diff --git a/src/finn/transformation/fpgadataflow/templates.py b/src/finn/transformation/fpgadataflow/templates.py
index 55a5af2ad887e4a8cfa5e3836bef00f2defe7284..8959a4dcd21dd1d07f31fa54438d3eb4bf21cf4f 100644
--- a/src/finn/transformation/fpgadataflow/templates.py
+++ b/src/finn/transformation/fpgadataflow/templates.py
@@ -103,21 +103,21 @@ from finn.util.data_packing import (
from finn.core.datatype import DataType
class RemoteTest():
- def __init__(
- self,
- exec_mode,
- N,
- bitfile="resizer.bit",
- inputfile="input.npy",
- outputfile="output.npy"):
-
- self.exec_mode = exec_mode
+ def __init__(self, N, bitfile):
+
self.N = N
- self.inputfile = inputfile
- self.outputfile = outputfile
self.ol = Overlay(bitfile)
self.dma = self.ol.axi_dma_0
self.ctrl_regs = self.ol.resize_accel_0
+ # input FINN DataType
+ self.idt = $INPUT_FINN_DATATYPE$
+ # output FINN DataType
+ self.odt = $OUTPUT_FINN_DATATYPE$
+ # input and output shapes
+ self.ishape_normal = $INPUT_SHAPE_NORMAL$
+ self.oshape_normal = $OUTPUT_SHAPE_NORMAL$
+ self.ishape_folded = $INPUT_SHAPE_FOLDED$
+ self.oshape_folded = $OUTPUT_SHAPE_FOLDED$
self.ishape_packed = $INPUT_SHAPE_PACKED$
self.oshape_packed = $OUTPUT_SHAPE_PACKED$
# neuron folding factor of output = iterations per sample
@@ -126,120 +126,113 @@ class RemoteTest():
# used by TLastMarker to signal end of transmission for AXI CDMA
self.REG_OFFSET_NUM_ITERS = 0x10
- def load_input(self):
+ def load_input(self, inputfile):
N = self.N
- ishape_normal = $INPUT_SHAPE_NORMAL$
# load desired input .npy file
- ibuf_normal = np.load(self.inputfile)
+ ibuf_normal = np.load(inputfile)
# ensure that shape is as expected
- assert ibuf_normal.shape == ishape_normal
+ assert ibuf_normal.shape == self.ishape_normal
return ibuf_normal
def pack_input(self, ibuf_normal):
N = self.N
- # input FINN DataType
- idt = $INPUT_FINN_DATATYPE$
- ishape_folded = $INPUT_SHAPE_FOLDED$
# convert to folded form
- ibuf_folded = ibuf_normal.reshape(ishape_folded)
+ ibuf_folded = ibuf_normal.reshape(self.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
+ ibuf_folded, self.idt, reverse_endian=True, reverse_inner=True
)
return ibuf_packed
def unpack_output(self, obuf_packed):
N = self.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
+ obuf_packed, self.odt, self.oshape_folded, reverse_endian=True, reverse_inner=True
)
return obuf_folded
- def save_output(self, obuf_folded):
+ def save_output(self, obuf_folded, outputfile):
N = self.N
# convert to normal reshape and save
- oshape_normal = $OUTPUT_SHAPE_NORMAL$
- obuf_normal = obuf_folded.reshape(oshape_normal)
- np.save(self.outputfile, obuf_normal)
-
- def allocate_pynqbuffer(self, shape, data=None):
- buf_device = allocate(shape=shape, dtype=np.uint8)
+ obuf_normal = obuf_folded.reshape(self.oshape_normal)
+ np.save(outputfile, obuf_normal)
+ def allocate_pynqbuffers(self, data=None):
+ ibuf_packed_device = allocate(shape=self.ishape_packed, dtype=np.uint8)
# if necessary copy the packed data into the PYNQ buffer
# TODO optimization: pack directly into the PYNQ buffer?
if data is not None:
- np.copyto(buf_device, data)
-
- return buf_device
+ np.copyto(ibuf_packed_device, data)
+ obuf_packed_device = allocate(shape=self.oshape_packed, dtype=np.uint8)
+ return [ibuf_packed_device, obuf_packed_device]
- def run_nw(self):
- exec_mode = self.exec_mode
- if exec_mode == "remote_pynq":
- ibuf_normal = self.load_input()
- ibuf_packed = self.pack_input(ibuf_normal)
- elif exec_mode != "throughput_test":
- raise Exception("Exec mode has to be set to remote_pynq or throughput_test")
-
+ def setup_TLastMarker(self):
+ N = self.N
# set up TLastMarker with correct num. samples
self.ctrl_regs.write(self.REG_OFFSET_NUM_ITERS, N*self.itersPerSample)
- # allocate a PYNQ buffer for the packed input buffer
- if exec_mode == "remote_pynq":
- ibuf_packed_device = self.allocate_pynqbuffer(self.ishape_packed, ibuf_packed)
- else:
- ibuf_packed_device = self.allocate_pynqbuffer(self.ishape_packed)
-
- # allocate a PYNQ buffer for the returned packed output buffer
- obuf_packed = self.allocate_pynqbuffer(self.oshape_packed)
-
- if exec_mode == "throughput_test":
- # measure runtime of network
- start = time.time()
- res={}
+ def run_nw(self, ibuf_packed_device, obuf_packed_device):
# set up the DMA and wait until all transfers complete
dma = self.dma
dma.sendchannel.transfer(ibuf_packed_device)
- dma.recvchannel.transfer(obuf_packed)
+ dma.recvchannel.transfer(obuf_packed_device)
dma.sendchannel.wait()
dma.recvchannel.wait()
-
- if exec_mode == "throughput_test":
- end = time.time()
- runtime = end - start
- res["runtime[ms]"] = runtime*1000
- res["throughput[images/s]"] = N / runtime
- res["DRAM_in_bandwidth[Mb/s]"] = np.prod(self.ishape_packed)*0.000001 / runtime
- res["DRAM_out_bandwidth[Mb/s]"] = np.prod(self.oshape_packed)*0.000001 / runtime
- file = open("nw_metrics.txt", "w")
- file.write(str(res))
- file.close()
- else:
- obuf_folded = self.unpack_output(obuf_packed)
- self.save_output(obuf_folded)
-
+ return obuf_packed_device
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Set exec mode, batchsize N, bitfile name, inputfile name and outputfile name')
- parser.add_argument('exec_mode', help='Please select functional verification ("remote_pynq") or throughput test ("throughput_test")')
- parser.add_argument('N', help='number of samples for inference', type=int)
- parser.add_argument('bitfile', default="resizer.bit")
- parser.add_argument('inputfile', default="input.npy")
- parser.add_argument('outputfile', default="output.npy")
+ parser.add_argument('--exec_mode', help='Please select functional verification ("remote_pynq") or throughput test ("throughput_test")')
+ parser.add_argument('--batchsize', help='number of samples for inference', type=int)
+ parser.add_argument('--bitfile', default="resizer.bit")
+ parser.add_argument('--inputfile', default="input.npy")
+ parser.add_argument('--outputfile', default="output.npy")
args = parser.parse_args()
exec_mode = args.exec_mode
- N = args.N
+ N = args.batchsize
bitfile = args.bitfile
inputfile = args.inputfile
outputfile = args.outputfile
- Test = RemoteTest(exec_mode, N, bitfile, inputfile, outputfile)
- Test.run_nw()
+ NW_test = RemoteTest(N, bitfile)
+
+ if exec_mode == "remote_pynq":
+ ibuf_normal = NW_test.load_input(inputfile)
+ ibuf_packed = NW_test.pack_input(ibuf_normal)
+ elif exec_mode != "throughput_test":
+ raise Exception("Exec mode has to be set to remote_pynq or throughput_test")
+
+ NW_test.setup_TLastMarker()
+
+ # allocate a PYNQ buffer for the packed input and buffer
+ if exec_mode == "remote_pynq":
+ [ibuf_packed_device, obuf_packed_device] = NW_test.allocate_pynqbuffers(ibuf_packed)
+ else:
+ [ibuf_packed_device, obuf_packed_device] = NW_test.allocate_pynqbuffers()
+
+ if exec_mode == "throughput_test":
+ # measure runtime of network
+ start = time.time()
+ res={}
+
+ obuf_packed_device = NW_test.run_nw(ibuf_packed_device, obuf_packed_device)
+
+ if exec_mode == "throughput_test":
+ end = time.time()
+ runtime = end - start
+ res["runtime[ms]"] = runtime*1000
+ res["throughput[images/s]"] = N / runtime
+ res["DRAM_in_bandwidth[Mb/s]"] = np.prod(NW_test.ishape_packed)*0.000001 / runtime
+ res["DRAM_out_bandwidth[Mb/s]"] = np.prod(NW_test.oshape_packed)*0.000001 / runtime
+ file = open("nw_metrics.txt", "w")
+ file.write(str(res))
+ file.close()
+ else:
+ obuf_folded = NW_test.unpack_output(obuf_packed_device)
+ NW_test.save_output(obuf_folded, outputfile)
"""