, 3), strides=(2, 2), activation='linear', input_shape=input_shape,
                        kernel_quantizer=quantized_bits(15, 7, alpha=1),
                        bias_quantizer=quantized_bits(15, 7, alpha=1), padding='valid'))
keras_model.add(BatchNormalization())
keras_model.add(QActivation("quantized_bits(15, 7, alpha=1)"))

print(keras_model.summary())

# Set some random weights
rnd.seed(42)
for layer in keras_model.layers:
     old_weights = layer.get_weights()
     if len(old_weights) > 0:
          new_weights = []
          for w in old_weights:
               new_w = []
               for i in range(np.prod(w.shape)):
                   new_w.append(rnd.uniform(0, 2))
               new_w = np.asarray(new_w).reshape(w.shape)
               new_weights.append(new_w)
          layer.set_weights(new_weights)

# Create conversion config
yaml_config = {}
yaml_config['KerasModel'] = keras_model
yaml_config['OutputDir'] = 'pr299_test'
yaml_config['ProjectName'] = 'myproject'
yaml_config['XilinxPart'] = 'xcvu9p-flgb2104-2-e'
yaml_config['ClockPeriod'] = 5
yaml_config['IOType'] = 'io_stream'
yaml_config['HLSConfig'] = {
    'Model': {
        'Precision': 'ap_fixed<16,8>',
        'ReuseFactor': 1
    }
}

# Prepare some data
np.random.seed(42)
x = np.random.rand(np.prod(keras_model.input.shape[1:])).reshape(keras_model.input.shape[1:])
# Predict with Keras
y_gold = keras_model.predict(np.expand_dims(x, axis=0))
print('Keras predictions:')
print(y_gold)

# Predict with hls4ml (strategy: latency)
hls_model = hls4ml.converters.keras_to_hls(yaml_config)
hls_model.compile()
y_latency = hls_model.predict(x)
print('HLS4ML predictions (strategy: latency):')
print(y_latency)

# Predict with hls4ml (strategy: resource)
yaml_config['HLSConfig']['Model']['Strategy'] = 'Resource'
hls_model = hls4ml.converters.keras_to_hls(yaml_config)
hls_model.compile()
y_resource = hls_model.predict(x)
print('HLS4ML predictions (strategy: resource):')
print(y_resource)

print('HLS4ML strategies are equal:', np.array_equal(y_latency, y_resource))