From 505a0a93e75a2d7e4cfc9290f857d4d50c081165 Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <maltanar@gmail.com>
Date: Thu, 26 Mar 2020 19:16:17 +0000
Subject: [PATCH] [StreamingFC] add shape and type inference to StreamingFC
---
.../fpgadataflow/streamingfclayer_batch.py | 22 ++++++++++++++++---
1 file changed, 19 insertions(+), 3 deletions(-)
diff --git a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
index 22421abc8..606c02778 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
@@ -32,7 +32,7 @@ from shutil import copy
import numpy as np
from pyverilator import PyVerilator
-
+from onnx import TensorProto, helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow import HLSCustomOp
from finn.util.basic import interleave_matrix_outer_dim_from_partitions
@@ -113,10 +113,26 @@ class StreamingFCLayer_Batch(HLSCustomOp):
return mh // pe
def make_shape_compatible_op(self, model):
- pass
+ oshape = self.get_normal_output_shape()
+ # implement tensor with correct shape
+ values = np.random.randn(*oshape).astype(np.float32)
+ return helper.make_node(
+ "Constant",
+ inputs=[],
+ outputs=[self.onnx_node.output[0]],
+ value=helper.make_tensor(
+ name="const_tensor",
+ data_type=TensorProto.FLOAT,
+ dims=values.shape,
+ vals=values.flatten().astype(float),
+ ),
+ )
def infer_node_datatype(self, model):
- pass
+ node = self.onnx_node
+ # data type stays the same
+ dtype = model.get_tensor_datatype(node.input[0])
+ model.set_tensor_datatype(node.output[0], dtype)
def verify_node(self):
info_messages = []
--
GitLab