def __init__(self, config):
self.config = config
self.backend = get_backend(self.config.get('Backend', 'Vivado'))
self.writer = get_writer(self.config.get('Backend', 'Vivado'))
self.model_precision = {}
self.layer_type_precision = {}
self.layer_name_precision = {}
self.model_rf = None
self.layer_type_rf = {}
self.layer_name_rf = {}
self.model_strategy = 'Latency'
self.layer_type_strategy = {}
self.layer_name_strategy = {}
self.model_compression = False
self.layer_type_compression = {}
self.layer_name_compression = {}
self.trace_output = self.get_config_value('TraceOutput', False)
self._parse_hls_config()
self._validate_hls_config()
def __init__(self, config, data_reader, layer_list, inputs=None, outputs=None):
self.config = HLSConfig(config)
self.backend = get_backend(config.get('Backend', 'Vivado'))
self.reader = data_reader
# If not provided, assumes layer_list[0] is input, and layer_list[-1] is output
self.inputs = inputs if inputs is not None else [layer_list[0]['name']]
self.outputs = outputs if outputs is not None else [layer_list[-1]['name']]
self.index = 0
self.graph = OrderedDict()
self.output_vars = {}
self._top_function_lib = None
self._make_graph(layer_list)
self._optimize_model(self.config.optimizers)
batchnorm_quantized_tanh_config_template = """struct config{index} : nnet::batchnorm_quantized_tanh_config {{
static const unsigned n_in = {n_in};
static const unsigned n_filt = {n_filt};
static const unsigned io_type = nnet::{iotype};
static const unsigned reuse_factor = {reuse};
}};\n"""
batchnorm_quantized_tanh_function_template = 'nnet::normalize_{quantize}_tanh<{input_t}, {config}>({input}, {output}, {threshold});'
# Register the layer types to the layer map
hls_model.register_layer('BatchNormalizationQuantizedTanh',
BatchNormalizationQuantizedTanh)
# Register the templates for config and function
templates.get_backend('Vivado').register_templates(
'BatchNormalizationQuantizedTanh',
batchnorm_quantized_tanh_function_template,
batchnorm_quantized_tanh_config_template)
class MergeBatchNormAndQuantizedTanh(OptimizerPass):
def match(self, node):
is_match = (node.__class__.__name__ == 'Activation'
and node.get_attr('activation')
in ['binary_tanh', 'ternary_tanh']
and node.get_input_node().__class__.__name__
== 'BatchNormalization')
return is_match
def transform(self, model, node):
bn_layer = node.get_input_node()
# Remove the Activation layer