From 898f2294ae2bc2beadc00a2f9ca1fdb3e29c4d7a Mon Sep 17 00:00:00 2001
From: mmrahorovic <mmrahorovic@hotmail.com>
Date: Sun, 20 Feb 2022 22:42:32 +0000
Subject: [PATCH] [custom_op]: MaxPool support for ceil mode
---
.../fpgadataflow/streamingmaxpool_batch.py | 16 ++++++++++++----
1 file changed, 12 insertions(+), 4 deletions(-)
diff --git a/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py b/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
index daa8319cd..d2e1406d2 100755
--- a/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
@@ -32,6 +32,7 @@ import warnings
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
+from finn.custom_op.general.maxpoolnhwc import compute_pool_output_dim
from finn.util.data_packing import npy_to_rtlsim_input, rtlsim_output_to_npy
@@ -44,6 +45,7 @@ class StreamingMaxPool_Batch(HLSCustomOp):
"PoolDim": ("ints", True, []), # [H, W] = [Y, X]
"NumChannels": ("i", True, 0),
"PE": ("i", True, 0),
+ "CeilMode": ("i", False, 0),
# FINN DataTypes for inputs/outputs
"dataType": ("s", True, ""),
}
@@ -96,6 +98,7 @@ class StreamingMaxPool_Batch(HLSCustomOp):
ifm_dim_h, ifm_dim_w = self.get_nodeattr("ImgDim")
k_h, k_w = tuple(self.get_nodeattr("PoolDim"))
ifm_ch = self.get_nodeattr("NumChannels")
+ ceil_mode = self.get_nodeattr("CeilMode")
if not self.is_1d():
assert (
ifm_dim_h % k_h == 0
@@ -103,8 +106,8 @@ class StreamingMaxPool_Batch(HLSCustomOp):
assert (
ifm_dim_w % k_w == 0
), "StreamingMaxPool needs ImgDim_w % PoolDim_w == 0"
- ofm_dim_h = int(np.floor(ifm_dim_h / k_w))
- ofm_dim_w = int(np.floor(ifm_dim_w / k_w))
+ ofm_dim_h = compute_pool_output_dim(ifm_dim_h, k_h, k_h, 0, ceil_mode)
+ ofm_dim_w = compute_pool_output_dim(ifm_dim_w, k_w, k_w, 0, ceil_mode)
oshape = (1, ofm_dim_h, ofm_dim_w, ifm_ch)
return oshape
@@ -197,15 +200,19 @@ class StreamingMaxPool_Batch(HLSCustomOp):
def defines(self, var):
numReps = 1
ifm_dim, k, ifm_ch = self.get_1d_attrs_normalized()
+ ceil_mode = self.get_nodeattr("CeilMode")
+ output_size = compute_pool_output_dim(ifm_dim[1], k[1], k[1], 0, ceil_mode)
if self.is_1d():
self.code_gen_dict["$DEFINES$"] = [
"""#define ImgDim {}\n #define PoolDim {}\n
- #define NumChannels {}\n #define PE {}\n #define numReps {}""".format(
+ #define NumChannels {}\n #define PE {}\n #define OutputSize {}
+ \n #define numReps {}""".format(
ifm_dim[1],
k[1],
self.get_nodeattr("NumChannels"),
self.get_nodeattr("PE"),
+ output_size,
numReps,
)
]
@@ -264,7 +271,8 @@ class StreamingMaxPool_Batch(HLSCustomOp):
if self.is_1d():
op = "StreamingMaxPool_Precision_1d"
self.code_gen_dict["$DOCOMPUTE$"] = [
- "%s<ImgDim, PoolDim, NumChannels, PE, %s, %s>(in0, out);"
+ """%s<ImgDim, PoolDim, NumChannels, PE,
+ OutputSize, %s, %s>(in0, out);"""
% (op, dtype_hls, minval_str)
]
else:
--
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