From 9a449205dad1015c08ac9013503e9c5e5049b51b Mon Sep 17 00:00:00 2001
From: auphelia <jakobapk@web.de>
Date: Wed, 22 Apr 2020 13:29:07 +0100
Subject: [PATCH] [Trafo-templates] First draft of restructured driver.py
template
---
.../transformation/fpgadataflow/templates.py | 64 +++++++++++--------
1 file changed, 37 insertions(+), 27 deletions(-)
diff --git a/src/finn/transformation/fpgadataflow/templates.py b/src/finn/transformation/fpgadataflow/templates.py
index 7c2bb26eb..01f0307ed 100644
--- a/src/finn/transformation/fpgadataflow/templates.py
+++ b/src/finn/transformation/fpgadataflow/templates.py
@@ -95,6 +95,43 @@ from finn.util.data_packing import (
)
from finn.core.datatype import DataType
+def load_input(N):
+ ishape_normal = $INPUT_SHAPE_NORMAL$
+ # load desired input .npy file
+ ibuf_normal = np.load("input.npy")
+ # ensure that shape is as expected
+ assert ibuf_normal.shape == ishape_normal
+ return ibuf_normal
+
+def pack_input(ibuf_normal, N):
+ # input FINN DataType
+ idt = $INPUT_FINN_DATATYPE$
+ ishape_folded = $INPUT_SHAPE_FOLDED$
+ # convert to folded form
+ ibuf_folded = ibuf_normal.reshape(ishape_folded)
+ # pack the input buffer, reversing both SIMD dim and endianness
+ ibuf_packed = finnpy_to_packed_bytearray(
+ ibuf_folded, idt, reverse_endian=True, reverse_inner=True
+ )
+ return ibuf_packed
+
+def unpack_output(obuf_packed, N):
+ # output FINN DataType
+ odt = $OUTPUT_FINN_DATATYPE$
+ oshape_folded = $OUTPUT_SHAPE_FOLDED$
+ # unpack the packed output buffer from accelerator
+ obuf_folded = packed_bytearray_to_finnpy(
+ obuf_packed, odt, oshape_folded, reverse_endian=True, reverse_inner=True
+ )
+ return obuf_folded
+
+def save_output(obuf_folded, N):
+ # convert to normal reshape and save
+ oshape_normal = $OUTPUT_SHAPE_NORMAL$
+ obuf_normal = obuf_folded.reshape(oshape_normal)
+ np.save("output.npy", obuf_normal)
+
+
bitfile_path = "resizer.bit"
ol = Overlay(bitfile_path)
dma=ol.axi_dma_0
@@ -107,33 +144,13 @@ REG_OFFSET_NUM_ITERS = 0x10
N = 1
# declare input/output types and shapes for the accelerator
-# input FINN DataType
-idt = $INPUT_FINN_DATATYPE$
-# normal, folded and packed input shapes
-ishape_normal = $INPUT_SHAPE_NORMAL$
-ishape_folded = $INPUT_SHAPE_FOLDED$
ishape_packed = $INPUT_SHAPE_PACKED$
-# output FINN DataType
-odt = $OUTPUT_FINN_DATATYPE$
-# normal, folded and packed output shapes
-oshape_normal = $OUTPUT_SHAPE_NORMAL$
-oshape_folded = $OUTPUT_SHAPE_FOLDED$
oshape_packed = $OUTPUT_SHAPE_PACKED$
# set up TLastMarker with correct num. samples
ctrl_regs.write(REG_OFFSET_NUM_ITERS, N)
-# load desired input .npy file
-ibuf_normal = np.load("input.npy")
-# ensure that shape is as expected
-assert ibuf_normal.shape == ishape_normal
-# convert to folded form
-ibuf_folded = ibuf_normal.reshape(ishape_folded)
-# pack the input buffer, reversing both SIMD dim and endianness
-ibuf_packed = finnpy_to_packed_bytearray(
- ibuf_folded, idt, reverse_endian=True, reverse_inner=True
-)
# allocate a PYNQ buffer for the packed input buffer
ibuf_packed_device = allocate(shape=ishape_packed, dtype=np.uint8)
# copy the packed data into the PYNQ buffer
@@ -158,11 +175,4 @@ file = open("nw_runtime.txt", "w")
file.write(str(runtime))
file.close()
-# unpack the packed output buffer from accelerator
-obuf_folded = packed_bytearray_to_finnpy(
- obuf_packed, odt, oshape_folded, reverse_endian=True, reverse_inner=True
-)
-# convert to normal reshape and save
-obuf_normal = obuf_folded.reshape(oshape_normal)
-np.save("output.npy", obuf_normal)
"""
--
GitLab