diff --git a/docker/Dockerfile.finn b/docker/Dockerfile.finn
index 5ce7cf3553ffab2dd0b928823157995f698b0dd8..1572ba2872a46a8eaa9331a601a77aada0b0aa1c 100644
--- a/docker/Dockerfile.finn
+++ b/docker/Dockerfile.finn
@@ -86,7 +86,7 @@ RUN pip install -e git+https://github.com/fbcotter/dataset_loading.git@0.0.4#egg
# git-based Python repo dependencies
# these are installed in editable mode for easier co-development
-ARG FINN_BASE_COMMIT="1fdf06c068f77ed5a312cd3a6edad098f64b09ed"
+ARG FINN_BASE_COMMIT="7c2603a95e90e4de2575020e575c24eab6a15889"
ARG FINN_EXP_COMMIT="f82c0d9868bb88ea045dfadb28508d327d287221"
ARG BREVITAS_COMMIT="462f86cdc60f9915baf13afd1676fb21da44c2ee"
ARG PYVERILATOR_COMMIT="0c3eb9343500fc1352a02c020a736c8c2db47e8e"
diff --git a/src/finn/custom_op/fpgadataflow/addstreams_batch.py b/src/finn/custom_op/fpgadataflow/addstreams_batch.py
index 856f84fae0cb90abcc08fc098d684872b1a6a6a1..fa80e47485eef4f289b0272fd73ac185bd1c2c5e 100644
--- a/src/finn/custom_op/fpgadataflow/addstreams_batch.py
+++ b/src/finn/custom_op/fpgadataflow/addstreams_batch.py
@@ -29,7 +29,6 @@
import numpy as np
import os
import warnings
-from onnx import TensorProto, helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -84,19 +83,7 @@ class AddStreams_Batch(HLSCustomOp):
assert ishape == exp_ishape, "Unexpected input1 shape."
ishape = tuple(model.get_tensor_shape(self.onnx_node.input[1]))
assert ishape == exp_ishape, "Unexpected input2 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),
- ),
- )
+ return super().make_const_shape_op(oshape)
def infer_node_datatype(self, model):
node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py b/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py
index 3cd6a7dfdbb2db7df08e1c726ee522b3c2ed20a0..4961f6148231252d255c1830ced418308032ce41 100644
--- a/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py
+++ b/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py
@@ -30,7 +30,6 @@ import numpy as np
import os
import warnings
from math import ceil
-from onnx import TensorProto, helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -125,18 +124,7 @@ class ChannelwiseOp_Batch(HLSCustomOp):
def make_shape_compatible_op(self, model):
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),
- ),
- )
+ return super().make_const_shape_op(oshape)
def infer_node_datatype(self, model):
node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
index 19732e44398665cfd9b97f9a1abcec56372e2523..a4018836846257c15ad203b1cef54c03cd081e45 100644
--- a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
+++ b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
@@ -29,7 +29,6 @@
import math
import numpy as np
import os
-from onnx import TensorProto, helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -148,18 +147,7 @@ class ConvolutionInputGenerator(HLSCustomOp):
ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
assert ishape == exp_ishape, "Unexpect input shape for ConvInpGen."
# 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),
- ),
- )
+ return super().make_const_shape_op(oshape)
def infer_node_datatype(self, model):
node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator1d.py b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator1d.py
index 1f9fcade0f974df2b2f21171d42e63f4af5e7eac..c4cf804126328b27fd56091d70f0b6e658b5b3c1 100644
--- a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator1d.py
+++ b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator1d.py
@@ -29,7 +29,6 @@
import math
import numpy as np
import os
-from onnx import TensorProto, helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -137,19 +136,7 @@ class ConvolutionInputGenerator1D(HLSCustomOp):
oshape = self.get_normal_output_shape()
ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
assert ishape == exp_ishape, "Unexpect input shape for ConvInpGen."
- # 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),
- ),
- )
+ return super().make_const_shape_op(oshape)
def infer_node_datatype(self, model):
node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/downsampler.py b/src/finn/custom_op/fpgadataflow/downsampler.py
index e8948c322a2543ee8ecbf682ee4a7989270ee3ad..6a0667f67dc9f127637bd80cc3e88f682a320cbb 100644
--- a/src/finn/custom_op/fpgadataflow/downsampler.py
+++ b/src/finn/custom_op/fpgadataflow/downsampler.py
@@ -1,7 +1,6 @@
import numpy as np
import os
import warnings
-from onnx import TensorProto, helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -83,19 +82,7 @@ class DownSampler(HLSCustomOp):
oshape = self.get_normal_output_shape()
ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
assert ishape == exp_ishape, "Unexpect input shape for DownSampler."
- # 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),
- ),
- )
+ return super().make_const_shape_op(oshape)
def infer_node_datatype(self, model):
node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/fmpadding_batch.py b/src/finn/custom_op/fpgadataflow/fmpadding_batch.py
index 03d3436346aa716e9ea9e49027fdbb17bee74311..f29ea431ffc95d6291407c35c17d943647cf45df 100644
--- a/src/finn/custom_op/fpgadataflow/fmpadding_batch.py
+++ b/src/finn/custom_op/fpgadataflow/fmpadding_batch.py
@@ -1,7 +1,6 @@
import numpy as np
import os
import warnings
-from onnx import TensorProto, helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -99,19 +98,7 @@ class FMPadding_Batch(HLSCustomOp):
oshape = self.get_normal_output_shape()
ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
assert ishape == exp_ishape, "Unexpect input shape for SameResize."
- # 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),
- ),
- )
+ return super().make_const_shape_op(oshape)
def infer_node_datatype(self, model):
node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/globalaccpool_batch.py b/src/finn/custom_op/fpgadataflow/globalaccpool_batch.py
index eabdcf599d23d35ed13069cb81afa3ec4999e8e7..6d4a55ee5c86b68776f4c7c2e58930034bb0be02 100644
--- a/src/finn/custom_op/fpgadataflow/globalaccpool_batch.py
+++ b/src/finn/custom_op/fpgadataflow/globalaccpool_batch.py
@@ -29,7 +29,6 @@
import numpy as np
import os
import warnings
-from onnx import TensorProto, helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -95,19 +94,7 @@ class GlobalAccPool_Batch(HLSCustomOp):
oshape = self.get_normal_output_shape()
ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
assert ishape == exp_ishape, "Unexpected input 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(),
- ),
- )
+ return super().make_const_shape_op(oshape)
def infer_node_datatype(self, model):
node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/iodma.py b/src/finn/custom_op/fpgadataflow/iodma.py
index 4fa74e35dbec902d6c4d980c48ffaa69cbd5ccd9..802c7e78515336ef884e5ff09356085b5cc6069f 100644
--- a/src/finn/custom_op/fpgadataflow/iodma.py
+++ b/src/finn/custom_op/fpgadataflow/iodma.py
@@ -29,7 +29,6 @@
import math
import numpy as np
import warnings
-from onnx import TensorProto, helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -146,19 +145,7 @@ class IODMA(HLSCustomOp):
oshape = self.get_normal_output_shape()
ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
assert ishape == exp_ishape, "Unexpected input 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),
- ),
- )
+ return super().make_const_shape_op(oshape)
def infer_node_datatype(self, model):
node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/labelselect_batch.py b/src/finn/custom_op/fpgadataflow/labelselect_batch.py
index d70d0f6a9b0cacb491ce748b84c8c7c474605170..1eb5962fdbc54092eaeb4796806b3a623c65aea8 100644
--- a/src/finn/custom_op/fpgadataflow/labelselect_batch.py
+++ b/src/finn/custom_op/fpgadataflow/labelselect_batch.py
@@ -102,18 +102,14 @@ class LabelSelect_Batch(HLSCustomOp):
oshape = self.get_normal_output_shape()
ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
assert ishape == exp_ishape, "Unexpected input shape."
- # implement tensor with correct shape
- values = np.random.randn(*oshape).astype(np.int64)
return helper.make_node(
- "Constant",
+ "RandomNormal",
inputs=[],
outputs=[self.onnx_node.output[0]],
- value=helper.make_tensor(
- name="const_tensor",
- data_type=TensorProto.INT64,
- dims=values.shape,
- vals=values.flatten(),
- ),
+ mean=0.0,
+ scale=1.0,
+ dtype=TensorProto.INT64,
+ shape=list(oshape),
)
def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/pool_batch.py b/src/finn/custom_op/fpgadataflow/pool_batch.py
index f4638e6de3616c568da295f091a1ad39262e6dd8..ba8a446f2cf7541c0bd2e1dff731afe2397942ef 100644
--- a/src/finn/custom_op/fpgadataflow/pool_batch.py
+++ b/src/finn/custom_op/fpgadataflow/pool_batch.py
@@ -28,7 +28,6 @@
import numpy as np
import os
-from onnx import TensorProto, helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -163,19 +162,7 @@ class Pool_Batch(HLSCustomOp):
oshape = self.get_normal_output_shape()
ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
assert ishape == exp_ishape, "Unexpected input shape for Pool_Batch."
- # 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),
- ),
- )
+ return super().make_const_shape_op(oshape)
def infer_node_datatype(self, model):
node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py b/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py
index 11809b9bc267d00c8cd630163cc969187efc7417..1791706afa217d5eb453064547c1ea66b306d227 100644
--- a/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py
@@ -30,7 +30,6 @@ import math
import numpy as np
import os
import warnings
-from onnx import TensorProto, helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -165,19 +164,7 @@ class StreamingDataWidthConverter_Batch(HLSCustomOp):
oshape = self.get_normal_output_shape()
ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
assert ishape == tuple(exp_ishape), "Unexpect input shape for StreamingDWC."
- # 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),
- ),
- )
+ return super().make_const_shape_op(oshape)
def infer_node_datatype(self, model):
node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
index 968c9a6bad0364813c4e70829628da3d07152fbd..90abb66e66bc54bc9d1f4c7a08c58ca58e6d1741 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
@@ -31,7 +31,6 @@ import numpy as np
import os
import textwrap
import warnings
-from onnx import TensorProto, helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -151,19 +150,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
def make_shape_compatible_op(self, model):
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),
- ),
- )
+ return super().make_const_shape_op(oshape)
def infer_node_datatype(self, model):
node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/streamingfifo.py b/src/finn/custom_op/fpgadataflow/streamingfifo.py
index c8ae83cc90e1199831da5286d5051fdb969e825a..91f6ed5b8d29fd72ea1fbb8a3da94cfc103af88e 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfifo.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfifo.py
@@ -30,7 +30,6 @@ import numpy as np
import os
import subprocess
import warnings
-from onnx import TensorProto, helper
from shutil import copy
from finn.core.datatype import DataType
@@ -78,19 +77,7 @@ class StreamingFIFO(HLSCustomOp):
oshape = self.get_normal_output_shape()
ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
assert ishape == tuple(exp_ishape), "Unexpect input shape for StreamingFIFO."
- # 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),
- ),
- )
+ return super().make_const_shape_op(oshape)
def infer_node_datatype(self, model):
node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py b/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
index 87ecde8f9c7c50b7a22213db2b856d7439050421..1e66a5c204cc62bb7620907f82fcd5b2072bc184 100644
--- a/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
@@ -29,7 +29,6 @@
import numpy as np
import os
import warnings
-from onnx import TensorProto, helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -140,19 +139,7 @@ class StreamingMaxPool_Batch(HLSCustomOp):
oshape = self.get_normal_output_shape()
ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
assert ishape == exp_ishape, "Unexpect input shape for StreamingMaxPool."
- # 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),
- ),
- )
+ return super().make_const_shape_op(oshape)
def infer_node_datatype(self, model):
node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/thresholding_batch.py b/src/finn/custom_op/fpgadataflow/thresholding_batch.py
index bd136d000e675c6b2e1d7fd84ecdd6ac5002ff06..610139f44ee7e8be1320b47c99222667fa6ed850 100644
--- a/src/finn/custom_op/fpgadataflow/thresholding_batch.py
+++ b/src/finn/custom_op/fpgadataflow/thresholding_batch.py
@@ -31,7 +31,6 @@ import os
import textwrap
import warnings
from math import ceil, log2
-from onnx import TensorProto, helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -112,19 +111,7 @@ class Thresholding_Batch(HLSCustomOp):
def make_shape_compatible_op(self, model):
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),
- ),
- )
+ return super().make_const_shape_op(oshape)
def infer_node_datatype(self, model):
node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/upsampler.py b/src/finn/custom_op/fpgadataflow/upsampler.py
index 8331610dc11bb440e6bb56924bbf8efeed2653c2..d5f809305b6bf30285e58e1c702bfe9d21f5fa03 100644
--- a/src/finn/custom_op/fpgadataflow/upsampler.py
+++ b/src/finn/custom_op/fpgadataflow/upsampler.py
@@ -1,7 +1,6 @@
import numpy as np
import os
import warnings
-from onnx import TensorProto, helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -69,19 +68,7 @@ class UpsampleNearestNeighbour_Batch(HLSCustomOp):
assert (
ishape == exp_ishape
), "Unexpect input shape for UpsampleNearestNeighbour_Batch."
- # 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),
- ),
- )
+ return super().make_const_shape_op(oshape)
def infer_node_datatype(self, model):
node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/vector_vector_activate_batch.py b/src/finn/custom_op/fpgadataflow/vector_vector_activate_batch.py
index fa990f28087c0ec47a071e85ed1af6ff9328f70f..c67eb0f21bd3a3a21bd92d3b4595ab66bd703b93 100644
--- a/src/finn/custom_op/fpgadataflow/vector_vector_activate_batch.py
+++ b/src/finn/custom_op/fpgadataflow/vector_vector_activate_batch.py
@@ -2,7 +2,6 @@ import math
import numpy as np
import os
import warnings
-from onnx import TensorProto, helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -129,19 +128,7 @@ class Vector_Vector_Activate_Batch(HLSCustomOp):
def make_shape_compatible_op(self, model):
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),
- ),
- )
+ return super().make_const_shape_op(oshape)
def infer_node_datatype(self, model):
node = self.onnx_node