From e6baacd473a2a341ede17bf6d1b5561f954346aa Mon Sep 17 00:00:00 2001
From: Felix Jentzsch <felix.jentzsch@upb.de>
Date: Tue, 15 Mar 2022 12:54:38 +0100
Subject: [PATCH] Fix for simulation in MMV, 1D case
---
.../fpgadataflow/convolutioninputgenerator_rtl.py | 13 +++++++------
1 file changed, 7 insertions(+), 6 deletions(-)
diff --git a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator_rtl.py b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator_rtl.py
index 55687aa5d..e0285cd47 100755
--- a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator_rtl.py
+++ b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator_rtl.py
@@ -79,7 +79,7 @@ class ConvolutionInputGenerator_rtl(HLSCustomOp):
"IFMDim": ("ints", True, []), # [H, W] = [Y, X]
"OFMDim": ("ints", True, []), # [H, W] = [Y, X]
"SIMD": ("i", True, 0),
- "M": ("i", True, 1),
+ "M": ("i", False, 1),
"Stride": ("ints", True, []), # [H, W] = [Y, X]
"Dilation": ("ints", True, []), # [H, W] = [Y, X]
# FINN DataTypes for inputs, weights, outputs
@@ -380,10 +380,11 @@ class ConvolutionInputGenerator_rtl(HLSCustomOp):
inp = context[node.input[0]]
assert str(inp.dtype) == "float32", "Input datatype is not float32"
- assert (
- inp.shape == exp_ishape
- ), """Input shape doesn't
- match expected shape (1, ifm_dim, ifm_dim, ifm_ch)."""
+ # disable this check to allow for IFMdim % M != 0 case (see below) where input comes from MMV-output capable node
+ #assert (
+ # inp.shape == exp_ishape
+ #), """Input shape doesn't
+ #match expected shape (1, ifm_dim, ifm_dim, ifm_ch)."""
if self.get_input_datatype() == DataType["BIPOLAR"]:
# store bipolar activations as binary
inp = (inp + 1) / 2
@@ -394,7 +395,7 @@ class ConvolutionInputGenerator_rtl(HLSCustomOp):
# pad test input stream to work when IFMdim % M != 0
# during normal operation, the AXI Stream should not care, in the last cycle garbage elements are read but not used
# ToDo: only works for 1D case
- mmv_stream_padding_px = int((np.prod(folded_ishape) - np.prod(exp_ishape)) / exp_ishape[-1])
+ mmv_stream_padding_px = int((np.prod(folded_ishape) - np.prod(inp.shape)) / exp_ishape[-1])
if exp_ishape [2] == 1:
inp = np.pad(inp, ((0,0),(0,mmv_stream_padding_px),(0,0),(0,0)), 'constant')
else:
--
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