From 68acbf1c15b690d82f6c9058c81a9cca696bdd11 Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <yamanu@xilinx.com>
Date: Tue, 22 Feb 2022 16:26:58 +0100
Subject: [PATCH] [ConvertToHLS] further constrain StreamingMaxPool conversion,
add warning
---
.../fpgadataflow/convert_to_hls_layers.py | 23 +++++++++++--------
1 file changed, 14 insertions(+), 9 deletions(-)
diff --git a/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py b/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
index 652136c82..46a97f6fa 100644
--- a/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
+++ b/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
@@ -349,15 +349,15 @@ class InferStreamingMaxPool(Transformation):
graph = model.graph
node_ind = 0
graph_modified = False
- for n in graph.node:
+ for node in graph.node:
node_ind += 1
- if n.op_type == "MaxPoolNHWC":
- mp_input = n.input[0]
- mp_output = n.output[0]
+ if node.op_type == "MaxPoolNHWC":
+ mp_input = node.input[0]
+ mp_output = node.output[0]
mp_in_shape = model.get_tensor_shape(mp_input)
# mp_out_shape = model.get_tensor_shape(mp_output)
dt = model.get_tensor_datatype(mp_input)
- mp_inst = getCustomOp(n)
+ mp_inst = getCustomOp(node)
k_h, k_w = mp_inst.get_nodeattr("kernel_shape")
ifm_ch = mp_in_shape[-1]
ifm_dim_h = mp_in_shape[1]
@@ -365,8 +365,11 @@ class InferStreamingMaxPool(Transformation):
pe = 1
ceil_mode = mp_inst.get_nodeattr("ceil_mode")
is_1d = (ifm_dim_h == 1 and k_h == 1) or (ifm_dim_w == 1 and k_w == 1)
- is_divisable = ifm_dim_h % k_h == 0 or ifm_dim_w % k_w == 0
- if is_1d or is_divisable:
+ is_divisable = (ifm_dim_h % k_h == 0) or (ifm_dim_w % k_w == 0)
+ is_bipolar = dt == DataType["BIPOLAR"]
+ pass_1d = is_1d and (not is_bipolar)
+ pass_2d = (not is_1d) and is_divisable
+ if pass_1d or pass_2d:
# create equivalent StreamingMaxPool_Batch node
new_node = helper.make_node(
"StreamingMaxPool_Batch",
@@ -380,12 +383,14 @@ class InferStreamingMaxPool(Transformation):
dataType=dt.name,
PE=pe,
CeilMode=ceil_mode,
- name="StreamingMaxPool_Batch_" + n.name,
+ name="StreamingMaxPool_Batch_" + node.name,
)
graph.node.insert(node_ind, new_node)
# remove old nodes
- graph.node.remove(n)
+ graph.node.remove(node)
graph_modified = True
+ else:
+ warnings.warn(node.name + ": could not convert to HLS")
if graph_modified:
model = model.transform(InferShapes())
model = model.transform(InferDataTypes())
--
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