remove passing of size to stream clone and repack

hls4ml/backends/fpga/passes/repack_stream.py

@@ -51,7 +51,7 @@ def match(self, node):
         # do not run optimizer pass for a flatten layer (1 output dimension)
         if not isinstance(node, Reshape):
             return False
-        return len(node.get_output_variable().shape) > 1 or node.name in node.model.outputs
+        return len(node.get_output_variable().shape) > 1 or node.outputs[0] in node.model.outputs
 
     def transform(self, model, node):
         if model.config.get_config_value('IOType') != 'io_stream':

hls4ml/backends/oneapi/passes/clone_templates.py

@@ -17,7 +17,7 @@ def format(self, node):
 
         output_pipes = ', '.join([f'{{output{i + 1}_pipe}}' for i in range(len(node.outputs))])
 
-        template = f'task_sequence<nnet::clone_stream<{{input_pipe}}, {output_pipes}, {{size}}>> {{name}};'
+        template = f'task_sequence<nnet::clone_stream<{{input_pipe}}, {output_pipes}>> {{name}};'
         return template.format(**params)
 
 

hls4ml/backends/oneapi/passes/reshaping_templates.py

@@ -1,5 +1,3 @@
-import numpy as np
-
 from hls4ml.backends.oneapi.oneapi_template import StreamFunctionCallTemplate, TaskSequenceTemplate
 from hls4ml.backends.template import FunctionCallTemplate, LayerConfigTemplate
 from hls4ml.model.layers import Reshape, Resize, Transpose, ZeroPadding1D, ZeroPadding2D
@@ -215,7 +213,7 @@ def format(self, node):
 
 
 # Reshape template (only used in streaming)
-reshape_task_sequence_template = 'task_sequence<nnet::repack_stream<{input_pipe}, {output_pipe}, {size}>> {name};'
+reshape_task_sequence_template = 'task_sequence<nnet::repack_stream<{input_pipe}, {output_pipe}>> {name};'
 reshape_include_list = ['nnet_utils/nnet_stream.h']
 
 
@@ -244,5 +242,4 @@ def __init__(self):
 
     def format(self, node):
         params = self._default_function_params(node)
-        params['size'] = int(np.prod(node.get_output_variable().shape))
         return self.template.format(**params)

hls4ml/converters/pytorch/reshape.py

@@ -26,6 +26,9 @@ def parse_reshape_layer(operation, layer_name, input_names, input_shapes, node,
                 cl.remove(-1)
                 layer['target_shape'][i] = int(size / np.prod(cl))
 
+    # remove the batch dimension
+    layer['target_shape'] = layer['target_shape'][1:]
+
     output_shape = input_shapes[0][:1] + layer['target_shape']
 
     return layer, output_shape